damccorm commented on code in PR #29619: URL: https://github.com/apache/beam/pull/29619#discussion_r1439800801
########## website/www/site/content/en/blog/scaling-streaming-workload.md: ########## @@ -0,0 +1,291 @@ +--- +layout: post +title: "Scaling a streaming workload on Apache Beam, 1 million events per second and beyond" +date: 2023-12-01 00:00:01 -0800 Review Comment: ```suggestion date: 2023-01-02 00:00:01 -0800 ``` ########## website/www/site/content/en/blog/scaling-streaming-workload.md: ########## @@ -0,0 +1,291 @@ +--- +layout: post +title: "Scaling a streaming workload on Apache Beam, 1 million events per second and beyond" +date: 2023-12-01 00:00:01 -0800 +categories: + - blog +authors: + - pabs +--- +<!-- +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at +http://www.apache.org/licenses/LICENSE-2.0 +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +--> + +# Scaling a streaming workload on Apache Beam + +<img class="center-block" + src="/images/blog/scaling-streaming-workload/0-intro.png" + alt="Streaming Processing"> + +Scaling a streaming workload is critical for ensuring that a pipeline can process large amounts of data while also minimizing latency and executing efficiently. Without proper scaling, a pipeline may experience performance issues or even fail entirely, delaying the time to insights for the business. + +Given the Apache Beam support for the sources and sinks needed by the workload, developing a streaming pipeline can be easy. You can focus on the processing (transformations, enrichments, or aggregations) and on setting the right configurations for each case. + +However, you need to identify the key performance bottlenecks and make sure that the pipeline has the resources it needs to handle the load efficiently. This can involve right-sizing the number of workers, understanding the settings needed for the source and sinks of the pipeline, optimizing the processing logic, and even determining the transport formats. + +This article illustrates how to manage the problem of scaling and optimizing a streaming workload developed in Apache Beam and run on Google Cloud using Dataflow. The goal is to reach one million events per second, while also minimizing latency and resource use during execution. The workload uses Pub/Sub as the streaming source and BigQuery as the sink. We describe the reasoning behind the configuration settings and code changes we used to help the workload achieve the desired scale and beyond. + +The progression described in this article maps to the evolution of a real-life workload, with simplifications. After the initial business requirements for the pipeline were achieved, the focus shifted to optimizing the performance and reducing the resources needed for the pipeline execution. + +## Execution setup + +For this article, we created a test suite that creates the necessary components for the pipelines to execute. You can find the code in [this Github repository](https://github.com/prodriguezdefino/apache-beam-streaming-tests). You can find the subsequent changes that are introduced on every run in the [`https://github.com/prodriguezdefino/apache-beam-streaming-tests/tree/main/scaling-streaming-workload-blog`](https://github.com/prodriguezdefino/apache-beam-streaming-tests/tree/main/scaling-streaming-workload-blog) folder as scripts that you can run to achieve similar results. Review Comment: ```suggestion For this article, we created a test suite that creates the necessary components for the pipelines to execute. You can find the code in [this Github repository](https://github.com/prodriguezdefino/apache-beam-streaming-tests). You can find the subsequent changes that are introduced on every run in the [https://github.com/prodriguezdefino/apache-beam-streaming-tests/tree/main/scaling-streaming-workload-blog](https://github.com/prodriguezdefino/apache-beam-streaming-tests/tree/main/scaling-streaming-workload-blog) folder as scripts that you can run to achieve similar results. ``` I think block quoting this creates weird formatting - https://apache-beam-website-pull-requests.storage.googleapis.com/29619/blog/scaling-streaming-workload/index.html#:~:text=the%20pipeline%20execution.-,Execution%20setup,-For%20this%20article -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
