ReductStore

# ReductStore

## A high-performance time series database for blob data

ReductStore is a time series database that is specifically designed for storing and managing large amounts of blob data. It boasts high performance for both writing and real-time querying, with the added benefit of batching data. This makes it an ideal solution for edge computing, computer vision, and IoT applications where network latency is a concern. For more information, please visit https://www.reduct.store/

## Why Does It Exist?

There are numerous time-series databases available in the market that provide remarkable functionality and scalability. However, all of them concentrate on numeric data and have limited support for unstructured data, which may be represented as strings.

On the other hand, S3-like object storage solutions could be the best place to keep blob objects, but they don't provide an API to work with data in the time domain.

There are many kinds of applications where we need to collect unstructured data such as images, high-frequency sensor data, binary packages, or huge text documents and provide access to their history. Many companies build a storage solution for these applications based on a combination of TSDB and Blob storage in-house. It might be a working solution; however, it is a challenging development task to keep data integrity in both databases, implement retention policies, and provide data access with good performance.

The ReductStore project aims to solve the problem of providing a complete solution for applications that require unstructured data to be stored and accessed at specific time intervals. It guarantees that your data will not overflow your hard disk and batches records to reduce the number of critical HTTP requests for networks with high latency.

All of these features make the database the right choice for edge computing and IoT applications if you want to avoid development costs for your in-house solution.

## Features

• Storing and accessing unstructured data as time series
• No limit for maximum size of blob
• Real-time FIFO bucket quota based on size to avoid disk space shortage
• HTTP(S) API
• Append-only replication
• Optimized for small objects (less than 1 MB)
• Labeling data for annotation and filtering
• Iterative data querying
• Batching records in an HTTP response for write and read operations
• Embedded Web Console
• Token authorization for managing data access