Operations like .collect,.take and takeSample deliver data to the driver and hence, the driver needs enough memory to allocate such data. http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-part-2/ some ML algo that needs to materialize results and broadcast them on the next iteration, then your job becomes dependent of the amount of data passing through the driver. To learn more, see our tips on writing great answers. So I think a few GBs will just be OK for your Driver. For example, if you join two tables through Spark SQL, Spark's CBO may decide to broadcast a smaller table (or a smaller dataframe) across to make join run faster. /bin/spark-submit --class
Any help will be appreciated and would really help with my understanding of Spark. Thanks for contributing an answer to Stack Overflow! How did this note help previous owner of this old film camera?
How to deal with executor memory and driver memory in Spark? And don't go above 5. Making statements based on opinion; back them up with references or personal experience. It seems that just by increasing the memory overhead by a small amount of 1024(1g) it leads to the successful run of the job with driver memory of only 2g and the MEMORY_TOTAL is only 2.524g! Another benefit is that Spark's shared variables (accumulators and broadcast variables) will have just one copy per executor, not per task - so switching to multiple tasks per executor is a direct memory saving right there. Few 100's of MB will do. Is the fact that ZFC implies that 1+1=2 an absolute truth? Is it against the law to sell Bitcoin at a flea market? As you don't know which one, each one of your executors will need to have >> 20Gb. Both the third and fourth case succeeds and I understand that it is because I am giving more memory which solves the memory problems. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA.
Apache Spark: Understanding terminology of Driver and Executor Configuration, Identifying a novel about floating islands, dragons, airships and a mysterious machine. How we can set the memory and CPU resources limits with spark operators? Connect and share knowledge within a single location that is structured and easy to search. Here 384 MB is maximum memory (overhead) value that may be utilized by Spark when executing jobs. When you reduce the partitioning to 1, that single partition will be in one of the executors. If you are familiar with MapReduce, your map tasks & reduce tasks are all executed in Executor(in Spark, they are called ShuffleMapTasks & ResultTasks), and also, whatever RDD you want to cache is also in executor's JVM's heap & disk. The memory you need to assign to the driver depends on the job. If I run the program with the same driver memory but higher executor memory, the job runs longer (about 3-4 minutes) than the first case and then it will encounter a different error from earlier which is a Container requesting/using more memory than allowed and is being killed because of that. How can I use parentheses when there are math parentheses inside? From what I have gathered, this is related to memory not being enough. If the job requires the driver to participate in the computation, like e.g. /bin/spark-submit --class
I will provide some background information and post my questions and describe the cases that I have experienced after them below. So, from the formula, I can see that my job requires MEMORY_TOTAL of around 12.154g to run successfully which explains why I need more than 10g for the driver memory setting. However, I am confused and do not understand completely why it happens and would appreciate if someone can provide me with some guidance and explanation. e.g. : I can't find now reference where it was recommended to go above 1 cores per executor. For simple development, I executed my Python code in standalone cluster mode (8 workers, 20 cores, 45.3 G memory) with spark-submit. If I run my program with any driver memory less than 11g, I will get the error below which is the SparkContext being stopped or a similar error which is a method being called on a stopped SparkContext. I'd advise you to find an alternative solution. = 12.154g. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Spark shell required memory = (Driver Memory + 384 MB) + (Number of executors * (Executor memory + 384 MB)). 512m, 2g). The job will run successfully with this setting (driver memory 2g and executor memory 1g but increasing the driver memory overhead(1g) and the executor memory overhead(1g). Apache Spark: The number of cores vs. the number of executors, Number of workers in SPARK standalone cluster mode, Apache Spark Effects of Driver Memory, Executor Memory, Driver Memory Overhead and Executor Memory Overhead on success of job runs. Start with --executor-cores 2, double --executor-memory (because --executor-cores tells also how many tasks one executor will run concurently), and see what it does for you. = 2.524g. My code recursively filters an RDD to make it smaller (removing examples as part of an algorithm), then does mapToPair and collect to gather the results and save them within a list. What would the ancient Romans have called Hercules' Club? I guess tasks in the same executor may peak its memory consumption at different times, so you don't waste/don't have to overprovision memory just to make it work. = 2g + 0.524g Why dont second unit directors tend to become full-fledged directors? Any setting with driver memory greater than 10g will lead to the job being able to run successfully. However, in the third case, spark.driver.memory + spark.yarn.driver.memoryOverhead = the memory By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. It is the best practice to go above 1. Is moderated livestock grazing an effective countermeasure for desertification? How APIs can take the pain out of legacy system headaches (Ep. Proof that When all the sides of two triangles are congruent, the angles of those triangles must also be congruent (Side-Side-Side Congruence). Announcing the Stacks Editor Beta release! /bin/spark-submit --class
Asking for help, clarification, or responding to other answers. In a Spark Application, Driver is responsible for task scheduling and Executor is responsible for executing the concrete tasks in your job. Find centralized, trusted content and collaborate around the technologies you use most. Is there a political faction in Russia publicly advocating for an immediate ceasefire? Is there a suffix that means "like", or "resembling"? From the Spark documentation, the definition for executor memory is. = 2 + (driverMemory * 0.07, with minimum of 384m) If the job is based purely on transformations and terminates on some distributed output action like rdd.saveAsTextFile, rdd.saveToCassandra, then the memory needs of the driver will be very low. I am doing some memory tuning on my Spark job on YARN and I notice different settings would give different results and affect the outcome of the Spark job run. Is "Occupation Japan" idiomatic? Are the two errors linked in some way? /bin/spark-submit --class
What are workers, executors, cores in Spark Standalone cluster? Why increasing the memory overhead (for both driver and executor) allows my job to complete successfully with a lower MEMORY_TOTAL (12.154g vs 2.524g)? Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. I am confused about dealing with executor memory and driver memory in Spark.
(instead of occupation of Japan, occupied Japan or Occupation-era Japan). If you have an rdd of 3GB in the cluster and call val myresultArray = rdd.collect, then you will need 3GB of memory in the driver to hold that data plus some extra room for the functions mentioned in the first paragraph. What are the "disks" seen on the walls of some NASA space shuttles? apache-sparkhadoopmemorymemory-managementout-of-memory. = 11g + 1.154g We use Spark 1.5 and stopped using --executor-cores 1 quite some time ago as it was giving GC problems; it looks also like a Spark bug, because just giving more memory wasn't helping as much as just switching to having more tasks per container. Even if you don't use Spark shared variables explicitly, Spark very likely creates them internally anyway. that YARN will create a JVM, = 11g + (driverMemory * 0.07, with minimum of 384m) Cannot Get Optimal Solution with 16 nodes of VRP with Time Windows. 465), Design patterns for asynchronous API communication.
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