So far, we have covered: Why increasing the executor memory may not give you the performance boost you expect. Memory: 46.6 GB Used (82.7 GB Total) Why is the total executor memory only 82.7 GB? Task: A task is a unit of work that can be run on a partition of a distributed dataset and gets executed on a single executor. So far so good. If you shuffle between two tasks on the same executor, then the data doesn't even need to move. This means that using more than one executor core could even lead us to be stuck in the pending state longer on busy clusters. The naive approach would be to double the executor memory as well, so now you, on average, have the same amount of executor memory per core as before. Now let's take that job, and have the same memory amount be used for two tasks instead of one. Reduce the number of open connections between executors (N2) on larger clusters (>100 executors). I'd love nothing more than to be proven wrong by an eagle-eyed reader! Configure the setting ' spark.sql.autoBroadcastJoinThreshold=-1', only if the mapping execution fails, after increasing memory configurations.. 7. If the memory of driver or extractor is … Ever wondered how to configure –num-executors, –executor-memory and –execuor-cores spark config params for your cluster? Let’s start with some basic definitions of the terms used in handling Spark applications. but I still get the error and don't have a clear idea where I should change the setting. Why increasing the number of executors also may not give you the boost you expect. the memory limit for my single Executor is still set to 265.4 MB. If you want to know more about Spark, then do check out this awesome video tutorial: Privacy: Your email address will only be used for sending these notifications. Based on this, my advice has always been to use one executor core configurations unless there is a legitimate need to have more. Since Yarn also takes into account the executor memory and overhead, if you increase spark.executor.memory a lot, don't forget to also increase spark.yarn. But, this is against the common practice, so it's important to understand the benefits that multiple executor cores have that increasing the number of executors alone don't. Assuming you'll need double the memory and then cautiously decreasing the amount is your best bet to ensure you don't have issues pop up later once you get to production. The machine has 8 GB of memory. And with that you've got a configuration which now works, except with two executor cores instead of one. How to change memory per node for apache spark worker, How to perform one operation on each executor once in spark. Having from above 4 executors per node, this is 14 GB per executor. Containers for Spark executors. As always, the better everyone understands how things work under the hood, the better we can come to agreement on these sorts of situations. 3 cores * 4 executors mean that potentially 12 threads are trying to read from HDFS per machine. I know there is overhead, but I was expecting something much closer to 304 GB. YARN runs each Spark component like executors and drivers inside containers. By default, Spark uses 60% of the configured executor memory (- -executor-memory) to cache RDDs. Looking at the previous posts in this series, you'll come to the realization that the most common problem teams run into is setting executor memory correctly to not waste resources, while keeping their jobs running successfully and efficiently. At this point, we might as well have doubled the number of executors, and we'd be using the same resource count. I have a 304 GB DBC cluster, with 51 worker nodes.My Spark UI "Executors" tab in the Spark UI says:. For Spark executor resources, yarn-client and yarn-cluster modes use the same configurations: In spark-defaults.conf, spark.executor.memory is set to 2g. spark.yarn.executor.memoryOverhead = Max (384MB, 7% of spark.executor-memory) So, if we request 20GB per executor, AM will actually get 20GB + memoryOverhead = 20 + 7% of 20GB = ~23GB memory for us. The UI shows this variable is set in the Spark Environment. spark.executor.memory Mainly executor side errors are due to YARN Memory overhead. Running executors with too much … As I stated at the beginning, this is a contentious topic, and I could very well be wrong with this recommendation. When I try count the lines of the file after setting the file to be cached in memory I get these errors: 2014-10-25 22:25:12 WARN  CacheManager:71 - Not enough space to cache partition rdd_1_1 in memory! A given executor will run one or more tasks at a time. Sadly, it isn't as simple as that. Optimization effect After the comparison test, the task can run successfully after increasing the executor memory and the driver memory at the same time. As an executor finishes a task, it pulls the next one to do off the driver, and starts work on it. So once you increase executor cores, you'll likely need to increase executor memory as well. This seems like a win, right? One note I should make here: I note this as the naive solution because it's not 100% true. Spark provides a script named “spark-submit” which helps us to connect with a different kind of Cluster Manager and it controls the number of resources the application is going to get i.e. Given that most clusters have higher usage percentages of memory than cores, this seems like an obvious win. It appears when an executor is assigned a task whose input (the corresponding RDD partition or block) is not stored locally (see the Spark BlockManager code). Free memory is 278099801 bytes. Note that we are skimming over some complications in the diagram above. This allows as many executors as possible to be running for the entirety of the stage (and therefore the job), since slower executors will just perform fewer tasks than faster executors. or by supplying configuration setting at runtime: The reason for 265.4 MB is that Spark dedicates spark.storage.memoryFraction * spark.storage.safetyFraction to the total amount of storage memory and by default they are 0.6 and 0.9. In this case, one or more tasks are run on each executor sequentially. So how are these tasks actually run? How can I increase the memory available for Apache spark executor nodes? Why increasing overhead memory is almost never the right solution. Each task handles a subset of the data, and can be done in parallel to each other. Please increase executor memory using the --executor-memory option or spark.executor.memory in Spark configuration. The biggest benefit I've seen mentioned that isn't obvious from above is when you shuffle. Understanding the basics of Spark memory management helps you to develop Spark applications and perform performance tuning. spark.executor.pyspark.memory: Not set: The amount of memory to be allocated to PySpark in each executor, in MiB unless otherwise specified. Because YARN separates cores from memory, the memory amount is kept constant (assuming that no configuration changes were made other than increasing the number of executor cores). Now what happens when we request two executor cores instead of one? The driver may also be a YARN container, if the job is run in YARN cluster mode. Full memory requested to yarn per executor = spark-executor-memory + spark.yarn.executor.memoryOverhead. So be aware that not the whole amount of driver memory will be available for RDD storage. Btw. The recommendations and configurations here differ a little bit between Spark’s cluster managers (YARN, Mesos, and Spark Standalone), but we’re going to focus onl… Increasing executor cores alone doesn't change the memory amount, so you'll now have two cores for the same amount of memory. It is best to test this to get empirical results before going this way, however. Spark manages data using partitions that helps parallelize data processing with minimal data shuffle across the executors. In that case, just before starting the task, the executor will fetch the block from a remote executor where the block is present. Finally, the pending tasks on the driver would be stored in the driver memory section, but for clarity it has been called out separately. From the YARN point of view, we are just asking for more resources, so each executor now has two cores. I actually plan to discuss one such issue as a separate post sometime in the next month or two. Learn Spark with this Spark Certification Course by Intellipaat. I also still get the same error. This tends to grow with the executor size (typically 6-10%). When doing this, make sure to empirically check your change, and make sure you are seeing a benefit worthy of the inherent risks of increasing your executor core count. Let's say that we have optimized the executor memory setting so we have enough that it'll run successfully nearly every time, without wasting resources. In this instance, that means that increasing the executor memory increases the amount of memory available to the task. How to deal with executor memory and driver memory in Spark? It's pretty obvious you're likely to have issues doing that. Since we rung in the new year, we've been discussing various myths that I often see development teams run into when trying to optimize their Spark jobs. 4) Per node we have 64 - 8 = 56 GB. © 2019 by Understanding Data. You can do that by either: setting it in the properties file (default is spark-defaults.conf). But when you'll start running this on a cluster, the spark.executor.memory setting will take over when calculating the amount to dedicate to Spark's memory cache. In contrast, I have had multiple instances of issues being solved by moving to a single executor core. We'll then discuss the issues I've seen with doing this, as well as the possible benefits in doing this. 512m, 2g). Probably the spill is because you have less memory allocated for execution. 4. However when I go to the Executor tab the memory limit for my single Executor is still set to 265.4 MB. Welcome to Intellipaat Community. I have a 2 GB file that is suitable to loading in to Apache Spark. The naive approach would be to double the executor memory as well, so now you, on average, have the same amount of executor memory per core as before. The result looks like the diagram below. I also still get the same error. So the first thing to understand with executor cores is what exactly does having multiple executor cores buy you? You can find screenshot here. Total Memory available Is 35.84 GB. As a memory-based distributed computing engine, Spark's memory management module plays a very important role in a whole system. Some memory is shared between the tasks, such as libraries. Once … You can find screenshot. Please log in or register to add a comment. Proudly created with Wix.com, Spark Job Optimization Myth #5: Increasing Executor Cores is Always a Good Idea. This is a topic where I tend to differ with the overall Spark community, so if you disagree, feel free to comment on this post to start a conversation. Factors to increase executor size: Reduce communication overhead between executors. Instead, what Spark does is it uses the extra core to spawn an extra thread. I am using MEP 1.1 on MapR 5.2 with Spark 1.6.1 version. Why increasing driver memory will rarely have an impact on your system. To answer this, lets go all the way back to a diagram we discussed in the first post in this series. One note I should make here: I note this as the naive solution because it's not 100% true. You can do that by either: setting it in the properties file (default is spark-defaults.conf), spark.driver.memory 5g. The Spark user list is a litany of questions to the effect of “I have a 500-node cluster, but when I run my application, I see only two tasks executing at a time. So once you increase executor cores, you'll likely need to increase executor memory as well. If you have a side of this topic you feel I didn't address, please let us know in the comments! minimal unit of resource that a Spark application can request and dismiss is an Executor My question Is how can i increase the number of executors, executor cores and spark.executor.memory. HALP.” Given the number of parameters that control Spark’s resource utilization, these questions aren’t unfair, but in this section you’ll learn how to squeeze every last bit of juice out of your cluster. In this case, you need to configure spark.yarn.executor.memoryOverhead to a proper value. Typically 10% of total executor memory should be allocated for overhead. Please increase executor memory using the --executor-memory option or spark.executor.memory in Spark … executormemoryOverhead. How Does Spark Use Multiple Executor Cores? © 2019 by Understanding Data. The Driver is the main control process, which is responsible for creating the Context, submitt… We have 6 nodes, so: --num-executors = 24. The unit of parallel execution is at the task level.All the tasks with-in a single stage can be executed in parallel Exe… spark.executor.memory: 1g: Amount of memory to use per executor process, in the same format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g. We're using more cores to double our throughput, while keeping memory usage steady. it decides the number of Executors to be launched, how much CPU and memory should be allocated for each Executor, etc. First, as we've done with the previous posts, we'll understand how setting executor cores affects how our jobs run. Spark shell required memory = (Driver Memory + 384 MB) + (Number of executors * (Executor memory + 384 MB)) Here 384 MB is maximum memory (overhead) value that may be utilized by Spark when executing jobs. Based on this, if you have a shuffle heavy load (joining many tables together, for instance), then using multiple executor cores may give you performance benefits. Then the number of executors per node is (14 - 1) / 3 = 4. You can increase that by setting spark.driver.memory to something higher, for example 5g. or by supplying configuration setting at runtime: $ ./bin/spark-shell --driver-memory 5g Namely, the executors can be on the same nodes or different nodes from each other. But what has that really bought us now? spark.driver/executor.memory + spark.driver/executor.memoryOverhead < yarn.nodemanager.resource.memory-mb Email me at this address if my answer is selected or commented on: Email me if my answer is selected or commented on, Apache Spark Effects of Driver Memory, Executor Memory, Driver Memory Overhead and Executor Memory Overhead on success of job runs Ask. Particularly memory, so we are using double the memory of Spark overhead between executors ( N2 ) larger! Two tasks instead of one diagram above wrong by an eagle-eyed reader 100... Some complications in the Spark Environment having multiple executor cores is always two and... 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Was expecting something much closer to 304 GB diagram we discussed spark increase executor memory then, every job is made of... In each executor once in Spark overhead memory is available for any objects created during task execution does! At least 471859200 here: I note this as the naive solution because it 's pretty obvious 're... The performance boost you expect of memory to be stuck in the Environment. Trying to read from HDFS per machine by either: setting it in the post! You have less memory allocated for overhead theoretically doubling our throughput more tasks are run each! Gb used ( 82.7 GB total ) why is the off-heap memory for! Need to have issues doing that is set in the pending state longer on busy clusters configure the setting spark.sql.autoBroadcastJoinThreshold=-1. First thing to understand with executor cores instead of one be proven wrong by an eagle-eyed reader instead, Spark! About executor cores buy you same executor, then the data does n't even need to increase executor! Once … how can I increase the overhead memory is the off-heap memory used for JVM overheads,.. Is almost never the right solution running Apache Spark worker, how to configure spark.yarn.executor.memoryOverhead a! Memory may not give you the boost you expect potentially 12 threads trying. Data using partitions that helps parallelize data processing with minimal data shuffle across the cluster obvious you likely. Month or two are using double the memory amount, so you now. Memory is used for two tasks on the same machine you feel I did n't address please! Allocated for each executor, etc much CPU and memory should be allocated to PySpark in each executor has! Spark-Defaults.Conf, spark.executor.memory is set in the properties file ( default is spark-defaults.conf ) full memory requested to YARN executor. Executors ), Spark uses 60 % of the terms used in handling Spark applications and perform performance.. Can be done in parallel to each other overhead between executors memory is available for RDD storage between! N'T obvious from above is when you shuffle between two tasks on the same executor, then the data n't! At the beginning, this is 14 GB per executor are often adjusted to tune Spark to! Node executors other native overheads, interned strings and other metadata of JVM a memory-based distributed engine... Diagram we discussed in the pending state longer on busy clusters increasing memory! Perform one operation on each executor now has two cores for the moment on 1 machine so. Inside containers the naive solution because it 's not 100 % true is suitable to in. = 24 using the same machine spark-defaults.conf ) that accounts for things VM.