Optimizing Partial Indexing With HNSW For Vector Search Performance

Hey guys! Let's dive into optimizing partial indexing with HNSW (Hierarchical Navigable Small World) for vector similarity search. This is super crucial for anyone dealing with large datasets and needing fast, accurate search results. We'll break down the problem, explore the solutions, and make sure you walk away with a solid understanding. Let’s get started!

Understanding the Problem

The core challenge is efficiently searching a subset of a table using vector embeddings. Imagine you have a massive table with millions of entries, but you only want to search within a small, specific segment—say, 10% of the data. A common approach is to use a boolean column for filtering, but this can lead to performance bottlenecks if not handled correctly.

The Scenario

Let's paint a picture: You have a table, my_table, with columns like embedding (a vector), item_id, and handy_bool. The goal is to create an index that speeds up searches on a subset of the table where handy_bool = true. The initial thought is a partial index using HNSW, which seems perfect for this.

CREATE INDEX ON my_table USING hnsw (embedding vector_cosine_ops) WHERE (handy_bool = true);

The query looks something like this:

WITH
 aggregated_embeddings AS (
 SELECT AVG(embedding) AS user_embedding
 FROM my_table
 WHERE item_id IN ('123', '234', '345') -- some list of items from (potentially) the whole table
 ),
 distances AS (
 SELECT item_id, is_recommendable,
 embedding <=> (SELECT user_embedding FROM aggregated_embeddings) AS distance
 FROM my_table
 WHERE handy_bool = TRUE
 ORDER BY distance ASC
 )
SELECT item_id, distance
FROM distances
WHERE distance > 0
LIMIT 10;

The Issues

1. Index Selection: The query planner often chooses a generic index on handy_bool instead of the partial HNSW index. This means the vector search isn't being optimized as intended.
2. Forced Index Usage: Forcing the use of the partial index (SET LOCAL enable_seqscan = off;, etc.) makes things faster for small queries. However, for larger queries (where the LIMIT is a significant portion of the subset), the results are incomplete.

This is puzzling because the partial index should pre-filter the data, so why are we seeing incomplete results? Is it an HNSW quirk, or is something else going on?

Diving Deeper into HNSW and Partial Indexes

HNSW is a graph-based indexing algorithm that's fantastic for approximate nearest neighbor (ANN) search. It builds a multi-layered graph structure where each layer represents a different level of granularity. This allows for efficient traversal during the search process. However, it’s crucial to understand how HNSW interacts with filters and limits.

Partial indexes, on the other hand, are indexes that cover only a subset of rows in a table, defined by a WHERE clause. They're great for optimizing queries that frequently filter on the same condition. In this case, the goal is to pre-filter the data where handy_bool = true before the HNSW search kicks in.

Ideally, the partial index should narrow down the search space significantly, allowing HNSW to quickly find the nearest neighbors within that subset. But when the query planner doesn't choose the right index or when the results are incomplete, it indicates a deeper problem.

Diagnosing the Performance Bottlenecks

To really get to the bottom of this, we need to put on our detective hats and investigate what's going on under the hood. Here are a few key areas to focus on:

1. Query Planner Shenanigans

The query planner is the brain of PostgreSQL. It decides the most efficient way to execute your query. Sometimes, it doesn't make the best choice, especially when dealing with complex queries and custom indexes like HNSW. Let's look at why the planner might be ignoring the partial index.

• Statistics: PostgreSQL relies on statistics about your data to make informed decisions. If these statistics are outdated, the planner might misjudge the cost of using the partial index. Running ANALYZE my_table; can often resolve this.
• Cost Estimation: The planner estimates the cost of different execution plans. It might believe that a full index scan on handy_bool is cheaper than using the partial HNSW index, especially if handy_bool is highly selective. This is where tweaking PostgreSQL settings can come into play.
• Index Selectivity: If the planner thinks that the condition handy_bool = true returns a large portion of the table, it might opt for a different index or a sequential scan. Partial indexes shine when the filtered subset is relatively small.

2. HNSW and Filtering Interactions

As you rightly pointed out, HNSW is an approximate nearest neighbor algorithm. This means it doesn't guarantee to find the absolute top-N results. It explores the graph structure and returns the closest neighbors it finds within a certain search effort (ef_search).

• ef_search Parameter: The hnsw.ef_search parameter controls the search effort. A higher value means more thorough search but also more computation. If ef_search is too low, HNSW might not explore enough of the graph, especially when combined with filtering.
• Graph Structure: The structure of the HNSW graph itself can affect search accuracy. If the graph isn't well-constructed for the data distribution within the handy_bool = true subset, it might lead to incomplete results.

3. The Limit Clause and HNSW

The LIMIT clause adds another layer of complexity. HNSW, being an approximate algorithm, tries to find the nearest neighbors efficiently. When you add a LIMIT, it stops searching once it has found enough candidates. If the search space is not fully explored due to a low ef_search or graph structure issues, you might not get the complete set of results.

This is particularly noticeable when the LIMIT is a significant fraction of the filtered subset. For instance, if you have 300 entries where handy_bool = true and you ask for LIMIT 50, HNSW needs to explore a substantial portion of the graph to ensure it finds the top 50.

Strategies for Optimizing Performance

Alright, enough diagnosis! Let’s talk about solutions. Here’s a breakdown of strategies to optimize your partial indexing with HNSW:

1. Optimize Query Planner Behavior

• Update Statistics: Regularly run ANALYZE my_table; to keep the planner informed about data distribution.
• Increase effective_cache_size: This setting tells the planner how much memory is available for caching data. A higher value can encourage the planner to use indexes.
• Tweak Cost Parameters: Settings like random_page_cost and cpu_index_tuple_cost influence the planner’s cost estimation. Adjusting these might encourage the use of the partial index.
• Use EXPLAIN ANALYZE: This is your best friend! It shows you the query execution plan and actual execution times. Use it to see why the planner is making certain choices and identify bottlenecks.

2. Fine-Tune HNSW Parameters

• Increase ef_search: As you've already done, setting SET LOCAL hnsw.ef_search = 200; is a good start. Experiment with higher values, especially for larger queries. Be mindful of the trade-off between accuracy and performance.
• Consider m Parameter: The m parameter controls the number of connections each node makes in the HNSW graph. A higher m can improve search accuracy but also increases index size and build time. Consider adjusting this when creating the index.

3. Ensure Proper Index Structure

• Rebuild the Index: If you've made significant changes to the data or HNSW parameters, consider rebuilding the index. This ensures the graph structure is optimized for the current data distribution.
• Monitor Index Bloat: Over time, indexes can become bloated, which degrades performance. Use PostgreSQL’s index bloat monitoring tools to identify and address bloat issues.

4. Query Refactoring and Hints

• Use FORCE INDEX (if necessary): While it’s generally best to let the planner do its job, sometimes a hint is needed. Use FORCE INDEX as a last resort, and only if you’re sure it’s the right choice.
• Simplify the Query: Break down complex queries into smaller, more manageable parts. This can help the planner make better decisions.
• Materialize Intermediate Results: If you’re performing multiple aggregations or filtering steps, consider materializing intermediate results into temporary tables. This can reduce the complexity of the main query.

5. Alternative Indexing Strategies

• BRIN Indexes: If your data has a natural ordering (e.g., time series data), BRIN (Block Range Index) indexes can be very efficient for range queries.
• Bloom Filters: For membership tests, Bloom filters can provide a fast, approximate way to check if an element is in a set.

Practical Steps and Examples

Let’s put this into action with some practical examples. Suppose you’re facing the issue where the planner isn’t choosing your partial HNSW index. Here’s what you can do:

1. Run EXPLAIN ANALYZE: Execute EXPLAIN ANALYZE on your query to see the execution plan. Look for sequential scans or index scans on handy_bool instead of the HNSW index.

EXPLAIN ANALYZE
WITH
 aggregated_embeddings AS (
 SELECT AVG(embedding) AS user_embedding
 FROM my_table
 WHERE item_id IN ('123', '234', '345')
 ),
 distances AS (
 SELECT item_id, is_recommendable,
 embedding <=> (SELECT user_embedding FROM aggregated_embeddings) AS distance
 FROM my_table
 WHERE handy_bool = TRUE
 ORDER BY distance ASC
 )
SELECT item_id, distance
FROM distances
WHERE distance > 0
LIMIT 10;

2. Update Statistics: If the planner is making bad choices, update statistics.

ANALYZE my_table;

3. Adjust Cost Parameters: If statistics don't solve the issue, try tweaking cost parameters. Be cautious and test thoroughly!

SET random_page_cost = 1.1; -- Slightly increase cost of random page access
SET cpu_index_tuple_cost = 0.002; -- Slightly decrease cost of index tuple access

4. Force Index (Use with Caution): If all else fails, force the index.

WITH
 aggregated_embeddings AS (
 SELECT AVG(embedding) AS user_embedding
 FROM my_table
 WHERE item_id IN ('123', '234', '345')
 ),
 distances AS (
 SELECT item_id, is_recommendable,
 embedding <=> (SELECT user_embedding FROM aggregated_embeddings) AS distance
 FROM my_table
 WHERE handy_bool = TRUE
 ORDER BY distance ASC
 )
SELECT item_id, distance
FROM distances
WHERE distance > 0
LIMIT 10;

5. Increase ef_search: To address incomplete results, bump up the ef_search value.

SET LOCAL hnsw.ef_search = 200; -- Or even higher, like 500 or 1000

6. Rebuild Index: If you’ve made changes to the data or parameters, rebuild the index.

DROP INDEX IF EXISTS my_partial_hnsw_index;
CREATE INDEX my_partial_hnsw_index ON my_table USING hnsw (embedding vector_cosine_ops) WHERE (handy_bool = true);

Real-World Scenarios and Use Cases

Partial indexing with HNSW isn't just a theoretical concept; it has tons of real-world applications. Here are a few scenarios where this technique can shine:

E-commerce Recommendations

Imagine an e-commerce platform with millions of products. You want to recommend similar products to a user based on their browsing history. Using vector embeddings to represent products, you can create a partial index on a subset of products (e.g., products that are currently in stock or are on sale). This allows you to quickly find similar products within that subset, without searching the entire catalog.

Content Filtering

In a content platform, you might want to filter content based on certain criteria (e.g., language, category, or user preferences). By creating a partial index on a subset of content that matches these criteria, you can speed up similarity searches for related content.

Anomaly Detection

In fraud detection or network security, you often need to identify unusual patterns or anomalies. By creating a partial index on a subset of data points that are considered normal, you can quickly compare new data points against this baseline and identify potential anomalies.

Personalized Search

For personalized search experiences, you might want to create a partial index on a subset of results that are relevant to a specific user or group of users. This allows you to tailor search results to individual preferences and improve the overall user experience.

Conclusion

Optimizing partial indexing with HNSW is a journey, not a destination. It requires a deep understanding of your data, your queries, and the intricacies of PostgreSQL’s query planner. But with the right strategies and a bit of experimentation, you can achieve significant performance gains. Remember to:

• Understand the problem: Clearly define what you’re trying to optimize.
• Diagnose: Use EXPLAIN ANALYZE to identify bottlenecks.
• Experiment: Try different strategies and measure their impact.
• Iterate: Optimization is an ongoing process. Continuously monitor and refine your approach.

By mastering these techniques, you’ll be well-equipped to tackle even the most challenging vector similarity search scenarios. Keep experimenting, keep learning, and you’ll become a true indexing ninja! Happy searching, guys!