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Apache Flink vs Apex: What are the differences?
Introduction: Apache Flink and Apex are both open-source stream processing platforms that provide real-time processing capabilities. While they share similarities in their functionality, there are key differences that set them apart.
Processing Model: Apache Flink uses a parallel data processing model, where data is processed as streams or batches, with support for event time processing. In contrast, Apex follows a DAG (Directed Acyclic Graph) model, which allows for building complex data processing pipelines with parallel and sequential processing stages.
Fault Tolerance: Apache Flink provides fault-tolerance through a mechanism called Checkpointing, where the state of the system is periodically saved for recovery in case of failures. On the other hand, Apex offers fault-tolerance through the use of an in-memory distributed parallel system that can recover from failures without losing any data.
Event Processing Guarantees: Apache Flink offers strong consistency guarantees for event processing, ensuring that events are processed exactly once or at most once. In comparison, Apex provides customizable event processing guarantees, allowing developers to choose between at least once, at most once, or exactly once processing semantics.
Language Support: Apache Flink supports multiple programming languages, including Java, Scala, and Python, making it versatile for developers with different language preferences. Apex primarily focuses on Java, providing a programming model that is tailored for Java developers.
Integration with Ecosystem: Apache Flink integrates well with other Apache projects like Apache Kafka, Apache Hadoop, and Apache Cassandra, allowing for seamless data ingestion and processing workflows. While Apex also supports integration with various data sources and sinks, its ecosystem integration may not be as extensive as Apache Flink.
Summary: In summary, Apache Flink and Apex differ in their processing models, fault tolerance mechanisms, event processing guarantees, language support, and integration with ecosystems.
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 Apex
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
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