Operations | Monitoring | ITSM | DevOps | Cloud

This article explores the PostgreSQL implementation of the B-Tree (the B stands for Balanced) and hash index data structures. As PostgreSQL grows in popularity as an open-source database system for developers and as a target for migrating from Oracle workloads, understanding how PostgreSQL indexes work is extremely important for database developers and administrators. PostgreSQL has several other types of indexes, such as GIN indexes, GiST indexes, and BRIN indexes.

In this part 1 of a blog series on serverless monitoring, we will learn how to run the observIQ Distro For OpenTelemetry Collector, referred to as “oiq-otel-collector”, in Google Cloud Run. There are many reasons that someone may want to run monitoring in a serverless state. In our example, we will be monitoring MongoDB Atlas, a cloud hosted version of MongoDB.

In March 2022, Grafana Labs released Grafana Mimir, the most scalable, most performant open source time series database in the world. Mimir provides significant scale — 1 billion active series and beyond — with easy deployment, multi-tenancy, durable storage, high availability, and super fast query performance. From launch, Grafana Mimir could natively consume Prometheus metrics.

CockroachDB is a highly resilient distributed SQL database developed by Cockroach Labs. CockroachDB assures ACID semantics and aims to make it easy to scale horizontally by adding nodes instead of manually sharding the database. Built to be resilient (much like its namesake insect) and highly available as it scales, CockroachDB readily recovers from node failures by repairing and rebalancing automatically.

In the era of technology defined by cloud computing, features evolve at the blinding speed of continuous deployment. When large software development organizations like Microsoft deliver semi-annual releases of products, like SQL Server, the volume of new features can be so large they can be hard to grasp. Microsoft continues to push the boundaries on what’s possible, both in on-premises and cloud data platforms, and they aim to make the life of data professionals much more manageable.

Many DevOps teams struggle to achieve consistent builds and releases due to ineffective collaboration and communication strategies. Over 71% of software teams today are working remotely from global locations, according to a survey by Perforce and DevOps.com. Interestingly, this consistency challenge can be easily solved by a simple approach – database version control.

Earlier this year we introduced the world to Grafana Mimir, a highly scalable open source time series database for Prometheus. One of Mimir’s guarantees is 100% compatibility with PromQL, which it achieves by reusing the Prometheus PromQL engine. However, the execution of a query in the Prometheus PromQL engine is only performed in a single thread, so no matter how many CPU cores you throw at it, it will only ever use one core to run a single query.

In the era of technology defined by cloud computing, features evolve at the blinding speed of continuous deployment. When large software development organizations like Microsoft deliver semi-annual releases of products, like SQL Server, the volume of new features can be so large they can be hard to grasp. Microsoft continues to push the boundaries on what’s possible, both in on-premises and cloud data platforms, and they aim to make the life of data professionals much more manageable.