Abdon Pijpelink
757767919b
* Change bulk upload from tutorial into guide * Add 'Mitigate Read-Write Contention' section to bulk upload guide * Update meta descriptions
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title, short_description, description, weight, partition
| title | short_description | description | weight | partition |
|---|---|---|---|---|
| Manage Data | Structure, store, and organize data in Qdrant — points, vectors, payloads, collections, indexes, and quantization. | Manage data in Qdrant by configuring collections, points, payloads, vector indexes, and quantization to control how records are stored, indexed, and retrieved. | 205 | develop |
Manage Data
Learn how to structure, store, and organize your data in Qdrant. These pages cover the core building blocks — from individual records and the vectors that represent them, to collections, payloads, and the indexing and quantization options that control how data is stored and retrieved.
Points
Points are the fundamental unit of data in Qdrant — each point is a record consisting of a vector and an optional payload.
Vectors
Vectors define how data is represented in vector space, including support for dense, sparse, and multivector configurations.
Payload
A Payload is structured metadata you can attach to a point, enabling filtering and enriched search results.
Collections
Collections are named groups of points that share the same vector configuration and serve as the top-level organizational unit in Qdrant.
Storage
Storage describes how Qdrant persists vector and payload data, including segment structure and in-memory vs. on-disk options.
Indexing
Indexing covers the available index types — payload, vector, sparse, and filterable — and how they accelerate search and filtering.
Quantization
Quantization reduces memory usage by compressing vectors, with options for scalar, product, and binary quantization.
Multitenancy
Multitenancy explains strategies for isolating data across multiple users or tenants within a single Qdrant deployment.
Bulk Upload
Bulk Upload covers strategies for ingesting large datasets efficiently, including batching, parallelization, and index configuration.