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.

Hackers are once again finding unsecured MongoDB databases carelessly left exposed on the internet, wiping their contents, and leaving a ransom note demanding a cryptocurrency payment for the data’s safe return. As ZDNet reports, ransom notes have been left on almost 23,000 MongoDB databases that were let unprotected on the public internet without a password. Unsecured MongoDB databases being attacked by hackers is nothing new, of course.