03_Aggregation.ipynb

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   "source": [
    "# サーチエンジンとしての利用手順(集計)\n",
    "----\n",
    "検索したドキュメントをさらに集計する 方法について説明します。"
   ]
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   },
   "source": [
    "## 準備"
   ]
  },
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   "source": [
    "本章のコマンドを実行するための設定を行います。  \n",
    "\n",
    "セットアップ済みの環境のうちコマンドを送る先であるCoordinating(Client) Nodeのホストアドレスを、次のセルに記入して実行し、保存してください。"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: ES_HOST=XXX.XXX.XXX.233:9200\n"
     ]
    }
   ],
   "source": [
    "%env ES_HOST=XXX.XXX.XXX.233:9200"
   ]
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   "source": [
    "また、データを蓄積する先のインデックスとタイプを、次のセルを実行して保存してください。  "
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: INDEX=meteorological-data-*\n",
      "env: TYPE=logs\n"
     ]
    }
   ],
   "source": [
    "%env INDEX=meteorological-data-*\n",
    "%env TYPE=logs"
   ]
  },
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   "source": [
    "## Aggregationとは"
   ]
  },
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   "source": [
    "Aggregationとは、検索結果を集約・集計するための機能です。  \n",
    "[02_Search](02_Search.ipynb)の「ドキュメントの検索」で説明したような検索の結果に対して、\n",
    "SQLで言うと集合関数（SUM, MIN, MAX, AVGなど）や、グループ化（GROUP BY）に相当する処理を実現できます。  \n",
    "また、複数のAggregationを1回のリクエストでまとめて実行したり、入れ子にして多段の計算をしたりすることも可能です。"
   ]
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   "source": [
    "Aggregationの種類は次の通りです。"
   ]
  },
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   "source": [
    "| 種類 | 内容 | \n",
    "|------|------|\n",
    "|Metrics Aggregations|複数のドキュメントが持つ数値データを集約します。<br>最大値、最小値、平均値等を求めることができます。|\n",
    "|Bucket Aggregations|ドキュメントのBucket（入れ物）を生成します。<br>Bucketは大抵の場合、複数生成されます。<br>SQLで言うGROUP BY句に似た動作をします。<br>子のAggregationを指定することで、Bucketごとの集計結果を得ることもできます。|\n",
    "|Pipeline Aggregations|他のAggregationの結果に対して、さらに集約処理をします。<br>SQLで言うサブクエリのような動作をします。|\n",
    "|Matrix Aggregations|複数のフィールドを入力とし、結果を行列で返します。<br>相関行列を求めたりすることができます。|"
   ]
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   },
   "source": [
    "## リクエストの基本構文"
   ]
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   "source": [
    "例えば、2015年8月の東京都の平均気温の最大値を確認するには、次の形式でリクエストを送信します。"
   ]
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     "text": [
      "{\n",
      "  \"took\" : 8,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 65,\n",
      "    \"successful\" : 65,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 31,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"max_temperature\" : {\n",
      "      \"value\" : 37.70000076293945\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   730  100   287  100   443  26143  40353 --:--:-- --:--:-- --:--:-- 44300\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/$INDEX/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"bool\": {\n",
    "      \"must\": [\n",
    "        {\n",
    "          \"term\": {\n",
    "            \"location\": \"tokyo\"\n",
    "          }\n",
    "        },\n",
    "        {\n",
    "          \"range\": {\n",
    "            \"@timestamp\": {\n",
    "              \"gte\": \"2015-08-01\",\n",
    "              \"lt\": \"2015-09-01\",\n",
    "              \"time_zone\": \"+09:00\"\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      ]\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"max_temperature\" : {\n",
    "      \"max\" : {\n",
    "        \"field\" : \"temperature_max\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
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   "source": [
    "この例の \"**aggs**\" の下の部分が、Aggregationを指定するためのJSON文です。  \n",
    "\"query\" による条件指定と同様に、リクエストのBody部に記述します。  \n",
    "\"query\" の検索結果に対して、\"aggs\"のパラメータで指定した内容で処理します。"
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   "source": [
    "構文は次の通りです。"
   ]
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   "source": [
    "\"aggs\" : {　　…(1)\n",
    "    \"<Aggregation名 1>\" : {　　…(2)\n",
    "        \"<Aggregation種別>\" : {　　…(3)\n",
    "            <AggregationのBody>　　…(4)\n",
    "        }\n",
    "        ,\"aggs\" : { <子のAggregation> }　　…(5) \n",
    "    }\n",
    "    ,\"<Aggregation名 2>\" : { …… }　　…(6)\n",
    "    ,\"<Aggregation名 3>\" : { …… }\n",
    "    ……\n",
    "}"
   ]
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   "source": [
    "|| 例での対応箇所 | 設定する内容 |\n",
    "|----|----|----|\n",
    "|(1)|\"aggs\"|Aggregationの指定が始まることを示す予約語です。<br>\"aggregations\"と、略さずに書くこともできます。 | \n",
    "|(2)|\"temperature_max\"| Aggregation名として任意の名前を付与できます。<br>例のような1件だけの場合はあまり意味がありませんが、<br>複数のAggregationを指定したとき応答を区別するためなどに役立ちます。|\n",
    "|(3)|\"max\"| Aggregationの種別です。<br>例ではMetrics Aggregationsの1つであるmaxを指定することで、<br>最大値を計算させています。|\n",
    "|(4)|\"field\" : \"templature\"|AggregationのBodyとして、集計のパラメータ等を指定します。<br>例では最大値を計算する対象として、<br>気温のフィールドを指定しています。|\n",
    "|(5)|なし|\"aggs\"を内部に指定することで、<br>集計結果を入れ子にして計算させることができます。|\n",
    "|(6)|なし|\"aggs\"の下に、複数のAggregationを並列に指定できます。<br>そうすることで複数の集計を同時に要求できます。<br>応答結果は指定したAggregation分だけ別々に表示され、<br>その際にAggregation名でどの指定に対応する結果かを区別できます。|"
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   "source": [
    "詳細については、Elasticsearch Referenceの[Aggregations](https://www.elastic.co/guide/en/elasticsearch/reference/5.x/search-aggregations.html)を参考にしてください。"
   ]
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   "source": [
    "なお、リクエストの例の先頭で"
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   "source": [
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "     ……"
   ]
  },
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   "source": [
    "のように件数として0件を指定していますが、これはqueryの結果が応答に混ざるのを抑制し、集計結果だけを表示するための指定です。  \n",
    "\n",
    "\"size\"は、[02_Search](02_Search.ipynb)の「検索結果の件数指定」で説明した通り、\"query\"で検索した結果の件数を制限するための指定です。  \n",
    "この指定がない場合、例にあるリクエストを送信すると、\n",
    "- \"query\"での検索結果（つまり2000年8月の東京都の気象データ全て）\n",
    "- \"aggs\"での集計結果の最大値\n",
    "\n",
    "の両方が応答に出てきてしまいます。  \n",
    "今回、特に\"query\"に対応する検索結果は不要ですので、\"size\"に0件を指定することで検索結果を応答からカットしています。  \n",
    "\n",
    "※\"size\"は\"query\"にのみ影響するため、\"aggs\"配下のAggregation結果が何件であっても、Aggregationの応答の数を制限しません。\n"
   ]
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   },
   "source": [
    "## レスポンスの基本構文"
   ]
  },
  {
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   "source": [
    "Aggregationを利用して検索した場合のレスポンスは次のようになります。"
   ]
  },
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    "{\n",
    "  \"took\" : 10,\n",
    "  \"timed_out\" : false,\n",
    "  \"_shards\" : {\n",
    "    \"total\" : 5,\n",
    "    \"successful\" : 5,\n",
    "    \"failed\" : 0\n",
    "  },\n",
    "  \"hits\" : {\n",
    "    \"total\" : 12,\n",
    "    \"max_score\" : 0.0,\n",
    "    \"hits\" : [ ]\n",
    "  },\n",
    "  \"aggregations\" : {\n",
    "    \"summer_days_count\" : {\n",
    "      \"value\" : 12\n",
    "    }\n",
    "  }\n",
    "}"
   ]
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     "previous": "94c430f8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"took\"から\"hits\"までの内容は、[レスポンスの基本構文（ドキュメントID以外で検索した場合）](02_Search.ipynb#レスポンスの基本構文（ドキュメントID以外で検索した場合）)と同様です。  \n",
    "\"aggregations\"以下について、レスポンスの例と対応させると次のような設定内容になっています。  \n",
    "\n",
    "| 構成要素 | 例での対応箇所 | 設定される内容 |\n",
    "|----|----|----|\n",
    "|Aggregation名|summer_days_count|リクエストで利用したAggregation名が設定されます。<BR>1回のリクエストで複数のAggregationを行うときに結果を識別するために利用します。|\n",
    "|Aggregation結果|value|Aggregation結果の値が設定されます。<BR>Aggregation種別によって、複数の結果を返す場合があります。詳細は各Aggregationの説明を参照してください。|"
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   "source": [
    "## Metrics Aggregations"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Metrics Aggregationsは、複数のドキュメントが持つ数値データを集約するためのAggregationsです。\n",
    "\n",
    "Metrics Aggregationsの種類は次の通りです。  \n",
    "リンクになっているAggregationは、この後に説明があります。"
   ]
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   "source": [
    "| 名称 | Aggregation種別 | 内容 | \n",
    "|----------------------------|-----------------|------|\n",
    "|[Value Count Aggregation](#Value-Count-Aggregation（個数のカウント）)|value_count|データの個数を集計します。|\n",
    "|Cardinality Aggregation|cardinality|異なる値のおおよその数を集計します。|\n",
    "|[Min Aggregation](#Min-/-Max-Aggregation（最小-/-最大）)|min|数値の最小値を集計します。|\n",
    "|[Max Aggregation](#Min-/-Max-Aggregation（最小-/-最大）)|max|数値の最大値を集計します。|\n",
    "|[Avg Aggregation](#Avg-Aggregation（平均）)|avg|数値の平均値を集計します。|\n",
    "|[Sum Aggregation](#Sum-Aggregation（合計）)|sum|数値の合計値を集計します。|\n",
    "|[Stats Aggregation](#Stats-Aggregation（各種集計の一括実施）)|stats|データの個数、最小値、最大値、平均値、合計値を集計します。|\n",
    "|Extended Stats Aggregation|extended_stats|Stats Aggregationの結果に加えて、<br>合計値の二乗、分散、標準偏差、平均値±標準偏差を集計します。|\n",
    "|[Percentiles Aggregation](#Percentiles-Aggregation（パーセンタイル）)|percentiles|データのパーセンタイル値を返します。|\n",
    "|Percentile Ranks Aggregation|percentile_ranks|データのパーセンタイルランクを返します。|\n",
    "|Top hits Aggregation|top_hits|関連性の高い上位何件かを返します。|\n",
    "|Scripted Metric Aggregation|scripted_metric|スクリプトを利用して、独自の集約を行うことができます。|\n",
    "|Geo Bounds Aggregation|geo_bounds|全ての位置座標（geo_point）を含む、長方形の座標を返します。|\n",
    "|Geo Centroid Aggregation|geo_centroid|全ての位置座標（geo_point）の、中心の座標を返します。|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c439fe-152a-11e7-b31a-0242ac12000d",
     "next": "94c43bca-152a-11e7-b31a-0242ac12000d",
     "previous": "94c43832-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "全Aggregationの説明については、Elasticsearch Referenceの[Metrics Aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics.html)を参照してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c43bca-152a-11e7-b31a-0242ac12000d",
     "next": "94c43d96-152a-11e7-b31a-0242ac12000d",
     "previous": "94c439fe-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Value Count Aggregation（個数のカウント）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c43d96-152a-11e7-b31a-0242ac12000d",
     "next": "94c43f76-152a-11e7-b31a-0242ac12000d",
     "previous": "94c43bca-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "データの個数を集計するには、Value Count Aggregationを利用します。  \n",
    "カウントしたいフィールド名をfiledパラメータとして渡します。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c43f76-152a-11e7-b31a-0242ac12000d",
     "next": "94c44142-152a-11e7-b31a-0242ac12000d",
     "previous": "94c43d96-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"value_count\" : {\n",
    "    \"field\" : \"<フィールド名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c44142-152a-11e7-b31a-0242ac12000d",
     "next": "94c44304-152a-11e7-b31a-0242ac12000d",
     "previous": "94c43f76-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年8月の東京で、temperature_maxが「30℃以上」かつ「35℃未満」だった日を数えるを検索する(真夏日の日数を得る)コマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c44304-152a-11e7-b31a-0242ac12000d",
     "next": "94c444da-152a-11e7-b31a-0242ac12000d",
     "previous": "94c44142-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 10,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 5,\n",
      "    \"successful\" : 5,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 12,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"summer_days_count\" : {\n",
      "      \"value\" : 12\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   679  100   273  100   406  21640  32183 --:--:-- --:--:-- --:--:-- 33833\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.08/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"bool\": {\n",
    "      \"must\": [\n",
    "        {\n",
    "          \"term\": {\n",
    "            \"location\": \"tokyo\"\n",
    "          }\n",
    "        },\n",
    "        {\n",
    "          \"range\" : {\n",
    "            \"temperature_max\" : {\n",
    "              \"gte\" : 30,\n",
    "              \"lt\" : 35\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      ]\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"summer_days_count\" : {\n",
    "      \"value_count\" : {\n",
    "        \"field\" : \"temperature_max\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c444da-152a-11e7-b31a-0242ac12000d",
     "next": "94c446ba-152a-11e7-b31a-0242ac12000d",
     "previous": "94c44304-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Min / Max Aggregation（最小 / 最大）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c446ba-152a-11e7-b31a-0242ac12000d",
     "next": "94c44886-152a-11e7-b31a-0242ac12000d",
     "previous": "94c444da-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "データの最小値を集計するには、Min Aggregationを利用します。  \n",
    "filedパラメータに渡したフィールドの最小値が集計されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c44886-152a-11e7-b31a-0242ac12000d",
     "next": "94c44a48-152a-11e7-b31a-0242ac12000d",
     "previous": "94c446ba-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"min\" : {\n",
    "    \"field\" : \"<フィールド名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c44a48-152a-11e7-b31a-0242ac12000d",
     "next": "94c44c0a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c44886-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "最大値を集計するMax Aggregationの場合は、\"min\"を\"max\"にするだけです。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c44c0a-152a-11e7-b31a-0242ac12000d",
     "next": "94c44dd6-152a-11e7-b31a-0242ac12000d",
     "previous": "94c44a48-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"max\" : {\n",
    "    \"field\" : \"<フィールド名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c44dd6-152a-11e7-b31a-0242ac12000d",
     "next": "94c44fa2-152a-11e7-b31a-0242ac12000d",
     "previous": "94c44c0a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年1月の東京で、\n",
    "- temperature_minの最小値(最も寒い日の最低気温を集計する)\n",
    "- temperature_maxの最大値(最も暖かい日の最高気温を集計する)\n",
    "\n",
    "を得るコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c44fa2-152a-11e7-b31a-0242ac12000d",
     "next": "94c45182-152a-11e7-b31a-0242ac12000d",
     "previous": "94c44dd6-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 2,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 5,\n",
      "    \"successful\" : 5,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 30,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"max_temperature\" : {\n",
      "      \"value\" : 16.399999618530273\n",
      "    },\n",
      "    \"min_temperature\" : {\n",
      "      \"value\" : -2.200000047683716\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   617  100   354  100   263  55915  41541 --:--:-- --:--:-- --:--:-- 59000\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.01/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"min_temperature\" : {\n",
    "      \"min\" : {\n",
    "        \"field\" : \"temperature_min\"\n",
    "      }\n",
    "    },\n",
    "    \"max_temperature\" : {\n",
    "      \"max\" : {\n",
    "        \"field\" : \"temperature_max\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c45182-152a-11e7-b31a-0242ac12000d",
     "next": "94c45344-152a-11e7-b31a-0242ac12000d",
     "previous": "94c44fa2-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Avg Aggregation（平均）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c45344-152a-11e7-b31a-0242ac12000d",
     "next": "94c45510-152a-11e7-b31a-0242ac12000d",
     "previous": "94c45182-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Avg Aggregationは検索結果のドキュメントに含まれる数値の平均値を集計します。   \n",
    "filedパラメータに渡したフィールドの平均値が集計されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c45510-152a-11e7-b31a-0242ac12000d",
     "next": "94c456d2-152a-11e7-b31a-0242ac12000d",
     "previous": "94c45344-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"avg\" : {\n",
    "    \"field\" : \"<フィールド名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c456d2-152a-11e7-b31a-0242ac12000d",
     "next": "94c4589e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c45510-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京都の平均気温の平均値を得るコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4589e-152a-11e7-b31a-0242ac12000d",
     "next": "94c45a74-152a-11e7-b31a-0242ac12000d",
     "previous": "94c456d2-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 16,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 65,\n",
      "    \"successful\" : 65,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 359,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"avg_temperature\" : {\n",
      "      \"value\" : 16.424512537076946\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   751  100   290  100   461  15648  24875 --:--:-- --:--:-- --:--:-- 25611\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/$INDEX/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"bool\": {\n",
    "      \"must\": [\n",
    "        {\n",
    "          \"term\": {\n",
    "            \"location\": \"tokyo\"\n",
    "          }\n",
    "        },\n",
    "        {\n",
    "          \"range\": {\n",
    "            \"@timestamp\": {\n",
    "              \"gte\": \"2015-01-01T00:00:00\",\n",
    "              \"lt\": \"2016-01-01T00:00:00\",\n",
    "              \"time_zone\": \"+09:00\"\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      ]\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"avg_temperature\" : {\n",
    "      \"avg\" : {\n",
    "        \"field\" : \"temperature_avg\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c45a74-152a-11e7-b31a-0242ac12000d",
     "next": "94c45c36-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4589e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Sum Aggregation（合計）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c45c36-152a-11e7-b31a-0242ac12000d",
     "next": "94c45e02-152a-11e7-b31a-0242ac12000d",
     "previous": "94c45a74-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "データの合計を集計するには、Sum Aggregationを利用します。  \n",
    "合計したいフィールド名をfiledパラメータとして渡します。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c45e02-152a-11e7-b31a-0242ac12000d",
     "next": "94c45fc4-152a-11e7-b31a-0242ac12000d",
     "previous": "94c45c36-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"sum\" : {\n",
    "    \"field\" : \"<フィールド名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c45fc4-152a-11e7-b31a-0242ac12000d",
     "next": "94c46190-152a-11e7-b31a-0242ac12000d",
     "previous": "94c45e02-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年1月の東京で、sunshine_duration(日照時間)を合計するコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c46190-152a-11e7-b31a-0242ac12000d",
     "next": "94c464b0-152a-11e7-b31a-0242ac12000d",
     "previous": "94c45fc4-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 6,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 5,\n",
      "    \"successful\" : 5,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 30,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"total_sunshine_duration\" : {\n",
      "      \"value\" : 175.39999996125698\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   479  100   294  100   185  28795  18119 --:--:-- --:--:-- --:--:-- 29400\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.01/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"total_sunshine_duration\" : {\n",
    "      \"sum\" : {\n",
    "        \"field\" : \"sunshine_duration\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c464b0-152a-11e7-b31a-0242ac12000d",
     "next": "94c466d6-152a-11e7-b31a-0242ac12000d",
     "previous": "94c46190-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Stats Aggregation（各種集計の一括実施）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c466d6-152a-11e7-b31a-0242ac12000d",
     "next": "94c468a2-152a-11e7-b31a-0242ac12000d",
     "previous": "94c464b0-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "ここまで、データの個数、最小、最大、平均、合計を個別に集計してきましたが、Stats Aggregationを利用すればそれらをまとめて取得できます。  \n",
    "集計したいフィールド名をfiledパラメータとして渡します。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c468a2-152a-11e7-b31a-0242ac12000d",
     "next": "94c46a6e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c466d6-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"stats\" : {\n",
    "    \"field\" : \"<フィールド名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c46a6e-152a-11e7-b31a-0242ac12000d",
     "next": "94c46c44-152a-11e7-b31a-0242ac12000d",
     "previous": "94c468a2-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年1月の東京で、sunshine_duration(日照時間)を個数、最小、最大、平均、合計を取得するコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c46c44-152a-11e7-b31a-0242ac12000d",
     "next": "94c46e1a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c46a6e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 5,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 5,\n",
      "    \"successful\" : 5,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 30,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"statsu_sunshine_duration\" : {\n",
      "      \"count\" : 29,\n",
      "      \"min\" : 0.0,\n",
      "      \"max\" : 9.600000381469727,\n",
      "      \"avg\" : 6.048275860732999,\n",
      "      \"sum\" : 175.39999996125698\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   586  100   398  100   188  52743  24913 --:--:-- --:--:-- --:--:-- 56857\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.01/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"statsu_sunshine_duration\" : {\n",
    "      \"stats\" : {\n",
    "        \"field\" : \"sunshine_duration\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c46e1a-152a-11e7-b31a-0242ac12000d",
     "next": "94c46fdc-152a-11e7-b31a-0242ac12000d",
     "previous": "94c46c44-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Percentiles Aggregation（パーセンタイル）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c46fdc-152a-11e7-b31a-0242ac12000d",
     "next": "94c471b2-152a-11e7-b31a-0242ac12000d",
     "previous": "94c46e1a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Percentiles Aggregationは数値のパーセンタイルを集計します。   \n",
    "filedパラメータに渡したフィールドのパーセンタイルが集計されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c471b2-152a-11e7-b31a-0242ac12000d",
     "next": "94c47388-152a-11e7-b31a-0242ac12000d",
     "previous": "94c46fdc-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"percentiles\" : {\n",
    "    \"field\" : \"<フィールド名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c47388-152a-11e7-b31a-0242ac12000d",
     "next": "94c4755e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c471b2-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京都の最高気温について、パーセンタイルを得るコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4755e-152a-11e7-b31a-0242ac12000d",
     "next": "94c4772a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c47388-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 33,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 65,\n",
      "    \"successful\" : 65,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 359,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"percentiles_temperature\" : {\n",
      "      \"values\" : {\n",
      "        \"1.0\" : 5.013999986648559,\n",
      "        \"5.0\" : 8.199999809265137,\n",
      "        \"25.0\" : 13.474999904632568,\n",
      "        \"50.0\" : 21.399999618530273,\n",
      "        \"75.0\" : 27.0,\n",
      "        \"95.0\" : 33.73000087738037,\n",
      "        \"99.0\" : 35.385999565124514\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   720  100   529  100   191  14130   5101 --:--:-- --:--:-- --:--:-- 14297\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/$INDEX/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"percentiles_temperature\" : {\n",
    "      \"percentiles\" : {\n",
    "        \"field\" : \"temperature_max\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4772a-152a-11e7-b31a-0242ac12000d",
     "next": "94c47900-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4755e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "## Bucket Aggregations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c47900-152a-11e7-b31a-0242ac12000d",
     "next": "94c47acc-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4772a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Bucket Aggregationsは、Metrics Aggregationsのようにフィールドの集計計算をするのではなく、  \n",
    "ドキュメントのBucket（入れ物）を生成するAggregationです。  \n",
    "Bucketは大抵の場合、複数生成されます。  \n",
    "SQLで言うGROUP BY句に似た動作をします。\n",
    "\n",
    "子のAggregationを指定することで、Bucketごとの集計結果を得ることもできます。\n",
    "\n",
    "Bucket Aggregationsの種類は次の通りです。  \n",
    "リンクになっているAggregationは、この後に説明があります。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c47acc-152a-11e7-b31a-0242ac12000d",
     "next": "94c47ca2-152a-11e7-b31a-0242ac12000d",
     "previous": "94c47900-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "| 名称 | Aggregation種別 | 内容 | \n",
    "|---------------------------|------|------|\n",
    "|Adjacency Matrix <br>Aggregation|adjacency_matrix|フィルターで指定した集計対象を要素とし、<br>各要素間の関連を隣接行列の形式で返す集計処理です。|\n",
    "|Children Aggregation|children|親子関係を持ったドキュメントを扱うための特殊な集計処理です。<br>親子関係については、The Definitive Guideの[Parent-Child Relationship](https://www.elastic.co/guide/en/elasticsearch/guide/master/parent-child.html)を参照してください。|\n",
    "|[Date Histogram Aggregation](#Date-Histogram-Aggregation（期間ごとの集計）)|date_histogram|一定期間ごとに集計します。|\n",
    "|Date Range Aggregation|date_range|複数の日時の範囲ごとに区切って集計します。|\n",
    "|Diversified Sampler Aggregation|diversified_sampler|集計対象を数に応じてサンプル抽出して制限します。<br>このとき指定されたフィールドについて、その値がバラバラになるよう抽出します。|\n",
    "|[Filter Aggregation](#Filter-Aggregation（フィルタ）)|filter|集計対象を単一の設定でフィルタします。|\n",
    "|[Filters Aggregation](#Filters-Aggregation（複数のフィルタ）)|filters|複数のフィルタごとに集計します。|\n",
    "|Geo Distance Aggregation|geo_distance|中心位置の座標（緯度・経度で指定）からの距離ごとに集計します。|\n",
    "|GeoHash grid Aggregation|geohash_grid|位置座標をグリッドで区切り、その範囲ごとに集計します。|\n",
    "|Global Aggregation|global|queryの指定を無視して全ドキュメントを集計対象とします。|\n",
    "|Histogram Aggregation|histogram|数値フィールドを一定数ごとに区切って集計します。|\n",
    "|IP Range Aggregation|ip_range|IPアドレスの範囲ごとに集計します。|\n",
    "|Missing Aggregation|missing|指定されたフィールドが無いドキュメントに集計対象を制限します。|\n",
    "|Nested Aggregation|nested|[Nested](https://www.elastic.co/guide/en/elasticsearch/reference/current/nested.html)な種別のドキュメントを扱うための特殊な集計処理です。|\n",
    "|[Range Aggregation](#Range-Aggregation（値の範囲ごとの集計）)|range|数値フィールドを複数の数値範囲ごとに区切って集計します。|\n",
    "|Reverse nested Aggregation|reverse_nested|Nestedなドキュメントから親のドキュメントを扱うための特殊な集計処理です。<br>Nested Aggregationの内部に定義します。|\n",
    "|Sampler Aggregation|sampler|集計対象を数に応じてサンプル抽出して制限します。|\n",
    "|Significant Terms Aggregation|significant_terms|指定されたフィールドについて、値の種類ごとに集計します。<br>この時、重要さのスコアを付与して返します。|\n",
    "|[Terms Aggregation](#Terms-Aggregation（種類ごとの集計）)|terms|指定されたフィールドについて、値の種類ごとに集計します。|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c47ca2-152a-11e7-b31a-0242ac12000d",
     "next": "94c47e6e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c47acc-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "全Aggregationの説明については、Elasticsearch Referenceの[Bucket Aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket.html)を参照してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c47e6e-152a-11e7-b31a-0242ac12000d",
     "next": "94c4803a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c47ca2-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### 基本的な利用方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4803a-152a-11e7-b31a-0242ac12000d",
     "next": "94c481fc-152a-11e7-b31a-0242ac12000d",
     "previous": "94c47e6e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Bucket Aggregationsは、名前の通り集計対象となるドキュメントを複数のBucket（入れ物）に仕分けします。  \n",
    "(Filter, Grobal Aggregationなど単一のBucketにするような種類のものや、Nested, Reverse nested Aggregationなどの特殊用途のものもありますが、少数です）  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c481fc-152a-11e7-b31a-0242ac12000d",
     "next": "94c483c8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4803a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "基本的には、Aggregationの種別と、仕分けのためのパラメータを指定します。  \n",
    "次の例は、日時の期間で仕分けするDate Histogram Aggregationを用い、timestampフィールドの値で、月ごと仕分けされるようにする指定です。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c483c8-152a-11e7-b31a-0242ac12000d",
     "next": "94c48594-152a-11e7-b31a-0242ac12000d",
     "previous": "94c481fc-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"aggs\" : {\n",
    "  \"count_by_month\" : {\n",
    "    \"date_histogram\" : {\n",
    "      \"field\" : \"timestamp\",\n",
    "      \"interval\":  \"month\"\n",
    "    }\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c48594-152a-11e7-b31a-0242ac12000d",
     "next": "94c4876a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c483c8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Bucket Aggregationを単体で指定すると、各Bucketに入るドキュメントの数が集計されます。  \n",
    "上の例であれば、月ごとの数が応答されます。\n",
    "\n",
    "単に数を出すのでなく、Bucketごとの集計（最小、最大、合計など）を取りたいのであれば、  \n",
    "Bucket Aggregationに子のAggregationを指定します。  \n",
    "例えば、月ごとの平均気温を得る指定は次のようになります。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4876a-152a-11e7-b31a-0242ac12000d",
     "next": "94c4892c-152a-11e7-b31a-0242ac12000d",
     "previous": "94c48594-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"aggs\" : {\n",
    "  \"avg_by_month\" : {\n",
    "    \"date_histogram\" : {\n",
    "      \"field\" : \"timestamp\",\n",
    "      \"interval\" : \"month\"\n",
    "    },\n",
    "    \"aggs\": {\n",
    "      \"avg_temperature\": {\n",
    "        \"avg\": {\n",
    "          \"field\": \"temperature_avg\"\n",
    "        }\n",
    "      }\n",
    "    }            \n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4892c-152a-11e7-b31a-0242ac12000d",
     "next": "94c48af8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4876a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Date Histogram Aggregation（期間ごとの集計）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c48af8-152a-11e7-b31a-0242ac12000d",
     "next": "94c48cce-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4892c-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "区切られた期間ごとに集計するには、Date Histogram Aggregationを利用します。  \n",
    "期間の設定は、年・月・日・時・分・秒のどれでも指定できます。  \n",
    "\n",
    "区切る対象となる日時のフィールド名をfiledパラメータとして、期間の設定をintervalパラメータとして渡します。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c48cce-152a-11e7-b31a-0242ac12000d",
     "next": "94c48e9a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c48af8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"date_histogram\" : {\n",
    "    \"field\" : \"<フィールド名>\",\n",
    "    \"interval\":  \"<期間指定>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c48e9a-152a-11e7-b31a-0242ac12000d",
     "next": "94c4905c-152a-11e7-b31a-0242ac12000d",
     "previous": "94c48cce-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "<期間指定>の部分に  \n",
    "year, quarter, month, week, day, hour, minute, second を指定すれば、それぞれ  \n",
    "年、四半期、月、週、日、時、分、秒 ごとの区切りとなります。\n",
    "\n",
    "また、 \"6h\" や \"30m\" というように数値と特定の略語を指定することで、  \n",
    "\"6時間ごと\"、\"30分ごと\" のように区切ることも可能です。  \n",
    "\n",
    "詳細は[Date Histogram Aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-datehistogram-aggregation.html)や、[time-units](https://www.elastic.co/guide/en/elasticsearch/reference/current/common-options.html#time-units) を参照してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4905c-152a-11e7-b31a-0242ac12000d",
     "next": "94c4921e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c48e9a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、temperature_maxが30℃以上だった日数を月ごとに数えるコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4921e-152a-11e7-b31a-0242ac12000d",
     "next": "94c493f4-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4905c-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 4,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 47,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"hot_day_by_month\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-05-01T00:00:00.000Z\",\n",
      "          \"key\" : 1430438400000,\n",
      "          \"doc_count\" : 3\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-06-01T00:00:00.000Z\",\n",
      "          \"key\" : 1433116800000,\n",
      "          \"doc_count\" : 2\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-07-01T00:00:00.000Z\",\n",
      "          \"key\" : 1435708800000,\n",
      "          \"doc_count\" : 20\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-08-01T00:00:00.000Z\",\n",
      "          \"key\" : 1438387200000,\n",
      "          \"doc_count\" : 19\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-09-01T00:00:00.000Z\",\n",
      "          \"key\" : 1441065600000,\n",
      "          \"doc_count\" : 3\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  1371  100   963  100   408   140k  61141 --:--:-- --:--:-- --:--:--  156k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"bool\": {\n",
    "      \"must\": [\n",
    "        {\n",
    "          \"term\": {\n",
    "            \"location\": \"tokyo\"\n",
    "          }\n",
    "        },\n",
    "        {\n",
    "          \"range\" : {\n",
    "            \"temperature_max\" : {\n",
    "              \"gte\" : 30\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      ]\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"hot_day_by_month\" : {\n",
    "      \"date_histogram\" : {\n",
    "        \"field\" : \"@timestamp\",\n",
    "        \"interval\":  \"month\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c493f4-152a-11e7-b31a-0242ac12000d",
     "next": "94c495ca-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4921e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "日時フィールドで区切りたいのでなく、数値のフィールドで区切って集計したい場合は、  \n",
    "[Histogram Aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-histogram-aggregation.html)\n",
    "を参照してください。  \n",
    "例えば気温のフィールドを5℃ずつ区切って数えることなどが可能です。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c495ca-152a-11e7-b31a-0242ac12000d",
     "next": "94c49796-152a-11e7-b31a-0242ac12000d",
     "previous": "94c493f4-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Filter Aggregation（フィルタ）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c49796-152a-11e7-b31a-0242ac12000d",
     "next": "94c49962-152a-11e7-b31a-0242ac12000d",
     "previous": "94c495ca-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Aggregationで集計する対象を絞りたいときには、Filter Aggregationを利用できます。\n",
    "\n",
    "\"term\"や\"range\"等を利用して、対象を絞り込んだ上でAggregationを適用します。  \n",
    "  \n",
    "\"term\"を利用する場合は<フィールド名>と<値>の組み合わせで集計対象の限定範囲を指定します。  \n",
    "<子Aggregation>はその限定範囲内で集計を処理します。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c49962-152a-11e7-b31a-0242ac12000d",
     "next": "94c49b2e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c49796-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"filter\" : { \"term\": { \"<フィールド名>\": \"<値>\" } },\n",
    "  \"aggs\" : {\n",
    "    <子Aggregation>\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c49b2e-152a-11e7-b31a-0242ac12000d",
     "next": "94c49d04-152a-11e7-b31a-0242ac12000d",
     "previous": "94c49962-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、晴れた日に限定したときのtemperature_max(最高気温)の平均を求めるコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c49d04-152a-11e7-b31a-0242ac12000d",
     "next": "94c49ed0-152a-11e7-b31a-0242ac12000d",
     "previous": "94c49b2e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 8,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"sunny_day_temperature\" : {\n",
      "      \"doc_count\" : 41,\n",
      "      \"avg_temperature\" : {\n",
      "        \"value\" : 15.709756153385813\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   673  100   357  100   316  31113  27540 --:--:-- --:--:-- --:--:-- 32454\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"sunny_day_temperature\" : {\n",
    "      \"filter\" : { \"term\": { \"information_daytime\": \"晴\" } },\n",
    "      \"aggs\" : {\n",
    "        \"avg_temperature\" : {\n",
    "          \"avg\" : {\n",
    "            \"field\" : \"temperature_max\"\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c49ed0-152a-11e7-b31a-0242ac12000d",
     "next": "94c4a0a6-152a-11e7-b31a-0242ac12000d",
     "previous": "94c49d04-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Filter Aggregationでの範囲限定は、queryの指定でも実現できますが、   \n",
    "Filter Aggregationであれば、次のように複数個の条件を列挙することで、異なる内容の集計を同時にリクエストできる点がメリットとなります。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4a0a6-152a-11e7-b31a-0242ac12000d",
     "next": "94c4a27c-152a-11e7-b31a-0242ac12000d",
     "previous": "94c49ed0-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "  \"aggs\" : {\n",
    "    \"sunny_day_max_temperature\" : {\n",
    "      \"filter\" : { \"term\": { \"information_daytime\": \"晴\" } },\n",
    "      \"aggs\" : {\n",
    "        \"avg_temperature\" : {\n",
    "          \"avg\" : {\n",
    "            \"field\" : \"temperature_max\"\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    \"rainy_day_min_temperature\" : {\n",
    "      \"filter\" : { \"term\": { \"information_daytime\": \"雨\" } },\n",
    "      \"aggs\" : {\n",
    "        \"avg_temperature\" : {\n",
    "          \"avg\" : {\n",
    "            \"field\" : \"temperature_min\"\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4a27c-152a-11e7-b31a-0242ac12000d",
     "next": "94c4a498-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4a0a6-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Filters Aggregation（複数のフィルタ）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4a498-152a-11e7-b31a-0242ac12000d",
     "next": "94c4a65a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4a27c-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Filter Aggregationでは１つの条件を指定しましたが、複数を指定したい場合はFilters Aggregationが利用できます。\n",
    "\n",
    "\"term\"を利用する場合は、\"filters\"の下に<フィールド名>と<値>の組み合わせを必要なだけ繰り返し記述します。  \n",
    "<子Aggregation>の集計は、<フィールド名>と<値>の組み合わせごとに別個に処理されます。  \n",
    "処理結果を区別するために<キー名>を指定します。応答には<キー名>ごとの集計結果が表示されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4a65a-152a-11e7-b31a-0242ac12000d",
     "next": "94c4a844-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4a498-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"filters\" : {\n",
    "    \"filters\" : {\n",
    "      \"<キー名1>\" : { \"term\" : { \"<フィールド名1>\": \"<値1>\" }},\n",
    "      \"<キー名2>\" : { \"term\" : { \"<フィールド名2>\": \"<値2>\" }},\n",
    "        :\n",
    "        : (以下繰り返し)\n",
    "        :\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    <子Aggregation>\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4a844-152a-11e7-b31a-0242ac12000d",
     "next": "94c4aa10-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4a65a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、晴れ、雨、曇りの日ごとのtemperature_max(最高気温)の平均を求めるコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4aa10-152a-11e7-b31a-0242ac12000d",
     "next": "94c4abdc-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4a844-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 9,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"temperature_by_weather\" : {\n",
      "      \"buckets\" : {\n",
      "        \"cloudy\" : {\n",
      "          \"doc_count\" : 8,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 26.774999856948853\n",
      "          }\n",
      "        },\n",
      "        \"rainy\" : {\n",
      "          \"doc_count\" : 2,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 19.949999809265137\n",
      "          }\n",
      "        },\n",
      "        \"sunny\" : {\n",
      "          \"doc_count\" : 41,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 15.709756153385813\n",
      "          }\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  1234  100   719  100   515  46812  33530 --:--:-- --:--:-- --:--:-- 47933\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"temperature_by_weather\" : {\n",
    "      \"filters\" : {\n",
    "        \"filters\" : {\n",
    "          \"sunny\"  : { \"term\" : { \"information_daytime\": \"晴\" }},\n",
    "          \"rainy\"  : { \"term\" : { \"information_daytime\": \"雨\" }},\n",
    "          \"cloudy\" : { \"term\" : { \"information_daytime\": \"曇\" }}\n",
    "        }\n",
    "      },      \n",
    "      \"aggs\" : {\n",
    "        \"avg_temperature\" : {\n",
    "          \"avg\" : {\n",
    "            \"field\" : \"temperature_max\"\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4abdc-152a-11e7-b31a-0242ac12000d",
     "next": "94c4ada8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4aa10-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Range Aggregation（値の範囲ごとの集計）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4ada8-152a-11e7-b31a-0242ac12000d",
     "next": "94c4af74-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4abdc-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Histogram AggregationやDate Histogram Aggregationでは、一定の数値や期間で区切られた範囲を集計できますが、それぞれの範囲の大きさを個別に増減させることはできません。  \n",
    "個別に増減させたいときはRange Aggragationで範囲ごとの大きさを調整できます。\n",
    "\n",
    "区切る対象となる数値のフィールド名をfiledパラメータとして指定します。  \n",
    "範囲の大きさはrangesパラメータの下で、toとfromで指定します。それを設定したい範囲ごとに繰り返します。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4af74-152a-11e7-b31a-0242ac12000d",
     "next": "94c4b14a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4ada8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"range\" : {\n",
    "    \"field\" : \"<フィールド名>\",\n",
    "    \"ranges\" : [\n",
    "      { \"from\" : <開始値1>, \"to\" : <終了値1> },\n",
    "      { \"from\" : <開始値2>, \"to\" : <終了値2> },\n",
    "      　：\n",
    "      　：（範囲の数だけ繰り返し）\n",
    "      　：\n",
    "    ]\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4b14a-152a-11e7-b31a-0242ac12000d",
     "next": "94c4b320-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4af74-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "なお、fromと同じ値は範囲に入るよう扱われ、逆にtoと同じ値は範囲に入りません。  \n",
    "また、fromかtoのどちらかは省略することも可能です。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4b320-152a-11e7-b31a-0242ac12000d",
     "next": "94c4b4e2-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4b14a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、temperature_maxが\n",
    "- 15℃未満\n",
    "- 15℃以上、30℃未満\n",
    "- 30℃以上\n",
    "\n",
    "である日が、3つの範囲ごとに何日ずつあったか数えるコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4b4e2-152a-11e7-b31a-0242ac12000d",
     "next": "94c4b6ae-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4b320-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 8,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"day_by_range\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key\" : \"*-15.0\",\n",
      "          \"to\" : 15.0,\n",
      "          \"doc_count\" : 105\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"15.0-30.0\",\n",
      "          \"from\" : 15.0,\n",
      "          \"to\" : 30.0,\n",
      "          \"doc_count\" : 205\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"30.0-*\",\n",
      "          \"from\" : 30.0,\n",
      "          \"doc_count\" : 47\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   896  100   607  100   289  52860  25167 --:--:-- --:--:-- --:--:-- 55181\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"day_by_range\" : {\n",
    "      \"range\" : {\n",
    "        \"field\" : \"temperature_max\",\n",
    "        \"ranges\" : [\n",
    "          { \"to\" : 15 },\n",
    "          { \"from\" : 15, \"to\" :30 },\n",
    "          { \"from\" : 30 }\n",
    "        ]\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4b6ae-152a-11e7-b31a-0242ac12000d",
     "next": "94c4b884-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4b4e2-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Terms Aggregation（種類ごとの集計）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4b884-152a-11e7-b31a-0242ac12000d",
     "next": "94c4ba50-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4b6ae-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "分類対象のフィールドが数値や期間などでなく、ある選択肢のうちどれかを選ぶようなものであった場合（例えば何かの種類の名前など）、その種類ごとに集計するにはTerms Aggregationを利用します。\n",
    "\n",
    "設定内容としては、分類したいフィールド名をfiledパラメータとして指定するのみです。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4ba50-152a-11e7-b31a-0242ac12000d",
     "next": "94c4bc1c-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4b884-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"terms\" : {\n",
    "    \"field\" : \"<フィールド名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4bc1c-152a-11e7-b31a-0242ac12000d",
     "next": "94c4bde8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4ba50-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、information_daytime(日中の天気)ごとの日数を数えるコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4bde8-152a-11e7-b31a-0242ac12000d",
     "next": "94c4bfb4-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4bc1c-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 77,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"day_by_info\" : {\n",
      "      \"doc_count_error_upper_bound\" : 0,\n",
      "      \"sum_other_doc_count\" : 114,\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key\" : \"晴\",\n",
      "          \"doc_count\" : 41\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"一時曇\",\n",
      "          \"doc_count\" : 30\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"一時雨\",\n",
      "          \"doc_count\" : 22\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"時々曇\",\n",
      "          \"doc_count\" : 21\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"一時晴\",\n",
      "          \"doc_count\" : 16\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"後曇\",\n",
      "          \"doc_count\" : 15\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"時々晴\",\n",
      "          \"doc_count\" : 11\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"後一時雨\",\n",
      "          \"doc_count\" : 9\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"後薄曇\",\n",
      "          \"doc_count\" : 9\n",
      "        },\n",
      "        {\n",
      "          \"key\" : \"後一時曇\",\n",
      "          \"doc_count\" : 8\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  1315  100  1138  100   177  13106   2038 --:--:-- --:--:-- --:--:-- 13232\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"day_by_info\" : {\n",
    "      \"terms\" : {\n",
    "        \"field\" : \"information_daytime\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4bfb4-152a-11e7-b31a-0242ac12000d",
     "next": "94c4c194-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4bde8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "## Pipeline Aggregations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4c194-152a-11e7-b31a-0242ac12000d",
     "next": "94c4c36a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4bfb4-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Pipeline Aggregationsは、他のAggregationの結果に対して、さらに集約処理をするAggregationsです。\n",
    "SQLで言うサブクエリのような動作をします。\n",
    "\n",
    "Pipeline Aggregationsの種類は次の通りです。  \n",
    "リンクになっているAggregationは、この後に説明があります。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4c36a-152a-11e7-b31a-0242ac12000d",
     "next": "94c4c540-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4c194-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "| 名称 | Aggregation種別 | 内容 | \n",
    "|-----------------------------|-----------------|------|\n",
    "|[Min Bucket Aggregation](#Min-/-Max-Bucket-Aggregation（最小-/-最大）)|min_bucket|集計結果の最小値を集計します。|\n",
    "|[Max Bucket Aggregation](#Min-/-Max-Bucket-Aggregation（最小-/-最大）)|max_bucket|集計結果の最大値を集計します。|\n",
    "|[Avg Bucket Aggregation](#Avg-Bucket-Aggregation（平均）)|avg_bucket|集計結果の平均値を集計します。|\n",
    "|[Sum Bucket Aggregation](#Sum-Bucket-Aggregation（合計）)|sum_bucket|集計結果の合計値を集計します。|\n",
    "|[Derivative Aggregation](#Derivative-Aggregation（差分）)|derivative|時系列に並んだ集計結果に対し、1つ前のデータとの差分を集計します。|\n",
    "|Serial Differencing Aggregation|serial_diff|時系列に並んだ集計結果に対し、いくつか前のデータとの差分を返します。|\n",
    "|[Stats Bucket Aggregation](#Stats-Bucket-Aggregation（各種集計の一括実施）)|stats_bucket|集計結果の個数、最小値、最大値、平均値、合計値を集計します。|\n",
    "|Extended Stats Bucket Aggregation|extended_stats_bucket|Stats Bucket　Aggregationの結果に加えて、<br>合計値の二乗、分散、標準偏差、平均値±標準偏差を集計します。|\n",
    "|[Percentiles Bucket Aggregation](#Percentiles-Bucket-Aggregation（パーセンタイル）)|percentiles_bucket|集計結果のパーセンタイル値を返します。|\n",
    "|[Moving Average Aggregation](#Moving-Average-Aggregation（移動平均）)|moving_avg|時系列に並んだ集計結果に対し、移動平均を返します。|\n",
    "|Cumulative Sum Aggregation|cumulative_sum|集計結果の累積和を返します。|\n",
    "|Bucket Script Aggregation|bucket_script|集計結果に対して、スクリプトを適用した結果を返します。|\n",
    "|Bucket Selector Aggregation|bucket_selector|集計結果に対して、スクリプトに記述した条件を満たす結果を返します。|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4c540-152a-11e7-b31a-0242ac12000d",
     "next": "94c4c702-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4c36a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "全Aggregationの説明については、Elasticsearch Referenceの[Pipeline Aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-pipeline.html)を参照してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4c702-152a-11e7-b31a-0242ac12000d",
     "next": "94c4c8ec-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4c540-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### 基本的な利用方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4c8ec-152a-11e7-b31a-0242ac12000d",
     "next": "94c4cab8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4c702-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Pipeline Aggregationsは、他のAggregationによる集計結果を集計します。  \n",
    "集計対象は、Aggregationの親子関係等で勝手に決まるのではなく、\"**buckets_path**\"という専用のパラメータに対象Aggregationの名前を記述することで指定します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4cab8-152a-11e7-b31a-0242ac12000d",
     "next": "94c4cc84-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4c8ec-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "次の例は、月ごとの平均気温を集計し、その中からさらに最も高いものを集計するリクエストです。  \n",
    "(3)が他の集計結果から最大のものを得るというPipeline Aggregationsを指定している部分です。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4cc84-152a-11e7-b31a-0242ac12000d",
     "next": "94c4ce50-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4cab8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"aggs\" : {\n",
    "  \"temperature_per_month\" : {　　…(1)\n",
    "    \"date_histogram\" : {\n",
    "      \"field\" : \"date\",\n",
    "      \"interval\" : \"month\"\n",
    "    },\n",
    "    \"aggs\": {\n",
    "      \"avg_temperature\": {　　…(2)\n",
    "        \"avg\": {\n",
    "          \"field\": \"temperature_avg\"\n",
    "        }\n",
    "      }\n",
    "    }            \n",
    "  },\n",
    "  \"max_temperature_month\" : {　　…(3)\n",
    "    \"max_bucket\" : {\n",
    "      \"buckets_path\" : \"temperature_per_month>avg_temperature\"\n",
    "    }\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4ce50-152a-11e7-b31a-0242ac12000d",
     "next": "94c4d012-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4cc84-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "(1)と(2)は、前の章までで説明しているBucket AggregationsとMetrics Aggregationsの組み合わせです。  \n",
    "\n",
    "- (1)のData Histgram Aggregationで月ごとのBucketを生成\n",
    "- (2)のAvg Aggregationで、(1)で生成されたBucketごとに平均値を集計\n",
    "\n",
    "と、ここまでで「月ごと」の「平均気温」が集計されています。\n",
    "\n",
    "(3)の\"max_bucket\"が、Max Bucket AggregationというPipeline Aggregationsの1つを指定している部分です。  \n",
    "※名前に\"Bucket\"と入っていますが、BucketでなくPipeline Aggregationsの一種です。  \n",
    "\n",
    "\"buckets_path\"パラメータは集計の対象を指定します。基本的には、"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4d012-152a-11e7-b31a-0242ac12000d",
     "next": "94c4d1e8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4ce50-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "起点のAggregation名>子のAggregation名>孫のAggregation名>……"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4d1e8-152a-11e7-b31a-0242ac12000d",
     "next": "94c4d3b4-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4d012-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "と、起点になるAggregationから順に子のAggregation名を記述し、\">\"で区切ります。  \n",
    "起点のAggregationは、Pipeline Aggregationsの兄弟関係にある物になります。  \n",
    "今回の場合であれば、(3)と兄弟関係にある(1)の\"temperature_per_month\"が起点になります。\n",
    "\n",
    "よって、"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4d3b4-152a-11e7-b31a-0242ac12000d",
     "next": "94c4d576-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4d1e8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"buckets_path\" : \"temperature_per_month>avg_temperature\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4d576-152a-11e7-b31a-0242ac12000d",
     "next": "94c4d742-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4d3b4-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\n",
    "と記述することで、(1),(2)の出力結果が集計対象であることを指定しています。  \n",
    "Max Bucket Aggregationは、集計対象のうち最大のものを取得しますので、  \n",
    "この例の場合は、「月ごと」の「平均気温」のうち「最大のもの」を返します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4d742-152a-11e7-b31a-0242ac12000d",
     "next": "94c4d904-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4d576-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"buckets_path\"等の指定方法の詳細については、Elasticsearch Referenceの[Pipeline Aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-pipeline.html)を参照してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4d904-152a-11e7-b31a-0242ac12000d",
     "next": "94c4dada-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4d742-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Min / Max Bucket Aggregation（最小 / 最大）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4dada-152a-11e7-b31a-0242ac12000d",
     "next": "94c4dca6-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4d904-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "集計データに対する最小値を集計するには、Min Bucket Aggregationを利用します。  \n",
    "指定したパス名の集計結果の最小値が集計されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4dca6-152a-11e7-b31a-0242ac12000d",
     "next": "94c4de72-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4dada-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"min_bucket\" : {\n",
    "    \"buckets_path\" : \"<パス名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4de72-152a-11e7-b31a-0242ac12000d",
     "next": "94c4e048-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4dca6-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "最大値を集計するMax Bucket Aggregationの場合は、\"min_bucket\"を\"max_bucket\"にするだけです。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4e048-152a-11e7-b31a-0242ac12000d",
     "next": "94c4e20a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4de72-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"max_bucket\" : {\n",
    "    \"buckets_path\" : \"<パス名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4e20a-152a-11e7-b31a-0242ac12000d",
     "next": "94c4e3cc-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4e048-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、平均気温がもっとも高い月を求めるケースを考えます。この場合、\n",
    "- Date Histogram Aggregationで各月の平均気温を求める。\n",
    "- 各月の平均気温のうち、Max Bucket Aggregationでもっとも平均気温が高い月を求める。\n",
    "\n",
    "というクエリを実行します。\n",
    "\n",
    "この結果を得るコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4e3cc-152a-11e7-b31a-0242ac12000d",
     "next": "94c4e58e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4e20a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 12,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"temperature_per_month\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-01T00:00:00.000Z\",\n",
      "          \"key\" : 1420070400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 5.7\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-02-01T00:00:00.000Z\",\n",
      "          \"key\" : 1422748800000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 5.821428537368774\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-03-01T00:00:00.000Z\",\n",
      "          \"key\" : 1425168000000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 10.47741940713698\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-04-01T00:00:00.000Z\",\n",
      "          \"key\" : 1427846400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 14.743333212534587\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-05-01T00:00:00.000Z\",\n",
      "          \"key\" : 1430438400000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 21.155172676875672\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-06-01T00:00:00.000Z\",\n",
      "          \"key\" : 1433116800000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 22.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-07-01T00:00:00.000Z\",\n",
      "          \"key\" : 1435708800000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 26.570967643491684\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-08-01T00:00:00.000Z\",\n",
      "          \"key\" : 1438387200000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 26.45483878351027\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-09-01T00:00:00.000Z\",\n",
      "          \"key\" : 1441065600000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 22.443333244323732\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-10-01T00:00:00.000Z\",\n",
      "          \"key\" : 1443657600000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 18.21290317658455\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-01T00:00:00.000Z\",\n",
      "          \"key\" : 1446336000000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 13.553571428571429\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-01T00:00:00.000Z\",\n",
      "          \"key\" : 1448928000000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 9.296666701634726\n",
      "          }\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    \"max_temperature_month\" : {\n",
      "      \"value\" : 26.570967643491684,\n",
      "      \"keys\" : [\n",
      "        \"2015-07-01T00:00:00.000Z\"\n",
      "      ]\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  3550  100  3065  100   485   179k  29139 --:--:-- --:--:-- --:--:--  187k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"temperature_per_month\" : {\n",
    "      \"date_histogram\" : {\n",
    "        \"field\" : \"@timestamp\",\n",
    "        \"interval\" : \"month\"\n",
    "      },\n",
    "      \"aggs\": {\n",
    "        \"avg_temperature\": {\n",
    "          \"avg\": {\n",
    "            \"field\": \"temperature_avg\"\n",
    "          }\n",
    "        }\n",
    "      }            \n",
    "    },\n",
    "    \"max_temperature_month\" : {\n",
    "      \"max_bucket\" : {\n",
    "        \"buckets_path\" : \"temperature_per_month>avg_temperature\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4e58e-152a-11e7-b31a-0242ac12000d",
     "next": "94c4e764-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4e3cc-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Avg Bucket Aggregation（平均）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4e764-152a-11e7-b31a-0242ac12000d",
     "next": "94c4e926-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4e58e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "集計データに対する平均値を集計するには、Avg Bucket Aggregationを利用します。  \n",
    "指定したパス名の集計結果の平均値が集計されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4e926-152a-11e7-b31a-0242ac12000d",
     "next": "94c4eae8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4e764-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"avg_bucket\" : {\n",
    "    \"buckets_path\" : \"<パス名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4eae8-152a-11e7-b31a-0242ac12000d",
     "next": "94c4ecc8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4e926-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、毎月の最小気温の平均値を求めるケースを考えます。この場合、\n",
    "- Date Histogram Aggregationで各月の気温の最小値を求める。\n",
    "- 各月の平均気温の最小値に対して、Avg Bucket Aggregationで毎月の最小気温の平均値を求める。\n",
    "\n",
    "というクエリを実行します。\n",
    "\n",
    "この結果を得るコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4ecc8-152a-11e7-b31a-0242ac12000d",
     "next": "94c4ee9e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4eae8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 8,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"temperature_per_month\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-01T00:00:00.000Z\",\n",
      "          \"key\" : 1420070400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : -2.200000047683716\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-02-01T00:00:00.000Z\",\n",
      "          \"key\" : 1422748800000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : -2.4000000953674316\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-03-01T00:00:00.000Z\",\n",
      "          \"key\" : 1425168000000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : -0.4000000059604645\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-04-01T00:00:00.000Z\",\n",
      "          \"key\" : 1427846400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 2.4000000953674316\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-05-01T00:00:00.000Z\",\n",
      "          \"key\" : 1430438400000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 11.600000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-06-01T00:00:00.000Z\",\n",
      "          \"key\" : 1433116800000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 13.399999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-07-01T00:00:00.000Z\",\n",
      "          \"key\" : 1435708800000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 17.600000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-08-01T00:00:00.000Z\",\n",
      "          \"key\" : 1438387200000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 17.899999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-09-01T00:00:00.000Z\",\n",
      "          \"key\" : 1441065600000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 14.800000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-10-01T00:00:00.000Z\",\n",
      "          \"key\" : 1443657600000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 9.5\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-01T00:00:00.000Z\",\n",
      "          \"key\" : 1446336000000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 3.9000000953674316\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-01T00:00:00.000Z\",\n",
      "          \"key\" : 1448928000000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"min_temperature\" : {\n",
      "            \"value\" : 1.899999976158142\n",
      "          }\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    \"avg_temperature_month\" : {\n",
      "      \"value\" : 7.333333350718021\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  3507  100  3022  100   485   177k  29220 --:--:-- --:--:-- --:--:--  184k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"temperature_per_month\" : {\n",
    "      \"date_histogram\" : {\n",
    "        \"field\" : \"@timestamp\",\n",
    "        \"interval\" : \"month\"\n",
    "      },\n",
    "      \"aggs\": {\n",
    "        \"min_temperature\": {\n",
    "          \"min\": {\n",
    "            \"field\": \"temperature_min\"\n",
    "          }\n",
    "        }\n",
    "      }            \n",
    "    },\n",
    "    \"avg_temperature_month\" : {\n",
    "      \"avg_bucket\" : {\n",
    "        \"buckets_path\" : \"temperature_per_month>min_temperature\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4ee9e-152a-11e7-b31a-0242ac12000d",
     "next": "94c4f074-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4ecc8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Sum Bucket Aggregation（合計）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4f074-152a-11e7-b31a-0242ac12000d",
     "next": "94c4f24a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4ee9e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "集計データに対する合計値を集計するには、Sum Bucket Aggregationを利用します。  \n",
    "指定したパス名の集計結果の合計値が集計されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4f24a-152a-11e7-b31a-0242ac12000d",
     "next": "94c4f416-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4f074-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"sum_bucket\" : {\n",
    "    \"buckets_path\" : \"<パス名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4f416-152a-11e7-b31a-0242ac12000d",
     "next": "94c4f5e2-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4f24a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、毎月の日照時間の平均を全ての月で求め、それらを合計するコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4f5e2-152a-11e7-b31a-0242ac12000d",
     "next": "94c4f7ae-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4f416-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 6,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"sunshine_per_month\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-01T00:00:00.000Z\",\n",
      "          \"key\" : 1420070400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 6.048275860732999\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-02-01T00:00:00.000Z\",\n",
      "          \"key\" : 1422748800000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 5.617857109223094\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-03-01T00:00:00.000Z\",\n",
      "          \"key\" : 1425168000000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 6.2645161382613646\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-04-01T00:00:00.000Z\",\n",
      "          \"key\" : 1427846400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 5.390000047783057\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-05-01T00:00:00.000Z\",\n",
      "          \"key\" : 1430438400000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 7.806896599459237\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-06-01T00:00:00.000Z\",\n",
      "          \"key\" : 1433116800000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 4.144827553938175\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-07-01T00:00:00.000Z\",\n",
      "          \"key\" : 1435708800000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 6.151612865828699\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-08-01T00:00:00.000Z\",\n",
      "          \"key\" : 1438387200000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 4.161290310803921\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-09-01T00:00:00.000Z\",\n",
      "          \"key\" : 1441065600000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 3.806666672726472\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-10-01T00:00:00.000Z\",\n",
      "          \"key\" : 1443657600000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 6.0870967827016305\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-01T00:00:00.000Z\",\n",
      "          \"key\" : 1446336000000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 3.9535713954163447\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-01T00:00:00.000Z\",\n",
      "          \"key\" : 1448928000000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_sunshine\" : {\n",
      "            \"value\" : 5.0999999538064005\n",
      "          }\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    \"sum_sunshine_month\" : {\n",
      "      \"value\" : 64.53261129068139\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  3458  100  2986  100   472   285k  46192 --:--:-- --:--:-- --:--:--  291k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"sunshine_per_month\" : {\n",
    "      \"date_histogram\" : {\n",
    "        \"field\" : \"@timestamp\",\n",
    "        \"interval\" : \"month\"\n",
    "      },\n",
    "      \"aggs\": {\n",
    "        \"avg_sunshine\": {\n",
    "          \"avg\": {\n",
    "            \"field\": \"sunshine_duration\"\n",
    "          }\n",
    "        }\n",
    "      }            \n",
    "    },\n",
    "    \"sum_sunshine_month\" : {\n",
    "      \"sum_bucket\" : {\n",
    "        \"buckets_path\" : \"sunshine_per_month>avg_sunshine\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4f7ae-152a-11e7-b31a-0242ac12000d",
     "next": "94c4f970-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4f5e2-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Derivative Aggregation（差分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4f970-152a-11e7-b31a-0242ac12000d",
     "next": "94c4fb46-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4f7ae-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "時系列に並んだ集計結果に対し、1つ前のデータとの差分を集計するには、Derivative Aggregationを利用します。  \n",
    "Derivative Aggregationの親階層は、Date Histogram AggregationもしくはHistogram Aggregationである必要があります。\n",
    "  \n",
    "指定したパス名の集計結果の差分が集計されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4fb46-152a-11e7-b31a-0242ac12000d",
     "next": "94c4fd12-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4f970-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"derivative\" : {\n",
    "    \"buckets_path\" : \"<パス名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4fd12-152a-11e7-b31a-0242ac12000d",
     "next": "94c4ff88-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4fb46-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、毎月の最小気温の前月との差分を得るコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c4ff88-152a-11e7-b31a-0242ac12000d",
     "next": "94c5017c-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4fd12-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 9,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 328,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"temperature_per_month\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-02-01T00:00:00.000Z\",\n",
      "          \"key\" : 1422748800000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : -2.4000000953674316\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-03-01T00:00:00.000Z\",\n",
      "          \"key\" : 1425168000000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : -0.4000000059604645\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : 2.000000089406967\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-04-01T00:00:00.000Z\",\n",
      "          \"key\" : 1427846400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 2.4000000953674316\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : 2.800000101327896\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-05-01T00:00:00.000Z\",\n",
      "          \"key\" : 1430438400000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.600000381469727\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : 9.200000286102295\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-06-01T00:00:00.000Z\",\n",
      "          \"key\" : 1433116800000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 13.399999618530273\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : 1.7999992370605469\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-07-01T00:00:00.000Z\",\n",
      "          \"key\" : 1435708800000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 17.600000381469727\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : 4.200000762939453\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-08-01T00:00:00.000Z\",\n",
      "          \"key\" : 1438387200000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 17.899999618530273\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : 0.2999992370605469\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-09-01T00:00:00.000Z\",\n",
      "          \"key\" : 1441065600000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 14.800000190734863\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : -3.09999942779541\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-10-01T00:00:00.000Z\",\n",
      "          \"key\" : 1443657600000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 9.5\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : -5.300000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-01T00:00:00.000Z\",\n",
      "          \"key\" : 1446336000000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 3.9000000953674316\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : -5.599999904632568\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-01T00:00:00.000Z\",\n",
      "          \"key\" : 1448928000000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 1.899999976158142\n",
      "          },\n",
      "          \"derivative_temperature_month\" : {\n",
      "            \"value\" : -2.0000001192092896\n",
      "          }\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  4138  100  3668  100   470   274k  35984 --:--:-- --:--:-- --:--:--  298k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"temperature_per_month\" : {\n",
    "      \"date_histogram\" : {\n",
    "        \"field\" : \"@timestamp\",\n",
    "        \"interval\" : \"month\"\n",
    "      },\n",
    "      \"aggs\": {\n",
    "        \"temperature\": {\n",
    "          \"min\": {\n",
    "            \"field\": \"temperature_min\"\n",
    "          }\n",
    "        },\n",
    "        \"derivative_temperature_month\" : {\n",
    "          \"derivative\" : {\n",
    "            \"buckets_path\" : \"temperature\"\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c5017c-152a-11e7-b31a-0242ac12000d",
     "next": "94c50352-152a-11e7-b31a-0242ac12000d",
     "previous": "94c4ff88-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Stats Bucket Aggregation（各種集計の一括実施）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c50352-152a-11e7-b31a-0242ac12000d",
     "next": "94c50514-152a-11e7-b31a-0242ac12000d",
     "previous": "94c5017c-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "集計データの個数、最小、最大、平均、合計を一括して取得するには、Stats Bucket Aggregationを利用します。\n",
    "\n",
    "指定したパス名の集計結果に対し、各種の集計が一括実施されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c50514-152a-11e7-b31a-0242ac12000d",
     "next": "94c506e0-152a-11e7-b31a-0242ac12000d",
     "previous": "94c50352-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"stats_bucket\" : {\n",
    "    \"buckets_path\" : \"<パス名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c506e0-152a-11e7-b31a-0242ac12000d",
     "next": "94c508a2-152a-11e7-b31a-0242ac12000d",
     "previous": "94c50514-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京で、月ごとの平均気温の個数、最小、最大、平均、合計を一括して取得するコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c508a2-152a-11e7-b31a-0242ac12000d",
     "next": "94c50a78-152a-11e7-b31a-0242ac12000d",
     "previous": "94c506e0-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 9,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"temperature_per_month\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-01T00:00:00.000Z\",\n",
      "          \"key\" : 1420070400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 5.7\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-02-01T00:00:00.000Z\",\n",
      "          \"key\" : 1422748800000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 5.821428537368774\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-03-01T00:00:00.000Z\",\n",
      "          \"key\" : 1425168000000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 10.47741940713698\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-04-01T00:00:00.000Z\",\n",
      "          \"key\" : 1427846400000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 14.743333212534587\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-05-01T00:00:00.000Z\",\n",
      "          \"key\" : 1430438400000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 21.155172676875672\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-06-01T00:00:00.000Z\",\n",
      "          \"key\" : 1433116800000,\n",
      "          \"doc_count\" : 29,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 22.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-07-01T00:00:00.000Z\",\n",
      "          \"key\" : 1435708800000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 26.570967643491684\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-08-01T00:00:00.000Z\",\n",
      "          \"key\" : 1438387200000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 26.45483878351027\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-09-01T00:00:00.000Z\",\n",
      "          \"key\" : 1441065600000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 22.443333244323732\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-10-01T00:00:00.000Z\",\n",
      "          \"key\" : 1443657600000,\n",
      "          \"doc_count\" : 31,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 18.21290317658455\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-01T00:00:00.000Z\",\n",
      "          \"key\" : 1446336000000,\n",
      "          \"doc_count\" : 28,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 13.553571428571429\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-01T00:00:00.000Z\",\n",
      "          \"key\" : 1448928000000,\n",
      "          \"doc_count\" : 30,\n",
      "          \"avg_temperature\" : {\n",
      "            \"value\" : 9.296666701634726\n",
      "          }\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    \"stats_temperature_month\" : {\n",
      "      \"count\" : 12,\n",
      "      \"min\" : 5.7,\n",
      "      \"max\" : 26.570967643491684,\n",
      "      \"avg\" : 16.369136234336032,\n",
      "      \"sum\" : 196.4296348120324\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  3598  100  3109  100   489   242k  39091 --:--:-- --:--:-- --:--:--  253k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"term\": {\n",
    "      \"location\": \"tokyo\"\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"temperature_per_month\" : {\n",
    "      \"date_histogram\" : {\n",
    "        \"field\" : \"@timestamp\",\n",
    "        \"interval\" : \"month\"\n",
    "      },\n",
    "      \"aggs\": {\n",
    "        \"avg_temperature\": {\n",
    "          \"avg\": {\n",
    "            \"field\": \"temperature_avg\"\n",
    "          }\n",
    "        }\n",
    "      }            \n",
    "    },\n",
    "    \"stats_temperature_month\" : {\n",
    "      \"stats_bucket\" : {\n",
    "        \"buckets_path\" : \"temperature_per_month>avg_temperature\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c50a78-152a-11e7-b31a-0242ac12000d",
     "next": "94c50c3a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c508a2-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Percentile Bucket Aggregation（パーセンタイル）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c50c3a-152a-11e7-b31a-0242ac12000d",
     "next": "94c50e2e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c50a78-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "集計データに対するパーセンタイルを集計するには、Percentile Bucket Aggregationを利用します。  \n",
    "\n",
    "指定したパス名の集計結果のパーセンタイルが集計されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c50e2e-152a-11e7-b31a-0242ac12000d",
     "next": "94c5100e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c50c3a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"percentiles_bucket\" : {\n",
    "    \"buckets_path\" : \"<パス名>\"\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c5100e-152a-11e7-b31a-0242ac12000d",
     "next": "94c511ee-152a-11e7-b31a-0242ac12000d",
     "previous": "94c50e2e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の日ごとの最高気温のパーセンタイルを得るコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c511ee-152a-11e7-b31a-0242ac12000d",
     "next": "94c513ba-152a-11e7-b31a-0242ac12000d",
     "previous": "94c5100e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 24,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"temperature_per_day\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-01T00:00:00.000Z\",\n",
      "          \"key\" : 1420070400000,\n",
      "          \"doc_count\" : 2,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 7.900000095367432\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-02T00:00:00.000Z\",\n",
      "          \"key\" : 1420156800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 8.899999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-03T00:00:00.000Z\",\n",
      "          \"key\" : 1420243200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 9.300000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-04T00:00:00.000Z\",\n",
      "          \"key\" : 1420329600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 12.899999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-05T00:00:00.000Z\",\n",
      "          \"key\" : 1420416000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 16.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-06T00:00:00.000Z\",\n",
      "          \"key\" : 1420502400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 10.199999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-07T00:00:00.000Z\",\n",
      "          \"key\" : 1420588800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.199999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-08T00:00:00.000Z\",\n",
      "          \"key\" : 1420675200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-09T00:00:00.000Z\",\n",
      "          \"key\" : 1420761600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.199999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-10T00:00:00.000Z\",\n",
      "          \"key\" : 1420848000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 10.800000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-11T00:00:00.000Z\",\n",
      "          \"key\" : 1420934400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 9.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-12T00:00:00.000Z\",\n",
      "          \"key\" : 1421020800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.800000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-13T00:00:00.000Z\",\n",
      "          \"key\" : 1421107200000,\n",
      "          \"doc_count\" : 0,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : null\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-14T00:00:00.000Z\",\n",
      "          \"key\" : 1421193600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 8.199999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-15T00:00:00.000Z\",\n",
      "          \"key\" : 1421280000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 13.199999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-16T00:00:00.000Z\",\n",
      "          \"key\" : 1421366400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.600000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-17T00:00:00.000Z\",\n",
      "          \"key\" : 1421452800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 10.899999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-18T00:00:00.000Z\",\n",
      "          \"key\" : 1421539200000,\n",
      "          \"doc_count\" : 0,\n",
      "          \"temperature\" : {\n",
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      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-19T00:00:00.000Z\",\n",
      "          \"key\" : 1421625600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.199999809265137\n",
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      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-20T00:00:00.000Z\",\n",
      "          \"key\" : 1421712000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 3.799999952316284\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-21T00:00:00.000Z\",\n",
      "          \"key\" : 1421798400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 5.599999904632568\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-22T00:00:00.000Z\",\n",
      "          \"key\" : 1421884800000,\n",
      "          \"doc_count\" : 1,\n",
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      "        {\n",
      "          \"key_as_string\" : \"2015-01-23T00:00:00.000Z\",\n",
      "          \"key\" : 1421971200000,\n",
      "          \"doc_count\" : 1,\n",
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      "            \"value\" : 8.100000381469727\n",
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      "        {\n",
      "          \"key_as_string\" : \"2015-01-24T00:00:00.000Z\",\n",
      "          \"key\" : 1422057600000,\n",
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      "        {\n",
      "          \"key_as_string\" : \"2015-01-25T00:00:00.000Z\",\n",
      "          \"key\" : 1422144000000,\n",
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      "        {\n",
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      "          \"key\" : 1422230400000,\n",
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      "        {\n",
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      "        {\n",
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      "          \"key\" : 1422403200000,\n",
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      "        {\n",
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      "        {\n",
      "          \"key_as_string\" : \"2015-01-30T00:00:00.000Z\",\n",
      "          \"key\" : 1422576000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 10.5\n",
      "          }\n",
      "        },\n",
      "        {\n",
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      "        {\n",
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      "        {\n",
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      "        {\n",
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      "        {\n",
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      "        {\n",
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      "        {\n",
      "          \"key_as_string\" : \"2015-02-19T00:00:00.000Z\",\n",
      "          \"key\" : 1424304000000,\n",
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      "          \"key_as_string\" : \"2015-11-22T00:00:00.000Z\",\n",
      "          \"key\" : 1448150400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 13.199999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-23T00:00:00.000Z\",\n",
      "          \"key\" : 1448236800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 18.700000762939453\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-24T00:00:00.000Z\",\n",
      "          \"key\" : 1448323200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 12.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-25T00:00:00.000Z\",\n",
      "          \"key\" : 1448409600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.5\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-26T00:00:00.000Z\",\n",
      "          \"key\" : 1448496000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 14.699999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-27T00:00:00.000Z\",\n",
      "          \"key\" : 1448582400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 15.699999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-28T00:00:00.000Z\",\n",
      "          \"key\" : 1448668800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 14.5\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-29T00:00:00.000Z\",\n",
      "          \"key\" : 1448755200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 14.300000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-11-30T00:00:00.000Z\",\n",
      "          \"key\" : 1448841600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 16.200000762939453\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-01T00:00:00.000Z\",\n",
      "          \"key\" : 1448928000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.300000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-02T00:00:00.000Z\",\n",
      "          \"key\" : 1449014400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 13.5\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-03T00:00:00.000Z\",\n",
      "          \"key\" : 1449100800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 14.199999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-04T00:00:00.000Z\",\n",
      "          \"key\" : 1449187200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 16.100000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-05T00:00:00.000Z\",\n",
      "          \"key\" : 1449273600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 13.100000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-06T00:00:00.000Z\",\n",
      "          \"key\" : 1449360000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 12.600000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-07T00:00:00.000Z\",\n",
      "          \"key\" : 1449446400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.300000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-08T00:00:00.000Z\",\n",
      "          \"key\" : 1449532800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 12.699999809265137\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-09T00:00:00.000Z\",\n",
      "          \"key\" : 1449619200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 12.800000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-10T00:00:00.000Z\",\n",
      "          \"key\" : 1449705600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 24.100000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-11T00:00:00.000Z\",\n",
      "          \"key\" : 1449792000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 15.600000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-12T00:00:00.000Z\",\n",
      "          \"key\" : 1449878400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-13T00:00:00.000Z\",\n",
      "          \"key\" : 1449964800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 13.899999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-14T00:00:00.000Z\",\n",
      "          \"key\" : 1450051200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 14.899999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-15T00:00:00.000Z\",\n",
      "          \"key\" : 1450137600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 17.399999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-16T00:00:00.000Z\",\n",
      "          \"key\" : 1450224000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 13.399999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-17T00:00:00.000Z\",\n",
      "          \"key\" : 1450310400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.800000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-18T00:00:00.000Z\",\n",
      "          \"key\" : 1450396800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 12.100000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-19T00:00:00.000Z\",\n",
      "          \"key\" : 1450483200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.100000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-20T00:00:00.000Z\",\n",
      "          \"key\" : 1450569600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 13.300000190734863\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-21T00:00:00.000Z\",\n",
      "          \"key\" : 1450656000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 15.899999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-22T00:00:00.000Z\",\n",
      "          \"key\" : 1450742400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 8.600000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-23T00:00:00.000Z\",\n",
      "          \"key\" : 1450828800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 15.399999618530273\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-24T00:00:00.000Z\",\n",
      "          \"key\" : 1450915200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 15.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-25T00:00:00.000Z\",\n",
      "          \"key\" : 1451001600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 12.5\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-26T00:00:00.000Z\",\n",
      "          \"key\" : 1451088000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 10.5\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-27T00:00:00.000Z\",\n",
      "          \"key\" : 1451174400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 10.600000381469727\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-28T00:00:00.000Z\",\n",
      "          \"key\" : 1451260800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.5\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-29T00:00:00.000Z\",\n",
      "          \"key\" : 1451347200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.0\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-30T00:00:00.000Z\",\n",
      "          \"key\" : 1451433600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.0\n",
      "          }\n",
      "        }\n",
      "      ]\n",
      "    },\n",
      "    \"percentiles__temperature_day\" : {\n",
      "      \"values\" : {\n",
      "        \"1.0\" : 4.900000095367432,\n",
      "        \"5.0\" : 8.199999809265137,\n",
      "        \"25.0\" : 13.5,\n",
      "        \"50.0\" : 21.399999618530273,\n",
      "        \"75.0\" : 27.0,\n",
      "        \"95.0\" : 33.900001525878906,\n",
      "        \"99.0\" : 35.5\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100 79146  100 78719  100   427  2256k  12533 --:--:-- --:--:-- --:--:-- 2329k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"aggs\" : {\n",
    "    \"temperature_per_day\" : {\n",
    "      \"date_histogram\" : {\n",
    "        \"field\" : \"@timestamp\",\n",
    "        \"interval\" : \"day\"\n",
    "      },\n",
    "      \"aggs\": {\n",
    "        \"temperature\": {\n",
    "          \"max\": {\n",
    "            \"field\": \"temperature_max\"\n",
    "          }\n",
    "        }\n",
    "      }            \n",
    "    },\n",
    "    \"percentiles_temperature_day\" : {\n",
    "      \"percentiles_bucket\" : {\n",
    "        \"buckets_path\" : \"temperature_per_day>temperature\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c513ba-152a-11e7-b31a-0242ac12000d",
     "next": "94c515a4-152a-11e7-b31a-0242ac12000d",
     "previous": "94c511ee-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Moving Average Aggregation（移動平均）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c515a4-152a-11e7-b31a-0242ac12000d",
     "next": "94c51784-152a-11e7-b31a-0242ac12000d",
     "previous": "94c513ba-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "時系列に並んだ集計結果に対し、移動平均を集計するには、Moving Average Aggregationを利用します。  \n",
    "Moving Average Aggregationの親階層は、Date Histogram AggregationもしくはHistogram Aggregationである必要があります。\n",
    "\n",
    "指定したパス名の移動平均が集計されます。windowにはウィンドウのサイズを指定します。  \n",
    "例えばパス名に指定したのが日ごとのデータであり、ウィンドウのサイズに10を指定すれば、10日間の平均値が、期間を1日ずつ後ろにずらして連続して出力されます。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c51784-152a-11e7-b31a-0242ac12000d",
     "next": "94c5195a-152a-11e7-b31a-0242ac12000d",
     "previous": "94c515a4-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"moving_avg\" : {\n",
    "    \"buckets_path\" : \"<パス名>\",\n",
    "    \"window\": <ウィンドウのサイズ>\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c5195a-152a-11e7-b31a-0242ac12000d",
     "next": "94c51b30-152a-11e7-b31a-0242ac12000d",
     "previous": "94c51784-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "他にもパラメータはありますが、詳細はElasticsearch Referenceの[Moving Average Aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-pipeline-movavg-aggregation.html)を参照してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c51b30-152a-11e7-b31a-0242ac12000d",
     "next": "94c51cfc-152a-11e7-b31a-0242ac12000d",
     "previous": "94c5195a-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "日ごとの最高気温の最大値について、10日間の移動平均を得るコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c51cfc-152a-11e7-b31a-0242ac12000d",
     "next": "94c51edc-152a-11e7-b31a-0242ac12000d",
     "previous": "94c51b30-152a-11e7-b31a-0242ac12000d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 26,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"temperature_per_day\" : {\n",
      "      \"buckets\" : [\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-01T00:00:00.000Z\",\n",
      "          \"key\" : 1420070400000,\n",
      "          \"doc_count\" : 2,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 7.900000095367432\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-02T00:00:00.000Z\",\n",
      "          \"key\" : 1420156800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 8.899999618530273\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 7.900000095367432\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-03T00:00:00.000Z\",\n",
      "          \"key\" : 1420243200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 9.300000190734863\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 8.399999856948853\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-04T00:00:00.000Z\",\n",
      "          \"key\" : 1420329600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 12.899999618530273\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 8.699999968210856\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-05T00:00:00.000Z\",\n",
      "          \"key\" : 1420416000000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 16.0\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 9.74999988079071\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-06T00:00:00.000Z\",\n",
      "          \"key\" : 1420502400000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 10.199999809265137\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 10.999999904632569\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-07T00:00:00.000Z\",\n",
      "          \"key\" : 1420588800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.199999809265137\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 10.866666555404663\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-01-08T00:00:00.000Z\",\n",
      "          \"key\" : 1420675200000,\n",
      "          \"doc_count\" : 0,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : null\n",
      "          }\n",
      "        },\n",
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      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 12.620000076293945\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-28T00:00:00.000Z\",\n",
      "          \"key\" : 1451260800000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.5\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 12.500000095367431\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-29T00:00:00.000Z\",\n",
      "          \"key\" : 1451347200000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.0\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 12.440000057220459\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"key_as_string\" : \"2015-12-30T00:00:00.000Z\",\n",
      "          \"key\" : 1451433600000,\n",
      "          \"doc_count\" : 1,\n",
      "          \"temperature\" : {\n",
      "            \"value\" : 11.0\n",
      "          },\n",
      "          \"10day_moving_avg\" : {\n",
      "            \"value\" : 12.430000019073486\n",
      "          }\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  106k  100  106k  100   433  2975k  12114 --:--:-- --:--:-- --:--:-- 3038k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"size\": 0,\n",
    "  \"aggs\" : {\n",
    "    \"temperature_per_day\" : {\n",
    "      \"date_histogram\" : {\n",
    "        \"field\" : \"@timestamp\",\n",
    "        \"interval\" : \"day\"\n",
    "      },\n",
    "      \"aggs\": {\n",
    "        \"temperature\": {\n",
    "          \"max\": {\n",
    "            \"field\": \"temperature_max\"\n",
    "          }\n",
    "        },\n",
    "        \"10day_moving_avg\" : {\n",
    "          \"moving_avg\" : {\n",
    "            \"buckets_path\" : \"temperature\",\n",
    "            \"window\" : 10\n",
    "          }\n",
    "        }\n",
    "      }            \n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c51edc-152a-11e7-b31a-0242ac12000d",
     "next": "94c520ee-152a-11e7-b31a-0242ac12000d",
     "previous": "94c51cfc-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "## Matrix Aggregations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c520ee-152a-11e7-b31a-0242ac12000d",
     "next": "94c522ba-152a-11e7-b31a-0242ac12000d",
     "previous": "94c51edc-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Matrix Aggregationsは、複数のフィールドを元に行列形式の集計結果を返すようなAggregationsです。\n",
    "\n",
    "Matrix Aggregationsの種類は次の通りです。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c522ba-152a-11e7-b31a-0242ac12000d",
     "next": "94c524a4-152a-11e7-b31a-0242ac12000d",
     "previous": "94c520ee-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "| 名称 | Aggregation種別 | 内容 | \n",
    "|---------------------------|-----------------|------|\n",
    "|[Matrix Stats](#Matrix-Stats)|matrix_stats|複数のフィールドを指定して、平均値、分散、相関行列などを返します。|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c524a4-152a-11e7-b31a-0242ac12000d",
     "next": "94c52670-152a-11e7-b31a-0242ac12000d",
     "previous": "94c522ba-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "詳細については、Elasticsearch Referenceの[Matrix Aggregations](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-matrix.html)を参照してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c52670-152a-11e7-b31a-0242ac12000d",
     "next": "94c52850-152a-11e7-b31a-0242ac12000d",
     "previous": "94c524a4-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "### Matrix Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c52850-152a-11e7-b31a-0242ac12000d",
     "next": "94c52a26-152a-11e7-b31a-0242ac12000d",
     "previous": "94c52670-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Matrix Statsは複数のフィールドを指定して、次の統計情報を返します。\n",
    "\n",
    "| データの種類 | レスポンスのフィールド名 | 内容 |\n",
    "|--------------|----------|------|\n",
    "|データ数|count|指定したフィールドのデータ数|\n",
    "|平均値|mean|指定したフィールドの平均値|\n",
    "|分散|variance|指定したフィールドの分散|\n",
    "|歪度|skewness|指定したフィールドの歪度|\n",
    "|尖度|kurtosis|指定したフィールドの尖度|\n",
    "|共分散|covariance|指定したフィールド間の共分散|\n",
    "|相関係数|correlation|指定したフィールド間の相関係数|\n",
    "\n",
    "aggregation名にmatrix_statsを指定し、複数のフィールド名を指定します。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c52a26-152a-11e7-b31a-0242ac12000d",
     "next": "94c52bfc-152a-11e7-b31a-0242ac12000d",
     "previous": "94c52850-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"<Aggregation名>\" : {\n",
    "  \"matrix_stats\": {\n",
    "    \"fields\": [\"フィールド名1\", \"フィールド名2\", ……, \"フィールド名N\"]\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c52bfc-152a-11e7-b31a-0242ac12000d",
     "next": "94c52dc8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c52a26-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "フィールド名は3個以上指定することもできます。\n",
    "\n",
    "詳細は、Elasticsearch Referenceの[Matrix Stats](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-matrix-stats-aggregation.html)を参照してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c52dc8-152a-11e7-b31a-0242ac12000d",
     "next": "94c52fa8-152a-11e7-b31a-0242ac12000d",
     "previous": "94c52bfc-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "2015年の東京都の、1日の平均気温(temperature_avg)と平均湿度(humidity_avg)のMatrix Statsを確認するには、次の形式でリクエストを送信します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c52fa8-152a-11e7-b31a-0242ac12000d",
     "next": "94c53188-152a-11e7-b31a-0242ac12000d",
     "previous": "94c52dc8-152a-11e7-b31a-0242ac12000d"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 9,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 60,\n",
      "    \"successful\" : 60,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 358,\n",
      "    \"max_score\" : 0.18232156,\n",
      "    \"hits\" : [\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.09\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-09-12\",\n",
      "        \"_score\" : 0.18232156,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-09-12\",\n",
      "          \"wind_speed_avg\" : \"2.8\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"29.5\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"6.1\",\n",
      "          \"sunshine_duration\" : \"9.0\",\n",
      "          \"temperature_avg\" : \"24.7\",\n",
      "          \"temperature_min\" : \"20.5\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : null,\n",
      "          \"information_night\" : null,\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : null,\n",
      "          \"information_daytime\" : \"一時曇\",\n",
      "          \"@timestamp\" : \"2015-09-11T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"75.0\",\n",
      "          \"humidity_min\" : \"53.0\",\n",
      "          \"atmospheric_pressure\" : \"1006.5\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"9.8\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1009.3\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.09\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-09-28\",\n",
      "        \"_score\" : 0.18232156,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-09-28\",\n",
      "          \"wind_speed_avg\" : \"2.5\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"27.8\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"5.3\",\n",
      "          \"sunshine_duration\" : \"9.2\",\n",
      "          \"temperature_avg\" : \"23.3\",\n",
      "          \"temperature_min\" : \"19.3\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : \"北西\",\n",
      "          \"information_night\" : \"時々晴\",\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : \"南東\",\n",
      "          \"information_daytime\" : null,\n",
      "          \"@timestamp\" : \"2015-09-27T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"73.0\",\n",
      "          \"humidity_min\" : \"48.0\",\n",
      "          \"atmospheric_pressure\" : \"1012.4\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"10.3\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1015.2\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.02\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-02-02\",\n",
      "        \"_score\" : 0.18232156,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-02-02\",\n",
      "          \"wind_speed_avg\" : \"4.7\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"9.6\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"9.1\",\n",
      "          \"sunshine_duration\" : \"9.6\",\n",
      "          \"temperature_avg\" : \"4.7\",\n",
      "          \"temperature_min\" : \"1.0\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : \"西\",\n",
      "          \"information_night\" : null,\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : \"西\",\n",
      "          \"information_daytime\" : \"晴\",\n",
      "          \"@timestamp\" : \"2015-02-01T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"34.0\",\n",
      "          \"humidity_min\" : \"22.0\",\n",
      "          \"atmospheric_pressure\" : \"1018.8\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"15.4\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1021.8\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.02\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-02-09\",\n",
      "        \"_score\" : 0.18232156,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-02-09\",\n",
      "          \"wind_speed_avg\" : \"3.2\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"4.9\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"7.0\",\n",
      "          \"sunshine_duration\" : \"1.7\",\n",
      "          \"temperature_avg\" : \"2.0\",\n",
      "          \"temperature_min\" : \"-0.6\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : \"北西\",\n",
      "          \"information_night\" : null,\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : \"北西\",\n",
      "          \"information_daytime\" : \"後時々晴\",\n",
      "          \"@timestamp\" : \"2015-02-08T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"49.0\",\n",
      "          \"humidity_min\" : \"26.0\",\n",
      "          \"atmospheric_pressure\" : \"1007.3\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"15.1\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1010.3\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.04\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-04-02\",\n",
      "        \"_score\" : 0.18232156,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-04-02\",\n",
      "          \"wind_speed_avg\" : \"3.8\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"17.4\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"7.1\",\n",
      "          \"sunshine_duration\" : \"11.1\",\n",
      "          \"temperature_avg\" : \"11.6\",\n",
      "          \"temperature_min\" : \"6.5\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : null,\n",
      "          \"information_night\" : \"時々曇\",\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : null,\n",
      "          \"information_daytime\" : null,\n",
      "          \"@timestamp\" : \"2015-04-01T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"64.0\",\n",
      "          \"humidity_min\" : \"38.0\",\n",
      "          \"atmospheric_pressure\" : \"1016.3\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"11.6\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1019.2\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.04\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-04-27\",\n",
      "        \"_score\" : 0.18232156,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-04-27\",\n",
      "          \"wind_speed_avg\" : \"2.9\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"25.0\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"6.4\",\n",
      "          \"sunshine_duration\" : \"12.3\",\n",
      "          \"temperature_avg\" : \"19.3\",\n",
      "          \"temperature_min\" : \"13.3\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : \"南東\",\n",
      "          \"information_night\" : \"晴\",\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : \"東\",\n",
      "          \"information_daytime\" : \"晴\",\n",
      "          \"@timestamp\" : \"2015-04-26T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"68.0\",\n",
      "          \"humidity_min\" : \"46.0\",\n",
      "          \"atmospheric_pressure\" : \"1014.8\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"10.3\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1017.7\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.04\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-04-22\",\n",
      "        \"_score\" : 0.13353139,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-04-22\",\n",
      "          \"wind_speed_avg\" : \"2.6\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"21.4\",\n",
      "          \"precipitation_day\" : \"2.5\",\n",
      "          \"wind_speed_max\" : \"7.0\",\n",
      "          \"sunshine_duration\" : \"4.8\",\n",
      "          \"temperature_avg\" : \"15.7\",\n",
      "          \"temperature_min\" : \"11.2\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : \"東\",\n",
      "          \"information_night\" : \"後晴一時曇\",\n",
      "          \"precipitation_max_10min\" : \"1.0\",\n",
      "          \"precipitation_max_hour\" : \"1.5\",\n",
      "          \"wind_direction\" : \"東\",\n",
      "          \"information_daytime\" : \"後曇一時雨\",\n",
      "          \"@timestamp\" : \"2015-04-21T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"62.0\",\n",
      "          \"humidity_min\" : \"28.0\",\n",
      "          \"atmospheric_pressure\" : \"1015.7\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"10.9\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1018.6\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.04\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-04-23\",\n",
      "        \"_score\" : 0.13353139,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-04-23\",\n",
      "          \"wind_speed_avg\" : \"2.5\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"24.8\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"6.4\",\n",
      "          \"sunshine_duration\" : \"11.6\",\n",
      "          \"temperature_avg\" : \"18.0\",\n",
      "          \"temperature_min\" : \"11.9\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : \"南東\",\n",
      "          \"information_night\" : \"曇\",\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : \"南東\",\n",
      "          \"information_daytime\" : null,\n",
      "          \"@timestamp\" : \"2015-04-22T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"66.0\",\n",
      "          \"humidity_min\" : \"25.0\",\n",
      "          \"atmospheric_pressure\" : \"1016.8\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"10.1\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1019.7\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.04\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-04-26\",\n",
      "        \"_score\" : 0.13353139,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-04-26\",\n",
      "          \"wind_speed_avg\" : \"3.3\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"23.8\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"6.5\",\n",
      "          \"sunshine_duration\" : \"12.4\",\n",
      "          \"temperature_avg\" : \"17.8\",\n",
      "          \"temperature_min\" : \"12.4\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : \"南東\",\n",
      "          \"information_night\" : \"晴\",\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : \"南東\",\n",
      "          \"information_daytime\" : null,\n",
      "          \"@timestamp\" : \"2015-04-25T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"50.0\",\n",
      "          \"humidity_min\" : \"33.0\",\n",
      "          \"atmospheric_pressure\" : \"1017.0\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"12.2\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1019.8\"\n",
      "        }\n",
      "      },\n",
      "      {\n",
      "        \"_index\" : \"meteorological-data-2015.05\",\n",
      "        \"_type\" : \"logs\",\n",
      "        \"_id\" : \"tokyo_15-05-15\",\n",
      "        \"_score\" : 0.13353139,\n",
      "        \"_source\" : {\n",
      "          \"date\" : \"15-05-15\",\n",
      "          \"wind_speed_avg\" : \"3.4\",\n",
      "          \"snowfall\" : null,\n",
      "          \"temperature_max\" : \"27.8\",\n",
      "          \"precipitation_day\" : null,\n",
      "          \"wind_speed_max\" : \"6.9\",\n",
      "          \"sunshine_duration\" : \"8.8\",\n",
      "          \"temperature_avg\" : \"22.7\",\n",
      "          \"temperature_min\" : \"18.9\",\n",
      "          \"@version\" : \"1\",\n",
      "          \"wind_direction_max_moment\" : \"東\",\n",
      "          \"information_night\" : \"曇後一時雨\",\n",
      "          \"precipitation_max_10min\" : null,\n",
      "          \"precipitation_max_hour\" : null,\n",
      "          \"wind_direction\" : \"東\",\n",
      "          \"information_daytime\" : \"後曇\",\n",
      "          \"@timestamp\" : \"2015-05-14T15:00:00.000Z\",\n",
      "          \"humidity_avg\" : \"59.0\",\n",
      "          \"humidity_min\" : \"34.0\",\n",
      "          \"atmospheric_pressure\" : \"1007.3\",\n",
      "          \"location\" : \"tokyo\",\n",
      "          \"wind_speed_max_moment\" : \"11.1\",\n",
      "          \"snowfall_max\" : null,\n",
      "          \"sea_level_pressure\" : \"1010.1\"\n",
      "        }\n",
      "      }\n",
      "    ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"agg_name\" : {\n",
      "      \"fields\" : [\n",
      "        {\n",
      "          \"name\" : \"humidity_avg\",\n",
      "          \"count\" : 357,\n",
      "          \"mean\" : 67.61624649859942,\n",
      "          \"variance\" : 296.9731060963713,\n",
      "          \"skewness\" : -0.19963971039820227,\n",
      "          \"kurtosis\" : 2.3628170740552825,\n",
      "          \"covariance\" : {\n",
      "            \"humidity_avg\" : 296.9731060963713,\n",
      "            \"temperature_avg\" : 51.78742804546196\n",
      "          },\n",
      "          \"correlation\" : {\n",
      "            \"humidity_avg\" : 1.0,\n",
      "            \"temperature_avg\" : 0.39175344679154095\n",
      "          }\n",
      "        },\n",
      "        {\n",
      "          \"name\" : \"temperature_avg\",\n",
      "          \"count\" : 357,\n",
      "          \"mean\" : 16.49579832133125,\n",
      "          \"variance\" : 58.84450473165005,\n",
      "          \"skewness\" : -0.04395726272066842,\n",
      "          \"kurtosis\" : 1.8964425808761805,\n",
      "          \"covariance\" : {\n",
      "            \"humidity_avg\" : 51.78742804546196,\n",
      "            \"temperature_avg\" : 58.84450473165005\n",
      "          },\n",
      "          \"correlation\" : {\n",
      "            \"humidity_avg\" : 0.39175344679154095,\n",
      "            \"temperature_avg\" : 1.0\n",
      "          }\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100 12669  100 12494  100   175   979k  14050 --:--:-- --:--:-- --:--:-- 1016k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/meteorological-data-2015.*/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"query\": {\n",
    "    \"term\" : { \"location\" : \"tokyo\" } \n",
    "  },\n",
    "  \"aggs\": {\n",
    "    \"agg_name\": {\n",
    "      \"matrix_stats\": {\n",
    "        \"fields\": [\"temperature_avg\", \"humidity_avg\"]\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c53188-152a-11e7-b31a-0242ac12000d",
     "next": "94c5335e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c52fa8-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "## 実行クエリ解析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c5335e-152a-11e7-b31a-0242ac12000d",
     "next": "94c5353e-152a-11e7-b31a-0242ac12000d",
     "previous": "94c53188-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "クエリを解析するためにProfile APIを使用します。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c5353e-152a-11e7-b31a-0242ac12000d",
     "next": "94c53714-152a-11e7-b31a-0242ac12000d",
     "previous": "94c5335e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "Profile APIは、実行クエリについて詳細情報を提供します。  \n",
    "クエリがどのように実行されるかをユーザーに示し、特定のクエリが遅い理由を理解し、改善するために使用します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lc_cell_meme": {
     "current": "94c53714-152a-11e7-b31a-0242ac12000d",
     "next": "94c538e0-152a-11e7-b31a-0242ac12000d",
     "previous": "94c5353e-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "例として、Profile APIのコマンドは次の通りです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "lc_cell_meme": {
     "current": "94c538e0-152a-11e7-b31a-0242ac12000d",
     "next": "94c53aac-152a-11e7-b31a-0242ac12000d",
     "previous": "94c53714-152a-11e7-b31a-0242ac12000d"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"took\" : 16,\n",
      "  \"timed_out\" : false,\n",
      "  \"_shards\" : {\n",
      "    \"total\" : 70,\n",
      "    \"successful\" : 70,\n",
      "    \"failed\" : 0\n",
      "  },\n",
      "  \"hits\" : {\n",
      "    \"total\" : 329,\n",
      "    \"max_score\" : 0.0,\n",
      "    \"hits\" : [ ]\n",
      "  },\n",
      "  \"aggregations\" : {\n",
      "    \"avg_temperature\" : {\n",
      "      \"value\" : 17.40243161340615\n",
      "    }\n",
      "  },\n",
      "  \"profile\" : {\n",
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      "      {\n",
      "        \"id\" : \"[sjROHXMsTxKfXSdoCbpRSQ][meteorological-data-2014.12][3]\",\n",
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      "      },\n",
      "      {\n",
      "        \"id\" : \"[mBJcG4SZRF6BiVftkttXkQ][meteorological-data-2015.03][2]\",\n",
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      "        \"aggregations\" : [\n",
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      "            \"type\" : \"org.elasticsearch.search.aggregations.metrics.avg.AvgAggregator\",\n",
      "            \"description\" : \"avg_temperature\",\n",
      "            \"time\" : \"0.01913200000ms\",\n",
      "            \"breakdown\" : {\n",
      "              \"reduce\" : 0,\n",
      "              \"build_aggregation\" : 14255,\n",
      "              \"build_aggregation_count\" : 1,\n",
      "              \"initialize\" : 4875,\n",
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      "              \"collect\" : 0,\n",
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      "        ]\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100  261k  100  260k  100   479  6970k  12802 --:--:-- --:--:-- --:--:-- 7048k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -XGET \"http://$ES_HOST/$INDEX/_search?pretty\" -H 'Content-Type:application/json' -d @- << EOF\n",
    "{\n",
    "  \"profile\": true,\n",
    "  \"size\": 0,\n",
    "  \"query\": {\n",
    "    \"bool\": {\n",
    "      \"must\": [\n",
    "        {\n",
    "          \"term\": {\n",
    "            \"location\": \"tokyo\"\n",
    "          }\n",
    "        },\n",
    "        {\n",
    "          \"range\": {\n",
    "            \"@timestamp\": {\n",
    "              \"gte\": \"2015-01-01T00:00:00\",\n",
    "              \"lt\": \"2016-01-01T00:00:00\",\n",
    "              \"time_zone\": \"+09:00\"\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      ]\n",
    "    }\n",
    "  },\n",
    "  \"aggs\" : {\n",
    "    \"avg_temperature\" : {\n",
    "      \"avg\" : {\n",
    "        \"field\" : \"temperature_avg\"\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "EOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "current": "94c53aac-152a-11e7-b31a-0242ac12000d",
     "next": null,
     "previous": "94c538e0-152a-11e7-b31a-0242ac12000d"
    }
   },
   "source": [
    "\"profile\"配下の\"shards\"配下にある\"aggregations\"に、実行したAggregationの情報があります。\n",
    "\n",
    "Profile APIの詳細は、Elasticsearch Referenceの[Profile API](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-profile.html)を参照してください。"
   ]
  }
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