Flowman Quick Start Guide#

This quick start guide will walk you through the first steps working with Flowman. We will be using Flowman in Docker to save setup and configuration of Spark and Flowman.

Of course running Flowman inside Docker will probably prevent a proper integration with any existing Hadoop environment like Cloudera or EMR. This requires a local installation of Flowman on your Linux (or Windows) machine, which is described in more detail in installation guide.

1. Start Docker image#

Of course, you need a working Docker installation, which should be quite easy. Then you can start a Docker image containing Flowman via the following command:

docker run --rm -ti dimajix/flowman:1.2.0-oss-spark3.4-hadoop3.3 bash   

Note that this simply starts a bash shell, but Flowman is only away some fingertips.

Mounting volumes#

You probably might want to mount some local directory into the Docker container running Flowman. For example, you may want to make local data accessible to Flowman or you may simply want to execute some Flowman project stored on your local machine. This can be easily achieved as follows:

docker run --rm -ti -v /home/kaya/flowman/example/weather:/home/flowman/local dimajix/flowman:1.2.0-oss-spark3.4-hadoop3.3 bash   

This will mount the local directory /home/kaya/flowman/example/weather on your host computer into the Docker container at /home/flowman/local.

2. Flowman Shell#

The example data is stored in a publicly accessible S3 bucket. Since the data is publicly available and the project is configured to use anonymous AWS authentication, you do not need to provide your AWS credentials (you even do not even need to have an account on AWS)

Start interactive Flowman shell#

We start Flowman by running the interactive Flowman shell. While this is not the tool that would be used in automatic batch processing (flowexec is the right tool for that scenario), it gives us a good idea how ETL projects in Flowman are organized.

flowshell -f examples/weather

Inspecting Relations#

Now we can inspect some of the relations defined in the project. First we list all relations

flowman:weather> relation list

Now we can peek inside the relations stations_raw and measurements_raw. Since the second relation is partitioned by years, we explicitly specify the year via the option -p year=2011

flowman:weather> relation show stations_raw
flowman:weather> relation show measurements_raw -p year=2011

Running a Job#

Now we want to execute the projects main job. Again the job is parametrized by year, so we need to specify the year that we’d like to process.

flowman:weather> job build main year=2011

Inspecting Mappings#

Now we’d like to inspect some of the mappings which have been used during job execution. Since some mappings depend on job-specific variables, we need to create a job context, which can be done by job enter <job-name> <job-args> as follows:

flowman:weather> job enter main year=2011

Note how the prompt has changed and will now include the job name. Now we can inspect some mappings:

flowman:weather/main> mapping list
flowman:weather/main> mapping show measurements_raw
flowman:weather/main> mapping show measurements_extracted
flowman:weather/main> mapping show stations_raw

Finally, we’d like to leave the job context again.

flowman:weather/main> job leave

Inspecting Results#

The job execution has written its results into some relations again. We can now inspect them again

flowman:weather> relation show stations
flowman:weather> relation show measurements
flowman:weather> relation show aggregates -p year=2011


Flowman also provides an execution history. In the trivial deployment, this information is stored locally in a Derby database, but other databases like MySQL, MariaDB etc. are also supported.

flowman:weather> history job search
flowman:weather> history target search -P weather

Generating Documentation#

Flowman cannot only execute all the data transformations specified in the example project, it can also generate a documentation, which will be stored as an HTML file

flowman:weather> documentation generate

This will create a file in the directory examples/weather/generated-documentation/project.html which can be viewed by any web browser of your choice.


Finally, we quit the Flowman shell via the quit command.

flowman:weather> quit

3. Flowman Batch Execution#

So far we have only used the Flowman shell for interactive work with projects. Actually, the shell was developed as a second step to help to analyze problems and debugging data flows. The primary command for working with Flowman projects is flowexec which is used for non-interactive batch execution, for example within cron jobs.

It shares a lot of code with the Flowman shell, so the commands are often exactly the same. The main difference is that with flowexec you specify the commands on the command line while flowshell provides its own prompt.

For example for running the “build” lifecycle of the weather project for the year 2014, you only need to run:

flowexec -f examples/weather job build main year=2014

4. Congratulations!#

A very special “Thank You!” goes to all of you who try to follow the example hands-on on your local machine. If you have problems with following the example, please leave me a note — it’s always difficult to streamline such a process, and I might have overseen some issues.