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Understanding Apache Flume: Its Purpose and Applications

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apache flume

All businesses, regardless of their size or industry, utilize log files to record all events that occur on their web servers. However, in the era of digital dominance, these events are becoming increasingly numerous, resulting in log files storing an exponential amount of data. To effectively handle this data, network administrators and DevOps professionals require efficient tools. This is where Apache Flume comes into play. What is it? What are its advantages and disadvantages? Find all the answers in this article.

What is Apache Flume?

As the amount of data collected by logs increases, new tools are emerging to facilitate their exploitation. One such tool is Apache Flume.

This is a tool for collecting, aggregating and moving large quantities of logs. This solution has been specially designed to handle high volumes and throughputs.

To this end, Apache Flume is written in an HDFS. In other words, a distributed file system that manages large data sets.

The idea is to enable users to access files on the shared storage system. And they can do so from any server on the network. All resources can thus be shared much more easily.

Thanks to its ability to handle large volumes of data, Apache Flume is particularly well suited to peak workloads.

After several redesigns due to an overly complex architecture and indebted code, Flume OG (old generation) has become Flume NG (new generation).

This has enabled the tool to offer users more advanced functionalities and simpler operation. In fact, it was listed as a Top Level Apache Project in 2012. These are open source projects that aim to bring about innovative changes in the IT world.

How does Flume work?

Flume architecture

As we saw earlier, Apache Flume is a distributed solution. As such, its architecture is made up of a large number of agents.

We will therefore need to define the distributed agents responsible for these tasks:

  • retrieve data from a multitude of sources ;
  • consolidate logs and write them to a centralized repository
  • repository (such as an HDFS cluster or an HBase database).

Let’s take a closer look at their role.

The agents

Traditionally, the agent performs routes that materialize as follows: Source -> Channel -> Sink.

Each of these elements fulfills a specific function:

The Flume source

The idea is to retrieve the message from an external source, such as an application, network traffic, social media, e-mail, and many other sources.

There are different types of Flume source, each with its own specific characteristics. The most common are the following:

  • Avro: enables communication between different Flume Apache agents.
  • Spooling Directory Source: facilitates reading of incoming files.
  • Syslog (TCP or UDP): the idea is to capture events from a syslog server.
  • HTTP: translates POST and GET requests.


This list is not exhaustive. Agents can use a multitude of sources, depending on the organization’s specific requirements.

The channel (or path)

This is where logs are stored by the agent. Here again, there are different channels:

  • Memory: events are stored in memory.
  • JDBC: this is a database storage channel.
  • File: this is a filesystem for storing logs.
The sink

This allows log data to be rewritten in its destination repository. Events can then be pushed to HDFS, IRC, HBase, ElasticSearch, File (or local file). Avro can also be used to facilitate communication with another agent.


If the previous path is the most classic, it is always possible to customize the route. For example, by adding an interceptor whose role is to sort and filter logs, directing them to the right repository.

In the same spirit, IT teams can create links between several agents, add several channels or sinks, or implement their own sources, channels or sinks from Java interfaces.

The route can then be much more complex than before, but it makes the data flow more intelligent. In all cases, the idea is to tailor log processing to the needs of the organization.

What are the advantages of Flume?

In demand by many companies, Apache Flume offers a multitude of advantages:

  • Simplicity: whether in terms of installation, configuration or operation, Apache Flume is very easy to use.
  • Customization: organizations can implement Java interfaces to meet specific business requirements. This provides additional functionality.
  • Compatibility: Flume was developed by Cloudera, a prominent player in the Hadoop ecosystem, under the Apache license. It is, therefore, a tool that belongs to the open-source Big Data ecosystem of Hadoop. As a result, it integrates seamlessly with most distributions of the Hadoop framework, allowing interaction with various technologies.

  • Performance: Being a distributed solution, Flume achieves excellent levels of performance and scalability. Businesses with complex information systems that handle thousands of events per second can effectively utilize this tool.

  • Accessibility: Apache Flume is a SaaS (Software as a Service) tool, making it compatible with all operating systems, including Windows, Mac, and mobile OS. This accessibility enables users to access it from any web browser.

  • Fault Tolerance: In the event of detecting faulty components, Flume utilizes backup components that automatically replace them, preventing service interruptions.

What are the drawbacks?

Although Flume has many advantages, it does have a few weaknesses:

  • Slow writing to disk: to maximize performance, data should be written to memory.
  • Lack of elasticity: adding a new node to the topology is not detectable.
  • Configuration: to optimize throughput speed, DevOps needs to configure as many routes as there are CPU cores available. This involves several hundred (or almost) identical configuration lines. To simplify reading, it is therefore necessary to equip yourself with a data generation script.

How does Flume Apache differ from other tools?

For log management, there are other tools such as Logstash and Kafka. So what are the differences between these solutions?

Flume Vs Logstasch

While Flume and Logstash share many similar features, it’s important to highlight the differences between the two.

In Flume, sending data to HDFS (Hadoop) is natively supported, whereas with Logstash, you need to install a plugin for this functionality. Additionally, Flume also allows for the use of Avro to optimize the tool’s serialization performance.

On the other hand, Logstash is generally considered to be much simpler to configure compared to Flume.

Flume vs Kafta

If Flume and Kafka are two Apache-licensed tools, they have several differences.

Indeed, Flume is responsible for collecting, aggregating, and moving large amounts of logs from various sources. The tool can continuously receive data from multiple sources, store it, and analyze it within Hadoop.

On the other hand, Kafka is specifically designed for the ingestion and real-time processing of continuous data. To do this, it treats each topic partition as an ordered set of messages.

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