Yothalot is an application for running parallel map/reduce algorithms on big data clusters. If you have a lot of data and want to process it using either native C++ or simple PHP scripts, Yothalot is the tool for you.

Yothalot was designed with simplicity in mind: you can start the cluster by simply starting up the Yothalot process on each of the servers in your network. The map/reduce jobs that you assign to the Yothalot cluster are automatically split up in smaller tasks and are sent to cluster nodes that have local access to the files being processed.

To keep things simple, Yothalot relies heavily on existing and proven open source technologies: GlusterFS for the distributed file system, RabbitMQ for robust inter process communication, and PHP as the simple script language that you can use for writing the jobs (although there is a C++ API too). To see how easy it is to use Yothalot you can have a look at our mapreduce counterpart of Hello world!.

Clustered file system

Yothalot is based on GlusterFS, a distributed network file system, that behaves just like a normal (POSIX) file system - but that happens to be distributed over many different servers. Because GlusterFS acts like a regular file system, you can use traditional tools and functions to read, write and manipulate files, without needing special tools or libraries to work with files.

GlusterFS automatically distributes files over the servers in the cluster, keeps track which files are stored on which server, and (based on your GlusterFS configuration) replicates files which ensures that no data is lost when a server in the cluster crashes or goes offline. The Yothalot job manager automatically assigns tasks to servers that hold a local copy of the file, so that reading or writing from files does not consume network bandwidth.

Fail safe message queues

Yothalot works with RabbitMQ for inter process message queuing. All jobs that you assign to Yothalot, and the communication between jobs use RabbitMQ message queues. RabbitMQ is a highly reliable tool, that automatically requeues messages when servers or jobs crash, making it an ideal solution for Yothalot.

RabbitMQ automatically detects when a job or server crashes (because a message is not acknowledged), and automatically puts these jobs back in the queue and reschedules them. This is exactly the sort of reliability that is needed when running jobs in large clusters, in which a single job or single node could fail.

RabbitMQ comes with a very handy web based user interface, and can be started in a cluster configuration, so that your message queues keep running even when one of the RabbitMQ nodes goes offline.


Yothalot comes with a straightforward and powerful PHP API that allows you to write and deploy your map/reduce jobs. Because PHP is crazy simple and at the same time hugely popular, you can easily write map/reduce jobs - or find people who can do that for you.

Because PHP is a scripting language, you do not have to compile any code. When you're testing and debugging your algorithm, you can simply save your changes, and schedule your job. Writing map/reduce algorithms has never been easier.

Native API

If, however, you prefer speed over simplicity, Yothalot also comes with a C++ API. With this API you can write super fast C++ applications that run map/reduce jobs.

Interested? You can go to the installation page to read how Yothalot can be obtained and installed, you can read why we have created Yothalot and are not using Hadoop, or look or mapreduce Hello world! example.