prioritizing partitions / task execution in spark
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I have a spark job with skewed data. The data needs to be partitioned based on a column. I would like to tell spark to start processing the biggest partitions first so that I get to use the available resources more efficiently.
The reasoning would go as follows: I have 10000 partitions in total of which 9999 partitions take just 1 minute to process and 1 partition that takes 10 minutes to process. If I get the heavy partition first I can do the job in 11 minutes if I get it as last it would take 18 minutes.
Is there a way to prioritize partitions? Does this make sense to you?
I sketched the two scenarios on a spreadsheet
Your reasoning is correct afa: if the big task were started immediately then your overall job will finish ealier. But it's also true that you can not control the ordering (/prioritization) of the tasks - since the spark task scheduler does not provide an interface to define that ordering.
prioritizing partitions / task execution in spark, I have a spark job with skewed data. The data needs to be partitioned based on a column. I would like to tell spark to start processing the biggest partitions first How Spark Internally Executes a Program In this article, I will try to explain how Spark works internally and what the components of execution are: jobs, tasks, and stages. by
if your data is skewed i.e majority of your data exists in single partition and your data is not equally divided across all the partitions.In such case you cannt make the best use of spark parallelizm.
I have 10000 partitions in total of which 9999 partitions take just 1 minute to process and 1 partition that takes 10 minutes to process. If I get the heavy partition first I can do the job in 11 minutes if I get it as last it would take 18 minutes.
It does not work like that. Each partition will be taken up for execution by only one task or thread.So in your case all other tasks get completed and only task will take time to complete due to data skewness.
Way to handle and speed up the process.
Evenly distribute your data
For example if you dont have any partitioning key then add a new column as sequence number%numberOfpartitionsyouwant to your dataframe. For example if you want 10 partitions then
numberOfpartitionsyouwant = 10
so each data will be partitioned between numbers 0 to 9. Then partitionBy the new dataframe using this new column.
Spark partitions taking uneven time to execute with frequent , prioritizing partitions / task execution in spark. I have a spark job with skewed data. The data needs to be partitioned based on a column. I would like to tell spark Spark assigns one task per partition and each worker can process one task at a time. Hash Partitioning vs. Range Partitioning in Apache Spark Apache Spark supports two types of partitioning: hash
Long-running tasks often a result of skewed data. The right solution here is to re-partition your data to ensure even distribution among tasks.
1.Evenly distribute your data using repartition as said by @Chandan 2.There might be encounter network issues while dealing with skewed data where an executor’s heartbeat times out.In such cases, consider increasing your **spark.network.timeout** and **spark.executor.heartbeatInterval**.
Important suggestion look for data locality level. The locality level, as far as I know, indicates which type of access to data has been performed. When a node finishes all its work and its CPU become idle, Spark may decide to start other pending task that require obtaining data from other places. So ideally, all your tasks should be process local as it is associated with lower data access latency.
You can configure the wait time before moving to other locality levels using:
1.Spark official docs on data locality
2.Explanation on data locality refer
Job Scheduling - Spark 2.4.5 Documentation, Apache Spark allows developers to run multiple tasks in parallel across executing Spark application with the different number of partitions is We can associate the spark stage with many other dependent parent stages. However, it can only work on the partitions of a single RDD. Also, with the boundary of a stage in spark marked by shuffle dependencies. Ultimately, submission of Spark stage triggers the execution of a series of dependent parent stages.
Apache Spark Performance Tuning – Degree of Parallelism, Best Practices for Scaling and Optimizing Apache Spark Holden Karau, Since tasks are executed on the child partition, the number of tasks executed The coalesce function prioritizes evenly distributing the data across the child partitions. Sparkling: Identification of Task Skew and Speculative Partition of Data for Spark Applications Download Slides Apache Spark demonstrated its advantages over Hadoop’s MapReduce computation engine, in terms of both the runtime performance and the broader range of computation workloads that it can handle.
High Performance Spark: Best Practices for Scaling and Optimizing , In hadoop MapReduce, the tuples generated in Map and Reduce task are  paper discusses PrIter, which is the prioritized execution of iterative When nodes fail, Spark shows recovery strategy by rebuilding only the lost RDD partitions. Spark splits data into partitions and executes computations on the partitions in parallel. You should understand how data is partitioned and when you need to manually adjust the partitioning to
Proceedings of the International Conference on Information , This is the class that actually takes “tasks” from a Spark application and sends of as a series of functions applied to a single Spark partition of data. Inside the TaskSet tasks are sorted on a priority order based on 4 But what if we have NODE-LOCAL tasks for a specific node and run out of execution so when rdd3 is (lazily) computed, spark will generate a task per partition of rdd1 and each task will execute both the filter and the map per line to result in rdd3. The number of tasks is determined by the number of partitions. Every RDD has a defined number of partitions. For a source RDD that is read from HDFS ( using sc.textFile(
- Thing is I need all the data with a given value for the partitioning column on a single executor. So I can partition more but data will stay skewed. I want just the tasks associated with bigger partition to be allocated first.
- how will it help you if it will be allocated first. In spark each task will be assigned to only one partition. If you use one executor with 4 cores then 4 threads will take 4 partitions. One partition won’t be allocated to multiple threads or tasks
- Well with 4 executors that won't matter much, with a thousand though...