Aug 162013

Recently I was asked to become a review for a book Apache Kafka published by the PACKT publishing. I was happy to accept the offer and now I am more than happy to share my view of the technology.

Apache Kafka is a distributed publish-subscribe messaging system that is used in many top IT companies, such as LinkedIn, Twitter, Spotify, DataSift, Square etc. The system guarantees the following properties: persistent messaging with a constant access to disc structures and high performance; high throughput; distributiveness 🙂 as the access to the system is both load balanced and partitioned to reduce the pressure on the one node; real-time properties and Hadoop and Storm integration.

Installation and building the system is very easy and won’t take much time. Depending on your needs a various set ups for the cluster are possible: single node – one broker (core Kafka process), single node – multiple brokers, multiple nodes – multiple brokers. Depending on the choice, the Kafka system should be configured properly, e.g. configuration properties of each broker need to be known by the producer (message sources).

The following figure shows the simplest case, when the messages are published by producers and, through the Kafka broker, they are delivered to the consumers. In this scenario (single node – single broker) all producers, broker and consumers are run on different machines.



Among important design solutions are the following: message caching, opportunity to re consume messages, group messages (reduce network overhead), local maintenance of the consumed messages, the system is purely decentralized and uses zookeeper for load balancing, both asynchronous and synchronous messaging is supported; moreover, a message compression to reduce the network load is used, e.g., gzip or google snappy. Additionally, the need of replication of any active system puts Kafka to the new level. Apache Kafka allow mirroring of an active data cluster into a passive one. It simply consume the messages form the source cluster and republish them on the target cluster. This feature is highly useful if the reliability and fault-tolerance are important concerns. Additionally, a simple replication is implemented within the system. This mainly is reached by the partitioning of messages (hashes) and a presence of a lead replica for each partition (this way both synch and asynch replication can be arranged).

The API provide a sufficient support to create both producer and consumers. Additionally, consumer can be one of two different type: one that ignore further processing of messages and just need the message to be delivered and another that require further processing of the message upon delivery. Consumer implementation can be both single threaded and multithreaded. However to prevent any unpredictable behavior the number of threads should correspond to the number of topics to consume. This way the full utilization of the threads and necessary message order will be preserved.

Kafka can be integrated with the the following technologies: Twitter Storm and Apache Hadoop for further online stream processing and offline analysis respectively. Storm integration is implemented on the consumer side. That is, Kafka consumer represented as regular Storm spouts that reads the data from the Kafka cluster. On the other hand, integration with Hadoop is bilateral, as it can be integrated with Hadoop as both producer and consumer.

In the former, Hadoop is integrated as a bridge for publishing the data to the Kafka broker. Hadoop producer extracts the data from the system in two ways: (a) use Pig scripts for writing data in binary Avro format (here, writes to multiple topics are made easier), (b) use Kafka “OutputFormat” class which publish data as bytes and provides control over output.

In the latter, Hadoop consumer represents a Hadoop job that pulls information from the Kafka system to HDFS. This might happen both sequentially and in parallel.

Pros: Easy to use and deploy; scalable; fast; distributed.

Cons: Might lack configurability and automatization; replication improvements required; fault-tolerance optimization.

I am going to actually test the framework and compare it with the current project I am working on at EPFL (awesome highly dynamic bft decentralized scalable pub/sub system). Stay tuned:)

  One Response to “Apache Kafka as a Technology”

  1. Add a larger diagram 🙂