databend

Databend implements a complete SQL query pipeline with AST-based parsing, semantic binding, cost-based optimization, and vectorized physical execution. The system uses a multi-stage planner that converts SQL into optimized execution plans with columnar data processing, enabling efficient OLAP workloads. Query optimization leverages statistics-driven cost models to select optimal join orders, aggregation strategies, and data access patterns across distributed compute nodes.

Databend provides built-in vector search capabilities with support for vector data types, similarity metrics (cosine, L2, Hamming), and index structures for fast approximate nearest neighbor (ANN) search. The system integrates vector operations directly into the SQL query engine, allowing users to perform vector similarity searches alongside traditional analytics without requiring separate vector database infrastructure. Vector indexes are stored and managed through the FUSE storage engine with automatic index maintenance during data mutations.

Databend implements a stage system for managing temporary data files used in COPY operations and data ingestion workflows. Stages can be internal (stored in object storage) or external (user-provided S3 buckets). The system provides caching layers for frequently accessed data, metadata caching for table statistics, and query result caching. Cache invalidation is automatic when underlying data changes, and cache policies can be configured per-table or globally.

Databend provides a Python sandbox environment for executing user-defined functions (UDFs) and analytical scripts within the database. The sandbox uses process isolation and resource limits to safely execute untrusted Python code. UDFs can be registered with type signatures and integrated into SQL expressions, enabling data transformation logic to be colocated with data. The system supports both scalar and aggregate Python functions with automatic vectorization.

Databend implements comprehensive multi-tenancy support through role-based access control (RBAC) with fine-grained permissions at database, table, and column levels. The system supports user authentication via multiple methods (password, OAuth, LDAP) and maintains separate namespaces for different tenants. Metadata isolation ensures that users can only see objects they have permission to access, and query execution is subject to row-level and column-level security policies.

Databend supports streaming data ingestion through multiple protocols (HTTP, Kafka, Kinesis) with automatic schema inference from incoming data. The system batches incoming records and writes them to the FUSE storage engine in optimized columnar format. Schema evolution is handled automatically; new columns are added to the table schema and backfilled with NULL values. Streaming ingestion is integrated with the query engine, enabling real-time analytics on freshly ingested data.

Databend implements distributed query execution across multiple compute nodes with adaptive resource allocation based on query characteristics and cluster load. The query planner generates distributed execution plans that partition work across nodes, with data shuffling and aggregation stages. The system monitors query resource usage (CPU, memory, I/O) and adjusts parallelism and batch sizes dynamically to optimize performance. Query scheduling respects resource quotas and prioritization policies.

Databend implements full-text search capabilities using inverted index structures that enable efficient text and JSON document search. The system supports tokenization, stemming, and relevance ranking through TF-IDF and BM25 scoring. Inverted indexes are built and maintained incrementally through the FUSE storage engine, allowing text search to be combined with SQL analytics in unified queries without external search infrastructure.

Databend provides geospatial data types (Point, LineString, Polygon, MultiGeometry) and spatial indexing structures (R-tree variants) for efficient geographic queries. The system supports spatial predicates (contains, intersects, distance), geographic functions, and spatial joins. Spatial indexes are managed through the FUSE storage engine, enabling geographic analytics to be combined with traditional SQL and vector search in unified queries.

Databend implements strict separation of compute and storage layers through a stateless query service (databend-query) that processes SQL requests without maintaining local state, while all data resides in object storage (S3, GCS, Azure Blob). Query nodes are ephemeral and can be scaled up/down independently from storage, with metadata managed by a separate Raft-consensus metadata service (databend-meta). This architecture enables elastic scaling, high availability, and cost-effective resource utilization.

Databend implements FUSE (Fast Universal Storage Engine), a columnar storage format optimized for object storage backends. FUSE stores data in Parquet-compatible columnar blocks with automatic compaction, versioning, and time-travel capabilities. The engine handles data layout optimization, block pruning, and metadata management through a hierarchical block structure stored in object storage. Compaction strategies (horizontal and vertical) automatically merge small files and optimize column encoding for query performance.

Databend manages cluster metadata (table schemas, user permissions, cluster state) through a dedicated metadata service (databend-meta) using Raft consensus for consistency. The system implements sophisticated metadata versioning with three key attributes (min_reader_version, min_writer_version, snapshot_version) enabling backward/forward compatibility across cluster upgrades. Metadata is serialized using Protocol Buffers and stored in a key-value store with transaction support, enabling atomic multi-object updates.

Databend exposes a flexible HTTP query API that supports multiple protocol handlers (MySQL, PostgreSQL, Clickhouse, REST) through a pluggable architecture. The HTTP interface accepts SQL queries, manages sessions, and returns results in multiple formats (JSON, CSV, Arrow, Parquet). The system implements connection pooling, query timeout management, and streaming result delivery for large result sets. Protocol handlers abstract away dialect differences, enabling clients written for MySQL or PostgreSQL to work with Databend.

Databend implements comprehensive session management that maintains per-connection state including variables, settings, temporary tables, and transaction context. The system uses query context objects to track execution state, table bindings, and expression evaluation environments. Session isolation ensures that concurrent queries from different connections don't interfere with each other's state, while transaction context manages ACID semantics for multi-statement transactions. Settings can be configured globally, per-session, or per-query with hierarchical override semantics.

Databend implements a comprehensive expression evaluation system with static type checking, implicit type coercion, and dynamic function dispatch. The system maintains a function registry with 500+ built-in functions (scalar, aggregate, window) with overload resolution based on argument types. Expression evaluation uses a columnar evaluation model where functions operate on entire columns at once for vectorized performance. Type coercion follows SQL standard rules with configurable strictness levels.