{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-databendlabs--databend","slug":"databendlabs--databend","name":"databend","type":"mcp","url":"https://docs.databend.com","page_url":"https://unfragile.ai/databendlabs--databend","categories":["mcp-servers"],"tags":["ai","bigdata","cloud-native","database","elasticsearch","geospatial","lakehouse","olap","rust","serverless","snowflake","sql","vector-database","vector-search"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-databendlabs--databend__cap_0","uri":"capability://data.processing.analysis.vectorized.sql.query.execution.with.cost.based.optimization","name":"vectorized sql query execution with cost-based optimization","description":"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.","intents":["Execute complex analytical SQL queries at scale on object storage","Optimize query performance through cost-based plan selection","Process large datasets with columnar vectorized execution","Distribute query execution across multiple compute nodes"],"best_for":["Data engineers building analytics pipelines on cloud object storage","Teams migrating from Snowflake or Redshift seeking open-source alternatives","Organizations requiring OLAP workloads with independent compute/storage scaling"],"limitations":["Cost-based optimizer effectiveness depends on accurate table statistics; stale statistics can lead to suboptimal plans","Vectorized execution adds memory overhead compared to row-oriented engines for small datasets","Query optimization time increases with complex multi-join queries (>10 joins may require manual hints)"],"requires":["S3, GCS, Azure Blob, or compatible object storage","Rust 1.70+ for building from source","Minimum 4GB RAM per query node for vectorized processing"],"input_types":["SQL queries (ANSI SQL with Databend extensions)","Table schemas with statistics metadata"],"output_types":["Query execution plans (JSON/text format)","Result sets (Arrow columnar format, JSON, CSV)"],"categories":["data-processing-analysis","query-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_1","uri":"capability://search.retrieval.native.vector.similarity.search.with.indexing","name":"native vector similarity search with indexing","description":"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.","intents":["Build RAG (Retrieval-Augmented Generation) systems with semantic search over embeddings","Perform similarity search on high-dimensional vector data at scale","Combine vector search with traditional SQL analytics in unified queries","Index and query AI-generated embeddings without external vector databases"],"best_for":["AI/ML engineers building RAG pipelines and semantic search applications","Teams consolidating vector database and analytics infrastructure","Developers prototyping LLM-powered applications with embedding-based retrieval"],"limitations":["Vector index performance degrades with very high dimensionality (>2000 dims) without careful tuning","Index maintenance overhead during bulk inserts can impact write throughput by 15-30%","Limited to exact vector type definitions; schema evolution of vector columns requires table recreation"],"requires":["Vector data type support in table schema","Minimum 8GB RAM for efficient index operations on large vector datasets","FUSE storage engine enabled (default configuration)"],"input_types":["Vector columns (float32/float64 arrays)","Similarity metrics (cosine, L2, Hamming)","Query vectors for ANN search"],"output_types":["Ranked result sets with similarity scores","Vector index metadata and statistics"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_10","uri":"capability://data.processing.analysis.stage.and.cache.management.for.data.ingestion.and.temporary.storage","name":"stage and cache management for data ingestion and temporary storage","description":"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.","intents":["Load data from external sources (S3, local files) into Databend tables","Manage temporary data files during ETL workflows","Cache query results and metadata for performance optimization","Implement data staging pipelines with automatic cleanup"],"best_for":["Data engineers building ETL pipelines","Teams performing bulk data loads from external sources","Organizations optimizing query performance through caching"],"limitations":["Stage file cleanup is manual; orphaned files can accumulate if COPY operations fail","Cache invalidation is conservative; changes to underlying data may not immediately invalidate all dependent caches","External stages require proper S3 credentials and bucket permissions; misconfiguration can cause silent failures"],"requires":["Object storage access (S3, GCS, Azure Blob) for internal stages","External S3 credentials for external stages","Minimum 1GB free space for stage files and cache"],"input_types":["Data files (CSV, Parquet, JSON, etc.)","Stage configuration (location, credentials, format)","COPY command parameters"],"output_types":["Loaded data in Databend tables","Stage file metadata and status","Cache statistics and hit rates"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_11","uri":"capability://code.generation.editing.python.sandbox.execution.for.user.defined.functions.and.scripts","name":"python sandbox execution for user-defined functions and scripts","description":"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.","intents":["Execute custom Python logic for data transformation without external processes","Register Python functions as SQL UDFs for use in queries","Run analytical scripts directly on data without data movement","Implement complex business logic that's difficult to express in SQL"],"best_for":["Data scientists implementing custom transformations","Teams with existing Python data processing logic","Developers building complex analytical applications"],"limitations":["Python sandbox has performance overhead; UDFs are 5-10x slower than native SQL functions","Sandbox resource limits (memory, CPU time) may cause UDF execution to fail on large datasets","Limited Python standard library support; external package imports require pre-installation in sandbox environment","Debugging Python UDFs is difficult; error messages may not clearly indicate root cause"],"requires":["Python 3.8+ installed in Databend environment","Python UDF code with proper type annotations","Memory allocation for Python interpreter (minimum 256MB per query node)"],"input_types":["Python function definitions with type signatures","Input data (columnar Arrow arrays)","UDF parameters and configuration"],"output_types":["Transformed data (columnar Arrow arrays)","UDF execution logs and error messages"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_12","uri":"capability://safety.moderation.multi.tenant.isolation.with.role.based.access.control","name":"multi-tenant isolation with role-based access control","description":"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.","intents":["Isolate data and access between different tenants or departments","Implement fine-grained access control at table and column levels","Enforce data governance policies through role-based permissions","Support multi-tenant SaaS applications on shared Databend infrastructure"],"best_for":["SaaS platforms built on Databend","Enterprise teams with complex access control requirements","Organizations with strict data governance policies"],"limitations":["Row-level security requires query rewriting; complex RLS policies can significantly impact query performance","Column-level security is enforced at query time; metadata about column existence may leak through error messages","RBAC configuration complexity increases with number of roles and permissions; misconfiguration can lead to unintended access"],"requires":["User authentication method configured (password, OAuth, LDAP)","Role definitions with appropriate permissions","Metadata isolation enabled in cluster configuration"],"input_types":["User credentials and authentication tokens","Role and permission definitions","Access control policies"],"output_types":["Authenticated user context","Filtered metadata based on permissions","Query results with RLS/CLS applied"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_13","uri":"capability://data.processing.analysis.streaming.data.ingestion.with.automatic.schema.inference","name":"streaming data ingestion with automatic schema inference","description":"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.","intents":["Ingest streaming data from Kafka, Kinesis, or HTTP sources","Automatically infer and evolve table schemas from incoming data","Enable real-time analytics on freshly ingested data","Build event-driven data pipelines without external ETL tools"],"best_for":["Teams building real-time analytics platforms","Organizations ingesting event streams from IoT or application logs","Developers building event-driven data pipelines"],"limitations":["Automatic schema inference can produce incorrect types for ambiguous data; manual schema specification is recommended","Streaming ingestion latency is typically 1-5 seconds due to batching; sub-second latency is not supported","Schema evolution can cause query compatibility issues; existing queries may fail if new columns have unexpected types"],"requires":["Streaming data source (Kafka, Kinesis, HTTP endpoint)","Network connectivity from Databend to streaming source","Minimum 2GB RAM for streaming ingestion buffers"],"input_types":["Streaming records (JSON, CSV, Avro, Protobuf)","Schema inference configuration","Batching and flushing parameters"],"output_types":["Data written to FUSE storage","Schema evolution metadata","Ingestion statistics and error logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_14","uri":"capability://data.processing.analysis.distributed.query.execution.with.adaptive.resource.allocation","name":"distributed query execution with adaptive resource allocation","description":"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.","intents":["Execute large queries across multiple compute nodes in parallel","Optimize resource allocation based on query characteristics","Prioritize queries based on user roles or SLA requirements","Monitor and control resource consumption per query"],"best_for":["Teams running large-scale analytical queries","Organizations with multi-tenant workloads requiring resource isolation","Developers optimizing query performance on distributed clusters"],"limitations":["Data shuffling between nodes adds network overhead; queries with large intermediate results can be 2-3x slower than single-node execution","Adaptive resource allocation has tuning overhead; suboptimal parameters can lead to query failures or poor performance","Query scheduling complexity increases with cluster size; scheduling decisions may not be optimal for all workload patterns"],"requires":["Multiple Databend query nodes (minimum 2 for distributed execution)","Network connectivity between query nodes with <100ms latency recommended","Resource quota configuration and monitoring infrastructure"],"input_types":["SQL queries","Resource quota and priority configuration","Cluster topology information"],"output_types":["Distributed execution plans","Query resource usage statistics","Query scheduling decisions and priorities"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_2","uri":"capability://search.retrieval.full.text.search.with.inverted.indexing","name":"full-text search with inverted indexing","description":"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.","intents":["Search text and JSON documents by keyword with relevance ranking","Build search-enabled applications without Elasticsearch or Solr","Combine full-text search with structured SQL queries on the same dataset","Index and query unstructured text data at scale"],"best_for":["Teams consolidating search and analytics infrastructure","Developers building search features into data applications","Organizations seeking to reduce operational complexity of multi-system stacks"],"limitations":["Inverted index memory footprint can be 20-40% of raw data size for text-heavy datasets","Index rebuild time increases linearly with dataset size; full reindex of 1TB+ datasets may require hours","Limited to single-language tokenization; multilingual search requires custom tokenizer configuration"],"requires":["Text or JSON columns in table schema","FUSE storage engine enabled","Minimum 2GB RAM for index structures on moderate datasets (100GB+)"],"input_types":["Text columns","JSON documents","Search queries (keyword-based)"],"output_types":["Ranked result sets with relevance scores","Index statistics and term frequency data"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_3","uri":"capability://data.processing.analysis.geospatial.data.processing.with.spatial.indexing","name":"geospatial data processing with spatial indexing","description":"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.","intents":["Query geographic data with spatial predicates (point-in-polygon, distance-based filtering)","Perform spatial joins between location-based datasets","Build location-aware analytics and mapping applications","Index and search geographic features at scale"],"best_for":["GIS analysts and geospatial data engineers","Teams building location-based services and mapping applications","Organizations analyzing geographic patterns in large datasets"],"limitations":["Spatial index performance degrades with highly skewed geographic distributions (e.g., dense urban clusters)","Complex spatial joins on large datasets can require significant memory; 10M+ point datasets may need 16GB+ RAM","Limited to 2D/3D geometries; 4D+ coordinate systems require custom handling"],"requires":["Geospatial data types defined in table schema","FUSE storage engine enabled","Minimum 8GB RAM for efficient spatial index operations on large geographic datasets"],"input_types":["Geometry types (Point, LineString, Polygon, MultiGeometry)","Coordinate reference systems (WGS84, Web Mercator, etc.)","Spatial predicates and distance thresholds"],"output_types":["Filtered result sets based on spatial predicates","Distance calculations and spatial relationship metadata"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_4","uri":"capability://automation.workflow.compute.storage.separation.with.stateless.query.nodes","name":"compute-storage separation with stateless query nodes","description":"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.","intents":["Scale compute resources independently from storage based on query workload","Deploy Databend across multiple cloud regions with shared data","Achieve high availability through stateless query node redundancy","Reduce infrastructure costs by using commodity object storage"],"best_for":["Cloud-native teams building data platforms on AWS/GCP/Azure","Organizations with variable query workloads requiring elastic scaling","Teams seeking to minimize operational overhead of database infrastructure"],"limitations":["Network latency to object storage impacts query performance; queries with many small reads can be 2-3x slower than local SSD-backed systems","Metadata service (databend-meta) requires Raft consensus; cluster formation takes 10-30 seconds and requires minimum 3 nodes for HA","Stateless design prevents local caching optimizations; hot data must be cached at object storage layer or in query node memory"],"requires":["S3-compatible object storage (AWS S3, MinIO, etc.) or GCS/Azure Blob","Network connectivity from query nodes to object storage (minimum 100 Mbps recommended)","Raft-compatible metadata service deployment (databend-meta)","Kubernetes or container orchestration for elastic query node scaling (optional but recommended)"],"input_types":["SQL queries","Object storage credentials and bucket paths"],"output_types":["Query results","Cluster topology and node status metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_5","uri":"capability://data.processing.analysis.fuse.storage.engine.with.columnar.format.and.compaction","name":"fuse storage engine with columnar format and compaction","description":"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.","intents":["Store analytics data efficiently in columnar format on object storage","Enable time-travel queries to access historical data versions","Optimize storage layout and compression for analytical workloads","Manage data lifecycle with automatic compaction and cleanup"],"best_for":["Data engineers managing large-scale analytics data on cloud object storage","Teams requiring data versioning and audit trails","Organizations optimizing storage costs through compression and compaction"],"limitations":["Compaction process is asynchronous and can lag behind writes; queries may see uncompacted small files affecting performance","Time-travel queries require metadata retention; keeping 30+ days of history increases metadata storage by 5-10%","Block pruning effectiveness depends on data clustering; randomly ordered data may require scanning 80%+ of blocks despite predicate pushdown"],"requires":["Object storage with S3-compatible API or native GCS/Azure Blob support","Minimum 1GB free space for metadata and compaction operations","FUSE storage engine enabled in table creation (default)"],"input_types":["Data from INSERT, COPY, or streaming ingestion","Compaction configuration parameters"],"output_types":["Columnar Parquet-compatible blocks in object storage","Metadata snapshots with version history"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_6","uri":"capability://data.processing.analysis.metadata.management.with.raft.consensus.and.versioning","name":"metadata management with raft consensus and versioning","description":"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.","intents":["Maintain consistent cluster state across distributed query nodes","Enable zero-downtime cluster upgrades through metadata versioning","Manage table schemas, user permissions, and access control","Provide transactional metadata updates for schema changes"],"best_for":["Teams deploying Databend in production with multiple query nodes","Organizations requiring high availability and zero-downtime upgrades","Developers building multi-tenant systems on Databend"],"limitations":["Raft consensus requires minimum 3 nodes for HA; single-node deployments lack fault tolerance","Metadata service latency (typically 10-50ms per operation) adds overhead to DDL operations; schema changes on large catalogs (10k+ tables) can take minutes","Metadata versioning complexity increases operational burden; incorrect version configuration can cause cluster incompatibility"],"requires":["Minimum 3 databend-meta nodes for HA (1 node acceptable for development)","Network connectivity between meta nodes with <100ms latency recommended","Persistent storage for Raft log (local disk or network storage)","Protocol Buffers 3.x for metadata serialization"],"input_types":["Schema definitions (CREATE TABLE, ALTER TABLE)","User and permission configurations","Cluster topology changes"],"output_types":["Metadata snapshots with version information","Raft log entries and consensus state"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_7","uri":"capability://tool.use.integration.http.query.api.with.protocol.handler.abstraction","name":"http query api with protocol handler abstraction","description":"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.","intents":["Execute SQL queries via HTTP without database-specific drivers","Support multiple client libraries (MySQL, PostgreSQL, Clickhouse clients)","Stream large result sets without loading entire results into memory","Integrate Databend into REST-based microservices and serverless functions"],"best_for":["Developers building REST APIs that query Databend","Teams using serverless functions (Lambda, Cloud Functions) for analytics","Organizations with heterogeneous client ecosystems (Python, Node.js, Go, etc.)"],"limitations":["HTTP protocol overhead adds 5-10ms latency per query compared to native TCP connections","Streaming results require chunked transfer encoding; some clients may buffer entire responses in memory","Protocol handler emulation (MySQL/PostgreSQL) has edge cases; complex dialect-specific features may not work identically"],"requires":["HTTP client library (curl, requests, fetch, etc.)","Network connectivity to Databend HTTP endpoint (default port 8000)","Optional: MySQL/PostgreSQL client libraries for protocol emulation"],"input_types":["SQL queries (string)","Query parameters and session configuration","Authentication credentials"],"output_types":["JSON result sets","CSV format","Arrow/Parquet binary format","Streaming chunked responses"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_8","uri":"capability://data.processing.analysis.session.and.query.context.management.with.isolation","name":"session and query context management with isolation","description":"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.","intents":["Maintain connection-specific state across multiple queries","Execute multi-statement transactions with ACID guarantees","Configure query behavior through session variables and settings","Isolate concurrent queries to prevent state interference"],"best_for":["Applications executing multiple related queries in a session","Teams requiring transactional consistency for multi-statement operations","Developers tuning query performance through session-level configuration"],"limitations":["Session state is maintained in query node memory; session failover requires reconnection and state reestablishment","Temporary tables are session-scoped and lost on disconnection; no persistence across sessions","Transaction isolation level (READ COMMITTED) may not satisfy all consistency requirements; serializable isolation not available"],"requires":["Active HTTP or protocol handler connection to Databend","Session timeout configuration (default 24 hours)","Memory allocation for session state (typically <1MB per session)"],"input_types":["SET statements for variable configuration","BEGIN/COMMIT/ROLLBACK for transaction control","SQL queries with implicit session context"],"output_types":["Session variable values","Transaction status and isolation level information","Query results with session-specific configuration applied"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-databendlabs--databend__cap_9","uri":"capability://data.processing.analysis.expression.evaluation.with.type.coercion.and.function.dispatch","name":"expression evaluation with type coercion and function dispatch","description":"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.","intents":["Evaluate complex SQL expressions with type safety and coercion","Dispatch function calls to appropriate implementations based on argument types","Execute aggregate and window functions over columnar data","Optimize expression evaluation through vectorized computation"],"best_for":["Query engine developers implementing SQL semantics","Teams building custom functions and expression extensions","Developers optimizing analytical query performance"],"limitations":["Type coercion rules can be surprising for users familiar with other databases; implicit conversions may mask data quality issues","Function overload resolution is deterministic but complex; ambiguous function calls require explicit type casting","Columnar evaluation model requires functions to operate on entire columns; scalar-only functions may have suboptimal performance"],"requires":["SQL expressions with valid type signatures","Function registry populated with built-in and custom functions","Columnar data format (Arrow) for vectorized evaluation"],"input_types":["SQL expressions (SELECT, WHERE, HAVING clauses)","Function definitions with type signatures","Columnar data (Arrow arrays)"],"output_types":["Evaluated expression results (typed values)","Function dispatch decisions and overload resolution"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["S3, GCS, Azure Blob, or compatible object storage","Rust 1.70+ for building from source","Minimum 4GB RAM per query node for vectorized processing","Vector data type support in table schema","Minimum 8GB RAM for efficient index operations on large vector datasets","FUSE storage engine enabled (default configuration)","Object storage access (S3, GCS, Azure Blob) for internal stages","External S3 credentials for external stages","Minimum 1GB free space for stage files and cache","Python 3.8+ installed in Databend environment"],"failure_modes":["Cost-based optimizer effectiveness depends on accurate table statistics; stale statistics can lead to suboptimal plans","Vectorized execution adds memory overhead compared to row-oriented engines for small datasets","Query optimization time increases with complex multi-join queries (>10 joins may require manual hints)","Vector index performance degrades with very high dimensionality (>2000 dims) without careful tuning","Index maintenance overhead during bulk inserts can impact write throughput by 15-30%","Limited to exact vector type definitions; schema evolution of vector columns requires table recreation","Stage file cleanup is manual; orphaned files can accumulate if COPY operations fail","Cache invalidation is conservative; changes to underlying data may not immediately invalidate all dependent caches","External stages require proper S3 credentials and bucket permissions; misconfiguration can cause silent failures","Python sandbox has performance overhead; UDFs are 5-10x slower than native SQL functions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6509766172046101,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-03T07:49:27Z"},"community":{"stars":9275,"forks":870,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=databendlabs--databend","compare_url":"https://unfragile.ai/compare?artifact=databendlabs--databend"}},"signature":"j7YsZF5ClnmX74PGXbSFdOWO33aI85TDBP8owRrOhIdF99dLkdSTW3cr3PHuMLGirx0nDihYpZGXiFdWw6XjBA==","signedAt":"2026-06-21T10:11:55.020Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/databendlabs--databend","artifact":"https://unfragile.ai/databendlabs--databend","verify":"https://unfragile.ai/api/v1/verify?slug=databendlabs--databend","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}