TPC-H 测试
本文将引导您对 PolarDB for PostgreSQL 进行 TPC-H 测试。
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背景
TPC-H 是专门测试数据库分析型场景性能的数据集。
测试准备
部署 PolarDB-PG
使用 Docker 快速拉起一个单机的 PolarDB for PostgreSQL 集群：
:::: code-group ::: code-group-item DockerHub
docker pull polardb/polardb_pg_local_instance:11
docker run -it \
--cap-add=SYS_PTRACE \
--privileged=true \
--name polardb_pg_htap \
--shm-size=512m \
polardb/polardb_pg_local_instance:11 \
bash
::: ::: code-group-item 阿里云 ACR
docker pull registry.cn-hangzhou.aliyuncs.com/polardb_pg/polardb_pg_local_instance:11
docker run -it \
--cap-add=SYS_PTRACE \
--privileged=true \
--name polardb_pg_htap \
--shm-size=512m \
registry.cn-hangzhou.aliyuncs.com/polardb_pg/polardb_pg_local_instance:11 \
bash
::: ::::
或者参考 进阶部署 部署一个基于共享存储的 PolarDB for PostgreSQL 集群。
生成 TPC-H 测试数据集
通过 tpch-dbgen 工具来生成测试数据。
$ git clone https://github.com/ApsaraDB/tpch-dbgen.git
$ cd tpch-dbgen
$ ./build.sh --help
1) Use default configuration to build
./build.sh
2) Use limited configuration to build
./build.sh --user=postgres --db=postgres --host=localhost --port=5432 --scale=1
3) Run the test case
./build.sh --run
4) Run the target test case
./build.sh --run=3. run the 3rd case.
5) Run the target test case with option
./build.sh --run --option="set polar_enable_px = on;"
6) Clean the test data. This step will drop the database or tables, remove csv
and tbl files
./build.sh --clean
7) Quick build TPC-H with 100MB scale of data
./build.sh --scale=0.1
通过设置不同的参数，可以定制化地创建不同规模的 TPC-H 数据集。build.sh 脚本中各个参数的含义如下：
--user：数据库用户名--db：数据库名--host：数据库主机地址--port：数据库服务端口--run：执行所有 TPC-H 查询，或执行某条特定的 TPC-H 查询--option：额外指定 GUC 参数--scale：生成 TPC-H 数据集的规模，单位为 GB
该脚本没有提供输入数据库密码的参数，需要通过设置 PGPASSWORD 为数据库用户的数据库密码来完成认证：
export PGPASSWORD=<your password>
生成并导入 100MB 规模的 TPC-H 数据：
./build.sh --scale=0.1
生成并导入 1GB 规模的 TPC-H 数据：
./build.sh
执行 PostgreSQL 单机并行执行
以 TPC-H 的 Q18 为例，执行 PostgreSQL 的单机并行查询，并观测查询速度。
在 tpch-dbgen/ 目录下通过 psql 连接到数据库：
cd tpch-dbgen
psql
-- 打开计时
\timing on
-- 设置单机并行度
SET max_parallel_workers_per_gather = 2;
-- 查看 Q18 的执行计划
\i finals/18.explain.sql
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------
Sort (cost=3450834.75..3450835.42 rows=268 width=81)
Sort Key: orders.o_totalprice DESC, orders.o_orderdate
-> GroupAggregate (cost=3450817.91..3450823.94 rows=268 width=81)
Group Key: customer.c_custkey, orders.o_orderkey
-> Sort (cost=3450817.91..3450818.58 rows=268 width=67)
Sort Key: customer.c_custkey, orders.o_orderkey
-> Hash Join (cost=1501454.20..3450807.10 rows=268 width=67)
Hash Cond: (lineitem.l_orderkey = orders.o_orderkey)
-> Seq Scan on lineitem (cost=0.00..1724402.52 rows=59986052 width=22)
-> Hash (cost=1501453.37..1501453.37 rows=67 width=53)
-> Nested Loop (cost=1500465.85..1501453.37 rows=67 width=53)
-> Nested Loop (cost=1500465.43..1501084.65 rows=67 width=34)
-> Finalize GroupAggregate (cost=1500464.99..1500517.66 rows=67 width=4)
Group Key: lineitem_1.l_orderkey
Filter: (sum(lineitem_1.l_quantity) > '314'::numeric)
-> Gather Merge (cost=1500464.99..1500511.66 rows=400 width=36)
Workers Planned: 2
-> Sort (cost=1499464.97..1499465.47 rows=200 width=36)
Sort Key: lineitem_1.l_orderkey
-> Partial HashAggregate (cost=1499454.82..1499457.32 rows=200 width=36)
Group Key: lineitem_1.l_orderkey
-> Parallel Seq Scan on lineitem lineitem_1 (cost=0.00..1374483.88 rows=24994188 width=22)
-> Index Scan using orders_pkey on orders (cost=0.43..8.45 rows=1 width=30)
Index Cond: (o_orderkey = lineitem_1.l_orderkey)
-> Index Scan using customer_pkey on customer (cost=0.43..5.50 rows=1 width=23)
Index Cond: (c_custkey = orders.o_custkey)
(26 rows)
Time: 3.965 ms
-- 执行 Q18
\i finals/18.sql
c_name | c_custkey | o_orderkey | o_orderdate | o_totalprice | sum
--------------------+-----------+------------+-------------+--------------+--------
Customer#001287812 | 1287812 | 42290181 | 1997-11-26 | 558289.17 | 318.00
Customer#001172513 | 1172513 | 36667107 | 1997-06-06 | 550142.18 | 322.00
...
Customer#001288183 | 1288183 | 48943904 | 1996-07-22 | 398081.59 | 325.00
Customer#000114613 | 114613 | 59930883 | 1997-05-17 | 394335.49 | 319.00
(84 rows)
Time: 80150.449 ms (01:20.150)
执行 ePQ 单机并行执行
PolarDB for PostgreSQL 提供了弹性跨机并行查询（ePQ）的能力，非常适合进行分析型查询。下面的步骤将引导您可以在一台主机上使用 ePQ 并行执行 TPC-H 查询。
在 tpch-dbgen/ 目录下通过 psql 连接到数据库：
cd tpch-dbgen
psql
首先需要对 TPC-H 产生的八张表设置 ePQ 的最大查询并行度：
ALTER TABLE nation SET (px_workers = 100);
ALTER TABLE region SET (px_workers = 100);
ALTER TABLE supplier SET (px_workers = 100);
ALTER TABLE part SET (px_workers = 100);
ALTER TABLE partsupp SET (px_workers = 100);
ALTER TABLE customer SET (px_workers = 100);
ALTER TABLE orders SET (px_workers = 100);
ALTER TABLE lineitem SET (px_workers = 100);
以 Q18 为例，执行查询：
-- 打开计时
\timing on
-- 打开 ePQ 功能的开关
SET polar_enable_px = ON;
-- 设置每个节点的 ePQ 并行度为 1
SET polar_px_dop_per_node = 1;
-- 查看 Q18 的执行计划
\i finals/18.explain.sql
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------------------
PX Coordinator 2:1 (slice1; segments: 2) (cost=0.00..257526.21 rows=59986052 width=47)
Merge Key: orders.o_totalprice, orders.o_orderdate
-> GroupAggregate (cost=0.00..243457.68 rows=29993026 width=47)
Group Key: orders.o_totalprice, orders.o_orderdate, customer.c_name, customer.c_custkey, orders.o_orderkey
-> Sort (cost=0.00..241257.18 rows=29993026 width=47)
Sort Key: orders.o_totalprice DESC, orders.o_orderdate, customer.c_name, customer.c_custkey, orders.o_orderkey
-> Hash Join (cost=0.00..42729.99 rows=29993026 width=47)
Hash Cond: (orders.o_orderkey = lineitem_1.l_orderkey)
-> PX Hash 2:2 (slice2; segments: 2) (cost=0.00..15959.71 rows=7500000 width=39)
Hash Key: orders.o_orderkey
-> Hash Join (cost=0.00..15044.19 rows=7500000 width=39)
Hash Cond: (orders.o_custkey = customer.c_custkey)
-> PX Hash 2:2 (slice3; segments: 2) (cost=0.00..11561.51 rows=7500000 width=20)
Hash Key: orders.o_custkey
-> Hash Semi Join (cost=0.00..11092.01 rows=7500000 width=20)
Hash Cond: (orders.o_orderkey = lineitem.l_orderkey)
-> Partial Seq Scan on orders (cost=0.00..1132.25 rows=7500000 width=20)
-> Hash (cost=7760.84..7760.84 rows=400 width=4)
-> PX Broadcast 2:2 (slice4; segments: 2) (cost=0.00..7760.84 rows=400 width=4)
-> Result (cost=0.00..7760.80 rows=200 width=4)
Filter: ((sum(lineitem.l_quantity)) > '314'::numeric)
-> Finalize HashAggregate (cost=0.00..7760.78 rows=500 width=12)
Group Key: lineitem.l_orderkey
-> PX Hash 2:2 (slice5; segments: 2) (cost=0.00..7760.72 rows=500 width=12)
Hash Key: lineitem.l_orderkey
-> Partial HashAggregate (cost=0.00..7760.70 rows=500 width=12)
Group Key: lineitem.l_orderkey
-> Partial Seq Scan on lineitem (cost=0.00..3350.82 rows=29993026 width=12)
-> Hash (cost=597.51..597.51 rows=749979 width=23)
-> PX Hash 2:2 (slice6; segments: 2) (cost=0.00..597.51 rows=749979 width=23)
Hash Key: customer.c_custkey
-> Partial Seq Scan on customer (cost=0.00..511.44 rows=749979 width=23)
-> Hash (cost=5146.80..5146.80 rows=29993026 width=12)
-> PX Hash 2:2 (slice7; segments: 2) (cost=0.00..5146.80 rows=29993026 width=12)
Hash Key: lineitem_1.l_orderkey
-> Partial Seq Scan on lineitem lineitem_1 (cost=0.00..3350.82 rows=29993026 width=12)
Optimizer: PolarDB PX Optimizer
(37 rows)
Time: 216.672 ms
-- 执行 Q18
c_name | c_custkey | o_orderkey | o_orderdate | o_totalprice | sum
--------------------+-----------+------------+-------------+--------------+--------
Customer#001287812 | 1287812 | 42290181 | 1997-11-26 | 558289.17 | 318.00
Customer#001172513 | 1172513 | 36667107 | 1997-06-06 | 550142.18 | 322.00
...
Customer#001288183 | 1288183 | 48943904 | 1996-07-22 | 398081.59 | 325.00
Customer#000114613 | 114613 | 59930883 | 1997-05-17 | 394335.49 | 319.00
(84 rows)
Time: 59113.965 ms (00:59.114)
可以看到比 PostgreSQL 的单机并行执行的时间略短。加大 ePQ 功能的节点并行度，查询性能将会有更明显的提升：
SET polar_px_dop_per_node = 2;
\i finals/18.sql
c_name | c_custkey | o_orderkey | o_orderdate | o_totalprice | sum
--------------------+-----------+------------+-------------+--------------+--------
Customer#001287812 | 1287812 | 42290181 | 1997-11-26 | 558289.17 | 318.00
Customer#001172513 | 1172513 | 36667107 | 1997-06-06 | 550142.18 | 322.00
...
Customer#001288183 | 1288183 | 48943904 | 1996-07-22 | 398081.59 | 325.00
Customer#000114613 | 114613 | 59930883 | 1997-05-17 | 394335.49 | 319.00
(84 rows)
Time: 42400.500 ms (00:42.401)
SET polar_px_dop_per_node = 4;
\i finals/18.sql
c_name | c_custkey | o_orderkey | o_orderdate | o_totalprice | sum
--------------------+-----------+------------+-------------+--------------+--------
Customer#001287812 | 1287812 | 42290181 | 1997-11-26 | 558289.17 | 318.00
Customer#001172513 | 1172513 | 36667107 | 1997-06-06 | 550142.18 | 322.00
...
Customer#001288183 | 1288183 | 48943904 | 1996-07-22 | 398081.59 | 325.00
Customer#000114613 | 114613 | 59930883 | 1997-05-17 | 394335.49 | 319.00
(84 rows)
Time: 19892.603 ms (00:19.893)
SET polar_px_dop_per_node = 8;
\i finals/18.sql
c_name | c_custkey | o_orderkey | o_orderdate | o_totalprice | sum
--------------------+-----------+------------+-------------+--------------+--------
Customer#001287812 | 1287812 | 42290181 | 1997-11-26 | 558289.17 | 318.00
Customer#001172513 | 1172513 | 36667107 | 1997-06-06 | 550142.18 | 322.00
...
Customer#001288183 | 1288183 | 48943904 | 1996-07-22 | 398081.59 | 325.00
Customer#000114613 | 114613 | 59930883 | 1997-05-17 | 394335.49 | 319.00
(84 rows)
Time: 10944.402 ms (00:10.944)
使用 ePQ 执行 Q17 和 Q18 时可能会出现 OOM。需要设置以下参数防止用尽内存：
SET polar_px_optimizer_enable_hashagg = 0;
执行 ePQ 跨机并行执行
在上面的例子中，出于简单考虑，PolarDB for PostgreSQL 的多个计算节点被部署在同一台主机上。在这种场景下使用 ePQ 时，由于所有的计算节点都使用了同一台主机的 CPU、内存、I/O 带宽，因此本质上是基于单台主机的并行执行。实际上，PolarDB for PostgreSQL 的计算节点可以被部署在能够共享存储节点的多台机器上。此时使用 ePQ 功能将进行真正的跨机器分布式并行查询，能够充分利用多台机器上的计算资源。
参考 进阶部署 可以搭建起不同形态的 PolarDB for PostgreSQL 集群。集群搭建成功后，使用 ePQ 的方式与单机 ePQ 完全相同。
如果遇到如下错误：
psql:queries/q01.analyze.sq1:24: WARNING: interconnect may encountered a network error, please check your network DETAIL: Failed to send packet (seq 1) to 192.168.1.8:57871 (pid 17766 cid 0) after 100 retries.可以尝试统一修改每台机器的 MTU 为 9000：
ifconfig <网卡名> mtu 9000