Articles

This post tells how a website with a distributed database reduced online serving latency from 1.11 s to 417.7 ms, and then to 123.6 ms. We found that some lessons learned on MySQL could be applied throughout the optimization process. But when we optimize a distributed database, we need to consider more.

The OSS Insight website displays the data changes of GitHub events in real time. It’s powered by TiDB Cloud, a MySQL-compatible distributed SQL database for elastic scale and real-time analytics.

Source de l’article sur DZONE

NoSQL data sets arose in the latter part of the 2000s as the expense of storage drastically diminished. The days of expecting to create a complicated, hard to-oversee data model to avoid data replication were long gone and the primary expense of programming and development was now focused on the developers themselves, and hence NoSQL databases were brought into the picture to enhance their productivity.

As storage costs quickly diminished, the measure of data that applications expected to store increased, and the query expanded as well. This data was received in all shapes and sizes — organized, semi-organized, and polymorphic — and characterizing the schema ahead of time turned out to be almost incomprehensible. NoSQL databases permitted the developers to store colossal measures of unstructured data, providing them with a ton of flexibility. 

Source de l’article sur DZONE

We are well past the experimental stage with the cloud. It’s become mission-critical, and we have entered a stage where our applications and services need to take advantage of the globally distributed nature of the cloud and deliver on the expectations of our consumers.

Legacy relational databases are simply not built for the cloud. They are difficult to scale in this environment and costly to maintain their uptime. NoSQL stores were built to address legacy limitations; however, they fall short when it comes to providing consistent transactions. They are casually consistent. Some of the most successful global organizations have purpose-built databases that achieve the reliability of the relational store with the benefits of scale and global coverage that comes with the cloud. These databases are a new breed called Distributed SQL.

Source de l’article sur DZONE


Brief Description

Interference is a simple java framework that enables you to run a distributed database service within your java application, using a JPA-like interface and annotations.

The basic unit of the interference service is a node — in fact, it is a java application, inside which a service instance is running.

Source de l’article sur DZONE

It’s easy for modern, distributed, high-scale applications to hide database performance and efficiency problems. Optimizing performance of such complex systems at scale requires some skill, but more importantly it requires a sound strategy and good observability, because you can’t optimize what you can’t measure. This session explains a performance measurement and optimization process anyone can use to deliver results predictably, optimizing customer experience while freeing up compute resources and saving money.

The session begins with what to measure and how; how to analyze it; how to categorize problems into one of three types; and three matching strategies to use in optimization as a result. It is a recursive method that can be used at any scale, from a data center with many types of databases cooperating as one, to a single server and drilling down to a single query. Along the way, we’ll discuss related concepts such as internally- and externally-focused golden signals of performance and resource sufficiency, workload quality of service, and more.

Source de l’article sur DZONE