Operations | Monitoring | ITSM | DevOps | Cloud

Today, we’ve added a magic dashboard to the Node integration that shows you the performance and volume of your PostgreSQL queries.

Proactive database performance monitoring is essential to maintain resource utilization and system performance. As data volumes grow, it is critical to monitor databases properly to deliver a seamless enduser experience, and lower IT infrastructure costs. Pinpointing database issues as they occur can assist in faster troubleshooting, and keeping the health of the application intact. Without monitoring, database outages may go unnoticed, and lead to loss of business reputation and profit.

Today, we’ll dive into N+1 queries⁠—what they are, how to spot them, why they have such an impact, and how to solve them.

RethinkDB is a document-oriented database that enables clients to listen for updates in real time using streams called changefeeds. RethinkDB was built for easy sharding and replication, and its query language integrates with popular programming languages, with no need for clients to parse commands from strings. The open source project began in 2012, and joined the Linux Foundation in 2017.

As we all know, the on-demand capacity mode of DynamoDB is great but can be cost-prohibitive in some cases (up to seven times more expensive than the Provisioned Capacity mode). The Provisioned mode, on the other hand, shifts to the development team the burden of predicting what level of capacity will be required by the application. And it’s not quite as straightforward to achieve the same level of scalability in the Provisioned mode as we enjoy in the On-demand one.

Logs are valuable. Logs generated by a major backend resource that provides clients with access to crucial data are more than just valuable; knowing where they are and being able to manage and understand the information that they contain can mean the difference between smooth, secure operation and degraded performance or even catastrophic failure for your application. MySQL Server produces a handful of basic logs.

NoSQL technology has become more popular in recent years thanks to the development of new open-source NoSQL databases that are relatively easy to install, use and integrate with web frameworks. An example of one of those popular frameworks on the internet is known as MEAN (MongoDb, Express.js, Angular.js, Node.js). These NoSQL frameworks have become very popular for things such as content management, catalogs and big data in general.

Today we’re pleased to announce the general availability of InfluxDB Enterprise on Microsoft’s Azure Marketplace. We’ll dive into all of these below, but first, let’s take a step back in case you’re not familiar with time series databases. If you’re looking for a time series database, here are three things to look for.

In today’s post, we’ll learn about Mnesia, see when you would use such a tool, and take a look at some of the pros and cons of using it. After covering the fundamentals of Mnesia, we’ll dive right into a sample application where we’ll build an Elixir application that uses Mnesia as its database. Let’s jump right in!

Elasticsearch and MongoDb are the two most popular distributed datastores used to manage NoSQL data. Both of these technologies are highly scalable and have document-oriented design at the core. There are differences between the two technologies, however, and it’s important to understand these differences in order to choose the right one for your use case. This blog post will examine the differences between these two technologies in a number of critical areas.