Streaming with Pub/Sub

This page provides a conceptual overview of Dataflow's integration with Pub/Sub. The overview describes some optimizations that are available in the Dataflow runner's implementation of the Pub/Sub I/O connector. Pub/Sub is a scalable, durable event ingestion and delivery system. Dataflow compliments Pub/Sub's scalable, at-least-once delivery model with message deduplication and exactly-once, in-order processing if you use windows and buffering. To use Dataflow, write your pipeline using the Apache Beam SDK and then execute the pipeline code on the Dataflow service.

Before you begin, learn about the basic concepts of Apache Beam and streaming pipelines. Read the following resources for more information:

• Intro to Apache Beam concepts such as PCollections, triggers, windows, and watermarks
• After Lambda: Exactly-once processing in Dataflow Part 1 and Part 3: Sources and Sinks
• Streaming: The world beyond batch: 101 and 102
• Apache Beam programming guide

Warning: Do not use a single Pub/Sub subscription for multiple pipelines. The data from a single subscription gets divided nondeterministically between the entities consuming that data; therefore, if you have two pipelines reading from a single subscription, each pipeline receives part of the data in a nondeterministic manner which may cause duplication of messages, watermark lag, and inefficient autoscaling. Instead, create a separate subscription for each pipeline. Alternatively, reading from a Pub/Sub topic automatically creates a separate subscription for each pipeline.

Building streaming pipelines with Pub/Sub

To get the benefits of Dataflow's integration with Pub/Sub, you can build your streaming pipelines in any of the following ways:

• Use existing streaming pipeline example code from the Apache Beam GitHub repo, such as streaming word extraction (Java) and streaming wordcount (Python).

• Write a new pipeline using the Apache Beam API reference (Java or Python).

• Use Google-provided Dataflow templates and the corresponding template source code in Java.

  Google provides a set of Dataflow templates that offer a UI-based way to start Pub/Sub stream processing pipelines. If you use Java, you can also use the source code of these templates as a starting point to create a custom pipeline.

  The following streaming templates export Pub/Sub data to different destinations:

  • Pub/Sub Subscription to BigQuery
  • Pub/Sub to Pub/Sub relay
  • Pub/Sub to Cloud Storage Avro
  • Pub/Sub to Cloud Storage Text
  • Storage Text to Pub/Sub (Stream)

  The following batch template imports a stream of data into a Pub/Sub topic:

  • Cloud Storage Text to Pub/Sub (Batch)

• Follow the Pub/Sub quickstart for stream processing with Dataflow to run a simple pipeline.

Note: To forward messages to Pub/Sub dead-letter topics using a Dataflow pipeline, see Handling Invalid Inputs in Dataflow.

Pub/Sub and Dataflow integration features

Apache Beam provides a reference I/O source implementation (PubsubIO) for Pub/Sub (Java and Python), which is used by non-Dataflow runners (such as the Apache Spark runner, Apache Flink runner, and the direct runner).

However, the Dataflow runner uses a different, private implementation of PubsubIO. This implementation takes advantage of Google Cloud-internal APIs and services to offer three main advantages: low latency watermarks, high watermark accuracy (and therefore data completeness), and efficient deduplication.

The Dataflow runner's implementation of PubsubIO automatically acknowledges messages once they have been written to persistent storage, either Shuffle or a sink. Therefore, messages are only acknowledged when Dataflow can guarantee that there will be no data loss if some component crashed, or a connection were lost.

Low latency watermarks

Dataflow has access to Pub/Sub's private API that provides the age of the oldest unacknowledged message in a subscription, with lower latency than is available in Cloud Monitoring. For comparison, the Pub/Sub backlog metrics that are available in Cloud Monitoring are typically delayed by two to three minutes, but the metrics are delayed only by approximately ten seconds for Dataflow. This makes it possible for Dataflow to advance pipeline watermarks and emit windowed computation results sooner.

High watermark accuracy

Another important problem solved natively by the Dataflow integration with Pub/Sub is the need for a robust watermark for windows defined in event time. The event time is a timestamp specified by the publisher application as an attribute of a Pub/Sub message, rather than the publish_time field set on a message by the Pub/Sub service itself. Because Pub/Sub computes backlog statistics only with respect to the service-assigned (or processing time) timestamps, estimating the event time watermark requires a separate mechanism.

To solve this problem, if the user elects to use custom event timestamps, the Dataflow service creates a second tracking subscription. This tracking subscription is used to inspect the event times of the messages in the backlog of the base subscription, and estimate the event time backlog. See the StackOverflow page that covers how Dataflow computes Pub/Sub watermarks for more information.

Efficient deduplication

Message deduplication is required for exactly-once message processing. Dataflow deduplicates messages with respect to the Pub/Sub message ID. As a result, all processing logic can assume that the messages are already unique with respect to the Pub/Sub message ID. The efficient, incremental aggregation mechanism to accomplish this is abstracted in the PubsubIO API.

If PubsubIO is configured to use custom message IDs, Dataflow deduplicates messages by maintaining a list of all the custom IDs it has seen in the last 10 minutes. If a new message's ID is in this list, the message is assumed to be a duplicate and discarded.