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Apache Flink vs Apache Spark: What are the differences?
Introduction
Apache Flink and Apache Spark are both powerful distributed processing frameworks that are widely used for big data processing and analytics. While they share some similarities, there are key differences between the two.
Processing Model: Apache Flink follows a true streaming model, where data is processed as it arrives in real-time. This provides low latency and ensures that processing is continuous and uninterrupted. On the other hand, Apache Spark operates on a micro-batch processing model, where data is processed in small batches, introducing slight latency. This makes Flink more suitable for applications requiring real-time data processing.
Fault Tolerance: Flink and Spark both provide fault tolerance mechanisms, but they differ in their approaches. Flink uses a mechanism called "lightweight snapshots", where only the necessary information is stored periodically to recover from failures. This enables fast recovery times and low overhead. Spark, on the other hand, uses Resilient Distributed Datasets (RDDs) to achieve fault tolerance. RDDs store the lineage of each dataset, allowing for recomputation in case of failures. This approach introduces a higher overhead.
Iterative Processing: Apache Flink was designed with iterative processing in mind, making it more efficient for machine learning and graph algorithms. Flink can keep data in memory between iterations, reducing the need for data serialization and deserialization. Spark also supports iterative processing, but it relies on RDDs, which have higher overhead and can be slower for iterative workloads.
Data Processing APIs: Flink and Spark provide different APIs for data processing. Flink offers a unified API that supports both batch and stream processing, making it more convenient for developers. Spark, on the other hand, has separate APIs for batch (RDD-based) and stream (DStream-based) processing. Flink's unified API allows for easier code reuse and better integration across different processing modes.
Memory Management: Flink and Spark use different memory management techniques. Flink has a managed memory model, where memory is allocated in fine-grained blocks and managed by the runtime. This allows Flink to efficiently manage memory and avoid out-of-memory errors. Spark, on the other hand, relies on Java's garbage collector for memory management, which can introduce longer pauses during processing.
State Management: Apache Flink provides built-in support for managing state, allowing for efficient handling of streaming data with complex dependencies. Flink's state management can handle data that spans multiple events, making it suitable for applications such as event time processing. Spark, on the other hand, does not provide built-in state management capabilities, requiring developers to implement custom solutions for state handling.
In summary, Apache Flink excels in true streaming, fault tolerance, iterative processing, unified API, memory management, and state management, making it a great choice for real-time data processing. Apache Spark, on the other hand, is more suitable for batch processing, offers RDD-based fault tolerance, and has a larger ecosystem of tools and libraries.
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 Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Apache Flink
Cons of Apache Spark
- Speed4
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What is Apache Spark?
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