Most relational databases - including MySQL, MariaDB, and SQL Server - support stored procedures and functions. Stored procedures and functions are actually very similar, and can in fact be utilized to accomplish the same task. That being said, there are some crucial differences between the two that need to be considered when deciding which to use for a given job. We'll go over these in today's blog
Regular expressions (regex) provide a way to match strings against a pattern so that your searches are "fuzzy" rather than exact. MongoDB comes with a regex engine built in so you can dig up documents even if you don't know exactly what the exact Field value is that you're looking for. In today's blog we'll learn how to use regexes in MongoDB, using Navicat for MongoDB.
Open up any document in a MongoDB database and you'll notice an _id field:
In fact, the ObjectId/_id is the only field that exists across every MongoDB document. In today's blog, we'll explore what it is and why it's important to your MongoDB database.
You've probably heard that column indexing is a great way to optimize query performance by minimizing the number of disk accesses required by the query. MongoDB has a specific application of field indexing called Covered Queries, where all of a query's columns are indexed. Covered Queries are very fast because MongoDB doesn't have to examine any documents apart from the indexed ones. In today's blog, we'll be learning how to use Covered Queries to query data faster.
If you've ever tried to locate a specific column in a large database, I'm sure that you'd agree that it can be a painstaking task. You can glean a lot of information about the DB structure from the information_schema schema. It has a list of all tables and all fields that are in a table. You can then run queries using the information that you have gotten from this table. The specific tables involved are SCHEMATA, TABLES and COLUMNS. There are foreign keys such that you can build up exactly how the tables are created in a schema.
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