Python has staked its claim as the most popular programming language among developers worldwide. Accessible via Windows, Linux, and Mac, it’s intuitive and easy to read, and its use of maths lends itself perfectly to Python for finance and data analysis.
You get one shot to keep shoppers on your ecommerce website with relevant search results. According to Harris Poll, 76% of online shoppers abandon a retail website after an unsuccessful search. Therefore, it’s critical to optimize your search experience so buyers can find what they need fast. That’s the theory behind a modern search experience: Nowadays it’s not enough to simply provide a search bar that returns matching products.
People often ask us, “What is the best query language to use with InfluxDB?” The answer to this question has evolved a bit over the years – and the current answer might surprise you.
Partitioning can provide a number of benefits to a sharding system, including faster query execution. Let’s see how it works. In a previous post, I described a sharding system to scale throughput and performance for query and ingest workloads. In this post, I will introduce another common technique, partitioning, that provides further advantages in performance and management for a sharding database.
At InfluxData, one of the common questions we regularly get asked by developers and architects alike the last few months is, “How does InfluxDB compare to MongoDB for time series workloads?” This question might be prompted for a few reasons. First, if they’re starting a brand new project and doing the due diligence of evaluating a few solutions head-to-head, it can be helpful in creating their comparison grid.
This article would not be possible without the contribution of Lars Stegman. The OPC UA Client Listener Plugin was his own contribution to a long-standing issue. Telegraf now includes a new plugin highly anticipated by the community. The OPC UA Client Listener Plugin. So you might be asking yourself: what is the big deal? There was already an OPC UA Plugin — how is this different?
Understand the two dimensions of scaling for database query and ingest workloads, and how sharding can make scaling elastic — or not. Scaling throughput and performance are critical design topics for all distributed databases, and sharding is usually a part of the solution. However, a design that increases throughput does not always help with performance and vice versa. Even when a design supports both, scaling them up and down at the same time is not always easy.
