Question 1

How to implement vector similarity search with Bleve?

Accepted Answer

Bleve supports vector search through approximate k-nearest neighbors queries by indexing vector fields and using the vector query type. Refer to the vectors documentation for configuration details, which allows semantic search alongside traditional methods.

Question 2

Bleve vs Elasticsearch for a Go application?

Accepted Answer

Bleve is ideal for embeddable search within Go apps, avoiding external dependencies, while Elasticsearch offers better scalability and features for distributed environments. Choose Bleve for Go-native simplicity, Elasticsearch for large-scale deployments.

Question 3

Can Bleve handle real-time indexing updates efficiently?

Accepted Answer

Yes, Bleve supports real-time indexing as it's embeddable, but performance depends on setup. For high-volume updates, you may need to tune batch sizes and index settings to prevent latency issues.

Question 4

What are the performance benchmarks for Bleve on large datasets?

Accepted Answer

Performance varies with data size and query complexity; Bleve is efficient for moderate loads, but specific benchmarks aren't provided in the README—consult community tests or conduct your own for accurate metrics.

Question 5

How to set up faceted navigation using Bleve?

Accepted Answer

Use Bleve's aggregations like terms facets or numeric range facets by indexing data with proper field mappings and querying with facet requests. This enables drill-down navigation in search results as described in the faceting support.

Question 6

Is Bleve production-ready for high-traffic applications?

Accepted Answer

Bleve is used in production by various projects, but for high-traffic apps, thorough testing and scaling strategies are essential. It may require custom optimizations compared to dedicated search services.

bleve

What is bleve?

Overview

Use Cases

Best For

Found a gem we're missing?

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions