10 Best Practices to Improve Database Performance

10 Best Practices to Improve Database Performance

Databases with poor design are counterproductive to efforts to enhance performance.

However, many businesses are unaware of the seemingly insignificant mistakes that are resulting in significant issues.

These ten best practices for database performance will assist you in significantly improving the direction and performance of your organization, additionally developing more fruitful search queries and elevating the standard of the info.

Here are Database Performance Practices:

1. Avoid Hints

There are countless hours and untold amounts of cash invested within the development of the foremost sophisticated and effective SQL engines by a number of the biggest RDBMS vendors. Hints prevent the RDBMS from using the foremost expedient route to access the information, which could be a significant performance hit. If you would like to touch, this means that there’s a controversy with the way your database or queries are designed, which has to be fixed.

2. Data types are the second piece of recommendation.

There are two styles of advantages that result from ensuring that the data types assigned to database performance attributes are accurate and appropriate:

• It makes certain that the proper reasonable information is added to the database. As an example, the worth 20010229 cannot be added to an attribute of the DATE data type; however, this value is added to an attribute of the INTEGER data type.
• It gives the UPDATE STATISTICS command better insight, which is what it needs. If you decide to store ‘Dates’ using an INTEGER data type instead of a DATE data type, the amounts of possible values that may be assigned to the attribute are off by one. As an example, an INTEGER can tackle a complete of 100 different values between the range of 20010101 and 20010201, whereas a DATE data type can only tackle a complete of 31 different values.

3. Remove Any Attributes from Your Queries that are not Necessary

In order to realize the simplest possible database performance, you ought to restrict the SELECT clause of the query to only include those attributes that are absolutely necessary to fulfill the business requirement. Once you restrict the attributes, the quantity of knowledge that has to be read from the disc is impeded, and more useful information will be stored on each page of memory. This may cause a more practical execution of the query. Note: The asterisk (*) should never be employed in any production query that uses the SELECT clause.

4. Consider Your Relationships

When developing new relationships, it’s essential to require into consideration the context during which those relationships will eventually be utilized (i.e. OLTP, Operational Data Store, Data Warehouse, etc.).

The following components are essential when developing new relationships or making adjustments to existing ones:

• The different levels of knowledge normalization that may be implemented;
• Data types are consistent across all of the relations;
• The attributes and order of the first key and secondary index;
• The cardinality of every attribute

5. Always Maintain Up-to-Date Statistics

The SQL engine gains knowledge and insights into the information through the utilization of statistics. The UPDATE STATISTICS process is liable for collecting statistics, which are then utilized by the OPTIMIZER to work out the info access path that’s the foremost time and resource effectively. It’s possible that the access paths into the info will change over time alongside the info itself. As a result, it’s essential to confirm that statistics are always up to now and to frequently recompile programs.

6. Avoid Doing Table or Index Scans

Accessing data that a question will never use is one of the foremost inefficient uses of system resources (such as space, processing power, and memory, among other things) in order to improve database performance. Scanning of information that’s not necessary occurs in most cases as a consequence of 1 of the subsequent factors:

• Incomplete or absent predicates in a very query
• A query with a poor structure
• Attributes that are neither keyed nor indexed that is used as a predicate
• Compound key with poor structural integrity
• Outdated statistics that cause a less effective QEP (Query Execution Plan)
• JOIN between relations that are either insufficient or poorly structured.
• A function that’s supported the predicate

7. Limit the employment of Temporary Tables

 A great feature that’s included within the majority of SQL implementations is named a brief table. This feature enables an intermediate data set to be used over once by a question or a stored procedure. Nevertheless, there is a variety of things to contemplate when deciding whether or to not use temporary tables, including the following: RDBMS engines have advanced to the purpose where they’re now ready to automatically provide this functionality at the time of runtime.

The additional I/O operations required to READ the initial data, WRITE the info to the temporary table, READ data from the temporary table, and so finally drop the temporary table have a negative impact on the quantity of your time it takes for queries to be completely executed. The execution of the query has been paused so as to attend for the temporary table to be populated.

8. Restrict Your Use of the highest Function to the maximum amount Possible

It is a widespread misunderstanding that question execution will come to an end once the highest condition has been satisfied. The unfortunate reality is that this can be not the least bit the case. Before having the ability to perform an evaluation of the highest condition, the engine of an RDBMS has to have a materialized intermediate result set.

9. In a query that needs sorted data, you’re required to incorporate the ORDER BY clause

There are many reasons for processing data during a specific order; however, it’s imperative to limit the ORDER BY clause to queries where the business requirements demand the info to be during a specific order. This can be because there are many alternative reasons for processing data in a very specific order. Without making use of a form operation, an alternative would be to structure the information base using the first key and/or an index in such the simplest way that it produces the data within the order required to satisfy the business requirements. This is able to eliminate the necessity for the type of operation.

10. Make Use of ‘Cover’ Indexes Whenever Possible

Utilizing multiple indices allows for more efficient access to information apart from the first key, which is one of the benefits of doing so. However, the bulk of indexes is inbuilt in such a way that it’s necessary to conduct a secondary READ operation on the bottom table so as to get all of the info that’s necessary for the query. Due to the way it’s constructed, a canopy index will always contain attributes that aren’t suitable to be used as predicates but which are included to fulfill the info needs of the query.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of clouds, RDBMS, NoSQL, and machine learning database platforms.