Table types

CONTRIBUTING.md

@@ -70,7 +70,8 @@ Some of this style guide is automated with GitHub Actions, but feel free to run
 * Always format SQL code, variable names, function names, etc. as code. For example, when talking about the `CREATE TABLE` statement, the keywords should be formatted as code.
 * When presenting SQL statements, do not include the DuckDB prompt (`D `).
 * SQL statements should end with a semicolon (`;`) to allow readers to quickly paste them into a SQL console.
-* Narrow tables – that do not span horizontally across the entire page – should be prepended with an empty div that has the `narrow_table` class: `<div class="narrow_table"></div>`.
+* Tables with predominantly code output (e.g., the result of a `DESCRIBE` statement) should be prepended with an empty div that has the `monospace_table` class: `<div class="monospace_table"></div>`.
+* Tables where the headers should be center-aligned (opposed to the left-aligned default) should be prepended with an empty div that has the `center_aligned_header_table` class: `<div class="center_aligned_header_table"></div>`.
 * Do not introduce hard line breaks if possible. Therefore, avoid using the `<br/>` HTML tag and avoid [double spaces at the end of a line in Markdown](https://spec.commonmark.org/0.28/#hard-line-breaks).
 * Single and double quote characters (`'` and `"`) are not converted to smart quotation marks automatically. To insert these, use `“` `”` and `‘` `’`.
 * When referencing other articles, put their titles in quotes, e.g., `see the [“Lightweight Compression in DuckDB” blog post]({% post_url 2022-10-28-lightweight-compression %})`.

_posts/2022-05-04-friendlier-sql.md

@@ -118,7 +118,6 @@ CREATE TABLE mandalorian AS SELECT 1 AS "THIS_IS_THE_WAY";
 SELECT this_is_the_way FROM mandalorian;
 ```  
 
-<div class="narrow_table"></div>
 
 | THIS_IS_THE_WAY |
 |----------------:|
@@ -163,7 +162,6 @@ Even as SQL fans, we know that SQL can learn a thing or two from newer languages
 SELECT 'I love you! I know'[:-3] AS nearly_soloed;
 ```  
 
-<div class="narrow_table"></div>
 
 |  nearly_soloed  |
 |:---|
@@ -189,7 +187,6 @@ SELECT
 FROM (SELECT ['A-Wing', 'B-Wing', 'X-Wing', 'Y-Wing'] AS starfighter_list);
 ```  
 
-<div class="narrow_table"></div>
 
 | dont_forget_the_b_wing |
 |:---|
@@ -249,7 +246,6 @@ FROM (
     ) theyre_coming_in_too_fast;
 ```  
 
-<div class="narrow_table"></div>
 
 | tie_fighter | tie_fighter:1 |
 |:---|:---|
@@ -270,7 +266,6 @@ JOIN sith_count_varchar s_char
   ON s_int.sith_count = s_char.sith_count;
 ```
 
-<div class="narrow_table"></div>
 
 | sith_count | sith_count |
 |---:|---:|

_posts/2023-07-07-python-udf.md

@@ -236,7 +236,6 @@ native_res = con.sql("SELECT sum(add_built_in_type(i)) FROM numbers").fetchall()
 arrow_res = con.sql("SELECT sum(add_arrow_type(i)) FROM numbers").fetchall()
 ```
 
-<div class="narrow_table"></div>
 
 |    Name     | Time (s) |
 |-------------|---------:|
@@ -295,7 +294,6 @@ con.sql("SELECT sum(strlen_arrow(i)) FROM strings tbl(i)").fetchall()
 exec_external(con)
 ```
 
-<div class="narrow_table"></div>
 
 |    Name     | Time (s) | Peak memory consumption (MB) |
 |-------------|---------:|-----------------------------:|

_posts/2023-08-23-even-friendlier-sql.md

@@ -65,7 +65,6 @@ SELECT
      substr(intro, starship_loc + len('starship') + 1) AS trimmed_intro;
 ```
 
-<div class="narrow_table"></div>
 
 |                        intro                        | starship_loc | trimmed_intro |
 |:---|:---|:---|
@@ -90,7 +89,6 @@ CREATE TABLE trek_facts AS
 DESCRIBE trek_facts;
 ```
 
-<div class="narrow_table"></div>
 
 |               column_name               | column_type | null | key  | default | extra |
 |:---|:---|:---|:---|:---|:---|
@@ -124,7 +122,6 @@ SELECT
 FROM trek_facts;
 ```
 
-<div class="narrow_table"></div>
 
 | episode_num | cnt_warp_speed_orders | highest_warp_speed_issued |
 |:---|:---|:---|
@@ -145,7 +142,6 @@ SELECT
 FROM trek_facts;
 ```
 
-<div class="narrow_table"></div>
 
 | max(trek_facts.cnt_warp_speed_orders) | max(trek_facts.highest_warp_speed_issued) |
 |:---|:---|
@@ -165,7 +161,6 @@ WHERE
     -- highest_warp_speed_issued >= 2
 ```
 
-<div class="narrow_table"></div>
 
 | episode_num | cnt_warp_speed_orders | highest_warp_speed_issued |
 |:---|:---|:---|
@@ -184,7 +179,6 @@ SELECT
 FROM trek_facts;
 ```
 
-<div class="narrow_table"></div>
 
 | max(trek_facts.<br>episode_num) | max(trek_facts.<br>aired_date) | max(trek_facts.<br>cnt_kirk_hookups) | ... | max(trek_facts.<br>bool_enterprise_saved_the_day) |
 |:---|:---|:---|:---|:---|
@@ -198,7 +192,6 @@ SELECT
 FROM trek_facts;
 ```
 
-<div class="narrow_table"></div>
 
 | max(trek_facts.<br>season_num) | max(trek_facts.<br>episode_num) | max(aired_date := <br>CAST(aired_date AS TIMESTAMP)) | ... | max(trek_facts.<br>bool_enterprise_saved_the_day) |
 |:---|:---|:---|:---|:---|
@@ -219,7 +212,6 @@ WHERE
     COLUMNS(col -> col LIKE '%warp%') >= 2;
 ```
 
-<div class="narrow_table"></div>
 
 | episode_num | cnt_warp_speed_orders | highest_warp_speed_issued |
 |:---|:---|:---|
@@ -245,7 +237,6 @@ SELECT
 FROM 'https://raw.githubusercontent.com/vlad-saling/star-trek-ipsum/master/src/content/content.json';
 ```
 
-<div class="narrow_table"></div>
 
 |                                                                            starship                                                                            |
 |:---|
@@ -293,7 +284,6 @@ SELECT
           .concat('.') AS im_not_messing_around_number_one;
 ```
 
-<div class="narrow_table"></div>
 
 | im_not_messing_around_number_one |
 |:---|
@@ -312,7 +302,6 @@ SELECT
      '.') AS oof;
 ```
 
-<div class="narrow_table"></div>
 
 |      oof      |
 |:---|
@@ -336,7 +325,6 @@ CREATE TABLE proverbs AS
 FROM proverbs;
 ```
 
-<div class="narrow_table"></div>
 
 |               klingon_proverb                |      borg_proverb       |
 |:---|:---|
@@ -358,7 +346,6 @@ INSERT INTO proverbs BY NAME
 SELECT * FROM proverbs;
 ```
 
-<div class="narrow_table"></div>
 
 |               klingon_proverb                |      borg_proverb       |
 |:---|:---|
@@ -386,7 +373,6 @@ INSERT INTO purchases
 FROM purchases;
 ```
 
-<div class="narrow_table"></div>
 
 |       item       | year | count |
 |:---|:---|:---|
@@ -409,7 +395,6 @@ CREATE TABLE pivoted_purchases AS
 FROM pivoted_purchases;
 ```
 
-<div class="narrow_table"></div>
 
 |       item       | 2155 | 2156  | 2157  |
 |:---|:---|:---|:---|
@@ -430,7 +415,6 @@ UNPIVOT pivoted_purchases
           VALUE count;
 ```
 
-<div class="narrow_table"></div>
 
 |       item       | year | count |
 |:---|:---|:---|
@@ -457,7 +441,6 @@ SELECT
           .list_transform(x -> x.string_split(' ')[1]) AS short_name;
 ```
 
-<div class="narrow_table"></div>
 
 |            ship_name             |
 |:---|
@@ -471,7 +454,6 @@ SELECT
           .list_filter(x -> x.contains('1701')) AS the_original;
 ```
 
-<div class="narrow_table"></div>
 
 |     the_original      |
 |:---|
@@ -490,7 +472,6 @@ SELECT
      IF x.contains('1701')] AS ready_to_boldly_go;
 ```
 
-<div class="narrow_table"></div>
 
 | ready_to_boldly_go |
 |:---|
@@ -509,7 +490,6 @@ SELECT
      casualties.*;
 ```
 
-<div class="narrow_table"></div>
 
 | gold_casualties | blue_casualties | red_casualties |
 |:---|:---|:---|
@@ -529,7 +509,6 @@ FROM officers
 SELECT officers;
 ```
 
-<div class="narrow_table"></div>
 
 |                   officers                   |
 |:---|
@@ -554,7 +533,6 @@ SELECT 6 UNION ALL
 SELECT 'First Contact';
 ```
 
-<div class="narrow_table"></div>
 
 |       movie        |
 |:---|
@@ -582,7 +560,6 @@ SELECT
      movie.num;
 ```
 
-<div class="narrow_table"></div>
 
 |       movie        | type |        name        | num |
 |:---|:---|:---|:---|

_posts/2023-09-15-asof-joins-fuzzy-temporal-lookups.md

@@ -35,7 +35,6 @@ which can be cumbersome and slow to implement in standard SQL.
 Let's start with a concrete example.
 Suppose we have a table of stock [`prices`](/data/prices.csv) with timestamps:
 
-<div class="narrow_table"></div>
 
 | ticker | when | price |
 | :----- | :--- | ----: |
@@ -51,7 +50,6 @@ Suppose we have a table of stock [`prices`](/data/prices.csv) with timestamps:
 
 We have another table containing portfolio [`holdings`](/data/holdings.csv) at various points in time:
 
-<div class="narrow_table"></div>
 
 | ticker | when | shares |
 | :----- | :--- | -----: |
@@ -77,7 +75,6 @@ FROM holdings h ASOF JOIN prices p
 
 This attaches the value of the holding at that time to each row:
 
-<div class="narrow_table"></div>
 
 | ticker | when | value |
 | :----- | :--- | ----: |
@@ -107,7 +104,6 @@ ORDER BY ALL;
 As you might expect, this will produce `NULL` prices and values instead of dropping left side rows
 when there is no ticker or the time is before the prices begin.
 
-<div class="narrow_table"></div>
 
 | ticker | when | value |
 | :----- | :--- | ----: |
@@ -148,7 +144,6 @@ INNER JOIN state s
 The default value of `infinity` is used to make sure there is an end value for the last row that can be compared.
 Here is what the `state` CTE looks like for our example:
 
-<div class="narrow_table"></div>
 
 | ticker | price |        when         |         end         |
 |:-------|------:|:--------------------|:--------------------|
@@ -227,7 +222,6 @@ But AsOf can now use any inequality, which allows it to handle other types of ev
 To explore this, let's use two very simple tables with no equality conditions.
 The build side will just have four integer "timestamps" with alphabetic values:
 
-<div class="narrow_table"></div>
 
 | Time | Value |
 | ---: | ----: |
@@ -240,7 +234,6 @@ The probe table will just be the time values plus the midpoints,
 and we can make a table showing what value each probe time matches
 for greater than or equal to:
 
-<div class="narrow_table"></div>
 
 | Probe | >=  |
 | ----: | --- |
@@ -258,7 +251,6 @@ This shows us that the interval a probe value matches is in the half-open interv
 
 Now let's see what happens if use strictly greater than as the inequality:
 
-<div class="narrow_table"></div>
 
 | Probe |  >  |
 | ----: | --- |
@@ -278,7 +270,6 @@ This means that for this inequality type, the time is not part of the interval.
 
 What if the inequality goes in the other direction, say less than or equal to?
 
-<div class="narrow_table"></div>
 
 | Probe | <=  |
 | ----: | --- |
@@ -303,7 +294,6 @@ when non-strict inequalities are used.
 
 We can check this by looking at the last inequality: strictly less than:
 
-<div class="narrow_table"></div>
 
 | Probe |  <  |
 | ----: | --- |
@@ -323,7 +313,6 @@ and it is a less than, so the time is the end of the interval.
 
 To sum up, here is the full list:
 
-<div class="narrow_table"></div>
 
 | Inequality | Interval   |
 | -- | ---------- |
@@ -445,7 +434,6 @@ CREATE OR REPLACE TABLE probe AS (
 
 The `build` table looks like this:
 
-<div class="narrow_table"></div>
 
 | k |          t          | v |
 |---|---------------------|---|
@@ -457,7 +445,6 @@ The `build` table looks like this:
 
 and the probe table looks like this (with only even values for k):
 
-<div class="narrow_table"></div>
 
 | k |          t          |
 |---|---------------------|
@@ -501,7 +488,6 @@ than inequalities and generates a hash join with a filter.
 
 Running the benchmark, we get results like this:
 
-<div class="narrow_table"></div>
 
 | Algorithm  | Median of 5 |
 | :--------- | ----------: |
@@ -551,7 +537,6 @@ INNER JOIN state s
        AND p.k = s.k;
 ```
 
-<div class="narrow_table"></div>
 
 | Algorithm  | Median of 5 runs |
 | :--------- | ---------------: |

_posts/2023-09-26-announcing-duckdb-090.md

@@ -66,7 +66,6 @@ SELECT count(*) FROM (SELECT DISTINCT * FROM tbl);
 
 If we keep all the data in memory, the query should use around 6GB. However, we can still complete the query if less memory is available. In the table below, we can see how the runtime is affected by lowering the memory limit:
 
-<div class="narrow_table"></div>
 
 |  memory limit |  v0.8.1  |  v0.9.0  |
 |--------------:|---------:|---------:|
@@ -135,7 +134,6 @@ SELECT
 FROM tripdata;
 ```
 
-<div class="narrow_table"></div>
 
 | Version | Run time |
 |--------:|---------:|
@@ -153,7 +151,6 @@ CREATE TABLE integers(i INTEGER PRIMARY KEY);
 INSERT INTO integers FROM range(10000000);
 ```
 
-<div class="narrow_table"></div>
 
 | Version | Size |
 | -- | --: |