Oozie SSH Action Failed With “externalId cannot be empty” Error

Last week I was working with an issue that when running a very simple SSH action through Oozie, the job kept failing with “externalId cannot be empty” error. The workflow only had one single SSH action, and nothing else. See the workflow example below:

<workflow-app name="SSH Action Test" xmlns="uri:oozie:workflow:0.5">
    <start to="ssh-5c4d"/>
    <kill name="Kill">
        <message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
    </kill>
    <action name="ssh-5c4d">
        <ssh xmlns="uri:oozie:ssh-action:0.1">
            <host>user1@another-server-url</host>
            <command>ls / &gt;&gt; /tmp/test.log</command>
            <capture-output/>
        </ssh>
        <ok to="End"/>
        <error to="Kill"/>
    </action>
    <end name="End"/>
</workflow-app>

And the error message from the Oozie server looked like below:

2018-01-03 06:12:45,347 ERROR org.apache.oozie.command.wf.ActionStartXCommand: 
SERVER[{oozie-server-url}] USER[admin] GROUP[-] TOKEN[] APP[SSH Action Test] JOB[0000000-180103010440574-ooz
ie-oozi-W] ACTION[0000000-180103010440574-oozie-oozi-W@ssh-5c4d] Exception,
java.lang.IllegalArgumentException: externalId cannot be empty
        at org.apache.oozie.util.ParamChecker.notEmpty(ParamChecker.java:90)
        at org.apache.oozie.util.ParamChecker.notEmpty(ParamChecker.java:74)
        at org.apache.oozie.WorkflowActionBean.setStartData(WorkflowActionBean.java:503)
        at org.apache.oozie.command.wf.ActionXCommand$ActionExecutorContext.setStartData(ActionXCommand.java:387)
        at org.apache.oozie.action.ssh.SshActionExecutor.start(SshActionExecutor.java:269)
        at org.apache.oozie.command.wf.ActionStartXCommand.execute(ActionStartXCommand.java:232)
        at org.apache.oozie.command.wf.ActionStartXCommand.execute(ActionStartXCommand.java:63)
        at org.apache.oozie.command.XCommand.call(XCommand.java:286)
        at org.apache.oozie.service.CallableQueueService$CompositeCallable.call(CallableQueueService.java:332)
        at org.apache.oozie.service.CallableQueueService$CompositeCallable.call(CallableQueueService.java:261)
        at java.util.concurrent.FutureTask.run(FutureTask.java:262)
        at org.apache.oozie.service.CallableQueueService$CallableWrapper.run(CallableQueueService.java:179)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
        at java.lang.Thread.run(Thread.java:745)

We confirmed that the passwordless connection from the Ooize server to the remote server worked correctly without issues.

After digging through the Oozie source code, I found out that it was due to the fact that Oozie uses Java’s Runtime.exec library to execute the commands remotely. And Runtime.exec does not work in the same way as shell, especially when re-directing output to a file, which Runtime.exec does not support at all. What happened under the hood was that Oozie will split the full command “ls / >> /tmp/test.log” into tokens “ls”, “/”, “>>”, “/tmp/test.log”, and pass all of them into Runtime.exec. And when Runtime.exec executed the command, it treated all tokens, apart from “ls” as the parameters to “ls” command. As you would expect, “>>” is not a file, and “ls” command will fail complain that file does not exist, hence will return exit status of 1, rather than 0.

Oozie tried to capture the PID of the remote process, but failed, and hence returned “externalId cannot be empty” error.

The workaround is simple, just store the full command you want to run into a new script file and ask Oozie to execute that script instead:

1. Create a file “ssh-action.sh” on the target host, for example, under /home/{user}/scripts/ssh-action.sh
2. Add command “ls / >> /tmp/ssh.log” to the file
3. Make the file executable by running:

chmod 744 /home/{user}/scripts/ssh-action.sh

4. Update Oozie workflow to run the new shell script instead:

<ssh xmlns="uri:oozie:ssh-action:0.1">
    <host>user@remote-server-url</host>
    <command>/home/{user}/scripts/ssh-action.sh</command>
    <capture-output/>
</ssh>

And then the SSH action should work perfectly.

Hive CLI Prints SLF4J Error to Standard Output

If you have both Hive and Spark running on the same cluster, chances are that Hive CLI will probably produce the following WARNING message upon exit of each session:

WARN: The method class org.apache.commons.logging.impl.SLF4JLogFactory#release() was invoked. 
WARN: Please see http://www.slf4j.org/codes.html#release for an explanation.

Sample full output looks like below:

[root@localhost ~]# hive -e "show tables;"

Logging initialized using configuration in jar:file:/opt/cloudera/parcels/CDH-5.12.1-1.cdh5.12.1.p0.3/jars/hive-common-1.1.0-cdh5.12.1.jar!/hive-log4j.properties
OK
sample_07
sample_08
web_logs
Time taken: 1.281 seconds, Fetched: 3 row(s)
WARN: The method class org.apache.commons.logging.impl.SLF4JLogFactory#release() was invoked.
WARN: Please see http://www.slf4j.org/codes.html#release for an explanation.

Even though Hive CLI has been deprecated in CDH, there are still lots of enterprise users out there are still stuck with Hive CLI due to legacy reasons and it is not easy for them to migrate to use Beeline or query Hive through ODBC or JDBC in their applications.

This is not an issue if you just run Hive CLI from command line and view the output. However, if you want to capture the result set from Hive CLI’s stdout, it will be a trouble, see test case below:

[root@localhost ~]# output=`hive -e "show tables;"`

Logging initialized using configuration in jar:file:/opt/cloudera/parcels/CDH-5.12.1-1.cdh5.12.1.p0.3/jars/hive-common-1.1.0-cdh5.12.1.jar!/hive-log4j.properties
OK
Time taken: 1.281 seconds, Fetched: 3 row(s)

And when you echo out the variable $output, it will contain the WARNING message:

[root@localhost ~]# echo $output
sample_07
sample_08
web_logs
WARN: The method class org.apache.commons.logging.impl.SLF4JLogFactory#release() was invoked.
WARN: Please see http://www.slf4j.org/codes.html#release for an explanation.

And when you want to use this output as other inputs in your application, things will go crazy.

This happens due to the below code under hive’s script file (/opt/cloudera/parcels/CDH/lib/hive/bin/hive) that loads Spark’s JAR file into Hive’s CLASSPATH:

# add Spark assembly jar to the classpath
if [[ -n "$SPARK_HOME" && !("$HIVE_SKIP_SPARK_ASSEMBLY" = "true") ]]
then
  sparkAssemblyPath=`ls ${SPARK_HOME}/lib/spark-assembly-*.jar`
  CLASSPATH="${CLASSPATH}:${sparkAssemblyPath}"
fi

Luckily, the latest CDH release, in fact from CDH 5.12.0, Cloudera has backported an upstream JIRA HIVE-12179, which added a checking for environment variable called “HIVE_SKIP_SPARK_ASSEMBLY”. So we can use this variable to disable the loading of Spark JARs for Hive CLI if you do not need to use Hive on Spark.

So the workaround is as simple as setting “HIVE_SKIP_SPARK_ASSEMBLY” to “true” so that the “if” statement will be skipped. See below example:

[root@localhost ~]# output=`export HIVE_SKIP_SPARK_ASSEMBLY=true; hive -e "show tables;"`

Logging initialized using configuration in jar:file:/opt/cloudera/parcels/CDH-5.12.1-1.cdh5.12.1.p0.3/jars/hive-common-1.1.0-cdh5.12.1.jar!/hive-log4j.properties
OK
Time taken: 1.281 seconds, Fetched: 3 row(s)

And the output will be clean like below:

[root@localhost ~]# echo $output
sample_07
sample_08
web_logs

This workaround will not work if you need to use Hive on Spark in Hive CLI, because it essentially disables the loading of Spark JARs. And of course, using of Hive CLI is strongly NOT recommended, and migration to Beeline or use ODBC/JDBC to connect to HiveServer2 is the right way to go in the long run.

Hope above information helps.

Hive Long Queries Causing ZooKeeper Fail With OutOfMemory Error

I have seen lots of Hadoop users are not using Big Data technology correctly. Hadoop is designed for Big Data, so it works well with large file sizes and this is why we have block size for HDFS as 256MB or 512MB depending on use cases. However, lots of users, even from big corporate companies are not utilizing such technology by having lots of small files and partitions for a given Hive table. Some particular users have millions of partitions and hundreds of millions of files stored in HDFS, each file is in KB of size. This puts lots of pressure on all components in the Hadoop echo system, including HDFS, HiveServer2, Impala, ZooKeeper just to name a few.

In this particular post, I am going to discuss one of the side effect of such usage pattern that it will crash ZooKeeper with OutOfMemory error, combined with large string of Hive query being run.

The following was what happened:

1. User runs a Hive query with very long string (contains 100KB of characters)
2. This Hive query runs against a table with millions of partitions
3. The query will scan through about 20,000 partition
4. Since hive.support.concurrency=true, when the query is running, Hive will try to create one ZNode per partition in ZooKeeper to indicate that those partitions are locked
5. Hive will also store the full Hive query string against each ZNode for debugging purpose, so that when issue happened, user can check ZNode and see which query locks the partition
6. So we have 20,000 partitions * 100K each, we will end up creating 2GB of data in ZooKeeper, just for this query alone
7. If we have multiple similar queries, ZooKeeper can reach to memory limit easily in no time

To overcome this problem, Hive introduced a new feature to control the number of characters to be stored against each ZNode in such scenario, via upstream JIRA HIVE-16334. This JIRA has been backported into CDH since 5.12.0.

However, the default size is 1MB (1,000,000 bytes), which is still big and above case will still happen. To work around this issue, we can simply reduce the number of Hive query being stored, say to 10K. (Storing of the query string is purely for debugging purpose, so in theory we can reduce to a very small size, but probably not a good idea if you want to troubleshoot other issues, so 10K should be a good starting point).

To do so, please follow below steps (assuming that you are using Cloudera Manager):

1. Go go CM > Hive > Configuration > HiveServer2 Advanced Configuration Snippet (Safety Valve) for hive-site.xml
2. Enter below into textarea (view as XML):

<property>
    <name>hive.lock.query.string.max.length</name>
    <value>10000</value>
    <description>The maximum length of the query string to store in the lock. Set it to 10K.</description>
</property>

3. Save and restart HiveServer2

After that, we should have less chance of hitting ZooKeeper OutOfMemory in the above scenario. However, the root cause was due to too many partitions, so the first priority is to reduce as much as possible so that each query will not scan more than 1000 partitions to get good performance.

For users using CDH older than CDH 5.12.0, suggestion is to upgrade.

Hope above helps.

SparkHistory Server Keeps Crashing With OutOfMemory Error

This article explains what to do when you are unable to start up SparkHistory server which keeps crashing with OutOfMemory errors after using Spark for some time.

To confirm that Spark History Server keeps failing with OutOfMemory error on start up, we can check the run time process directory for Spark under /var/run/cloudera-scm-agent/process directory if you using using CDH version of Spark. The stdout.log file contains the following error:

#
# java.lang.OutOfMemoryError: Java heap space
# -XX:OnOutOfMemoryError="/usr/lib64/cmf/service/common/killparent.sh"
# Executing /bin/sh -c "/usr/lib64/cmf/service/common/killparent.sh"...

One of the possible reason for the failure was due to some large job history files under /user/spark/sparkApplicationHistory (or /user/spark/spark2ApplicationHistory for Spark2). On SparkHistory server startup, it will try to load those files into memory, and if those history files are too big, it will cause history server to crash with OutOfMemory error unless heap size is increased through Cloudera Manager interface.

To confirm this, just run:

hdfs dfs -ls /user/spark/sparkApplicationHistory

or

hdfs dfs -ls /user/spark/spark2ApplicationHistory

and see if there are any files that are in hundreds of MBs in size, if yes, then that will be a problem.

You might also notice that some files might have “.inprogress” extension, like below:

/user/spark/spark2ApplicationHistory/application_1503451614878_0337.inprogress

Those files can become stale in the HDFS directory in the case that Spark job failed prematurely, and Spark did not get a chance to clean up those files. Since SparkHistory server has no way of knowing if those files were left over from a failed Spark job, or if they were still being processed, hence it will just blindly load everything under the HDFS directory into memory, which will cause failure if those files are too big.

Spark has a clean up job to remove any old files that are longer than a pre-defined time period, however, it does not remove stale .inprogress files. This issue was reported in SPARK-8617.

Once SPARK-8617 is fixed, we should not see those stale .inprogress files anymore. But at the time of writing, it has not been backported into CDH yet.

For now, we just need to delete all the files under /user/spark/sparkApplicationHistory or /user/spark/spark2ApplicationHistory directory in HDFS that are older than, say one week, so that those big files can be cleaned up.

After that, we should be able to get SparkHistory server startup without issues.

Show Create Table Output Truncated for VIEW in Hive

Yesterday i was working on a very weird issue in Hive that the SHOW CREATE TABLE for a VIEW returned partial output. This happened in Hue, Beeline as well as Hive CLI. Please see below test case:

1. From Hue interface:

2. From Beeline:

0: jdbc:hive2://localhost:10000/default> SHOW CREATE TABLE test_view;
+------------------------------------+--+
|           createtab_stmt           |
+------------------------------------+--+
| CREATE VIEW `test_view` AS SELECT  |
|                                    |
| FROM `default`.`sample_07`         |
+------------------------------------+--+

3. From Hive CLI:

hive> SHOW CREATE TABLE test_view;
OK
CREATE VIEW `test_view` AS SELECT

FROM `default`.`sample_07`

After some researching and testing in varies CDH versions, I found out that the issue was caused by having “\t” character in the VIEW’s create statement, and it only happens in CDH version before 5.13.1. Any characters after “\t” will be removed in the output.

What happens in CDH 5.13.1 version is that Hive will remove white spaces in the query string before saving to database, so this will not happen, however, simply upgrade CDH will not help to resolve the issue, as the code that reads from Hive MetaStore database does not change.

To fix the issue, we need to update the database stored in the Hive MetaStore backend, please follow below steps (this is for MySQL, for other database types, please consult with vendor):

1. STOP Hive services, so that no once an use the it to update Hive Metadata by running any queries
2 Dump the whole MySQL database out into text a file using “mysqldump” command line tool (please make sure all data is dumped correctly so that we can load data back)
3. Use text editor to replace “tab” with spaces for all the data stored in “TBLS” table
4. Since we can not RENAME databases, just drop it and re-create it
5. Reload the modified dump file back into newly created database
6. Restart Hive and confirm if issue is fixed.

It is better to test this in the DEV/TEST cluster before applying the same change in PROD to minimize mistakes.

Hope above helps.