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https://issues.apache.org/jira/browse/TEZ-4442?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Authur Wang updated TEZ-4442:
-----------------------------
Description:
We have a UDF which loads about 5 million records into memory, and
matchs the data in the memory according to the user's input, and finally return
the output. Each input record of the UDF will lead to one output.
Based on heapdump analysis, this udf occupies about 100MB of memory.
The UDF runs stably in hive on MR, hive on spark and native spark, and only
needs about 4GB of memory for that situation. However, if we use tez engine,
we adjust the memory from 4G to 8g, the task will fail. Even if we adjust the
memory to 12g, the task will fail with a high probability. Why does tez engine
need so much memory compared to Mr and spark? Is there a good tuning method to
control the amount of memory ?
command is as follows:
beeline -u
'jdbc:hive2://bg21146.hadoop.com:10000/default;principal=hive/[bg21146.hadoop....@bg.com|mailto:bg21146.hadoop....@bg.com]'
--hiveconf tez.queue.name=root.000kjb.bdhmgmas_bas -e "
create temporary function get_card_rank as
'com.unionpay.spark.udf.GenericUDFCupsCardMediaProc' using jar
'hdfs:///user/lib/spark-udf-0.0.1-SNAPSHOT.jar';
set tez.am.log.level=debug;
set tez.am.resource.memory.mb=8192;
set hive.tez.container.size=8192;
set tez.task.resource.memory.mb=2048;
set tez.runtime.io.sort.mb=1200;
set hive.auto.convert.join.noconditionaltask.size=500000000;
set tez.runtime.unordered.output.buffer.size-mb=800;
set tez.grouping.min-size=33554432;
set tez.grouping.max-size=536870912;
set hive.tez.auto.reducer.parallelism=true;
set hive.tez.min.partition.factor=0.25;
set hive.tez.max.partition.factor=2.0;
set hive.exec.reducers.bytes.per.reducer=268435456;
set mapreduce.map.memory.mb=4096;
set ipc.maximum.response.length=1536000000;
select
get_card_rank(ext_pri_acct_no) as ext_card_media_proc_md,
count(\*)
from bs_comdb.tmp_bscom_glhis_ct_settle_dtl_bas_swt a
where a.hp_settle_dt = '20200910'
group by get_card_rank(ext_pri_acct_no)
;
"
was:
We have a UDF which loads about 5 million records into memory, and
matchs the data in the memory according to the user's input, and finally return
the output. Each input record of the UDF will lead to one output.
Based on heapdump analysis, this udf occupies about 100MB of memory.
The UDF runs stably in hive on MR, hive on spark and native spark, and only
needs about 4GB of memory for that situation. However, if we use tez engine,
we adjust the memory from 4G to 8g, the task will fail. Even if we adjust the
memory to 12g, the task will fail with a high probability. Why does tez engine
need so much memory compared to Mr and spark? Is there a good tuning method to
control the amount of memory ?
command is as follows:
beeline -u
'jdbc:hive2://bg21146.hadoop.com:10000/default;principal=hive/[bg21146.hadoop....@bg.com|mailto:bg21146.hadoop....@bg.com]'
--hiveconf tez.queue.name=root.000kjb.bdhmgmas_bas -e "
create temporary function get_card_rank as
'com.unionpay.spark.udf.GenericUDFCupsCardMediaProc' using jar
'hdfs:///user/lib/spark-udf-0.0.1-SNAPSHOT.jar';
set tez.am.log.level=debug;
set tez.am.resource.memory.mb=8192;
set hive.tez.container.size=8192;
set tez.task.resource.memory.mb=2048;
set tez.runtime.io.sort.mb=1200;
set hive.auto.convert.join.noconditionaltask.size=500000000;
set tez.runtime.unordered.output.buffer.size-mb=800;
set tez.grouping.min-size=33554432;
set tez.grouping.max-size=536870912;
set hive.tez.auto.reducer.parallelism=true;
set hive.tez.min.partition.factor=0.25;
set hive.tez.max.partition.factor=2.0;
set hive.exec.reducers.bytes.per.reducer=268435456;
set mapreduce.map.memory.mb=4096;
set ipc.maximum.response.length=1536000000;
select
get_card_rank(ext_pri_acct_no) as ext_card_media_proc_md,
count(*)
from bs_comdb.tmp_bscom_glhis_ct_settle_dtl_bas_swt a
where a.hp_settle_dt = '20200910'
group by get_card_rank(ext_pri_acct_no)
;
"
> tez unable to control the memory size when UDF occupies 100MB memory
> ---------------------------------------------------------------------
>
> Key: TEZ-4442
> URL: https://issues.apache.org/jira/browse/TEZ-4442
> Project: Apache Tez
> Issue Type: Bug
> Affects Versions: 0.9.1
> Environment: we use CDP7.1.7SP1 with the 0.91 tez version
> Reporter: Authur Wang
> Priority: Critical
> Attachments: app.log, application_1659706606596_0047.log.gz,
> spark-udf-0.0.1-SNAPSHOT.jar, spark-udf-src.zip
>
>
> We have a UDF which loads about 5 million records into memory, and
> matchs the data in the memory according to the user's input, and finally
> return the output. Each input record of the UDF will lead to one output.
> Based on heapdump analysis, this udf occupies about 100MB of
> memory. The UDF runs stably in hive on MR, hive on spark and native spark,
> and only needs about 4GB of memory for that situation. However, if we use tez
> engine, we adjust the memory from 4G to 8g, the task will fail. Even if we
> adjust the memory to 12g, the task will fail with a high probability. Why
> does tez engine need so much memory compared to Mr and spark? Is there a good
> tuning method to control the amount of memory ?
>
>
> command is as follows:
> beeline -u
> 'jdbc:hive2://bg21146.hadoop.com:10000/default;principal=hive/[bg21146.hadoop....@bg.com|mailto:bg21146.hadoop....@bg.com]'
> --hiveconf tez.queue.name=root.000kjb.bdhmgmas_bas -e "
>
> create temporary function get_card_rank as
> 'com.unionpay.spark.udf.GenericUDFCupsCardMediaProc' using jar
> 'hdfs:///user/lib/spark-udf-0.0.1-SNAPSHOT.jar';
>
> set tez.am.log.level=debug;
> set tez.am.resource.memory.mb=8192;
> set hive.tez.container.size=8192;
> set tez.task.resource.memory.mb=2048;
> set tez.runtime.io.sort.mb=1200;
> set hive.auto.convert.join.noconditionaltask.size=500000000;
> set tez.runtime.unordered.output.buffer.size-mb=800;
> set tez.grouping.min-size=33554432;
> set tez.grouping.max-size=536870912;
> set hive.tez.auto.reducer.parallelism=true;
> set hive.tez.min.partition.factor=0.25;
> set hive.tez.max.partition.factor=2.0;
> set hive.exec.reducers.bytes.per.reducer=268435456;
> set mapreduce.map.memory.mb=4096;
> set ipc.maximum.response.length=1536000000;
>
>
> select
> get_card_rank(ext_pri_acct_no) as ext_card_media_proc_md,
> count(\*)
> from bs_comdb.tmp_bscom_glhis_ct_settle_dtl_bas_swt a
> where a.hp_settle_dt = '20200910'
> group by get_card_rank(ext_pri_acct_no)
> ;
> "
>
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