influxdb-odata-server

Odata provider (server) interfaces influxdb by exposing using REST based odata endpoints. Supports Influxdb 1.x and 2.x Connects InfluxDB with BI apps including Excel, PowerBI and Tableau.

Installation and Setup

Current version has been tested on python 3.6. Few features might break in 2.x.

Install all dependencies via pip

pip install -r requirements.txt

Configure Odata server host and port details in settings.conf Sample config file is well documented. Choose the influxdb_version in metadata section and accordingly provide influxdb connection details. Also, enable authentication if required. Refer to Authentication section

Before spinning up the server, create an xml metadata defining the fields/tags of each measurement in an influx database (or bucket) using

python startServer.py -c production.conf -m

-m or --createmetadata flag creates Odata schema metadata file at the location defined by the metadata_file tag in the configuration. It doesn't spin up the server. Schema is generated for all measurements specified by the databases or bucket tags in the configuration. Multiple db/buckets can be configured by separating each by comma(,)

Once the metadata file is generated, spin up the Odata server by python startServer.py -c settings.conf

Supported features

• Odata 2 provider (server) implemetation.
• Supports both Influxdb 1.x and Influx 2.0 with Flux support. Based on the request, it internally converts into influxql/flux query (based on influx version in use).
• Works with any BI tool supporting Odata 2 as data source. Tested for Excel 2016, 2019, Office 365, Tableau Desktop 2019.
• Supports only one database/bucket per service. Multiple instances on separate ports can be initiated to handle multiple databas/bucket.

Supported Influx Functions and Usage

• Database/Bucket and Measuremnt
• Select
• Filter (time based and tag-value based)
• Aggregate Function
• Group by time
• Limit

For example, the endpoint

http://localhost:8080/data/monitoring__cpu?$filter=timestamp ge datetime'2020-04-03T00:00:00' and timestamp lt datetime'2020-04-06T00:00:00' and host eq '192.168.1.12'&$top=100&groupByTime=1h&aggregate=mean&$select=usage_guest,usage_guest_nice,usage_idle,usage_iowait, usage_steal,usage_system,usage_user

queries measurement cpu inmonitoring database for timestamp greater equal to 2020-04-03T00:00:00 and less than 2020-04-06T00:00:00 and tag host equal to string value 192.168.1.12, group by every 1 hour and returns aggregated mean on fields usage_guest, usage_guest_nice,usage_idle,usage_iowait, usage_steal,usage_system, usage_user

Authentication

Current version supports only basic HTTP authentication via Simple Login and AWS Cognito. Simple Login method stores username and password in configuration file (in plain text).

Authentication validator module is called dynamically based on the configuration. Additional modules can be added in the authentication directory of the project.

Deployment

• startServer.py spins up a werkzeug server using run_simple module. This is not suitable for production and doesn't perform well on concurrent requests.

• The code can also be hosted AWS Lambda and served with AWS API Gateway for serveless implementation. Code has tested with Zappa framework. Detailed instructions to be released soon.

• Docker image to be released soon.