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[2.25.0] - Unreleased

Highlights

  • Splittable DoFn is opt-out for Java based runners (Direct, Flink, Jet, Samza, Twister2) using --experiments=use_deprecated_read. For all other runners, users can opt-in using --experiments=use_sdf_read. (Java) (BEAM-10670)
  • New highly anticipated feature X added to Python SDK (BEAM-X).
  • New highly anticipated feature Y added to Java SDK (BEAM-Y).

I/Os

  • Added cross-language support to Java's KinesisIO, now available in the Python module apache_beam.io.kinesis (BEAM-10138, BEAM-10137).
  • Update Snowflake JDBC dependency for SnowflakeIO (BEAM-10864)
  • Added cross-language support to Java's SnowflakeIO.Write, now available in the Python module apache_beam.io.snowflake (BEAM-9898).
  • Added delete function to Java's ElasticsearchIO#Write. Now, Java's ElasticsearchIO can be used to selectively delete documents using withIsDeleteFn function (BEAM-5757).

New Features / Improvements

  • Support for repeatable fields in JSON decoder for ReadFromBigQuery added. (Python) (BEAM-10524)
  • Added an opt-in, performance-driven runtime type checking system for the Python SDK (BEAM-10549). More details will be in an upcoming blog post.
  • Added support for Python 3 type annotations on PTransforms using typed PCollections (BEAM-10258). More details will be in an upcoming blog post.
  • Improved the Interactive Beam API where recording streaming jobs now start a long running background recording job. Running ib.show() or ib.collect() samples from the recording (BEAM-10603).
  • In Interactive Beam, ib.show() and ib.collect() now have "n" and "duration" as parameters. These mean read only up to "n" elements and up to "duration" seconds of data read from the recording (BEAM-10603).
  • Initial preview of Dataframes support. See also example at apache_beam/examples/wordcount_dataframe.py
  • Fixed support for type hints on @ptransform_fn decorators in the Python SDK. (BEAM-4091) This has not enabled by default to preserve backwards compatibility; use the --type_check_additional=ptransform_fn flag to enable. It may be enabled by default in future versions of Beam.
  • X feature added (Java/Python) (BEAM-X).

Breaking Changes

  • Python 2 and Python 3.5 support dropped (BEAM-10644, BEAM-9372).
  • Pandas 1.x allowed. Older version of Pandas may still be used, but may not be as well tested.

Deprecations

  • Python transform ReadFromSnowflake has been moved from apache_beam.io.external.snowflake to apache_beam.io.snowflake. The previous path will be removed in the future versions.
  • X behavior is deprecated and will be removed in X versions (BEAM-X).

Known Issues

  • Fixed X (Java/Python) (BEAM-X).

[2.24.0] - 2020-09-18

Highlights

  • Apache Beam 2.24.0 is the last release with Python 2 and Python 3.5 support.

I/Os

  • New overloads for BigtableIO.Read.withKeyRange() and BigtableIO.Read.withRowFilter() methods that take ValueProvider as a parameter (Java) (BEAM-10283).
  • The WriteToBigQuery transform (Python) in Dataflow Batch no longer relies on BigQuerySink by default. It relies on a new, fully-featured transform based on file loads into BigQuery. To revert the behavior to the old implementation, you may use --experiments=use_legacy_bq_sink.
  • Add cross-language support to Java's JdbcIO, now available in the Python module apache_beam.io.jdbc (BEAM-10135, BEAM-10136).
  • Add support of AWS SDK v2 for KinesisIO.Read (Java) (BEAM-9702).
  • Add streaming support to SnowflakeIO in Java SDK (BEAM-9896)
  • Support reading and writing to Google Healthcare DICOM APIs in Python SDK (BEAM-10601)
  • Add dispositions for SnowflakeIO.write (BEAM-10343)
  • Add cross-language support to SnowflakeIO.Read now available in the Python module apache_beam.io.external.snowflake (BEAM-9897).

New Features / Improvements

  • Shared library for simplifying management of large shared objects added to Python SDK. Example use case is sharing a large TF model object across threads (BEAM-10417).
  • Dataflow streaming timers are not strictly time ordered when set earlier mid-bundle (BEAM-8543).
  • OnTimerContext should not create a new one when processing each element/timer in FnApiDoFnRunner (BEAM-9839)
  • Key should be available in @OnTimer methods (Spark Runner) (BEAM-9850)

Breaking Changes

  • WriteToBigQuery transforms now require a GCS location to be provided through either custom_gcs_temp_location in the constructor of WriteToBigQuery or the fallback option --temp_location, or pass method="STREAMING_INSERTS" to WriteToBigQuery (BEAM-6928).
  • Python SDK now understands typing.FrozenSet type hints, which are not interchangeable with typing.Set. You may need to update your pipelines if type checking fails. (BEAM-10197)

[2.23.0] - 2020-06-29

Highlights

I/Os

  • Support for reading from Snowflake added (Java) (BEAM-9722).
  • Support for writing to Splunk added (Java) (BEAM-8596).
  • Support for assume role added (Java) (BEAM-10335).
  • A new transform to read from BigQuery has been added: apache_beam.io.gcp.bigquery.ReadFromBigQuery. This transform is experimental. It reads data from BigQuery by exporting data to Avro files, and reading those files. It also supports reading data by exporting to JSON files. This has small differences in behavior for Time and Date-related fields. See Pydoc for more information.

New Features / Improvements

  • Update Snowflake JDBC dependency and add application=beam to connection URL (BEAM-10383).

Breaking Changes

  • RowJson.RowJsonDeserializer, JsonToRow, and PubsubJsonTableProvider now accept "implicit nulls" by default when deserializing JSON (Java) (BEAM-10220). Previously nulls could only be represented with explicit null values, as in {"foo": "bar", "baz": null}, whereas an implicit null like {"foo": "bar"} would raise an exception. Now both JSON strings will yield the same result by default. This behavior can be overridden with RowJson.RowJsonDeserializer#withNullBehavior.
  • Fixed a bug in GroupIntoBatches experimental transform in Python to actually group batches by key. This changes the output type for this transform (BEAM-6696).

Deprecations

  • Remove Gearpump runner. (BEAM-9999)
  • Remove Apex runner. (BEAM-9999)
  • RedisIO.readAll() is deprecated and will be removed in 2 versions, users must use RedisIO.readKeyPatterns() as a replacement (BEAM-9747).

Known Issues

  • Fixed X (Java/Python) (BEAM-X).

[2.22.0] - 2020-06-08

Highlights

I/Os

  • Basic Kafka read/write support for DataflowRunner (Python) (BEAM-8019).
  • Sources and sinks for Google Healthcare APIs (Java)(BEAM-9468).
  • Support for writing to Snowflake added (Java) (BEAM-9894).

New Features / Improvements

  • --workerCacheMB flag is supported in Dataflow streaming pipeline (BEAM-9964)
  • --direct_num_workers=0 is supported for FnApi runner. It will set the number of threads/subprocesses to number of cores of the machine executing the pipeline (BEAM-9443).
  • Python SDK now has experimental support for SqlTransform (BEAM-8603).
  • Add OnWindowExpiration method to Stateful DoFn (BEAM-1589).
  • Added PTransforms for Google Cloud DLP (Data Loss Prevention) services integration (BEAM-9723):
    • Inspection of data,
    • Deidentification of data,
    • Reidentification of data.
  • Add a more complete I/O support matrix in the documentation site (BEAM-9916).
  • Upgrade Sphinx to 3.0.3 for building PyDoc.
  • Added a PTransform for image annotation using Google Cloud AI image processing service (BEAM-9646)
  • Dataflow streaming timers are not strictly time ordered when set earlier mid-bundle (BEAM-8543).

Breaking Changes

  • The Python SDK now requires --job_endpoint to be set when using --runner=PortableRunner (BEAM-9860). Users seeking the old default behavior should set --runner=FlinkRunner instead.

Deprecations

Known Issues

[2.21.0] - 2020-05-27

Highlights

I/Os

  • Python: Deprecated module apache_beam.io.gcp.datastore.v1 has been removed as the client it uses is out of date and does not support Python 3 (BEAM-9529). Please migrate your code to use apache_beam.io.gcp.datastore.v1new. See the updated datastore_wordcount for example usage.
  • Python SDK: Added integration tests and updated batch write functionality for Google Cloud Spanner transform (BEAM-8949).

New Features / Improvements

  • Python SDK will now use Python 3 type annotations as pipeline type hints. (#10717)

    If you suspect that this feature is causing your pipeline to fail, calling apache_beam.typehints.disable_type_annotations() before pipeline creation will disable is completely, and decorating specific functions (such as process()) with @apache_beam.typehints.no_annotations will disable it for that function.

    More details will be in Ensuring Python Type Safety and an upcoming blog post.

  • Java SDK: Introducing the concept of options in Beam Schema’s. These options add extra context to fields and schemas. This replaces the current Beam metadata that is present in a FieldType only, options are available in fields and row schemas. Schema options are fully typed and can contain complex rows. Remark: Schema aware is still experimental. (BEAM-9035)

  • Java SDK: The protobuf extension is fully schema aware and also includes protobuf option conversion to beam schema options. Remark: Schema aware is still experimental. (BEAM-9044)

  • Added ability to write to BigQuery via Avro file loads (Python) (BEAM-8841)

    By default, file loads will be done using JSON, but it is possible to specify the temp_file_format parameter to perform file exports with AVRO. AVRO-based file loads work by exporting Python types into Avro types, so to switch to Avro-based loads, you will need to change your data types from Json-compatible types (string-type dates and timestamp, long numeric values as strings) into Python native types that are written to Avro (Python's date, datetime types, decimal, etc). For more information see https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-avro#avro_conversions.

  • Added integration of Java SDK with Google Cloud AI VideoIntelligence service (BEAM-9147)

  • Added integration of Java SDK with Google Cloud AI natural language processing API (BEAM-9634)

  • docker-pull-licenses tag was introduced. Licenses/notices of third party dependencies will be added to the docker images when docker-pull-licenses was set. The files are added to /opt/apache/beam/third_party_licenses/. By default, no licenses/notices are added to the docker images. (BEAM-9136)

Breaking Changes

  • Dataflow runner now requires the --region option to be set, unless a default value is set in the environment (BEAM-9199). See here for more details.
  • HBaseIO.ReadAll now requires a PCollection of HBaseIO.Read objects instead of HBaseQuery objects (BEAM-9279).
  • ProcessContext.updateWatermark has been removed in favor of using a WatermarkEstimator (BEAM-9430).
  • Coder inference for PCollection of Row objects has been disabled (BEAM-9569).
  • Go SDK docker images are no longer released until further notice.

Deprecations

  • Java SDK: Beam Schema FieldType.getMetadata is now deprecated and is replaced by the Beam Schema Options, it will be removed in version 2.23.0. (BEAM-9704)
  • The --zone option in the Dataflow runner is now deprecated. Please use --worker_zone instead. (BEAM-9716)

Known Issues

[2.20.0] - 2020-04-15

Highlights

I/Os

  • Java SDK: Adds support for Thrift encoded data via ThriftIO. (BEAM-8561)
  • Java SDK: KafkaIO supports schema resolution using Confluent Schema Registry. (BEAM-7310)
  • Java SDK: Add Google Cloud Healthcare IO connectors: HL7v2IO and FhirIO (BEAM-9468)
  • Python SDK: Support for Google Cloud Spanner. This is an experimental module for reading and writing data from Google Cloud Spanner (BEAM-7246).
  • Python SDK: Adds support for standard HDFS URLs (with server name). (#10223).

New Features / Improvements

  • New AnnotateVideo & AnnotateVideoWithContext PTransform's that integrates GCP Video Intelligence functionality. (Python) (BEAM-9146)
  • New AnnotateImage & AnnotateImageWithContext PTransform's for element-wise & batch image annotation using Google Cloud Vision API. (Python) (BEAM-9247)
  • Added a PTransform for inspection and deidentification of text using Google Cloud DLP. (Python) (BEAM-9258)
  • New AnnotateText PTransform that integrates Google Cloud Natural Language functionality (Python) (BEAM-9248)
  • ReadFromBigQuery now supports value providers for the query string (Python) (BEAM-9305)
  • Direct runner for FnApi supports further parallelism (Python) (BEAM-9228)
  • Support for @RequiresTimeSortedInput in Flink and Spark (Java) (BEAM-8550)

Breaking Changes

  • ReadFromPubSub(topic=) in Python previously created a subscription under the same project as the topic. Now it will create the subscription under the project specified in pipeline_options. If the project is not specified in pipeline_options, then it will create the subscription under the same project as the topic. (BEAM-3453).
  • SpannerAccessor in Java is now package-private to reduce API surface. SpannerConfig.connectToSpanner has been moved to SpannerAccessor.create. (BEAM-9310).
  • ParquetIO hadoop dependency should be now provided by the users (BEAM-8616).
  • Docker images will be deployed to apache/beam repositories from 2.20. They used to be deployed to apachebeam repository. (BEAM-9063)
  • PCollections now have tags inferred from the result type (e.g. the keys of a dict or index of a tuple). Users may expect the old implementation which gave PCollection output ids a monotonically increasing id. To go back to the old implementation, use the force_generated_pcollection_output_ids experiment.

Deprecations

Bugfixes

  • Fixed numpy operators in ApproximateQuantiles (Python) (BEAM-9579).
  • Fixed exception when running in IPython notebook (Python) (BEAM-X9277).
  • Fixed Flink uberjar job termination bug. (BEAM-9225)
  • Fixed SyntaxError in process worker startup (BEAM-9503)
  • Key should be available in @OnTimer methods (Java) (BEAM-1819).

Known Issues

[2.19.0] - 2020-01-31

  • For versions 2.19.0 and older release notes are available on Apache Beam Blog.