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Best practices to design Spark application

Understand Your Data and Workload#

  1. Data Size: Understand the volume of data your application will process. This will influence the infrastructure needed and the parallelism level in your application.

  2. Data Skewness: Identify if your data is skewed, as this can cause certain tasks to take longer to complete and negatively impact performance. Techniques like key salting can be applied to handle skewness.

  3. Workload Type: Understand the type of operations your application will perform. For example, analytical workloads may benefit from columnar data formats like Parquet.

Application Design#

  1. Transformations: Use transformations like map, filter, reduceByKey over actions as much as possible, as transformations are lazily evaluated and can benefit from Spark's optimization.

  2. Shuffling: Minimize operations that cause data shuffling across the network, as they are expensive. Avoid operations like groupByKey in favor of reduceByKey or aggregateByKey.

  3. Broadcasting: Small datasets that are used across transformations should be broadcasted to improve performance.

  4. Partitioning: Use the right level of partitioning. Too few partitions can lead to fewer concurrent tasks and underutilization of resources. Too many partitions might lead to excessive overhead

Resource Allocation and Configuration#

  1. Memory Allocation: Properly allocate memory to Spark executors, driver, and overhead. Monitor the memory usage of your application to fine-tune these settings.

  2. Dynamic Resource Allocation: Enable dynamic resource allocation if supported by your cluster manager. This allows Spark to adjust the resources based on workload.

  3. Parallelism: Configure the level of parallelism (number of partitions) based on the data volume and infrastructure. Infrastructure Consideration:

  4. Storage: Use fast storage (like SSDs) to store your data, as I/O operations can become a bottleneck in large data processing.

  5. Network: A high-speed network is important, especially if your workload involves data shuffling.

  6. Nodes and Cores: More nodes with multiple cores can increase parallelism and data processing speed.

  7. Data Locality: Aim for data locality, i.e., running tasks on nodes where the data is stored, to reduce network I/O

Monitor and Iterate#

Use Spark's built-in web UI to monitor your applications. Look for stages that take a long time to complete, tasks that fail and are retried, storage and computation bottlenecks, and executor memory usage.