How are Global Indexes implemented in PostgreSQL?

In PostgreSQL, global indexes are implemented using a shared-nothing architecture, which means that the index data is partitioned and distributed across multiple physical nodes or “shards.” This allows for parallel query processing and improved performance for large-scale database workloads.

The implementation of global indexes in PostgreSQL is based on the concept of “table partitioning,” which involves dividing a large table into smaller, more manageable partitions based on a partition key. Each partition contains a subset of the table’s data and is stored on a separate physical node or shard.

To create a global index in PostgreSQL, you must first define a partitioned table and specify the partition key. Then, you can create an index on the partition key, which will be distributed across all partitions in the table. This allows for fast query processing and efficient use of system resources.

When a query is executed on a partitioned table with a global index, the query planner will automatically route the query to the appropriate partition based on the partition key. This allows for parallel query processing across multiple shards, which can significantly improve performance for large-scale workloads.

Global indexes in PostgreSQL also support various indexing types, such as B-tree, hash, and GiST (Generalized Search Tree) indexes, which provide additional flexibility and performance optimization options for different types of workloads.

Overall, the implementation of global indexes in PostgreSQL is designed to provide scalable and efficient indexing for large-scale database workloads, while also supporting various indexing types and optimization strategies.

Explain Global Indexes in PostgreSQL with example

In PostgreSQL, a global index is an index that spans multiple partitions of a partitioned table. Global indexes are useful for large-scale database workloads because they allow for fast query processing and efficient use of system resources across multiple physical nodes or “shards.”

To demonstrate how global indexes work in PostgreSQL, let’s consider an example of a large-scale e-commerce application with a partitioned table for order data. The table is partitioned by order date, with each partition representing a specific date range. The table has the following columns: order_id, customer_id, order_date, total_price, and shipping_address.

To create a global index on the order_date column, we can use the following SQL command:

This command will create a global index on the order_date column, which will be distributed across all partitions of the orders table. When a query is executed on the orders table that involves the order_date column, the query planner will automatically route the query to the appropriate partition based on the partition key.

For example, suppose we want to retrieve all orders from the month of January. We can use the following SQL query:

When this query is executed, the query planner will automatically route the query to the partition that contains data for the month of January. This allows for fast query processing and efficient use of system resources, even for large-scale data sets.

In summary, global indexes in PostgreSQL allow for efficient indexing of large-scale database workloads by distributing index data across multiple physical nodes or “shards.” Global indexes are implemented using a shared-nothing architecture, which allows for parallel query processing and improved performance.

About Shiv Iyer 444 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.