Memo: Avoid Functions in Database QueriesOctober 21st, 2010 by Scott Selikoff
The most common type of questions in the JavaRanch JDBC forum tends to be about improving performance in a database (that, and “Where can I download a JDBC Driver?”). While remote trouble-shooting performance issues can be tricky, we often spot issues with the presented queries and offer alternatives to improve performance. One issue I see from time to time is the use of database built-in functions in database queries. In general, these should be avoided at all costs for commonly-executed queries, especially ones that could trigger the function to be applied to every record in a table. This article will address this issue of using functions in database queries in more detail and provide explanations and tips for avoiding them in the future.
1. Throwing away indexes
By default, databases search an entire table for records. The purpose of indexes, created automatically by some DBMS systems, is to allow records to be retrieved faster. Using Big O notation, a sorted set of records can find an item within a range in O(log(n)) while an unsorted set would have to search the entire table, or O(n). For hash searches on a specific key, the search time is near O(constant) for properly balanced data structures.
Developers can create indexes on multiple columns in a table, but they have no direct control over how and when they are used. It is the job of the DBMS query optimizer to apply what it thinks are the best indexes at the time the query is requested. Queries that apply a function to a column in a database will likely throw away any index that the query optimizer could take advantage of to search the table, resulting in a full-table scan every time the table is queried. If you are lucky and your table only contains a few dozen records, this issue might not be noticeable, but for the rest of us, this could pose a serious problem. While there are some DBMSs, Oracle being one of them, that support function-based indexes, but they are far from standard practice.
2. Goodbye Portability
My biggest issue with functions, like any database-specific feature, is the fact that they are database specific features! Once you write a query that takes advantage of a specific function, porting your application to a different database system becomes much more difficult. Oftentimes, functions are used because there is a muddling of the data, application, and presentation layer. I have seen developers use database-specific functions to format data (most commonly, dates) from SELECT queries that are transmitted from JDBC directly to the user. If you have a strong mid-tier platform like Java, it is better to leverage the formatting functions within the language, not database-specific ones, to present data to the user.
Porting an application to a different database is non-trivial at best, but the use of database-specific functions will make the job much more difficult. And to those developers out there who often comment that switching databases never happens, it does. I’ve done it. And it’s not for the faint of heart.
3. Slow if used incorrectly
There are correct ways to use functions in database queries that are often overlooked when writing the query. For example, compare the following two MySQL-based queries, both of which search for orders placed on February 15, 2010 and use built-in functions:
SELECT * FROM Widgets WHERE DATE_FORMAT(orderDate,"%Y-%m-%d") = '2010-02-15'; SELECT * FROM Widgets WHERE orderDate = (CAST '2010-02-15' as DATETIME);
First, question is, are they equivalent? The answer is no, but let’s skip that for now and discuss performance. Which is likely to perform better on a database that contains a tree-based index on Widgets.orderDate?
Give up? The second query, of course! The second query applies a function to a constant, and most database query analyzers are intelligent enough to only apply this function once. It then uses the range index to find the records in Log(n) time. On the other hand, the first query performs a function call on every record in the table and therefore ignores the index – resulting in a slow table scan.
As for accuracy, if orderDate is a DATETIME, the second query will return all the results placed only at midnight on February 2nd (2010-02-15 00:00:00) while the second query will return all results placed during the entire day. No worries – there is an easy fix that still uses the index on orderDate for optimal performance:
SELECT * FROM Widgets WHERE orderDate >= (CAST '2010-02-15' as DATETIME) AND orderDate < (CAST '2010-02-16' as DATETIME);
Even though this adds a second parameter to the search, the sorted index can be applied to both.
Like many things in the world, database functions are necessary evil that are required to solve certain problems. The goal shouldn’t be to never use them, but to keep using them to a minimum. One common solution, if the function is being applied to a column repeatedly, is to denormalize the data and add a new column containing the value of the original column with the function applied to it. This is equivalent to how a function-based index works, for databases that do not have this feature built-in. The only overhead is the cost of maintaining the value in the new column each time the original value is updated, although since reads are more common than writes, this is often easy in practice. I am aware some developers have issues with denormalized data, but it is often necessary on large tables with queries that can run for minutes or even hours.
In the previous example, if searching on specific single dates is extremely common, the developer could add an orderDateSTR column that contains strings such as ‘2010-02-15’. A hash index could then be built on the column which would allow searches for single dates to be accomplished in near constant time. Granted, this is not useful for range queries, but would be useful in situations where single date searching needed to be fast on large data sets.