{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"duckdb","slug":"duckdb","name":"DuckDB","type":"repo","url":"https://github.com/duckdb/duckdb","page_url":"https://unfragile.ai/duckdb","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"duckdb__cap_0","uri":"capability://data.processing.analysis.columnar.vectorized.query.execution.on.external.files","name":"columnar vectorized query execution on external files","description":"Executes SQL queries directly on Parquet, CSV, and JSON files using a columnar vectorized execution engine that processes data in SIMD-friendly chunks (DataChunk vectors) without materializing entire datasets into memory. The engine uses the Vector and DataChunk abstraction layer from the type system to enable cache-efficient batch processing of billions of rows, with lazy evaluation and predicate pushdown to minimize I/O.","intents":["Run analytical SQL queries on multi-gigabyte CSV or Parquet files without loading into a database server","Process data locally on a laptop without spinning up infrastructure","Perform exploratory analysis on raw data files with standard SQL syntax"],"best_for":["Data analysts and engineers doing local data exploration","ML practitioners preparing training datasets without cloud infrastructure","Teams migrating from pandas/dask to SQL-based workflows"],"limitations":["Single-machine processing — no distributed query execution across clusters","Vectorized execution adds overhead for very small datasets (< 10K rows) compared to row-oriented engines","Memory-bound for extremely large aggregations without spilling to disk (configurable via buffer management)"],"requires":["C++17 compiler for building from source","Python 3.7+ for Python API, or Node.js 14+ for JavaScript bindings","Parquet files must use supported compression codecs (snappy, gzip, zstd)"],"input_types":["Parquet files","CSV files (with configurable delimiters, quoting, escaping)","JSON files (line-delimited or nested structures)","Arrow IPC format"],"output_types":["Arrow RecordBatch (zero-copy)","Pandas DataFrame","Polars DataFrame","CSV export","Parquet export"],"categories":["data-processing-analysis","query-execution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_1","uri":"capability://data.processing.analysis.parquet.schema.inference.and.predicate.pushdown","name":"parquet schema inference and predicate pushdown","description":"Automatically infers Parquet file schemas and applies filter predicates at the file-reading layer to skip row groups and columns that don't match query conditions. Uses the Parquet Integration module to parse metadata without reading full column data, enabling sub-millisecond filtering decisions on multi-terabyte datasets. Supports nested type handling via the Variant Type system for complex Parquet structures.","intents":["Query only relevant partitions of a large Parquet dataset without scanning the entire file","Automatically detect column types and nullability from Parquet metadata","Handle Parquet files with nested arrays, structs, and maps without manual schema definition"],"best_for":["Data engineers working with partitioned Parquet lakes (e.g., Hive-style directories)","Analytics teams querying multi-gigabyte fact tables with selective filters","ML pipelines that need schema flexibility for evolving data formats"],"limitations":["Predicate pushdown only works for simple column comparisons — complex expressions require full column scan","Nested type filtering (e.g., filtering on array elements) requires materializing the nested column","Parquet files with missing statistics in metadata cannot use row-group skipping"],"requires":["Parquet files with valid metadata (row group statistics)","C++ standard library with filesystem support (C++17)","Optional: Parquet files compressed with snappy, gzip, zstd, or uncompressed"],"input_types":["Parquet files (single or multiple via glob patterns)","Parquet metadata (inferred automatically)"],"output_types":["Filtered Arrow RecordBatch","Row count estimates","Schema information (column names, types, nullability)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_10","uri":"capability://planning.reasoning.query.profiling.and.performance.monitoring","name":"query profiling and performance monitoring","description":"Provides the Query Profiler System that captures detailed execution metrics (operator timing, row counts, memory usage) for each query operator. Integrates with the Logging Infrastructure to record profiling data and enable performance analysis. Supports both per-query profiling and aggregate statistics across multiple queries.","intents":["Identify slow query operators and bottlenecks","Monitor memory usage and spilling behavior during query execution","Analyze query performance trends over time"],"best_for":["Database administrators optimizing query performance","Data engineers debugging slow ETL pipelines","Teams building performance monitoring dashboards"],"limitations":["Profiling overhead adds 5-10% latency to query execution","Detailed profiling data requires significant disk space for long-running queries","Profiling metrics are operator-level and may not identify CPU cache misses or I/O stalls"],"requires":["Query profiling enabled via PRAGMA or configuration","Sufficient disk space for profiling logs","Access to DuckDB's internal metrics (requires C++ API or Python introspection)"],"input_types":["SQL queries to profile","Profiling configuration (verbosity level, metrics to capture)"],"output_types":["Profiling report (operator timing, row counts, memory usage)","Query plan with actual execution metrics","Performance logs for analysis"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_11","uri":"capability://data.processing.analysis.sorting.and.scanning.with.configurable.execution.strategies","name":"sorting and scanning with configurable execution strategies","description":"Implements the Sorting, Scanning, and Execution Pipeline with multiple sort strategies (in-memory quicksort, external merge sort with spilling). The scanning layer supports both full table scans and index-based scans with filter pushdown. Uses the Buffer Management layer to handle memory pressure during sorting operations, automatically spilling to disk when necessary.","intents":["Sort large datasets that exceed available memory","Scan tables efficiently with filter predicates applied at the storage layer","Execute ORDER BY clauses with configurable memory limits"],"best_for":["Queries with ORDER BY on large tables","Range queries with filter predicates","Analytical workloads requiring sorted output"],"limitations":["External merge sort with spilling has 2-5x performance penalty vs. in-memory sort","Scanning with complex filter expressions may require materializing intermediate columns","Index-based scans require pre-built indexes (not automatically created)"],"requires":["Sufficient memory for in-memory sort (configurable via max_memory)","Disk space for spill files if sorting larger than available memory","Sortable column types (numeric, string, temporal)"],"input_types":["Arrow RecordBatch to sort","Sort key columns and order (ASC/DESC)","Filter predicates for scanning"],"output_types":["Sorted Arrow RecordBatch","Scan results with filters applied","Sort statistics (spill events, memory usage)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_12","uri":"capability://data.processing.analysis.in.process.database.with.persistent.storage","name":"in-process database with persistent storage","description":"Provides an in-process database engine that can operate in both memory-only mode (for ephemeral analysis) and persistent mode (with data stored in DuckDB's native format). Uses the Storage Engine with row groups and column data organization to maintain data durability while preserving columnar format. Supports both read-only and read-write modes with configurable access patterns.","intents":["Create a local analytical database without running a separate server process","Persist analytical results to disk for later querying","Switch between in-memory and persistent storage modes based on use case"],"best_for":["Data scientists building reproducible analysis notebooks","Teams building embedded analytics into applications","Organizations deploying analytics on edge devices or laptops"],"limitations":["Single-process access — no multi-process concurrency (use file locking for safety)","Persistent storage uses DuckDB's proprietary format (not compatible with other databases)","In-memory mode requires sufficient RAM for entire dataset"],"requires":["Disk space for persistent storage (if using persistent mode)","Write permissions to database directory","Single-process access (or external locking for multi-process)"],"input_types":["SQL CREATE TABLE statements","Data from files (Parquet, CSV, JSON) or DataFrames","INSERT/UPDATE/DELETE statements"],"output_types":["Persistent database file (.duckdb)","Query results from stored tables","Schema information"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_13","uri":"capability://tool.use.integration.arrow.ipc.integration.for.zero.copy.data.exchange","name":"arrow ipc integration for zero-copy data exchange","description":"Integrates with Apache Arrow's Inter-Process Communication (IPC) format to enable zero-copy data exchange with other Arrow-compatible systems (Pandas, Polars, PyArrow, R, etc.). Uses Arrow RecordBatch as the internal representation, allowing data to be shared across language boundaries without serialization. Supports both reading and writing Arrow IPC files and streaming Arrow data.","intents":["Share query results with other Arrow-compatible libraries without copying data","Build polyglot data pipelines that mix Python, R, and other languages","Integrate DuckDB with existing Arrow-based infrastructure"],"best_for":["Data science teams using multiple languages (Python, R, Rust)","Organizations with existing Arrow infrastructure","Teams building high-performance data pipelines"],"limitations":["Zero-copy only works with Arrow-compatible formats — other formats require serialization","Arrow schema must be compatible with DuckDB types (some type mismatches require conversion)","Memory layout differences between systems may require copying in some cases"],"requires":["Apache Arrow library (included with DuckDB)","Arrow-compatible data source or consumer","Compatible type definitions"],"input_types":["Arrow RecordBatch","Arrow Table","Arrow IPC files"],"output_types":["Arrow RecordBatch","Arrow Table","Arrow IPC files"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_14","uri":"capability://data.processing.analysis.type.system.with.nested.types.struct.list.map.and.custom.types","name":"type system with nested types (struct, list, map) and custom types","description":"Implements a comprehensive type system that includes scalar types (INTEGER, VARCHAR, TIMESTAMP) and nested types (STRUCT for objects, LIST for arrays, MAP for key-value pairs). 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The engine selects join strategies based on table sizes and available memory, with support for both in-memory hash tables and spilling to disk when memory pressure exceeds configured thresholds. Uses the Buffer Management and Compression layer to manage memory efficiently during large joins.","intents":["Join multiple large tables efficiently without materializing cross products","Perform semi-joins and anti-joins for filtering operations","Handle out-of-core joins when hash table size exceeds available RAM"],"best_for":["Analytics queries with multiple table joins (2-10 tables)","Data engineers building ETL pipelines with complex join logic","Teams processing datasets larger than available RAM with join operations"],"limitations":["Hash joins require the build-side table to fit in memory (or spill to disk with 2-3x performance penalty)","Join order optimization is cost-based but may not find optimal order for > 10 tables","Spilling to disk requires configured temporary directory with sufficient free space"],"requires":["Sufficient RAM for hash table (configurable via max_memory setting)","Disk space for spill files if joining tables larger than available memory","Both tables must have compatible join key types"],"input_types":["Two or more Arrow RecordBatch streams","SQL join predicates (equality conditions)"],"output_types":["Joined Arrow RecordBatch","Join statistics (rows processed, spill events)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_3","uri":"capability://data.processing.analysis.aggregation.and.window.function.computation","name":"aggregation and window function computation","description":"Executes aggregate functions (SUM, COUNT, AVG, MIN, MAX, GROUP_CONCAT, etc.) and window functions (ROW_NUMBER, RANK, LAG, LEAD, etc.) using vectorized operators that process DataChunk batches. Supports both simple aggregations and complex window frames with partition-by and order-by clauses. Uses the Aggregation and Window Functions operator pattern with streaming aggregation for memory efficiency.","intents":["Compute group-by aggregations on large datasets with multiple aggregate expressions","Calculate running totals, ranks, and percentiles using window functions","Perform complex analytics like cumulative sums and moving averages"],"best_for":["Data analysts computing summary statistics and KPIs","ML engineers creating time-series features with window functions","Business intelligence teams building aggregated reports"],"limitations":["Window functions with large frames (e.g., ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) require materializing entire partition in memory","Streaming aggregation for GROUP BY requires hash table proportional to number of distinct groups","Complex window frames with multiple ORDER BY columns have higher CPU overhead"],"requires":["Input data must be properly typed (numeric types for SUM/AVG, comparable types for MIN/MAX)","Window function ORDER BY columns must be sortable","Sufficient memory for hash table (number of groups × state size)"],"input_types":["Arrow RecordBatch with numeric, string, or temporal columns","Aggregate function specifications (function name, input columns, filter conditions)","Window frame specifications (PARTITION BY, ORDER BY, frame bounds)"],"output_types":["Aggregated Arrow RecordBatch with computed columns","Window function results with same row count as input","Aggregate statistics (count, sum, avg, min, max, etc.)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_4","uri":"capability://planning.reasoning.query.optimization.with.cost.based.join.ordering","name":"query optimization with cost-based join ordering","description":"Applies the Optimizer Pipeline to transform logical query plans into optimized physical plans using cost-based heuristics. The Join Order Optimization module evaluates different join orderings and selects the lowest-cost plan based on estimated row counts and cardinality. Uses the Binder System to validate query semantics and the Table Catalog Management to resolve table references and statistics.","intents":["Automatically reorder joins to minimize intermediate result sizes","Push down filters to reduce data scanned before joins","Select appropriate physical operators (hash join vs. nested loop) based on table sizes"],"best_for":["Developers writing complex multi-table queries without manual optimization","Data engineers building query templates that work across different data distributions","Teams migrating from hand-optimized SQL to declarative query writing"],"limitations":["Cost estimation relies on table statistics — queries on unanalyzed tables may choose suboptimal plans","Join order optimization is exponential in number of tables (> 10 tables may timeout)","Cardinality estimation errors propagate through the plan, leading to poor physical operator selection"],"requires":["Table statistics (row count, column cardinality) — can be auto-generated via ANALYZE","Accurate type information for all columns","Cost model calibration for specific hardware (CPU, memory, disk speed)"],"input_types":["SQL query (SELECT with multiple JOINs, WHERE, GROUP BY, ORDER BY)","Table metadata (schema, statistics, indexes)"],"output_types":["Optimized physical query plan","Plan explanation (EXPLAIN output with estimated costs)","Optimization decisions (join order, filter pushdown, operator selection)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_5","uri":"capability://data.processing.analysis.csv.reading.with.configurable.parsing.and.type.inference","name":"csv reading with configurable parsing and type inference","description":"Reads CSV files with automatic type inference and configurable parsing options (delimiter, quote character, escape character, header detection). Uses the CSV Reading and Writing module to handle edge cases like quoted fields with embedded delimiters and newlines. Supports streaming reads for files larger than memory via the Multi-File Reader pattern, with parallel file reading for multiple CSV inputs.","intents":["Load CSV files into DuckDB without pre-specifying column types","Handle non-standard CSV formats (tab-delimited, pipe-delimited, custom quoting)","Read multiple CSV files with consistent schema via glob patterns"],"best_for":["Data analysts working with exported CSV files from Excel or databases","ML engineers preparing datasets from diverse CSV sources","Teams ingesting data from legacy systems that export CSV"],"limitations":["Type inference requires scanning sample rows — may infer incorrect types for columns with sparse data types","Large CSV files with inconsistent quoting or escaping may fail parsing","Parallel reading of multiple CSVs requires consistent schema across files"],"requires":["CSV file with valid UTF-8 encoding (or specified encoding)","Sufficient memory for type inference sample (configurable, default 8KB)","Disk space for temporary files if using parallel reading"],"input_types":["CSV files (single or multiple via glob patterns)","CSV parsing options (delimiter, quote, escape, header row number)"],"output_types":["Arrow RecordBatch with inferred types","Schema information (column names, inferred types)","Parsing error reports (line numbers, error descriptions)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_6","uri":"capability://data.processing.analysis.json.data.extraction.and.querying","name":"json data extraction and querying","description":"Parses and queries JSON data (line-delimited JSON, nested objects, arrays) using the JSON Extension. Supports JSON path expressions for extracting nested fields and converting JSON to relational tables. Uses the Variant Type system to represent JSON values natively, enabling SQL operations on semi-structured data without explicit schema definition.","intents":["Query JSON APIs or log files without converting to CSV first","Extract nested fields from JSON objects using SQL syntax","Convert JSON arrays to relational tables for analysis"],"best_for":["Data engineers ingesting JSON from APIs or log systems","ML practitioners working with semi-structured data","Teams analyzing JSON-formatted logs or events"],"limitations":["JSON parsing is slower than Parquet for large datasets due to text parsing overhead","Variant Type operations have higher CPU cost than native types","Complex JSON path expressions may require materializing intermediate results"],"requires":["Valid JSON format (line-delimited or nested structures)","UTF-8 encoding","Sufficient memory for JSON parsing (proportional to largest JSON object)"],"input_types":["JSON files (line-delimited or nested)","JSON path expressions (e.g., '$.field.nested[0].value')"],"output_types":["Arrow RecordBatch with Variant columns","Extracted scalar values (strings, numbers, booleans)","Relational tables from JSON arrays"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_7","uri":"capability://tool.use.integration.python.api.with.pandas.polars.integration","name":"python api with pandas/polars integration","description":"Provides a Python API that integrates seamlessly with Pandas and Polars DataFrames, enabling zero-copy data exchange via Arrow IPC format. Supports both eager execution (returning results immediately) and lazy evaluation (building query plans). The Python API wraps the C API and uses Arrow RecordBatch as the internal representation for efficient data transfer.","intents":["Use DuckDB as a SQL engine for Pandas DataFrames without data copying","Build Python data pipelines that mix SQL queries with Python transformations","Leverage DuckDB's query optimization for complex Pandas operations"],"best_for":["Python data scientists using Jupyter notebooks for exploratory analysis","ML engineers building feature engineering pipelines","Teams migrating from Pandas to SQL-based workflows"],"limitations":["Zero-copy integration only works with Arrow-compatible formats (Pandas with PyArrow backend, Polars)","Lazy evaluation requires explicit .collect() call to materialize results","Python GIL may limit parallelism for CPU-bound operations"],"requires":["Python 3.7+","duckdb Python package (pip install duckdb)","Optional: pandas, polars, pyarrow for DataFrame integration"],"input_types":["Pandas DataFrame","Polars DataFrame","Arrow Table","SQL query string","File paths (CSV, Parquet, JSON)"],"output_types":["Pandas DataFrame","Polars DataFrame","Arrow Table","Python list of tuples","Iterator for streaming results"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_8","uri":"capability://tool.use.integration.extension.system.with.pluggable.functions.and.types","name":"extension system with pluggable functions and types","description":"Provides an Extension Architecture that allows developers to register custom functions, types, and table functions via the Function Registry and Autoloading system. Extensions are loaded dynamically at runtime and integrated into the query optimizer and execution engine. Supports both built-in extensions (JSON, Delta, Parquet) and user-defined extensions via C++ or SQL.","intents":["Add custom SQL functions without modifying DuckDB core","Implement domain-specific types (e.g., geospatial, financial)","Build table-valued functions for custom data sources"],"best_for":["Database extension developers building specialized functionality","Teams with domain-specific data types or operations","Organizations building internal data platforms on DuckDB"],"limitations":["Extension development requires C++ knowledge and understanding of DuckDB internals","Extensions must be compiled for each target platform (Linux, macOS, Windows)","No sandbox isolation — malicious extensions can access all system resources"],"requires":["C++17 compiler for building extensions","DuckDB development headers and CMake build system","Understanding of DuckDB's Function Registry and type system"],"input_types":["C++ extension code","Function signatures (parameter types, return types)","Custom type definitions"],"output_types":["Registered SQL functions","Custom types available in queries","Table-valued functions for data sources"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckdb__cap_9","uri":"capability://data.processing.analysis.transaction.support.with.acid.guarantees","name":"transaction support with acid guarantees","description":"Implements transaction support via the Table Storage and Transactions module, providing ACID guarantees for INSERT, UPDATE, and DELETE operations. Uses row-group versioning and write-ahead logging to ensure durability and isolation. Supports both read-committed and serializable isolation levels with configurable transaction behavior.","intents":["Ensure data consistency when performing multiple INSERT/UPDATE/DELETE operations","Rollback failed transactions without corrupting the database","Support concurrent read and write operations with isolation"],"best_for":["Applications requiring data consistency (financial systems, inventory management)","Teams building ETL pipelines with transactional guarantees","Systems that need to handle concurrent updates safely"],"limitations":["Write-ahead logging adds latency to INSERT/UPDATE/DELETE operations","Serializable isolation level may cause transaction conflicts under high concurrency","Transaction rollback requires replaying log entries, which can be slow for large transactions"],"requires":["Persistent storage (not in-memory mode) for durability","Sufficient disk space for write-ahead log","Proper file permissions for database directory"],"input_types":["SQL 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