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A sysadmin in the high performance computing world since 2008, Wilfried Roset is now working with the open source databases and observability environment at OVHcloud. He leads a team focused on building industrialized, resilient, and efficient solutions. For nearly two decades, OVHcloud has been a leader in cloud hosting and has been Europe’s largest provider since 2011. To serve our 1.4 million customers globally, we need a reliable and scalable observability platform.

Memory databases are known for their ability to store and manage large volumes of data in memory. Their memory-based architecture allows users to quickly retrieve critical information and benefit from performant data reading. Thanks to these characteristics, businesses use memory databases for various applications that require prompt data access playing a vital role within their digital resources.

Here at InfluxData, we recently announced InfluxDB 3.0, which expands the number of use cases that are feasible with InfluxDB. One of the primary benefits of the new storage engine that powers InfluxDB 3.0 is its ability to store traces, metrics, events, and logs in a single database. Each of these types of time series data has unique workloads, which leaves some unanswered questions. For example: Luckily this is where our work within OpenTelemetry comes into play.

This article was originally published in The New Stack and is reposted here with permission. They require different approaches for storage and querying, making it a challenge to use a single solution. But InfluxDB is working to consolidate them into one. Time series data has unique characteristics that distinguish it from other types of data. But even within the scope of time series data, there are different types of data that require different workloads.