Operations | Monitoring | ITSM | DevOps | Cloud

Query tuning is generally considered one of the fastest ways to accelerate your Microsoft SQL Server performance. System-level server performance improvement activities can be expensive and ineffective. Moreover, expert developers agree most SQL Server performance issues can be traced directly to poorly written queries and ineffective indexing rather than hardware constraints. In fact, many performance problems can only be resolved through query optimization and tuning.

This is the story of how I used Honeycomb to troubleshoot redis/redis-rb#924 and discovered a surprising workaround.

Apache Ignite is a computing platform for storing and processing large datasets in memory. Ignite can leverage hardware RAM as both a caching and storage layer to serve as a distributed, in-memory database or data grid. This allows Ignite to ingest and process complex datasets—such as those from real-time machine learning and analytics systems—in parallel and at faster speeds than traditional databases supported by only disk storage.

Hazelcast is a distributed, in-memory computing platform for processing large data sets with extremely low latency. Its in-memory data grid (IMDG) sits entirely in random access memory, which provides significantly faster access to data than disk-based databases. And with high availability and scalability, Hazelcast IMDG is ideal for use cases like fraud detection, payment processing, and IoT applications.

Most modern web applications are heavily reliant on persisting data with relational databases, and so it’s no surprise that a large part of application performance monitoring relates to keeping an eye on database performance to ensure that our SQL queries are as efficient as possible. With this in mind, Scout features a Database Addon module, and in this video we are going to take a closer look at what it has to offer.

You’ve pored over the MongoDB documentation, crafted highly polished and well-tuned queries, and confidently deployed your new code to production. Everything ran great at first, but once CPU or RAM usage hit a certain point, your queries suddenly slowed to a crawl. What happened, and how can you prepare for situations like this in the future? This is an unfortunate but common scenario with databases like MongoDB.

If you are anything like us here at Sematext, you are likely always trying to automate any tedious, repetitive tasks. Repetitio est mater… boringdorum. Setting up monitoring falls in that category. You either do it manually every time you provision a new piece of infrastructure or service, or you automate it. Note that by “service” I mean either an instance of your own application or something like Nginx or Elasticsearch or MySQL or …

Like application monitoring, database performance monitoring is a critical discipline. If there isn’t a code issue, there’s a good chance you have a database issue. Key metrics such as CPU and memory usage can give you important insights into your database’s performance. In addition, by monitoring slow queries, and inordinate number of database requests, you can combine this knowledge to optimize both.