paradedb/paradedb

Features

• Hybrid Retrieval - Combines vector similarity and full-text keyword matching in a single query to improve retrieval accuracy.
• Columnar Analytics - Calculates high-performance aggregates, buckets, and facets using specialized columnar storage.
• Columnar Storage Engines - Stores non-text fields in a columnar format optimized for analytical workloads and high-speed filtering.
• Data Analytics Engines - Provides a high-performance computational backend for executing complex analytical queries over massive datasets.
• Analytical Operation Optimizers - Provides high-performance count and group-by operations using specialized scan nodes to optimize analytical queries.
• Full-Text Search Integrations - Integrates advanced text search, highlighting, and ranking capabilities directly into a Postgres database.
• Data Replication - Synchronizes data from external Postgres instances via logical replication to ensure consistency and availability.
• Logical Replication Ingestion - Ingests data changes from external database instances using logical replication protocols to eliminate manual ETL processes.
• Full Text Search - Executes advanced full-text search using tokenization to find relevant documents within a relational engine.
• Postgres-Based Engines - Functions as a storage engine built upon a PostgreSQL backend to add search and analytical capabilities.
• Query Aggregates - Executes JSON aggregate queries using a columnar index to accelerate data counting and summarization.
• Real-Time Analytics - Provides high-performance aggregate queries and columnar scans for immediate operational insights without external analytical tools.
• Columnar-Inverted Hybrid Indexes - Implements indexing strategies that combine inverted indices for text search with columnar storage for analytical aggregations.
• Vector Databases - Implements storage and querying of dense and sparse vector embeddings directly within the database for semantic search.
• Vector Search - Finds similar items using dense and sparse vector embeddings for semantic retrieval and similarity matching.
• Predicate Pushdown Joins - Implements predicate pushdown during join operations to reduce latency by utilizing indexes before accessing base tables.
• Indexed Predicate Filtering - Accelerates filter execution by including non-text columns in the search index to enable indexed predicate filtering.
• Field Aggregates - Computes fast field aggregates in parallel to analyze data distributions across indexed fields.
• Asynchronous Segment Merging - Buffers incoming writes and merges index segments asynchronously in the background to maintain high ingestion throughput.
• Predicate Pushdown - Filters data at the storage layer during index scans to reduce data movement and processing overhead.
• Search Index Synchronizers - Maintains real-time consistency between the primary relational data and search indexes using multi-version concurrency control.
• Search Index Pushdowns - Reduces processing overhead by pushing search predicates and aggregate functions into the index during joins.
• Faceted Search Engines - Provides the backend capability to aggregate results into categories using single-query counts for faceted search.
• Faceted Search Implementation - Enables the development of category-based filtering and data distribution summaries using single-query aggregates.
• Search Ranking Algorithms - Calculates relevance scores based on indexed fields to prioritize the most pertinent search results.
• Search Result Filtering - Uses standard database clauses and operators to narrow search results based on text and non-text fields.
• Search Result Fusion Algorithms - Combines multiple ranked result sets into a single unified list using Reciprocal Rank Fusion.
• Asynchronous Indexing - Increases ingestion speed by buffering writes and merging index segments asynchronously in the background.
• High Availability Clustering - Uses read replicas and logical replication to maintain service uptime and distribute read loads across a cluster.
• Data Synchronization and Consistency - Ensures atomic changes and consistent query results by synchronizing indexes with multi-version concurrency control.
• Rank Fusion Algorithms - Merges multiple ranked result sets into a single unified list by normalizing and combining individual relevance scores.
• Database Systems - Postgres-based search and analytics engine.
• Extensions and Plugins - Extension enabling full-text search using modern ranking algorithms.

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