Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
As a fast-growing open source NewSQL database, TiDB frequently releases new features and improvements. If you are a TiDB user, you may have found it hard to decide whether or not to upgrade your version. You may have also wondered how to make your upgrade journey safer, smoother, and even unnoticed by business.
On the one hand, new TiDB versions have new features that can support some of the new demands in your business or can fix some known security loopholes or bugs.
In the article Deep Neural Network implemented in pure SQL over BigQuery, the author claimed to use pure SQL to implement a deep neural network model. But after I opened his repo, I found that he used Python to implement iterative training, which was not truly pure SQL.
In this post, I’ll share how I trained a machine learning model in pure SQL on TiDB, an open-source distributed SQL database. Major steps included:
In the previous article “An Introduction to DistSQL” written by Haoran Meng, the Apache ShardingSphere Committer shared the motivating reasons behind the design of DistSQL, explained its syntax system, and impressively showcased how you can use one SQL to create a sharding table.
Recently, the ShardingSphere community has redesigned the SCTL grammar and the execution engine, integrating SCTL into the DistSQL syntax system. Now RAL contains the old SCTL function, making ShardingSphere’s command language even more convenient for database management. Today, our community author would like to introduce the changes and elaborate on how you can use the new RAL command lines. We always pursue a better user experience, and the upgrade we developed this time is just another typical example.
PalFish is a fast-growing online education platform that focuses on English learning. It offers tailored English speaking experience to English as a Second Language (ESL) students. As of October 2020, PalFish has over 40 million users, of which more than 2 million are paid users.
As our business rapidly grew, the surge of data posed a severe challenge to our MongoDB database. MongoDB (2.x and 3.x) does not support transactions and has no predefined schema to directly regulate data. This blocked our business growth. To solve these problems, we migrated from MongoDB to TiDB, an open-source, MySQL-compatible, distributed SQL database that supports Hybrid Transactional/Analytical Processing (HTAP) workloads. This turned out to be the right move.
Over the past decade, applications have become more and more data-intensive. Dynamic data, analytics, and models are now at the core of any application that matters. To support these requirements, there is a commonly held, but often incorrect, belief that modern applications need to be built on top of a variety of special-purpose databases, each built for a specific workload. It is said that this allows you to pick the best ones to solve your application needs.
This trend is apparent when you look at the plethora of open-source data tools that have proliferated in recent years. Each one was built to scratch an itch, optimized for specific, narrow use cases seen in a smattering of projects. In response, some of the cloud vendors have packaged up these multiple database technologies for you to choose from, commonly forking from existing open-source projects. You’re then meant to wire together several of these tools into the needed data solution for each application.
The SQL execution plan is a critical factor that affects SQL statement performance. The stability of the SQL execution plans heavily influences the entire cluster's performance. If a relational database's optimizer chooses a wrong execution plan for a query, it usually has a negative impact on the system; for example, operations might take longer to respond or the database might get overloaded.
We've done a lot of work on optimizer stability for TiDB. However, SQL execution plans are affected by various factors. The execution plan may encounter unanticipated changes. As a result, the execution time might be too long.