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extract_trfc_page_kpi的hive sql如下:
set mapred.job.queue.name=pms; set hive.exec.reducers.max=8; set mapred.reduce.tasks=8; set mapred.job.name=extract_trfc_page_kpi; insert overwrite table pms.extract_trfc_page_kpi partition(ds='$yesterday') select distinct page_type_id, pv, uv, '$yesterday' update_time from ( --针对PC、H5 select page_type_id, sum(pv) as pv, sum(uv) as uv from dw.rpt_trfc_page_kpi where ds = '$yesterday' and stat_type = 1 group by page_type_id union all --PC搜索页特殊处理 select 5 as page_type_id, sum(pv) as pv, sum(uv) as uv from dw.rpt_trfc_page_kpi where ds = '$yesterday' and stat_type = 1 and page_type_id in (51, 52) union all --针对APP select a.page_type_id, sum(pv) as pv, sum(uv) as uv from dw.rpt_trfc_page_kpi a left outer join ( select distinct page_type_id, old_page_type_id from tandem.mobile_backend_page_url_rule where is_delete = 0 ) b on (a.page_type_id = b.old_page_type_id) where a.ds = '$yesterday' and stat_type = 1 group by a.page_type_id ) t;
上面的sql中存在两个union all操作,顺序执行下来的话,需要耗时20分钟。
分析以上的sql,其中union all前后的三个查询操作并无直接关联,因此没有必要顺序执行,因此优化的思路是让这三个查询操作并行执行,hive提供了如下参数实现job的并行操作:
// 开启任务并行执行
set hive.exec.parallel=true;
// 同一个sql允许并行任务的最大线程数
set hive.exec.parallel.thread.number=8;
在执行sql时加上上面的两个hive参数,如:
set mapred.job.queue.name=pms; set hive.exec.reducers.max=8; set mapred.reduce.tasks=8; set hive.exec.parallel=true; set hive.exec.parallel.thread.number=8; set mapred.job.name=extract_trfc_page_kpi; insert overwrite table pms.extract_trfc_page_kpi partition(ds='$yesterday') select distinct page_type_id, pv, uv, '$yesterday' update_time from ( --针对PC、H5 select page_type_id, sum(pv) as pv, sum(uv) as uv from dw.rpt_trfc_page_kpi where ds = '$yesterday' and stat_type = 1 group by page_type_id union all --PC搜索页特殊处理 select 5 as page_type_id, sum(pv) as pv, sum(uv) as uv from dw.rpt_trfc_page_kpi where ds = '$yesterday' and stat_type = 1 and page_type_id in (51, 52) union all --针对APP select a.page_type_id, sum(pv) as pv, sum(uv) as uv from dw.rpt_trfc_page_kpi a left outer join ( select distinct page_type_id, old_page_type_id from tandem.mobile_backend_page_url_rule where is_delete = 0 ) b on (a.page_type_id = b.old_page_type_id) where a.ds = '$yesterday' and stat_type = 1 group by a.page_type_id ) t;
在hive-site.xml中进行设置,查看当前版本hive的配置参数:
hive> set -v; ... hive.exec.orc.zerocopy=false hive.exec.parallel=false hive.exec.parallel.thread.number=8 hive.exec.perf.logger=org.apache.hadoop.hive.ql.log.PerfLogger hive.exec.rcfile.use.explicit.header=true hive.exec.rcfile.use.sync.cache=true hive.exec.reducers.bytes.per.reducer=1000000000 hive.exec.reducers.max=999 hive.exec.rowoffset=false hive.exec.scratchdir=/tmp/hive-pms hive.exec.script.allow.partial.consumption=false hive.exec.script.maxerrsize=100000 hive.exec.script.trust=false hive.exec.show.job.failure.debug.info=true ...
这些参数是配置在$HIVE_HOME/conf/hive-site.xml中的,现在在这个配置文件中加入:
<property>
<name>hive.exec.parallel</name>
<value>true</value>
</property>
<property>
<name>hive.exec.parallel.thread.number</name>
<value>16</value>
</property>
重新启动hive,看到刚刚配置的参数已经生效了:
hive> set -v;
...
hive.exec.orc.skip.corrupt.data=false
hive.exec.orc.zerocopy=false
hive.exec.parallel=true
hive.exec.parallel.thread.number=16
hive.exec.perf.logger=org.apache.hadoop.hive.ql.log.PerfLogger
hive.exec.rcfile.use.explicit.header=true
hive.exec.rcfile.use.sync.cache=true
hive.exec.reducers.bytes.per.reducer=1000000000
hive.exec.reducers.max=999
hive.exec.rowoffset=false
hive.exec.scratchdir=/tmp/hive-pms
hive.exec.script.allow.partial.consumption=false
...
结论
经过测试,添加了这两个参数以后,extract_trfc_page_kpi脚本执行时间从耗时20分钟,优化为耗时3分钟。
接!
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