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Apache Flink vs CDAP: What are the differences?
Apache Flink and CDAP are two popular data processing frameworks used for real-time data processing. In this comparison, we will highlight the key differences between Apache Flink and CDAP.
1. **Programming Model**: Apache Flink follows a DataStream API model where data is processed as a stream of events, providing low latency processing for real-time applications. CDAP, on the other hand, offers a batch processing model where data is processed in micro-batches, which is suitable for large-scale data processing.
2. **Use Cases**: Apache Flink is often preferred for real-time stream processing use cases where low latency and high throughput are critical, such as real-time analytics and monitoring. CDAP, on the other hand, is more suitable for ETL (Extract, Transform, Load) processes, batch processing, and data lake applications.
3. **Ecosystem Integration**: Apache Flink has a rich ecosystem with support for various connectors and libraries for stream processing and integration with technologies like Apache Kafka and Apache Hadoop. CDAP, on the other hand, provides integration with various storage systems, databases, and services through its plugins and extensions.
4. **Scalability**: Apache Flink is designed for horizontal scalability, allowing users to scale their processing clusters dynamically based on the workload. CDAP also supports horizontal scalability but is more focused on simplifying the development and deployment of data applications rather than large-scale processing.
5. **Resource Management**: Apache Flink comes with built-in support for resource management using Apache YARN, Apache Mesos, or Kubernetes, providing efficient cluster utilization and fault tolerance. CDAP provides resource management through its CDAP Master service, which manages the deployment and execution of data applications across the cluster.
6. **Ease of Use**: Apache Flink requires understanding of stream processing concepts and APIs, making it more suitable for developers with experience in real-time data processing. CDAP, on the other hand, provides a higher level of abstraction with visual tools and a drag-and-drop interface, making it easier for developers to create data pipelines without deep knowledge of underlying technologies.
In Summary, Apache Flink and CDAP differ in their programming models, use cases, ecosystem integration, scalability, resource management, and ease of use, making each framework more suitable for specific types of data processing applications.
Advice on CDAP and Apache Flink
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 516.9K views
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.
Replies (2)
lvhuyen
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.
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 361.4K views
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"
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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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What is CDAP?
Cask Data Application Platform (CDAP) is an open source application development platform for the Hadoop ecosystem that provides developers with data and application virtualization to accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements.
What is Apache Flink?
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
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