MongoDB is a cross-platform NoSQL database that uses JSON-like documents with optional schema to store data. It was designed for high availability, high performance for high-data persistance use cases, and automatic scaling. Of course, all with the right infrastructure in mind. It is usually a good choice for document-oriented use cases when you need quick prototyping or massive scale. With the massive scale comes massive traffic, though.

Staying in control of your users’ digital experience and their level of satisfaction is the most important thing you can do as a software-based business. Yet, that’s impossible to do without a monitoring strategy and tools that enable you to visualize how customers interact with your app or website from their perspective.

Redis is an open-sourced, BSD 3 licensed, highly efficient in-memory data store that can be easily used as a distributed, in-memory key-value store, cache, or message broker. It is known for being extremely fast, reliable, and supporting a wide variety of data structures, making it a very versatile tool widely adopted across the industry. Redis was architectured with speed in mind and is designed in a way that it keeps all the data in memory.

OK, so you want to sort your documents by something that can’t be implemented with Solr’s built-in functions. This calls for a custom function, which you can implement through your own ValueSourceParser. To address the elephant in the room, Elasticsearch and OpenSearch have script sorting. This is easier to implement, but not as close to Lucene. Though of course you can use a native script as well.

When we say “logs” we really mean any kind of time-series data: events, social media, you name it. See Jordan Sissel’s definition of time + data. And when we talk about autoscaling, what we really want is a hands-off approach at handling Elasticsearch/OpenSearch clusters. In this post, we’ll show you how to use a Kubernetes Operator to autoscale Elasticsearch clusters, going through the following with just a few commands.

When new web browser versions are released, new bugs are inevitably introduced, which can degrade a website’s performance and increase the overall page load time. This can severely impact a user’s engagement and a business’s bottom line.

Website performance is important as it directly impacts your business bottom line, this is why picking the right website monitoring service crucial! They perform regular tests and alert you whenever your site is down, making it easier for you to spot track down issues and solve them. There are lots of options out there from simple uptime monitoring or transaction monitoring to complex web performance monitoring solutions.

The Apache Tomcat is an open-source implementation of the Jakarta Servlet, Jakarta Server Pages, Jakarta Expression Language, Jakarta WebSocket, Jakarta Annotations and Jakarta Authentication specifications, all being a part of the Jakarta EE Platform. That is the official description of Apache Tomcat.

If you’re using Solr, for sure there are times when you change the schema and need to reindex. Quite often the source of truth is a database, so you can use streaming expressions via the JDBC source to reindex. But sometimes that’s not possible or adds too much load to the DB. So how can we use Solr itself as a source?

For this Black Friday, Sematext wishes you: Now seriously, applications tend to generate a lot more logs on Black Friday, and they also tend to break down more – making those logs even more precious. If you’re using the Elastic Stack for log management, in this post we’ll share some tips and tricks to prepare you for this extra traffic.