Tune Ecu Map Database

[Francisca Noggles]

Thereare various techniques with which you can configure the optimal performance of a particular database. Database tuning overlaps with query tuning; so, good indexing and avoiding improper queries help in increasing the database efficiency. In addition, increasing storage, updating to latest database versions and investing in a more powerful CPU (if needed) are also some of the general techniques.

Normalization is the process of removing of duplicate data from a database. We can normalize a database by breaking down larger tables into smaller related tables. This increases the performance of database as it requires less time to retrieve data from small tables instead of one large table.

In SQL, indexes are the pointers (memory address) to the location of specific data in database. We use indexes in our database to reduce query time, as the database engine can jump to the location of a specific record using its index instead of scanning the entire database.

Choosing the correct query to retrieve data efficiently also improves the performance of a database. For example, choosing to retrieve an entire table when we only need the data in a single column will unnecessarily increase query time. So, query the database wisely.

In large databases, we should always retrieve only the required columns from the database instead of retrieving all the columns, even when they are not needed. We can easily do this by specifying the column names in the SELECT statement instead of using the SELECT (*) statement.

Wildcards (%) are characters that we use to search for data based on patterns. These wildcards paired with indexes only improves performance because the database can quickly find the data that matches the pattern.

SQL JOINs are used to combine two tables based on a common column. There are two ways of creating a JOIN implicit join and explicit join. Explicit Join notation use the JOIN keyword with the ON clause to join two tables while the implicit join notation does not use the JOIN keyword and works with the WHERE clause.

Performance wise, they are both on the same level. However, in more complicated cases, the implicit join notation might produce completely different results than intended. Therefore, Explicit Joining is preferred.

The OR operator is used to combine multiple conditions when filtering a database. Whenever we use OR in a filter condition each statement is processed separately. This degrades database performance as the entire table must be scanned multiple times to retrieve the data that matches the filter condition.

Instead, we can use a more optimized solution; by breaking the different OR conditions into separate queries, which can be processed parallelly by the database. Then, the results from these queries can be combined using UNION.

The WHERE and HAVING clause are both used to filter data in SQL. However, WHERE clause is more efficient than HAVING. With WHERE clause, only the records that match the condition are retrieved. But with HAVING clause, it first retrieves all the records and then filters them based on a condition. Therefore, the WHERE clause is preferable.

However, when the database tables are altered by deleting or updating the records, indexes are also altered to suit the changes made. This will cause the indexes to be scattered across the storage. Physical locations also lose the contiguous allocation. Thus, reducing the database performance.

Defragmentation is the solution this problem. It will reorganize/rebuild the logical ordering of the indexes to match the physical ordering. But, this process first analyses the indexes and chooses whether they only need to be reorganized or rebuilt completely.

Database tuning describes a group of activities used to optimize and homogenize the performance of a database. It usually overlaps with query tuning, but refers to design of the database files, selection of the database management system (DBMS) application, and configuration of the database's environment (operating system, CPU, etc.).

Database tuning aims to maximize use of system resources to perform work as efficiently and rapidly as possible. Most systems are designed to manage their use of system resources, but there is still much room to improve their efficiency by customizing their settings and configuration for the database and the DBMS.

Hardware and software configuration of disk subsystems are examined: RAID levels and configuration,[1] block and stripe size allocation, and the configuration of disks, controller cards, storage cabinets, and external storage systems such as SANs. Transaction logs and temporary spaces are heavy consumers of I/O, and affect performance for all users of the database. Placing them appropriately is crucial.

Frequently joined tables and indexes are placed so that as they are requested from file storage, they can be retrieved in parallel from separate disks simultaneously. Frequently accessed tables and indexes are placed on separate disks to balance I/O and prevent read queuing.

DBMS tuning refers to tuning of the DBMS and the configuration of the memory and processing resources of the computer running the DBMS. This is typically done through configuring the DBMS, but the resources involved are shared with the host system.

Tuning the DBMS can involve setting the recovery interval (time needed to restore the state of data to a particular point in time), assigning parallelism (the breaking up of work from a single query into tasks assigned to different processing resources), and network protocols used to communicate with database consumers.

Memory is allocated for data, execution plans, procedure cache, and work space[clarify]. It is much faster to access data in memory than data on storage, so maintaining a sizable cache of data makes activities perform faster. The same consideration is given to work space. Caching execution plans and procedures means that they are reused instead of recompiled when needed. It is important to take as much memory as possible, while leaving enough for other processes and the OS to use without excessive paging of memory to storage.

Processing resources are sometimes assigned to specific activities to improve concurrency. On a server with eight processors, six could be reserved for the DBMS to maximize available processing resources for the database.

On a heavily used database, the transaction log grows rapidly. Transaction log entries must be removed from the log to make room for future entries. Frequent transaction log backups are smaller, so they interrupt database activity for shorter periods of time.

DBMS use statistic histograms to find data in a range against a table or index. Statistics updates should be scheduled frequently and sample as much of the underlying data as possible. Accurate and updated statistics allow query engines to make good decisions about execution plans, as well as efficiently locate data.

Defragmentation of table and index data increases efficiency in accessing data. The amount of fragmentation depends on the nature of the data, how it is changed over time, and the amount of free space in database pages to accept inserts of data without creating additional pages.

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Tuning involves accelerating query response, improving indexing, deploying clusters, and reconfiguring OSes according to how they're best used to support system function and end-user experience. MySQL and Oracle are prominent examples of database management systems (DBMS) on which DBAs generally perform database tuning.

SQL query performance tuning uses different techniques and procedures to reduce query response time and overall resource utilization. Generating an optimized execution plan, keeping track of resource consumption, reviewing index table alignment, and analyzing SQL statements are common ways to better SQL query performance.

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