Spark SQL 部署和测试（下）

数据库技术闲谈 2022-06-08

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接上文《Spark SQL 部署和测试（上）》分享 Spark SQL 里测试 TPC-H 以及查看执行计划的方法。

Spark SQL 执行计划不及传统关系数据库那么强大但基本的都有，原理很容易理解。学习后对理解新兴的分布式数据库执行计划也有帮助。

TPC-H 测试

TPC-H（商业智能计算测试）是国际交易处理效能委员会(TPC,Transaction Processing Performance Council) 组织制定的用来模拟决策支持类应用的一个测试方案。

数据文件初始化

TPC-H 从官方网站下载：

https://www.tpc.org/tpc_documents_current_versions/current_specifications5.asp

下载后解压缩即可直接用，里面包含了数据初始化程序 dbgen。由于要支持Hive表(Spark SQL兼容Hive SQL)语法，需要对源码文件做一些修改。

修改 makefile

指定数据库类型为 SQLSERVER（TPC-H不支持HIVE，用SQLServer替代一下），以及指定编译工具为 gcc 。

[root@sfx111188 dbgen]# cp makefile.suitemakefile
[root@sfx111188 dbgen]# vim makefile


100 ################
101 ## CHANGE NAME OF ANSI COMPILER HERE
102 ################
103 CC     = gcc
104 # Current values for DATABASE are:INFORMIX, DB2, TDAT (Teradata)
105 #                                  SQLSERVER,SYBASE, ORACLE, VECTORWISE
106 # Current values for MACHINE are:  ATT, DOS, HP, IBM, ICL, MVS,
107 #                                  SGI, SUN,U2200, VMS, LINUX, WIN32
108 # Current values for WORKLOAD are:  TPCH
109 DATABASE= SQLSERVER
110 MACHINE = LINUX
111 WORKLOAD = TPCH
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修改头文件tpcd.h

修改对应的 SQLSERVER 类型的语法为 HIVE 语法。

 [root@sfx111188 dbgen]# vim tpcd.h 


 #ifdef  SQLSERVER
 #define GEN_QUERY_PLAN "explain;"
 #define START_TRAN      "starttransaction;\n"
 #define END_TRAN       "commit;\n"
 #define SET_OUTPUT      ""
 #define SET_ROWCOUNT    "limit %d;\m"
 #define SET_DBASE       "use%s;\n"
 #endif
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初始化数据

在当前目录编译并运行，并生成数据。

[hadoop@sfx111188 dbgen]$ make 
[hadoop@sfx111188 dbgen]$ mkdir tpch_350
[hadoop@sfx111188 dbgen]$ cp dbgen tpch_350/
[hadoop@sfx111188 dbgen]$ cp qgen tpch_350/
[hadoop@sfx111188 dbgen]$ cp dists.dss tpch_350/
[hadoop@sfx111188 dbgen]$ cd tpch_350
[hadoop@sfx111188 tpch_350]$ ./dbgen -s 350
TPC-H Population Generator (Version 3.0.0)
Copyright Transaction Processing Performance Council 1994 - 2010


[hadoop@sfx111188 tpch_350]$ du -sh *.tbl
8.1G    customer.tbl
263G    lineitem.tbl
4.0K    nation.tbl
59G     orders.tbl
41G     partsupp.tbl
8.1G    part.tbl
4.0K    region.tbl
480M    supplier.tbl


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TPC-H 建表

首先在 Spark SQL 里建外部表，导入TPC-H 数据。

创建数据库 tpch。并登录。

spark-sql> create database tpch;
Time taken: 0.048 seconds
spark-sql> use tpch;
Time taken: 0.016 seconds
spark-sql>
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提前创建这个 HDFS 目录。

[hadoop@sfx111188 ~]$ hdfs dfs -mkdir -p /tpch/
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创建外部表，外部表存储路径是 HDFS 根路径：/tpch/。

use tpch;


create external table lineitem (
  l_orderkey int,
  l_partkey int,
  l_suppkey int,
  l_linenumber int,
  l_quantity double,
  l_extendedprice double,
  l_discount double,
  l_tax double,
  l_returnflag string,
  l_linestatus string,
  l_shipdate string,
  l_commitdate string,
  l_receiptdate string,
  l_shipinstruct string,
  l_shipmode string,
  l_comment string)
row format delimited
fields terminated by '|'
stored as textfile
location'/tpch/lineitem';


create external table nation (
  n_nationkey int,
  n_name string,
  n_regionkey int,
  n_comment string)
row format delimited
fields terminated by '|'
stored as textfile
location'/tpch/nation';


create external table region (
  r_regionkey int,
  r_name string,
  r_comment string)
row format delimited
fields terminated by '|'
stored as textfile
location '/tpch/region';


create external table part (
  p_partkey int,
  p_name string,
  p_mfgr string,
  p_brand string,
  p_type string,
  p_size int,
  p_container string,
  p_retailprice double,
  p_comment string)
row format delimited
fields terminated by '|'
stored as textfile
location '/tpch/part';


create external table supplier (
  s_suppkey int,
  s_name string,
  s_address string,
  s_nationkey int,
  s_phone string,
  s_acctbal double,
  s_comment string)
row format delimited
fields terminated by '|'
stored as textfile
location '/tpch/supplier';


create external table partsupp (
  ps_partkey int,
  ps_suppkey int,
  ps_availqty int,
  ps_supplycost double,
  ps_comment string)
row format delimited
fields terminated by '|'
stored as textfile
location '/tpch/partsupp';


create external table customer (
  c_custkey int,
  c_name string,
  c_address string,
  c_nationkey int,
  c_phone string,
  c_acctbal double,
  c_mktsegment string,
  c_comment string)
row format delimited
fields terminated by '|'
stored as textfile
location '/tpch/customer';


create external table orders (
  o_orderkey int,
  o_custkey int,
  o_orderstatus string,
  o_totalprice double,
  o_orderdate date,
  o_orderpriority string,
  o_clerk string,
  o_shippriority int,
  o_comment string)
row format delimited
fields terminated by '|'
stored as textfile
location '/tpch/orders';
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导入 TPC-H 数据文件到外部表。

load data local
inpath '/data/sfdv1n1/tpch350/region.tbl' into table region;
load data local
inpath '/data/sfdv1n1/tpch350/customer.tbl' into table customer;
load data local
inpath '/data/sfdv1n1/tpch350/lineitem.tbl' into table lineitem;
load data local
inpath '/data/sfdv1n1/tpch350/nation.tbl' into table nation;
load data local
inpath '/data/sfdv1n1/tpch350/orders.tbl' into table orders;
load data local inpath '/data/sfdv1n1/tpch350/part.tbl' into table part;
load data local
inpath '/data/sfdv1n1/tpch350/partsupp.tbl' into table partsupp;
load data local
inpath '/data/sfdv1n1/tpch350/supplier.tbl' into table supplier;


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上面建的表是外部表，再新建一组 Spark 表，不开启压缩，使用 parquet 列存格式。然后将外部表数据导入到 Spark 表中。

create database tpch_uncompressed;
set spark.sql.parquet.compression.codec=uncompressed;


Create table tpch_uncompressed.lineitem (L_ORDERKEY INT, L_PARTKEY INT, L_SUPPKEY INT,
L_LINENUMBER INT, L_QUANTITY DOUBLE, L_EXTENDEDPRICE DOUBLE, L_DISCOUNT DOUBLE,
L_TAX DOUBLE, L_RETURNFLAG STRING, L_LINESTATUS STRING, L_SHIPDATE STRING,
L_COMMITDATE STRING, L_RECEIPTDATE STRING, L_SHIPINSTRUCT STRING, L_SHIPMODE
STRING, L_COMMENT STRING) stored as parquet;
create table tpch_uncompressed.part (P_PARTKEY INT, P_NAME STRING, P_MFGR STRING,
P_BRAND STRING, P_TYPE STRING, P_SIZE INT, P_CONTAINER STRING, P_RETAILPRICE
DOUBLE, P_COMMENT STRING) stored as parquet;
create table tpch_uncompressed.supplier (S_SUPPKEY INT, S_NAME STRING, S_ADDRESS
STRING, S_NATIONKEY INT, S_PHONE STRING, S_ACCTBAL DOUBLE, S_COMMENT STRING)
stored as parquet;
create table tpch_uncompressed.partsupp (PS_PARTKEY INT, PS_SUPPKEY INT, PS_AVAILQTY
INT, PS_SUPPLYCOST DOUBLE, PS_COMMENT STRING) stored as parquet;
create table tpch_uncompressed.nation (N_NATIONKEY INT, N_NAME STRING, N_REGIONKEY
INT, N_COMMENT STRING) stored as parquet;
create
table tpch_uncompressed.region (R_REGIONKEY INT, R_NAME STRING, R_COMMENT
STRING) stored as parquet;
create table tpch_uncompressed.orders (O_ORDERKEY INT, O_CUSTKEY INT, O_ORDERSTATUS
STRING, O_TOTALPRICE DOUBLE, O_ORDERDATE STRING, O_ORDERPRIORITY STRING,
O_CLERK STRING, O_SHIPPRIORITY INT, O_COMMENT STRING) stored as parquet;
create table tpch_uncompressed.customer (C_CUSTKEY INT, C_NAME STRING, C_ADDRESS
STRING, C_NATIONKEY INT, C_PHONE STRING, C_ACCTBAL DOUBLE, C_MKTSEGMENT STRING,
C_COMMENT STRING) stored as parquet;


use tpch_uncompressed; 
insert into customer select * from tpch.customer;
insert into lineitem select * from tpch.lineitem;
insert into part select * from tpch.part;
insert into supplier select * from tpch.supplier;
insert into partsupp select * from tpch.partsupp;
insert into nation select * from tpch.nation;
insert into region select * from tpch.region;
insert into orders select * from tpch.orders;
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parquet 简介

parquet 是面相分析型业务的列式存储格式。相比行存格式有以下优势：

谓词下推，可以跳过不符合条件的数据，只读取需要的数据，降低 IO 数据量。
使用更高效的存储编码，进一步压缩存储空间。
映射下推，只需读取需要的列，支持向量运算，能获得更好的扫描性能。

HDFS 文件分析

hdfs 查看方式

HDFS 的目录可以通过 WEB 页面查看，监听端口默认 9870 。

也可以通过命令 hdfs 查看。

[hadoop@sfx111188bcache]$ hdfs dfs -du -s -h tpch/*
8.1 G    8.1 G   tpch/customer
263.0 G  263.0 G tpch/lineitem
2.2 K    2.2 K   tpch/nation
58.6 G   58.6 G  tpch/orders
8.0 G    8.0 G   tpch/part
48.1 G   48.1 G  tpch/partsupp
389      389     tpch/region
479.6 M  479.6 M tpch/supplier


[hadoop@sfx111188 ~]$ hdfs dfs -du -h user/hadoop/warehouse/tpch_uncompressed.db/
7.8 G    7.8 G    user/hadoop/warehouse/tpch_uncompressed.db/customer
112.4 G  112.4 G  user/hadoop/warehouse/tpch_uncompressed.db/lineitem
3.8 K    3.8 K    user/hadoop/warehouse/tpch_uncompressed.db/nation
43.9 G   43.9 G   user/hadoop/warehouse/tpch_uncompressed.db/orders
4.2 G    4.2 G    user/hadoop/warehouse/tpch_uncompressed.db/part
37.7 G   37.7 G   user/hadoop/warehouse/tpch_uncompressed.db/partsupp
1.5 K    1.5 K    user/hadoop/warehouse/tpch_uncompressed.db/region
498.0 M  498.0 M  user/hadoop/warehouse/tpch_uncompressed.db/supplier
[hadoop@sfx111188 ~]$


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从上面可以看出，每个表的原始数据导入到 parquet 格式表后，数据感觉都被压缩了。

文件块大小是 128MB，实际块大小可能会比块小。如 lineitem 表的块平均为 56MB 左右。

CSD 数据压缩比

当 HDFS 文件放在 CSD 2000上时，可以看数据压缩比。

查看 lineitem 原始文件的一个块信息（找到 blockid）

查看该Block ID 对应的文件的 CSD 压缩比。文件有2个，数据文件部分压缩比在 2.15 左右。

[hadoop@sfx111188subdir0]$ find . |grep 1073743944 |sudo xargs sfx-filesize -h
  Logical Alocated  Physical   Ratio File
     128M     128M    59.50M    2.15 ./subdir8/blk_1073743944
 
  Logical Alocated  Physical   Ratio File
    1.00M    1.00M     1.00M    1.00 ./subdir8/blk_1073743944_3121.meta
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再查看该表 lineitem（parquet格式）的一个数据块信息。

同样查看该数据块在 CSD 2000 上压缩比，约为 1.64 。

[hadoop@sfx111188 dfs]$ find . |grep 1073745231 |sudo xargs sfx-filesize -h |grep .
  Logical  Alocated  Physical   Ratio File
  442.50K      444K   442.77K    1.00 ./data/current/BP-294683394-127.0.0.1-1651761708423/current/finalized/subdir0/subdir13/blk_1073745231_4407.meta
  Logical  Alocated  Physical   Ratio File
   55.31M    55.31M    33.64M    1.64 ./data/current/BP-294683394-127.0.0.1-1651761708423/current/finalized/subdir0/subdir13/blk_1073745231
 
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TPC-H 查询

Spark SQL 能满足 TPC-H 22个查询需求。下面给两个例子。

q1_pricing_summary_report.sql

SELECT
  L_RETURNFLAG, L_LINESTATUS, SUM(L_QUANTITY), SUM(L_EXTENDEDPRICE), SUM(L_EXTENDEDPRICE*(1-L_DISCOUNT)), SUM(L_EXTENDEDPRICE*(1-L_DISCOUNT)*(1+L_TAX)), AVG(L_QUANTITY), AVG(L_EXTENDEDPRICE), AVG(L_DISCOUNT), COUNT(1)
FROM
  lineitem
WHERE
  L_SHIPDATE<='1998-09-02'
GROUP BY L_RETURNFLAG, L_LINESTATUS
ORDER BY L_RETURNFLAG, L_LINESTATUS;
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q2_minimum_cost_supplier.sql

select
    s_acctbal,
    s_name,
    n_name,
    p_partkey,
    p_mfgr,
    s_address,
    s_phone,
    s_comment
from
    part,
    supplier,
    partsupp,
    nation,
    region
where
    p_partkey = ps_partkey
    and s_suppkey = ps_suppkey
    and p_size = 15
    and p_type like '%BRASS'
    and s_nationkey = n_nationkey
    and n_regionkey = r_regionkey
    and r_name = 'EUROPE'
    and ps_supplycost = (
        select
            min(ps_supplycost)
        from
            partsupp,
            supplier,
            nation,
            region
        where
            p_partkey = ps_partkey
            and s_suppkey = ps_suppkey
            and s_nationkey = n_nationkey
            and n_regionkey = r_regionkey
            and r_name = 'EUROPE'
    )
order by
    s_acctbal desc,
    n_name,
    s_name,
    p_partkey
limit 100;
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查询执行方式：

[hadoop@sfx111188 ~]$ spark-sql --database tpch_uncompressed -f tpch_queries/q1_pricing_summary_report.sql
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
2022-05-23 14:54:52,272 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Spark master: local[*], Application Id: local-1653288893920
A       F       1.3214595889E10 1.981526484204869E13    1.8824474573410145E13   1.9577452155215535E13   25.500070938291696      38237.31451022952       0.05000213280898179     518218005
N       F       3.44841059E8    5.171030920476401E11    4.91243409840054E11     5.108955072236212E11    25.49886259627669       38236.57406246567       0.050010318859026945    13523782
N       O       2.6023136781E10 3.902133887866225E13    3.707025733533616E13    3.855306479917973E13    25.50024030295549       38237.26273749808       0.049999221336791196    1020505551
R       F       1.3214518093E10 1.9815185947287605E13   1.882442544492562E13    1.9577409002481715E13   25.50045772433984       38237.96736230632       0.05000015503457316     518207094
Time taken: 52.726 seconds, Fetched 4 row(s)
[hadoop@sfx111188 ~]$
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