TPCx-BB源码分析
2024-03-17 09:13:54
Copy from: 一篇文章看懂TPCx-BB(大数据基准测试工具）源码
TPCx-BB是大数据基准测试工具,它通过模拟零售商的30个应用场景，执行30个查询来衡量基于Hadoop的大数据系统的包括硬件和软件的性能。其中一些场景还用到了机器学习算法（聚类、线性回归等）。为了更好地了解被测试的系统的性能，需要对TPCx-BB整个测试流程深入了解。本文详细分析了整个TPCx-BB测试工具的源码，希望能够对大家理解TPCx-BB有所帮助。
代码结构
主目录（$BENCH_MARK_HOME）下有：
- bin
- conf
- data-generator
- engines
- tools
几个子目录。
bin下有几个 module ,是执行时需要用到的脚本：bigBench、cleanLogs、logEnvInformation、runBenchmark、zipLogs等
conf下有两个配置文件：bigBench.properties 和 userSettings.conf
bigBench.properties 主要设置 workload（执行的benchmarkPhases）和 power_test_0（POWER_TEST 阶段需要执行的SQL查询）
默认 workload ：
workload=CLEAN_ALL,ENGINE_VALIDATION_DATA_GENERATION,ENGINE_VALIDATION_LOAD_TEST,ENGINE_VALIDATION_POWER_TEST,ENGINE_VALIDATION_RESULT_VALIDATION,CLEAN_DATA,DATA_GENERATION,BENCHMARK_START,LOAD_TEST,POWER_TEST,THROUGHPUT_TEST_1,BENCHMARK_STOP,VALIDATE_POWER_TEST,VALIDATE_THROUGHPUT_TEST_1默认 power_test_0 ：1-30
userSetting.conf 是一些基本设置，包括JAVA environment 、default settings for benchmark（database、engine、map_tasks、scale_factor ...）、HADOOP environment、
HDFS config and paths、Hadoop data generation options(DFS_REPLICATION、HADOOP_JVM_ENV...)
data-generator下是跟数据生成相关的脚本及配置文件。详细内容在下面介绍。
engines下是TPCx-BB支持的4种引擎：biginsights、hive、impala、spark_sql。默认引擎为hive。实际上，只有hive目录下不为空，其他三个目录下均为空，估计是现在还未完善。
tools下有两个jar包：HadoopClusterExec.jar 和 RunBigBench.jar 。其中 RunBigBench.jar 是执行TPCx-BB测试的一个非常重要的文件，大部分程序都在该jar包内。
数据生成
数据生成相关程序和配置都在 data-generator 目录下。该目录下有一个 pdgf.jar 包和 config、dicts、extlib 三个子目录。
pdgf.jar是数据生成的Java程序。config下有两个配置文件：bigbench-generation.xml 和 bigbench-schema.xml 。
bigbench-generation.xml 主要设置生成的原始数据（不是数据库表）包含哪几张表、每张表的表名、表的大小以及表输出的目录、表文件的后缀、分隔符、字符编码等。
<schema name="default">
<tables>
<!-- not refreshed tables -->
<!-- tables not used in benchmark, but some tables have references to them. not refreshed. Kept for legacy reasons -->
<table name="income_band"></table>
<table name="reason"></table>
<table name="ship_mode"></table>
<table name="web_site"></table>
<!-- /tables not used in benchmark -->
<!-- Static tables (fixed small size, generated only on node 1, skipped on others, not generated during refresh) -->
<table name="date_dim" static="true"></table>
<table name="time_dim" static="true"></table>
<table name="customer_demographics" static="true"></table>
<table name="household_demographics" static="true"></table>
<!-- /static tables -->
<!-- "normal" tables. split over all nodes. not generated during refresh -->
<table name="store"></table>
<table name="warehouse"></table>
<table name="promotion"></table>
<table name="web_page"></table>
<!-- /"normal" tables.-->
<!-- /not refreshed tables -->
<!--
refreshed tables. Generated on all nodes.
Refresh tables generate extra data during refresh (e.g. add new data to the existing tables)
In "normal"-Phase generate table rows: [0,REFRESH_PERCENTAGE*Table.Size];
In "refresh"-Phase generate table rows: [REFRESH_PERCENTAGE*Table.Size+1, Table.Size]
.Has effect only if ${REFRESH_SYSTEM_ENABLED}==1.
-->
<table name="customer">
<scheduler name="DefaultScheduler">
<partitioner
name="pdgf.core.dataGenerator.scheduler.TemplatePartitioner">
<prePartition><![CDATA[
if(${REFRESH_SYSTEM_ENABLED}>0){
int tableID = table.getTableID();
int timeID = 0;
long lastTableRow=table.getSize()-1;
long rowStart;
long rowStop;
boolean exclude=false;
long refreshRows=table.getSize()*(1.0-${REFRESH_PERCENTAGE});
if(${REFRESH_PHASE}>0){
//Refresh part
rowStart = lastTableRow - refreshRows +1;
rowStop = lastTableRow;
if(refreshRows<=0){
exclude=true;
}
}else{
//"normal" part
rowStart = 0;
rowStop = lastTableRow - refreshRows;
}
return new pdgf.core.dataGenerator.scheduler.Partition(tableID, timeID,rowStart,rowStop,exclude);
}else{
//DEFAULT
return getParentPartitioner().getDefaultPrePartition(project, table);
}
]]></prePartition>
</partitioner>
</scheduler>
</table><output name="SplitFileOutputWrapper">
<!-- DEFAULT output for all Tables, if no table specific output is specified-->
<output name="CSVRowOutput">
<fileTemplate><![CDATA[outputDir + table.getName() +(nodeCount!=1?"_"+pdgf.util.StaticHelper.zeroPaddedNumber(nodeNumber,nodeCount):"")+ fileEnding]]></fileTemplate>
<outputDir>output/</outputDir>
<fileEnding>.dat</fileEnding>
<delimiter>|</delimiter>
<charset>UTF-8</charset>
<sortByRowID>true</sortByRowID>
</output>
<output name="StatisticsOutput" active="1">
<size>${item_size}</size><!-- a counter per item .. initialize later-->
<fileTemplate><![CDATA[outputDir + table.getName()+"_audit" +(nodeCount!=1?"_"+pdgf.util.StaticHelper.zeroPaddedNumber(nodeNumber,nodeCount):"")+ fileEnding]]></fileTemplate>
<outputDir>output/</outputDir>
<fileEnding>.csv</fileEnding>
<delimiter>,</delimiter>
<header><!--"" + pdgf.util.Constants.DEFAULT_LINESEPARATOR-->
</header>
<footer></footer>bigbench-schema.xml 设置了很多参数，有跟表的规模有关的，比如每张表的大小（记录的条数）;绝大多数是跟表的字段有关的，比如时间的起始、结束、性别比例、结婚比例、指标的上下界等。还具体定义了每个字段是怎么生成的，以及限制条件。示例如下：
生成的数据大小由 SCALE_FACTOR（-f） 决定。如 -f 1，则生成的数据总大小约为1G；-f 100，则生成的数据总大小约为100G。那么SCALE_FACTOR（-f） 是怎么精确控制生成的数据的大小呢？
原因是 SCALE_FACTOR（-f） 决定了每张表的记录数。如下，customer 表的记录数为 100000.0d * ${SF_sqrt}，即如果 -f 1 则 customer 表的记录数为 100000*sqrt(1)= 10万条 ;如果 -f 100 则 customer 表的记录数为 100000*sqrt(100)= 100万条
<property name="${customer_size}" type="long">100000.0d * ${SF_sqrt}</property><property name="${DIMENSION_TABLES_START_DAY}" type="datetime">2000-01-03 00:00:00</property>
<property name="${DIMENSION_TABLES_END_DAY}" type="datetime">2004-01-05 00:00:00</property><property name="${gender_likelihood}" type="double">0.5</property>
<property name="${married_likelihood}" type="double">0.3</property><property name="${WP_LINK_MIN}" type="double">2</property>
<property name="${WP_LINK_MAX}" type="double">25</property> <field name="d_date" size="13" type="CHAR" primary="false">
<gen_DateTime>
<disableRng>true</disableRng>
<useFixedStepSize>true</useFixedStepSize>
<startDate>${date_dim_begin_date}</startDate>
<endDate>${date_dim_end_date}</endDate>
<outputFormat>yyyy-MM-dd</outputFormat>
</gen_DateTime>
</field> <field name="t_time_id" size="16" type="CHAR" primary="false">
<gen_ConvertNumberToString>
<gen_Id/>
<size>16.0</size>
<characters>ABCDEFGHIJKLMNOPQRSTUVWXYZ</characters>
</gen_ConvertNumberToString>
</field><field name="cd_dep_employed_count" size="10" type="INTEGER" primary="false">
<gen_Null probability="${NULL_CHANCE}">
<gen_WeightedListItem filename="dicts/bigbench/ds-genProbabilities.txt" list="dependent_count" valueColumn="0" weightColumn="0" />
</gen_Null>
</field>dicts下有city.dict、country.dict、male.dict、female.dict、state.dict、mail_provider.dict等字典文件，表里每一条记录的各个字段应该是从这些字典里生成的。
extlib下是引用的外部程序jar包。有 lucene-core-4.9.0.jar、commons-net-3.3.jar、xml-apis.jar和log4j-1.2.15.jar等
总结：
pdgf.jar根据bigbench-generation.xml 和 bigbench-schema.xml两个文件里的配置（表名、字段名、表的记录条数、每个字段生成的规则），从 dicts 目录下对应的 .dict
文件获取表中每一条记录、每个字段的值，生成原始数据。
customer 表里的某条记录如下：
0 AAAAAAAAAAAAAAAA 1824793 3203 2555 28776 14690 Ms. Marisa Harrington N 17 4 1988 UNITED ARAB EMIRATES RRCyuY3XfE3a Marisa.Harrington@lawyer.com gdMmGdU9如果执行 TPCx-BB 测试时指定 -f 1（SCALE_FACTOR = 1） 则最终生成的原始数据总大小约为 1G(977M+8.6M)
[root@node-20-100 ~]# hdfs dfs -du -h /user/root/benchmarks/bigbench/data
12.7 M 38.0 M /user/root/benchmarks/bigbench/data/customer
5.1 M 15.4 M /user/root/benchmarks/bigbench/data/customer_address
74.2 M 222.5 M /user/root/benchmarks/bigbench/data/customer_demographics
14.7 M 44.0 M /user/root/benchmarks/bigbench/data/date_dim
151.5 K 454.4 K /user/root/benchmarks/bigbench/data/household_demographics
327 981 /user/root/benchmarks/bigbench/data/income_band
405.3 M 1.2 G /user/root/benchmarks/bigbench/data/inventory
6.5 M 19.5 M /user/root/benchmarks/bigbench/data/item
4.0 M 12.0 M /user/root/benchmarks/bigbench/data/item_marketprices
53.7 M 161.2 M /user/root/benchmarks/bigbench/data/product_reviews
45.3 K 135.9 K /user/root/benchmarks/bigbench/data/promotion
3.0 K 9.1 K /user/root/benchmarks/bigbench/data/reason
1.2 K 3.6 K /user/root/benchmarks/bigbench/data/ship_mode
3.3 K 9.9 K /user/root/benchmarks/bigbench/data/store
4.1 M 12.4 M /user/root/benchmarks/bigbench/data/store_returns
88.5 M 265.4 M /user/root/benchmarks/bigbench/data/store_sales
4.9 M 14.6 M /user/root/benchmarks/bigbench/data/time_dim
584 1.7 K /user/root/benchmarks/bigbench/data/warehouse
170.4 M 511.3 M /user/root/benchmarks/bigbench/data/web_clickstreams
7.9 K 23.6 K /user/root/benchmarks/bigbench/data/web_page
5.1 M 15.4 M /user/root/benchmarks/bigbench/data/web_returns
127.6 M 382.8 M /user/root/benchmarks/bigbench/data/web_sales
8.6 K 25.9 K /user/root/benchmarks/bigbench/data/web_site执行流程
要执行TPCx-BB测试，首先需要切换到TPCx-BB源程序的目录下，然后进入bin目录，执行以下语句：
./bigBench runBenchmark -f 1 -m 8 -s 2 -j 5其中，-f、-m、-s、-j都是参数，用户可根据集群的性能以及自己的需求来设置。如果不指定，则使用默认值，默认值在 conf 目录下的 userSetting.conf 文件指定，如下：
export BIG_BENCH_DEFAULT_DATABASE="bigbench"
export BIG_BENCH_DEFAULT_ENGINE="hive"
export BIG_BENCH_DEFAULT_MAP_TASKS="80"
export BIG_BENCH_DEFAULT_SCALE_FACTOR="1000"
export BIG_BENCH_DEFAULT_NUMBER_OF_PARALLEL_STREAMS="2"
export BIG_BENCH_DEFAULT_BENCHMARK_PHASE="run_query"默认 MAP_TASKS 为 80（-m 80）、SCALE_FACTOR 为 1000（-f 1000）、NUMBER_OF_PARALLEL_STREAMS 为 2（-s 2）。
所有可选参数及其意义如下：
General options:
-d 使用的数据库 (默认: $BIG_BENCH_DEFAULT_DATABASE -> bigbench)
-e 使用的引擎 (默认: $BIG_BENCH_DEFAULT_ENGINE -> hive)
-f 数据集的规模因子（scale factor） (默认: $BIG_BENCH_DEFAULT_SCALE_FACTOR -> 1000)
-h 显示帮助
-m 数据生成的`map tasks`数 (default: $BIG_BENCH_DEFAULT_MAP_TASKS)"
-s 并行的`stream`数 (默认: $BIG_BENCH_DEFAULT_NUMBER_OF_PARALLEL_STREAMS -> 2)
Driver specific options:
-a 伪装模式执行
-b 执行期间将调用的bash脚本在标准输出中打印出来
-i 指定需要执行的阶段 (详情见$BIG_BENCH_CONF_DIR/bigBench.properties)
-j 指定需要执行的查询 (默认：1-30共30个查询均执行)"
-U 解锁专家模式若指定了-U,即解锁了专家模式，则：
echo "EXPERT MODE ACTIVE"
echo "WARNING - INTERNAL USE ONLY:"
echo "Only set manually if you know what you are doing!"
echo "Ignoring them is probably the best solution"
echo "Running individual modules:"
echo "Usage: `basename $0` module [options]"
-D 指定需要debug的查询部分. 大部分查询都只有一个单独的部分
-p 需要执行的benchmark phase (默认: $BIG_BENCH_DEFAULT_BENCHMARK_PHASE -> run_query)"
-q 指定需要执行哪个查询（只能指定一个）
-t 指定执行该查询时用第哪个stream
-v metastore population的sql脚本 (默认: ${USER_POPULATE_FILE:-"$BIG_BENCH_POPULATION_DIR/hiveCreateLoad.sql"})"
-w metastore refresh的sql脚本 (默认: ${USER_REFRESH_FILE:-"$BIG_BENCH_REFRESH_DIR/hiveRefreshCreateLoad.sql"})"
-y 含额外的用户自定义查询参数的文件 (global: $BIG_BENCH_ENGINE_CONF_DIR/queryParameters.sql)"
-z 含额外的用户自定义引擎设置的文件 (global: $BIG_BENCH_ENGINE_CONF_DIR/engineSettings.sql)"
List of available modules:
$BIG_BENCH_ENGINE_BIN_DIR回到刚刚执行TPCx-BB测试的语句：
./bigBench runBenchmark -f 1 -m 8 -s 2 -j 5bigBench
bigBench是主脚本，runBenchmark是module。
bigBench 里设置了很多环境变量（包括路径、引擎、STREAM数等等），因为后面调用 runBigBench.jar 的时候需要在Java程序里读取这些环境变量。
bigBench 前面都是在做一些基本工作，如设置环境变量、解析用户输入参数、赋予文件权限、设置路径等等。到最后一步调用 runBenchmark 的 runModule() 方法：
设置基本路径
export BIG_BENCH_VERSION="1.0"
export BIG_BENCH_BIN_DIR="$BIG_BENCH_HOME/bin"
export BIG_BENCH_CONF_DIR="$BIG_BENCH_HOME/conf"
export BIG_BENCH_DATA_GENERATOR_DIR="$BIG_BENCH_HOME/data-generator"
export BIG_BENCH_TOOLS_DIR="$BIG_BENCH_HOME/tools"
export BIG_BENCH_LOGS_DIR="$BIG_BENCH_HOME/logs"指定
core-site.xml和hdfs-site.xml的路径数据生成时要用到Hadoop集群，生成在hdfs上
export BIG_BENCH_DATAGEN_CORE_SITE="$BIG_BENCH_HADOOP_CONF/core-site.xml"
export BIG_BENCH_DATAGEN_HDFS_SITE="$BIG_BENCH_HADOOP_CONF/hdfs-site.xml"赋予整个包下所有可执行文件权限（.sh/.jar/.py）
find "$BIG_BENCH_HOME" -name '*.sh' -exec chmod 755 {} +
find "$BIG_BENCH_HOME" -name '*.jar' -exec chmod 755 {} +
find "$BIG_BENCH_HOME" -name '*.py' -exec chmod 755 {} +设置
userSetting.conf的路径并sourceUSER_SETTINGS="$BIG_BENCH_CONF_DIR/userSettings.conf"
if [ ! -f "$USER_SETTINGS" ]
then
echo "User settings file $USER_SETTINGS not found"
exit 1
else
source "$USER_SETTINGS"
fi解析输入参数和选项并根据选项的内容作设置
第一个参数必须是
module_name如果没有输入参数或者第一个参数以"-"开头，说明用户没有输入需要运行的module。
if [[ $# -eq 0 || "`echo "$1" | cut -c1`" = "-" ]]
then
export MODULE_NAME=""
SHOW_HELP="1"
else
export MODULE_NAME="$1"
shift
fi
export LIST_OF_USER_OPTIONS="$@"解析用户输入的参数
根据用户输入的参数来设置环境变量
while getopts ":d:D:e:f:hm:p:q:s:t:Uv:w:y:z:abi:j:" OPT; do
case "$OPT" in
# script options
d)
#echo "-d was triggered, Parameter: $OPTARG" >&2
USER_DATABASE="$OPTARG"
;;
D)
#echo "-D was triggered, Parameter: $OPTARG" >&2
DEBUG_QUERY_PART="$OPTARG"
;;
e)
#echo "-e was triggered, Parameter: $OPTARG" >&2
USER_ENGINE="$OPTARG"
;;
f)
#echo "-f was triggered, Parameter: $OPTARG" >&2
USER_SCALE_FACTOR="$OPTARG"
;;
h)
#echo "-h was triggered, Parameter: $OPTARG" >&2
SHOW_HELP="1"
;;
m)
#echo "-m was triggered, Parameter: $OPTARG" >&2
USER_MAP_TASKS="$OPTARG"
;;
p)
#echo "-p was triggered, Parameter: $OPTARG" >&2
USER_BENCHMARK_PHASE="$OPTARG"
;;
q)
#echo "-q was triggered, Parameter: $OPTARG" >&2
QUERY_NUMBER="$OPTARG"
;;
s)
#echo "-t was triggered, Parameter: $OPTARG" >&2
USER_NUMBER_OF_PARALLEL_STREAMS="$OPTARG"
;;
t)
#echo "-s was triggered, Parameter: $OPTARG" >&2
USER_STREAM_NUMBER="$OPTARG"
;;
U)
#echo "-U was triggered, Parameter: $OPTARG" >&2
USER_EXPERT_MODE="1"
;;
v)
#echo "-v was triggered, Parameter: $OPTARG" >&2
USER_POPULATE_FILE="$OPTARG"
;;
w)
#echo "-w was triggered, Parameter: $OPTARG" >&2
USER_REFRESH_FILE="$OPTARG"
;;
y)
#echo "-y was triggered, Parameter: $OPTARG" >&2
USER_QUERY_PARAMS_FILE="$OPTARG"
;;
z)
#echo "-z was triggered, Parameter: $OPTARG" >&2
USER_ENGINE_SETTINGS_FILE="$OPTARG"
;;
# driver options
a)
#echo "-a was triggered, Parameter: $OPTARG" >&2
export USER_PRETEND_MODE="1"
;;
b)
#echo "-b was triggered, Parameter: $OPTARG" >&2
export USER_PRINT_STD_OUT="1"
;;
i)
#echo "-i was triggered, Parameter: $OPTARG" >&2
export USER_DRIVER_WORKLOAD="$OPTARG"
;;
j)
#echo "-j was triggered, Parameter: $OPTARG" >&2
export USER_DRIVER_QUERIES_TO_RUN="$OPTARG"
;;
\?)
echo "Invalid option: -$OPTARG" >&2
exit 1
;;
:)
echo "Option -$OPTARG requires an argument." >&2
exit 1
;;
esac
done设置全局变量。如果用户指定了某个参数的值，则采用该值，否则使用默认值。
export BIG_BENCH_EXPERT_MODE="${USER_EXPERT_MODE:-"0"}"
export SHOW_HELP="${SHOW_HELP:-"0"}"
export BIG_BENCH_DATABASE="${USER_DATABASE:-"$BIG_BENCH_DEFAULT_DATABASE"}"
export BIG_BENCH_ENGINE="${USER_ENGINE:-"$BIG_BENCH_DEFAULT_ENGINE"}"
export BIG_BENCH_MAP_TASKS="${USER_MAP_TASKS:-"$BIG_BENCH_DEFAULT_MAP_TASKS"}"
export BIG_BENCH_SCALE_FACTOR="${USER_SCALE_FACTOR:-"$BIG_BENCH_DEFAULT_SCALE_FACTOR"}"
export BIG_BENCH_NUMBER_OF_PARALLEL_STREAMS="${USER_NUMBER_OF_PARALLEL_STREAMS:-"$BIG_BENCH_DEFAULT_NUMBER_OF_PARALLEL_STREAMS"}"
export BIG_BENCH_BENCHMARK_PHASE="${USER_BENCHMARK_PHASE:-"$BIG_BENCH_DEFAULT_BENCHMARK_PHASE"}"
export BIG_BENCH_STREAM_NUMBER="${USER_STREAM_NUMBER:-"0"}"
export BIG_BENCH_ENGINE_DIR="$BIG_BENCH_HOME/engines/$BIG_BENCH_ENGINE"
export BIG_BENCH_ENGINE_CONF_DIR="$BIG_BENCH_ENGINE_DIR/conf"检测 -s -m -f -j的选项是否为数字
if [ -n "`echo "$BIG_BENCH_MAP_TASKS" | sed -e 's/[0-9]*//g'`" ]
then
echo "$BIG_BENCH_MAP_TASKS is not a number"
fi
if [ -n "`echo "$BIG_BENCH_SCALE_FACTOR" | sed -e 's/[0-9]*//g'`" ]
then
echo "$BIG_BENCH_SCALE_FACTOR is not a number"
fi
if [ -n "`echo "$BIG_BENCH_NUMBER_OF_PARALLEL_STREAMS" | sed -e 's/[0-9]*//g'`" ]
then
echo "$BIG_BENCH_NUMBER_OF_PARALLEL_STREAMS is not a number"
fi
if [ -n "`echo "$BIG_BENCH_STREAM_NUMBER" | sed -e 's/[0-9]*//g'`" ]
then
echo "$BIG_BENCH_STREAM_NUMBER is not a number"
fi检查引擎是否存在
if [ ! -d "$BIG_BENCH_ENGINE_DIR" ]
then
echo "Engine directory $BIG_BENCH_ENGINE_DIR not found. Aborting script..."
exit 1
fi
if [ ! -d "$BIG_BENCH_ENGINE_CONF_DIR" ]
then
echo "Engine configuration directory $BIG_BENCH_ENGINE_CONF_DIR not found. Aborting script..."
exit 1
fi设置
engineSetting.conf路径并sourceENGINE_SETTINGS="$BIG_BENCH_ENGINE_CONF_DIR/engineSettings.conf"
if [ ! -f "$ENGINE_SETTINGS" ]
then
echo "Engine settings file $ENGINE_SETTINGS not found"
exit 1
else
source "$ENGINE_SETTINGS"
fi检查module是否存在
当输入某个module时，系统会先到
$BIG_BENCH_ENGINE_BIN_DIR/目录下去找该module是否存在，如果存在，就source "$MODULE"；如果该目录下不存在指定的module，再到export MODULE="$BIG_BENCH_BIN_DIR/"目录下找该module，如果存在，就source "$MODULE"；否则，输出Module $MODULE not found, aborting script.export MODULE="$BIG_BENCH_ENGINE_BIN_DIR/$MODULE_NAME"
if [ -f "$MODULE" ]
then
source "$MODULE"
else
export MODULE="$BIG_BENCH_BIN_DIR/$MODULE_NAME"
if [ -f "$MODULE" ]
then
source "$MODULE"
else
echo "Module $MODULE not found, aborting script."
exit 1
fi
fi检查module里的runModule（）、helpModule ( )、runEngineCmd()方法是否有定义
MODULE_RUN_METHOD="runModule"
if ! declare -F "$MODULE_RUN_METHOD" > /dev/null 2>&1
then
echo "$MODULE_RUN_METHOD was not implemented, aborting script"
exit 1
fi运行
module如果module是runBenchmark，执行
runCmdWithErrorCheck "$MODULE_RUN_METHOD"
也就是runCmdWithErrorCheck runModule（）
由上可以看出，bigBench脚本主要执行一些如设置环境变量、赋予权限、检查并解析输入参数等基础工作，最终调用runBenchmark的runModule()方法继续往下执行。
runBenchmark
接下来看看runBenchmark脚本。
runBenchmark里有两个函数：helpModule ()和runModule ()。
helpModule ()就是显示帮助。
runModule ()是运行runBenchmark模块时真正调用的函数。该函数主要做四件事：
- 清除之前生成的日志
- 调用
RunBigBench.jar来执行 - logEnvInformation
- 将日志文件夹打包成zip
源码如下：
runModule () {
#check input parameters
if [ "$BIG_BENCH_NUMBER_OF_PARALLEL_STREAMS" -le 0 ]
then
echo "The number of parallel streams -s must be greater than 0"
return 1
fi
"${BIG_BENCH_BIN_DIR}/bigBench" cleanLogs -U $LIST_OF_USER_OPTIONS
"$BIG_BENCH_JAVA" -jar "${BIG_BENCH_TOOLS_DIR}/RunBigBench.jar"
"${BIG_BENCH_BIN_DIR}/bigBench" logEnvInformation -U $LIST_OF_USER_OPTIONS
"${BIG_BENCH_BIN_DIR}/bigBench" zipLogs -U $LIST_OF_USER_OPTIONS
return $?
}相当于运行runBenchmark模块时又调用了cleanLogs、logEnvInformation、zipLogs三个模块以及RunBigBench.jar。其中RunBigBench.jar是TCPx-BB测试执行的核心代码，用Java语言编写。接下来分析RunBigBench.jar源码。
runModule()
runModule()函数用来执行某个module。我们已知，执行某个module需要切换到主目录下的bin目录，然后执行：
./bigBench module_name arguments在runModule()函数里，cmdLine用来生成如上命令。
ArrayList cmdLine = new ArrayList();
cmdLine.add("bash");
cmdLine.add(this.runScript);
cmdLine.add(benchmarkPhase.getRunModule());
cmdLine.addAll(arguments);其中，this.runScript为：
this.runScript = (String)env.get("BIG_BENCH_BIN_DIR") + "/bigBench";benchmarkPhase.getRunModule()用来获得需要执行的module。
arguments为用户输入的参数。
至此，cmdLine为：
bash $BIG_BENCH_BIN_DIR/bigBench module_name arguments那么，怎么让系统执行该bash命令呢？答案是调用runCmd()方法。
boolean successful = this.runCmd(this.homeDir, benchmarkPhase.isPrintStdOut(), (String[])cmdLine.toArray(new String[0]));接下来介绍rumCmd()方法
runCmd()
runCmd()方法通过ProcessBuilder来创建一个操作系统进程，并用该进程执行以上的bash命令。
ProcessBuilder还可以设置工作目录和环境。
ProcessBuilder pb = new ProcessBuilder(command);
pb.directory(new File(workingDirectory));
Process p = null;
---
p = pb.start();getQueryList()
getQueryList()用来获得需要执行的查询列表。从$BIG_BENCH_LOGS_DIR/bigBench.properties文件中读取。与$BIG_BENCH_HOME/conf/bigBench.properties内容一致。
bigBench.properties里power_test_0=1-30规定了powter_test_0阶段需要执行的查询及其顺序。
可以用区间如 5-12 或者单个数字如 21 表示，中间用 , 隔开。
power_test_0=28-25,2-5,10,22,30表示powter_test_0阶段需要执行的查询及其顺序为：28,27,26,25,2,3,4,5,10,22,30
如果想让30个查询按顺序执行，则：
power_test_0=1-30获得查询列表的源码如下：
private List<Integer> getQueryList(BigBench.BenchmarkPhase benchmarkPhase, int streamNumber) {
String SHUFFLED_NAME_PATTERN = "shuffledQueryList";
BigBench.BenchmarkPhase queryOrderBasicPhase = BigBench.BenchmarkPhase.POWER_TEST;
String propertyKey = benchmarkPhase.getQueryListProperty(streamNumber);
boolean queryOrderCached = benchmarkPhase.isQueryOrderCached();
if(queryOrderCached && this.queryListCache.containsKey(propertyKey)) {
return new ArrayList((Collection)this.queryListCache.get(propertyKey));
} else {
Object queryList;
String basicPhaseNamePattern;
if(!this.properties.containsKey(propertyKey)) {
if(benchmarkPhase.isQueryOrderRandom()) {
if(!this.queryListCache.containsKey("shuffledQueryList")) {
basicPhaseNamePattern = queryOrderBasicPhase.getQueryListProperty(0);
if(!this.properties.containsKey(basicPhaseNamePattern)) {
throw new IllegalArgumentException("Property " + basicPhaseNamePattern + " is not defined, but is the basis for shuffling the query list.");
}
this.queryListCache.put("shuffledQueryList", this.getQueryList(queryOrderBasicPhase, 0));
}
queryList = (List)this.queryListCache.get("shuffledQueryList");
this.shuffleList((List)queryList, this.rnd);
} else {
queryList = this.getQueryList(queryOrderBasicPhase, 0);
}
} else {
queryList = new ArrayList();
String[] var11;
int var10 = (var11 = this.properties.getProperty(propertyKey).split(",")).length;
label65:
for(int var9 = 0; var9 < var10; ++var9) {
basicPhaseNamePattern = var11[var9];
String[] queryRange = basicPhaseNamePattern.trim().split("-");
switch(queryRange.length) {
case 1:
((List)queryList).add(Integer.valueOf(Integer.parseInt(queryRange[0].trim())));
break;
case 2:
int startQuery = Integer.parseInt(queryRange[0]);
int endQuery = Integer.parseInt(queryRange[1]);
int i;
if(startQuery > endQuery) {
i = startQuery;
while(true) {
if(i < endQuery) {
continue label65;
}
((List)queryList).add(Integer.valueOf(i));
--i;
}
} else {
i = startQuery;
while(true) {
if(i > endQuery) {
continue label65;
}
((List)queryList).add(Integer.valueOf(i));
++i;
}
}
default:
throw new IllegalArgumentException("Query numbers must be in the form X or X-Y, comma separated.");
}
}
}
if(queryOrderCached) {
this.queryListCache.put(propertyKey, new ArrayList((Collection)queryList));
}
return new ArrayList((Collection)queryList);
}
}parseEnvironment()
parseEnvironment()读取系统的环境变量并解析。
Map env = System.getenv();
this.version = (String)env.get("BIG_BENCH_VERSION");
this.homeDir = (String)env.get("BIG_BENCH_HOME");
this.confDir = (String)env.get("BIG_BENCH_CONF_DIR");
this.runScript = (String)env.get("BIG_BENCH_BIN_DIR") + "/bigBench";
this.datagenDir = (String)env.get("BIG_BENCH_DATA_GENERATOR_DIR");
this.logDir = (String)env.get("BIG_BENCH_LOGS_DIR");
this.dataGenLogFile = (String)env.get("BIG_BENCH_DATAGEN_STAGE_LOG");
this.loadLogFile = (String)env.get("BIG_BENCH_LOADING_STAGE_LOG");
this.engine = (String)env.get("BIG_BENCH_ENGINE");
this.database = (String)env.get("BIG_BENCH_DATABASE");
this.mapTasks = (String)env.get("BIG_BENCH_MAP_TASKS");
this.numberOfParallelStreams = Integer.parseInt((String)env.get("BIG_BENCH_NUMBER_OF_PARALLEL_STREAMS"));
this.scaleFactor = Long.parseLong((String)env.get("BIG_BENCH_SCALE_FACTOR"));
this.stopAfterFailure = ((String)env.get("BIG_BENCH_STOP_AFTER_FAILURE")).equals("1");并自动在用户指定的参数后面加上 -U (解锁专家模式)
this.userArguments.add("-U");如果用户指定了 PRETEND_MODE、PRINT_STD_OUT、WORKLOAD、QUERIES_TO_RUN，则以用户指定的参数为准，否则使用默认值。
if(env.containsKey("USER_PRETEND_MODE")) {
this.properties.setProperty("pretend_mode", (String)env.get("USER_PRETEND_MODE"));
}
if(env.containsKey("USER_PRINT_STD_OUT")) {
this.properties.setProperty("show_command_stdout", (String)env.get("USER_PRINT_STD_OUT"));
}
if(env.containsKey("USER_DRIVER_WORKLOAD")) {
this.properties.setProperty("workload", (String)env.get("USER_DRIVER_WORKLOAD"));
}
if(env.containsKey("USER_DRIVER_QUERIES_TO_RUN")) {
this.properties.setProperty(BigBench.BenchmarkPhase.POWER_TEST.getQueryListProperty(0), (String)env.get("USER_DRIVER_QUERIES_TO_RUN"));
}读取 workload 并赋值 benchmarkPhases。如果 workload 里不包含 BENCHMARK_START 和 BENCHMARK_STOP，自动在 benchmarkPhases 的首位和末位分别加上 BENCHMARK_START 和 BENCHMARK_STOP。
this.benchmarkPhases = new ArrayList();
Iterator var7 = Arrays.asList(this.properties.getProperty("workload").split(",")).iterator();
while(var7.hasNext()) {
String benchmarkPhase = (String)var7.next();
this.benchmarkPhases.add(BigBench.BenchmarkPhase.valueOf(benchmarkPhase.trim()));
}
if(!this.benchmarkPhases.contains(BigBench.BenchmarkPhase.BENCHMARK_START)) {
this.benchmarkPhases.add(0, BigBench.BenchmarkPhase.BENCHMARK_START);
}
if(!this.benchmarkPhases.contains(BigBench.BenchmarkPhase.BENCHMARK_STOP)) {
this.benchmarkPhases.add(BigBench.BenchmarkPhase.BENCHMARK_STOP);
}run()
run() 方法是 RunBigBench.jar 里核心的方法。所有的执行都是通过 run() 方法调用的。比如 runQueries()、runModule()、generateData()等。runQueries()、runModule()、generateData() 又通过调用 runCmd() 方法来创建操作系统进程，执行bash命令，调用bash脚本。
run() 方法里通过一个 while 循环来逐一执行 workload 里的每一个 benchmarkPhase。 不同的 benchmarkPhase 会调用 runQueries()、runModule()、generateData()...中的不同方法。
try {
long e = 0L;
this.log.finer("Benchmark phases: " + this.benchmarkPhases);
Iterator startCheckpoint = this.benchmarkPhases.iterator();
long throughputStart;
while(startCheckpoint.hasNext()) {
BigBench.BenchmarkPhase children = (BigBench.BenchmarkPhase)startCheckpoint.next();
if(children.isPhaseDone()) {
this.log.info("The phase " + children.name() + " was already performed earlier. Skipping this phase");
} else {
try {
switch($SWITCH_TABLE$io$bigdatabenchmark$v1$driver$BigBench$BenchmarkPhase()[children.ordinal()]) {
case 1:
case 20:
throw new IllegalArgumentException("The value " + children.name() + " is only used internally.");
case 2:
this.log.info(children.getConsoleMessage());
e = System.currentTimeMillis();
break;
case 3:
if(!BigBench.BenchmarkPhase.BENCHMARK_START.isPhaseDone()) {
throw new IllegalArgumentException("Error: Cannot stop the benchmark before starting it");
}
throughputStart = System.currentTimeMillis();
this.log.info(String.format("%-55s finished. Time: %25s", new Object[]{children.getConsoleMessage(), BigBench.Helper.formatTime(throughputStart - e)}));
this.logTreeRoot.setCheckpoint(new BigBench.Checkpoint(BigBench.BenchmarkPhase.BENCHMARK, -1L, -1L, e, throughputStart, this.logTreeRoot.isSuccessful()));
break;
case 4:
case 15:
case 18:
case 22:
case 27:
case 28:
case 29:
this.runModule(children, this.userArguments);
break;
case 5:
case 10:
case 11:
this.runQueries(children, 1, validationArguments);
break;
case 6:
case 9:
this.runModule(children, validationArguments);
break;
case 7:
this.generateData(children, false, validationArguments);
break;
case 8:
this.generateData(children, true, validationArguments);
break;
case 12:
case 19:
case 24:
this.runQueries(children, 1, this.userArguments);
break;
case 13:
case 14:
case 21:
case 23:
case 25:
case 26:
this.runQueries(children, this.numberOfParallelStreams, this.userArguments);
break;
case 16:
this.generateData(children, false, this.userArguments);
break;
case 17:
this.generateData(children, true, this.userArguments);
}
children.setPhaseDone(true);
} catch (IOException var21) {
this.log.info("==============\nBenchmark run terminated\nReason: An error occured while running a command in phase " + children + "\n==============");
var21.printStackTrace();
if(this.stopAfterFailure || children.mustSucceed()) {
break;
}
}
}
}这里的 case 1-29 并不是 1-29 条查询，而是枚举类型里的 1-29 个 benmarkPhase 。如下所示：
private static enum BenchmarkPhase {
BENCHMARK((String)null, "benchmark", false, false, false, false, "BigBench benchmark"),
BENCHMARK_START((String)null, "benchmark_start", false, false, false, false, "BigBench benchmark: Start"),
BENCHMARK_STOP((String)null, "benchmark_stop", false, false, false, false, "BigBench benchmark: Stop"),
CLEAN_ALL("cleanAll", "clean_all", false, false, false, false, "BigBench clean all"),
ENGINE_VALIDATION_CLEAN_POWER_TEST("cleanQuery", "engine_validation_power_test", false, false, false, false, "BigBench engine validation: Clean power test queries"),
ENGINE_VALIDATION_CLEAN_LOAD_TEST("cleanMetastore", "engine_validation_metastore", false, false, false, false, "BigBench engine validation: Clean metastore"),
ENGINE_VALIDATION_CLEAN_DATA("cleanData", "engine_validation_data", false, false, false, false, "BigBench engine validation: Clean data"),
ENGINE_VALIDATION_DATA_GENERATION("dataGen", "engine_validation_data", false, false, false, true, "BigBench engine validation: Data generation"),
ENGINE_VALIDATION_LOAD_TEST("populateMetastore", "engine_validation_metastore", false, false, false, true, "BigBench engine validation: Populate metastore"),
ENGINE_VALIDATION_POWER_TEST("runQuery", "engine_validation_power_test", false, false, false, false, "BigBench engine validation: Power test"),
ENGINE_VALIDATION_RESULT_VALIDATION("validateQuery", "engine_validation_power_test", false, false, true, false, "BigBench engine validation: Check all query results"),
CLEAN_POWER_TEST("cleanQuery", "power_test", false, false, false, false, "BigBench clean: Clean power test queries"),
CLEAN_THROUGHPUT_TEST_1("cleanQuery", "throughput_test_1", false, false, false, false, "BigBench clean: Clean first throughput test queries"),
CLEAN_THROUGHPUT_TEST_2("cleanQuery", "throughput_test_2", false, false, false, false, "BigBench clean: Clean second throughput test queries"),
CLEAN_LOAD_TEST("cleanMetastore", "metastore", false, false, false, false, "BigBench clean: Load test"),
CLEAN_DATA("cleanData", "data", false, false, false, false, "BigBench clean: Data"),
DATA_GENERATION("dataGen", "data", false, false, false, true, "BigBench preparation: Data generation"),
LOAD_TEST("populateMetastore", "metastore", false, false, false, true, "BigBench phase 1: Load test"),
POWER_TEST("runQuery", "power_test", false, true, false, false, "BigBench phase 2: Power test"),
THROUGHPUT_TEST((String)null, "throughput_test", false, false, false, false, "BigBench phase 3: Throughput test"),
THROUGHPUT_TEST_1("runQuery", "throughput_test_1", true, true, false, false, "BigBench phase 3: First throughput test run"),
THROUGHPUT_TEST_REFRESH("refreshMetastore", "throughput_test_refresh", false, false, false, false, "BigBench phase 3: Throughput test data refresh"),
THROUGHPUT_TEST_2("runQuery", "throughput_test_2", true, true, false, false, "BigBench phase 3: Second throughput test run"),
VALIDATE_POWER_TEST("validateQuery", "power_test", false, false, true, false, "BigBench validation: Power test results"),
VALIDATE_THROUGHPUT_TEST_1("validateQuery", "throughput_test_1", false, false, true, false, "BigBench validation: First throughput test results"),
VALIDATE_THROUGHPUT_TEST_2("validateQuery", "throughput_test_2", false, false, true, false, "BigBench validation: Second throughput test results"),
SHOW_TIMES("showTimes", "show_times", false, false, true, false, "BigBench: show query times"),
SHOW_ERRORS("showErrors", "show_errors", false, false, true, false, "BigBench: show query errors"),
SHOW_VALIDATION("showValidation", "show_validation", false, false, true, false, "BigBench: show query validation results");
private String runModule;
private String namePattern;
private boolean queryOrderRandom;
private boolean queryOrderCached;
private boolean printStdOut;
private boolean mustSucceed;
private String consoleMessage;
private boolean phaseDone;
private BenchmarkPhase(String runModule, String namePattern, boolean queryOrderRandom, boolean queryOrderCached, boolean printStdOut, boolean mustSucceed, String consoleMessage) {
this.runModule = runModule;
this.namePattern = namePattern;
this.queryOrderRandom = queryOrderRandom;
this.queryOrderCached = queryOrderCached;
this.printStdOut = printStdOut;
this.mustSucceed = mustSucceed;
this.consoleMessage = consoleMessage;
this.phaseDone = false;
}3对应 BENCHMARK_STOP，4对应 CLEAN_ALL,29对应 SHOW_VALIDATION，依此类推...
可以看出：
CLEAN_ALL、CLEAN_LOAD_TEST、LOAD_TEST、THROUGHPUT_TEST_REFRESH、SHOW_TIMES、SHOW_ERRORS、SHOW_VALIDATION等benchmarkPhases调用的是
this.runModule(children, this.userArguments);方法是 runModule ，参数是 this.userArguments。
ENGINE_VALIDATION_CLEAN_POWER_TEST、ENGINE_VALIDATION_POWER_TEST、ENGINE_VALIDATION_RESULT_VALIDATION 调用的是
this.runQueries(children, 1, validationArguments);方法是 runQueries ，参数是 1(stream number) 和 validationArguments。
ENGINE_VALIDATION_CLEAN_LOAD_TEST 和 ENGINE_VALIDATION_LOAD_TEST 调用的是
this.runModule(children, validationArguments);ENGINE_VALIDATION_CLEAN_DATA 调用的是
this.generateData(children, false, validationArguments);ENGINE_VALIDATION_DATA_GENERATION 调用的是
this.generateData(children, true, validationArguments);CLEAN_POWER_TEST、POWER_TEST、VALIDATE_POWER_TEST 调用的是
this.runQueries(children, 1, this.userArguments);CLEAN_THROUGHPUT_TEST_1``CLEAN_THROUGHPUT_TEST_2``THROUGHPUT_TEST_1``THROUGHPUT_TEST_2``VALIDATE_THROUGHPUT_TEST_1 VALIDATE_THROUGHPUT_TEST_2 调用的是
this.runQueries(children, this.numberOfParallelStreams, this.userArguments);CLEAN_DATA 调用的是
this.generateData(children, false, this.userArguments);DATA_GENERATION 调用的是
this.generateData(children, true, this.userArguments);总结一下以上的方法调用可以发现：
- 跟
ENGINE_VALIDATION相关的benchmarkPhase用的参数都是validationArguments。其余用的是userArguments（ validationArguments 和 userArguments 唯一的区别是 validationArguments 的SCALE_FACTOR恒为1） - 跟
POWER_TEST相关的都是调用runQueries()方法，因为POWER_TEST就是执行SQL查询 - 跟
CLEAN_DATADATA_GENERATION相关的都是调用generateData()方法 - 跟
LOAD_TESTSHOW相关的都是调用runModule()方法
benchmarkPhase 和 module 对应关系
具体每个 benchmarkPhase 跟 module（执行的脚本）的对应关系如下：
CLEAN_ALL -> "cleanAll"
ENGINE_VALIDATION_CLEAN_POWER_TEST -> "cleanQuery"
ENGINE_VALIDATION_CLEAN_LOAD_TEST -> "cleanMetastore",
ENGINE_VALIDATION_CLEAN_DATA -> "cleanData"
ENGINE_VALIDATION_DATA_GENERATION -> "dataGen"
ENGINE_VALIDATION_LOAD_TEST -> "populateMetastore"
ENGINE_VALIDATION_POWER_TEST -> "runQuery"
ENGINE_VALIDATION_RESULT_VALIDATION -> "validateQuery"
CLEAN_POWER_TEST -> "cleanQuery"
CLEAN_THROUGHPUT_TEST_1 -> "cleanQuery"
CLEAN_THROUGHPUT_TEST_2 -> "cleanQuery"
CLEAN_LOAD_TEST -> "cleanMetastore"
CLEAN_DATA -> "cleanData"
DATA_GENERATION -> "dataGen"
LOAD_TEST -> "populateMetastore"
POWER_TEST -> "runQuery"
THROUGHPUT_TEST -> (String)null
THROUGHPUT_TEST_1 -> "runQuery"
THROUGHPUT_TEST_REFRESH -> "refreshMetastore"
THROUGHPUT_TEST_2 -> "runQuery"
VALIDATE_POWER_TEST -> "validateQuery"
VALIDATE_THROUGHPUT_TEST_1 -> "validateQuery"
VALIDATE_THROUGHPUT_TEST_2 -> "validateQuery"
SHOW_TIMES -> "showTimes"
SHOW_ERRORS -> "showErrors"
SHOW_VALIDATION -> "showValidation"当执行某个 benchmarkPhase 时会去调用如上该 benchmarkPhase 对应的 module （脚本位于 $BENCH_MARK_HOME/engines/hive/bin 目录下）
cmdLine.add(benchmarkPhase.getRunModule());程序调用流程
bigBench.png
接下来介绍每个module的功能
module
cleanAll
1. DROP DATABASE
2. 删除hdfs上的源数据echo "dropping database (with all tables)"
runCmdWithErrorCheck runEngineCmd -e "DROP DATABASE IF EXISTS $BIG_BENCH_DATABASE CASCADE;"
echo "cleaning ${BIG_BENCH_HDFS_ABSOLUTE_HOME}"
hadoop fs -rm -r -f -skipTrash "${BIG_BENCH_HDFS_ABSOLUTE_HOME}"cleanQuery
1. 删除对应的 Query 生成的临时表
2. 删除对应的 Query 生成的结果表runCmdWithErrorCheck runEngineCmd -e "DROP TABLE IF EXISTS $TEMP_TABLE1; DROP TABLE IF EXISTS $TEMP_TABLE2; DROP TABLE IF EXISTS $RESULT_TABLE;"
return $?cleanMetastore
1. 调用 `dropTables.sql` 将23张表依次DROPecho "cleaning metastore tables"
runCmdWithErrorCheck runEngineCmd -f "$BIG_BENCH_CLEAN_METASTORE_FILE"export BIG_BENCH_CLEAN_METASTORE_FILE="$BIG_BENCH_CLEAN_DIR/dropTables.sql"dropTables.sql 将23张表依次DROP,源码如下：
DROP TABLE IF EXISTS ${hiveconf:customerTableName};
DROP TABLE IF EXISTS ${hiveconf:customerAddressTableName};
DROP TABLE IF EXISTS ${hiveconf:customerDemographicsTableName};
DROP TABLE IF EXISTS ${hiveconf:dateTableName};
DROP TABLE IF EXISTS ${hiveconf:householdDemographicsTableName};
DROP TABLE IF EXISTS ${hiveconf:incomeTableName};
DROP TABLE IF EXISTS ${hiveconf:itemTableName};
DROP TABLE IF EXISTS ${hiveconf:promotionTableName};
DROP TABLE IF EXISTS ${hiveconf:reasonTableName};
DROP TABLE IF EXISTS ${hiveconf:shipModeTableName};
DROP TABLE IF EXISTS ${hiveconf:storeTableName};
DROP TABLE IF EXISTS ${hiveconf:timeTableName};
DROP TABLE IF EXISTS ${hiveconf:warehouseTableName};
DROP TABLE IF EXISTS ${hiveconf:webSiteTableName};
DROP TABLE IF EXISTS ${hiveconf:webPageTableName};
DROP TABLE IF EXISTS ${hiveconf:inventoryTableName};
DROP TABLE IF EXISTS ${hiveconf:storeSalesTableName};
DROP TABLE IF EXISTS ${hiveconf:storeReturnsTableName};
DROP TABLE IF EXISTS ${hiveconf:webSalesTableName};
DROP TABLE IF EXISTS ${hiveconf:webReturnsTableName};
DROP TABLE IF EXISTS ${hiveconf:marketPricesTableName};
DROP TABLE IF EXISTS ${hiveconf:clickstreamsTableName};
DROP TABLE IF EXISTS ${hiveconf:reviewsTableName};cleanData
1. 删除hdfs上 /user/root/benchmarks/bigbench/data 目录
2. 删除hdfs上 /user/root/benchmarks/bigbench/data_refresh 目录echo "cleaning ${BIG_BENCH_HDFS_ABSOLUTE_INIT_DATA_DIR}"
hadoop fs -rm -r -f -skipTrash "${BIG_BENCH_HDFS_ABSOLUTE_INIT_DATA_DIR}"
echo "cleaning ${BIG_BENCH_HDFS_ABSOLUTE_REFRESH_DATA_DIR}"
hadoop fs -rm -r -f -skipTrash "${BIG_BENCH_HDFS_ABSOLUTE_REFRESH_DATA_DIR}"dataGen
1. 创建目录 /user/root/benchmarks/bigbench/data 并赋予权限
2. 创建目录 /user/root/benchmarks/bigbench/data_refresh 并赋予权限
3. 调用 HadoopClusterExec.jar 和 pdgf.jar 生成 base data 到 /user/root/benchmarks/bigbench/data 目录下
4. 调用 HadoopClusterExec.jar 和 pdgf.jar 生成 refresh data 到 /user/root/benchmarks/bigbench/data_refresh 目录下创建目录 /user/root/benchmarks/bigbench/data 并赋予权限
runCmdWithErrorCheck hadoop fs -mkdir -p "${BIG_BENCH_HDFS_ABSOLUTE_INIT_DATA_DIR}"
runCmdWithErrorCheck hadoop fs -chmod 777 "${BIG_BENCH_HDFS_ABSOLUTE_INIT_DATA_DIR}"创建目录 /user/root/benchmarks/bigbench/data_refresh 并赋予权限
runCmdWithErrorCheck hadoop fs -mkdir -p "${BIG_BENCH_HDFS_ABSOLUTE_REFRESH_DATA_DIR}"
runCmdWithErrorCheck hadoop fs -chmod 777 "${BIG_BENCH_HDFS_ABSOLUTE_REFRESH_DATA_DIR}"调用 HadoopClusterExec.jar 和 pdgf.jar 生成 base data
runCmdWithErrorCheck hadoop jar "${BIG_BENCH_TOOLS_DIR}/HadoopClusterExec.jar" -archives "${PDGF_ARCHIVE_PATH}" ${BIG_BENCH_DATAGEN_HADOOP_EXEC_DEBUG} -taskFailOnNonZeroReturnValue -execCWD "${PDGF_DISTRIBUTED_NODE_DIR}" ${HadoopClusterExecOptions} -exec ${BIG_BENCH_DATAGEN_HADOOP_JVM_ENV} -cp "${HADOOP_CP}:pdgf.jar" ${PDGF_CLUSTER_CONF} pdgf.Controller -nc HadoopClusterExec.tasks -nn HadoopClusterExec.taskNumber -ns -c -sp REFRESH_PHASE 0 -o "'${BIG_BENCH_HDFS_ABSOLUTE_INIT_DATA_DIR}/'+table.getName()+'/'" ${BIG_BENCH_DATAGEN_HADOOP_OPTIONS} -s ${BIG_BENCH_DATAGEN_TABLES} ${PDGF_OPTIONS} "$@" 2>&1 | tee -a "$BIG_BENCH_DATAGEN_STAGE_LOG" 2>&1调用 HadoopClusterExec.jar 和 pdgf.jar 生成 refresh data
runCmdWithErrorCheck hadoop jar "${BIG_BENCH_TOOLS_DIR}/HadoopClusterExec.jar" -archives "${PDGF_ARCHIVE_PATH}" ${BIG_BENCH_DATAGEN_HADOOP_EXEC_DEBUG} -taskFailOnNonZeroReturnValue -execCWD "${PDGF_DISTRIBUTED_NODE_DIR}" ${HadoopClusterExecOptions} -exec ${BIG_BENCH_DATAGEN_HADOOP_JVM_ENV} -cp "${HADOOP_CP}:pdgf.jar" ${PDGF_CLUSTER_CONF} pdgf.Controller -nc HadoopClusterExec.tasks -nn HadoopClusterExec.taskNumber -ns -c -sp REFRESH_PHASE 1 -o "'${BIG_BENCH_HDFS_ABSOLUTE_REFRESH_DATA_DIR}/'+table.getName()+'/'" ${BIG_BENCH_DATAGEN_HADOOP_OPTIONS} -s ${BIG_BENCH_DATAGEN_TABLES} ${PDGF_OPTIONS} "$@" 2>&1 | tee -a "$BIG_BENCH_DATAGEN_STAGE_LOG" 2>&1populateMetastore
该过程是真正的创建数据库表的过程。建表的过程调用的是 $BENCH_MARK_HOME/engines/hive/population/ 目录下的 hiveCreateLoad.sql ,通过该sql文件来建数据库表。
- 从 /user/root/benchmarks/bigbench/data 路径下读取 .dat 的原始数据，生成 TEXTFILE 格式的外部临时表
- 用
select * from 临时表来创建最终的 ORC 格式的数据库表 - 删除外部临时表。
从 /user/root/benchmarks/bigbench/data 路径下读取 .dat 的原始数据，生成 TEXTFILE 格式的外部临时表
DROP TABLE IF EXISTS ${hiveconf:customerTableName}${hiveconf:temporaryTableSuffix};
CREATE EXTERNAL TABLE ${hiveconf:customerTableName}${hiveconf:temporaryTableSuffix}
( c_customer_sk bigint --not null
, c_customer_id string --not null
, c_current_cdemo_sk bigint
, c_current_hdemo_sk bigint
, c_current_addr_sk bigint
, c_first_shipto_date_sk bigint
, c_first_sales_date_sk bigint
, c_salutation string
, c_first_name string
, c_last_name string
, c_preferred_cust_flag string
, c_birth_day int
, c_birth_month int
, c_birth_year int
, c_birth_country string
, c_login string
, c_email_address string
, c_last_review_date string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '${hiveconf:fieldDelimiter}'
STORED AS TEXTFILE LOCATION '${hiveconf:hdfsDataPath}/${hiveconf:customerTableName}'
;用 select * from 临时表 来创建最终的 ORC 格式的数据库表
DROP TABLE IF EXISTS ${hiveconf:customerTableName};
CREATE TABLE ${hiveconf:customerTableName}
STORED AS ${hiveconf:tableFormat}
AS
SELECT * FROM ${hiveconf:customerTableName}${hiveconf:temporaryTableSuffix}
;删除外部临时表
DROP TABLE ${hiveconf:customerTableName}${hiveconf:temporaryTableSuffix};runQuery
1. runQuery 调用每个query下的 run.sh 里的 `query_run_main_method()` 方法
2. `query_run_main_method()` 调用 `runEngineCmd` 来执行query脚本（qxx.sql）runQuery 调用每个query下的 run.sh 里的 query_run_main_method() 方法
QUERY_MAIN_METHOD="query_run_main_method"
-----------------------------------------
"$QUERY_MAIN_METHOD" 2>&1 | tee -a "$LOG_FILE_NAME" 2>&1query_run_main_method() 调用 runEngineCmd 来执行query脚本（qxx.sql）
query_run_main_method () {
QUERY_SCRIPT="$QUERY_DIR/$QUERY_NAME.sql"
if [ ! -r "$QUERY_SCRIPT" ]
then
echo "SQL file $QUERY_SCRIPT can not be read."
exit 1
fi
runCmdWithErrorCheck runEngineCmd -f "$QUERY_SCRIPT"
return $?
}一般情况下 query_run_main_method () 方法只是执行对应的query脚本，但是像 q05、q20... 这些查询，用到了机器学习算法，所以在执行对应的query脚本后会把生成的结果表作为输入，然后调用执行机器学习算法（如聚类、逻辑回归）的jar包继续执行，得到最终的结果。
runEngineCmd () {
if addInitScriptsToParams
then
"$BINARY" "${BINARY_PARAMS[@]}" "${INIT_PARAMS[@]}" "$@"
else
return 1
fi
}
--------------------------
BINARY="/usr/bin/hive"
BINARY_PARAMS+=(--hiveconf BENCHMARK_PHASE=$BIG_BENCH_BENCHMARK_PHASE --hiveconf STREAM_NUMBER=$BIG_BENCH_STREAM_NUMBER --hiveconf QUERY_NAME=$QUERY_NAME --hiveconf QUERY_DIR=$QUERY_DIR --hiveconf RESULT_TABLE=$RESULT_TABLE --hiveconf RESULT_DIR=$RESULT_DIR --hiveconf TEMP_TABLE=$TEMP_TABLE --hiveconf TEMP_DIR=$TEMP_DIR --hiveconf TABLE_PREFIX=$TABLE_PREFIX)
INIT_PARAMS=(-i "$BIG_BENCH_QUERY_PARAMS_FILE" -i "$BIG_BENCH_ENGINE_SETTINGS_FILE")
INIT_PARAMS+=(-i "$LOCAL_QUERY_ENGINE_SETTINGS_FILE")
if [ -n "$USER_QUERY_PARAMS_FILE" ]
then
if [ -r "$USER_QUERY_PARAMS_FILE" ]
then
echo "User defined query parameter file found. Adding $USER_QUERY_PARAMS_FILE to hive init."
INIT_PARAMS+=(-i "$USER_QUERY_PARAMS_FILE")
else
echo "User query parameter file $USER_QUERY_PARAMS_FILE can not be read."
return 1
fi
fi
if [ -n "$USER_ENGINE_SETTINGS_FILE" ]
then
if [ -r "$USER_ENGINE_SETTINGS_FILE" ]
then
echo "User defined engine settings file found. Adding $USER_ENGINE_SETTINGS_FILE to hive init."
INIT_PARAMS+=(-i "$USER_ENGINE_SETTINGS_FILE")
else
echo "User hive settings file $USER_ENGINE_SETTINGS_FILE can not be read."
return 1
fi
fi
return 0validateQuery
1. 调用每个query下的 run.sh 里的 `query_run_validate_method()` 方法
2. `query_run_validate_method()` 比较 `$BENCH_MARK_HOME/engines/hive/queries/qxx/results/qxx-result` 和hdfs上 `/user/root/benchmarks/bigbench/queryResults/qxx_hive_${BIG_BENCH_BENCHMARK_PHASE}_${BIG_BENCH_STREAM_NUMBER}_result` 两个文件，如果一样，则验证通过，否则验证失败。if diff -q "$VALIDATION_RESULTS_FILENAME" <(hadoop fs -cat "$RESULT_DIR/*")
then
echo "Validation of $VALIDATION_RESULTS_FILENAME passed: Query returned correct results"
else
echo "Validation of $VALIDATION_RESULTS_FILENAME failed: Query returned incorrect results"
VALIDATION_PASSED="0"
fiSF为1时(-f 1)，用上面的方法比较，SF不为1（>1）时,只要hdfs上的结果表中行数大于等于1即验证通过
if [ `hadoop fs -cat "$RESULT_DIR/*" | head -n 10 | wc -l` -ge 1 ]
then
echo "Validation passed: Query returned results"
else
echo "Validation failed: Query did not return results"
return 1
firefreshMetastore
1. 调用 `$BENCH_MARK_HOME/engines/hive/refresh/` 目录下的 `hiveRefreshCreateLoad.sql` 脚本
2. `hiveRefreshCreateLoad.sql` 将hdfs上 `/user/root/benchmarks/bigbench/data_refresh/` 目录下每个表数据插入外部临时表
3. 外部临时表再将每个表的数据插入Hive数据库对应的表中hiveRefreshCreateLoad.sql 将hdfs上 /user/root/benchmarks/bigbench/data_refresh/ 目录下每个表数据插入外部临时表
DROP TABLE IF EXISTS ${hiveconf:customerTableName}${hiveconf:temporaryTableSuffix};
CREATE EXTERNAL TABLE ${hiveconf:customerTableName}${hiveconf:temporaryTableSuffix}
( c_customer_sk bigint --not null
, c_customer_id string --not null
, c_current_cdemo_sk bigint
, c_current_hdemo_sk bigint
, c_current_addr_sk bigint
, c_first_shipto_date_sk bigint
, c_first_sales_date_sk bigint
, c_salutation string
, c_first_name string
, c_last_name string
, c_preferred_cust_flag string
, c_birth_day int
, c_birth_month int
, c_birth_year int
, c_birth_country string
, c_login string
, c_email_address string
, c_last_review_date string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '${hiveconf:fieldDelimiter}'
STORED AS TEXTFILE LOCATION '${hiveconf:hdfsDataPath}/${hiveconf:customerTableName}'
;
-----------------
set hdfsDataPath=${env:BIG_BENCH_HDFS_ABSOLUTE_REFRESH_DATA_DIR};外部临时表再将每个表的数据插入Hive数据库对应的表中
INSERT INTO TABLE ${hiveconf:customerTableName}
SELECT * FROM ${hiveconf:customerTableName}${hiveconf:temporaryTableSuffix}
;附录
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