Memory usage in Spark largely falls under one of two categories: execution and storage. Execution memory refers to that used for computation in shuffles, joins, sorts and aggregations, while storage memory refers to that used for caching and propagating internal data across the cluster.

Table of Contents

What is Spark memory?

This memory pool is managed by Spark. This is responsible for storing intermediate state while doing task execution like joins or to store the broadcast variables. All the cached/persisted data will be stored in this segment, specifically in the storage memory of this segment.

How is memory Spark calculated?

  1. spark.executor.cores. Tiny Approach – Allocating one executor per core. …
  2. spark.excutor.cores = 5. spark.executor.instances.
  3. =15/5 = 3.
  4. = 27-1 = 26.
  5. spark.executor.memory.
  6. = 63/3 = 21.
  7. spark.executor.memory = 21 * 0.90 = 19GB.
  8. spark.yarn.executor.memoryOverhead = 21 * 0.10 = 2GB.

How is Spark executor memory divided?

In each executor, Spark allocates a minimum of 384 MB for the memory overhead and the rest is allocated for the actual workload. The formula for calculating the memory overhead — max(Executor Memory * 0.1, 384 MB).

How will you do memory tuning in Spark?

  1. Avoid the nested structure with lots of small objects and pointers.
  2. Instead of using strings for keys, use numeric IDs or enumerated objects.
  3. If the RAM size is less than 32 GB, set JVM flag to –xx:+UseCompressedOops to make a pointer to four bytes instead of eight.

What is the difference between driver memory and executor memory in Spark?

Executors are worker nodes’ processes in charge of running individual tasks in a given Spark job and The spark driver is the program that declares the transformations and actions on RDDs of data and submits such requests to the master.

How does Spark catalyst Optimizer work?

The Spark SQL Catalyst Optimizer improves developer productivity and the performance of their written queries. Catalyst automatically transforms relational queries to execute them more efficiently using techniques such as filtering, indexes and ensuring that data source joins are performed in the most efficient order.

👉 For more insights, check out this resource.

How do I set executor memory in Spark submit?

  1. setting it in the properties file (default is $SPARK_HOME/conf/spark-defaults.conf ), spark.driver.memory 5g.
  2. or by supplying configuration setting at runtime $ ./bin/spark-shell –driver-memory 5g.

How do you calculate the number of executors in Spark?

Number of available executors = (total cores/num-cores-per-executor) = 150/5 = 30.

How is Spark driver memory determined?

Determine the memory resources available for the Spark application. Multiply the cluster RAM size by the YARN utilization percentage. Provides 5 GB RAM for available drivers and 50 GB RAM available for worker nodes. Discount 1 core per worker node to determine the executor core instances.

👉 Discover more in this in-depth guide.

Article first time published on

What happens if a Spark executor fails?

If an executor runs into memory issues, it will fail the task and restart where the last task left off. If that task fails after 3 retries (4 attempts total by default) then that Stage will fail and cause the Spark job as a whole to fail.

How do I set executor cores in Spark?

Every Spark executor in an application has the same fixed number of cores and same fixed heap size. The number of cores can be specified with the –executor-cores flag when invoking spark-submit, spark-shell, and pyspark from the command line, or by setting the spark. executor. cores property in the spark-defaults.

How do I get better performance with Spark?

  1. Use DataFrame/Dataset over RDD.
  2. Use coalesce() over repartition()
  3. Use mapPartitions() over map()
  4. Use Serialized data format’s.
  5. Avoid UDF’s (User Defined Functions)
  6. Caching data in memory.
  7. Reduce expensive Shuffle operations.
  8. Disable DEBUG & INFO Logging.

How can I improve my Databrick performance?

  1. Optimize performance with file management. Compaction (bin-packing) Data skipping. Z-Ordering (multi-dimensional clustering) Tune file size. Notebooks. …
  2. Auto Optimize.
  3. Optimize performance with caching.
  4. Dynamic file pruning.
  5. Isolation levels.
  6. Bloom filter indexes.
  7. Low Shuffle Merge.
  8. Optimize join performance.

How can I make my spark go faster?

To accomplish ideal performance in Sort Merge Join: Make sure the partitions have been co-located. Otherwise, there will be shuffle operations to co-locate the data as it has a pre-requirement that all rows having the same value for the join key should be stored in the same partition.

What is data skewness in spark?

Usually, in Apache Spark, data skewness is caused by transformations that change data partitioning like join, groupBy, and orderBy. For example, joining on a key that is not evenly distributed across the cluster, causing some partitions to be very large and not allowing Spark to process data in parallel.

What are AST in spark?

AstBuilder converts SQL statements into Spark SQL’s relational entities (i.e. data types, Catalyst expressions, logical plans or TableIdentifiers ) using visit callback methods. AstBuilder is the AST builder of AbstractSqlParser (i.e. the base SQL parsing infrastructure in Spark SQL).

What is logical plan in spark?

What is Spark Logical Plan? Logical Plan refers to an abstract of all transformation steps that need to be executed. However, it does not provide details about the Driver(Master Node) or Executor (Workder Node). The SparkContext is responsible for generating and storing it.

When should I increase Spark driver memory?

Managing memory resources The – -driver-memory flag controls the amount of memory to allocate for a driver, which is 1GB by default and should be increased in case you call a collect() or take(N) action on a large RDD inside your application.

How much memory does a Spark driver need?

ComponentDefault sizeSuggested initial sizeSpark worker1 GB1 GBSpark driver1 GB2 GBSpark executor1 GB2 GBTotal4 GB6 GB

How does Spark executor work?

Executors are worker nodes’ processes in charge of running individual tasks in a given Spark job. They are launched at the beginning of a Spark application and typically run for the entire lifetime of an application. Once they have run the task they send the results to the driver.

What is the default Spark executor memory?

Spark propertyDefault valuespark.driver.memory1 GBspark.driver.maxResultSize1 GBspark.executor.memory1 GBspark.memory.fraction0.6

What is lazy evaluation in Spark?

Lazy evaluation means that if you tell Spark to operate on a set of data, it listens to what you ask it to do, writes down some shorthand for it so it doesn’t forget, and then does absolutely nothing. It will continue to do nothing, until you ask it for the final answer.

What is off-heap memory in Spark?

Off-heap refers to objects (serialised to byte array) that are managed by the operating system but stored outside the process heap in native memory (therefore, they are not processed by the garbage collector).

What happens when we submit a Spark job?

What happens when a Spark Job is submitted? When a client submits a spark user application code, the driver implicitly converts the code containing transformations and actions into a logical directed acyclic graph (DAG).

What happens when we do Spark submit?

Once you do a Spark submit, a driver program is launched and this requests for resources to the cluster manager and at the same time the main program of the user function of the user processing program is initiated by the driver program.

Why Spark executors are dead?

An executor is considered as dead if, at the time of checking, its last heartbeat message is older than the timeout value specified in spark.network. timeout entry. On removal, the driver informs task scheduler about executor lost. Later the scheduler handles the lost of tasks executing on the executor.

How is fault tolerance achieved in Spark?

To achieve fault tolerance for all the generated RDDs, the achieved data replicates among multiple Spark executors in worker nodes in the cluster. … Data received but buffered for replication – The data is not replicated thus the only way to recover fault is by retrieving it again from the source.

What are the challenges you faced in Spark?

Job-Level ChallengesCluster-Level Challenges1. Executor and core allocation6. Resource allocation2. Memory allocation7. Observability3. Data skew / small files8. Data partitioning vs. SQL queries / inefficiency4. Pipeline optimization9. Use of auto-scaling

What is Spark master?

Spark Master (often written standalone Master) is the resource manager for the Spark Standalone cluster to allocate the resources (CPU, Memory, Disk etc…) among the Spark applications. The resources are used to run the Spark Driver and Executors.

How do you stop shuffle read and write in spark?

  1. Tune the spark. sql. shuffle. partitions .
  2. Partition the input dataset appropriately so each task size is not too big.
  3. Use the Spark UI to study the plan to look for opportunity to reduce the shuffle as much as possible.
  4. Formula recommendation for spark. sql. shuffle. partitions :