TimescaleDB

This blog provides a brief guide on how to start with database automation. It will walk you through the main points and essentials of database automation, such as what can be and what should not be automated, and discusses some useful automation tools.

Role-Based Access Control (RBAC) in ClusterControl allows super administrators to provide fine-grained restrictive access based on a role.  This blog guides you in using RBAC, teaching you the differences among available roles and how to use roles and their privileges to achieve a desired level of security.

Hybrid Cloud vs Full Public Cloud dares how organizations and companies have to decide and design the infrastructure to achieve high availability, scalability but with tightened security. In this blog, we showcase how both differ and how you are going to utilize them whenever you engage with these infrastructure solutions for your business requirements.

Object storage in the cloud is inexpensive, reliable and durable, and is a great way of archiving backup data. In this blog, we will share some tips on how to store TimescaleDB backups on AWS S3 and Google Cloud Storage.

If you need to handle time-series data in your current PostgreSQL installation, you should consider enabling TimescaleDB there to manage it in a more performant way. In this blog, we will see how to enable TimescaleDB in an existing PostgreSQL installation both manually and using ClusterControl.

In this blog post, we look deeper into one of the most advanced features offered by ClusterControl called automatic database recovery and failover. ClusterControl is able to recover clusters, nodes and database-related processes to ensure you achieve the best database uptime possible.

PostgreSQL and its underlying platform solutions can help you cut down the high costs associated with an Oracle Database license; all with small to moderate changes. This blog walks you through some of the advantages of migrating from Oracle to PostgreSQL for your SQL database needs.

While setting up a single instance of Timescale on a Docker container is a pretty straight-forward task, deploying a replication cluster to achieve high availability is not. Customizing something from the OS side, or choosing a different OS image altogether makes it even harder. In this blog, we’ll deploy TimescaleDB on Docker and discuss a way to make it easier to configure a replication with ClusterControl using a clean OS image.

In the new time-series database world, TimescaleDB and InfluxDB are two popular options with fundamentally different architectures. One is based off a relational database, PostgreSQL, the other build as a NoSQL engine. In this blog, we’ll give you a short description of those two, and how they stack against each other.

Managing backup environments can be complex, and, in case of failure, it’s important to reduce downtime and loss of data as much as possible. Full, differential and incremental backups are good options, they also help reduce the disk space usage. There are other important features like backup verification, uploading to remote systems or cloud, and compression, to complete our backup strategy. In this blog, we’ll see how we can use pgBackRest for backing up PostgreSQL and TimescaleDB and how we can manage all of these features from the same system.

TimescaleDB uses a stream of write-ahead log (WAL) records to keep the standby databases synchronized. In this blog, we’ll review some concepts related to Streaming Replication and how ClusterControl can help us configure it in an easy way.

How do you scale TimescaleDB? How do you convert a single TimescaleDB instance to cluster with no downtime? How does the application know which database node to access? This blog post provides an overview of how to use ClusterControl to scale your time-series data with minimal effort.

Failover is the process of moving to a healthy standby component, during a failure or maintenance event. The quicker it can be done, the faster you can be back online. If you’re looking at minimizing downtime and meet your SLAs through an automated approach for TimescaleDB, then this blog is for you.

Doing a performance monitoring in TimescaleDB is always a challenging task, especially for a very huge time-series data. ClusterControl offers a way to help a DBA or a developer to make things feasible and productive with the aid of the different features integrated within ClusterControl. Check out this blog as we showcases what are these and how you can leverage ClusterControl to monitor your TimescaleDB cluster.

Managing backup environments can be complex, and there are a number of options to consider - full, differential, incremental, point in time recovery, automatic restores for backup verification, uploading to remote systems or cloud, and so on. So how can we backup TimescaleDB? In this blog, we’ll see how ClusterControl can help us implement our backup management strategy for TimescaleDB.

TimescaleDB is based on PostgreSQL, and therefore, supports streaming replication as the primary method of replication. However, PostgreSQL does not come with automatic failover. This can be a problem in a high availability production environment, as manual failover implies prolonged downtime.The good news is that failover in TimescaleDB can be completely automated in ClusterControl 1.7.2. This blog shows us how to deploy such a setup.

Included in the 1.7.2 release of ClusterControl we are proud to announce an expansion of the databases we support to include TimescaleDB, a revolutionary new time-series that leverages the stability, maturity and power of PostgreSQL.

ClusterControl 1.7.2 includes support for TimescaleDB, MySQL 8.0 and Maxscale 2.2 as well as improved support for PostgreSQL monitoring & backups. It also launches two new features, CC Spotlight (a new search tool) and CMON HA (distributed fault-tolerant controllers).

Time-series data is becoming more and more present, as we capture and analyze data at a faster rate. This increases the demand for complex queries and scalable database solutions. This blog provides a high-level overview of the new database system “TimescaleDB”, and a quick hands-on to get you going.

Time-series databases have emerged as a best-in-class approach for storing and analyzing huge amounts of data generated by users and IoT devices.But what are time-series databases? Why have they become so important in the database landscape? In this blog post, we try to shed some light on the subject.