Indexes

Learn how to implement spatial search in RavenDB with Python. Covers radius queries, custom polygon shapes, WKT syntax, and reverse point-in-polygon lookup using the Flat Finder demo.

Why RavenDB queries can return stale results, when that is perfectly fine, and how to force fresh results per query, per session, or across the whole document store.

Run semantic searches over your documents with RavenDB 7.0 vector search: generate embeddings on the server with no external tools, and query them through static indexes or auto-indexes.

Discover 7 common RavenDB index anti-patterns, including misusing LoadDocument and storing too many fields, and learn how to fix each one for better query performance.

Track down the RavenDB index that's consuming your disk I/O using the Studio Indexing Performance view, iotop on Linux, and Resource Monitor on Windows, then optimize the culprit.

How RavenDB projections return only the fields you need, why projected data comes from documents by default, and how to store fields in a static index and tune projection behavior for faster reads.

Match accented terms like Kraków against both Kraków and Krakow. Compares manual diacritics normalization with a custom ASCIIFoldingFilter analyzer for Lucene and Corax indexes.

Diagnose and fix high memory usage in RavenDB: understand how RavenDB manages memory with memory-mapped files, spot trouble on Windows, Linux, and in the Studio, then trace the cause with admin logs, debug endpoints, and dotnet-dump.

Extend RavenDB's indexing process with machine learning in C#: train an ML.NET model to flag toxic comments during indexing, and use a pre-trained Catalyst model to detect each document's language, all through AdditionalAssemblies and AdditionalSources.

How to use dynamic fields in RavenDB static indexes to query on properties that aren't defined at index-creation time, plus the pitfalls to watch for with duplicate field names and stored projections.