Apache Spark Partitions – AWS S3
Reading data from AWS S3
When Apache Spark reads data from Amazon S3 (Simple Storage Service), the process of creating partitions is different from reading data from HDFS.
In the case of S3, Spark does not directly align partitions with the concept of HDFS blocks, as there is no block-based storage system like HDFS in S3. Instead, Spark processes data based on the objects stored in S3.
Here’s how Spark creates partitions when reading data from AWS S3
- File-based Partitioning:
- Spark generally creates partitions based on the number of files in the S3 storage. Each file becomes a separate partition, and Spark processes them in parallel.
- S3A Connector and InputSplits:
- Spark uses the S3A connector when reading data from S3. The S3A connector creates InputSplits, which represent portions of the data to be processed. However, in the context of S3, these InputSplits are not based on block sizes but rather on the size of the files in S3.
- Parallelism:
- The level of parallelism in Spark when reading from S3 is influenced by the number of files and their sizes. Spark aims to process files in parallel, and the number of partitions is often determined by the number of files.
- Configurable Parameters:
- Spark provides configuration parameters that allow users to control the partitioning behavior when reading data from S3. For example,
spark.sql.files.maxPartitionBytes and spark.sql.files.openCostInBytes can influence the number of partitions based on the file sizes.
- Coalesce and Repartition:
- Users can use the repartition or coalesce methods on DataFrames after reading the data to explicitly control the number of partitions
It’s important to note that the characteristics of Spark partitioning in S3 are different from those in HDFS. The absence of block-based storage in S3 means that Spark relies on the organization of files in S3 to create partitions.
Users may need to experiment with configurations and consider factors like the number of files, their sizes, and the desired level of parallelism to optimize Spark performance when reading data from S3.
Additional Considerations:
- Small Files: For many small files, Spark might create too many partitions, potentially leading to overhead. Consider techniques like file consolidation or coalescing partitions in those cases.
- Experimentation: The optimal partition size often depends on your specific use case, so experimentation and analysis are often necessary to find the best configuration.