Results of 1 - 10 of about 28 for Structure (0.003 sec.)
- JSON Processing Functions 16075
- JSON Extraction Functions There are two extraction functions, which have their respective operators. The operators can only be used if the string is stored as the JSON logical type. These functions support the same two location notations as
...ST , the result will be LIST of LIST of VARCHAR . json_pretty(json) Return json in a pretty-printed, indented format. json_structure(json) Return the structure of json . Defaults to JSON if the structure is inconsistent (e.g., incompatible types in an arr...FROM example; [4] SELECT json_type(j) FROM example; OBJECT SELECT json_keys(j) FROM example; [family, species] SELECT json_structure(j) FROM example; {"family":"VARCHAR","species":["VARCHAR"]} SELECT json_structure('["duck", {"family": "anatidae"}]'); ["J...SON"] Because JSON has no native temporal or UUID types, json_structure reports DATE , TIME , TIMESTAMP , and UUID values as VARCHAR , since that is how they are represented in JSON. If you feed... - https://man.mtool.work/duckdb/2.0/data/json/json_functions.html
- R-Tree Indexes 9733
- The spatial extension provides support for spatial indexing through the R-tree extension index type. Why Should I Use an R-Tree Index? When working with geospatial datasets, it is very common that you want to filter rows based on their spat
...possible to accelerate these types of queries significantly. How Do R-Tree Indexes Work? An R-tree is a balanced tree data structure that stores the approximate minimum bounding rectangle of each geometry (and the internal ID of the corresponding row) in...can be computed from the beginning. Additionally, using the bulk loading algorithm tends to create a R-tree with a better structure (less overlap between bounding boxes), which usually leads to better query performance. If you find that the performance o...you specific workload, you might want to experiment with the max_node_capacity and min_node_capacity options to change the structure of the R-tree and how it responds to insertions and deletions, see the full list of options here . In general, a tree with... - https://man.mtool.work/duckdb/2.0/core_extensions/spatial/r-tree_indexes.html
- Extension Distribution 9389
- 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
.... A DuckDB repository is an HTTP, HTTPS, S3, or local file based directory that serves the extensions files in a specific structure. This structure is described in the “Downloading Extensions Directly from S3” section , and is the same for local paths an...on of this statement will first look for icu.duckdb_extension.gz , then icu.duckdb_extension in the repository's directory structure. If the custom repository is served over HTTPS or S3, the httpfs extension is required. DuckDB will attempt to autoload th... - https://man.mtool.work/duckdb/2.0/extensions/extension_distribution.html
- Variant Type 8832
- The VARIANT type stores typed, binary data where each row is self-contained with its own type information. This differs from the JSON type , which is physically stored as text. Because type metadata is embedded per-value, VARIANT provides b
...etter compression and query performance than JSON for semi-structured data. The VARIANT type is inspired by Snowflake's semi-structured VARIANT data type . It is available in Parquet since 20...roduced in DuckDB 2.0. When storing VARIANT columns in a DuckDB database file, DuckDB 2.0 automatically detects the common structure of the values and shreds them, i.e., stores the fields that share a type as separate, typed columns. Shredded data compres...ng VARIANT columns to Parquet, DuckDB can automatically shred (decompose) the variant data into typed columns based on the structure of the first row group. This auto-shredding improves read performance by enabling predicate pushdown and efficient column... - https://man.mtool.work/duckdb/2.0/sql/data_types/variant.html
- Overview 8488
- The pages in this section are intended for developers who contribute to DuckDB or build extensions for it. If you would like to contribute, please also read the contributing guidelines . Project Structure and Processes DuckDB Repositories l
... - https://man.mtool.work/duckdb/2.0/dev/overview.html
- Execution Format 8488
- Vector is the container format used to store in-memory data during execution. DataChunk is a collection of Vectors, used for instance to represent a column list in a PhysicalProjection operator. Data Flow DuckDB uses a vectorized query exec
...ter; struct { uint32_t length; char inlined[12]; } inlined; } value; }; Short strings ( <= 12 bytes ) are inlined into the structure, while larger strings are stored with a pointer to the data in the auxiliary string buffer. The length is used throughout...ap vectors are stored as a LIST[STRUCT(key KEY_TYPE, value VALUE_TYPE)] . Union Vectors Internally UNION utilizes the same structure as a STRUCT . The first “child” is always occupied by the Tag Vector of the UNION , which records for each row which of th... - https://man.mtool.work/duckdb/2.0/internals/vector.html
- Variant Functions 8373
- This section describes functions that operate on the VARIANT type . New Most variant_* functions were introduced in DuckDB 2.0. The variant_extract , variant_normalize and variant_typeof functions were already available in DuckDB 1.5. Name
...Description variant_typeof(v) Return the full type of a VARIANT value, including nested structure. variant_type(v) Return the top-level type category of a VARIANT value. variant_keys(v) Return the keys of a VARIANT objec...ues of x into a VARIANT array. variant_typeof(v) | Description | Return the full type of a VARIANT value, including nested structure. | | Example | variant_typeof('{"a": 1}'::JSON::VARIANT) | | Result | OBJECT(a) | variant_type(v) | Description | Return t...group_array function was added in DuckDB 2.0. Example The variant_* functions are useful for inspecting and filtering semi-structured data stored in a VARIANT column: CREATE TABLE events (payload VARIANT); INSERT INTO events VALUES ('{"user": {"id": 42, "... - https://man.mtool.work/duckdb/2.0/sql/functions/variant.html
- Advanced Installation Methods 8258
- Downloading Extensions Directly from S3 Downloading an extension directly can be helpful when building a Lambda service or container that uses DuckDB. DuckDB extensions are stored in public S3 buckets, but the directory structure of those b
... - https://man.mtool.work/duckdb/2.0/extensions/advanced_installation_methods.html
- Python Client API 7914
- 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
...ARCHAR', 'JSON']), 'set': ('json_set', 0, ['JSON', 'VARCHAR', 'JSON']), 'strip_nulls': ('json_strip_nulls', 0, ['JSON']), 'structure': ('json_structure', 0, ['JSON']), 'transform': ('json_transform', 0, ['JSON', 'VARCHAR']), 'transform_strict': ('json_tra...trip_nulls ( * args : ValueOrExpr ) → JsonExpr The document without its null-valued object fields. SQL: json_strip_nulls . structure ( * args : ValueOrExpr ) → JsonExpr The shape of the document, with types in place of values. SQL: json_structure . transf...orm ( * args : ValueOrExpr ) → Expression The json_transform function. Arguments: structure. SQL: json_transform . transform_strict ( * args : ValueOrExpr ) → Expression The json_transform_strict function. Argument... - https://man.mtool.work/duckdb/2.0/clients/python/reference/
- Hive Partitioning 7914
- Examples Read data from a Hive partitioned dataset: SELECT * FROM read_parquet('orders/*/*/*.parquet', hive_partitioning = true); Write a table to a Hive partitioned dataset: COPY orders TO 'orders' (FORMAT parquet, PARTITION_BY (year, mont
...nth FROM services) TO 'test' (PARTITION_BY (year, month)); When reading, the partition columns are read from the directory structure and can be included or excluded depending on the hive_partitioning parameter. FROM read_parquet('test/*/*/*.parquet', hive... - https://man.mtool.work/duckdb/2.0/data/partitioning/hive_partitioning.html
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