Kafka and Flume both are used for real time event processing system. They both are developed by Apache. Kafka is a publish-subscribe model messaging system. It can be used to communicate between publisher and subscriber using topic. One of the best features of Kafka is, it is highly available and resilient to node failures and supports automatic recovery.So let see what are the Difference between Apache Kafka and Flume.
On the other hand, flume is mainly designed for Hadoop and it is a part of Hadoop ecosystem. It is used to collect data from different sources and transfer data to the centralized data store. Flume was mainly designed in order to collect streaming data (log data) from various web servers to HDFS.
Apache Kafka:
It is an open-source stream-processing software platform written in Java and Scala. It is made by LinkedIn which is given to the Apache Software Foundation. Apache Kafka aims to provide a high throughput, unified, low-latency platform for handling the real-time data feeds. Kafka generally used TCP based protocol which optimized for efficiency. It is very fast and performs 2 million writes per second.
It also guarantees zero percent data loss.
Apache Kafka generally used for real-time analytics, ingestion data into the Hadoop and to spark, error recovery, website activity tracking.
Kafka is an open-source stream processing platform developed by the Apache. It is a mediator between source and destination for a real-time streaming process where we can persist the data for a specific time period. Kafka is a distributed messaging system. Where we can use that persisted data for the real-time process. It runs as a service on one or more servers. The Kafka stores stream of records in categories called topics. Each stream record consists of key, value, and timestamp.
Following are the main component of Kafka
Source: This will trigger when a new CDC (Change Data Capture) or new insert occurs at the source. For that, we have to define a key column to identify the change.
Broker: Which is responsible for holding data. Each Broker holds no of partition.
Topic: It categorizes the data. Topics in Kafka are always subscribed by multiple consumers that subscribe to the data written to it.
Partition: Topics are further splited into partition for parallel processing.
Producer: Producer is responsible for publishing the data. It will push the data to the topics of their choice. The producer will choose which record to assign to which partition within the topic.
Consumer: Consumers will consume data from topics. A consumer will be a label with their consumer group. If the same topic has multiple consumers from different consumer group then each copy has been sent to each group of consumers.
Kafka has better throughput and has features like built-in partitioning, replication, and fault-tolerance which makes it the best solution for huge scale message or stream processing applications.
Apache Flume:
Apache Flume is a reliable, distributed, and available software for efficiently aggregating, collecting, and moving large amounts of log data. It has a flexible and simple architecture based on streaming data flows. It is written in java. It has its own query processing engine which makes it to transform each new batch of data before it is moved to the intended sink. It has a flexible design.
Apache Flume is a tool which is used to collect, aggregate and transfer data streams from different sources to a centralized data store such as HDFS (Hadoop Distributed File System). Flume is highly reliable, configurable and manageable distributed data collection service which is designed to gather streaming data from different web servers to HDFS. It is also an open source data collection service.
Apache Flume is based on streaming data flows and has a flexible architecture. Flume offers highly fault-tolerant, robust and reliable mechanism for fail-over and recovery with the capability to collect data in both batch and in stream modes. Flume’s capabilities are leveraged by enterprises to manage high volume streams of data to land in HDFS. For instance, data streams include application logs, sensors and machine data and social media, and so on. These data, when landed in Hadoop, can be analyzed by running interactive queries in Apache Hive or serve as real-time data for business dashboards in Apache HBase. Some of the features include,
- Gather data from multiple sources, and efficiently ingest into HDFS
- A variety of source and destination types are supported
- Flume can be easily customized, reliable, scalable and fault-tolerant
- Can store data in any centralized store (eg., HDFS, HBase)
Difference between Apache Kafka and Flume:
Below is a table of differences between Apache Kafka and Apache Flume:
| Apache Kafka | Apache Flume |
| Apache Kafka is a distributed data system. | Apache Flume is a available, reliable, and distributed system. |
| It is optimized for ingesting and processing streaming data in real-time. | It is efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store. |
| It is basically working as a pull model. | It is basically working as a push model . |
| It is easy to scale. | It is not scalable in comparison with Kafka. |
| An fault-tolerant, efficient and scalable messaging system. | It is specially designed for Hadoop. |
| It supports automatic recovery if resilient to node failure. | You will lose events in the channel in case of flume-agent failure. |
| Kafka runs as a cluster which handles the incoming high volume data streams in the real time. | Flume is a tool to collect log data from distributed web servers. |
| Kafka will treat each topic partition as an ordered set of messages. | Flume can take in streaming data from the multiple sources for storage and analysis which use in Hadoop. |
Conclusion:
In summary, Apache Kafka vs Flume offer reliable, distributed and fault-tolerant systems for aggregating and collecting large volumes of data from multiple streams and big data applications. Both Apache Kafka and Flume systems can be scaled and configured to suit different computing needs. Kafka’s architecture provides fault-tolerance, but Flume can be tuned to ensure fail-safe operations. Users planning to implement these systems must first understand the use case and implement appropriately to ensure high performance and realize full benefits.