[jira] [Resolved] (TEZ-4442) tez unable to control the memory size when UDF occupies 100MB memory

["László Bodor (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/TEZ-4442?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

László Bodor resolved TEZ-4442.
-------------------------------
    Resolution: Invalid

> 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: CDP7.1.7SP1
> tez 0.9.1
> hive 3.1.3
>  
>            Reporter: Authur Wang
>            Priority: Critical
>         Attachments: app.log, application_1659706606596_0047.log.gz, hiveserver2.out, java heap1.png, java heap2.png, 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.com@BG.COM|mailto:bg21146.hadoop.com@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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