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[Solved] Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)

Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB) in python. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

How Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB) Error Occurs?

Today I get the following error Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB) in python.

How To Solve Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB) Error ?

  1. How To Solve Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB) Error ?

    To Solve Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB) Error It seems like the problem is the amount of data you are trying to pull back to to your driver is too large.

  2. Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)

    To Solve Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB) Error It seems like the problem is the amount of data you are trying to pull back to to your driver is too large.

Solution 1

It seems like the problem is the amount of data you are trying to pull back to to your driver is too large. Most likely you are using the collect method to retrieve all values from a DataFrame/RDD. The driver is a single process and by collecting a DataFrame you are pulling all of that data you had distributed across the cluster back to one node. This defeats the purpose of distributing it! It only makes sense to do this after you have reduced the data down to a manageable amount.

You have two options:

  1. If you really need to work with all that data, then you should keep it out on the executors. Use HDFS and Parquet to save the data in a distributed manner and use Spark methods to work with the data on the cluster instead of trying to collect it all back to one place.
  2. If you really need to get the data back to the driver, you should examine whether you really need ALL of the data or not. If you only need summary statistics then compute that out on the executors before calling collect. Or if you only need the top 100 results, then only collect the top 100.

Update:

There is another reason you can run into this error that is less obvious. Spark will try to send data back the driver beyond just when you explicitly call collect. It will also send back accumulator results for each task if you are using accumulators, data for broadcast joins, and some small status data about each task. If you have LOTS of partitions (20k+ in my experience) you can sometimes see this error. This is a known issue with some improvements made, and more in the works.

The options for getting past if if this is your issue are:

  1. Increase spark.driver.maxResultSize or set it to 0 for unlimited
  2. If broadcast joins are the culprit, you can reduce spark.sql.autoBroadcastJoinThreshold to limit the size of broadcast join data
  3. Reduce the number of partitions

Solution 2

Cause: caused by actions like RDD’s collect() that send big chunk of data to the driver

Solution: set by SparkConf: conf.set("spark.driver.maxResultSize", "4g") OR set by spark-defaults.conf: spark.driver.maxResultSize 4g OR set when calling spark-submit: --conf spark.driver.maxResultSize=4g

Summery

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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