Debugging Hive with Hadoop in the Cloud - Hadoop Summit 2014


Published on

Anyone that has used Hadoop knows that jobs sometimes get stuck. Hadoop is powerful, and it’s experiencing a tremendous rate of innovation, but it also has many rough edges. As Hadoop practitioners, we all spend a lot of effort dealing with these rough edges in order to keep Hadoop and Hadoop jobs running well for our customers and/or organizations.

For this session, we will look at typical problems encountered by Hadoop users when running Hive, and discuss implications for the future of Hadoop development. We will also go through the solution to this kind of problem using step-by-step instructions and the specific code and logs we used to identify the issue.

As a community, we need to work together to improve this kind of experience for our industry. Now that Hadoop 2 has been shipped, we believe the Hadoop community will be able to focus its energies on rounding off rough edges like these, and this session should provide advanced users with some tools and strategies to identify issues with jobs and how to keep these running smoothly.

Published in: Technology, Education
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Debugging Hive with Hadoop in the Cloud - Hadoop Summit 2014

  1. 1. DEBUGGING HIVE WITH HADOOP IN THE CLOUD Soam Acharya, David Chaiken, Denis Sheahan, Charles Wimmer Altiscale, Inc. June 4, 2014
  2. 2. WHO ARE WE? • Altiscale: Infrastructure Nerds! • Hadoop As A Service • Rack and build our own Hadoop clusters • Provide a suite of Hadoop tools o Hive, Pig, Oozie o Others as needed: R, Python, Spark, Mahout, Impala, etc. • Monthly billing plan: compute, storage • • @Altiscale #HadoopSherpa
  3. 3. 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
  4. 4. 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, …
  5. 5. ALTISCALE PERSPECTIVE • Service provider o 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 approach o How to use Hadoop 2 cluster tools and logs to debug and to tune o This talk will not focus on query optimization
  6. 6. 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
  7. 7. 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
  8. 8. HADOOP 1 VS HADOOP 2 • No more JobTrackers, TaskTrackers • YARN ~ Operating System for Clusters o MapReduce is implemented as a YARN application o Bring on the applications! (Spark is just the start…) • Should be Transparent to Hive users
  9. 9. HADOOP 2 DEBUGGING TOOLS • Monitoring o 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
  10. 10. HADOOP 2 DEBUGGING TOOLS • Hadoop logs o 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
  11. 11. ACCESSING LOGS IN HADOOP 2 • To view the logs for a job, click on the link under the ID column in Resource Manager UI.
  12. 12. ACCESSING LOGS IN HADOOP 2 • To view application top level logs, click on logs. • To view individual logs for the mappers and reducers, click on History.
  13. 13. ACCESSING LOGS IN HADOOP 2 • Log output for the entire application.
  14. 14. ACCESSING LOGS IN HADOOP 2 • Click on the Map link for mapper logs and the Reduce link for reducer logs.
  15. 15. ACCESSING LOGS IN HADOOP 2 • Clicking on a single link under Name provides an overview for that particular map job.
  16. 16. ACCESSING LOGS IN HADOOP 2 • Finally, clicking on the logs link will take you to the log output for that map job.
  17. 17. ACCESSING LOGS IN HADOOP 2 • Fun, fun, donuts, and more fun…
  18. 18. HIVE + HADOOP 2 ARCHITECTURE • Hive 0.10+ Hadoop 2 Cluster Hive CLI Hive Metastore HiveserverJDBC/ODBC Kettle, Hue, …
  19. 19. HIVE LOGS • Query Log location • From /etc/hive/hive-site.xml: <property> <name>hive.querylog.location</name> <value>/home/hive/log/${}</value> </property> SessionStart SESSION_ID="soam_201402032341" TIME="1391470900594"
  20. 20. HIVE CLIENT LOGS • /etc/hive/ o hive.log.dir=/var/log/hive/${} 2014-05-29 19:51:09,830 INFO parse.ParseDriver ( - Parsing command: select count(*) from dogfood_job_data 2014-05-29 19:51:09,852 INFO parse.ParseDriver ( - Parse Completed 2014-05-29 19:51:09,852 INFO ql.Driver ( - </PERFLOG method=parse start=1401393069830 end=1401393069852 duration=22> 2014-05-29 19:51:09,853 INFO ql.Driver ( - <PERFLOG method=semanticAnalyze> 2014-05-29 19:51:09,890 INFO parse.SemanticAnalyzer ( - Starting Semantic Analysis 2014-05-29 19:51:09,892 INFO parse.SemanticAnalyzer ( - Completed phase 1 of Semantic Analysis 2014-05-29 19:51:09,892 INFO parse.SemanticAnalyzer ( - Get metadata for source tables 2014-05-29 19:51:09,906 INFO parse.SemanticAnalyzer ( - Get metadata for subqueries 2014-05-29 19:51:09,909 INFO parse.SemanticAnalyzer ( - Get metadata for destination tables
  21. 21. HIVE METASTORE LOGS • /etc/hive-metastore/ o hive.log.dir=/service/log/hive-metastore/${} 2014-05-29 19:50:50,179 INFO metastore.HiveMetaStore ( - 200: source:/ get_table : db=default tbl=dogfood_job_data 2014-05-29 19:50:50,180 INFO HiveMetaStore.audit ( - ugi=chaiken ip=/ cmd=source:/ get_table : db=default tbl=dogfood_job_data 2014-05-29 19:50:50,236 INFO metastore.HiveMetaStore ( - 200: source:/ get_table : db=default tbl=dogfood_job_data 2014-05-29 19:50:50,236 INFO HiveMetaStore.audit ( - ugi=chaiken ip=/ cmd=source:/ get_table : db=default tbl=dogfood_job_data 2014-05-29 19:50:50,261 INFO metastore.HiveMetaStore ( - 200: source:/ get_table : db=default tbl=dogfood_job_data
  22. 22. HIVE ISSUES + CASE STUDIES • Hive Issues o Hive client out of memory o Hive map/reduce task out of memory o Hive metastore out of memory o Hive launches too many tasks • Case Studies: o Hive “stuck” job o Hive “missing directories” o Analyze Hive Query Execution
  23. 23. HIVE CLIENT OUT OF MEMORY • Memory intensive client side hive query (map-side join) Number of reduce tasks not specified. Estimated from input data size: 999 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer=<number> In order to limit the maximum number of reducers: set hive.exec.reducers.max=<number> In order to set a constant number of reducers: set mapred.reduce.tasks=<number> java.lang.OutOfMemoryError: Java heap space at java.nio.CharBuffer.wrap( at java.nio.CharBuffer.wrap( at java.lang.StringCoding$StringDecoder.decode(
  24. 24. 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! • 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 cached Mem: 1695 1388 306 0 60 424 -/+ buffers/cache: 903 791 Swap: 895 101 794
  25. 25. 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
  26. 26. HIVE TASK OUT OF MEMORY • Fix is to increase task RAM allocation… set<new RAM allocation>; set mapreduce.reduce.memory.mb=<new RAM allocation>; • Also watch out for… set<heap size>m; set<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
  27. 27. 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 • After notifying hive users about downtime: service hcat restart
  28. 28. HIVE LAUNCHES TOO MANY TASKS • Typically a function of the input data set • Lots of little files
  29. 29. HIVE LAUNCHES TOO MANY TASKS • Set mapred.max.split.size to appropriate fraction of data size • Also verify that
  30. 30. 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?”
  31. 31. 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
  32. 32. 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: File does not exist: hdfs://opaque_hostname:8020/HiveTableDir/FileNa
  33. 33. 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
  34. 34. 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?”
  35. 35. HIVE “MISSING DIRECTORIES” • 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- blk_3560522076897293424_2448396{blockUCState=UNDER_C ONSTRUCTION, primaryNodeIndex=-1, replicas=[ReplicaUnderConstruction[ 010|RBW], ReplicaUnderConstruction[|RBW], ReplicaUnderConstruction[|RBW]]}
  36. 36. HIVE “MISSING DIRECTORIES” • 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 • Used time of deletion to inspect hive logs
  37. 37. HIVE “MISSING DIRECTORIES” 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"
  38. 38. HIVE “MISSING DIRECTORIES” • 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
  39. 39. HIVE “MISSING DIRECTORIES” • 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)
  40. 40. HIVE “MISSING DIRECTORIES” • 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).
  41. 41. 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
  42. 42. 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 memory o set mapreduce.reduce.memory.mb=5120 o Reduces slots and extends reduce time • Query fails to launch Stage-2 with out of memory o set HADOOP_HEAPSIZE=1024 on client machine • Query has 250,000 Mappers in Stage-2 which causes failure o set mapred.max.split.size=5368709120 to reduce Mappers
  43. 43. ANALYSIS: HOW TO VISUALIZE? • Next challenge - how to visualize job execution? • Existing hadoop/hive logs not sufficient for this task • Wrote internal tools o parse job history files o plot mapper and reducer execution
  45. 45. Single reduce task ANALYSIS: REDUCE STAGE-1
  48. 48. 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.”
  49. 49. CONCLUSIONS • Hive + Hadoop debugging can get very complex o 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 opportunity o 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!