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While this works, the built-in Postgres mechanisms may not be enough. As shown by various benchmarks, building an index may take 24+ hours for big (100+ GB) corpora and fail with out-of-memory issues easily.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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TimescaleDB
An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
Timescale is an extension for PostgreSQL that transforms it into an efficient time series database. It optimizes time series data processing using features like intelligent chunking, hypertables, and IMMV with aggregates. Hypertables automatically partition the data by time, but to the user, the table still appears as a regular table.
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One of the solutions that supercharge OLAP in Postgres is ParadeDB which uses columnar storage. It’s an extension to PostgreSQL that uses pg_lakehouse extension to run the queries using DuckDB which is an in-process OLAP database. It’s highly optimized thanks to its columnar storage and can outperform native PostgreSQL by orders of magnitude. They claim they are 94 times faster but it can be even more depending on your use case.
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hydra
Hydra: Column-oriented Postgres. Add scalable analytics to your project in minutes. (by hydradatabase)
Hydra enhances aggregate query processing through pg_ivm, leveraging materialized views for storing computed analytical information. This approach minimizes redundant computations as it eliminates the need to recalculate stored view results every time a new query is performed on these precomputed aggregates. You can see how much it improves performance in this benchmark.
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FerretDB is an open-source database designed as a lightweight alternative to MongoDB. It acts as a translation layer between MongoDB queries and a traditional SQL database, such as PostgreSQL. This allows users to interact with a document database API (like MongoDB) while storing the data in PostgreSQL. In essence, FerretDB combines the strengths of MongoDB's developer-friendly API with the reliability and features of SQL databases, offering a flexible solution for users who prefer or need to work within the open-source ecosystem. You can see some benchmarks in this article.
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Hydra enhances aggregate query processing through pg_ivm, leveraging materialized views for storing computed analytical information. This approach minimizes redundant computations as it eliminates the need to recalculate stored view results every time a new query is performed on these precomputed aggregates. You can see how much it improves performance in this benchmark.
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pgai
A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL
If pgvector is not enough, pgai streamlines the development of search and Retrieval Augmented Generation (RAG) AI applications directly within PostgreSQL. With pgai, embedding and AI generation models are more tightly integrated into the database. This allows you to perform several tasks directly through SQL queries, including:
