Apache Flume

What is Apache Flume?

Apache Flume is a tool devised by Cloudera to assist in the collection, aggregation, and movement of large sums of log data. It is primarily used for online analytic applications. As a distributed and reliable service, it provides robust fault-tolerance mechanisms and tunable reliability for managing data flow in an efficient manner.


Developed by Cloudera, Apache Flume was initially designed to ingest log data in Hadoop Distributed File System (HDFS). It became a part of the Apache Hadoop ecosystem in 2012, rapidly being embraced for its efficiency in moving large volumes of data.

Functionality and Features

Apache Flume's key features include:

  • Distributed and reliable data collection.
  • Support for a large set of data sources including log4j logs, syslogs, etc.
  • The ability to write data into various types of data stores like HDFS, HBase, and Solr.
  • Scalability and fault-tolerant handling of data flows.


Apache Flume's architecture is streamlined and conceptually straightforward. It comprises three key components: sources, channels, and sinks.

  • Sources: Data generators kicking off the data flow.
  • Channels: Temporary stores where incoming data are held.
  • Sinks: Data consumers or destinations where the processed data ends up.

Benefits and Use Cases

Apache Flume is an excellent choice for real-time log streaming. It offers flexibility, reliability, and scalability. Its common use-cases include log data aggregation, populating Hadoop with data, and social media data collection.

Challenges and Limitations

While Apache Flume efficiently handles data streaming, it lacks certain elements such as support for event processing, transformation, and advanced queuing functionalities. Also, being a high-latency tool, it may not be ideal for millisecond-sensitive applications.

Integration with Data Lakehouse

Flume can be vital in a data lakehouse setup, where it can serve as the data ingestion layer, enabling the streaming of data into the lakehouse in real-time. It can efficiently feed log data or any streaming data into Hadoop systems, which could form the storage base of the data lakehouse.

Security Aspects

Flume supports secure and non-secure modes of operation. It uses Simple Authentication and Security Layer (SASL) for communication between Flume agents, delivering security through authentication and wire encryption.


Apache Flume's performance is determined by its configuration and the nature of use. With proper tuning, Flume can offer high throughput rates, sufficient for handling most large-scale data streaming tasks.


  • What kind of data can Apache Flume handle? Apache Flume is versatile and can handle any kind of data, but it's particularly effective with event-based data like log files.
  • What part does Apache Flume play in big data? Apache Flume is a key player in big data architectures for transporting massive volumes of data to Hadoop for further processing and analysis.
  • What is a Flume agent? A Flume agent is an independent daemon process in Apache Flume responsible for collecting, aggregating, and transferring data.


  • Flume Event: The unit of data flow in Apache Flume, generally equivalent to one log entry.
  • Flume Source: The component that ingests data into the Flume architecture.
  • Flume Sink: The component that removes data from Flume and sends it to its final destination.
  • Flume Channel: The component that temporarily stores Flume Events between the Source and Sink.
  • Flume Agent: A JVM process that hosts the Flume components.
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