Storm in pictures

We’ve been using Storm for some time now. It is quite easy to start using it in a local environment (e.g. your IDE), but it can be a bit daunting to install a cluster and then really understand what Storm is actually doing to execute your topology. The documentation has improved over time, and some blogs have appeared, notably the one by Michael Noll, that explain how Storm works. Nevertheless I still felt the need to explain to myself how Storm works and hopefully this will prove useful to others as well.

We’ll start with some Storm terminology. The following picture shows a topology:


  • A topology is a description of a workflow. It defines how the code you write is put together and executed.
  • A spout is responsible for data input. Storm does not impose any restriction on the source of the data. Hadoop, for example, wants the data to be in its own filesystem, HDFS. With Storm, you can use any data source you want as long as you’re able to write a piece of code for it that fetches this data. Typically, the input data comes from a queue such as Kafka or ActiveMQ, but databases, filesystems or web servers are also fine.
  • A bolt is responsible for processing the data. It gets data from a spout or another bolt in the form of tuples. A tuple is a lightweight data format provided by Storm that you can use to wrap the data you actually need to process. A bolt either processes the input data and sends it out as another tuple (or set of tuples), or it stores it in some external system. This could be yet another queue, database, filesystem, etc.
  • Storm 0.9 added the notion of a metrics consumer. It is often helpful to know what is going on in your topology. The Storm UI provides some insight, but this is on a general level, i.e. it shows how many tuples were transmitted between spouts and bolts, and how many were acknowledged or failed. It can’t tell anything about the internal state of your spouts and bolts, because that is application specific. This is where the metrics framework, and the metrics consumer in particular, comes into play. The metrics framework allows you to create metrics variables in your spouts and bolts. These metrics are transmitted to the metrics consumer. Like the bolts, it is then your responsibility to push these metrics to an external system for storage or visualization.

The architecture of Storm is illustrated by the following picture:


  • There is a single master node that is responsible for the scheduling of tasks in the cluster. One process on this machine is called Nimbus, which performs the actual scheduling of tasks. Another process is the Storm UI, which can be used to view the cluster and the topologies.
  • There are several slave nodes that actually execute your code. One process on these machines is the Supervisor, which supervises the process that actually executes your code (see below for more detail). Another process, new since Storm 0.9, is the Logviewer. It is now possible to use the Storm UI, pinpoint any problem in the execution of your code and then click through to the logfile on the slave node that executed the code.
  • Storm uses ZooKeeper to perform cluster management. In a development environment, a single ZooKeeper node is fine. In a production environment, it is necessary to use three, five or more (2n+1) nodes.

Storm tries to make use of as much parallelism as possible. To achieve this, it uses multiple machines (supervisors), runs several Java virtual machines on each machine (workers) and uses many threads per JVM (executors). The following picture illustrates this:


The number of supervisors obviously depends on the number of machines you have installed the Storm supervisor process on. The number of workers each of these machines run is configured in the storm.yaml configuration file. By default there are four worker processes (JVMs) per supervisor, but this can be changed by adding port numbers to this file.

  - 6700
  - 6701
  - 6702
  - 6703

The number of supervisors and workers that are useable by Storm are now set. Now we can tell Storm how we want to run our topology on this cluster:


We start by creating a Config object and setting the number of workers we want to use for this specific topology. In this case we selected two of them. Then we create a TopologyBuilder object and set our spout, our bolts and our metrics consumer. We can set two parameters that tell Storm how many to have of these components and how parallel they should be executed:

  • The first parameter is the parallelism hint. This tells Storm how many executors (threads) should be used for this component. By default this number is equal to 1.
  • The second number we can set is the number of tasks. This tells Storm how many times the component should be present in total. If the number of tasks is higher than the parallelism hint, then there will be executors that run more than one task serially. For example, when using a parallelism hint of 2 and a number of tasks of 6, there will be 2 executors that run 3 components serially. By default the number of tasks is equal to the parallelism hint.

5 thoughts on “Storm in pictures

  1. Permalink  ⋅ Reply


    August 8, 2014 at 6:58pm

    Excellent explanation. Could you please guide how we should decide that which no. of workers for a topology will be sufficient.

  2. Permalink  ⋅ Reply


    February 24, 2015 at 4:59pm

    Thank you for this explanation!

  3. Permalink  ⋅ Reply

    Kritika Kapoor

    March 14, 2016 at 6:47am

    Great post!!!
    I found one of the good Apache Storm tutorial at which is useful for beginners as well as Advance learner’s to learn about the various concepts of Apache Storm .

  4. Permalink  ⋅ Reply


    September 7, 2016 at 4:57pm

    extremely good explanation of parallelism in storm.No where on internet it is explained so clearly.

  5. Permalink  ⋅ Reply


    April 4, 2017 at 7:02am

    Brilliant doubt what is task here ?, Task means a set of instruction as per my understanding. But here in storm suppose a bolt is executing its execute() method so if we have multiple threads to execute this method so it can be run in parallel for multiple tuples. But what is task here ?

Leave a Reply

Your email will not be published. Name and Email fields are required.