AppSignal was built because we were tired of slow and clumsy monitoring setups. Instead, we built monitoring that’s easy yet powerful - an intuitive interface enables you to figure out what’s happening in no time. Today, our team made finding slow events effortless. We’ve fully overhauled our slow events feature, helping developers to quickly find and fix slow queries and API requests.
MySQL is the de facto choice for open-source relational databases, and you should learn how to use MySQL database logs to improve efficiency and security. As an open-source product, it is free to use and has a large and active developer community. It is crucial to understand how to diagnose and monitor the performance of a MySQL instance in the long run. Logs to the rescue!
While tuning the performance of your application at the code level or sizing the JVM appropriately are important for enhancing performance, it is equally important to look at how to tune accesses to the backend database. After all, response time for a web request is dependent on the processing time in the Java application tier as well as the query processing time in the database tier.
Imagine some users complaining that querying PostgreSQL is slow (this never happened right?), and we have to troubleshoot this problem. It could be one of two things: I would normally first check on the environment, specifically PostgreSQL metrics over time. Such monitoring shows if the CPU is too high or how many disk reads were buffer reads. PostgreSQL logs also give information about the environment, such as how many statements were run and if any errors occurred.
Evolving MySQL operations requires understanding how MySQL works. A good monitoring tool alerts on issues before they impact end users and helps reduce the MTTR of incidents when they do occur. But choosing a database monitoring solution can be tough due to the vast number of solutions available, each with their own pros and cons. In this blog post, I’ll review some of the best MySQL monitoring tools available that can help measure and improve database performance.
MarkLogic is a multi-model NoSQL database with support for queries across XML and JSON documents (including geospatial data), binary data, and semantic triples—as well as full-text searches—plus a variety of interfaces and storage layers. Customers include large organizations like Airbus, the BBC, and the U.S. Department of Defense. Because MarkLogic can process terabytes of data across hundreds of clustered nodes, maintaining a deployment is a complex business.
For the last few months, I have been actively contributing to the InfluxDB community by building InfluxDB Templates for InfluxDB 2.0. InfluxDB is the purpose-built time series database used for metric collection and storage. Let’s build an InfluxDB Template to monitor PostgreSQL together! 👏
Our goal is to support both MySQL and PostgreSQL as a backend wherever we need a database. Our latest addition in this area was PostgreSQL support for reporting, which will be released in the next few weeks. We don’t have PostgreSQL support for Icinga Certificate Monitoring yet. But it has already been worked on and the pull request for it is waiting to be tried. We would be happy to receive feedback in this regard so that we can fully merge PostgreSQL support as soon as possible.
