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Redis is designed to be fast. In most cases, it is. However, there are times when Redis may be slow, due to network issues, disk latency, or other factors. When this happens, it is important to be able to detect the slow down and investigate the cause. Latency is the maximum delay between the time a client issues a command and the time the reply to the command is received by the client. Redis has strict requirements on average and worst case latency.

I find it amazing how much opportunity and flexibility cloud environments are creating for organizations of all sizes. I’m seeing more and more companies experimenting with open-source software (OSS) relational database systems, which years ago would’ve been too complicated for the customer to set up. With Azure, you can spin up OSS systems like MySQL or PostgreSQL quickly to determine if the engine fits their needs. If it does, you can continue development on it.

Redis is an open-sourced, BSD 3 licensed, highly efficient in-memory data store. It is used widely in the industry because of its incredible performance and ease of use. It can easily be used as a distributed, in-memory key-value store, cache, or message broker. It can hold virtually any data structure, making it highly versatile. Redis was architectured and developed with speed in mind and designed to keep all the data in memory.

Azure Cosmos DB for PostgreSQL is a fully managed relational database service for PostgreSQL that is powered by the open source Citus extension. With remote query execution and support for JSON-B, geospatial data, rich indexing, and high-performance scale-out, Cosmos DB for PostgreSQL enables users to build applications on single- or multi-node clusters.

As organizations adopt cloud technologies and modernize their applications, the data they generate and ingest often grows exponentially, leaving them with difficult choices for storing and using this data. Customers are beginning to explore moving away from traditional relational database management systems (RDBMS) because of the data volume to be ingested, as these RDBMS often cannot handle workloads.

For many workloads, using a time series database is a smart choice that saves time and storage space. Developers and companies have more database choices than ever. Choosing the right database for a project saves time when writing and querying data. As companies work with larger datasets to make increasingly intelligent and automated systems, efficiency is key. For many workloads, using a time series database is a smart choice that saves time and storage space.