“Container killed by YARN for exceeding memory lim

Question

I'm running a 5 node Spark cluster on AWS EMR each sized m3.xlarge (1 master 4 slaves). I successfully ran through a 146Mb bzip2 compressed CSV file and ended up with a perfectly aggregated result.

Now I'm trying to process a ~5GB bzip2 CSV file on this cluster but I'm receiving this error:

16/11/23 17:29:53 WARN TaskSetManager: Lost task 49.2 in stage 6.0 (TID xxx, xxx.xxx.xxx.compute.internal): ExecutorLostFailure (executor 16 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 10.4 GB of 10.4 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead.

I'm confused as to why I'm getting a ~10.5GB memory limit on a ~75GB cluster (15GB per 3m.xlarge instance)...

Here is my EMR config:

[
 {
  "classification":"spark-env",
  "properties":{

  },
  "configurations":[
     {
        "classification":"export",
        "properties":{
           "PYSPARK_PYTHON":"python34"
        },
        "configurations":[

        ]
     }
  ]
},
{
  "classification":"spark",
  "properties":{
     "maximizeResourceAllocation":"true"
  },
  "configurations":[

  ]
 }
]

From what I've read, setting the maximizeResourceAllocation property should tell EMR to configure Spark to fully utilize all resources available on the cluster. Ie, I should have ~75GB of memory available... So why am I getting a ~10.5GB memory limit error? Here is the code I'm running:

def sessionize(raw_data, timeout):
# https://www.dataiku.com/learn/guide/code/reshaping_data/sessionization.html
    window = (pyspark.sql.Window.partitionBy("user_id", "site_id")
              .orderBy("timestamp"))
    diff = (pyspark.sql.functions.lag(raw_data.timestamp, 1)
            .over(window))
    time_diff = (raw_data.withColumn("time_diff", raw_data.timestamp - diff)
                 .withColumn("new_session", pyspark.sql.functions.when(pyspark.sql.functions.col("time_diff") >= timeout.seconds, 1).otherwise(0)))
    window = (pyspark.sql.Window.partitionBy("user_id", "site_id")
              .orderBy("timestamp")
              .rowsBetween(-1, 0))
    sessions = (time_diff.withColumn("session_id", pyspark.sql.functions.concat_ws("_", "user_id", "site_id", pyspark.sql.functions.sum("new_session").over(window))))
    return sessions
def aggregate_sessions(sessions):
    median = pyspark.sql.functions.udf(lambda x: statistics.median(x))
    aggregated = sessions.groupBy(pyspark.sql.functions.col("session_id")).agg(
        pyspark.sql.functions.first("site_id").alias("site_id"),
        pyspark.sql.functions.first("user_id").alias("user_id"),
        pyspark.sql.functions.count("id").alias("hits"),
        pyspark.sql.functions.min("timestamp").alias("start"),
        pyspark.sql.functions.max("timestamp").alias("finish"),
        median(pyspark.sql.functions.collect_list("foo")).alias("foo"),
    )
    return aggregated
 spark_context = pyspark.SparkContext(appName="process-raw-data")
spark_session = pyspark.sql.SparkSession(spark_context)
raw_data = spark_session.read.csv(sys.argv[1],
                                  header=True,
                                  inferSchema=True)
# Windowing doesn't seem to play nicely with TimestampTypes.
#
# Should be able to do this within the ``spark.read.csv`` call, I'd
# think. Need to look into it.
convert_to_unix = pyspark.sql.functions.udf(lambda s: arrow.get(s).timestamp)
raw_data = raw_data.withColumn("timestamp",
                               convert_to_unix(pyspark.sql.functions.col("timestamp")))
sessions = sessionize(raw_data, SESSION_TIMEOUT)
aggregated = aggregate_sessions(sessions)
aggregated.foreach(save_session)

Basically, nothing more than windowing and a groupBy to aggregate the data.

It starts with a few of those errors, and towards halting increases in the amount of the same error.

I've tried running spark-submit with --conf spark.yarn.executor.memoryOverhead but that doesn't seem to solve the problem either.

Answer 1

If you're not using spark-submit, and you're looking for another way to specify the yarn.nodemanager.vmem-check-enabled parameter mentioned by Duff, here are 2 other ways:

Method 2

If you're using a JSON Configuration file (that you pass to the AWS CLI or to your boto3 script), you'll have to add the following configuration:

[{
"Classification": "yarn-site", 
  "Properties": {
    "yarn.nodemanager.vmem-check-enabled": "false"
   }
}]

Method 3

If you use the EMR console, add the following configuration:

classification=yarn-site,properties=[yarn.nodemanager.vmem-check-enabled=false]

Answer 2

See,

I had the same problem in a huge cluster that I'm working now. The problem will not be solved to adding memory to the worker. Sometimes in process aggregation spark will use more memory than it has and the spark jobs will start to use off-heap memory.

One simple example is:

If you have a dataset that you need to reduceByKey it will, sometimes, agregate more data in one worker than other, and if this data exeeds the memory of one worker you get that error message.

Adding the option spark.yarn.executor.memoryOverhead will help you if you set for 50% of the memory used for the worker (just for test, and see if it works, you can add less with more tests).

But you need to understand how Spark works with the Memory Allocation in the cluster:

1. The more common way Spark uses 75% of the machine memory. The rest goes to SO.
2. Spark has two types of memory during the execution. One part is for execution and the other is the storage. Execution is used for Shuffles, Joins, Aggregations and Etc. The storage is used for caching and propagating data accross the cluster.

One good thing about memory allocation, if you are not using cache in your execution you can set the spark to use that sotorage space to work with execution to avoid in part the OOM error. As you can see this in documentation of spark:

This design ensures several desirable properties. First, applications that do not use caching can use the entire space for execution, obviating unnecessary disk spills. Second, applications that do use caching can reserve a minimum storage space (R) where their data blocks are immune to being evicted. Lastly, this approach provides reasonable out-of-the-box performance for a variety of workloads without requiring user expertise of how memory is divided internally.

But how can we use that?

You can change some configurations, Add the MemoryOverhead configuration to your job call but, consider add this too: spark.memory.fraction change for 0.8 or 0.85 and reduce the spark.memory.storageFraction to 0.35 or 0.2.

Other configurations can help, but it need to check in your case. Se all these configuration here.

Now, what helps in My case.

I have a cluster with 2.5K workers and 2.5TB of RAM. And we were facing OOM error like yours. We just increase the spark.yarn.executor.memoryOverhead to 2048. And we enable the dynamic allocation. And when we call the job, we don't set the memory for the workers, we leave that for the Spark to decide. We just set the Overhead.

But for some tests for my small cluster, changing the size of execution and storage memory. That solved the problem.

Answer 3

I had the same issue on small cluster running relatively small job on spark 2.3.1. The job reads parquet file, removes duplicates using groupBy/agg/first then sorts and writes new parquet. It processed 51 GB of parquet files on 4 nodes (4 vcores, 32Gb RAM).

The job was constantly failing on aggregation stage. I wrote bash script watch executors memory usage and found out that in the middle of the stage one random executor starts taking double memory for a few seconds. When I correlated time of this moment with GC logs it matched with full GC that empties big amount of memory.

At last I understood that the problem is related somehow to GC. ParallelGC and G1 causes this issue constantly but ConcMarkSweepGC improves the situation. The issue appears only with small amount of partitions. I ran the job on EMR where OpenJDK 64-Bit (build 25.171-b10) was installed. I don't know the root cause of the issue, it could be related to JVM or operating system. But it is definitely not related to heap or off-heap usage in my case.

UPDATE1

Tried Oracle HotSpot, the issue is reproduced.

Answer 4

Try repartition. It works in my case.

The dataframe was not so big at the very beginning when it was loaded with write.csv(). The data file amounted to be 10 MB or so, as may required say totally several 100 MB memory for each processing task in executor. I checked the number of partitions to be 2 at the time. Then it grows like a snowball during the following operations joining with other tables, adding new columns. And then I ran into the memory exceeding limits issue at a certain step. I checked the paritions number, it's still 2, derived from the original data frame I guess. So I tried to repartition it at the very beginning, and there was no more such issue.

I did not read many materials about Spark and YARN yet. What I know is there are executors in nodes, an executor could handle many a task depending upon the resources. My guess is one partition would be atomically mapped to one task. And its volume determines the resource usage. Spark could not slice it if one partition grows too big.

A reasonable strategy is to determine the nodes, and container memory first, either 10GB or 5GB. Ideally, both could serve any data processing job, just a matter of time. Given 5GB memory setting, the reasonable row for one partition you find, say is 1000 after testing (it won't failed any steps during the processing), we could do it as the following pseudo code:

RWS_PER_PARTITION = 1000
input_df = spark.write.csv("file_uri", *other_args)
total_rows = input_df.count()
original_num_partitions = input_df.getNumPartitions()
numPartitions = max(total_rows/RWS_PER_PARTITION, original_num_partitions)
input_df = input_df.repartition(numPartitions)

Wish it help!

Answer 5

I feel your pain..

We had similar issues of running out of memory with Spark on YARN. We have five 64GB, 16 core VMs and regardless of what we set spark.yarn.executor.memoryOverhead to, we just couldn't get enough memory for these tasks -- they would eventually die no matter how much memory we would give them. And this as a relatively straight-forward Spark application that was causing this to happen.

We figured out that the physical memory usage was quite low on the VMs but the virtual memory usage was extremely high. We set yarn.nodemanager.vmem-check-enabled in yarn-site.xml to false and our containers were no longer killed, and the application appeared to work as expected.

Doing more research, I found the answer to why this happens here: https://www.mapr.com/blog/best-practices-yarn-resource-management

Since on Centos/RHEL 6 there are aggressive allocation of virtual memory due to OS behavior, you should disable virtual memory checker or increase yarn.nodemanager.vmem-pmem-ratio to a relatively larger value.

That page had a link to a very useful page from IBM: https://www.ibm.com/developerworks/community/blogs/kevgrig/entry/linux_glibc_2_10_rhel_6_malloc_may_show_excessive_virtual_memory_usage?lang=en

In summary, glibc > 2.10 changed its memory allocation. And although huge amounts of virtual memory being allocated isn't the end of the world, it doesn't work with the default settings of YARN.

Instead of setting yarn.nodemanager.vmem-check-enabled to false, you could also play with setting the MALLOC_ARENA_MAX environment variable to a low number in hadoop-env.sh.

I recommend reading through both pages -- the information is very handy.