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- Window Functions 14413
- DuckDB supports window functions , which can use multiple rows to calculate a value for each row. Window functions are blocking operators , i.e., they require their entire input to be buffered, making them one of the most memory-intensive o
...are supported by major SQL database systems. Examples Generate a row_number column to enumerate rows: SELECT row_number() OVER () FROM sales; Tip If you only need a number for each row in a table, you can use the rowid pseudocolumn . Generate a row_...number column to enumerate rows, ordered by time : SELECT row_number() OVER (ORDER BY time) FROM sales; Generate a row_number column to enumerate rows, ordered by time and partitioned by region : SE...LECT row_number() OVER (PARTITION BY region ORDER BY time) FROM sales; Compute the difference between the current and the previous-by- time amoun... - https://man.mtool.work/duckdb/2.0/sql/functions/window_functions.html
- Relational API 13397
- The Relational API builds a query one step at a time from Python. A relation, a DuckDBPyRelation , is a piece of SQL text: the methods of the Transformation and Functions sections return a new relation whose text extends the one they were c
...runs nothing: DuckDB binds the text once to learn the columns and their types, and holds no data. For example, a relation over a billion rows is built at once: import duckdb duckdb_conn = duckdb.connect() rel = duckdb_conn.sql("FROM range(1_000_000_...eld.fetchall()) [(2,)] [(0,)] Relation Creation This section contains the details on how a relation is created. A relation over a query that reads is lazy evaluated . Name Description from_arrow Create a relation object from an Arrow object. from_csv...query each time it is read, parameter values bound at every run. Any other statement runs once, now, and gives a relation over what it returned. Statements before the last one run immediately. table Create a relation object for the named table. tabl... - https://man.mtool.work/duckdb/2.0/clients/python/relational_api.html
- QUALIFY Clause 10591
- The QUALIFY clause is used to filter the results of WINDOW functions . This filtering of results is similar to how a HAVING clause filters the results of aggregate functions applied based on the GROUP BY clause . The QUALIFY clause avoids t
...schema_name, function_name, -- In this example the function_rank column in the select clause is for reference row_number() OVER (PARTITION BY schema_name ORDER BY function_name) AS function_rank FROM duckdb_functions() QUALIFY row_number() OVER (PART...name) < 3; Filter based on a window function defined in the SELECT clause: SELECT schema_name, function_name, row_number() OVER (PARTITION BY schema_name ORDER BY function_name) AS function_rank FROM duckdb_functions() QUALIFY function_rank < 3; Filt...schema_name, function_name, -- In this example the function_rank column in the select clause is for reference row_number() OVER my_window AS function_rank FROM duckdb_functions() WINDOW my_window AS (PARTITION BY schema_name ORDER BY function_name) Q... - https://man.mtool.work/duckdb/2.0/sql/query_syntax/qualify.html
- CONNECT and DISCONNECT Statements 8787
- New Support for the CONNECT and DISCONNECT statements was introduced in DuckDB 2.0. The CONNECT statement routes the current session to a remote database, so that subsequent queries execute on that remote and their results stream back. DISC
...session to the local database. CONNECT is central to using DuckDB as a server : a DuckDB instance can serve its databases over the network, and any other DuckDB can attach to it and route queries there. Examples Attach a remote DuckDB server over th...n error. Supported Databases CONNECT works with databases that support remote execution, including: DuckDB servers reached over the Quack protocol . PostgreSQL and MySQL servers. For these, the remote pushdown optimizer ships SQL directly to the serv...er instead of pulling tables over the wire. In-process local databases, such as the default in-memory database or a DuckDB database file, do not support CON... - https://man.mtool.work/duckdb/2.0/sql/statements/connect.html
- Python Client API 8089
- Connection class duckdb. Appender Bases: object append_row ( self , * args ) → None Buffer one row, one value per column, each converted as an executemany parameter would be append_rows ( self , rows : object ) → None Buffer each row of an
...query each time it is read, parameter values bound at every run. Any other statement runs once, now, and gives a relation over what it returned. Statements before the last one run immediately. get_profiling_information ( self , format : str = 'json'...query each time it is read, parameter values bound at every run. Any other statement runs once, now, and gives a relation over what it returned. Statements before the last one run immediately. query_appender ( self , dml : str , columns : dict , * ,...rce : str | None = None ) → _duckdb.Appender Create an appender that buffers rows of the declared columns and runs the DML over them at each flush query_progress ( self ) → float Query progress of pending operation read_csv ( * args : object , ** kwa... - https://man.mtool.work/duckdb/2.0/clients/python/reference/
- HTTP CSV Import 7860
- To load a CSV file directly over HTTP(S), the httpfs extension is required. This can be installed using the INSTALL SQL command. This only needs to be run once. INSTALL httpfs; To load the httpfs extension for usage, use the LOAD SQL comman
...d: LOAD httpfs; After the httpfs extension is set up, CSV files can be read over http(s) the same way as a local file, using read_csv : SELECT * FROM read_csv('https://⟨domain⟩/path/to/file.csv'); Moreov...tps://⟨domain⟩/path/to/file.csv'; Example The following query reads an openly licensed (CC BY 4.0) real-world CSV directly over HTTP and aggregates it, with no local download step: SELECT area, round(avg(sunbed_price), 0) AS avg_sunbed_price_eur, cou... - https://man.mtool.work/duckdb/2.0/guides/network_cloud_storage/http_csv_import.h...
- Share a DuckDB Database That Only Stores Views 7771
- A DuckDB database file does not have to contain any data: it can store only view definitions that point at remote data such as Parquet files on object storage. The resulting file is only a few hundred kilobytes, yet it behaves like a shared
...le the underlying data stays in its original location and format. This is useful for publishing a curated “semantic layer” over a data lake, where the view definitions encode the joins, filters, and column names that consumers should use, without cop...large the referenced datasets are. Hosting the Catalog Upload the catalog.duckdb file to any location that DuckDB can read over the network, for example an s3:// bucket or an https:// endpoint. Querying the Catalog Consumers attach the catalog read-o...tations Consumers must be able to access both the catalog file and every data source that its views reference. Connections over HTTPS and the S3 API are read-only , so the catalog cannot be modified in place. Query performance depends on the network... - https://man.mtool.work/duckdb/2.0/guides/network_cloud_storage/duckdb_views_only...
- HTTP Parquet Import 7619
- To load a Parquet file over HTTP(S), the httpfs extension is required. This can be installed using the INSTALL SQL command. This only needs to be run once. INSTALL httpfs; To load the httpfs extension for usage, use the LOAD SQL command: LO
...AD httpfs; After the httpfs extension is set up, Parquet files can be read over http(s) : SELECT * FROM read_parquet('https://⟨domain⟩/path/to/file.parquet'); For example: SELECT * FROM read_parquet('ht...tps://duckdb.org/data/prices.parquet'); Moreover, the read_parquet function itself can also be omitted thanks to DuckDB's replacement scan mechanism : SELECT * FROM 'https... - https://man.mtool.work/duckdb/2.0/guides/network_cloud_storage/http_import.html
- Extension Distribution 7543
- Platforms Extension binaries are distributed for several platforms (see below). For platforms where packages for certain extensions are not available, users can build them from source and install the resulting binaries manually . Most offic
...ing Signed Extensions Extensions can be signed with a cryptographic key. By default, DuckDB uses its built-in public keys to verify the integrity of extensions before loading them. All core and community extensions are signed by the DuckDB team. In a...alled from it against these keys. Signing the extension simplifies their distribution, this is why they can be distributed over HTTP without the need for HTTPS, which itself is supported through an extension ( httpfs ). Unsigned Extensions Warning On...ly load unsigned extensions from sources you trust. Avoid loading unsigned extensions over HTTP. Consult the Securing DuckDB page for guidelines on how to set up DuckDB in a secure manner. If you wish to load your... - https://man.mtool.work/duckdb/2.0/extensions/extension_distribution.html
- Release Cycle 7467
- This document outlines the DuckDB and core extension release cycle framework. It is intended for developers working on DuckDB extensions to better understand the underlying processes. Overview DuckDB follows Semantic Versioning ( v<MAJOR>.<
...se Calendar Terminology In the release docs we use some basic terminology to describe versions and branches. We briefly go over them here. vx.y.z : The latest stable release vx.y-codename : The name of the branch that will produce vx.y.<n> releases v...and are free to follow their own release cycle. In this section we categorize different types of DuckDB extensions and go over their release cycles. To describe the release cycle of extensions, we need to first categorize extensions in three differe...of these three categories they belong to. In-tree extensions Unstable API extensions Stable API extensions We will now go over the release cycles of the three different categories, in order of increasing complexity. In-Tree Extensions For in-tree ex... - https://man.mtool.work/duckdb/2.0/dev/release_cycle.html
DuckDB 2.0 Documentation / man.mtool.work