# OpenData

## Docs

- [Append records](https://opendata.dev/docs/api-reference/append-records.md): Append one or more records to the log. Each record has a key (used for partitioning scans) and a value (arbitrary bytes, both base64-encoded in JSON).
- [Count entries](https://opendata.dev/docs/api-reference/count-entries.md): Count the number of log entries for a given key within an optional sequence range.
- [Get a vector by ID](https://opendata.dev/docs/api-reference/get-a-vector-by-id.md): Retrieve a single vector and its attributes by its user-provided ID.
- [Instant query](https://opendata.dev/docs/api-reference/instant-query.md): Evaluate a PromQL expression at a single point in time.
- [List keys](https://opendata.dev/docs/api-reference/list-keys.md): List distinct keys present in the log within an optional segment range.
- [List label names](https://opendata.dev/docs/api-reference/list-label-names.md): Return the sorted list of all label names present in the database. Optionally filtered by series selectors and/or a time range.
- [List label values](https://opendata.dev/docs/api-reference/list-label-values.md): Return the sorted list of known values for a given label name. Optionally filtered by series selectors and/or a time range.
- [List segments](https://opendata.dev/docs/api-reference/list-segments.md): List log segments whose start sequence falls within the given range. Segments are the physical storage units of the log.
- [List series](https://opendata.dev/docs/api-reference/list-series.md): Return the list of label sets (unique metric identities) that match a set of series selectors. This is useful for discovering which time series exist for a given metric name or label combination.
- [Range query](https://opendata.dev/docs/api-reference/range-query.md): Evaluate a PromQL expression over a range of time, returning a matrix of time series with regularly spaced samples.
- [Remote write](https://opendata.dev/docs/api-reference/remote-write.md): Ingest time series data using the [Prometheus Remote Write 1.0](https://prometheus.io/docs/specs/prw/remote_write_spec/) protocol.
- [Scan entries](https://opendata.dev/docs/api-reference/scan-entries.md): Scan log entries for a given key within an optional sequence range.
- [Search for nearest neighbors](https://opendata.dev/docs/api-reference/search-for-nearest-neighbors.md): Find the `k` nearest vectors to a given query vector. Optionally filter results by metadata attributes using a filter expression.
- [Upsert vectors](https://opendata.dev/docs/api-reference/upsert-vectors.md): Upsert one or more vectors into the collection. Each vector has a user-provided string ID (up to 64 bytes), a dense embedding in the `vector` attribute, and optional metadata attributes.
- [Architecture](https://opendata.dev/docs/buffer/architecture.md): How Buffer works
- [Configuration](https://opendata.dev/docs/buffer/configuration.md): Configure object storage, batching, and garbage collection for Buffer
- [Buffer](https://opendata.dev/docs/buffer/index.md): Highly-available ingestion buffer on object storage
- [Introduction](https://opendata.dev/docs/index.md)
- [Key-Value](https://opendata.dev/docs/keyvalue/index.md): Simple key-value storage built on SlateDB
- [Log](https://opendata.dev/docs/log/index.md): Key-oriented event streaming built on an LSM tree
- [Getting to Production](https://opendata.dev/docs/log/production.md): Deploy, monitor, and secure Log on Kubernetes with S3 storage
- [Quickstart](https://opendata.dev/docs/log/quickstart.md): Install Log locally and append your first events in minutes
- [Storage design](https://opendata.dev/docs/log/storage-design.md): How Log maps streams to LSM keys with segment-based compaction
- [Architecture](https://opendata.dev/docs/overview/architecture.md)
- [Deployment](https://opendata.dev/docs/overview/deployment.md)
- [Reading Data](https://opendata.dev/docs/overview/reading-data.md)
- [Storage](https://opendata.dev/docs/overview/storage.md)
- [Writing Data](https://opendata.dev/docs/overview/writing-data.md)
- [Configuration](https://opendata.dev/docs/timeseries/configuration.md): Configure scrape targets, storage backends, and retention for Timeseries
- [Timeseries](https://opendata.dev/docs/timeseries/index.md): Object-store-native time series database with Prometheus compatibility
- [Stateless Ingest](https://opendata.dev/docs/timeseries/ingest.md): Durable OTLP ingest for Timeseries via an object-storage queue
- [Getting to Production](https://opendata.dev/docs/timeseries/production.md): Deploy, monitor, and secure Timeseries on Kubernetes with S3 storage
- [Quickstart](https://opendata.dev/docs/timeseries/quickstart.md): Install Timeseries locally and query metrics in minutes
- [Storage design](https://opendata.dev/docs/timeseries/storage-design.md): How Timeseries organizes data in time buckets on SlateDB
- [Data Model](https://opendata.dev/docs/vector/data-model.md): Collections, records, attributes, and vectors
- [Vector](https://opendata.dev/docs/vector/index.md): Approximate nearest neighbor search on object storage
- [Getting to Production](https://opendata.dev/docs/vector/production.md): Deploy, monitor, and secure Vector on Kubernetes with S3 storage
- [Quickstart](https://opendata.dev/docs/vector/quickstart.md): Install Vector locally and search your first documents in minutes
- [Storage Design](https://opendata.dev/docs/vector/storage-design.md): How Vector indexes records to support efficient similarity search

## OpenAPI Specs

- [vector](https://opendata.dev/docs/openapi/vector.yaml)
- [log](https://opendata.dev/docs/openapi/log.yaml)
- [timeseries](https://opendata.dev/docs/openapi/timeseries.yaml)