Java and Clojure

To run local and remote computation clusters, streamparse relies upon a JVM technology called Apache Storm. The integration with this technology is lightweight, and for the most part, you don’t need to think about it.

However, to get the library running, you’ll need

  1. JDK 7+, which you can install with apt-get, homebrew, or an installler; and
  2. lein, which you can install from the project’s page or github

Confirm that you have lein installed by running:

> lein version

You should get output similar to this:

Leiningen 2.3.4 on Java 1.7.0_55 Java HotSpot(TM) 64-Bit Server VM

If lein isn’t installed, follow these directions.

Once that’s all set, you install streamparse using pip:

> pip install streamparse

Your First Project

When working with streamparse, your first step is to create a project using the command-line tool, sparse:

> sparse quickstart wordcount

Creating your wordcount streamparse project...
    create    wordcount
    create    wordcount/.gitignore
    create    wordcount/config.json
    create    wordcount/fabfile.py
    create    wordcount/project.clj
    create    wordcount/README.md
    create    wordcount/src
    create    wordcount/src/bolts/
    create    wordcount/src/bolts/__init__.py
    create    wordcount/src/bolts/wordcount.py
    create    wordcount/src/spouts/
    create    wordcount/src/spouts/__init__.py
    create    wordcount/src/spouts/words.py
    create    wordcount/tasks.py
    create    wordcount/topologies
    create    wordcount/topologies/wordcount.clj
    create    wordcount/virtualenvs
    create    wordcount/virtualenvs/wordcount.txt

Try running your topology locally with:

    cd wordcount
    sparse run

The quickstart project provides a basic wordcount topology example which you can examine and modify. You can inspect the other commands that sparse provides by running:

> sparse -h

Project Structure

streamparse projects expect to have the following directory layout:

File/Folder Contents
config.json Configuration information for all of your topologies.
fabfile.py Optional custom fabric tasks.
project.clj leiningen project file, can be used to add external JVM dependencies.
src/ Python source files (bolts/spouts/etc.) for topologies.
tasks.py Optional custom invoke tasks.
topologies/ Contains topology definitions written using the Clojure DSL for Storm.
virtualenvs/ Contains pip requirements files in order to install dependencies on remote Storm servers.

Defining Topologies

Storm’s services are Thrift-based and although it is possible to define a topology in pure Python using Thrift, it introduces a host of additional dependencies which are less than trivial to setup for local development. In addition, it turns out that using Clojure to define topologies, still feels fairly Pythonic, so the authors of streamparse decided this was a good compromise.

Let’s have a look at the definition file created by using the sparse quickstart command.

(ns wordcount
  (:use     [streamparse.specs])

(defn wordcount [options]
    ;; spout configuration
    {"word-spout" (python-spout-spec
    ;; bolt configuration
    {"count-bolt" (python-bolt-spec
          {"word-spout" :shuffle}
          ["word" "count"]
          :p 2

The first block of code we encounter effectively states “import the Clojure DSL functions for Storm”:

(ns wordcount
  (:use     [backtype.storm.clojure])

The next block of code actually defines the topology and stores it into a function named “wordcount”.

(defn wordcount [options]
    ;; spout configuration
    {"word-spout" (python-spout-spec
    ;; bolt configuration
    {"count-bolt" (python-bolt-spec
          {"word-spout" :shuffle}
          ["word" "count"]
          :p 2

It turns out, the name of the function doesn’t matter much; we’ve used wordcount above, but it could just as easily be bananas. What is important, is that the function must return an array with only two dictionaries and take one argument.

The first dictionary holds a named mapping of all the spouts that exist in the topology, the second holds a named mapping of all the bolts. The options argument contains a mapping of topology settings.

An additional benefit of defining topologies in Clojure is that we’re able to mix and match the types of spouts and bolts. In most cases, you may want to use a pure Python topology, but you could easily use JVM-based spouts and bolts or even spouts and bolts written in other languages like Ruby, Go, etc.

Since you’ll most often define spouts and bolts in Python however, we’ll look at two important functions provided by streamparse: python-spout-spec and python-bolt-spec.

When creating a Python-based spout, we provide a name for the spout and a definition of that spout via python-spout-spec:

{"sentence-spout-1" (python-spout-spec
                     ;; topology options passed in
                     ;; name of the python class to ``run``
                     ;; output specification, what named fields will this spout emit?
                     ;; configuration parameters, can specify multiple
                     :p 2)
 "sentence-spout-2" (shell-spout-spec

In the example above, we’ve defined two spouts in our topology: sentence-spout-1 and sentence-spout-2 and told Storm to run these components. python-spout-spec will use the options mapping to get the path to the python executable that Storm will use and streamparse will run the class provided. We’ve also let Storm know exactly what these spouts will be emitting, namely a single field called sentence.

You’ll notice that in sentence-spout-1, we’ve passed an optional map of configuration parameters :p 2, which sets the spout to have 2 Python processes. This is discussed in Parallelism and Workers.

Creating bolts is very similar and uses the python-bolt-spec function:

{"sentence-splitter" (python-bolt-spec
                      ;; topology options passed in
                      ;; inputs, where does this bolt recieve it's tuples from?
                      {"sentence-spout-1" :shuffle
                       "sentence-spout-2" :shuffle}
                      ;; class to run
                      ;; output spec, what tuples does this bolt emit?
                      ;; configuration parameters
                      :p 2)
 "word-counter" (python-bolt-spec
                 ;; recieves tuples from "sentence-splitter", grouped by word
                 {"sentence-splitter" ["word"]}
                 ["word" "count"])
 "word-count-saver" (python-bolt-spec
                     ;; topology options passed in
                     {"word-counter" :shuffle}
                     ;; does not emit any fields

In the example above, we define 3 bolts by name sentence-splitter, word-counter and word-count-saver. Since bolts are generally supposed to process some input and optionally produce some output, we have to tell Storm where a bolts inputs come from and whether or not we’d like Storm to use any stream grouping on the tuples from the input source.

In the sentence-splitter bolt, you’ll notice that we define two input sources for the bolt. It’s completely fine to add multiple sources to any bolts.

In the word-counter bolt, we’ve told Storm that we’d like the stream of input tuples to be grouped by the named field word. Storm offers comprehensive options for stream groupings, but you will most commonly use a shuffle or fields grouping:

  • Shuffle grouping: Tuples are randomly distributed across the bolt’s tasks in a way such that each bolt is guaranteed to get an equal number of tuples.
  • Fields grouping: The stream is partitioned by the fields specified in the grouping. For example, if the stream is grouped by the “user-id” field, tuples with the same “user-id” will always go to the same task, but tuples with different “user-id”’s may go to different tasks.

There are more options to configure with spouts and bolts, we’d encourage you to refer to Storm’s Concepts for more information.

Spouts and Bolts

The general flow for creating new spouts and bolts using streamparse is to add them to your src folder and update the corresponding topology definition.

Let’s create a spout that emits sentences until the end of time:

import itertools

from streamparse.spout import Spout

class SentenceSpout(Spout):

    def initialize(self, stormconf, context):
        self.sentences = [
            "She advised him to take a long holiday, so he immediately quit work and took a trip around the world",
            "I was very glad to get a present from her",
            "He will be here in half an hour",
            "She saw him eating a sandwich",
        self.sentences = itertools.cycle(self.sentences)

    def next_tuple(self):
        sentence = next(self.sentences)

    def ack(self, tup_id):
        pass  # if a tuple is processed properly, do nothing

    def fail(self, tup_id):
        pass  # if a tuple fails to process, do nothing

The magic in the code above happens in the initialize() and next_tuple() functions. Once the spout enters the main run loop, streamparse will call your spout’s initialize() method. After initialization is complete, streamparse will continually call the spout’s next_tuple() method where you’re expected to emit tuples that match whatever you’ve defined in your topology definition.

Now let’s create a bolt that takes in sentences, and spits out words:

import re

from streamparse.bolt import Bolt

class SentenceSplitterBolt(Bolt):

    def process(self, tup):
        sentence = tup.values[0]  # extract the sentence
        sentence = re.sub(r"[,.;!\?]", "", sentence)  # get rid of punctuation
        words = [[word.strip()] for word in sentence.split(" ") if word.strip()]
        if not words:
            # no words to process in the sentence, fail the tuple

        # tuple acknowledgement is handled automatically

The bolt implementation is even simpler. We simply override the default process() method which streamparse calls when a tuple has been emitted by an incoming spout or bolt. You are welcome to do whatever processing you would like in this method and can further emit tuples or not depending on the purpose of your bolt.

In the SentenceSplitterBolt above, we have decided to use the emit_many() method instead of emit() which is a bit more efficient when sending a larger number of tuples to Storm.

If your process() method completes without raising an Exception, streamparse will automatically ensure any emits you have are anchored to the current tuple being processed and acknowledged after process() completes.

If an Exception is raised while process() is called, streamparse automatically fails the current tuple prior to killing the Python process.

Failed Tuples

In the example above, we added the ability to fail a sentence tuple if it did not provide any words. What happens when we fail a tuple? Storm will send a “fail” message back to the spout where the tuple originated from (in this case SentenceSpout) and streamparse calls the spout’s fail() method. It’s then up to your spout implementation to decide what to do. A spout could retry a failed tuple, send an error message, or kill the topology. See Dealing With Errors for more discussion.

Bolt Configuration Options

You can disable the automatic acknowleding, anchoring or failing of tuples by adding class variables set to false for: auto_ack, auto_anchor or auto_fail. All three options are documented in streamparse.bolt.Bolt.


from streamparse.bolt import Bolt

class MyBolt(Bolt):

    auto_ack = False
    auto_fail = False

    def process(self, tup):
        # do stuff...
        if error:
          self.fail(tup)  # perform failure manually
        self.ack(tup)  # perform acknowledgement manually

Handling Tick Tuples

Ticks tuples are built into Storm to provide some simple forms of cron-like behaviour without actually having to use cron. You can receive and react to tick tuples as timer events with your python bolts using streamparse too.

The first step is to override process_tick() in your custom Bolt class. Once this is overridden, you can set the storm option topology.tick.tuple.freq.secs=<frequency> to cause a tick tuple to be emitted every <frequency> seconds.

You can see the full docs for process_tick() in streamparse.bolt.Bolt.


from streamparse.bolt import Bolt

class MyBolt(Bolt):

    def process_tick(self, freq):
        # An action we want to perform at some regular interval...

Then, for example, to cause process_tick() to be called every 2 seconds on all of your bolts that override it, you can launch your topology under sparse run by setting the appropriate -o option and value as in the following example:

$ sparse run -o "topology.tick.tuple.freq.secs=2" ...

Remote Deployment

Setting up a Storm Cluster

See Storm’s Setting up a Storm Cluster.


When you are satisfied that your topology works well via testing with:

> sparse run -d

You can submit your topology to a remote Storm cluster using the command:

sparse submit [--environment <env>] [--name <topology>] [-dv]

Before submitting, you have to have at least one environment configured in your project’s config.json file. Let’s create a sample environment called “prod” in our config.json file:

    "library": "",
    "topology_specs": "topologies/",
    "virtualenv_specs": "virtualenvs/",
    "envs": {
        "prod": {
            "user": "storm",
            "nimbus": "storm1.my-cluster.com",
            "workers": [
            "log": {
                "path": "/var/log/storm/streamparse",
                "max_bytes": 100000,
                "backup_count": 10,
                "level": "info"
            "use_ssh_for_nimbus": true,
            "virtualenv_root": "/data/virtualenvs/"

We’ve now defined a prod environment that will use the user storm when deploying topologies. Before submitting the topology though, streamparse will automatically take care of instaling all the dependencies your topology requires. It does this by sshing into everyone of the nodes in the workers config variable and building a virtualenv using the the project’s local virtualenvs/<topology_name>.txt requirements file.

This implies a few requirements about the user you specify per environment:

  1. Must have ssh access to all servers in your Storm cluster
  2. Must have write access to the virtualenv_root on all servers in your Storm cluster

streamparse also assumes that virtualenv is installed on all Storm servers.

Once an environment is configured, we could deploy our wordcount topology like so:

> sparse submit

Seeing as we have only one topology and environment, we don’t need to specify these explicitly. streamparse will now:

  1. Package up a JAR containing all your Python source files
  2. Build a virtualenv on all your Storm workers (in parallel)
  3. Submit the topology to the nimbus server

Disabling & Configuring Virtualenv Creation

If you do not have ssh access to all of the servers in your Storm cluster, but you know they have all of the requirements for your Python code installed, you can set "use_virtualenv" to false in config.json.

If you would like to pass command-line flags to virtualenv, you can set "virtualenv_flags" in config.json, for example:

"virtualenv_flags": "-p /path/to/python"

Note that this only applies when the virtualenv is created, not when an existing virtualenv is used.

Using unofficial versions of Storm

If you wish to use streamparse with unofficial versions of storm (such as the HDP Storm) you should set :repositories in your project.clj to point to the Maven repository containing the JAR you want to use, and set the version in :dependencies to match the desired version of Storm.

For example, to use the version supplied by HDP, you would set :repositories to:

:repositories {"HDP Releases" "http://repo.hortonworks.com/content/repositories/releases"}

Local Clusters

Streamparse assumes that your Storm cluster is not on your local machine. If it is, such as the case with VMs or Docker images, change "use_ssh_for_nimbus" in config.json to false.


The Storm supervisor needs to have access to the log.path directory for logging to work (in the example above, /var/log/storm/streamparse). If you have properly configured the log.path option in your config, streamparse will automatically set up a log files on each Storm worker in this path using the following filename convention:



  • topology_name: is the topology.name variable set in Storm
  • component_name: is the name of the currently executing component as defined in your topology definition file (.clj file)
  • task_id: is the task ID running this component in the topology
  • process_id: is the process ID of the Python process

streamparse uses Python’s logging.handlers.RotatingFileHandler and by default will only save 10 1 MB log files (10 MB in total), but this can be tuned with the log.max_bytes and log.backup_count variables.

The default logging level is set to INFO, but if you can tune this with the log.level setting which can be one of critical, error, warning, info or debug. Note that if you perform sparse run or sparse submit with the --debug set, this will override your log.level setting and set the log level to debug.

When running your topology locally via sparse run, your log path will be automatically set to /path/to/your/streamparse/project/logs.