TraceDB

Izizindalwazi zeVektha

Akushicilelwe

TraceDB is an AI-native transactional database designed to efficiently store and query candidate streams derived from committed records, using multiple native views such as lexical, vector, graph, temporal, and explainable candidates to facilitate advanced data analysis and pattern recognition. Its key features include embedded engine capabilities for schema management, record ingestion, querying, explanation, and data snapshotting, alongside a flexible HTTP API, SDK support for multiple programming languages, and modes for managing data freshness and consistency, thereby solving problems related to real-time data modeling, complex relationship tracking, and explainability for data scientists, developers, and organizations handling large-scale, multi-dimensional, and evolving datasets.

TraceDB ingatholakala ku Vector Databases izigaba.

Imikhiqizo efana kakhulu ne-TraceDB yile:

RogueDB Iphrofayela eqinisekisiwe Kukhuthazwe → TileDB → RegattaDB → YDB → VectorScaleDB Iphrofayela eqinisekisiwe →

Imininingwane

TraceDB Izici Eziyinhloko

• SDKs
  It offers software development kits for TypeScript, Python, and Rust to connect and work with the database.
• HTTP API
  It provides a set of APIs to interact with the database easily.
• HTTP server
  You can run the database as a server on your computer for online access.
• Policy filters
  It keeps user and field filters inside query rules.
• Run local demo
  You can run a demonstration on your computer to see how it works.
• Embedded engine
  You can run the database locally, apply schemas, ingest data, and manage records easily.
• Freshness modes
  It offers different ways to handle data freshness: strict, lazy, or allow dirty data.
• Lexical matching
  It can find text in records based on words and phrases.
• One commit epoch
  All changes are grouped into one commit period for easy tracking.
• Temporal queries
  It can ask data about specific times using time fields.
• Many native views
  It has different ways to view data, like lexical, vector, graph, and more.
• Read API contract
  It has a defined way to read data, which you can test or use.
• One logical record
  It stores each record as a single, complete unit.
• Graph relationships
  It organizes data as graphs with links between records.
• Explain candidate rows
  It shows explanations, scores, and timing for each result.
• No external sync drift
  The database does not lose sync with other systems on its own.
• Explain every candidate
  It can explain how it chose each data candidate during queries.
• Start with local product
  You can start by running a demo locally and then use the HTTP interface for testing.
• Stores committed records
  TraceDB saves records after they are finished and ready for use.
• Vector candidate streams
  It can find related data using embeddings without needing extra sync jobs.
• Multiple candidate streams
  It reads data in different ways using streams like lexical, vector, graph, temporal, freshness, policy, and explainable streams.
• Logical record and commit epoch
  It organizes data as logical records and groups changes by commit time.
• AI-native transactional database
  TraceDB is a database that uses artificial intelligence to help with transactions and data analysis.

Amabheji

<a href="https://saasbrowser.com/zu/saas/1537410/tracedb" target="_blank" rel="noopener"><img src="https://files.saasbrowser.com/yatq35rp6nrj6bqg38lme1u32733" alt="TraceDB - SaaS Browser Verified Badge" width="200" height="133" /></a>

Kopisha ibheji

<a href="https://saasbrowser.com/zu/saas/1537410/tracedb" target="_blank" rel="noopener"><img src="https://files.saasbrowser.com/f1fv8nmqm3v4rzk9obf9lf7q1r9u" alt="TraceDB - SaaS Browser Verified Badge" width="200" height="133" /></a>

Kopisha ibheji

TraceDB Okunye

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