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Self-contained layered data pipelines within Spark and python using luigi

Using pipeline.py to submit your spark app, you can have traditional luigi task trees run all within a pyspark context and handle resulting task statuses in yarn logs.

Install

Simple

bin/install

Manual
  • Tested against Hortonworks Hadoop 2.6.0, Python 3.5, Spark 2.0.1, CentOS
Hadoop 2.6.0.2.2.0.0-2041
Subversion [email protected]:hortonworks/hadoop.git -r 7d56f02902b436d46efba030651a2fbe7c1cf1e9
  • requires standard-lib python2 on Linux for install
cd ~/
git clone this-repo
  • fill out your luigi.cfg. Example in this repo
python setup.py install

  • follow the command prompts to accept the license and default install path

  • answer yes to Do you wish the installer to prepend the Anaconda3 install location to PATH in your /home/user.../.bashrc ?

  • To place your luigi.cfg into production use on hdfs, do

hadoop fs -put $DEFAULT_LUIGI_CONFIG_PATH_LOCAL $DEFAULT_LUIGI_CONFIG_PATH_HDFS
  • start the luigi deamon task monitor
luigid --background --pidfile ../pidfile --logdir ../logs --state-path ../statefile

  • then monitor your tasks on host:8082

  • to install this repo as a package and overwrite an existing installation do the following (needs to be run when deploying updates to this repo)

python setup.py install

Executables

  • bin/beeline: connects to hive server via beeline to run interactive queries
  • bin/install: installs this package and sets everything up
  • bin/luigid: starts luigi daemon process
  • bin/prod: sets up this repo ready for production usage
  • bin/pyenv: recreates the required python env
  • bin/pyspark: starts the pyspark shell for interactive usage/debugging
  • bin/pysparksql: executes arbitary sql using spark or hive contexts (no results returned)
  • bin/workflow: executes the user input'ed workflow/task
  • bin/kill: kills apps using -s SEARCHSTRING -t STATE -q QUEUE
  • bin/kill_app_after_x_min: kills all apps running for more than x minutes
  • bin/search_log: searches yarn logs for the given search string and returns the given logtype
  • bin/search_app: searches running apps for the given search string and returns the app's status

Running Workflows

  • Example:
source ~/.bashrc
export LUIGI_CONFIG_PATH=~/this-repo/luigi.cfg
cd this-repo/workflows
python3 -m luigi --module postgres_replication Run --workers 10

Running Tasks

  • Kicking off jobs can be scheduled / managed using clock.py, /workflows, or via a shell script. We typically use /workflows for our production task workflows
/workflows

  • Does something to determine dependencies and then kicks off the necessary pipeline.py SparkSubmitTask

  • Example: workflows/postgres_replication.py

    • Connects to Postgres db, determines based on the size which tables will be incremental or not, then launches the postgres.py Table spark job via pipeline.py SparkSubmitTask for each table required on the import
clock.py (also see schedule.py)

  • pure python alternative to cron

  • run python clock.py to start all jobs defined in clock.py

  • (screen)[https://www.gnu.org/software/screen/manual/screen.html] can be used to manage the clock.py application connection (Contrl C will insert you into the running clock.py process where you can dynamically schedule or clear jobs)

# list running screen sessions
screen -r
# list all screen sessions
screen -ls
# kill screen session 10031.pts-50.host1
screen -S 10031.pts-50.host1 -p 0 -X quit
# connect to existing screen session 10031.pts-50.host1
screen -d -r 10031.pts-50.host1
Running DDL SparkSQL
cd ~/this-repo/workflows/
export LUIGI_CONFIG_PATH=../luigi.cfg
python spark_sql.py test_master

Performance

  • Testing performance of a job can be done using sparklint and submitting the spark job with:
--conf spark.extraListeners=com.groupon.sparklint.SparklintListener 
--packages com.groupon.sparklint:sparklint-spark201_2.11:1.0.4
  • then open a browser and navigate to your driver node’s port 23763

Contributing

  • NOTE: Deploying any major change requires recreating the PythonENV so that spark-utils are available within any given spark-context
python pipeline.py PythonENV
  • Tests go in tests/
  • To execute a test file, run python module_test.py
  • Handle your test paths/imports using context.py
Example Layering - All layers connected to the Central Scheduler
  • Luigi Task Layer 1 - pipeline.py submits the spark job
  • Luigi Task Layer 2 - luigi runs the postgres.py Table task within Spark cluster and reports completion time, errors, status
  • Luigi Task Layer 1 - 'pipeline.py' checks the yarn logs to make sure the task succeeded

Class Descriptions

  • setup.py
  • class Error : handling general exceptions in this module
  • class Python : by default, install Anaconda python 3.5.2
  • interface.py
  • read_*_config : reads local or hdfs config
  • port_is_open : check if scheduler is running
  • get_task : cmdline parser to luigi.Task
  • build : build luigi workflow programmatically
  • run : thread safe alternative to build
  • build_and_run : wraps get_task and run
  • class decorators for custom logic to handle task statuses
  • scheduler.py
  • Scheduler : python implementation of cron alternative
  • SafeScheduler : scheduler loop will not fail if a job fails
  • Job : handles the actual execution of a job
  • google_sheet.py
  • class Sheet : Writes all contents of one tab from google sheet to a hive table
  • class SheetToHDFS : Writes all contents of one tab from google sheet to hdfs path folder
  • hdfs.py
  • class Directory : hdfs directory utility
  • class File : hdfs file utility (i.e. create/delete/read/write/describe)
  • pipeline.py
  • class PythonENV : creates a spark ready python env
  • inputs:
  • packages: list of python packages to be installed by pip
  • packages_conda: list of pyhton packages to be installed by conda
  • python_version: i.e. 3.5
  • env_name : name to call this env that is created (default = py3spark_env)
  • python_install : if true, then the class PythonInstall is ran before creating the env
  • output: resulting env.zip will be in ~/anaconda3/envs/ directory
  • class SparkSubmitTask: given inputs and spark parameters and job, attemps to submit it for you
  • an abstraction of spark-submit
  • postgres.py
  • class Table : replicates a postgres table to a hive table (incrementally or from scratch)
  • class Query : creates a hive table based on results of postgres Query
  • class QueryToHDFS : replicates a postgres table to hdfs folder path
  • hive.py:
  • HiveQueryTask : Uses beeline to run a query