HAProxy is generally the frontend layer of your application, which means it plays a critical role since all traffic first lands on this layer. Because of this, you need to make sure everything is working at this layer all the time, as any issue can directly impact your business. Therefore, having visibility on this layer is crucial. Visibility can come from two aspects: the metrics HAProxy emits and the logs it generates while handling requests.

Redis is an open-sourced, BSD 3 licensed, highly efficient in-memory data store. It is used widely in the industry because of its incredible performance and ease of use. It can easily be used as a distributed, in-memory key-value store, cache, or message broker. It can hold virtually any data structure, making it highly versatile. Redis was architectured and developed with speed in mind and designed to keep all the data in memory.

Logs are a critical aspect of any production workload, as they give you insight into what is happening in your system and tell you which components may be having issues. The traditional method of looking at logs involves basic Linux commands like tail, less, or sometimes cat.

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.