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As Luigi's homepage said, it's "a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in."

Luiti is built on top of Luigi, separates all your tasks into multiple packages, and forces one task per one Python file. Luiti task classes can be managed by the luiti command, supported operations are ls, new, generate, info, clean, run, and webui.

Luiti is born to build a layered database warehouse, corresponding to the different packages we just mentioned. A data warehouse consists of synced data sources, fact tables, dimension tables, regular or temporary business reports.

The essence of batching processing system is to separate a large task into small tasks, and the essence of business report is that a daily report or a weekly report is requried, so here comes TaskDay, TaskWeek, and more. Task classes also have a Hadoop version, such as TaskDayHadoop, TaskWeekHadoop, and so on.

You can pass any parameters into Luigi's tasks, but Luiti recommend you to pass only date_value parameter. So you can run Luiti tasks periodically, e.g. hourly, daily, weekly, etc. luiti = luigi + time.

Document guide

  1. Intro
  2. Luiti command tool
  3. Luiti WebUI screenshots
  4. Core concepts based on time management
  5. Built-in properties
  6. Manage multiple projects in luiti
  7. A simple guide to Luigi
  8. A simple example in luiti
  9. Installment and Development
  10. Task recommendation
  11. Task decorators
  12. MapReduce related
  13. Extend luiti
  14. FAQ
  15. Run tests
  16. Who uses Luiti?

Keynote Luiti - An Offline Task Management Framework

Luiti command tool

After installed package, you can use luiti command tool that contained in the package.

$ luiti
usage: luiti [-h] {ls,new,generate,info,clean,run,webui} ...

Luiti tasks manager.

optional arguments:
  -h, --help            show this help message and exit

  valid subcommands

    ls                  list all current luiti tasks.
    new                 create a new luiti project.
    generate            generate a new luiti task python file.
    info                show a detailed task.
    clean               manage files that outputed by luiti tasks.
    run                 run a luiti task.
    webui               start a luiti DAG visualiser.

Luiti WebUI screenshots

# or
luiti webui --project-dir your_main_luiti_package_path

Here's some screenshots from, just to give you an idea of how luiti's multiple Python packages works.

  1. Luiti WebUI list Luiti WebUI list

  2. Luiti WebUI show Luiti WebUI show

  3. Luiti Code show Luiti Code show

Core concepts based on time management

date type

Basic inheriting task classes:

  1. TaskBase (luigi.Task)
  2. TaskHour (TaskBase)
  3. TaskDay (TaskBase)
  4. TaskWeek (TaskBase)
  5. TaskMonth (TaskBase)
  6. TaskRange (TaskBase)

You can extend more date type by subclass TaskBase, and make sure the date types are added in TaskBase.DateTypes too.

Hadoop ineriting task classes:

  1. TaskDayHadoop (luigi.hadoop.HadoopExt, TaskDay)
  2. TaskWeekHadoop (luigi.hadoop.HadoopExt, TaskWeek)
  3. TaskRangeHadoop (luigi.hadoop.HadoopExt, TaskRange)

Other task classes:

  1. RootTask (luigi.Task)
  2. StaticFile (luigi.Task)
  3. MongoImportTask (TaskBase) # export json file from hdfs to mongodb.

Task specification and built-in properties and recommendation

Task naming conventions

  1. One Task class per file.
  2. Task class should be camel case ( e.g. EnglishStudentAllExamWeek), file name should be low case with underscore ( e.g. ).
  3. Task files should be under the directory of luiti_tasks. luiti use this convertion to linking tasks inner and outer of pacakges.
  4. Task class name should be ended with date type, e.g. Day, Week, etc. Please refer to TaskBase.DateTypes.

Task builtin properties.

  1. date_value. Required, even it's a Range type Task. This ensure that output will be written to a day directory.
  2. data_file. The absolute output file path, it's a string format.
  3. data_dir. The directory of the absolute output file path, it's a string format.
  4. root_dir. The root path of this package. data_file and data_dir are all under it.
  5. output. Basic Task's output class is LocalTarget, and Hadoop Task's output class is hdfs.HdfsTarget.
  6. date_str. A datetime string, such as "20140901".
  7. date_type. A string that generated from task class name, e.g. Day, Week, etc.
  8. date_value_by_type_in_last. If current date type is Week, and it'll return the previous week's date_value.
  9. date_value_by_type_in_begin. If current date type is Week, and it'll return Monday zero clock in the current week.
  10. date_value_by_type_in_end. If current date type is Week, and it'll return Sunday 11:59:59 clock in the current week.
  11. pre_task_by_self. Usually it returns previous task in the current date type. If reaches the time boundary of current date type, it returns RootTask.
  12. is_reach_the_edge. It's semester at 17zuoye business.
  13. instances_by_date_range. Class function, return all task intances list that belongs to current date range.
  14. task_class. Return current task class.

Manage multiple projects in luiti

The directory structure of a specific project.

We recommend you to organize every project's directory structure as the below form, and it means it's also a normal Python package, for example:

project_A                                            --- project directory                                           --- Python package install script
  README.markdown                                    --- project README
  project_A/                                         --- Python package install directory
  ├──                                    --- mark current directories on disk as a Python package directories
  └── luiti_tasks                                    --- a directory name which indicates it contains several luiti tasks
      ├──                                --- mark current directories on disk as a Python package directories
      ├──                            --- initialize luiti environment variables
      ├──               --- an example luiti task
      ├──                                 --- another example luiti task
      └── templates                                  --- some libraries

After installing luiti, you can run following command line to generate a project like above. bash luiti new --project-name project_A

If other luiti projects need to using this package, and you need to install this package, to make sure luiti could find them in the search path (sys.path) of Python modules.

Every luiti projects share the same structure, e.g. project_A/luiti_tasks/ After config luigi.plug_packages("project_B", "project_C==0.0.2"]) in, you can use @luigi.ref_tasks("ArtistStreamDay') to indicate current Task to find ArtistStreamDay Task in current package project_A, or related project_B, project_C packages.

A simple guide to Luigi

Luigi's core concept is to force you to separte a big task into many small tasks, and they're linked by atomic Input and Ouput. Luigi contains four parts mainly:

  1. Output. It must be implemented in output function, such as LocalTarget and hdfs.HdfsTarget.
  2. Input. It must be implemented in requires function, and the function is supposed to return some or None task instances.
  3. Parameters. Parameters should be inherited from luigi.Parameter, e.g. DateParameter, etc.
  4. Execute Logic. Use run function if running at local, or mapper and reducer if running on a distributed MapReduce YARN.

After finish the business logic implementation and test cases, You can submit your task to the luigid background daemon. luigid will process task dependencies automatically, this is done by checking output is already exists (It's the Target class's function). And luigi will guarantee that task instances are uniq in current luigid background process by the task class name and parameters.

A simple example in luiti

An official example from luigi.

Code below is copied from

import luigi
from collections import defaultdict

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

The same example written in luiti.

from luiti import *

class ArtistStreamDay(StaticFile):

    def filepath(self):
        return TargetUtils.hdfs("/tmp/streams_%s.tsv" % self.date_str
from luiti import *

class AggregateArtistsWeek(TaskWeek):

    def requires(self):
        return [self.ArtistStreamDay(d1) for d1 in self.days_in_week]

    def output(self):
        return TargetUtils.hdfs("/data/artist_streams_%s.tsv" % self.date_str

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

        for file1 in self.input():
            for line2 in TargetUtils.line_read(file1):
                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

Optimizition notes:

  1. luiti's task class is built in with date_value property, and converted into Arrow data type.
  2. In ArtistStreamDay, date_str is transformed from date_value, and converted from a function into a instance property after the first call.
  3. @luigi.ref_tasks bind ArtistStreamDay as AggregateArtistsWeek's instance property, so we can use self.ArtistStreamDay(d1) form to instantiate some task instances.
  4. After AggregateArtistsWeek is inherited from TaskWeek, it'll has self.days_in_week property automatically.
  5. TargetUtils.line_read replaced original function that needs two lines codes to complete the feature, and return a Generator directly.

Writing MapReduce in luiti

from luiti import *

class AggregateArtistsWeek(TaskWeekHadoop):

    def requires(self):
        return [self.ArtistStreamDay(d1) for d1 in self.days_in_week]

    def output(self):
        return TargetUtils.hdfs("/data/weeks/artist_streams_%s.tsv" % self.date_str

    def mapper(self, line1):
        timestamp, artist, track = line.strip().split()
        yield artist, 1

    def reducer(self, artist, counts):
        yield artist, len(counts)

Yes, it's almost no difference to luigi, except the self.days_in_week property and @luigi.ref_tasks decorator.

Installment and Develop requirements


pip install luiti

Or lastest source code

git clone
cd luiti
python install

Develop requirements

  1. Node.js & bower
  2. pip requirements from
  3. tox & nose

Time library

The time library is Arrow , every Task instance's date_value property is a arrow.Arrow type.

luiti will convert date paramters into local time zone automatically. If you want to customize time, please prefer to use ArrowParameter.get(*strs) and to make sure you use the local time zone.

Task recommendation


We highly recommend you to use cached_property, like werkzeug said, "A decorator that converts a function into a lazy property. The function wrapped is called the first time to retrieve the result and then that calculated result is used the next time you access the value".

This function is heavily used in 17zuoye everyday, we use it to cache lots of things, such as a big data dict.

class AnotherBussinessDay(TaskDayHadoop):

    def requires(self):
        return [task1, task2, ...]

    def mapper(self, line1):
        k1, v1 = process(line1)
        yield k1, v1

    def reducer(self, k1, vs1):
        for v1 in vs1:
            v2 = func2(v1, self.another_dict)
            yield k1, v2

    def another_dict(self):
        # lots of cpu/io
        return big_dict

Global utilities.

  1. Basic utilities, such as os, re, json, defaultdict, etc.
  2. Date processing utilities, they are arrow, ArrowParameter.
  3. Cache utilities, cached_property.
  4. Other utilities, such as IOUtils, DateUtils, TargetUtils, HDFSUtils, MRUtils, MathUtils, CommandUtils, CompressUtils.

Task decorators

# 1. Bind related tasks lazily, and can be used as instance property directly.

# 2. Support multiple file output in MapReduce

# 3. Run MapReduce in local mode by only add one decorator.

# 4. Check current task' data source's date range is satisfied.

# 5. Check current task can be runned in current date range.
@luigi.check_runtime_range(hour_num=[4,5,6], weekday_num=[1])

# 6. Let Task Templates under [luigi.contrib]( to follow with Luiti's Task convertion.

class AnotherBussinessDay(TaskDayHadoop):

Clean temporary file when a task fails.

When executing a MR job, luigi will write result to a file with timestamp instantly. If the task successes, then rename to the name that the task's original output file path. If the task fails, then YARN will delete the temporary file automatically.

Read file in a Generator way.

  1. Original way. for line1 in TargetUtils.line_read(hdfs1), line1 is an unicode type.
  2. Read by JSON. for json1 in TargetUtils.json_read(hdfs1), json1 is a valid Python object.
  3. Read in a K-V format. for k1, v1 in TargetUtils.mr_read(hdfs1), k1 is an unicode type, and v1 is a Python object.

HDFS file object

We recommend to use TargetUtils.hdfs(path1). This function compacts the MR file result data format that consists of "part-00000" file blocks.

MapReduce test cases

  1. Add MapReduce input and output to mrtest_input and mrtest_output, these mimic the MapReduce processing.
  2. In your test file, use @MrTestCase to decorator your test class, and add your task class to mr_task_names list.
  3. (Optional) Add some config dict to mrtest_attrs to mimic properties that generated in production mode.
  4. Run your test cases!

from luiti import *

class BuyFruitDay(TaskDayHadoop):

    def requries(self):

    def output(self):

    def mapper(self, line):
        yield uid, fruit

    def reducer(self, uid, fruits):
        price = sum([self.price_dict[fruit] for fruit in fruits])
        yield "", MRUtils.str_dump({"uid": uid, "price": price})

    def price_dict(self):
        result = dict()
        for json1 in TargetUtils.json_read(a_fruit_price_list_file):
            result[json1["name"]] = json1["price"]
        return result

    def mrtest_input(self):
        return """
{"uid": 3, "fruit": "Apple"}
{"uid": 3, "fruit": "Apple"}
{"uid": 3, "fruit": "Banana"}

    def mrtest_output(self):
        return """
{"uid": 3, "price": 7}

    def mrtest_attrs(self):
        return {
          "price_dict": {
            "Apple": 3,
            "Banana": 1,

test file

from luiti import MrTestCase

class TestMapReduce(unittest.TestCase):
    mr_task_names = [

if __name__ == '__main__':

Extend luiti

Using TaskBase's builtin extend class function to extend or overwrite the default properties or functions, for example:

    'property_1' : lambda self: "property_2",

extend class function compacts with function, property, cached_property, or any other attributes at the same time。When you want to overwrite property and cached_property, you just need a function value, and extend will automatically converted into property and cached_property type.


Q: How atomic file is supported?

A: As luigi's document mentioned that "Simple class that writes to a temp file and moves it on close() Also cleans up the temp file if close is not invoked", so use the self.input().open("r") or self.output().open("w") instead of open("some_file", "w").

Q: Can luigi detect the interdependent tasks?

A: It's not question inside of luigi, but it's a question about topological sorting as a general computer science topic. The task scheduler is implemented at luigi/ .

Q: How to pass more parameters into luiti tasks?

A: You can create a key-value dict, date_value is the key, and your customize parameters are the values.

If you have other unresolved questions, please feel free to ask questions at issues.

Run tests

# or
nosetests --with-coverage --cover-inclusive --cover-package=luiti
tox -e py27-cdh

Who uses Luiti?

Please let us know if your company wants to be featured on this list!