Five Tips for Faster Analytics with Postgres

Sooter Saalu

Postgres is a popular open source relational database with over thirty-five years of iterative development and a solid reputation for its reliability and performance. However, working with analytics in Postgres can present various challenges.

These challenges can include slow query performance when dealing with large data sets; extended execution times for complex operations like aggregates, joins, and data transformations; limitations in analytical workflows and automation capabilities; as well as difficulties related to scalability and resource management.

While Postgres provides a solid foundation for data analysis, effectively utilizing it for analytics, especially with large data sets, can be a daunting task. This article provides practical tips for improving the performance of your analytics workflows and achieving faster analytics within your Postgres environment. The tips are centered around five key areas:

We'll cover:

Hydra is an open source data warehouse built on Postgres. It adds vectorization and columnar storage to Postgres, resulting in large speed ups on aggregates and analytics workloads. By following these tips and leveraging Hydra's recent advancements, you can overcome the performance challenges Postgres historically faced for analytics. Build better analytics with Postgres.

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Materialized Views

Unlike regular views, which are virtual tables that run a query each time they are accessed, materialized views are precomputed database tables that store the results of a query and only update them when they are directly refreshed. Compared to performing equivalent queries on the underlying tables, materialized views can significantly enhance the speed of your queries, particularly for more complex queries.

Materialized views are typically used in scenarios where query performance is critical, queries are executed frequently against large and complex data sets, and the data being queried doesn't change as frequently. In cases where your queried data requires multiple aggregations, joins, and operations that are computed frequently for reporting or analytics, you can efficiently improve performance and reduce execution time using precomputed materialized views.

You can create materialized views using the following command structure:

    [ (column_name [, ...] ) ]
    [ USING method ]
    [ WITH ( storage_parameter [= value] [, ... ] ) ]
    [ TABLESPACE tablespace_name ]
    AS query
    [ WITH [ NO ] DATA ]

For example, here's a materialized view that computes a monthly sales summary containing the total products sold and sales gained:


    date_trunc('month', order_date) AS month,
    SUM(quantity) AS total_quantity,
    SUM(quantity * price) AS total_sales
    date_trunc('month', order_date),

Best Practices for Materialized Views

There are a couple of things you can do to make sure you're making the most of materialized views:

  • Refresh your materialized view: The frequency and methods you employ to refresh your materialized view are crucial factors that influence the overall effectiveness of your view. As refreshing a materialized view can be a resource-intensive operation, it's better suited to data that updates infrequently. Also, you can choose between fully refreshing your view or incrementally refreshing it so that only the updated changes are replaced.
  • Monitor disk space and performance: Materialized views can consume significant amounts of disk space, so it's essential to monitor their size and delete unused ones to free up space. You should also monitor their refresh performance to ensure that they don't negatively impact the overall performance.
  • Optimize as indexed and partitioned tables: Materialized views offer a distinct advantage by being stored as regular tables in Postgres. This means that they can fully leverage the benefits of indexing and partitioning techniques, leading to enhanced performance and efficient processing of large data sets.


Partitioning involves dividing a large table into smaller, more manageable pieces called partitions. Each partition is a separate table that stores a subset of the original table's data. In Postgres, row- and column-based partitions can be established using specific criteria, distinct values, or ranges. Postgres also allows you to establish multiple partitions under a partition by creating a sub-partition or composite partition. Partitioning large tables makes it easier and faster to manage and query data.

Partitioning is useful for large, complex tables and can help improve the performance of your database. By implementing partitioning in scenarios involving data sets with millions of rows or complex joins, you can significantly decrease the amount of data that needs to be scanned during query execution. It's particularly efficient for time-based or geographic data, as you can partition your large database by date or region, creating subsets of data that represent specific states or days.

Before partitioning, you need to consider your database requirements and determine the appropriate partitioning strategy. This decision will dictate how the table should be partitioned—whether it should be divided into equal parts, partitioned row-wise, or based on specific columns such as dates, regions, or customer IDs.

To partition your data, you first need to create a partitioned table and then create specific partitions based on your partitioning criteria. The following example creates partitions for a sales table based on the dates of the transactions.

First, you need to create a new partitioned table with the desired partitioning criteria. This table will serve as the parent table for your partitions:

    id              int not null,
    sales_date      date not null,
    customer_id     int,
    amount          int
) PARTITION BY RANGE (sales_date);

After creating the partitioned table, you need to create individual partitions for specific date ranges. These partitions inherit the structure and constraints of the parent table:

CREATE TABLE sales_y2006m02 PARTITION OF sales
    FOR VALUES FROM ('2006-02-01') TO ('2006-03-01');

CREATE TABLE sales_y2006m03 PARTITION OF sales
    FOR VALUES FROM ('2006-03-01') TO ('2006-04-01');

Best Practices for Partitioning

If you want to make the best use of partitioning, you should be sure to do the following:

  • Choose the right partitioning key: The partitioning key is the column that determines how the data is partitioned. It's important to choose a partitioning key that evenly distributes the data across partitions and is frequently used in queries. For example, if you're partitioning a sales table, you might choose to partition it by date since queries often filter by date.
  • Size your partitions appropriately: Each partition should be sized appropriately to balance performance and manageability. Large partitions can adversely impact query performance, while small partitions can lead to an unmanageable number of partitions, increasing your maintenance and backup overhead as well as overall system complexity. The ideal partition size will depend on your specific use case and hardware.
  • Monitor partition usage and growth: As data grows and changes over time, it's important to monitor partition usage and growth to ensure that your partitioning strategy is still effective. You may need to add or remove partitions as the data changes.


An index in Postgres is a separate object that contains a sorted version of some columns from the table along with a reference to the original row in the table. By creating indexes on frequently queried columns, Postgres can locate and retrieve the relevant data more efficiently, reducing the time required for query execution. Indexes can significantly speed up search, filtering, and sorting operations, improving the overall performance of analytics queries in Postgres.

Indexing is beneficial in various scenarios where large, complex tables are frequently queried, as it has a definite impact on the performance of your aggregation, grouping, joining, sorting, and search operations. Creating indexes can improve query execution time and enforce referential integrity for foreign key relationships. Indexing columns with a large number of distinct values can also help Postgres quickly identify and retrieve the specific data you need.

You can create indexes from one or more columns in Postgres using the following command structure:

CREATE INDEX index_name ON table_name (column1, column2, ...);

Best Practices for Indexing

To ensure you're not misusing indexing, you should consider the following recommendations:

  1. Only use indexes when necessary: Be cautious when using indexes, particularly multicolumn indexes, as they can impose additional overhead on your database system. Additionally, they may slow down the execution time of UPDATE and INSERT operations. It's advisable to use indexes only when necessary for optimal performance.
  2. Choose the right columns to index: Select the appropriate columns to create indexes based on your query patterns, frequency of queries, required data modification operations, and overall performance needs. Keep in mind that primary keys and foreign key columns in Postgres are automatically indexed by default, so avoid excessive indexing.
  3. Monitor index fragmentation: Regularly check for index fragmentation, especially for tables that undergo frequent updates or deletes. Fragmented indexes can impact query performance. You can use tools like pgstattuple or built-in functions like pg_stat_all_indexes to monitor and manage index fragmentation.

Stored Procedures

Stored procedures are precompiled and stored database objects that contain a set of SQL statements and procedural logic. They allow you to group multiple SQL statements together and execute them as a single unit of work whenever needed. Stored procedures encapsulate complex database logic, acting as containers for your queries and operations and making them easier to manage and maintain. They provide a core repository for executing frequently used SQL queries or performing complex calculations.

As stored procedures are easily reusable, they are an effective solution for frequently executed queries or essential business operations. They can be used to simplify your reporting process by consolidating all data manipulations, conditional operations, and SQL statements into a procedure that can be executed quickly. This reduces network traffic and minimizes the overhead of sending multiple individual queries. With stored procedures, you can ensure data consistency and integrity. You can also define access rights and permissions for each procedure, restricting certain procedures to specific users.

Here's a simple example of a stored procedure that inserts data into a users table:

CREATE PROCEDURE insert_data(a integer, b integer)
AS $$

Best Practices for Stored Procedures

The following recommendations will help you optimize your stored procedures:

  • Plan for reusability: You should design your stored procedures with reusability in mind. Aim to create modular and self-contained procedures that can be easily utilized. This promotes code efficiency and reduces duplication.
  • Document your stored procedures: Provide clear and comprehensive documentation for each stored procedure. Include information about the purpose, input parameters, expected output, and any relevant usage instructions. Proper documentation helps you and other developers understand and use the procedures effectively.
  • Handle errors and exceptions: Implement error handling within your stored procedures to gracefully handle exceptions and unexpected scenarios. Use appropriate error handling mechanisms to catch and handle errors effectively. This improves the stability and robustness of your code.
  • Regularly review and maintain them: Periodically review and update your stored procedures as your system evolves. Refactor and optimize procedures when necessary to align with changing requirements or to improve performance. Regularly review the necessity and usage of existing procedures to ensure they remain relevant.

Columnar Storage

Columnar storage in Postgres refers to a technique where data is organized and stored column by column instead of row by row. Values from the same column are stored together, creating separate data structures that can improve compression rates because they contain similar data values. Columnar storage is particularly well suited for aggregations, where calculations are performed on a single column or a subset of columns. Aggregations can be executed more efficiently as the column values are stored consecutively, reducing the need for scanning unnecessary data. In general, this improves query performance and enables efficient column data retrieval for analytic queries that need data from single columns or a subset of columns.

Columnar storage is commonly used in analytical databases and data warehousing systems where query performance and analytics processing speed are critical. It offers significant advantages for workloads that involve large data sets, complex queries, frequent aggregations, or selective column retrieval.

Best Practices for Columnar Storage

Consider the following recommendations to maximize the efficiency of columnar storage:

  • Identify suitable data: Effective utilization of columnar storage begins with identifying the data that is best suited for this storage format. Columnar storage excels in analytical workloads characterized by large data sets and queries that predominantly target specific columns or subsets of columns. To determine which data will derive the greatest benefits from columnar storage, consider factors such as data size, query patterns, and analytical requirements.
  • Consider hybrid storage models: Depending on your workload characteristics, consider adopting hybrid storage models that combine row-based and columnar storage techniques. This approach allows you to leverage the strengths of each storage type for different types of queries and optimize the overall performance of your analytical workflows.

Using Hydra to Further Enhance Analytics

As you can see, optimizing Postgres for efficient and speedy execution of analytical processes can be a demanding task that requires continuous attention and adaptation to evolving data and business logic. While Postgres itself offers a solid foundation, there are alternative options available that further enhance its analytics capabilities. One such option is Hydra, an open source data warehouse built on Postgres.

Hydra uses advanced techniques like columnar storage, vectorized execution, and query parallelization to efficiently handle online analytical processing (OLAP) queries. By taking advantage of these features, Hydra significantly improves the performance of analytical workloads in Postgres.

Hydra offers several advantages for analytics while maintaining compatibility with Postgres heap tables, indexing, and native partitioning. This ensures smooth handling of high-throughput transactional writes, enables quick lookup and operational queries, and supports hybrid transactional/analytical processing (HTAP). By incorporating Hydra into your analytics workflow, you can leverage these benefits to enhance the efficiency of your analytical processes.

Two specific Hydra features that can significantly impact your analytic workflows are incremental materialized views and columnar storage with query parallelization.

Incremental Materialized Views

Hydra's incremental materialized views feature offers a practical solution for managing materialized views in Postgres. By utilizing the pg_ivm extension, Hydra enables automatic updates to materialized views based on changes in the underlying base tables.

The key advantage of incremental materialized views is the ability to refresh the view efficiently by applying only the relevant changes from the base tables. This approach eliminates the need to recalculate the entire view, resulting in improved query performance and reduced computation overhead.

Hydra's incremental materialized views can ensure that your materialized views stay up-to-date without the time-consuming process of recomputation. You'll be able to devote more of your attention to exploring your most recent data and making decisions based on real-time information when you automate the process of refreshing the data.

Columnar Storage with Query Parallelization

Hydra's columnar storage with query parallelization significantly improves the performance and efficiency of analytics workloads in Postgres. By leveraging the benefits of columnar storage and query parallelization, Hydra enables faster and optimized query execution.

Hydra's columnar storage organizes data in a manner that enhances query performance by storing and accessing data more efficiently. Additionally, the query parallelization feature divides queries into smaller tasks executed concurrently by multiple workers, reducing execution time and improving resource utilization.

By combining these features, Hydra enhances the processing of analytical queries in Postgres. The result is faster query response times, improved scalability, and better resource management. This allows for efficient handling of large data sets, complex join operations, and demanding aggregations, enabling quicker and more effective extraction of valuable insights from your data.


Optimizing analytics workflows and achieving fast results in Postgres can be challenging, especially when dealing with large data sets and complex operations. However, by implementing the five practical tips discussed in this article—materialized views, partitioning, indexing, stored procedures, and columnar storage—you can enhance the performance of your analytics workflows and unlock the full potential of your data analysis capabilities in Postgres.

With the right strategies and tools in place, you can harness the power of Postgres for analytics and unlock valuable insights from your data more swiftly and effectively. Hydra is the open source Snowflake alternative. It's a data warehouse for developers that's fast, simple, and adaptable to future requirements. Star the Hydra GitHub repo to join the community and follow along for updates.

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