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, 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):

    <description>The maximum length of the query string to store in the lock. Set it to 10K.</description>

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/"
# Executing /bin/sh -c "/usr/lib64/cmf/service/common/"...

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


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:


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.