In last week's blog we learned about the potential uses and advantages to utilizing Database Partitioning when working with large data sets. In today's follow-up, we'll create a MySQL partition in Navicat for MySQL using the HASH partitioning criteria.
Any time that you categorize data into different types, the need to convert from one data type to another is inevitable. Off the top of my head, a common use case is to process variables that were passed in from a web form via a query parameter or POST request body. Serializing data in order to send it across the network tends to coerce all variables into strings. As such, they often need to be converted into a more appropriate data type, such as a number, date, or what-have-you.
The MAX() function is often used to return the largest value of a given column. It's not picky about types, so the column may contain salaries, best before dates, or last names. The question is, can the MAX() function also find the highest value across multiple columns? The short answer is Yes. The longer explanation is that it depends on the database you're using. In today's blog, we'll explore a few ways to obtain the maximum value among two or more columns, either using the MAX() function, or an even better alternative.
Like most database developers, you've probably written your fair share of queries that search for that proverbial needle in a haystack of text or binary data. I know I have! Perhaps even more important than the SELECT statements that you write against the database are the indexes that it contains. To that end, an inverted index can go a long way towards making mounds of data accessible in an expeditious manner. In today's blog, we'll learn what inverted indexes are, and how to use them in your databases, using MySQL as an example.
Avoiding and/or Minimizing Deadlocks
In relational database systems (RDBMS), a deadlock is a situation where two concurrent transactions cannot make progress because each one is waiting for the other to release the lock. In Part 1 of this series, we we established what Object Locking is in Relational Databases, the different types of locks, and deadlocking. Then, in Part 2, we compared the pros and cons of Pessimistic and Optimistic locking. In this installment, we'll be exploring a few causes of deadlocks, as well as strategies for avoiding, or at least, minimizing them.
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