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This repo contains code examples for using python-bindings with JSON and MapR-DB (via the OJAI, Open JSON Application Interface). Optionally, the code in the .py files posted here can be used to build a sample application with our partner, Visual Action. The HTML and Javascript files to build the full application are not present in this repo, but the complete JSON data flow can be built from these files and they serve as example reference code for getting started with OJAI and MapR-DB in Python.

You must have a MapR-DB instance with OJAI running to use this example code. Go to the MapR download page to download a virtual machine running MapR-DB pre-configured, or download the setup script to get started with your own MapR cluster for free.

If you are using one of the MapR pre-supplied VMs, you may need to install python3. Future versions of the sandbox will contain this preinstalled. To install python3 on the sandbox, follow these steps:

• As root, run: yum install zlib-devel yum install openssl-devel
• Follow the steps outlined here. You will need to follow all these steps, including installing the "Development tools" group which takes a few minutes. At the end of these steps you should be able to run python3 --version

To use Python with MapR-DB (as this example does), you will need the package python-bindings installed. You can install this by running pip3 install maprdb.

The code examples that load data into MapR-DB do not require that the tables be made in advance, but they will delete the existing tables if they are there. Edit load.py and change the variables TABLE_SENSORS_PATH and TABLE_MAINT_PATH to change the path of the tables. These default to /user/mapr/mdata and /user/mapr/mdata.

The data set used in this application is in this wellsensors repo. A small pair of datasets for the well and maintenance information (1M and 50K lines, respectively) is included in this repo. To generate a larger dataset on your own, use the schema from the above repo with Ted Dunning's excellent log-synth tool to generate a set of arbitrary size. Watch out, JSON can get big, fast!

Be sure to uncompress ws.json.gz and maint.json.gz before running the scripts. If you are generating the graphs with makeplots.py, edit the OUTPUT_PATH variable at the top of that file to set the output directory for the graph images.

The following files correspond to several code examples you can use to get familiar with how to load JSON into MapR-DB using Python.

• load.py - reads each dataset (ws.json and maint.json), loads the JSON documents into MapR-DB
• makeplots.py - reads the data and makes a series of plots, using Spark. This file can be executed in Spark by using spark-submit makeplots.py.
• summary.py - reads the data outputs some summary statistics using a Pandas dataframe

These files are meant to be run in sequence, i.e. run load.py first, then either makeplots.py using the spark-submit command, or simply run summary.py with python3 and you can view basic statistics about the dataset.

Follow the instructions in this video to build the application on your own machine.

Questions? Visit the MapR Community to post questions and for discussion on the platform.