PostgreSQL query optimization refers to the process of improving the performance and efficiency of SQL queries executed on a PostgreSQL database. It involves analyzing and adjusting various components of the database system, including the query planner, indexing, caching, and server configuration.

Here are some techniques and best practices for optimizing PostgreSQL queries:

EXPLAIN Command:
Use the EXPLAIN command to analyze query execution plans and identify potential performance bottlenecks. This command shows how the query is being executed and provides information about the indexes, join algorithms, and sort operations used.

Indexes:
Use indexes to speed up query execution. Indexes allow PostgreSQL to find data more quickly by creating a separate data structure that can be searched efficiently. Choosing the right indexes can significantly improve query performance.The following indexes can be implemented in PostgreSQL:
1. B-tree Index (default)
2. Hash Index
3. GiST (Generalized Search Tree) Index
4. SP-GiST (Space Partitioned Generalized Search Tree) Index
5. GIN (Generalized Inverted Index) Index
6. BRIN (Block Range INdex) Index
7. Bitmap Index

Functions:
Avoid using functions in WHERE clauses or JOIN conditions,as they can prevent the use of indexes and slow down queries. If possible, move the function call to the SELECT clause.

Prepared Statements:
Use prepared statements to avoid re-parsing queries each time they are executed. This can improve performance and reduce server load.

A prepared statement is a server-side object that can be used to optimize performance. When the PREPARE statement is executed, the specified statement is parsed, analyzed, and rewritten. When an EXECUTE command is subsequently issued, the prepared statement is planned and executed.

Partitioning:
Consider using partitioning to divide large tables into smaller, more manageable pieces. This can speed up queries that only need to access a subset of the data. The following forms of partitioning can be implemented in PostgreSQL:
1. Range Partitioning
2. List Partitioning

Configuration:
Adjust PostgreSQL server configuration settings, such as the shared_buffers and work_mem parameters, to optimize performance for your specific workload.

Tools:
Use query optimization tools and frameworks, such as pgAdmin, to automate the process of analyzing and optimizing queries. These tools can suggest improvements and provide detailed performance metrics.

BONUS:
Apache AGE is a PostgreSQL extension that provides graph database functionality. The goal of Apache AGE is to provide graph data processing and analytics capability to all relational databases. Through Apache AGE, PostgreSQL users will gain access to graph query modeling within the existing relational database. Users can read and write graph data in nodes and edges. They can also use various algorithms such as variable length and edge traversal when analyzing data.
Website: https://age.apache.org/
Github: https://github.com/apache/age