OLAP in Postgres: Features, Challenges, and Optimization Strategies

What Is OLAP? 

Online Analytical Processing (OLAP) is a technology used for complex analytical queries and data analysis, particularly in business reporting. It allows users to perform multidimensional queries on large datasets quickly, supporting operations like data mining, business intelligence, and reporting. 

OLAP systems enable users to aggregate data across various dimensions, such as time, geography, and product. OLAP systems are distinguished from transaction-based systems (OLTP) by their focus on read-heavy, complex queries rather than handling frequent updates. 

In OLAP, data is typically stored in a multidimensional format, allowing for swift data retrieval. This contrasts with relational databases that use a two-dimensional table format more suited to transactional applications. 

While PostgreSQL was not originally designed for OLAP workloads, it is increasingly used for analytics use cases. We’ll cover how OLAP works in Postgres, how Postgres supports OLAP-style analysis, as well as important challenges and optimization strategies to achieve high performance.

This is part of a series of articles about Postgres analytics.

How OLAP Works in PostgreSQL 

PostgreSQL was not originally designed for OLAP workloads, but can support OLAP workloads through a combination of query processing, indexing techniques, and specialized features that improve analytical performance. Unlike traditional OLAP systems that rely on dedicated data warehouses, PostgreSQL allows users to perform OLAP-style analysis directly on relational data, which improves flexibility and ease of use.

Key components of OLAP in PostgreSQL include:

• Multidimensional analysis: PostgreSQL enables data aggregation across multiple dimensions using GROUP BY, ROLLUP, and CUBE operations.
• Parallel query execution: PostgreSQL supports parallel query processing, improving performance for large analytical queries by distributing workloads across multiple CPU cores.
• Indexing for faster retrieval: Index types like BRIN (Block Range INdex) and GIN (Generalized Inverted Index) optimize query execution on large datasets, reducing scan times.
• Partitioning for performance: Table partitioning allows PostgreSQL to handle vast amounts of data by splitting tables based on predefined criteria, such as date ranges or categories.

How Does PostgreSQL Perform on OLAP vs Competitors? 

PostgreSQL can handle OLAP workloads effectively up to a certain scale, especially when enhanced with features like materialized views, parallel query execution, and advanced indexing. However, as data volumes and concurrency demands grow, it becomes more challenging to achieve good performance.

Performance and Scalability

PostgreSQL performs well for moderate analytical workloads, especially when queries are optimized and datasets are partitioned or indexed effectively. But as data scales into billions or trillions of records, performance may degrade. Query latency becomes unpredictable, and the system can struggle to support high-concurrency access patterns common in user-facing analytics. 

SQL Compatibility and Migration Complexity

One of PostgreSQL’s strengths is its mature and expressive SQL support. Some Postgres OLAP extensions require extensive query rewrites, particularly around joins and window functions. SQL compatibility is a critical factor in migration planning, as rewriting queries and adapting schemas can consume significant engineering resources.

Operational Complexity and Developer Experience

PostgreSQL offers a simple, well-understood deployment model, which appeals to experienced IT teams. But maintaining OLAP workloads on Postgres often requires careful tuning, complex index strategies, and workarounds for scaling. These increase operational overhead and slow down iteration cycles for development teams.

Integration and Data Movement

PostgreSQL has limited built-in support for real-time data ingestion at scale. It supports tools like foreign data wrappers and logical replication, but it can be challenging to achieve real-time streaming and synchronization.

PostgreSQL Features for OLAP 

Here are some aspects specific to Postgres that enable OLAP queries. 

Window Functions for Complex Calculations

PostgreSQL provides a set of window functions, which allow users to perform calculations across a defined set of rows related to the current row. Unlike aggregate functions, window functions retain individual row details while computing cumulative values.

Key use cases of window functions in OLAP include:

• Running totals and moving averages: Using SUM() OVER() or AVG() OVER() to calculate trends over time without grouping data.
• Rankings and percentiles: Functions like RANK(), DENSE_RANK(), and PERCENT_RANK() help analyze performance metrics and distributions.
• Lag and lead analysis: The LAG() and LEAD() functions allow comparisons between consecutive rows, useful for time-series analysis.

Common Table Expressions (CTEs) for Hierarchical Queries

Common Table Expressions (CTEs) simplify complex queries by allowing temporary result sets to be referenced multiple times within a query. Recursive CTEs are particularly useful for hierarchical data analysis, such as organizational structures, bill of materials, or pathfinding problems.

Example use cases of CTEs in OLAP:

• Hierarchical data retrieval: Recursive CTEs (WITH RECURSIVE) enable efficient querying of parent-child relationships.
• Data preprocessing: Complex transformations can be broken into smaller, readable steps using non-recursive CTEs.
• Query simplification: Instead of deeply nested subqueries, CTEs improve readability and maintainability.

Materialized Views for Query Optimization

Materialized views in PostgreSQL store precomputed query results, improving performance for repeated analytical queries. Unlike standard views, which execute queries on demand, materialized views persist data and can be refreshed periodically.

Benefits of using materialized views in OLAP:

• Faster query performance: Frequently used aggregations and joins are stored, reducing execution time.
• Reduced computational overhead: Complex calculations don’t need to be recomputed with every query.
• Incremental refresh support: PostgreSQL 12+ supports REFRESH MATERIALIZED VIEW CONCURRENTLY, allowing updates without locking reads.

Foreign Data Wrappers (FDWs) for Integrating External Data Sources

Foreign Data Wrappers (FDWs) allow PostgreSQL to query external data sources as if they were local tables. This capability is crucial for OLAP scenarios that require data integration from multiple sources, such as data lakes, NoSQL databases, or other PostgreSQL instances.

Key use cases of FDWs in OLAP:

• Cross-database queries: Seamlessly join data from PostgreSQL and external databases like MySQL or Oracle.
• Big data integration: Connect to Hadoop or cloud-based storage for large-scale analytics.
• ETL optimization: Use FDWs to pull in external data for preprocessing before transformation and analysis.

PostgreSQL supports various FDWs, including postgres_fdw, mysql_fdw, and oracle_fdw, enabling flexible and scalable OLAP architectures.

Challenges and Considerations of OLAP on PostgreSQL 

Here are some of the main potential issues to consider when working with OLAP scenarios in Postgres.

Scaling PostgreSQL for Large Datasets

PostgreSQL scales vertically by utilizing additional hardware (more CPU, RAM, and disk), but horizontal scaling is more complex.

Key strategies for scaling PostgreSQL in OLAP scenarios:

• Partitioning: PostgreSQL supports declarative partitioning, which improves query performance by dividing large tables into smaller, more manageable pieces based on key attributes like date or category.
• Parallel query execution: PostgreSQL distributes workloads across multiple CPU cores to speed up complex queries, reducing execution time for large datasets.
• Index optimization: Using BRIN, GIN, and B-tree indexes optimizes query performance by minimizing the need for full-table scans.
• Connection pooling: Tools like PgBouncer help manage database connections, reducing overhead from multiple users running analytical queries.

For extreme scalability, PostgreSQL can be extended with sharding solutions like Citus, which distributes data across multiple nodes, enabling near-linear scaling for OLAP workloads.

Balancing OLAP and OLTP Workloads

PostgreSQL is primarily an OLTP database but can be optimized for OLAP workloads. However, running both types of workloads on the same system requires careful resource management to prevent performance bottlenecks.

Best practices for balancing OLAP and OLTP in PostgreSQL:

• Use read replicas: Offloading analytical queries to read replicas prevents them from impacting transactional performance.
• Workload isolation: Dedicated resources (CPU, memory, and disk) for OLAP queries can prevent resource contention with OLTP operations.
• Materialized views: Precomputing complex aggregations reduces the need for real-time heavy queries.
• Scheduled query execution: Running OLAP queries during off-peak hours prevents them from slowing down OLTP transactions.

If workloads are heavily skewed towards OLAP, a hybrid approach using PostgreSQL alongside a dedicated data warehouse may be a better option.

Hardware and Infrastructure Requirements

For OLAP workloads, PostgreSQL benefits from hardware configurations optimized for high-throughput analytical queries.

Key infrastructure considerations:

• Memory (RAM): More RAM allows PostgreSQL to cache larger portions of data, reducing disk I/O bottlenecks.
• Storage (SSD vs. HDD): SSDs significantly improve query performance compared to HDDs due to faster random access speeds.
• CPU: Multi-core processors improve parallel query execution, reducing response times for complex aggregations.
• Network and disk throughput: High-bandwidth storage and fast network connections ensure smooth data movement, especially for distributed PostgreSQL setups.

Tuning PostgreSQL parameters, such as work_mem, shared_buffers, and effective_cache_size, further optimizes performance for OLAP queries.

Postgres OLAP Optimization Strategies 

Here’s a look at the main strategies used to optimize OLAP queries in Postgres.

1. Indexing Strategies

For analytical workloads that scan large tables, standard B-tree indexes are often too heavy and inefficient. BRIN (Block Range INdex) is more suitable. Instead of indexing individual rows, BRIN indexes store summary information for blocks of rows, which drastically reduces index size and speeds up large-range scans.

BRIN is especially effective on naturally ordered columns like timestamps or monotonically increasing IDs. It performs best when there’s high correlation between physical storage order and the indexed column. The pages_per_range parameter can be tuned to optimize for narrow or broad scan patterns, adjusting based on expected query selectivity.

BRIN should be avoided if the data is randomly distributed, highly selective queries are frequent, or the table sees frequent in-place updates. For use cases like time-series analytics or large fact tables with sequential insert patterns, BRIN can deliver significant performance and storage benefits.

2. Query Planning and Execution Analysis

OLAP queries typically involve joins, aggregations, and large scans, making effective planning critical. PostgreSQL's query planner can misestimate costs for large joins or aggregations, leading to inefficient plans. Adjusting planner-related parameters like effective_cache_size helps the optimizer make better decisions by informing it how much memory is likely available for caching.

For deep analysis, tools like EXPLAIN (ANALYZE, BUFFERS) help identify where queries are scanning too much data or spilling to disk. Monitoring execution plans regularly and tuning join order or using materialized CTEs can reduce planner mistakes and improve runtime.

3. Parallel Query Execution

PostgreSQL supports parallel execution for sequential scans, joins, aggregations, and some window functions. For OLAP workloads, enabling parallelism can significantly reduce query times by leveraging multiple CPU cores.

Key settings include:

• max_parallel_workers_per_gather: Controls how many workers can be used per operation
• parallel_tuple_cost and parallel_setup_cost: Adjust sensitivity of the planner to parallelize

Parallelism is most beneficial on large tables and queries that aggregate significant amounts of data. It's important to monitor CPU utilization, as parallel queries consume more total CPU while delivering faster results.

4. Tuning for OLAP Systems

OLAP tuning starts with adjusting memory-related parameters:

• work_mem: Controls memory for sort/hash operations. Increase it to avoid spilling to disk during aggregations and joins.
• effective_cache_size: Affects planner decisions. Set it high to reflect the actual available memory used for caching.

It is recommended to set maintenance_work_mem higher for faster index creation and maintenance on large tables. OLAP queries benefit from increased memory, but avoid overallocating system-wide.

PostgreSQL also requires careful configuration of vacuum settings for large analytics tables. Set appropriate autovacuum_vacuum_threshold and autovacuum_vacuum_scale_factor values to ensure timely cleanup without unnecessary overhead.

Finally, benchmark changes with tools like pgbench, and monitor query performance using extensions like pg_stat_statements to validate the impact of tuning decisions.

Improving Complex Query Performance in PostgreSQL with Epsio

While PostgreSQL offers robust capabilities for OLAP workloads, managing large-scale analytical queries can be challenging. Epsio addresses these challenges by providing incremental materialized views that optimize query performance.

What Is Epsio?

Epsio is a platform that integrates with PostgreSQL to maintain the results of complex queries incrementally. Instead of recalculating entire datasets upon each data change, Epsio updates only the affected parts of the query result. This approach significantly reduces query times and resource consumption.

How Epsio Works

Epsio connects to your existing PostgreSQL database and allows you to define views using the epsio.create_view function. Once a view is created, Epsio monitors changes to the underlying data and updates the view accordingly. This ensures that the view always reflects the current state of the data without the need for full recomputation.  

For example:

CALL epsio.create_view('revenue_per_genre', 
    'SELECT movies.genre, SUM(purchases.price) AS revenue
     FROM purchases
     LEFT JOIN movies ON movies.id = purchases.movie_id
     GROUP BY movies.genre');

After defining the view, you can query it like any other table:

SELECT * FROM revenue_per_genre;

Epsio ensures that this view remains up-to-date as new purchases are made or existing records are modified.

Benefits of Using Epsio for OLAP Workloads

• Performance Improvement: By updating only the necessary parts of a query result, Epsio reduces the time and resources required for complex analytical queries.

• Scalability: Epsio’s architecture supports high-throughput workloads, making it suitable for large datasets and concurrent users.

• Ease of Integration: Epsio integrates seamlessly with existing PostgreSQL deployments, including managed services like Amazon RDS and Google Cloud SQL.  

• Support for Complex Queries: Epsio supports a wide range of SQL features, including joins, CTEs, subqueries, GROUP BY, and window functions.  

For more information and detailed deployment guides, visit the Epsio documentation.