Download - Debugging Hive with Hadoop in the Cloud - Hadoop Summit 2014

DEBUGGING HIVE WITH HADOOP IN THE CLOUDSoam Acharya, David Chaiken, Denis Sheahan, Charles WimmerAltiscale, Inc.June 4, 2014

WHO ARE WE?

• Altiscale: Infrastructure Nerds!

• Hadoop As A Service

• Rack and build our own Hadoop clusters

• Provide a suite of Hadoop toolso Hive, Pig, Oozie

o Others as needed: R, Python, Spark, Mahout, Impala, etc.

• Monthly billing plan: compute, storage

• https://www.altiscale.com

• @Altiscale #HadoopSherpa

http://www.altiscale.com/



TALK ROADMAP

• Our Platform and Perspective

• Hadoop 2 Primer

• Hadoop Debugging Tools

• Accessing Logs in Hadoop 2

• Hive + Hadoop Architecture

• Hive Logs

• Hive Issues + Case Studies

• Conclusion: Making Hive Easier to Use

OUR DYNAMIC PLATFORM

• Hadoop 2.0.5 => Hadoop 2.2.0

• Hive 0.10 => Hive 0.12

• Hive, Pig and Oozie most commonly used tools

• Working with customers on:Spark, Stinger (Hive 0.13 + Tez), Impala, …

ALTISCALE PERSPECTIVE

• Service providero Hadoop Dialtone!

o Keep Hadoop/Hive + other tools running

o Service Level Agreements target application-level metrics

o Multiple clusters/customers

o Operational scalability

o Multi-tenancy

• Operational approacho How to use Hadoop 2 cluster tools and logs

to debug and to tune

o This talk will not focus on query optimization

Hadoop 2 Cluster

Name Node Hadoop Slave

Hadoop Slave

Hadoop Slave

Resource Manager

Secondary NameNode

Hadoop Slave

Node Managers+

Data Nodes

QUICK PRIMER – HADOOP 2

QUICK PRIMER – HADOOP 2 YARN

• Resource Manager (per cluster)o Manages job scheduling and execution

o Global resource allocation

• Application Master (per job)o Manages task scheduling and execution

o Local resource allocation

• Node Manager (per-machine agent)o Manages the lifecycle of task containers

o Reports to RM on health and resource usage

HADOOP 1 VS HADOOP 2

• No more JobTrackers, TaskTrackers

• YARN ~ Operating System for Clusterso MapReduce is implemented as a YARN application

o Bring on the applications! (Spark is just the start…)

• Should be Transparent to Hive users

HADOOP 2 DEBUGGING TOOLS

• Monitoringo System state of cluster:

CPU, Memory, Network, Disk

Nagios, Ganglia, Sensu!

Collectd, statd, Graphite

o Hadoop level HDFS usage

Resource usage:

• Container memory allocated vs used

• # of jobs running at the same time

• Long running tasks

HADOOP 2 DEBUGGING TOOLS

• Hadoop logso Daemon logs: Resource Manager, NameNode,

DataNode

o Application logs: Application Master, MapReduce tasks

o Job history file: resources allocated during job lifetime

o Application configuration files: store all Hadoop application parameters

• Source code instrumentation

ACCESSING LOGS IN HADOOP 2

• To view the logs for a job, click on the link under the ID column in Resource Manager UI.


• To view application top level logs, click on logs.

• To view individual logs for the mappers and reducers, click on History.


• Log output for the entire application.


• Click on the Map link for mapper logs and the Reduce link for reducer logs.


• Clicking on a single link under Name provides an overview for that particular map job.


• Finally, clicking on the logs link will take you to the log output for that map job.


• Fun, fun, donuts, and more fun…

HIVE + HADOOP 2 ARCHITECTURE

• Hive 0.10+

Hadoop 2 Cluster

Hive CLI Hive Metastore

HiveserverJDBC/ODBC

Kettle, Hue, …

HIVE LOGS

• Query Log location

• From /etc/hive/hive-site.xml:

<property>

<name>hive.querylog.location</name>

<value>/home/hive/log/${user.name}</value>

</property>

SessionStart SESSION_ID="soam_201402032341" TIME="1391470900594"

HIVE CLIENT LOGS

• /etc/hive/hive-log4j.properties:o hive.log.dir=/var/log/hive/${user.name}

2014-05-29 19:51:09,830 INFO parse.ParseDriver (ParseDriver.java:parse(179)) - Parsing command: select count(*) from dogfood_job_data

2014-05-29 19:51:09,852 INFO parse.ParseDriver (ParseDriver.java:parse(197)) - Parse Completed

2014-05-29 19:51:09,852 INFO ql.Driver (PerfLogger.java:PerfLogEnd(124)) - </PERFLOG method=parse start=1401393069830 end=1401393069852 duration=22>

2014-05-29 19:51:09,853 INFO ql.Driver (PerfLogger.java:PerfLogBegin(97)) - <PERFLOG method=semanticAnalyze>

2014-05-29 19:51:09,890 INFO parse.SemanticAnalyzer (SemanticAnalyzer.java:analyzeInternal(8305)) - Starting Semantic Analysis

2014-05-29 19:51:09,892 INFO parse.SemanticAnalyzer (SemanticAnalyzer.java:analyzeInternal(8340)) - Completed phase 1 of Semantic Analysis

2014-05-29 19:51:09,892 INFO parse.SemanticAnalyzer (SemanticAnalyzer.java:getMetaData(1060)) - Get metadata for source tables

2014-05-29 19:51:09,906 INFO parse.SemanticAnalyzer (SemanticAnalyzer.java:getMetaData(1167)) - Get metadata for subqueries

2014-05-29 19:51:09,909 INFO parse.SemanticAnalyzer (SemanticAnalyzer.java:getMetaData(1187)) - Get metadata for destination tables

HIVE METASTORE LOGS

• /etc/hive-metastore/hive-log4j.properties:o hive.log.dir=/service/log/hive-metastore/${user.name}

2014-05-29 19:50:50,179 INFO metastore.HiveMetaStore (HiveMetaStore.java:logInfo(454)) - 200: source:/10.252.18.94 get_table : db=default tbl=dogfood_job_data

2014-05-29 19:50:50,180 INFO HiveMetaStore.audit (HiveMetaStore.java:logAuditEvent(239)) - ugi=chaiken ip=/10.252.18.94 cmd=source:/10.252.18.94 get_table : db=default tbl=dogfood_job_data


2014-05-29 19:50:50,236 INFO HiveMetaStore.audit (HiveMetaStore.java:logAuditEvent(239)) - ugi=chaiken ip=/10.252.18.94 cmd=source:/10.252.18.94 get_table : db=default tbl=dogfood_job_data


HIVE ISSUES + CASE STUDIES

• Hive Issueso Hive client out of memory

o Hive map/reduce task out of memory

o Hive metastore out of memory

o Hive launches too many tasks

• Case Studies:o Hive “stuck” job

o Hive “missing directories”

o Analyze Hive Query Execution

HIVE CLIENT OUT OF MEMORY

• Memory intensive client side hive query (map-side join)

Number of reduce tasks not specified. Estimated from input data size: 999

In order to change the average load for a reducer (in bytes):

set hive.exec.reducers.bytes.per.reducer=<number>

In order to limit the maximum number of reducers:

set hive.exec.reducers.max=<number>

In order to set a constant number of reducers:

set mapred.reduce.tasks=<number>

java.lang.OutOfMemoryError: Java heap space

at java.nio.CharBuffer.wrap(CharBuffer.java:350)

at java.nio.CharBuffer.wrap(CharBuffer.java:373)

at java.lang.StringCoding$StringDecoder.decode(StringCoding.java:138)

HIVE CLIENT OUT OF MEMORY

• Use HADOOP_HEAPSIZE prior to launching Hive client

• HADOOP_HEAPSIZE=<new heapsize> hive <fileName>

• Watch out for HADOOP_CLIENT_OPTS issue in hive-env.sh!

• Important to know the amount of memory available on machine running client… Do not exceed or use disproportionate amount.$ free -m

total used free shared buffers cachedMem: 1695 1388 306 0 60 424-/+ buffers/cache: 903 791Swap: 895 101 794

HIVE TASK OUT OF MEMORY

• Query spawns MapReduce jobs that run out of memory

• How to find this issue?o Hive diagnostic message

o Hadoop MapReduce logs

HIVE TASK OUT OF MEMORY

• Fix is to increase task RAM allocation…set mapreduce.map.memory.mb=<new RAM allocation>;

set mapreduce.reduce.memory.mb=<new RAM allocation>;

• Also watch out for…set mapreduce.map.java.opts=-Xmx<heap size>m;

set mapreduce.reduce.java.opts=-Xmx<heap size>m;

• Not a magic bullet – requires manual tuning

• Increase in individual container memory size:o Decrease in overall containers that can be run

o Decrease in overall parallelism

HIVE METASTORE OUT OF MEMORY

• Out of memory issues not necessarily dumped to logs

• Metastore can become unresponsive

• Can’t submit queries

• Restart with a higher heap size:export HADOOP_HEAPSIZE in hcat_server.sh

• After notifying hive users about downtime:service hcat restart

HIVE LAUNCHES TOO MANY TASKS

• Typically a function of the input data set

• Lots of little files

HIVE LAUNCHES TOO MANY TASKS• Set mapred.max.split.size to appropriate fraction of data size

• Also verify that hive.input.format=org.apache.hadoop.hive.ql.io.CombineHiveInputFormat

CASE STUDY: HIVE STUCK JOB

From an Altiscale customer:

“This job [jobid] has been running now for 41 hours. Is it still progressing or has something hung up the map/reduce so it’s just spinning? Do you have any insight?”

HIVE STUCK JOB

1. Received jobId, application_1382973574141_4536, from client

2. Logged into client cluster.

3. Pulled up Resource Manager

4. Entered part of jobId (4536) in the search box.

5. Clicked on the link that says: application_1382973574141_4536

6. On resulting Application Overview page, clicked on link next to “Tracking URL” that said Application Master

HIVE STUCK JOB

7. On resulting MapReduce Application page, we clicked on the Job Id (job_1382973574141_4536).

8. The resulting MapReduce Job page displayed detailed status of the mappers, including 4 failed mappers

9. We then clicked on the 4 link on the Maps row in the Failed column.

10.Title of the next page was “FAILED Map attempts in job_1382973574141_4536.”

11.Each failed mapper generated an error message.

12.Buried in the 16th line:

Caused by: java.io.FileNotFoundException: File does not exist: hdfs://opaque_hostname:8020/HiveTableDir/FileName.log.date.seq

HIVE STUCK JOB

• Job was stuck for a day or so, retrying a mapper that would never finish successfully.

• During the job, our customers’ colleague realized input file was corrupted and deleted it.

• Colleague did not anticipate the affect of removing corrupted data on a running job

• Hadoop didn’t make it easy to find out:o RM => search => application link => AM overview

page => MR Application Page => MR Job Page => Failed jobs page => parse long logs

o Task retry without hope of success

HIVE “MISSING DIRECTORIES”

From an Altiscale customer:

“One problem we are seeing after the [Hive Metastore] restart is that we lost quite a few directories in [HDFS]. Is there a way to recover these?”


• Obtained list of “missing” directories from customer:o /hive/biz/prod/*

• Confirmed they were missing from HDFS

• Searched through NameNode audit log to get block IDs that belonged to missing directories.

13/07/24 21:10:08 INFO hdfs.StateChange: BLOCK* NameSystem.allocateBlock: /hive/biz/prod/incremental/carryoverstore/postdepuis/lmt_unmapped_pggroup_schema._COPYING_. BP-798113632-10.251.255.251-1370812162472 blk_3560522076897293424_2448396{blockUCState=UNDER_CONSTRUCTION, primaryNodeIndex=-1, replicas=[ReplicaUnderConstruction[10.251.255.177:50010|RBW], ReplicaUnderConstruction[10.251.255.174:50010|RBW], ReplicaUnderConstruction[10.251.255.169:50010|RBW]]}


• Used blockID to locate exact time of file deletion from Namenode logs:

13/07/31 08:10:33 INFO hdfs.StateChange: BLOCK* addToInvalidates: blk_3560522076897293424_2448396 to 10.251.255.177:50010 10.251.255.169:50010 10.251.255.174:50010

• Used time of deletion to inspect hive logs


QueryStart QUERY_STRING="create database biz_weekly location '/hive/biz/prod'" QUERY_ID=“usrprod_20130731043232_0a40fd32-8c8a-479c-ba7d-3bd8a2698f4b" TIME="1375245164667"

:

QueryEnd QUERY_STRING="create database biz_weekly location '/hive/biz/prod'" QUERY_ID=”usrprod_20130731043232_0a40fd32-8c8a-479c-ba7d-3bd8a2698f4b" QUERY_RET_CODE="0" QUERY_NUM_TASKS="0" TIME="1375245166203"

:

QueryStart QUERY_STRING="drop database biz_weekly" QUERY_ID=”usrprod_20130731073333_e9acf35c-4f07-4f12-bd9d-bae137ae0733" TIME="1375256014799"

:

QueryEnd QUERY_STRING="drop database biz_weekly" QUERY_ID=”usrprod_20130731073333_e9acf35c-4f07-4f12-bd9d-bae137ae0733" QUERY_NUM_TASKS="0" TIME="1375256014838"


• In effect, user “usrprod” issued:

At 2013-07-31 04:32:44: create database biz_weekly location '/hive/biz/prod'

At 2013-07-31 07:33:24: drop database biz_weekly

• This is functionally equivalent to:

hdfs dfs -rm -r /hive/biz/prod


• Customer manually placed their own data in /hive –the warehouse directory managed and controlled by hive

• Customer used CREATE and DROP db commands in their code

o Hive deletes database and table locations in /hive with impunity

• Why didn’t deleted data end up in .Trash?o Trash collection not turned on in configuration settings

o It is now, but need a –skipTrash option (HIVE-6469)


• Hadoop forensics: piece together disparate sources…o Hadoop daemon logs (NameNode)

o Hive query and metastore logs

o Hadoop config files

• Need better tools to correlate the different layers of the system: hive client, hive metastore, MapReduce job, YARN, HDFS, operating sytem metrics, …

By the way… Operating any distributed system would be totally insane without NTP and a standard time zone (UTC).

CASE STUDY – ANALYZE QUERY

• Customer provided Hive query + data sets(100GBs to ~5 TBs)

• Needed help optimizing the query

• Didn’t rewrite query immediately

• Wanted to characterize query performance and isolate bottlenecks first

ANALYZE AND TUNE EXECUTION

• Ran original query on the datasets in our environment:

o Two M/R Stages: Stage-1, Stage-2

• Long running reducers run out of memoryo set mapreduce.reduce.memory.mb=5120

o Reduces slots and extends reduce time

• Query fails to launch Stage-2 with out of memoryo set HADOOP_HEAPSIZE=1024 on client machine

• Query has 250,000 Mappers in Stage-2 which causes failure

o set mapred.max.split.size=5368709120to reduce Mappers

ANALYSIS: HOW TO VISUALIZE?

• Next challenge - how to visualize job execution?

• Existing hadoop/hive logs not sufficient for this task

• Wrote internal toolso parse job history files

o plot mapper and reducer execution

ANALYSIS: MAP STAGE-1

Single reduce task

ANALYSIS: REDUCE STAGE-1

ANALYSIS: MAP STAGE-2

ANALYSIS: REDUCE STAGE-2

ANALYZE EXECUTION: FINDINGS

• Lone, long running reducer in first stage of query

• Analyzed input data:o Query split input data by userId

o Bucketizing input data by userId

o One very large bucket: “invalid” userId

o Discussed “invalid” userid with customer

• An error value is a common pattern!o Need to differentiate between “Don’t know and don’t

care”or “don’t know and do care.”

CONCLUSIONS

• Hive + Hadoop debugging can get very complexo Sifting through many logs and screens

o Automatic transmission versus manual transmission

• Static partitioning induced by Java Virtual Machine has benefits but also induces challenges.

• Where there are difficulties, there’s opportunityo Better tooling

o Better instrumentation

o Better integration of disparate logs and metrics

• Hadoop as a Service: aggregate and share expertise

• Need to learn from the traditional database community!

QUESTIONS? COMMENTS?

Download - Debugging Hive with Hadoop in the Cloud - Hadoop Summit 2014

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