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Apache Flink

Fast and reliable large-scale data processing engine

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.
Apache Flink is a tool in the Big Data Tools category of a tech stack.
Apache Flink is an open source tool with 23.1K GitHub stars and 12.9K GitHub forks. Here’s a link to Apache Flink's open source repository on GitHub

Who uses Apache Flink?

59 companies reportedly use Apache Flink in their tech stacks, including CRED, Zalando, and Groww.

430 developers on StackShare have stated that they use Apache Flink.

Apache Flink Integrations

Kafka, Hadoop, HBase, Apache Zeppelin, and YARN Hadoop are some of the popular tools that integrate with Apache Flink. Here's a list of all 11 tools that integrate with Apache Flink.
Pros of Apache Flink
Unified batch and stream processing
Easy to use streaming apis
Out-of-the box connector to kinesis,s3,hdfs
Open Source
Low latency
Decisions about Apache Flink

Here are some stack decisions, common use cases and reviews by companies and developers who chose Apache Flink in their tech stack.

Needs advice
Apache FlinkApache Flink
Kafka StreamsKafka Streams

We currently have 2 Kafka Streams topics that have records coming in continuously. We're looking into joining the 2 streams based on a key with a window of 5 minutes based on their timestamp.

Should I consider kStream - kStream join or Apache Flink window joins? Or is there any other better way to achieve this?

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Needs advice
Apache FlinkApache Flink

Please tell me why you still choose Kafka after using both modules. Also, Apache Flink is faster then Kafka, isn't it?

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I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?

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Surabhi Bhawsar
Technical Architect at Pepcus · | 7 upvotes · 716.2K views
Needs advice
Apache FlinkApache Flink

I need to build the Alert & Notification framework with the use of a scheduled program. We will analyze the events from the database table and filter events that are falling under a day timespan and send these event messages over email. Currently, we are using Kafka Pub/Sub for messaging. The customer wants us to move on Apache Flink, I am trying to understand how Apache Flink could be fit better for us.

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Blog Posts

Mar 24 2021 at 12:57PM



Apache Flink's Features

  • Hybrid batch/streaming runtime that supports batch processing and data streaming programs.
  • Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.
  • Flexible and expressive windowing semantics for data stream programs
  • Built-in program optimizer that chooses the proper runtime operations for each program
  • Custom type analysis and serialization stack for high performance

Apache Flink Alternatives & Comparisons

What are some alternatives to Apache Flink?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Apache Storm
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
Apache Flume
It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
See all alternatives

Apache Flink's Followers
861 developers follow Apache Flink to keep up with related blogs and decisions.