Adding a schemaless, timestamp searchable data store to rt-ai Edge using Manifold

The MQTT-based heart of rt-ai Edge is ideal for constructing stream processing networks (SPNs) that are intended to run continuously. rt-ai Edge tools (such as rtaiDesigner) make it easy to modify and re-deploy SPNs across multiple nodes during the design phase but, once in full time operation, these SPNs just run by themselves. An existing stream processing element (SPE), PutNiFi, allows data from an rt-ai Edge network to be stored and processed by big data tools –  using Elasticsearch for example. However, these types of big data tools aren’t always appropriate, especially if low latency access is required as Java garbage collection can cause random delays.

For many applications, much simpler but reliably low latency storage is desirable. The Manifold system already has a storage app, ManifoldStore,  that is optimized for timestamp-based searches of historical data. A new SPE called PutManifold allows data from an SPN to flow into a Manifold networking surface. The SPN screen capture above shows two instances of the PutManifold SPE used to transfer audio and video data from the SPN. ManifoldStore grabs passing data and stores it using timestamp as the key. Manifold applications can then access historical data flows using streamId/timestamp pairs. It is particularly simple to coordinate access across multiple data streams. This is very useful when trying to correlate events across multiple data sources at a particular point or window in time.

ManifoldStore is intrinsically schemaless in that it can store anything that consists of a JSON part and a binary data part, as used in rt-ai Edge. A new application called rtaiView is a universal viewer that allows multiple streams of all types to be displayed in a traditional split-screen monitoring format. It uses ManifoldStore for its underlying storage and provides a window into the operation of the SPN.

Manifold is designed to be very flexible with various features that reduce configuration for ad-hoc uses. This makes it very easy to perform offline processing of stored data as and when required which is ideal for offline machine learning applications.

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