glaze VS ReductStore

Previously in the Glaze library, you would need to write out your key names along with the member object pointers. However, as of version 1.8.0, these key names are now optional. If the keys are not provided the member variable name will be reflected for serialization/deserialization. I'm even more excited about this reflection than the previously announced pure Clang reflection, because it works well with user customization and supports non-aggregate, non-default constructible, and non-constexpr types.

BEVE fully supports JSON messages. The Glaze C++ JSON library allows users to use the same API to encode/decode to either JSON or EVE binary. Glaze also encodes/decodes directly into your C++ structures and standard library containers, making it easy to use without additional copies.

I would either look at https://github.com/eyalz800/zpp_bits or https://github.com/stephenberry/glaze. FYI, glaze both supports json and binary.

Glaze is a software that uses artificial intelligence to create realistic images from text descriptions. It is based on Stable Diffusion, an open source framework for image synthesis using diffusion models. Glaze claims to offer enhanced model training with minimal style interference by using a technique called ControlNet1.

I ended up adopting this approach in some test code https://godbolt.org/z/GKW8Preva when I was thinking about adding automatic enum serialization/deserialization to stephenberry/glaze. But there are too many limitations.

Alternatively, use a preexisting library for binary serialization and deserialization. If you’re trying to serialize your own stuff, glaze is a good option since it supports both json and binary serialization/deserialization. https://github.com/stephenberry/glaze

This parse file has some of the chunk simd processing: https://github.com/stephenberry/glaze/blob/main/include/glaze/util/parse.hpp

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.