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Spark at eBay -
Troubleshooting the
everyday issues
Aug. 6, 2014
Seattle Spark Meetup
Don Watters - Sr. Manager of Architecture, eBay Inc.
Suzanne Monthofer - Solutions Architect, eBay Inc.
Agenda
– eBay Overview
– Spark Motivation
– Use Cases At eBay
– Troubleshooting the everyday issues
2
eBay Overview
3
> 50 thousand categories of products > 200 million items listed for sale on the site Average retailer has thousands of products
4
PLATFORM
5 5
Data @ eBay
5
>50 TB/day new data
>100 PB/day
>100 Trillion pairs of information
Millions of queries/day
>6000 business users & analysts
>50k chains of logic
24x7x365
99.98+% Availability
turning over a TB every second Active/Active
Near-Real-time
>100k data elements
Always online
Processed
Spark Motivation
– Great Promise!
– Fits our pattern well
– Iterative approach possible, like SQL
6
7
Agenda
– Use Cases At eBay
8
9
eBay Transformer = More Data
Agenda
– Troubleshooting the everyday issues
10
Tools and Skill sets
• JIRA issue tracking – internal and apache
• Github repository – source version control, documentation (.md)
• Compilation/dependencies - Maven – jar dependencies
• Java – versioning, debugging stack traces, environments, multiple JDK/JREs, compatibility errors
• POSIX OS – environment variables, directory structures, permissions, Shell scripting
• HDFS, hadoop queues, formats, compression
• Yarn/Mesos – environments, debugging, logs, killing
• JIRA internal wikis – global internal collaboration
• User groups, internal DLs, platform support teams, informal emails
• Ability to decipher Java Stack traces
• Stack Overflow, Googling, indirect clues
• Scrappiness:
when dwarfed by a challenge, compensating for seeming inadequacies through will, persistence and heart
11
Most Common Question: Yarn ShellException
(GiraphApplicationMaster.java:onContainersCompleted(574)) - Got container status for
containerID=container_1392317581183_0245_01_000003, state=COMPLETE, exitStatus=1, diagnostics=Exception from container-launch:
org.apache.hadoop.util.Shell$ExitCodeException: at org.apache.hadoop.util.Shell.runCommand(Shell.java:464)
at org.apache.hadoop.util.Shell.run(Shell.java:379)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:589)
at org.apache.hadoop.yarn.server.nodemanager.LinuxContainerExecutor.launchContainer(LinuxContainerExecutor.java:252)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:283)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:79)
at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:303)
at java.util.concurrent.FutureTask.run(FutureTask.java:138)
at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908)
at java.lang.Thread.run(Thread.java:662)
12
Means an error occurred in the Yarn container – need to search for Java
stack trace deeper in the Yarn logs…
Killing Yarn Jobs and Viewing Yarn
Logs and status in many places:
• Hadoop console (transient – disappear after job done)
• Aggregated Yarn logs – not available until job finishes or is killed
• Execution shell – only very high-level status
Killing: Ctrl-C, then
/apache/hadoop/bin/yarn application -kill application_1392973982912_7321
Viewing Logs: /apache/hadoop/bin/yarn logs -applicationId application_1392973982912_7321
Sifting to find text ”Exception”, ”Memory”, etc. | grep Exception -5
| grep Memory -5
• Would like easier debugging and exiting on errors
• May look at a log4j appenders
13
Biggest Challenge: Resource Allocation/Capacity
Scheduling
• Users must request needed resources
• Long-running jobs hang without releasing resources and must be killed
manually
• Created a dedicated Spark queue – still not equitable
• Capacity allocation prioritization is complex
• Spark shell hangs on to memory
• Many users deciding to wait for better stability and better guarantee of
resource availability and job completion
• Yarn vs. Mesos debate?
14
Tuning Spark – Hanging Jobs and Out-of-Memory
Errors
– spark.default.parallelism - # requested Yarn containers
– spark.executor.memory - ~75-90% requested Yarn container memory size
– spark.storage.memoryFraction - lower from default 0.6 to ~0.2 (if you are not pinning significant amount of data)
– Remove outliers from dataset (dual-pass with larger entities)
– Use primitive data types – avoid Strings
– Use Kryo serialization
– app UI at localhost:4040 (disabled on our cluster)
– Need to understand inner workings of Spark
– Community working to reduce the amount of configuration needed
Alex Rubensteyn blog post: “Spark should be better than MapReduce (if only it worked)”
http://blog.explainmydata.com/2014/05/spark-should-be-better-than-mapreduce.html?m=1
Patrick Wendell’s talk on performance at Spark Summit 2013:
https://spark-summit.org/talk/wendell-understanding-the-performance-of-spark-applications/
Tuning Guide: https://spark.apache.org/docs/latest/tuning.html
15
Yarn Improvements Needed for Spark
16
Great talk by Sandy Ryza from Cloudera at Spark Summit 2014
https://www.youtube.com/watch?v=N6pJhxCPe-Y
Rapid Pace of Change
17
18
SPARK-1203
spark-shell on yarn-client race in properly getting hdfs delegation
tokens - error on saveAsTextFile Exception in thread "main" org.apache.hadoop.ipc.RemoteException(java.io.IOException):
Delegation Token can be issued only with kerberos or web authentication at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getDelegationToken(FSNamesystem.java:6211)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.getDelegationToken(NameNodeRpcServer.java:461)
...
at org.apache.hadoop.hdfs.DFSClient.getDelegationToken(DFSClient.java:920)
at org.apache.hadoop.hdfs.DistributedFileSystem.getDelegationToken(DistributedFileSystem.java:1336)
at org.apache.hadoop.fs.FileSystem.collectDelegationTokens(FileSystem.java:527)
at org.apache.hadoop.fs.FileSystem.addDelegationTokens(FileSystem.java:505)
at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodesInternal(TokenCache.java:121)
at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodesInternal(TokenCache.java:100)
at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodes(TokenCache.java:80)
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:202)
Burnt by bugs in snapshots during incubating phase …
Check Spark JIRA issues https://issues.apache.org/jira/browse/SPARK/
Apache Shark – Hive on Spark
…NOW OBSOLETE…
• Google protobuf error (notorious) – had to replace bundled jar Caused by: java.lang.VerifyError: class org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$SetOwnerRequestProto overrides final method
getUnknownFields.()Lcom/google/protobuf/UnknownFieldSet;
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(Unknown Source)
• Had to replace hadoop core/security jars with eBay jars
• JDBC driver: mysql-connector-java-5.0.8-bin.jar
• Got it working on single node – able to access/query existing hive tables
• Couldn’t use for extremely large tables/joins yet (need multi-node)
• Requires JDK 1.7 – couldn’t run on multiple nodes in cluster (still 1.6)
• ./bin/shark-withinfo –skipRddReload to avoid a bad table error
• Performance 2-5x’s better than Hive for 8M row table count query
…Start Looking at Spark SQL!
19
Exception in thread "main" org.apache.hadoop.hive.ql.metadata.HiveException:
java.lang.RuntimeException: Unable to instantiate
org.apache.hadoop.hive.metastore.HiveMetaStoreClient at org.apache.hadoop.hive.ql.metadata.Hive.getAllDatabases(Hive.java:1072)
at shark.memstore2.TableRecovery$.reloadRdds(TableRecovery.scala:49)
at shark.SharkCliDriver.<init>(SharkCliDriver.scala:283)
at shark.SharkCliDriver$.main(SharkCliDriver.scala:162)
at shark.SharkCliDriver.main(SharkCliDriver.scala)
Caused by: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.metastore.HiveMetaStoreClient
at org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1139)
at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:51)
at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:61)
at org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:2288)
at org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:2299)
at org.apache.hadoop.hive.ql.metadata.Hive.getAllDatabases(Hive.java:1070)
... 4 more
Caused by: java.lang.reflect.InvocationTargetException at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
at java.lang.reflect.Constructor.newInstance(Unknown Source)
at org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1137)
... 9 more
Caused by: java.lang.VerifyError: class
org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$SetO
wnerRequestProto overrides final method
getUnknownFields.()Lcom/google/protobuf/UnknownFieldSet; at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(Unknown Source)
at java.security.SecureClassLoader.defineClass(Unknown Source))
20
Shark Jar Incompatibilities
21
Caused by: KrbException: Server not found in Kerberos database (7)
at sun.security.krb5.KrbTgsRep.<init>(Unknown Source)
at sun.security.krb5.KrbTgsReq.getReply(Unknown Source)
at sun.security.krb5.KrbTgsReq.sendAndGetCreds(Unknown Source)
at sun.security.krb5.internal.CredentialsUtil.serviceCreds(Unknown Source)
14/05/07 17:49:58 ERROR security.UserGroupInformation: PriviledgedActionException as:[email protected] cause:javax.security.sasl.SaslException: GSS initiate failed [Caused by
GSSException: No valid credentials provided (Mechanism level: Server not found in Kerberos
database (7))] 14/05/07 17:49:58 INFO security.UserGroupInformation: Initiating logout for [email protected]
14/05/07 17:49:58 INFO security.UserGroupInformation: Initiating re-login [email protected]
14/05/07 17:50:02 ERROR security.UserGroupInformation: PriviledgedActionException as:[email protected]
ause:javax.security.sasl.SaslException: GSS initiate failed [Caused by
GSSException: No valid credentials provided (Mechanism level: Server not found
in Kerberos database (7))] 14/05/07 17:50:02 WARN security.UserGroupInformation: Not attempting to re-login since the last re-login was
attempted less than 600 seconds before.
Shark vs. Hive, Spark SQL vs Shark
Big Data Benchmarks
22
https://amplab
.cs.berkeley.e
du/benchmark
/
http://databricks.com/
blog/2014/06/02/excit
ing-performance-
improvements-on-
the-horizon-for-
spark-sql.html
Compilation: Maven, sbt, ivy, ant
• Maven/sbt/ivy/munge can be complex, finicky
[info] Resolving com.ebay.incdata.metis#metis-matching-engine;1.0-SNAPSHOT ...
[warn] module not found: com.ebay.incdata.metis#metis-matching-engine;1.0-SNAPSHOT
[warn] ==== local: tried
[warn] /Users/smonthofer/.ivy2/local/com.ebay.incdata.metis/metis-matching-engine/1.0-SNAPSHOT/ivys/ivy.xml
[warn] ==== public: tried
[warn] http://repo1.maven.org/maven2/com/ebay/incdata/metis/metis-matching-engine/1.0-SNAPSHOT/metis-matching-engine-1.0-SNAPSHOT.pom
[warn] ==== Local Maven Repository: tried
[warn] file:///var/root/.m2/repository/com/ebay/incdata/metis/metis-matching-engine/1.0-SNAPSHOT/metis-matching-engine-1.0-SNAPSHOT.pomURI
has an authority component
at sbt.IvyActions$.sbt$IvyActions$$resolve(IvyActions.scala:213)
at sbt.IvyActions$$anonfun$update$1.apply(IvyActions.scala:122)
at sbt.IvyActions$$anonfun$update$1.apply(IvyActions.scala:121)
[warn] ::::::::::::::::::::::::::::::::::::::::::::::
[warn] :: UNRESOLVED DEPENDENCIES ::
[warn] ::::::::::::::::::::::::::::::::::::::::::::::
java.net.MalformedURLException: no protocol: /Users/smonthofer/.m2/repository
• build.sbt resolvers +=
"Local Maven Repository" at file:///Users/smonthofer/.m2/repository
• Needed 3 slashes (platform independence feature)!!! Grrrr…
23
Learned New Term:
Yak Shaving
24
From Urban Dictionary:
Any seemingly pointless activity which is actually
necessary to solve a problem which solves a
problem which, several levels of recursion later,
solves the real problem you're working on.
origin: MIT AI Lab, after 2000: orig. probably from a
Ren & Stimpy episode.
Building scalable systems is not all sexy roflscale fun.
It’s a lot of plumbing and yak shaving. A lot of
hacking together tools that really ought to exist
already, but all the open source solutions out there
are too bad (and yours ends up bad too, but at least
it solves your particular problem).
- Martin Kleppmann, LinkedIn, Founder of Rapportive
25
Simple documentation saves
time later for yourself and for
others
Cut/paste/collect things that work, errors,
common commands and put on a wiki page
(even email drafts are a fast holding place).
Source control/backups for working versions
– be able to start from scratch
Maven, sbt, dependencies – complex,
corruptible, bizarre tricks, multiple open
source projects – magic (also scary)
Get ahead of the curve on new technology
cause new challenges will always come up
From xkcd
If you want to succeed as badly as you want the air,
then you will get it… there is no other secret to success
- Socrates (lesson to his students)
Privileged and Confidential 26
Quoted by Spark User group user:
Spark at eBay -
Troubleshooting the
everyday issues
Aug. 6, 2014
Seattle Spark Meetup
Don Watters – [email protected]
Suzanne Monthofer – [email protected]