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Apache Flink vs Cloudera Enterprise: What are the differences?
Introduction
Apache Flink and Cloudera Enterprise are both widely used big data processing frameworks offering various capabilities, but they differ in several key aspects.
Processing Model: Apache Flink uses a stream processing model, allowing continuous data processing in real-time, while Cloudera Enterprise primarily focuses on batch processing, processing data in discrete chunks.
Native Integration: Cloudera Enterprise provides seamless integration with various components of the Hadoop ecosystem, such as HDFS and YARN, while Apache Flink has its own ecosystem and doesn't rely on Hadoop infrastructure.
Support for Stateful Computation: Apache Flink natively supports stateful computations, making it easier to maintain and process application state across different computations, whereas Cloudera Enterprise requires additional tools for managing states.
Language Support: Apache Flink supports both Java and Scala for programming, offering developers flexibility in choosing their preferred language, while Cloudera Enterprise primarily supports Java, making it less versatile in terms of language options.
Optimization Techniques: Cloudera Enterprise offers advanced optimization techniques for batch processing, making it more suitable for large-scale batch data processing, while Apache Flink is optimized for real-time stream processing, resulting in better performance for streaming workloads.
Deployment Flexibility: Apache Flink allows deployment in various environments, including standalone, YARN, and Mesos, offering more flexibility in deployment options compared to Cloudera Enterprise, which is typically deployed on Hadoop clusters.
In Summary, Apache Flink and Cloudera Enterprise differ in their processing models, native integration, support for stateful computation, language support, optimization techniques, and deployment flexibility.
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 Cloudera Enterprise
- Easily management1
- Cheeper1
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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