Apache Beam vs Apache Flink

Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Beam

Stacks183

Followers361

Votes14

Apache Beam vs Apache Flink: What are the differences?

Introduction

Apache Beam and Apache Flink are both powerful distributed data processing frameworks that offer similar features but with some key differences. In this comparison, we will explore six key differences between Apache Beam and Apache Flink.

Programming Model: Apache Beam provides a unified programming model that allows developers to write data processing pipelines in multiple languages such as Java, Python, and Go. On the other hand, Apache Flink primarily focuses on Java and Scala for writing data processing applications.
Batch and Stream Processing: While both Apache Beam and Apache Flink support both batch and stream processing, Apache Beam has a more flexible and abstracted approach. It treats batch processing as a special case of stream processing, allowing seamless integration between the two modes. Apache Flink, on the other hand, treats batch and stream processing as distinct execution models.
Execution Model: Apache Beam follows a portable execution model that allows pipelines to be executed on different processing engines. This enables developers to write pipelines once and execute them on different processing frameworks such as Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Flink, on the other hand, has its own distinct execution model optimized for its runtime environment.
Fault Tolerance: Both Apache Beam and Apache Flink offer fault tolerance mechanisms to handle failures during data processing. However, Apache Flink utilizes a fine-grained checkpointing mechanism that offers precise recovery points and strong durability guarantees. On the other hand, Apache Beam relies on the underlying execution engine's fault tolerance capabilities, which may vary depending on the chosen processing framework.
State Management: Apache Flink provides a built-in state management mechanism, allowing developers to maintain and update state across multiple processing stages in a fault-tolerant manner. In contrast, Apache Beam does not have native state management capabilities and relies on external state management systems, making it more flexible but also requiring more manual configuration.
Community and Ecosystem: Apache Flink has gained a significant user base and has a vibrant community with frequent releases and active development. It offers a rich ecosystem with various libraries and connectors specifically designed for Flink. Apache Beam, on the other hand, has a larger community and a more extensive ecosystem due to its compatibility with multiple processing frameworks.

In summary, Apache Beam and Apache Flink have some fundamental differences in their programming models, execution models, fault tolerance mechanisms, state management capabilities, and community support. These differences make them suitable for different use cases and require careful consideration when selecting the appropriate framework for your data processing needs.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Apache Flink, Apache Beam

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

576k views576k

Comments

Detailed Comparison

Apache Flink	Apache Beam
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.	It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
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	-
Statistics
GitHub Stars 25.4K	GitHub Stars -
GitHub Forks 13.7K	GitHub Forks -
Stacks 534	Stacks 183
Followers 879	Followers 361
Votes 38	Votes 14
Pros & Cons
Pros 16 Unified batch and stream processing 8 Easy to use streaming apis 8 Out-of-the box connector to kinesis,s3,hdfs 4 Open Source 2 Low latency	Pros 5 Cross-platform 5 Open-source 2 Unified batch and stream processing 2 Portable
Integrations
YARN Hadoop Hadoop HBase Kafka	No integrations available

What are some alternatives to Apache Flink, Apache Beam?

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.

Airflow

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Related Comparisons

Apache Beam vs Apache Flink: What are the differences?

Introduction

Programming Model: Apache Beam provides a unified programming model that allows developers to write data processing pipelines in multiple languages such as Java, Python, and Go. On the other hand, Apache Flink primarily focuses on Java and Scala for writing data processing applications.
Batch and Stream Processing: While both Apache Beam and Apache Flink support both batch and stream processing, Apache Beam has a more flexible and abstracted approach. It treats batch processing as a special case of stream processing, allowing seamless integration between the two modes. Apache Flink, on the other hand, treats batch and stream processing as distinct execution models.
Execution Model: Apache Beam follows a portable execution model that allows pipelines to be executed on different processing engines. This enables developers to write pipelines once and execute them on different processing frameworks such as Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Flink, on the other hand, has its own distinct execution model optimized for its runtime environment.
Fault Tolerance: Both Apache Beam and Apache Flink offer fault tolerance mechanisms to handle failures during data processing. However, Apache Flink utilizes a fine-grained checkpointing mechanism that offers precise recovery points and strong durability guarantees. On the other hand, Apache Beam relies on the underlying execution engine's fault tolerance capabilities, which may vary depending on the chosen processing framework.
State Management: Apache Flink provides a built-in state management mechanism, allowing developers to maintain and update state across multiple processing stages in a fault-tolerant manner. In contrast, Apache Beam does not have native state management capabilities and relies on external state management systems, making it more flexible but also requiring more manual configuration.
Community and Ecosystem: Apache Flink has gained a significant user base and has a vibrant community with frequent releases and active development. It offers a rich ecosystem with various libraries and connectors specifically designed for Flink. Apache Beam, on the other hand, has a larger community and a more extensive ecosystem due to its compatibility with multiple processing frameworks.

Apache Beam vs Apache Flink

Overview