Hive调优：让任务并行执行

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业务背景

extract_trfc_page_kpi的hive sql如下：

[mw_shl_code=bash,true]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;[/mw_shl_code]

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。

优化策略

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：

[mw_shl_code=bash,true]// 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;[/mw_shl_code]

方案一

在执行sql时加上上面的两个hive参数，如：

[mw_shl_code=bash,true]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;[/mw_shl_code]

方案二

在hive-site.xml中进行设置，查看当前版本hive的配置参数：

[mw_shl_code=bash,true]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
...[/mw_shl_code]

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：

[mw_shl_code=bash,true]<property>
<name>hive.exec.parallel</name>
<value>true</value>
</property>
<property>
<name>hive.exec.parallel.thread.number</name>
<value>16</value>
</property>[/mw_shl_code]

重新启动hive，看到刚刚配置的参数已经生效了：

[mw_shl_code=bash,true]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
...[/mw_shl_code]

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。