Definition of PostgreSQL Query Optimization
PostgreSQL provides the query optimization techniques to increase the performance of the database as well as it minimizes the complexity of the query with the query optimization technique. As developers, we always consider how we can increase the performance of the database and how we can avoid the complexity of the query. When we talk about relation operators in PostgreSQL the more difficult to process is JOIN. The number of solutions we can use for this problem to avoid complexity such as (nested loop, hashing, and B-tree, etc). Now a day’s PostgreSQL uses a near-exhaustive search method to optimize the query.
PostgreSQL Query Optimization Techniques
Single query optimization is used to increase the performance of the database. If we have a 10TB database then we can use a multi-column index. The speed of the database is increased by 112X. For more knowledge let’s see different techniques as follows.
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Explain analyze
PostgreSQL has two different commands such as EXPLAIN and EXPLAIN ANALYZE. The difference between EXPLAIN and EXPLAIN ANALYZE is that the EXPLAIN command is used to see the cost of query based on your system database and EXPLAIN ANALYZE command is used to show the process time of query at every stage. Most of the time we refer to the EXPLAIN ANALYZE command because it shows all details about the query that means the cost of query and processing time. On the other hand, the EXPLAIN command is used for specific indexes.
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One index per query
In this technique, it uses a specific column from the table so we can quickly find data from the table. In PostgreSQL index of the column is also maintained row identifier or we can say address of the row to speed up the table scan.
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Selection of random rows
In this approach we select random rows from the table at the time of row selection it uses row identifier or row address. Main purpose of query optimization is to increase the performance of the database and we advise creating a separate index per unique query to boost the performance of the database.
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Column sequence in Multicolumn indexing
Sometimes we need to assign column order in multicolumn indexing because to avoid overlapping of indexing or we can say to avoid redundancy of column name and it is helpful to boost the speed as well as to minimize the complexity of query in database.
Let’s see scan type and Joins for a better understanding the query optimization.
It is necessary to understand different scan types and join types because when we learn query optimization techniques we must know different scan types and join. A sequential scan technique is helpful to small tables to boost the performance of databases; different types of sequential scan and Join as follows.
Scan Types
Different types of scan as follows.
- Sequential Scan
- Brute force technique to access or retrieve data from disk.
- We can scan the entire
- A sequential scan is held full to a small
- Index scan
- Scan all rows or some rows from the table.
- Random seeks are costly for a spindle based disk.
- It is faster than a sequential scan when we access a small number of rows from the table.
- Indexing only scan
- It can scan all rows or some rows in the index.
- In which we already store the values so there is no need to lookup rows in the table.
- Bitmap Heap scan
- It scans indexing, it creates bitmap pages to visit and look up relevant pages from disk.
Join Types
- Nested Join
- In which we can scan rows from the outer table to match rows with the inner table.
- It is fast to scan and better for small size of a table.
- Merge Join
- It is good for large tables to increase the speed of databases. It required a high cost if it required additional sort.
- Hash Join
- In which we scan rows from the inner table to match rows with the outer
- It is useful for equality conditions, it requires a high cost but execution of query is fast.
Examples of PostgreSQL Query Optimization
Let’s see how EXPLAIN ANALYZE commands work as follows.
First, we need to create a table by using the create table statement so here we create an emp table with different attributes with different data types. Below snapshot shows details structure of emp table as follows.
select * from emp;
Now we use the EXPLAIN ANALYZE command to see the query plan as follows.
EXPLAIN ANALYZE select * from emp;
Explanation:
In the above statement, we use a select clause with explain analyze command to see the query of the emp table with details. It shows planning time and execution time of the query also shows a sequential scan of emp table. Illustrate the remaining end result of the above announcement by way of the usage of the following snapshot.
Now to see how we can create the index it has a simple syntax to create an index as follows.
Example #1
create index emp_org on emp(emp_id);
Explanation:
In the above example, we use the create index statement to create the index name as emp_org for emp_id column on emp table. Illustrate the remaining end result of the above announcement by way of the usage of the following snapshot.
We can see the query plan by executing the explain analyze command.
Example #2
explain analyze select * from emp where emp_id < 8;
Explanation:
In the above example, we use select and where clauses with explain analyze command. It shows all filtered with execution time, planning time. Illustrate the remaining end result of the above announcement by way of the usage of the following snapshot.
Conclusion
We hope from this article you have understood the PostgreSQL query optimization. From the above article, we have learned the basic syntax of how to create indexes in PostgreSQL as well as different commands to see query plans. We have additionally discovered how we can enforce them in PostgreSQL with different examples of every technique. From this article, we have learned how we can handle query optimization in PostgreSQL.
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