At the starting of this blog, my expectation was to understand spark configuration based on the amount of data.

Any cluster manager can be used as long as the executor processes are running and they communicate with each other. Spark acquires executors on nodes in cluster.

The number ofexecutors for a spark application can be specified inside the SparkConf or via the flag num-executors from command-line. Any idea how to calculate spark.dynamicAllocation.maxExecutor in case of Dynamic Allocation.

Keep sharing stuffs like this. Number of cores of 5 is same for good concurrency as explained above. Now for the first case, if we think we do not need 19 GB, and just 10 GB is sufficient based on the data size and computations involved, then following are the numbers: Number of executors for each node = 3. The formula for that overhead is max(384, .07 * spark.executor.memory), Calculating that overhead: .07 * 21 (Here 21 is calculated as above 63/3)= 1.47, Since 1.47 GB > 384 MB, the overhead is 1.47, Take the above from each 21 above => 21 1.47 ~ 19 GB, Final numbers Executors 17, Cores 5, Executor Memory 19 GB.

An executor stays up for the So we also need to change number of cores for each executor.

So rounding to 1GB as overhead, we get 10-1 = 9 GB, Final numbers Executors 35, Cores 5, Executor Memory 9 GB.

duration of the Spark Application and runs the tasks in multiple threads.

Now we try to understand, how to configure the best set of values to optimize a spark job.

Tasks are sent by SparkContext to the executors. I mean we have one property to set shuffle partition i.e.

Resource Allocation is an important aspect during the execution of any spark job. 6 cores, 24 GB RAM . Cluster Manager allocates resources across the other applications. . num-executors 20 executor-memory 6g executor-cores 2 queue quenemae_q1 conf spark.yarn.executor.memoryOverhead=2048 \ we have single node cluster 128 GB memory and 32 cores.

For optimal usage:

From the driver code, SparkContext connects to cluster manager (standalone/Mesos/YARN).

Application code (jar/python files/python egg files) is sent to executors.

Really nice informative article.

Partitions :A partition is a small chunk of a large distributed data set. However if dynamic allocation comes into picture, there would be different stages like the following: What is the number for executors to start with: Initial number of executors (spark.dynamicAllocation.initialExecutors) to start with. For instance, an application will add 1 executor in the first round, and then 2, 4, 8 and so on executors in the subsequent rounds.

These limits are for sharing between spark and other applications which run on YARN. Eachapplication has its own executors. Cluster Manager :An external service for acquiring resources on the cluster (e.g. Still 15/5 as calculated above.

I am also educating people on similar Spark Tutorial so if you are interested to know more you can watch this Spark training:-https://www.youtube.com/watch?v=dMDQz82FCqE, Hey It is really above article.

Executor runs tasks and keeps data in memory or disk storage across them.

Its such a wonderful read on Spark tutorial.

Example we have 1 TB.

Pingback: can we have more than one executor per application per node.?

Imitation of Intelligence : Exploring Artificial Intelligence! So how many nodes will required for certain amount of data. Any other than above references?

But if we are processing 20 to 30 GB data ,Is it really require to allocate this much core and memory per executor ? The above scenarios start with accepting number of cores as fixed and moving to the number of executors and memory. So we might think, more concurrent tasks for each executor will give better performance.

Excellent explanation.I really appreciate your explanation on this blog.Expecting one blog from you how to set number of partition for shuffle for best optimization.

Then final number is 36 1(for AM) = 35, 6 executors for each node. Coming to the next step, with 5 as cores per executor, and 15 as total available cores in one node (CPU) we come to3 executors per node which is 15/5.

Overhead is 12*.07=.84. Thanks .. it was very useful info, Could you kindly let me know the data that we can process with just 1 node with config: From the above steps, it is clear that the number of executors and their memory setting play a major role in a spark job.

no of executors 1 yarn-cluster mode A driver runs inside application master process, client goes away once the application is initialized. When to ask new executors or give away current executors: When do we request new executors (spark.dynamicAllocation.schedulerBacklogTimeout) This means that there have been pending tasks for this much duration. But what in case of small cluster with 4 node each with 4 cores and 30GB of RAM. Hi Shalin, the numbers came from the initial hardware setup configuration and the formulae used to calculate the resources. But research shows that any application with more than 5 concurrent tasks, would lead to a bad show.

Very nice article above!! Hey , Very nice explanation .

Hey.. the article is really nice.. but i have a doubt.

Spark manages data using partitions that helps parallelize data processing with minimaldata shuffle across the executors. There are two ways in which we configure the executor and core details to the Spark job.

And available RAM on each node is 63 GB. \ we can increase/decrease.please give us fully clarity. The parallel task numbers etc are derived as per requirement and the references are provided in the blog. So final number is 17 executors, This 17 is the number we give to spark using num-executors while running from spark-submit shell command, From above step, we have 3 executorsper node.

To conclude, if we need more control over the job execution time, monitor the job for unexpected data volume the static numbers would help.

## over head, 0.07X29 = ~ 2GB, so effective available is 27 GB for Executor

If not configured correctly, a spark job can consume entire cluster resources and make other applications starve for resources.

if yes then how they divide the task on that worker node program faq.

However small overhead memory is also needed to determine the full memory request to YARN for each executor.

Can you solve these problem please.

To handle 300 gb data what would be the configuration for executor memory and driver memory. if yes then how they divide the task on that worker node program faq, Impala Load Balancing with Amazon Elastic Load Balancer.

Assumption all nodes has equal configuration.

63/6 ~ 10.

Spark is agnostic to a cluster manager as long as itcan acquire executor processes and those can communicate with each other.We areprimarily interested in Yarn as the cluster manager. This blog helps to understand the basic flow in a Spark Application and then how to configure the number of executors, memory settings of each executors and the number of cores for a Spark Job. This helps the resources to be re-used for other applications. so at that time how much hardware requirement minimum i required?

In a cluster where wehave other applications running and they also need cores to run the tasks, we need to make sure that we assign the cores at cluster level. spark-submit master yarn \ default is spark.sql.shuffle.partiton = 200.what are the optimization way to increase and decrease this number.and on what basis So we can create a spark_user and then give cores (min/max) for that user.

The magic number 5 comes to 3 (any number less than or equal to 5).

(Number of Executor/No of Nodes) X (Executor Memory) < (Node RAM 2 GB), no of cores per executor 3

Task : A task is a unit of work that can be run on a partition of a distributed dataset andgets executed on a single executor.

Because with 6 executors per node and 5 cores it comes down to 30 cores per node, when we only have 16 cores.

Cores : A core is a basic computation unit of CPUand aCPU may have one or more cores to perform tasks at a given time.

Nice article,

executor memory 27.0, ## 3 cores and 29 GB available for JVM on each node

So the request for the number of executors requested in each round increases exponentially from the previous round. This number comes from the ability of an executor to run parallel tasks and not from how many cores a system has. Static Allocation The values are given as part of spark-submit.

So with 3 cores, and 15 available cores we get 5 executors per node, 29 executors ( which is (5*6 -1)) andmemory is 63/5 ~ 12.

Executor :An executor is a single JVM process which is launched for an application on a workernode.

In spark, this controls the number of parallel tasks an executor can run. A single node can run multiple executors andexecutors for an application can span multiple worker nodes.

This would eventually be the number what we give at spark-submit in static way.

ThankYou!!!

The time in which a job has to complete spark.dynamicAllocation.executorIdleTimeout, can we have more than one executor per application per node.? Here each application will get its own executor processes. And at the same time the performance want to show good.

We need to calculate the number of executors on each node and then get the total number for the job.

Hi,

This is mentioned in the document as a factor for deciding the Spark configuration but later in this document does not cover this factor. First on each node, 1 core and 1 GB is needed for Operating System and Hadoop Daemons, so we have 15 cores, 63 GB RAM for each node.

Dynamic Allocation The values are picked up based on the requirement (size of data, amount of computations needed) and released after use.

So the optimal value is 5.

We start with how to choose number of cores: Number of cores = Concurrent tasks an executor can run. 3 cores per executor, so 1 executor per node and 29 gb of Ram per executor

To understand dynamic allocation, we need to have knowledge of the following properties: spark.dynamicAllocation.enabled when this is set to true we need not mention executors.

Number of executors for each node = 32/5 ~ 6, So total executors = 6 * 6 Nodes = 36. Ans: 3 Cores, 4 executors and 27 GB for RAM. Understand Google api.ai and build Artificial Intelligent Assistant, Understanding Resource Allocation configurations for a Spark application, Kafka A great choice for large scale event processing, Installing Apache Zeppelin on a Hadoop Cluster, Installing and Configuring Apache Airflow, Static or dynamic allocation of resources. At a specific point, the above property max comes into picture.