New in version 3.0.0.


Storm topologies are described as a Directed Acyclic Graph (DAG) of Storm components, namely bolts and spouts.

Topology DSL

To simplify the process of creating Storm topologies, streamparse features a Python Topology DSL. It lets you specify topologies as complex as those you can in Java or Clojure, but in concise, readable Python.

Topology files are located in topologies in your streamparse project folder. There can be any number of topology files for your project in this directory.

  • topologies/
  • topologies/
  • topologies/
  • ...

A valid Topology may only have Bolt and Spout attributes.

Simple Python Example

The first step to putting together a topology, is creating the bolts and spouts, so let’s assume we have the following bolt and spout:

from collections import Counter

from redis import StrictRedis

from streamparse import Bolt

class WordCountBolt(Bolt):
    outputs = ['word', 'count']

    def initialize(self, conf, ctx):
        self.counter = Counter() = 0

    def _increment(self, word, inc_by):
        self.counter[word] += inc_by += inc_by

    def process(self, tup):
        word = tup.values[0]
        self._increment(word, 10 if word == "dog" else 1)
        if % 1000 == 0:
  "counted %i words",
        self.emit([word, self.counter[word]])

class RedisWordCountBolt(Bolt):
    def initialize(self, conf, ctx):
from itertools import cycle

from streamparse import Spout

class WordSpout(Spout):
    outputs = ['word']

    def initialize(self, stormconf, context):
        self.words = cycle(['dog', 'cat', 'zebra', 'elephant'])

    def next_tuple(self):
        word = next(self.words)

One important thing to note is that we have added an outputs attribute to these classes, which specify the names of the output fields that will be produced on their default streams. If we wanted to specify multiple streams, we could do that by specifying a list of Stream objects.

Now let’s hook up the bolt to read from the spout:

Word count topology (in memory)

from streamparse import Grouping, Topology

from bolts import WordCountBolt
from spouts import WordSpout

class WordCount(Topology):
    word_spout = WordSpout.spec()
    count_bolt = WordCountBolt.spec(inputs={word_spout: Grouping.fields('word')},


Your project’s src directory gets added to sys.path before your topology is imported, so you should use absolute imports based on that.

As you can see, streamparse.Bolt.spec() and streamparse.Spout.spec() methods allow us to specify information about the components in your topology and how they connect to each other. Their respective docstrings outline all of the possible ways they can be used.

Java Components

The topology DSL fully supports JVM-based bolts and spouts via the JavaBolt and JavaSpout classes.

Here’s an example of how we would use the Storm Kafka Spout:

Pixel count topology

from streamparse import Grouping, JavaSpout, Topology

from bolts.pixel_count import PixelCounterBolt
from bolts.pixel_deserializer import PixelDeserializerBolt

class PixelCount(Topology):
    pixel_spout = JavaSpout.spec(name="pixel-spout",
    pixel_deserializer = PixelDeserializerBolt.spec(name='pixel-deserializer-bolt',
    pixel_counter = PixelCounterBolt.spec(name='pixel-count-bolt',
                                          config={"topology.tick.tuple.freq.secs": 1})

One limitation of the Thrift interface we use to send the topology to Storm is that the constructors for Java components can only be passed basic Python data types: bool, bytes, float, int, and str.

Components in Other Languages

If you have components that are written in languages other than Java or Python, you can have those as part of your topology as well—assuming you’re using the corresponding multi-lang library for that language.

To do that you just need to use the streamparse.ShellBolt.spec() and streamparse.ShellSpout.spec() methods. They take command and script arguments to specify a binary to run and its string-separated arguments.

Multiple Streams

To specify that a component has multiple output streams, instead of using a list of strings for outputs, you must specify a list of Stream objects, as shown below.

class FancySpout(Spout):
    outputs = [Stream(fields=['good_data'], name='default'),
               Stream(fields=['bad_data'], name='errors')]

To select one of those streams as the input for a downstream Bolt, you simply use [] to specify the stream you want. Without any stream specified, the default stream will be used.

class ExampleTopology(Topology):
    fancy_spout = FancySpout.spec()
    error_bolt = ErrorBolt.spec(inputs=[fancy_spout['errors']])
    process_bolt = ProcessBolt.spec(inputs=[fancy_spout])


By default, Storm uses a SHUFFLE grouping to route tuples to particular executors for a given component, but you can also specify other groupings by using the appropriate Grouping attribute. The most common grouping is probably the fields() grouping, which will send all the tuples with the same value for the specified fields to the same executor. This can be seen in the prototypical word count topology:

Word count topology (in memory)

from streamparse import Grouping, Topology

from bolts import WordCountBolt
from spouts import WordSpout

class WordCount(Topology):
    word_spout = WordSpout.spec()
    count_bolt = WordCountBolt.spec(inputs={word_spout: Grouping.fields('word')},

Topology-Level Configuration

If you want to set a config option for all components in your topology, like topology.environment, you can do that by adding a config class attribute to your Topology that is a dict mapping from option names to their values. For example:

class WordCount(Topology):
    config = {'topology.environment': {'LD_LIBRARY_PATH': '/usr/local/lib/'}}

Running Topologies

What Streamparse Does

When you run a topology either locally or by submitting to a cluster, streamparse will

  1. Bundle all of your code into a JAR
  2. Build a Thrift Topology struct out of your Python topology definition.
  3. Pass the Thrift Topology struct to Nimbus on your Storm cluster.

If you invoked streamparse with sparse run, your code is executed directly from the src/ directory.

If you submitted to a cluster with sparse submit, streamparse uses lein to compile the src directory into a jar file, which is run on the cluster. Lein uses the project.clj file located in the root of your project. This file is a standard lein project file and can be customized according to your needs.

Dealing With Errors

When detecting an error, bolt code can call its fail() method in order to have Storm call the respective spout’s fail() method. Known error/failure cases result in explicit callbacks to the spout using this approach.

Exceptions which propagate without being caught will cause the component to crash. On sparse run, the entire topology will stop execution. On a running cluster (i.e. sparse submit), Storm will auto-restart the crashed component and the spout will receive a fail() call.

If the spout’s fail handling logic is to hold back the tuple and not re-emit it, then things will keep going. If it re-emits it, then it may crash that component again. Whether the topology is tolerant of the failure depends on how you implement failure handling in your spout.

Common approaches are to:

  • Append errant tuples to some sort of error log or queue for manual inspection later, while letting processing continue otherwise.
  • Attempt 1 or 2 retries before considering the tuple a failure, if the error was likely an transient problem.
  • Ignore the failed tuple, if appropriate to the application.

Parallelism and Workers

In general, use the ``par`` “parallelism hint” parameter per spout and bolt in your configuration to control the number of Python processes per component.

Reference: Understanding the Parallelism of a Storm Topology

Storm parallelism entities:

  • A worker process is a JVM, i.e. a Java process.
  • An executor is a thread that is spawned by a worker process.
  • A task performs the actual data processing. (To simplify, you can think of it as a Python callable.)

Spout and bolt specs take a par keyword to provide a parallelism hint to Storm for the number of executors (threads) to use for the given spout/bolt; for example, par=2 is a hint to use two executors. Because streamparse implements spouts and bolts as independent Python processes, setting par=N results in N Python processes for the given spout/bolt.

Many streamparse applications will need only to set this parallelism hint to control the number of resulting Python processes when tuning streamparse configuration. For the underlying topology workers, streamparse sets a default of 2 workers, which are independent JVM processes for Storm. This allows a topology to continue running when one worker process dies; the other is around until the dead process restarts.

Both sparse run and sparse sumbit accept a -p N command-line flag to set the number of topology workers to N. For convenience, this flag also sets the number of Storm’s underlying messaging reliability acker bolts to the same N value. In the event that you need it (and you understand Storm ackers), use the -a and -w command-line flags instead of -p to control the number of acker bolts and the number of workers, respectively. The sparse command does not support Storm’s rebalancing features; use sparse submit -f -p N to kill the running topology and redeploy it with N workers.

Note that the underlying Storm thread implementation, LMAX Disruptor, is designed with high-performance inter-thread messaging as a goal. Rule out Python-level issues when tuning your topology:

  • bottlenecks where the number of spout and bolt processes are out of balance
  • serialization/deserialization overhead of more data emitted than you need
  • slow routines/callables in your code