Reasons for Slow Database Performance
Reasons for Slow Database Performance
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Usually there are scenarios where the application does not perform as expected. A simple web page which fetches data from database and displays optimizes it for mobiles should be fast and turnaround times should be less than 30 seconds on a good network connection.
But still there are cases where these kinds of applications suffer the most performance issues. This is because the database in these cases is not designed by giving proper attention to the application requirements.
You can change one application design even after delivery but changing the database design once a number of application have been integrated with it is like explosion.
Here I am giving some points which should be kept in mind while designing application and database. Only basic idea is being provided. For details one can search each topic on the web as each point expands well to multiple articles.
1) Bind Variables: When a SQL query is sent to the database engine for processing and sending the result, it is compiled by the database compiler to get the tokens of the query. This involves parsing, optimizing and identifying the query. After a number of steps, the SQL query is passed to the database engine for processing. In a small application with a user base of less than 500, it is usually the same query which is executed more often than others. The use of bind variables helps in storing the compiled query once and executing it with different data at different times. For using bind variables, one needs to use PreparedStatement objects in Java.
2) Query is not well formed: Usually the same SQL query can be written in multiple ways. There are ways by which a query can be optimized to give the best performance. The corresponding SQL construct should be chosen depending upon requirement. I have scenarios where people have used WHERE clause instead of GROUP BY and are complaining of poor response times. Similarly Sub queries and Joins complement each other.
3) Database structure is not well defined/normalized: This is probably known to everybody that the database tables should be properly normalized as this is part of every DBMS course at graduation level. If the tables are not properly designed and normalized, anomalies set in.
4) Proper caching is not in place: Many applications make use of temporary caches on the application server to store the reference data or frequently accessed data as memory is less of an issue than the time with new generation servers.
5) Number of rows in the table too large: If the table itself has too much of data then the queries will take time to execute. Partitioning a table into multiple tables is recommended in these situations. For example: If a table has employee records of 1000000 employees then it could be split into 5 small tables each having 200000 rows. The advantage is we know beforehand in which smaller table to look for a particular employee code as the division of large table can be done on the employee id column.
6) Connections are not being pooled: If connections are not pooled then the each time a new connection is requested for a request to database. Maintaining a connection pool is much better than creating and destroying the connection for executing every SQL query. Of course, there are frameworks like Hibernate which take care of creating the connection pools and also allow the customization of these pools
7) Connections not closed/returned to pool in case of exceptions: When an exception occurs while performing database operations, it ought to be caught. Usually catching the exception is not the issue because SQLException is a checked exception but closing the connection is something that most of the times is left out. If the connection is not released, the same connection cannot be used for any other purpose till the connection is timed out.
8) Stored procedures for complex computations on database: Stored procedures are a good way to perform database intensive operations. This is because they are already compiled and there is less network trips for getting the same results as compared to SQL queries.
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