gunthercox/datahub repository overview

⁠DataHub

DataHub is an experimental high-throughput data feed designed for machine consumption.

⁠Use cases

I need a centralized data feed with a defined format so that distributed applications can consume and respond to events.

1. Robotics: Multitudes of sensors and systems will gather and post data to this data feed. Subscribing applications will watch the feed for relevant events that require actions to be performed.
2. Prediction: Event prediction can be performed by analyzing the data stream for patterns.

⁠Architecture / Tech Stack

• A scalable application layer written in Python⁠.
• Load balanced behind Nginx⁠.
• Data stored using Redis⁠.

Redis instances use a single thread which is optimal for robotics applications running on Raspberry Pis. Although a single instance of redis has exceptional performance, redis can be scaled as needed as an application usage grows.

The API design is optimized for scaling behind a load balancer in architectures where many separate systems transmit information to a central endpoint. This design means that only a single directional connection (from the sensor systems to the API) is required, which is optimal when data is being transmitted from devices that aren't publicly-accessible on a network.

⁠Data Format

Data should be sent to the API in the form of a POST request with the following JSON content.

• name: The name of the 'thing' that is reporting the data. For example, a GPS sensor.
• value: The sensor value being recorded.
• expires: Optional date at which the record is no longer valid. The application that sends the data determines when it expires.

⁠Performance benchmarks

For 1000 requests sent using tests/benchmark.py

Post time: 3.70 seconds
Get time:  1.80 seconds
Total execution time: 5.54 seconds

⁠Data Projections

The following projects the number requests per second that the system will need to be able to handle based on a hypothetical robotics project.

• Camera
  • Up to 5 events per second
• Audio
  • Up to 20 events per second
• Temperature sensor
  • 1 sample per minute
• Encoder readings
  • Up to 100 events per second

So about 125 requests per second for incoming event data.

Let's also estimate that there will be 50 subscribers reading the incoming data at a rate of 1 request per second.

That gives us a grand total of 175 request per second that this system needs to be able to handle. This is definitely doable.