Need advice about which tool to choose?Ask the StackShare community!
Apache Beam vs Apache Flink: What are the differences?
Introduction
Apache Beam and Apache Flink are both powerful distributed data processing frameworks that offer similar features but with some key differences. In this comparison, we will explore six key differences between Apache Beam and Apache Flink.
Programming Model: Apache Beam provides a unified programming model that allows developers to write data processing pipelines in multiple languages such as Java, Python, and Go. On the other hand, Apache Flink primarily focuses on Java and Scala for writing data processing applications.
Batch and Stream Processing: While both Apache Beam and Apache Flink support both batch and stream processing, Apache Beam has a more flexible and abstracted approach. It treats batch processing as a special case of stream processing, allowing seamless integration between the two modes. Apache Flink, on the other hand, treats batch and stream processing as distinct execution models.
Execution Model: Apache Beam follows a portable execution model that allows pipelines to be executed on different processing engines. This enables developers to write pipelines once and execute them on different processing frameworks such as Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Flink, on the other hand, has its own distinct execution model optimized for its runtime environment.
Fault Tolerance: Both Apache Beam and Apache Flink offer fault tolerance mechanisms to handle failures during data processing. However, Apache Flink utilizes a fine-grained checkpointing mechanism that offers precise recovery points and strong durability guarantees. On the other hand, Apache Beam relies on the underlying execution engine's fault tolerance capabilities, which may vary depending on the chosen processing framework.
State Management: Apache Flink provides a built-in state management mechanism, allowing developers to maintain and update state across multiple processing stages in a fault-tolerant manner. In contrast, Apache Beam does not have native state management capabilities and relies on external state management systems, making it more flexible but also requiring more manual configuration.
Community and Ecosystem: Apache Flink has gained a significant user base and has a vibrant community with frequent releases and active development. It offers a rich ecosystem with various libraries and connectors specifically designed for Flink. Apache Beam, on the other hand, has a larger community and a more extensive ecosystem due to its compatibility with multiple processing frameworks.
In summary, Apache Beam and Apache Flink have some fundamental differences in their programming models, execution models, fault tolerance mechanisms, state management capabilities, and community support. These differences make them suitable for different use cases and require careful consideration when selecting the appropriate framework for your data processing needs.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Apache Beam
- Open-source5
- Cross-platform5
- Portable2
- Unified batch and stream processing2
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
Sign up to add or upvote prosMake informed product decisions
What is Apache Beam?
What is Apache Flink?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Apache Flink?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Apache Beam?
What tools integrate with Apache Flink?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+6
+4
+7
+8