Skip to main content

The Kafka series (Part 1): what's Kafka?

I am starting today a new series of posts about Apache Kafka (http://kafka.apache.org/). Kafka is an Open Source message broker written in Scala (http://www.scala-lang.org/). Originally it has been developed by LinkedIn (https://ie.linkedin.com/), but then it has been released as Open Source in 2011 and it is currently maintained by the Apache Software Foundation (http://www.apache.org/). Why one should prefer Kafka to a traditional JMS message broker? Here's a short list of convincing reasons:
  • It's fast: a single Kafka broker running on commodity hardware can handle hundreds of megabytes of reads and writes per second from thousands of clients.
  • Great scalability: it can be easily and transparently expanded without downtime. 
  • Durability and Replication: messages are persisted on disk and replicated within the cluster to prevent data loss (setting a proper configuration using the high number of available configuration parameters you could achieve zero data loss).
  • Performances: each broker can handle terabytes of messages without performance impact. 
  • It allows real time stream processing.
  • It can be easily integrated with other popular systems for Big Data architectures like Hadoop, Spark and Storm (later posts of this series will cover the integration with them).

Basic core concepts

These are the core concepts of Kafka you need to get familiar with:
  • Topics: they are categories or feed names to which upcoming messages are published.
  • Producers: any entity that publish messages to a topic.
  • Consumers: any entity that subscribes to topics and consumes messages from them.
  • Brokers: services that handle read and write operations.

Architecture

And here's the architectural diagram of a typical Kafka cluster:

Kafka uses ZooKeeper (https://zookeeper.apache.org/). The Kafka binaries provide it, so if the hosting machines don't have ZooKeeper on board you can use the one bundled with Kakfa.
The communication between clients and servers happens using a high performant and language agnostic TCP protocol.
Kafka provides APIs to implement producers and consumers using different programming languages. In this series I will focus mainly on Java.

Use Cases

There are several use cases for Kafka. Here I am going to show a short list, but the scenarios could be much more:
  • Messaging
  • Stream processing
  • Log Aggregation
  • Metrics
  • Web activities tracking
  • Event Sourcing
As soon as you get familiar with Kafka you will realize what of your use cases could be well managed through this system.

What's next?

In Part 2 of this series we will walk among the steps to configure a Kafka cluster in a Linux environment.
Labels:

Comments

Post a Comment

Popular posts from this blog

Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do
Post a Comment
Read more

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv
6 comments
Read more

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You
5 comments
Read more