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Managing partitions for ETL output in Amazon Glue

Partitioning is an important technique for organizing datasets so they can be queried efficiently. It organizes data in a hierarchical directory structure based on the distinct values of one or more columns.

For example, you might decide to partition your application logs in Amazon Simple Storage Service (Amazon S3) by date, broken down by year, month, and day. Files that correspond to a single day's worth of data are then placed under a prefix such as s3://my_bucket/logs/year=2018/month=01/day=23/. Systems like Amazon Athena, Amazon Redshift Spectrum, and now Amazon Glue can use these partitions to filter data by partition value without having to read all the underlying data from Amazon S3.

Crawlers not only infer file types and schemas, they also automatically identify the partition structure of your dataset when they populate the Amazon Glue Data Catalog. The resulting partition columns are available for querying in Amazon Glue ETL jobs or query engines like Amazon Athena.

After you crawl a table, you can view the partitions that the crawler created. In the Amazon Glue console, choose Tables in the left navigation pane. Choose the table created by the crawler, and then choose View Partitions.

For Apache Hive-style partitioned paths in key=val style, crawlers automatically populate the column name using the key name. Otherwise, it uses default names like partition_0, partition_1, and so on. You can change the default names on the console. To do so, navigate to the table. Check if indexes exist under the Indexes tab. If that's the case, you need to delete them to proceed (you can recreate them using the new column names afterwards). Then, choose Edit Schema, and modify the names of the partition columns there.

In your ETL scripts, you can then filter on the partition columns. Because the partition information is stored in the Data Catalog, use the from_catalog API calls to include the partition columns in the DynamicFrame. For example, use create_dynamic_frame.from_catalog instead of create_dynamic_frame.from_options.

Partitioning is an optimization technique that reduces data scan. For more information about the process of identifying when this technique is appropriate, consult Reduce the amount of data scan in the Best practices for performance tuning Amazon Glue for Apache Spark jobs guide on Amazon Prescriptive Guidance.

Pre-filtering using pushdown predicates

In many cases, you can use a pushdown predicate to filter on partitions without having to list and read all the files in your dataset. Instead of reading the entire dataset and then filtering in a DynamicFrame, you can apply the filter directly on the partition metadata in the Data Catalog. Then you only list and read what you actually need into a DynamicFrame.

For example, in Python, you could write the following.

glue_context.create_dynamic_frame.from_catalog(
    database = "my_S3_data_set",
    table_name = "catalog_data_table",
    push_down_predicate = my_partition_predicate)

This creates a DynamicFrame that loads only the partitions in the Data Catalog that satisfy the predicate expression. Depending on how small a subset of your data you are loading, this can save a great deal of processing time.

The predicate expression can be any Boolean expression supported by Spark SQL. Anything you could put in a WHERE clause in a Spark SQL query will work. For example, the predicate expression pushDownPredicate = "(year=='2017' and month=='04')" loads only the partitions in the Data Catalog that have both year equal to 2017 and month equal to 04. For more information, see the Apache Spark SQL documentation, and in particular, the Scala SQL functions reference.

Server-side filtering using catalog partition predicates

The push_down_predicate option is applied after listing all the partitions from the catalog and before listing files from Amazon S3 for those partitions. If you have a lot of partitions for a table, catalog partition listing can still incur additional time overhead. To address this overhead, you can use server-side partition pruning with the catalogPartitionPredicate option that uses partition indexes in the Amazon Glue Data Catalog. This makes partition filtering much faster when you have millions of partitions in one table. You can use both push_down_predicate and catalogPartitionPredicate in additional_options together if your catalogPartitionPredicate requires predicate syntax that is not yet supported with the catalog partition indexes.

Python:

dynamic_frame = glueContext.create_dynamic_frame.from_catalog(
    database=dbname, 
    table_name=tablename,
    transformation_ctx="datasource0",
    push_down_predicate="day>=10 and customer_id like '10%'",
    additional_options={"catalogPartitionPredicate":"year='2021' and month='06'"}
)

Scala:

val dynamicFrame = glueContext.getCatalogSource(
    database = dbname,
    tableName = tablename, 
    transformationContext = "datasource0",
    pushDownPredicate="day>=10 and customer_id like '10%'",
    additionalOptions = JsonOptions("""{
        "catalogPartitionPredicate": "year='2021' and month='06'"}""")
    ).getDynamicFrame()

Writing partitions

By default, a DynamicFrame is not partitioned when it is written. All of the output files are written at the top level of the specified output path. Until recently, the only way to write a DynamicFrame into partitions was to convert it to a Spark SQL DataFrame before writing.

However, DynamicFrames now support native partitioning using a sequence of keys, using the partitionKeys option when you create a sink. For example, the following Python code writes out a dataset to Amazon S3 in the Parquet format, into directories partitioned by the type field. From there, you can process these partitions using other systems, such as Amazon Athena.

glue_context.write_dynamic_frame.from_options(
    frame = projectedEvents,
    connection_type = "s3",    
    connection_options = {"path": "$outpath", "partitionKeys": ["type"]},
    format = "parquet")