How can I configure an AWS Glue ETL job to output larger files?

3 minuto de leitura

I want to configure an AWS Glue ETL job to output a small number of large files instead of a large number of small files.


Use any of the following methods to reduce the number of output files for an AWS Glue ETL job.

Increase the value of the groupSize parameter

Grouping is automatically enabled when you use dynamic frames and when the Amazon Simple Storage Service (Amazon S3) dataset has more than 50,000 files. Increase this value to create fewer, larger output files. For more information, see Reading input files in larger groups.

In the following example, groupSize is set to 10485760 bytes (10 MB):

dyf = glueContext.create_dynamic_frame_from_options("s3", {'paths': ["s3://awsexamplebucket/"], 'groupFiles': 'inPartition', 'groupSize': '10485760'}, format="json")

Note: The groupSize and groupFiles parameters are supported only in the following data formats: csv, ion, grokLog, json, and xml. This option is not supported for avro, parquet, and orc.

Use coalesce(N) or repartition(N)

1.    (Optional) Calculate your target number of partitions (N) based on the input data set size. Use the following formula:

targetNumPartitions = 1 Gb * 1000 Mb/10 Mb = 100

Note: In this example, the input size is 1 GB, and the target output is 10 MB. This calculation allows you to control the size of your output file .

2.    Check the current number of partitions using the following code:

currentNumPartitions = dynamic_frame.getNumPartitions()

Note: When you repartition, targetNumPartitions should be smaller than currentNumPartitions.

  1.    Use an Apache Spark coalesce() operation to reduce the number of Spark output partitions before writing to Amazon S3. This reduces the number of output files. For example:

Keep in mind:

  • coalesce() performs Spark data shuffles, which can significantly increase the job run time.
  • If you specify a small number of partitions, then the job might fail. For example, if you run coalesce(1), Spark tries to put all data into a single partition. This can lead to disk space issues.
  • You can also use repartition() to decrease the number of partitions. However, repartition() reshuffles all data. The coalesce() operation uses existing partitions to minimize the number of data shuffles. For more information on using repartition(), see Spark Repartition on the eduCBA website.

Use maxRecordsPerFile

Use the Spark write() method to control the maximum record count per file. The following example sets the maximum record count to 20:

df.write.option("compression", "gzip").option("maxRecordsPerFile",20).json(s3_path)

Note: The maxRecordsPerFile option acts only as an upper limit for the record count per file. The record count of each file will be less than or equal to the number specified. If the value is zero or negative, then there is no limit.

Related information

Fix the processing of multiple files using grouping

AWS OFICIALAtualizada há um ano