What is Spark Executor

Spark Executor is a process that runs on a worker node in a Spark cluster and is responsible for executing tasks assigned to it by the Spark driver program. In this article, we shall discuss what is Spark Executor, the types of executors, configurations, uses, and the performance of executors.

1. Spark Executor

Executors are the workhorses of a Spark application, as they perform the actual computations on the data.

When a Spark driver program submits a task to a cluster, it is divided into smaller units of work called “tasks”. These tasks are then scheduled to run on available Executors in the cluster. Executors are responsible for executing these tasks in parallel and returning the results back to the driver program.

Each Executor is allocated a certain amount of memory and CPU resources when it is started, and it uses this memory to store data in memory for faster access during computations. Executors also manage the data stored in the cache and disk, and they handle shuffle operations (when data needs to be exchanged between nodes).

By default, Spark creates one Executor per node in the cluster, but you can configure the number of Executors based on your application’s needs. The number of Executors can affect the performance of your Spark application, so it’s important to choose the right number based on the available resources and the nature of the data processing tasks.

2. Types of Spark Executors

In Apache Spark, there are different types of Executors that can be used based on the requirements of the application. These are:

Default Executor: This is the default type of Executor in Spark, and it is used for general-purpose data processing tasks. Each node in the cluster runs one Default Executor by default.
Coarse-Grained Executor: Coarse-Grained Executors are used for tasks that require more memory, and they can be configured to have larger amounts of memory than the Default Executors. They are also used when the application has large datasets that need to be processed.
Fine-Grained Executor: Fine-Grained Executors are used for tasks that require less memory and are used when the application has many small tasks to perform. They are also useful in cases where the data is not evenly distributed across the nodes in the cluster.
External Executors: External Executors are used when the application needs to use external resources for processing. For example, if the application needs to use a GPU for processing, an External Executor can be used to offload the processing to the GPU.

Each type of Executor has its own advantages and disadvantages, and the choice of Executor depends on the requirements of the application. For example, if the application has a large dataset to process, a Coarse-Grained Executor might be more suitable, while if the application has many small tasks, a Fine-Grained Executor might be more appropriate.

3. Configurations of Spark Executors

Apache Spark provides a number of configuration options for Executors that can be used to optimize their performance and resource usage. Here are some of the key configuration options:

Executor Memory: This specifies the amount of memory that is allocated to each Executor. By default, this is set to 1g (1 gigabyte), but it can be increased or decreased based on the requirements of the application. This configuration option can be set using the --executor-memory flag when launching a Spark application.
Executor Cores: This specifies the number of CPU cores that are allocated to each Executor. By default, this is set to 1 core, but it can be increased or decreased based on the requirements of the application. This configuration option can be set using the --executor-cores flag when launching a Spark application.
Number of Executors: This specifies the number of Executors that are launched on each node in the Spark cluster. By default, this is set to 2, but it can be increased or decreased based on the requirements of the application. This configuration option can be set using the --num-executors flag when launching a Spark application.
Executor Garbage Collection (GC): Spark provides two different types of Garbage Collection algorithms for Executors: the default is Concurrent Mark and Sweep (CMS), but the alternative is the Garbage First (G1) collector. Depending on the workload and data size, different GC algorithms might perform better or worse, so it’s worth trying out different settings.
Overhead Memory: This specifies the amount of memory reserved for system processes such as JVM overhead and off-heap buffers. By default, this is set to 10% of the Executor Memory, but it can be increased or decreased based on the requirements of the application. This configuration option can be set using the --executor-memory-overhead flag when launching a Spark application.
Shuffle Memory: This specifies the amount of memory allocated for Spark’s shuffle operations, which are used to exchange data between Executors. By default, this is set to 384 MB, but it can be increased or decreased based on the requirements of the application. This configuration option can be set using the --spark.shuffle.memoryFraction flag when launching a Spark application.

These are just some of the configuration options available for Spark Executors. Optimizing these settings can significantly improve the performance and resource utilization of Spark applications.


spark-submit \
  --class com.example.MySparkApp \
  --master yarn \
  --deploy-mode client \
  --executor-memory 2g \
  --num-executors 4 \
  --executor-cores 2 \
  --conf spark.executor.extraJavaOptions "-Dsome.property=value" \
  --conf spark.dynamicAllocation.enabled=true \
  my-spark-app.jar

4. Performance of Spark Executors

The performance of Spark Executors can have a significant impact on the overall performance of a Spark application. Here are some factors that can affect the performance of Spark Executors:

Memory: Each Executor is allocated a certain amount of memory, and the amount of memory allocated can affect the performance of the Executor. If an Executor runs out of memory, it may need to spill data to disk, which can slow down processing.
CPU: Executors rely on CPU resources to perform computations, and the number of CPU cores allocated to an Executor can affect its performance. If an Executor is allocated fewer CPU cores than it needs, it may become a bottleneck and slow down processing.
Network: Executors need to communicate with each other to exchange data, and the network bandwidth available to the Executors can affect the performance of the application. Slow network connections can cause delays in data transfer, which can slow down processing.
Data Distribution: The way data is distributed across the cluster can affect the performance of the Executors. If data is skewed, meaning that some nodes have more data to process than others, it can cause some Executors to become bottlenecks and slow down processing.
Task Granularity: The granularity of the tasks assigned to Executors can affect their performance. If tasks are too small, there may be too much overhead associated with task scheduling and data transfer, which can slow down processing. Conversely, if tasks are too large, they may take longer to complete, which can also slow down processing.

In order to optimize the performance of Spark Executors, it’s important to balance the resources allocated to each Executor, tune the application to minimize data skew and optimize the task granularity, and choose the appropriate type of Executor based on the specific requirements of the application.

5. Uses of Spark Executors

Spark Executors are the building blocks of Apache Spark, and they play a critical role in processing data in a distributed manner. Here are some common use cases where Spark Executors are used:

Data Processing: Executors are responsible for processing data in parallel on a Spark cluster. They perform tasks such as filtering, aggregating, joining, and transforming data.
Machine Learning: Spark’s Machine Learning library, MLlib, uses Executors to perform machine learning tasks such as training models, making predictions, and evaluating models.
Streaming Data: Spark Streaming uses Executors to process streaming data in real-time. Executors are responsible for processing data in batches and updating the state of the streaming job.
Graph Processing: Spark’s GraphX library uses Executors to perform graph processing tasks such as graph traversal, vertex and edge mapping, and graph algorithms.
Interactive Analytics: Spark SQL, which provides a high-level API for querying structured and semi-structured data, uses Executors to execute SQL queries on large datasets.
Batch Processing: Spark can be used for batch processing tasks such as ETL (Extract, Transform, Load), data warehousing, and report generation. Executors are responsible for processing these tasks in parallel on a Spark cluster.

Overall, Spark Executors are a fundamental component of Apache Spark, and they are used in a wide range of data processing and analysis tasks, from simple data transformations to complex machine learning and graph processing tasks.

6. Conclusion

In conclusion, Spark Executors are an essential component of Apache Spark that enable parallel processing of data in a distributed computing environment. Executors are responsible for executing tasks on Spark worker nodes, processing data in parallel, and providing fault tolerance through data replication.

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