Here's a our scenario:
import sys from pyspark import SparkContext from pyspark.sql import SQLContext if __name__ == "__main__": sc = SparkContext() sqlContext = SQLContext( sc ) df_input = sqlContext.read.format( "com.databricks.spark.avro" ).load( "hdfs://nameservice1/path/to/our/data" ) df_filtered = df_input.where( "someattribute in ('filtervalue1', 'filtervalue2')" ) cnt = df_filtered.count() print( "Record count: %i" % cnt )
Submit the code:
spark-submit --master yarn --num-executors 50 --executor-memory 2G --driver-memory 50G --driver-cores 10 filter_large_data.py
Many thanks in advance,
I was searching for some case studies on related topic and found your question so thought I should help out. Your problem is one of these below and solution is below as well:-
Common Errors and their Solutions
1. Issue: Your application runs out of heap space on the executors.
– java.lang.OutOfMemoryError: Java heap space on the executors nodes
– java.lang.OutOfMemoryError: GC overhead limit exceeded
Possible Causes and Solutions
An executor might have to deal with partitions requiring more memory than what is assigned. Consider increasing the –executor memory or the executor memory overhead to a suitable value for your application.
Shuffles are expensive operations since they involve disk I/O, data serialization, and network I/O. Avoid shuffles whenever possible. Shuffle operations can result in large partitions where the data is not evenly distributed across them. Also, the shuffle block size is limited to two gigabytes.
Very large partitions may end up with writing blocks greater than two gigabytes. In cases like these, consider increasing the number of partitions in your job. This ensures that there is lesser data per partition.
If the data that you are dealing with has skewed partitions i.e. certain partitions having huge amount of data compared to the rest, then append some hash value to the end of your key. This will lead to better distribution of your data and you can have an additional aggregate step to remove the appended hash and get back all values for that key.
Another interesting point to remember while repartitioning is that Spark highly compresses the data if the number of partitions is greater than 2,000. So, if your job is pretty close to having about 2,000 partitions, repartition the data to 2,001 or more partitions.
While working with RDDs, avoid using groupByKeys. GroupBy keys tend to keep all values for a given key in memory. Keys having a very large value list that cannot be kept in memory will result in OOMs as they aren’t spilled to disk. One solution is to replace groupByKeys with reduceByKeys that does a map side combine and decreases the amount of data that is passed to the reducers.
The groupByKey functionality works better with dataframes and datasets because of the query optimizer where it may switch to using reduceByKey instead or applies a filter before grouping based on what your application does with grouped data.
While joining two datasets where one of them is considerably smaller in size, consider broadcasting the smaller dataset. Set spark.sql.autoBroadcastJoinThreshold to a value equal to or greater than the size of the smaller dataset or you could forcefully broadcast the right dataset by left.join(broadcast(right), columns).
Configure your application to run with more cores per executors. While this still maintains parallelism, it also allows for fewer shuffles than when the application runs with large number of executors and fewer cores.
A lot of time spent on GC is an indication that data didn’t fit into the heap space. One of the first and foremost things to do is to ensure there aren’t any memory leaks in your code (Check for large number of temporary objects created by doing a heap dump).
Allocate sufficient storage memory (increase `spark.memory.storageFraction`) for caching data and only cache them if they are being re-used elsewhere in the job.
Choose a different GC Policy (CMS, ParallelOld GC,G1GC) that is more suitable for your application. G1GC is more preferable as it brings in the benefits from both ParallelOld GC and CMS with higher throughput and lower latency.
2. Issue: Your application runs out of heap space on the driver node.
– java.lang.OutOfMemoryError: Java heap space on the driver node
Possible Causes and Solutions
First and foremost, consider increasing –driver-memory or driver memory overhead if set to a very low value.
When you perform actions on the data that ends up collecting the result on the driver end, for example, applying a collect() on the data is an unsafe operation that can lead to an OOM if the collected size is larger than what the driver can house in its memory. If the goal is to save the data, then instead of bringing back all the data to the driver, it is preferable to let the executors parallely write down each of the resultant partitions into separate files.
When your application has a really large number of tasks (partitions), an OOM on the driver end can occur easily. Every task sends a mapStatus object back to the driver. When there is a shuffle (re-partition, coalesce, reduceByKey, groupByKey, foldByKey, combineByKey, sortByKey, cogroup, join) operation involved, data gets re-distributed across executors and leads to the generation of map and reduce tasks.
Intermediate files are written to disk in the shuffle stage to avoid re-computation. The map status contains location information for the tasks which is sent back to the driver. A large number of tasks would lead to the driver receiving a lot of data and also having to deal with multiple Map output status requests in the reduce phase.
3. Issue: Cluster runs out of disk space.
– java.io.IOException: No space left on device
Possible Causes and Solutions
Check the executors for logs like this `UnsafeExternalSorter: Thread 75 spilling sort data of 141.0 MB to disk (90 times so far)`. This is an indication that the storage data is continuously evicted to disk. Exceptions like this occur when data becomes larger than what is configured to be stored on the node
Ensure that the `spark.memory.fraction` isn’t too low. The default being 0.6 of the heap space, setting it to a higher value will give more memory for both execution and storage data and will cause lesser spills.
Shuffles involve writing data to disk at the end of the shuffle stage. As a general practice avoid spilling to disk unless the cost of computation is much higher than reading from the disk. One such example to avoid shuffles is to broadcast the smaller table while joining or even partition both the datasets with the same hash partitioner so that keys with the same hash from both tables reside in the same partition.
If you are running the job with minimal number of nodes, consider adding more nodes to increase the DFS for the cluster.
By default, the spilled data and intermediate files are written to /tmp. You can mount more disks and specify them in spark.local.dirs to provide more disk space for the cluster.
4. Issue: Application takes too long to complete or is indefinitely stuck and does not show progress.
Possible Causes and Solutions
Long running tasks often referred to as stragglers are mostly a result of skewed data. This means there are a few partitions that have more data than the rest causing tasks that deal with them to run for a longer time. The right solution here is to re-partition your data to ensure even distribution among tasks.
Tasks are scheduled to the executors based on the lowest locality level of data. However, a task that is set to run on a particular executor will be handed over to another executor if it is free. This brings in the possibility of the re-assigned task to read data from over the network and might change the locality level of the data for the task.
Tasks dealing with higher locality levels will face delays due to the increase in network I/O. If you encounter this scenario where most of your stragglers are because of the higher locality levels, consider increasing spark.locality.wait to let the task wait a little longer before it gets reassigned to a free executor.
Apart from the above errors that revolve under what operations you perform on your data and how efficiently you use the memory resources available, one might also encounter network issues where an executor’s heartbeat times out. In such cases, consider increasing your spark.network.timeout and spark.executor.heartbeatInterval.
While avoiding pitfalls is essential, one is always interested in making jobs more performant. Stay tuned for the next post in the series that dives deeper into Spark’s memory configuration, on how to set the right parameters for your job and the best practices one must adopt.