new doc

Author: digoal

201707/20170703_01.md

@@ -48,7 +48,7 @@ Oracle主要通过几个方面来提升它在这个级别的性能：
 ### 单机OLTP性能如何? TPC-C  
 tpc-c是OLTP的工业测试标准之一，商业数据库，硬件厂商大都会用TPC-C的测试结果来彰显自己的性能。  
 
-PostgreSQL TPC-C在单机的一组测试数据(warehouses=3000, terminals=256)。  
+PostgreSQL TPC-C在单机的一组测试数据(warehouses=3000, terminals=256)如下，（这组测试数据是机器上有其他混合应用时的测试数据，还有较大的提升空间，到120万tpmC应该没有问题。）。   
 
 [《数据库界的华山论剑 tpc.org》](../201701/20170125_01.md)    
 
@@ -278,7 +278,7 @@ postgres=# explain (analyze,verbose,timing,costs,buffers) select sum(c_custkey),
 Time: 9709.400 ms (00:09.709)  
 ```  
 
-提升了N倍性能。  
+PG 10采样向量化插件提升了N倍性能，叠加并行化，甚至可以超过DP的性能。  
 
 使用向量化除了性能本身的提升，还可以更好的压缩数据。  
 
@@ -300,7 +300,92 @@ postgres=# \dt+ vops_customer
 (1 row)  
 ```  
 
-[VOPS guide](20170703_01_vops.html)  
+并行叠加向量计算的效果测试。  
+  
+PG 10: 439毫秒。   
+   
+```
+postgres=# set max_parallel_workers_per_gather =48;
+SET
+postgres=# alter table vops_customer set (parallel_workers =48);
+ALTER TABLE
+
+postgres=# explain (analyze,verbose,timing,costs,buffers) select sum(c_custkey),avg(c_custkey),min(c_custkey),max(c_custkey),sum(c_nationkey),avg(c_nationkey),min(c_nationkey),max(c_nationkey),sum(c_acctbal),min(c_acctbal),max(c_acctbal),avg(c_acctbal),countall(*) from vops_customer;  
+ Finalize Aggregate  (cost=470827.14..470827.15 rows=1 width=104) (actual time=315.794..315.794 rows=1 loops=1)
+   Output: sum(c_custkey), avg(c_custkey), min(c_custkey), max(c_custkey), sum(c_nationkey), avg(c_nationkey), min(c_nationkey), max(c_nationkey), sum(c_acctbal), min(c_acctbal), max(c_acctbal), avg(c_acctbal), countall(*)
+   Buffers: shared hit=476862
+   ->  Gather  (cost=470825.20..470825.21 rows=48 width=176) (actual time=315.483..315.770 rows=49 loops=1)
+         Output: (PARTIAL sum(c_custkey)), (PARTIAL avg(c_custkey)), (PARTIAL min(c_custkey)), (PARTIAL max(c_custkey)), (PARTIAL sum(c_nationkey)), (PARTIAL avg(c_nationkey)), (PARTIAL min(c_nationkey)), (PARTIAL max(c_nationkey)), (PAR
+TIAL sum(c_acctbal)), (PARTIAL min(c_acctbal)), (PARTIAL max(c_acctbal)), (PARTIAL avg(c_acctbal)), (PARTIAL countall(*))
+         Workers Planned: 48
+         Workers Launched: 48
+         Buffers: shared hit=476862
+         ->  Partial Aggregate  (cost=470825.20..470825.21 rows=1 width=176) (actual time=300.421..300.421 rows=1 loops=49)
+               Output: PARTIAL sum(c_custkey), PARTIAL avg(c_custkey), PARTIAL min(c_custkey), PARTIAL max(c_custkey), PARTIAL sum(c_nationkey), PARTIAL avg(c_nationkey), PARTIAL min(c_nationkey), PARTIAL max(c_nationkey), PARTIAL sum(c_
+acctbal), PARTIAL min(c_acctbal), PARTIAL max(c_acctbal), PARTIAL avg(c_acctbal), PARTIAL countall(*)
+               Buffers: shared hit=468750
+               Worker 0: actual time=286.534..286.534 rows=1 loops=1
+                 Buffers: shared hit=8616
+......
+               Worker 46: actual time=310.345..310.345 rows=1 loops=1
+                 Buffers: shared hit=9389
+               Worker 47: actual time=310.727..310.727 rows=1 loops=1
+                 Buffers: shared hit=9280
+......
+               ->  Parallel Seq Scan on public.vops_customer  (cost=0.00..469238.28 rows=48828 width=1584) (actual time=0.017..114.654 rows=47832 loops=49)
+                     Output: c_custkey, c_nationkey, c_acctbal
+                     Buffers: shared hit=468750
+                     Worker 0: actual time=0.021..107.447 rows=43080 loops=1
+                       Buffers: shared hit=8616
+                     Worker 1: actual time=0.019..109.747 rows=42325 loops=1
+......
+                     Worker 47: actual time=0.018..119.142 rows=46400 loops=1
+                       Buffers: shared hit=9280
+ Planning time: 0.135 ms
+ Execution time: 439.027 ms
+(208 rows)
+```
+   
+相对应的deepgreen测试如下: 532毫秒.      
+  
+```
+postgres=# select sum(c_custkey),avg(c_custkey),min(c_custkey),max(c_custkey),sum(c_nationkey),avg(c_nationkey),min(c_nationkey),max(c_nationkey),sum(c_acctbal),min(c_acctbal),max(c_acctbal),avg(c_acctbal),count(*) from customer;   
+        sum        |    avg     | min |    max    |    sum     |       avg        | min | max |       sum        |   min   |   max   |       avg        |   count   
+-------------------+------------+-----+-----------+------------+------------------+-----+-----+------------------+---------+---------+------------------+-----------
+ 11250000075000000 | 75000000.5 |   1 | 150000000 | 1800117761 | 12.0007850733333 |   0 |  24 | 675048124068.057 | -999.99 | 9999.99 | 4500.32082712038 | 150000000
+(1 row)
+
+Time: 531.973 ms
+
+postgres=# explain analyze select sum(c_custkey),avg(c_custkey),min(c_custkey),max(c_custkey),sum(c_nationkey),avg(c_nationkey),min(c_nationkey),max(c_nationkey),sum(c_acctbal),min(c_acctbal),max(c_acctbal),avg(c_acctbal),count(*) from customer;   
+                                                                 QUERY PLAN                                                                  
+---------------------------------------------------------------------------------------------------------------------------------------------
+ Aggregate  (cost=7151025.61..7151025.62 rows=1 width=200)
+   Rows out:  1 rows with 0.001 ms to first row, 330 ms to end, start offset by 1.624 ms.
+   ->  Gather Motion 48:1  (slice1; segments: 48)  (cost=7151025.03..7151025.53 rows=1 width=224)
+         Rows out:  48 rows at destination with 458 ms to end, start offset by 1.625 ms.
+         ->  Aggregate  (cost=7151025.03..7151025.04 rows=1 width=224)
+               Rows out:  Avg 1.0 rows x 48 workers.  Max 1 rows (seg0) with 0.002 ms to first row, 471 ms to end, start offset by 4.299 ms.
+               ->  Append-only Columnar Scan on customer  (cost=0.00..2276025.00 rows=3125000 width=24)
+                     Rows out:  0 rows (seg0) with 57 ms to end, start offset by 71 ms.
+ Slice statistics:
+   (slice0)    Executor memory: 443K bytes.
+   (slice1)    Executor memory: 18722K bytes avg x 48 workers, 18722K bytes max (seg0).
+ Statement statistics:
+   Memory used: 128000K bytes
+ Settings:  optimizer=off
+ Optimizer status: legacy query optimizer
+ Total runtime: 789.744 ms
+(16 rows)
+
+Time: 791.114 ms
+```
+  
+![pic](20170703_01_pic_007.jpg)  
+  
+PG 10 多核+向量计算组合后，已和Deepgreen的分析性能持平甚至略好。（要知道测试中，PG10 还没有使用正儿八经的列式存储呢，还有提升的潜力。）       
+  
+[PostgreSQL VOPS guide](20170703_01_vops.html)  
 
 #### 3、多核并行