Vector search engines and indexing strategies have revolutionized the way we query data in large databases, enabling far more efficient and accurate results for similarity search operations. Among the plethora of indexing options available, two have emerged as particularly noteworthy within the scope of Postgres and pgvector: IVFFlat and HNSW. In this article, we shall explore these indexing options, comparing their functionalities, efficiencies, and practical applications, to guide database architects and developers in selecting the most suitable option for their needs.


IVFFlat divides vectors into clusters represented as lists, and a query performs a search over a subset of these clusters deemed closest to the vector in question. With its straightforward implementation, IVFFlat promises faster index build times and less memory usage, which are pivotal factors for databases not only in terms of resources but also scalability.

IVFFlat index

IVFFlat index visualization

Practically, the performance of an IVFFlat index is influenced by the number of clusters chosen and the quantity of clusters (‘probes’) queried against. A reasonable starting point recommends a division of the number of rows by 1000 for databases up to a million entries, and for larger datasets, taking the square root of the number of rows. The ivfflat model offers the flexibility to adjust these parameters based on the specific data and query patterns observed, ensuring faster queries without compromising significantly on recall accuracy. Index creation could be improved a lot on GPU.


Conversely, the HNSW (Hierarchical Navigable Small Worlds) index takes a different approach by creating a multi-layered graph structure for the vectors, allowing for a more nuanced search mechanism that navigates the layers (or ‘zoom levels’) to find the nearest neighbors. This method, while being more memory-intensive and requiring longer build times, tends to outperform IVFFlat in speed-recall tradeoff metrics significantly.

HNSW graph

HNSW graph visualization

Importantly, HNSW is less sensitive to the initial size of the data, capable of being built without data due to its graph structure not relying on centroid calculation as in IVFFlat. This makes it particularly useful in applications where data is sparse at the onset or in scenarios of gradual data accumulation.

Practical application and considerations

When selecting between IVFFlat and HNSW, several factors merit consideration. Firstly, the nature of the dataset and the query requirements; IVFFlat, with its efficient use of memory and faster build times, could be more suited for applications where resources are constrained or where query volume is not exceedingly high. On the contrary, for high-throughput systems requiring the quickest query responses, the superior query performance of HNSW makes it the index of choice, despite its higher resource demands.

Also critical is the evolution of the dataset—HNSW’s structure affords a resilience to changes in data that IVFFlat lacks. IVFFlat’s clusters, once defined, do not adapt well to significant alterations in the dataset makeup, potentially necessitating periodic re-indexing to maintain query accuracy, a consideration that is significantly mitigated in HNSW due to its dynamic graph structure.


In choosing between IVFFlat and HNSW for indexing in pgvector, it’s crucial to understand their distinct features and how they cater to different requirements. Below is a comparison table that simplifies the decision-making process, aligning closely with the style and language of the article while ensuring clarity through simple terminology:

Feature IVFFlat HNSW
Indexing mechanism Clusters vectors into lists Creates a multi-layered graph structure
Memory usage Lower Higher
Index build time Faster Longer
Speed-recall tradeoff Good with adjustable parameters Superior
Sensitivity to data size More suitable for up to a million entries Less sensitive, adaptable to sparse data
Query performance Efficient for moderate query volumes Optimal for high-throughput systems
Adaptability to data changes May require re-indexing for significant changes More resilient to changes in dataset
Recommended use cases Resource-constrained environments, lower query volumes High-throughput systems requiring quick responses

This table summarizes the key aspects of IVFFlat and HNSW, providing a straightforward comparison to assist database architects and developers in selecting the most suitable indexing strategy based on their specific needs, such as query performance, resource utilization, and the nature of their dataset.


Both IVFFlat and HNSW offer compelling advantages for similarity search implementations within pgvector, tailored for different scenarios and requirements. Ultimately, the choice between them boils down to a balance between query performance and resource utilization, influenced by the particular demands of the application and the characteristics of the dataset. While IVFFlat might be the preferred option for scenarios emphasizing quicker setup times and lower resource consumption, HNSW stands out in environments where query speed and resilience to database evolution are paramount.

As the database landscape continues to evolve, understanding and leveraging the strengths of technologies like IVFFlat and HNSW will be crucial for building responsive, efficient, and future-proof data infrastructures.