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Workflow mgmgt + task scheduling + dependency resolution

Project Description

NOTE: For the latest code and documentation, please go to

Luigi is a Python package that helps you build complex pipelines of batch jobs. It handles dependency resolution,
workflow management, visualization, handling failures, command line integration, and much more.

The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to
chain many tasks, automate them, and failures *will* happen. These tasks can be anything, but are typically long running
things like [Hadoop]( jobs, dumping data to/from databases, running machine learning
algorithms, or anything else.

There are other software packages that focus on lower level aspects of data processing, like
[Hive](, [Pig](, or [Cascading]( Luigi is not
a framework to replace these. Instead it helps you stitch many tasks together, where each task can be a Hive query, a
Hadoop job in Java, a Python snippet, dumping a table from a database, or anything else. It's easy to build up long-
running pipelines that comprise thousands of tasks and take days or weeks to complete. Luigi takes care of a lot of the
workflow management so that you can focus on the tasks themselves and their dependencies.

You can build pretty much any task you want, but Luigi also comes with a *toolbox* of several common task templates that
you use. It includes native Python support for running mapreduce jobs in Hadoop, as well as Pig and Jar jobs. It also
comes with filesystem abstractions for HDFS and local files that ensures all file system operations are atomic. This is
important because it means your data pipeline will not crash in a state containing partial data.

Luigi was built at [Spotify](, mainly by [Erik Bernhardsson]( and
[Elias Freider](, but many other people have contributed.

## Dependency graph example

Just to give you an idea of what Luigi does, this is a screen shot from something we are running in production. Using
Luigi's visualizer, we get a nice visual overview of the dependency graph of the workflow. Each node represents a task
which has to be run. Green tasks are already completed whereas yellow tasks are yet to be run. Most of these tasks are
Hadoop jobs, but there are also some things that run locally and build up data files.

## Background

We use Luigi internally at [Spotify]( to run thousands of tasks every day, organized in complex
dependency graphs. Most of these tasks are Hadoop jobs. Luigi provides an infrastructure that powers all kinds of stuff
including recommendations, toplists, A/B test analysis, external reports, internal dashboards, etc. Luigi grew out of
the realization that powerful abstractions for batch processing can help programmers focus on the most important bits
and leave the rest (the boilerplate) to the framework.

Conceptually, Luigi is similar to [GNU Make]( where you have certain tasks and these
tasks in turn may have dependencies on other tasks. There are also some similarities to
[Oozie]( and [Azkaban]( One major
difference is that Luigi is not just built specifically for Hadoop, and it's easy to extend it with other kinds of

Everything in Luigi is in Python. Instead of XML configuration or similar external data files, the dependency graph is
specified *within Python*. This makes it easy to build up complex dependency graphs of tasks, where the dependencies can
involve date algebra or recursive references to other versions of the same task. However, the workflow can trigger
things not in Python, such as running Pig scripts or scp'ing files.

## Installing

Downloading and running *python install* should be enough. Note that you probably want
[Tornado]( Also [Mechanize]( is optional if you
want to run Hadoop jobs since it makes debugging easier. See [Configuration](#configuration) for how to configure Luigi.

## Example workflow – top artists

This is a very simplified case of something we do at Spotify a lot. All user actions are logged to HDFS where we run a
bunch of Hadoop jobs to transform the data. At some point we might end up with a smaller data set that we can bulk
ingest into Cassandra, Postgres, or some other format.

For the purpose of this excercise, we want to aggregate all streams, and find the top 10 artists. We will then put it
into Postgres.

This example is also available in *examples/*

### Step 1 - Aggregate artist streams

class AggregateArtists(luigi.Task):
date_interval = luigi.DateIntervalParameter()

def output(self):
return luigi.LocalTarget("data/artist_streams_%s.tsv" % self.date_interval)

def requires(self):
return [Streams(date) for date in self.date_interval]

def run(self):
artist_count = defaultdict(int)

for input in self.input():
with'r') as in_file:
for line in in_file:
timestamp, artist, track = line.strip().split()
artist_count[artist] += 1

with self.output().open('w') as out_file:
for artist, count in artist_count.iteritems():
print >> out_file, artist, count

There are several pieces of this snippet that deserve more explanation.

* Any *Task* may be customized by instantiating one or more *Parameter* objects on the class level.
* The *output* method tells Luigi where the result of running the task will end up. The path can be some function of the
* The *requires* tasks specifies other tasks that we need to perform this task. In this case it's an external dump named
*Streams* which takes the date as the argument.
* For plain Tasks, the *run* method implements the task. This could be anything, including calling subprocesses,
performing long running number crunching, etc. For some subclasses of *Task* you don't have to implement the *run*
method. For instance, for the *HadoopJobTask* subclass you implement a *mapper* and *reducer* instead.
* *HdfsTarget* is a built in class that makes it easy to read/write from/to HDFS. It also makes all file operations
atomic, which is nice in case your script crashes for any reason.

### Running this locally

Try running this using eg.

$ python examples/ AggregateArtists --local-scheduler --date-interval 2012-06

You can also try to view the manual using --help which will give you an overview of the options:

usage: [-h] [--local-scheduler] [--scheduler-host SCHEDULER_HOST]
[--lock] [--lock-pid-dir LOCK_PID_DIR] [--workers WORKERS]
[--date-interval DATE_INTERVAL]

optional arguments:
-h, --help show this help message and exit
--local-scheduler Use local scheduling
--scheduler-host SCHEDULER_HOST
Hostname of machine running remote scheduler [default:
--lock Do not run if the task is already running
--lock-pid-dir LOCK_PID_DIR
Directory to store the pid file [default:
--workers WORKERS Maximum number of parallel tasks to run [default: 1]
--date-interval DATE_INTERVAL

Running the command again will do nothing because the output file is already created. In that sense, any task in Luigi
is *idempotent* because running it many times gives the same outcome as running it once. Note that unlike Makefile, the
output will not be recreated when any of the input files is modified. You need to delete the output file manually.

The *--local-scheduler* flag tells Luigi not to connect to a scheduler server. This is not recommended for other purpose
than just testing things.

### Step 1b - running this in Hadoop

Luigi comes with native Python Hadoop mapreduce support built in, and here is how this could look like, instead of the
class above.

class AggregateArtistsHadoop(luigi.hadoop.JobTask):
date_interval = luigi.DateIntervalParameter()

def output(self):
return luigi.HdfsTarget("data/artist_streams_%s.tsv" % self.date_interval)

def requires(self):
return [StreamsHdfs(date) for date in self.date_interval]

def mapper(self, line):
timestamp, artist, track = line.strip().split()
yield artist, 1

def reducer(self, key, values):
yield key, sum(values)

Note that `luigi.hadoop.JobTask` doesn't require you to implement a `run` method. Instead, you typically implement a
`mapper` and `reducer` method.

### Step 2 – Find the top artists

At this point, we've counted the number of streams for each artists, for the full time period. We are left with a large
file that contains mappings of artist -> count data, and we want to find the top 10 artists. Since we only have a few
hundred thousand artists, and calculating artists is nontrivial to parallelize, we choose to do this not as a Hadoop
job, but just as a plain old for-loop in Python.

class Top10Artists(luigi.Task):
date_interval = luigi.DateIntervalParameter()
use_hadoop = luigi.BooleanParameter()

def requires(self):
if self.use_hadoop:
return AggregateArtistsHadoop(self.date_interval)
return AggregateArtists(self.date_interval)

def output(self):
return luigi.LocalTarget("data/top_artists_%s.tsv" % self.date_interval)

def run(self):
top_10 = nlargest(10, self._input_iterator())
with self.output().open('w') as out_file:
for streams, artist in top_10:
print >> out_file, self.date_interval.date_a, self.date_interval.date_b, artist, streams

def _input_iterator(self):
with self.input().open('r') as in_file:
for line in in_file:
artist, streams = line.strip().split()
yield int(streams), int(artist)

The most interesting thing here is that this task (*Top10Artists*) defines a dependency on the previous task
(*AggregateArtists*). This means that if the output of *AggregateArtists* does not exist, the task will run before

$ python examples/ Top10Artists --local-scheduler --date-interval 2012-07

This will run both tasks.

### Step 3 - Insert into Postgres

This mainly serves as an example of a specific subclass *Task* that doesn't require any code to be written. It's also an
example of how you can define task templates that you can reuse for a lot of different tasks.

class ArtistToplistToDatabase(luigi.postgres.CopyToTable):
date_interval = luigi.DateIntervalParameter()
use_hadoop = luigi.BooleanParameter()

host = "localhost"
database = "toplists"
user = "luigi"
password = "abc123" # ;)
table = "top10"

columns = [("date_from", "DATE"),
("date_to", "DATE"),
("artist", "TEXT"),
("streams", "INT")]

def requires(self):
return Top10Artists(self.date_interval, self.use_hadoop)

Just like previously, this defines a recursive dependency on the previous task. If you try to build the task, that will
also trigger building all its upstream dependencies.

### Using the central planner

The *--local-scheduler* flag tells Luigi not to connect to a central scheduler. This is recommended in order to get
started and or for development purposes. At the point where you start putting things in production we strongly recommend
running the central scheduler server. In addition to providing locking so the same task is not run by multiple
processes at the same time, this server also provides a pretty nice visualization of your current work flow.

If you drop the *--local-scheduler* flag, your script will try to connect to the central planner, by default at
localhost port 8082. If you run

PYTHONPATH=. python bin/luigid

in the background and then run

$ python --date 2012-W03

then in fact your script will now do the scheduling through a centralized server. You need
[Tornado]( for this to work.

Launching *http://localhost:8082* should show something like this:

Looking at the dependency graph for any of the tasks yields something like this:

In case your job crashes remotely due to any Python exception, Luigi will try to fetch the traceback and print it on
standard output. You need [Mechanize]( for it to work and you also need
connectivity to your tasktrackers.

## Conceptual overview

There are two fundamental building blocks of Luigi - the *Task* class and the *Target* class. Both are abstract classes
and expect a few methods to be implemented. In addition to those two concepts, the *Parameter* class is an important
concept that governs how a Task is run.

### Target

Broadly speaking, the Target class corresponds to a file on a disk. Or a file on HDFS. Or some kind of a checkpoint,
like an entry in a database. Actually, the only method that Targets have to implement is the *exists* method which
returns True if and only if the Target exists.

In practice, implementing Target subclasses is rarely needed. You can probably get pretty far with the *LocalTarget* and
*hdfs.HdfsTarget* classes that are available out of the box. These directly map to a file on the local drive, or a file
in HDFS, respectively. In addition these also wrap the underlying operations to make them atomic. They both implement
the *open(flag)* method which returns a stream object that could be read (flag = 'r') from or written to (flag = 'w').
Both LocalTarget and hdfs.HdfsTarget also optionally take a format parameter. Luigi comes with Gzip support by providing
*format=format.Gzip* . Adding support for other formats is pretty simple.

### Task

The *Task* class is a bit more conceptually interesting because this is where computation is done. There are a few
methods that can be implemented to alter its behavior, most notably *run*, *output* and *requires*.

The Task class corresponds to some type of job that is run, but in general you want to allow some form of
parametrization of it. For instance, if your Task class runs a Hadoop job to create a report every night, you probably
want to make the date a parameter of the class.

#### Parameter

In Python this is generally done by adding arguments to the constructor, but Luigi requires you to declare these
parameters instantiating Parameter objects on the class scope:

class DailyReport(luigi.hadoop.JobTask):
date = luigi.DateParameter(
# ...

By doing this, Luigi can do take care of all the boilerplate code that would normally be needed in the constructor.
Internally, the DailyReport object can now be constructed by running *DailyReport(, 5, 10))* or just
*DailyReport()*. Luigi also creates a command line parser that automatically handles the conversion from strings to
Python types. This way you can invoke the job on the command line eg. by passing *--date 2012-15-10*.

The parameters are all set to their values on the Task object instance, i.e.

d = DailyReport(, 5, 10))

will return the same date that the object was constructed with. Same goes if you invoke Luigi on the command line.

Python is not a typed language and you don't have to specify the types of any of your parameters. You can simply use
*luigi.Parameter* if you don't care. In fact, the reason DateParameter et al exist is just in order to support command
line interaction and make sure to convert the input to the corresponding type (i.e. instead of a string).

#### Task.requires

The *requires* method is used to specify dependencies on other Task object, which might even be of the same class. For
instance, an example implementation could be

def requires(self):
return OtherTask(, DailyReport( - datetime.timedelta(1))

In this case, the DailyReport task depends on two inputs created earlier, one of which is the same class. requires can
return other Tasks in any way wrapped up within dicts/lists/tuples/etc.

#### Task.output

The *output* method returns one or more Target objects. Similarly to requires, can return wrap them up in any way that's
convenient for you. However we recommend that any Task only return one single Target in output. If multiple outputs are
returned, atomicity will be lost unless the Task itself can ensure that the Targets are atomically created. (If
atomicity is not of concern, then it is safe to return multiple Target objects.)

class DailyReport(luigi.Task):
date = luigi.DateParameter()
def output(self):
return luigi.hdfs.HdfsTarget('/reports/%Y-%m-%d'))
# ...


The *run* method now contains the actual code that is run. Note that Luigi breaks down everything into two stages. First
it figures out all dependencies between tasks, then it runs everything. The *input()* method is an internal helper
method that just replaces all Task objects in requires with their corresponding output. For instance, in this example

class TaskA(luigi.Task):
def output(self):
return luigi.LocalTarget('xyz')

class FlipLinesBackwards(luigi.Task):
def requires(self):
return TaskA()

def output(self):
return luigi.LocalTarget('abc')

def run(self):
f = self.input().open('r') # this will return a file stream that reads from "xyz"
g = self.output().open('w')
for line in f:
g.write('%s\n', ''.join(reversed(line.strip().split()))
g.close() # needed because files are atomic

#### Running from the command line

Any task can be instantiated and run from the command line

class MyTask(luigi.Task):
x = IntParameter()
y = IntParameter(default=45)
def run(self):
print self.x + self.y

if __name__ == '__main__':

You can run this task from the command line like this:

python MyTask --x 123 --y 456

You can also pass *main_task_cls=MyTask* to and that way you can invoke it simply using

python --x 123 --y 456

#### Executing a Luigi workflow

As seen above, command line integration is achieved by simply adding

if __name__ == '__main__':

This will read the args from the command line (using argparse) and invoke everything.

In case you just want to run a Luigi chain from a Python script, you can do that internally without the command line
integration. The code will look something like

task = MyTask(123, 'xyz')
sch = scheduler.CentralPlannerScheduler()
w = worker.Worker(scheduler=sch)

#### Instance caching

In addition to the stuff mentioned above, Luigi also does some metaclass logic so that if eg.
*DailyReport(, 5, 10))* is instantiated twice in the code, it will in fact result in the same object.
This is needed so that each Task is run only once.

#### But I just want to run a Hadoop job?

The Hadoop code is integrated in the rest of the Luigi code because we really believe almost all Hadoop jobs benefit
from being part of some sort of workflow. However, in theory, nothing stops you from using the hadoop.JobTask class (and
also hdfs.HdfsTarget) without using the rest of Luigi. You can simply run it manually using

MyJobTask('abc', 123).run()

You can use the hdfs.HdfsTarget class anywhere by just instantiating it:

t = luigi.hdfs.HdfsTarget('/tmp/test.gz', format=format.Gzip)
f ='w')
# ...
f.close() # needed

#### Using the central scheduler

The central scheduler does not execute anything for you, or help you with job parallelization. The two purposes it
serves are to

* Make sure two instances of the same task are not running simultaneously
* Provide visualization of everything that's going on.

For running tasks periodically, the easiest thing to do is to trigger a Python script from cron or from a continuously
running process. There is no central process that automatically triggers job. This model may seem limited, but we
believe that it makes things far more intuitive and easy to understand.

## Luigi patterns

### Code reuse

One nice thing about Luigi is that it's super easy to depend on tasks defined in other repos. It's also trivial to have
"forks" in the execution path, where the output of one task may become the input of many other tasks.

Currently no semantics for "intermediate" output is supported, meaning that all output will be persisted indefinitely.
The upside of that is that if you try to run X -> Y, and Y crashes, you can resume with the previously built X. The
downside is that you will have a lot of intermediate results on your file system. A useful pattern is to put these files
in a special directory and have some kind of periodical garbage collection clean it up.

### Triggering many tasks

A common use case is to make sure some daily Hadoop job (or something else) is run every night. Sometimes for various
reasons things will crash for more than a day though. A useful pattern is to have a dummy Task at the end just declaring
dependencies on the past few days of tasks you want to run.

class AllReports(luigi.Task):
date = luigi.DateParameter(
lookback = luigi.IntParameter(default=14)
def requires(self):
for i in xrange(self.lookback):
date = - datetime.timedelta(i + 1)
yield SomeReport(date), SomeOtherReport(date), CropReport(date), TPSReport(date), FooBarBazReport(date)

This simple task will not do anything itself, but will invoke a bunch of other tasks.

## Configuration

All configuration can be done by adding a configuration file named client.cfg to your current working directory or
/etc/luigi (although this is further configurable).

* *default-scheduler-host* defaults the scheduler to some hostname so that you don't have to provide it as an argument
* *error-email* makes sure every time things crash, you will get an email (unless it was run on the command line)
* *luigi-history*, if set, specifies a filename for Luigi to write stuff (currently just job id) to in mapreduce job's
output directory. Useful in a configuration where no history is stored in the output directory by Hadoop.
* If you want to run Hadoop mapreduce jobs in Python, you should also a path to your streaming jar
* By default, Luigi is configured to work with the CDH4 release of Hadoop. There are some minor differences with
regards to the HDFS CLI in CDH3, CDH4 and the Apache releases of Hadoop. If you want to use a release other than CDH4,
you need to specify which version you are using.

### Example /etc/luigi/client.cfg

version: cdh4
jar: /usr/lib/hadoop-xyz/hadoop-streaming-xyz-123.jar

error-email: foo@bar.baz

All sections are optional based on what parts of Luigi you are actually using. By default, Luigi will not send error
emails when running through a tty terminal. If using the Apache release of Hive, there are slight differences when
compared to the CDH release, so specify this configuration setting accordingly.

## More info

Luigi is the sucessor to a couple of attempts that we weren't fully happy with. We learned a lot from our mistakes and
some design decisions include:

* Straightforward command line integration.
* As little boilerplate as possible.
* Focus on job scheduling and dependency resolution, not a particular platform. In particular this means no limitation
to Hadoop. Though Hadoop/HDFS support is built-in and is easy to use, this is just one of many types of things you can
* A file system abstraction where code doesn't have to care about where files are located.
* Atomic file system operations through this abstraction. If a task crashes it won't lead to a broken state.
* The depencies are decentralized. No big config file in XML. Each task just specifies which inputs it needs and cross-
module dependencies are trivial.
* A web server that renders the dependency graph and does locking etc for free.
* Trivial to extend with new file systems, file formats and job types. You can easily write jobs that inserts a Tokyo
Cabinet into Cassandra. Adding broad support S3, MySQL or Hive should be a stroll in the park. (Feel free to send us a
patch when you're done!)
* Date algebra included.
* Lots of unit tests of the most basic stuff

It wouldn't be fair not to mention some limitations with the current design:

* Its focus is on batch processing so it's probably less useful for near real-time pipelines or continuously running
* The assumption is that a each task is a sizable chunk of work. While you can probably schedule a few thousand jobs,
it's not meant to scale beyond tens of thousands.
* Luigi maintains a strict separation between scheduling tasks and running them. Dynamic for-loops and branches are non-
trivial to implement. For instance, it's tricky to iterate a numerical computation task until it converges.

It should actually be noted that all these limitations are not fundamental in any way. However, it would take some major
refactoring work.

Also it should be mentioned that Luigi is named after the world's second most famous plumber.

## Future ideas

* S3/EC2 - We have some old ugly code based on Boto that could be integrated in a day or two.
* Built in support for Pig/Hive.
* Better visualization tool - the layout gets pretty messy as the number of tasks grows.
* Integration with existing Hadoop frameworks like mrjob would be cool and probably pretty easy.
* Better support (without much boilerplate) for unittesting specific Tasks

## Getting help

* Find us on #luigi on freenode.
* Subscribe to the [luigi-user]( group and ask a question.

## Want to contribute?

Awesome! Let us know if you have any ideas. Feel free to contact where x = luigi and y = spotify.

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