comms_champion VS ReductStore

I've been developing CommsChampion Ecosystem for about 9 years as my pet project. It's about easy and compile-time configurable implementation of binary communication protocols using C++11 programming language, with main focus on embedded systems (including bare-metal ones).

If this is the case, for the third stage of the application specific protocol handling I recommend CommsChampion Ecosystem.

I suppose if you develop something that is going to be used in a single project / product without much of a customization, then meta-programming is not really justified. The meta-programming is justified in many cases when you implement some kind of a library, which can be used in multiple independent products, and these uses may require some product specific customizations. For example, I'm developing a solution for implementing binary communication protocols for embedded systems in C++, called CommsChampion Ecosystem. The core component of which is the COMMS library. Every single use of this library requires different customization. Every application may require different polymorphic interface to handle its message objects. I use template meta-programming there to define virtual functions only needed by the application and not adding unnecessary ones. I also use template meta programming to allow customization of the storage data structures and may use different, more optimized code for some.

I use commschamp. You describe the protocol in an XML format, it only supports C++ though

In edge computing, managing time series blob data efficiently is critical for performance-sensitive applications. This blog post will compare ReductStore, a specialized time series database for unstructured data, and MongoDB, a widely-used NoSQL database.

We are pleased to announce the release of the latest minor version of ReductStore, 1.9.0. ReductStore is a time series database designed for storing and managing large amounts of blob data.

Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.

ReductStore is a time-series database for blob data, optimized for edge computing and complements Kafka by providing a data storage solution for files larger than 1MB–Kafka's maximum message size.

Moreover, the challenge is even trickier when there is simply too much data to store the whole dataset on disk and we need to stream data from a remote database such as ReductStore.

The MQTT protocol is widely used in IoT applications because of its simplicity and ability to connect different data sources to applications using a publish/subscribe model. While many MQTT brokers support persistent sessions and can store message history as long as an MQTT client is not available, there may be cases where data needs to be stored for a longer period. In such cases, it is recommended to use a time series database. There are many options available, but if you need to store unstructured data such as images, sensor data, or Protobuf messages, consider using ReductStore. It is a time series database specifically designed for storing large amounts of blob data and optimized for IoT and edge computing.

ReductStore is an innovative time-series database designed specifically for managing Blob data, making it ideal for our needs in real-time unsupervised anomaly detection. The true strength of ReductStore lies in its ability to store not just raw data but also AI labels within the metadata and models at the edge.

We are pleased to announce the release of the latest minor version of ReductStore, 1.6.0. ReductStore is a time series database designed for storing and managing large amounts of blob data.

ReductStore{:target="_blank"} is a unique time-series database that offers a specialized database for storing MQTT data in the form of time-series blob data.