Overview

Apache Flink

Stacks533

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flume

Stacks49

Followers120

Votes0

Apache Flink vs Apache Flume: What are the differences?

Introduction

Apache Flink and Apache Flume are both open-source frameworks used for processing and analyzing data. However, there are significant differences between the two. In this article, we will discuss the key differences between Apache Flink and Apache Flume.

Processing Model: Apache Flink is a stream processing framework designed to handle both batch and real-time data processing. It provides a unified programming model for both batch and stream processing, allowing developers to build complex data pipelines. On the other hand, Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming data. It focuses on data ingestion from various sources into a centralized data store or data processing system.
Data Sources: Apache Flink supports various data sources, including message queues, event streams, file systems, and databases. It can consume data from static files, file systems like HDFS, and even external systems like Apache Kafka. In contrast, Apache Flume primarily focuses on collecting data from various sources like web servers, log files, and social media platforms. It is optimized for efficiently collecting and transporting large amounts of data from distributed sources to centralized data stores.
Fault Tolerance: Apache Flink provides strong fault-tolerance guarantees by leveraging its distributed processing model. It ensures that data is processed reliably even in the presence of failures. Flink achieves fault tolerance by leveraging data replication and checkpointing mechanisms. On the other hand, Apache Flume ensures reliability and fault tolerance by using a transactional approach for data collection. It can guarantee the delivery of data to the destination by using reliable and persistent channels.
Event Time Processing: Apache Flink has built-in support for event time processing, allowing developers to handle out-of-order data and achieve accurate results even in the presence of delayed or late data. This feature is crucial for stream processing use cases where data arrives with a timestamp. In contrast, Apache Flume does not have built-in support for event time processing. It primarily focuses on efficient data collection and transportation rather than complex event processing.
Built-in Machine Learning: Apache Flink provides built-in support for machine learning through its FlinkML library. It allows developers to train and deploy machine learning models directly within the Flink runtime. This makes it easy to incorporate machine learning algorithms and predictive analytics into data processing pipelines. On the other hand, Apache Flume does not provide built-in support for machine learning. It is mainly focused on data ingestion and transport.
Stream Processing Throughput: Apache Flink is known for its high-throughput stream processing capabilities. Its optimized runtime and execution engine enable it to process large volumes of data at low latency. Flink achieves high throughput by leveraging parallelism and efficient data distribution strategies. On the other hand, Apache Flume focuses on reliable and scalable data collection rather than high-throughput stream processing.

In summary, Apache Flink is a versatile and powerful stream processing framework that supports both batch and real-time processing, provides fault tolerance guarantees, supports event time processing, and includes built-in machine learning capabilities. On the other hand, Apache Flume is a specialized data collection and transport framework that focuses on efficiently collecting and moving large amounts of data from distributed sources to centralized data stores.

Advice on Apache Flink, Apache Flume

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.

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Comments

Detailed Comparison

Apache Flink	Apache Flume
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 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.
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 533	Stacks 49
Followers 879	Followers 120
Votes 38	Votes 0
Pros & Cons
Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency	No community feedback yet
Integrations
YARN Hadoop Hadoop HBase Kafka	No integrations available

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