Need advice about which tool to choose?Ask the StackShare community!
Apache Flink vs Faust: What are the differences?
Storage and Processing Model: Apache Flink and Faust have different models for storage and processing. Apache Flink is designed for distributed stream processing and batch processing, providing support for both event time and processing time semantics. Faust, on the other hand, is a stream processing library specifically built for Kafka, allowing you to define stream processors and connect them to Kafka topics.
Programming Paradigm: Apache Flink uses a unified programming model called the DataStream API, which enables users to write stream processing jobs in a high-level language similar to SQL. Faust, on the other hand, leverages Python as its programming language, making it more accessible to Python developers.
Supported Data Sources: Apache Flink supports a wide range of data sources and connectors, including Kafka, Hadoop, Amazon S3, and more. It also provides connectors for different databases and messaging systems. In contrast, Faust is tightly integrated with Kafka and focuses on supporting Kafka topics as the primary data source.
Fault Tolerance Mechanisms: Apache Flink is designed with fault tolerance in mind and provides built-in mechanisms to handle failures. It achieves fault tolerance through its distributed snapshotting and checkpointing mechanism. Faust, being a Kafka-specific library, relies on Kafka's own fault tolerance mechanisms, such as replication and leader election.
Processing Guarantees: Apache Flink provides strong processing guarantees by ensuring exactly once semantics for both event time and processing time. It achieves this by incorporating mechanisms like event deduplication and transactional writes into its processing pipelines. Faust, on the other hand, provides at-least-once processing guarantees by leveraging Kafka's message offset tracking.
Ecosystem and Community Support: Apache Flink has a mature ecosystem and a large community backing it. It is widely used in various industries and has a rich set of libraries and tools built around it. Faust, being a relatively new library, has a smaller ecosystem and community compared to Apache Flink.
In Summary, Apache Flink and Faust differ in their storage and processing models, programming paradigms, supported data sources, fault tolerance mechanisms, processing guarantees, and ecosystem/community support.
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 Faust
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 Faust?
What is Apache Flink?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to get full access to all the companiesMake informed product decisions
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