Apache Flink vs Apache Spark – A comparison guide

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In this tutorial, we will discuss the comparison between Apache Spark and Apache Flink.

Apache spark and Apache Flink both are open source platform for the batch processing as well as the stream processing at the massive scale which provides fault-tolerance and data-distribution for distributed computations.

This guide provides feature wise comparison between two booming big data technologies that is Apache Flink vs Apache Spark.

Apache Flink vs Apache Spark

FeaturesApache FlinkApache Spark
Computation ModelFlink is based on the operator-based computational model.Spark is based on the micro-batch modal.
Streaming engineApache Flink uses streams for all workloads: streaming, SQL, micro-batch and batch. Batch is a finite set of streamed data.Apache Spark uses micro-batches for all workloads. But it is not sufficient for use cases where we need to process large streams of live data and provide results in real time.
Iterative processingFlink API provides two dedicated iterations operation Iterate and Delta Iterate.Spark is based on non-native iteration which is implemented as regular for – loops outside the system.
OptimizationApache Flink comes with an optimizer that is independent with the actual programming interface.In Apache Spark jobs has to be manually optimized.
LatencyWith minimum efforts in configuration Apache Flink’s data streaming run-time achieves low latency and high throughput.Apache Spark has high latency as compared to Apache Flink.
PerformanceOverall performance of Apache Flink is excellent as compared to any other data processing system. Apache Flink uses native closed loop iterations operators which makes machine learning and graph processing more faster.Though Apache Spark has an excellent community background and now It is considered as most matured community. But Its stream processing is not much efficient than Apache Flink as it uses micro-batch processing.
Fault toleranceThe fault tolerance mechanism followed by Apache Flink is based on Chandy-Lamport distributed snapshots. The mechanism is lightweight, which results in maintaining high throughput rates and provide strong consistency guarantees at the same time.Spark Streaming recovers lost work and delivers exactly-once semantics out of the box with no extra code or configuration.(refer Spark fault tolerant feature guide )
Duplicate eliminationApache Flink process every records exactly one time hence eliminates duplication.Spark also process every record exactly one time hence eliminates duplication.
Window CriteriaFlink has a record-based or any custom user-defined Window criteria.Spark has a time-based Window criteria
Memory -ManagementFlink provides automatic memory management.Spark provides configurable memory management. Spark 1.6, Spark has moved towards automating memory management as well.
SpeedFlink processes data at lightening fast speedSpark’s processing model is slower than Flink


Apache Spark and Flink both are next generations Big Data tool grabbing industry attention. Both provide native connectivity with Hadoop and NoSQL Databases and can process HDFS data. Both are the nice solution to several Big Data problems.

But Flink is faster than Spark, due to its underlying architecture. Apache Spark is a most active component in Apache repository. Spark has very strong community support and has a good number of contributors. Spark has already been deployed in the production.

But as far as streaming capability is concerned Flink is far better than Spark (as spark handles stream in form of micro-batches) and has native support for streaming. Spark is considered as 3G of Big Data, whereas Flink is as 4G of Big Data.

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10 Responses

  1. Wahid says:

    Can you please provide a comparison between Apache Storm vs Spark

    • DF HD Team says:

      Apache Storm is a technology which provides solution only for real time processing. Apache Storm is very complex technology to develop such applications. Industry needs a such type of technology which can solve all the types of problems like Batch processing, stream processing interactive processing as well as iterative processing and all such kind of requirements are fulfilled by Apache Spark.For more detail you can refer this link.

  2. Guillermo says:

    I think Apache Storm is faster like Apache Flink in real time streaming, but it is faster than Spark Streaming, Storm is running in the millisecond level like Flink but Spark is running in the seconds level, that means Spark is slower than Flink or Storm , and in the new version of Storm it has a very good implementation for Windowing and Snapshot Chandy Lamport Algoritmn…

  3. Barrington says:

    Reading your content is pure pleasure for me as it provides lot many insights related to technology. Keep up the good work!!

  4. Garvit says:

    Thank you for sharing detailed comparison between Apache Flink and Apache Spark. It’s really nice blog to decide whether one should choose Flink or Spark as career development.

  5. JoshDonnells says:

    Nice article to explain difference between 2 of the latest Big data technologies- Apache Spark and Apache Flink.

  6. baziru says:

    Nicely explained key differences between flink vs spark with features of both that make them special.

  7. Jennifer says:

    Awesum blog on differences between flink and spark..Great work.

  8. Albert Heinle says:

    Love your article. However, I would love to see the comparisons outlined here being represented in numbers. What does “Spark’s processing model is slower than Flink” even mean? Like, how much slower? And in what cases? Please provide more details.

  9. Ghanty says:

    Thank you for such good insights and analysis laid out. As seen in many shops, which are predominantly batch oriented but run their batch on streaming system like apache kafka, (there practically no realtime streaming data here but there is anticipation of that happening some time in future) Now would you see any merits or demerits of using Flink for data pipelines (similar to etl pipelines) here?

    Your willingness to share insights on above will be greatly appreciated
    Kind Regards

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