Apache Flume is a distributed, reliable, and highly available service designed for efficiently collecting, aggregating, and moving large amounts of log data from various sources to a centralized data store. Its main purpose is to collect and aggregate streaming data, such as logs from web servers or event data, and move it into Hadoop Distributed File System (HDFS) or other big data stores like Apache HBase.
The architecture of Flume consists of several key components:
1. **Event**: An event is the basic unit of data that Flume deals with while transporting from source to destination. Examples of events can be log lines, packets, etc.
2. **Source**: Sources are the components responsible for accepting data from external systems and generating events from the data. Some examples of pre-built Flume sources are Avro, Thrift, Syslog, HTTP, and JMS.
3. **Sink**: Sinks are responsible for consuming events from channels and storing them to the destination system, such as HDFS, HBase, or other external systems. Some common pre-built Flume sinks are HDFS, HBase, and Solr.
4. **Channel**: Channels act as a temporary storage buffer between the data sources and sinks. They can be seen as queues that store the events generated by sources before they are consumed by sinks. Some examples of pre-built Flume channels are Memory Channel, File Channel, and JDBC Channel.
5. **Agent**: An agent is a JVM process that runs individual Flume components, such as sources, sinks, and channels. A typical Flume setup consists of multiple agents running on different machines.
6. **Interceptor**: Interceptors perform light-weight processing on events as they traverse through a Flume agent. They can be used to filter, route, or modify events before they reach the channels or sinks.
The overall data flow in a Flume agent looks like this:
‘Source(s) -> Interceptor(s) -> Channel(s) -> Sink(s)‘
Here is a high-level view of the Apache Flume architecture:
Let’s take an example to illustrate the architecture:
Suppose we have a web server generating logs that we want to store in HDFS using Flume.
- We would configure a Flume source (e.g., ‘Syslog Source‘) to accept the logs from the web server.
- We may have an interceptor to filter out irrelevant log events or modify them.
- A channel (e.g., ‘Memory Channel‘) would be used to buffer the events generated by the source.
- Finally, a sink (e.g., ‘HDFS Sink‘) would be configured to consume events from the channel and store them in HDFS.
In this setup, a single Flume agent, or multiple agents, can handle the entire process of collecting, processing, buffering, and storing log events.