Although MySQL DECIMAL and NUMERIC data types are both fixed-point values, they are still susceptible to rounding errors. The reason is that, no matter how many digits a type can accommodate (the maximum number of digits for DECIMAL is 65!) that number is still fixed. Moreover, DECIMAL columns can be assigned a precision or scale that could have the potential affect of truncation to the allowed number of digits.
MySQL provides several different log files that can help you find out what's going on inside your MySQL server instance. These include:
- error log
- isam log
- general query log
- binary log
- slow log
Of these, the slow query log is especially useful for finding inefficient or time-consuming queries, which can adversely affect database and overall server performance. This blog will describe how to read and interpret slow query log output to better debug query performance.
Usually, when your database runs slower for an extended period, the culprit is more often than not a "bad" query. That is to say, a query that is not fully optimized, poorly written, or gives users the ability to fetch an unlimited number of rows from the database. We can alleviate some pain by throwing more resources at the server, but this is really a short term fix and doe not address the underlying issue. The best course of action is to identify and fix the problem query or queries, which shouldn't be too difficult, given some time and effort. Of course, the first step is to identify which query or queries is/are the stalwarts. There are a few ways to do that, depending on your specific database type. Today's blog will highlight a few strategies for MySQL.
In last week's blog, we learned about the ramifications of poor indexing, as well as how to choose which columns to include as part of a clustered index. In this article, we'll cover how the same indexes that provide better performance for some operations, can add overhead for others.
It is common knowledge that judicious use of indexes can help SELECT queries execute significantly faster. This can tempt some database admins (DBAs) to try to milk as much performance gains as possible by adding indexes to every column that might possibly be included in a query. The downside to adding indexes to a table is that they affect the performance of writes. Moreover, improperly created indexes can even adversely affect SELECT queries! Any table configuration where performance suffers due to excessive, improper, or missing indexes is considered to be poor indexing. In today's blog, we'll learn about the ramifications of poor indexing, as well as cover how to choose which columns to include as part of a clustered index.
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