Beam Capability Matrix

Apache Beam provides a portable API layer for building sophisticated data-parallel processing pipelines that may be executed across a diversity of execution engines, or runners. The core concepts of this layer are based upon the Beam Model (formerly referred to as the Dataflow Model), and implemented to varying degrees in each Beam runner. To help clarify the capabilities of individual runners, we’ve created the capability matrix below.

Individual capabilities have been grouped by their corresponding What / Where / When / How question:

For more details on the What / Where / When / How breakdown of concepts, we recommend reading through the Streaming 102 post on O’Reilly Radar.

Note that in the future, we intend to add additional tables beyond the current set, for things like runtime characteristics (e.g. at-least-once vs exactly-once), performance, etc.

How to read the tables
Tools we are comparing
PropertiesDoes this tool have this property?Yes/Partially/No/Unverified
What do those signs mean?
Yes
~
Partially
?
Unverified
No

What is being computed?

Google Cloud DataflowPrism Local RunnerApache FlinkApache Spark (RDD/DStream based)Apache Spark Structured Streaming (Dataset based)Apache NemoHazelcast JetTwister2Python Direct FnRunner
ParDo
~
GroupByKey
~
~
Flatten
~
Combine
~
Composite Transforms
~
~
~
~
~
~
Side Inputs
~
~
Source API
~
Metrics
~
~
~
~
~
~
Stateful Processing
~
~
~
~
SEE DETAILS AND FULL VERSION HERE.

Bounded Splittable DoFn Support Status

Google Cloud DataflowPrism Local RunnerApache FlinkApache Spark (RDD/DStream based)Apache Spark Structured Streaming (Dataset based)Apache NemoHazelcast JetTwister2Python Direct FnRunner
Base
~
~
Side Inputs
~
~
Splittable DoFn Initiated Checkpointing
~
~
Dynamic Splitting
~
Bundle Finalization
~
SEE DETAILS AND FULL VERSION HERE.

Unbounded Splittable DoFn Support Status

Google Cloud DataflowPrism Local RunnerApache FlinkApache Spark (RDD/DStream based)Apache Spark Structured Streaming (Dataset based)Apache NemoHazelcast JetTwister2Python Direct FnRunner
Base
~
Side Inputs
Splittable DoFn Initiated Checkpointing
~
Dynamic Splitting
Bundle Finalization
~
SEE DETAILS AND FULL VERSION HERE.

Where in event time?

Google Cloud DataflowPrism Local RunnerApache FlinkApache Spark (RDD/DStream based)Apache Spark Structured Streaming (Dataset based)Apache NemoHazelcast JetTwister2Python Direct FnRunner
Global windows
~
Fixed windows
~
Sliding windows
~
Session windows
~
Custom windows
~
Custom merging windows
~
Timestamp control
~
SEE DETAILS AND FULL VERSION HERE.

When in processing time?

Google Cloud DataflowPrism Local RunnerApache FlinkApache Spark (RDD/DStream based)Apache Spark Structured Streaming (Dataset based)Apache NemoHazelcast JetTwister2Python Direct FnRunner
Configurable triggering
~
Event-time triggers
~
Processing-time triggers
~
Count triggers
~
Composite triggers
~
~
Allowed lateness
~
Timers
~
~
~
~
~
SEE DETAILS AND FULL VERSION HERE.

How do refinements relate?

Google Cloud DataflowPrism Local RunnerApache FlinkApache Spark (RDD/DStream based)Apache Spark Structured Streaming (Dataset based)Apache NemoHazelcast JetTwister2Python Direct FnRunner
Discarding
~
Accumulating
SEE DETAILS AND FULL VERSION HERE.

Additional common features not yet part of the Beam model

Google Cloud DataflowPrism Local RunnerApache FlinkApache Spark (RDD/DStream based)Apache Spark Structured Streaming (Dataset based)Apache NemoHazelcast JetTwister2Python Direct FnRunner
Drain
~
~
Checkpoint
~
~
Key-ordered delivery
~
~
?
?
?
?
?
?
SEE DETAILS AND FULL VERSION HERE.