【原创】大数据基础之Benchmark（2）TPC-DS

tpc

官方：http://www.tpc.org/

一 简介

The TPC is a non-profit corporation founded to define transaction processing and database benchmarks and to disseminate objective, verifiable TPC performance data to the industry.

TPC(The Transaction Processing Performance Council)是一个非盈利公司，致力于定义事务处理和数据库benchmark，同时向业界发布客观的可验证的tpc性能数据；

The term transaction is often applied to a wide variety of business and computer functions. Looked at as a computer function, a transaction could refer to a set of operations including disk read/writes, operating system calls, or some form of data transfer from one subsystem to another.

While TPC benchmarks certainly involve the measurement and evaluation of computer functions and operations, the TPC regards a transaction as it is commonly understood in the business world: a commercial exchange of goods, services, or money. A typical transaction, as defined by the TPC, would include the updating to a database system for such things as inventory control (goods), airline reservations (services), or banking (money).

In these environments, a number of customers or service representatives input and manage their transactions via a terminal or desktop computer connected to a database. Typically, the TPC produces benchmarks that measure transaction processing (TP) and database (DB) performance in terms of how many transactions a given system and database can perform per unit of time, e.g., transactions per second or transactions per minute.

TPC-DS is a Decision Support Benchmark

官方：http://www.tpc.org/tpcds/default.asp

文档：http://www.tpc.org/tpc_documents_current_versions/pdf/tpc-ds_v2.10.1.pdf

A simple schema for decision support systems or data warehouses is the star schema, where events are collected in large fact tables, while smaller supporting tables (dimensions) are used to describe the data.

The TPC-DS is an example of such a schema. It models a typical retail warehouse where the events are sales and typical dimensions are date of sale, time of sale, or demographic of the purchasing party.

决策支持系统的schema中，event被存放在大的事实表（fact table）中，而小的维度表（dimension table）用来描述数据；

数据库界最具挑战的一个测试基准TPC-DS，它模拟了一个典型的零售行业的数据仓库；

The TPC Benchmark DS (TPC-DS) is a decision support benchmark that models several generally applicable aspects of a decision support system, including queries and data maintenance. The benchmark provides a representative evaluation of performance as a general purpose decision support system. A benchmark result measures query response time in single user mode, query throughput in multi user mode and data maintenance performance for a given hardware, operating system, and data processing system configuration under a controlled, complex, multi-user decision support workload. The purpose of TPC benchmarks is to provide relevant, objective performance data to industry users. TPC-DS Version 2 enables emerging technologies, such as Big Data systems, to execute the benchmark.

二 使用

1 下载

http://www.tpc.org/tpc_documents_current_versions/current_specifications.asp

2 安装

# unzip TPC-DS_Tools_v2.10.1.zip
# cd v2.10.1rc3/tools
# make

生成两个工具：

dsdgen
dsqgen

3 初始化表结构sql脚本

tpcds.sql
tpcds_ri.sql
tpcds_source.sql

可能需要根据具体的db修改一些诸如字段类型；

4 生成测试数据

# mkdir /tmp/tpcdsdata
# ./dsdgen -SCALE 1GB -DIR /tmp/tpcdsdata -parallel 4 -child 4

其中 -SCALE 用于指定生成的数据规模，可以修改比如10GB，1TB

5 生成查询脚本

# ./dsqgen -input ../query_templates/templates.lst -directory ../query_templates -dialect oracle -scale 1GB -OUTPUT_DIR /tmp/tpcdsdata/query_oracle

默认支持dialect如下：

db2.tpl
netezza.tpl
oracle.tpl
sqlserver.tpl

可见默认都是针对传统的关系型数据库，下面看怎样应用于大数据场景；

三 测试hive

官方：https://github.com/hortonworks/hive-testbench

1 下载安装

$ wget https://github.com/hortonworks/hive-testbench/archive/hive14.zip
$ unzip hive14.zip
$ cd hive-testbench-hive14/
$ ./tpcds-build.sh

2 生成测试数据和查询脚本

$ export FORMAT=parquet
$ ./tpcds-setup.sh 1000

单位为G，修改FORMAT，比如orc、parquet等

生成日志

TPC-DS text data generation complete.
Loading text data into external tables.
Optimizing table date_dim (1/24).
Optimizing table time_dim (2/24).
Optimizing table item (3/24).
Optimizing table customer (4/24).
Optimizing table customer_demographics (5/24).
Optimizing table household_demographics (6/24).
Optimizing table customer_address (7/24).
Optimizing table store (8/24).
Optimizing table promotion (9/24).
Optimizing table warehouse (10/24).
Optimizing table ship_mode (11/24).
Optimizing table reason (12/24).
Optimizing table income_band (13/24).
Optimizing table call_center (14/24).
Optimizing table catalog_page (16/24).
Optimizing table web_page (15/24).
Optimizing table web_site (17/24).
Optimizing table store_sales (18/24).
Optimizing table store_returns (19/24).
Optimizing table web_sales (20/24).
Optimizing table web_returns (21/24).
Optimizing table catalog_sales (22/24).
Optimizing table inventory (24/24).
Optimizing table catalog_returns (23/24).
Data loaded into database tpcds_bin_partitioned_parquet_10.

生成结果

hive> use tpcds_bin_partitioned_parquet_10;
OK
Time taken: 0.025 seconds
hive> show tables;
OK
call_center
catalog_page
catalog_returns
catalog_sales
customer
customer_address
customer_demographics
date_dim
household_demographics
income_band
inventory
item
promotion
reason
ship_mode
store
store_returns
store_sales
time_dim
warehouse
web_page
web_returns
web_sales
web_site
Time taken: 0.049 seconds, Fetched: 24 row(s)

3 运行测试

测试sql脚本目录：sample-queries-tpcds

$ cd sample-queries-tpcds
hive> use tpcds_bin_partitioned_parquet_10;
hive> source query12.sql;

4 批量测试

根据需要修改hive配置：sample-queries-tpcds/testbench.settings
根据需要修改测试脚本（perl）：runSuite.pl

$ perl runSuiteCommon.pl
ERROR: one or more parameters not defined

Usage:
perl runSuiteCommon.pl [tpcds|tpch] [scale]

Description:
This script runs the sample queries and outputs a CSV file of the time it took each query to run. Also, all hive output is kept as a log file named 'queryXX.sql.log' for each query file of the form 'queryXX.sql'. Defaults to scale of 2.

核心代码：

my $suite = shift;

my $scale = shift || ;

dieWithUsage("suite name required") unless $suite eq "tpcds" or $suite eq "tpch";

chdir $SCRIPT_PATH;

if( $suite eq 'tpcds' ) {

        chdir "sample-queries-tpcds";

} else {

        chdir 'sample-queries-tpch';

} # end if

my @queries = glob '*.sql';

my $db = {

        'tpcds' => "tpcds_bin_partitioned_orc_$scale",

        'tpch' => "tpch_flat_orc_$scale"

};

print "filename,status,time,rows\n";

for my $query ( @queries ) {

        my $logname = "$query.log";

        my $cmd="echo 'use $db->{${suite}}; source $query;' | hive -i testbench.settings 2>&1  | tee $query.log";

这个脚本有两个参数：suite scale，比如tpcds 10

可以修改的更通用，一个是数据库硬编码orc，一个是硬编码hive命令，一个是打印正在执行的cmd，一个是启动命令有初始化环境的时间成本，直接使用beeline连接server的耗时更真实；修改之后可以用于其他测试，比如spark-sql、impala、drill等；

修改之后是这样：

#!/usr/bin/perl

use strict;
use warnings;
use POSIX;
use File::Basename;

# PROTOTYPES
sub dieWithUsage(;$);

# GLOBALS
my $SCRIPT_NAME = basename( __FILE__ );
my $SCRIPT_PATH = dirname( __FILE__ );

# MAIN
dieWithUsage("one or more parameters not defined") unless @ARGV >= ;
my $suite = shift;
my $scale = shift || ;
my $format = shift || ;
my $engineCmd = shift || ;
dieWithUsage("suite name required") unless $suite eq "tpcds" or $suite eq "tpch";
print "params: $suite, $scale, $format, $engineCmd;";

chdir $SCRIPT_PATH;
if( $suite eq 'tpcds' ) {
        chdir "sample-queries-tpcds";
} else {
        chdir 'sample-queries-tpch';
} # end if
my @queries = glob '*.sql';

my $db = {
        'tpcds' => "tpcds_bin_partitioned_${format}_$scale",
        'tpch' => "tpch_flat_${format}_$scale"
};

print "filename,status,time,rows\n";
for my $query ( @queries ) {
        my $logname = "$query.log";
        my $cmd="${engineCmd}/$db->{${suite}} -i conf.settings -f $query 2>&1  | tee $query.log";
#       my $cmd="cat $query.log";
        #print $cmd ; exit;
        my $currentTime = strftime("%Y-%m-%d %H:%M:%S", localtime(time));
        print "$currentTime : ";
        print "$cmd \n";

        my $hiveStart = time();

        my @hiveoutput=`$cmd`;
        die "${SCRIPT_NAME}:: ERROR:  hive command unexpectedly exited \$? = '$?', \$! = '$!'" if $?;

        my $hiveEnd = time();
        my $hiveTime = $hiveEnd - $hiveStart;
        my $is_success = ;
        foreach my $line ( @hiveoutput ) {
                if( $line =~ /[(\d+|No)]\s+row[s]? selected \(([\d\.]+) seconds\)/ ) {
                        $is_success = ;
                        print "$query,success,$hiveTime,$1\n";
                } # end if
        } # end while
        if( $is_success == ) {
                print "$query,failed,$hiveTime\n";
        }
} # end for

sub dieWithUsage(;$) {
        my $err = shift || '';
        if( $err ne '' ) {
                chomp $err;
                $err = "ERROR: $err\n\n";
        } # end if

        print STDERR <<USAGE;
${err}Usage:
        perl ${SCRIPT_NAME} [tpcds|tpch] [scale] [format] [engineCmd]

Description:
        This script runs the sample queries and outputs a CSV file of the time it took each query to run.  Also, all hive output is kept as a log file named 'queryXX.sql.log' for each query file of the form 'queryXX.sql'. Defaults to scale of .
USAGE
        exit ;
}

执行：

# beeline to hiveserver2

$ perl runSuite.pl tpcds 10 parquet "$HIVE_HOME/bin/beeline -u jdbc:hive2://localhost:10000"

# beeline to spark thrift server

$ perl runSuite.pl tpcds 10 parquet "$SPARK_HOME/bin/beeline -u jdbc:hive2://localhost:11111"

# beeline to impala

perl runSuite.pl tpcds 10 parquet "$HIVE_HOME/bin/beeline -d com.cloudera.impala.jdbc4.Driver -u jdbc:impala://localhost:21050"

批量测试脚本

#!/bin/sh

current_dir=`pwd`

scale="$1"
format="$2"

if [ -z "$scale" ]; then
        scale=10
fi
if [ -z "$format" ]; then
        format="parquet"
fi

#echo "$current_dir $component $scale $test_dir"
echo "mkdir merge"
echo ""

component="hive"
test_dir="test_$component"

echo "# test $component"
echo "mkdir $test_dir"
echo "cp -R sample-queries-tpcds $test_dir"
echo "ln -s $current_dir/runSuiteCommon.pl $test_dir/runSuite.pl"
echo "cd $test_dir"
echo "perl runSuite.pl tpcds ${scale} $format \"$HIVE_HOME/bin/beeline -i conf.settings -n hadoop -u jdbc:hive2://localhost:10000\" 2>&1| tee ${component}_${scale}_${format}.log"
echo "cd .."
echo "grep -e '^query' $test_dir/${component}_${scale}_${format}.log|sort > merge/${component}_${scale}_${format}.log"
echo "wc -l merge/${component}_${scale}_${format}.log"
echo ""

component="spark"
test_dir="test_$component"

echo "# test $component"
echo "mkdir $test_dir"
echo "cp -R sample-queries-tpcds $test_dir"
echo "ln -s $current_dir/runSuiteCommon.pl $test_dir/runSuite.pl"
echo "cd $test_dir"
echo "perl runSuite.pl tpcds ${scale} $format \"$SPARK_HOME/bin/beeline -i conf.settings -u jdbc:hive2://localhost:11111\" 2>&1| tee ${component}_${scale}_${format}.log"
echo "cd .."
echo "grep -e '^query' $test_dir/${component}_${scale}_${format}.log|sort > merge/${component}_${scale}_${format}.log"
echo "wc -l merge/${component}_${scale}_${format}.log"
echo ""

component="impala"
test_dir="test_$component"

echo "# test $component"
echo "mkdir $test_dir"
echo "cp -R sample-queries-tpcds $test_dir"
echo "ln -s $current_dir/runSuiteCommon.pl $test_dir/runSuite.pl"
echo "cd $test_dir"
echo "perl runSuite.pl tpcds ${scale} $format \"$HIVE_HOME/bin/beeline -i conf.settings -d com.cloudera.impala.jdbc4.Driver -u jdbc:impala://localhost:21050\" 2>&1| tee ${component}_${scale}_${format}.log"
echo "cd .."
echo "grep -e '^query' $test_dir/${component}_${scale}_${format}.log|sort > merge/${component}_${scale}_${format}.log"
echo "wc -l merge/${component}_${scale}_${format}.log"
echo ""

component="presto"
test_dir="test_$component"

echo "# test $component"
echo "mkdir $test_dir"
echo "cp -R sample-queries-tpcds $test_dir"
echo "ln -s $current_dir/runSuiteCommon.pl $test_dir/runSuite.pl"
echo "cd $test_dir"
echo "perl runSuite.pl tpcds ${scale} $format \"$HIVE_HOME/bin/beeline -i conf.settings -d com.facebook.presto.jdbc.PrestoDriver -n hadoop -u jdbc:presto://localhost:8080/hive\" 2>&1| tee ${component}_${scale}_${format}.log"
echo "cd .."
echo "grep -e '^query' $test_dir/${component}_${scale}_${format}.log|sort > merge/${component}_${scale}_${format}.log"
echo "wc -l merge/${component}_${scale}_${format}.log"

echo "awk -F ',' '{if(NF==4){print \$1\",\"\$4}else{print \$1\",0\"}}' merge/hive_${scale}_${format}.log > /tmp/hive_${scale}_${format}.log"
echo "awk -F ',' '{if(NF==4){print \$4}else{print \"0\"}}' merge/spark_${scale}_${format}.log > /tmp/spark_${scale}_${format}.log"
echo "awk -F ',' '{if(NF==4){print \$4}else{print \"0\"}}' merge/impala_${scale}_${format}.log > /tmp/impala_${scale}_${format}.log"
echo "awk -F ',' '{if(NF==4){print \$4}else{print \"0\"}}' merge/presto_${scale}_${format}.log > /tmp/presto_${scale}_${format}.log"
echo "paste -d\",\" /tmp/hive_${scale}_${format}.log /tmp/spark_${scale}_${format}.log /tmp/impala_${scale}_${format}.log /tmp/presto_${scale}_${format}.log > merge/result_${scale}_${format}.csv"
echo "sed -i \"1i sql_${scale}_${format},hive,spark,impala,presto\" merge/result_${scale}_${format}.csv"
echo ""

结果合并之后使用excel图形化显示