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jydoop: Efficient and Testable Hadoop map-reduce in Python


Querying Hadoop/HBase using custom java classes is complicated and tedious. It's very difficult to test and debug analyses on small sets of sample data, or without setting up a Hadoop/HBase cluster.

Writing analyses in Python allows for easier local development + testing without having to set up Hadoop or HBase. The same analysis scripts can then be deployed to a production cluster configuration.

Writing Scripts

The quickest way to get started is to have a look at some of the examples in scripts/.

The minimal jydoop job requires only two function definitions:

  • map(key, value, context) The map phase of MapReduce - called once for each input record, and data is written out by calling context.write(new_key, new_value)
  • setupjob(job, args) Determines how data is made available to the job script and processes incoming arguments. Usually you will use an existing implementation of this function.

For most Mozilla data sources, there are predefined setupjob functions available that you can reference in your script without implementing your own. There are examples of this included in the scripts/ dir.

In addition to the Mozilla data sources, there is also support for using the output of one jydoop job as the input of another. More on this below.

Besides the required map and setupjob functions, there are a number of optional functions you can implement for full MapReduce functionality:

  • reduce(key, values, context) The reduce phase of MapReduce - called once for each key (as output by the Map phase) with a list of all values seen for that key. If you do not define a reduce function in your script, it will run as a Map-only job and skip the Reduce phase entirely.
  • combine(key, values, context) An intermediate way of reducing partial value lists for a key. This function is entirely optional, but can improve performance if the logic for reducing values can be done in pieces. A good example of this is count-type jobs, where the overall reduce will still work fine even if some subsets of values have already been summed. The Hadoop Documentation has a nice description of the combine phase.
  • output(path, results) You may override how data is written out to the destination file by implementing this method. The default behaviour is usually fine. The results argument is an iterator on the (key, value) pairs that come from the Reduce step.
  • mapsetup(context) Called before the Map phase
  • mapcleanup(context) Called after the Map phase
  • skip_local_output() If this returns True, then output from the job is not downloaded from HDFS to a local file. The supplied output filename is used as the location in HDFS where data will be stored. If this function is omitted, the default behaviour is to output to a local text file, then remove any data from HDFS.

Testing Locally

To test scripts, use locally saved sample data and

python script/ sample.json analysis.out

where sample.json is a newline-separated json dump. See the examples in scripts/ for map-only or map-reduce jobs.

Local testing can be done on any machine with Python installed, and doesn't require access to any extra libraries beyond what is included with jydoop, nor does it require connectivity to the Hadoop cluster.

Production Setup

Fetch resources

Fetch dependent JARs using

make download

Note: You may need to set the http_proxy environment variable to allow curl to get out to the internet:

export http_proxy=http://myproxy:port

Running as a Hadoop Job

Python scripts are wrapped into driver.jar with the Java driver.

For example, to count the distribution of operating systems on Mozilla telemetry data for March 30th, 2013 run:

make hadoop ARGS="scripts/ outputfile 20130330 20130330"

Supported Types of Jobs

jydoop supports several different backend data sources which are of the following types:

  • HBase (mapper type HBASE)
  • Plain sequence files (mapper type TEXT)
  • Jydoop-formatted sequence files (mapper type JYDOOP)

The mapper type should be set by the setupjob function. Currently supported types are HBASE, TEXT, or JYDOOP. The default is HBASE so you do not need to specify this value for HBase data sources. Other mapper types should set the org.mozilla.jydoop.hbasecolumns key in the job Configuration. For example, the TestPilot setupjob function sets the mapper type using:

job.getConfiguration().set("org.mozilla.jydoop.mappertype", "TEXT")

All the different types require at least two arguments, namely the script to be run and the filename where output will be sent.


Telemetry jobs take two extra arguments, the start date and the end date of the range you want to analyze. Telemetry data is quite large, so it's best to keep the date range to a few days max.

The production example above uses the HBase support to access Telemetry data (using the telemetryutils.hbase_setupjob setup function).

You may also access the most recent 14 days' data in HDFS. This will make jobs finish somewhat more quickly than using HBase. To use the HDFS data, specify the following in your script:

setupjob = telemetryutils.setupjob

Check the script for an example.

Firefox Health Report Jobs

FHR jobs don't require any extra arguments beyond the script name and the output file.

To help reduce the boilerplate required to write Firefox Health Report jobs, a special decorator and Python class is made available. From your job script:

from healthreportutils import (

def map(key, payload, context):
    if payload.telemetry_enabled:

    for day, providers in payload.daily_data():

When the @FHRMapper decorator is applied to a map function, the 2nd argument to the function will automatically be converted to a healthreportutils.FHRPayload class. In addition, special arguments can be passed to the decorator to perform common filtering operations outside of your job.

See the source in for complete usage info.


TestPilot jobs access data in Sequence Files in HDFS.

Scripts accessing TestPilot data require three arguments: the TestPilot study name, the start date, and the end date. For example, to run the sample script against the testpilot_micropilot_search_study_2 study from June 10th to June 17th, 2013:

make hadoop ARGS="scripts/ test_out.txt testpilot_micropilot_search_study_2 2013-06-10 2013-06-17"

Jydoop output -> jydoop input

You can run a jydoop job against the output of a previous jydoop job.

This enables workflows where you first filter or preprocess your input data and store it back in HDFS, then write a number of different analysis scripts that work on the filtered data set.

Normally jydoop jobs remove their output in HDFS once the data has been saved locally.

If you want to keep the output in HDFS instead of saving locally, you can implement the skip_local_output function in your job script (and have it return True). This will cause the data not to be saved locally, and also prevent it from being deleted from HDFS when the job is complete.

You then use the job's output in another job by using the jydoop.setupjob function in your script.

As a simplistic example, if you have a two-stage job which first reads and filters TestPilot data and stores the result into a HDFS location interesting_output, then reads interesting_output to produce local data, you could do the following:

""" - Filter TestPilot input for an interesting person"""
import json
import testpilotutils
import jydoop
def map(key, value, context):
    payload = json.loads(value)
    if payload["personid"] == "interesting!":
        context.write(key, value)

def skip_local_output():
    return True

setupjob = testpilotutils.setupjob
""" - Count events by type"""
import json
import jydoop
def map(key, value, context):
    payload = json.loads(value)
    for event in payload["events"]:
        context.write(event["type"], 1)

reduce = jydoop.sumreducer
combine = jydoop.sumreducer
setupjob = jydoop.setupjob

Run the jobs:

# Generate the HDFS output:
make hadoop ARGS="scripts/ interesting_output testpilot_study_1337 2013-06-10 2013-06-24"

# Process HDFS data and output local data:
make hadoop ARGS="scripts/ final_result.txt interesting_output"

You can then run any other jobs against interesting_output without having to re-filter the original data.


Efficient Hadoop Map-Reduce in Python




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