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
JDK 7+, which you can install with apt-get, homebrew, or an installler; and
lein, which you can install from the Leiningen project page or github
Apache Storm development environment, which you can install from the Storm project page
You will need to have at least Apache Storm version 0.10.0 to cooperate with Streamparse.
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
Confirm that you have storm installed by running:
> storm version
You should get output similar to this:
Running: java -client -Ddaemon.name= -Dstorm.options= -Dstorm.home=/opt/apache-storm-1.0.1 -Dstorm.log.dir=/opt/apache-storm-1.0.1/logs -Djava.library.path=/usr/local/lib:/opt/local/lib:/usr/lib -Dstorm.conf.file= -cp /opt/apache-storm-1.0.1/lib/reflectasm-1.10.1.jar:/opt/apache-storm-1.0.1/lib/kryo-3.0.3.jar:/opt/apache-storm-1.0.1/lib/log4j-over-slf4j-1.6.6.jar:/opt/apache-storm-1.0.1/lib/clojure-1.7.0.jar:/opt/apache-storm-1.0.1/lib/log4j-slf4j-impl-2.1.jar:/opt/apache-storm-1.0.1/lib/servlet-api-2.5.jar:/opt/apache-storm-1.0.1/lib/disruptor-3.3.2.jar:/opt/apache-storm-1.0.1/lib/objenesis-2.1.jar:/opt/apache-storm-1.0.1/lib/storm-core-1.0.1.jar:/opt/apache-storm-1.0.1/lib/slf4j-api-1.7.7.jar:/opt/apache-storm-1.0.1/lib/storm-rename-hack-1.0.1.jar:/opt/apache-storm-1.0.1/lib/log4j-api-2.1.jar:/opt/apache-storm-1.0.1/lib/log4j-core-2.1.jar:/opt/apache-storm-1.0.1/lib/minlog-1.3.0.jar:/opt/apache-storm-1.0.1/lib/asm-5.0.3.jar:/opt/apache-storm-1.0.1/conf org.apache.storm.utils.VersionInfo Storm 1.0.1 URL https://git-wip-us.apache.org/repos/asf/storm.git -r b5c16f919ad4099e6fb25f1095c9af8b64ac9f91 Branch (no branch) Compiled by tgoetz on 2016-04-29T20:44Z From source with checksum 1aea9df01b9181773125826339b9587e
- If
leinisn't installed, - follow these directions to install it.
If storm isn't installed,
follow these directions.
Once that's all set, you install streamparse using pip:
> pip install streamparse
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/topologies
create wordcount/topologies/wordcount.py
create wordcount/virtualenvs
create wordcount/virtualenvs/wordcount.txt
Done.
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
If you see an error like:
Local Storm version, 1.0.1, is not the same as the version in your project.clj, 0.10.0. The versions must match.
You will have to edit your wordcount/project.clj file and change Apache Storm library version to match the one you have installed.
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 :ref:`topology_dsl`. |
| virtualenvs/ | Contains pip requirements files used to install dependencies on remote Storm servers. |
Storm's services are Thrift-based and although it is possible to define a topology in pure Python using Thrift. For details see :ref:`topology_dsl`.
Let's have a look at the definition file created by using the
sparse quickstart command.
.. literalinclude:: ../streamparse/bootstrap/project/topologies/wordcount.py
:language: python
In the count_bolt 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. This is the default grouping if no other is specified.
- 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 our :ref:`topology_dsl` docs or Storm's Concepts for more information.
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):
outputs = ['sentence']
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)
self.emit([sentence])
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 nothingThe 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):
outputs = ['word']
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
self.fail(tup)
return
for word in words:
self.emit([word])
# tuple acknowledgement is handled automaticallyThe 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.
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.
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
:meth:`~streamparse.storm.spout.Spout.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 :ref:`dealing-with-errors` for
more discussion.
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
:class:`streamparse.bolt.Bolt`.
Example:
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 manuallyTicks 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
:class:`streamparse.bolt.Bolt`.
Example:
from streamparse.bolt import Bolt
class MyBolt(Bolt):
def process_tick(self, freq):
# An action we want to perform at some regular interval...
self.flush_old_state()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" ...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:
{
"serializer": "json",
"topology_specs": "topologies/",
"virtualenv_specs": "virtualenvs/",
"envs": {
"prod": {
"user": "storm",
"nimbus": "storm1.my-cluster.com",
"workers": [
"storm1.my-cluster.com",
"storm2.my-cluster.com",
"storm3.my-cluster.com"
],
"log": {
"path": "/var/log/storm/streamparse",
"file": "pystorm_{topology_name}_{component_name}_{task_id}_{pid}.log",
"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:
- Must have ssh access to all servers in your Storm cluster
- Must have write access to the
virtualenv_rooton all servers in your Storm cluster
If you would like to use your system user for creating the SSH connection to
the Storm cluster, you can omit the user setting from your config.json.
By default the root user is used for creating virtualenvs when you do not
specify a user in your config.json. To override this, set the
sudo_user option in your config.json. sudo_user will default to
user if one is specified.
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:
- Package up a JAR containing all your Python source files
- Build a virtualenv on all your Storm workers (in parallel)
- Submit the topology to the
nimbusserver
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 have virtualenvs on your machines that you would like streamparse to
use, but not update or manage, you can set "install_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.
If you would like to share a single virtualenv across topologies, you can set
"virtualenv_name" in config.json which overrides the default behaviour
of using the topology name for virtualenv. Updates to a shared virtualenv should
be done after shutting down topologies, as code changes in running topologies
may cause errors.
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"}
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.
If you frequently use the same options to sparse submit in your project, you
can set them in config.json using the options key in your environment
settings. For example:
{
"topology_specs": "topologies/",
"virtualenv_specs": "virtualenvs/",
"envs": {
"vagrant": {
"user": "vagrant",
"nimbus": "streamparse-box",
"workers": [
"streamparse-box"
],
"virtualenv_root": "/data/virtualenvs",
"options": {
"topology.environment": {
"LD_LIBRARY_PATH": "/usr/local/lib/"
}
}
}
}
}You can also set the --worker and --acker parameters in config.json
via the worker_count and acker_count keys in your environment settings.
{
"topology_specs": "topologies/",
"virtualenv_specs": "virtualenvs/",
"envs": {
"vagrant": {
"user": "vagrant",
"nimbus": "streamparse-box",
"workers": [
"streamparse-box"
],
"virtualenv_root": "/data/virtualenvs",
"acker_count": 1,
"worker_count": 1
}
}
}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 use the value for the log.file option to set up log files for each
Storm worker in this path. The filename can be customized further by using
certain named placeholders. The default filename is set to:
pystorm_{topology_name}_{component_name}_{task_id}_{pid}.log
Where:
topology_name: is thetopology.namevariable set in Stormcomponent_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 topologypid: 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.