API Reference¶

`intake_sql.SQLManualPartition`
`intake_sql.SQLSource`(uri, sql_expr[, …])	One-shot SQL to dataframe reader (no partitioning)
`intake_sql.SQLSourceAutoPartition`(uri, …)	SQL table reader with automatic partitioning
`intake_sql.SQLSourceManualPartition`(uri, …)	SQL expression reader with explicit partitioning
`intake_sql.SQLCatalog`(uri[, views])	Makes data sources out of known tables in the given SQL service

class intake_sql.SQLSource(uri, sql_expr, sql_kwargs={}, metadata={})[source]¶

One-shot SQL to dataframe reader (no partitioning)

Caches entire dataframe in memory.

Parameters:	uri: str or None Full connection string in sqlalchemy syntax sql_expr: str Query expression to pass to the DB backend sql_kwargs: dict Further arguments to pass to pandas.read_sql
Attributes:	datashape description `hvplot` Returns a hvPlot object to provide a high-level plotting API. `plot` Returns a hvPlot object to provide a high-level plotting API.

Methods

`close`()	Close open resources corresponding to this data source.
`discover`()	Open resource and populate the source attributes.
`read`()	Load entire dataset into a container and return it
`read_chunked`()	Return iterator over container fragments of data source
`read_partition`(i)	Return a (offset_tuple, container) corresponding to i-th partition.
`to_dask`()	Return a dask container for this data source
`yaml`()	Return YAML representation of this data-source

read()[source]¶: Load entire dataset into a container and return it

class intake_sql.SQLSourceAutoPartition(uri, table, index, sql_kwargs={}, metadata={})[source]¶

SQL table reader with automatic partitioning

Only reads existing tables, not arbitrary SQL expressions.

For partitioning, require to provide the column to be used, which should be indexed in the database. Can then provide list of boundaries, number of partitions or target partition size; see dask.dataframe.read_sql_table and examples for a list of possibilities.

Parameters:	uri: str or None Full connection string in sqlalchemy syntax table: str Table to read index: str Column to use for partitioning and as the index of the resulting dataframe sql_kwargs: dict Further arguments to pass to dask.dataframe.read_sql
Attributes:	datashape description `hvplot` Returns a hvPlot object to provide a high-level plotting API. `plot` Returns a hvPlot object to provide a high-level plotting API.

Methods

`close`()	Close open resources corresponding to this data source.
`discover`()	Open resource and populate the source attributes.
`read`()	Load entire dataset into a container and return it
`read_chunked`()	Return iterator over container fragments of data source
`read_partition`(i)	Return a (offset_tuple, container) corresponding to i-th partition.
`to_dask`()	Return a dask container for this data source
`yaml`()	Return YAML representation of this data-source

read()[source]¶: Load entire dataset into a container and return it

to_dask()[source]¶: Return a dask container for this data source

class intake_sql.SQLSourceManualPartition(uri, sql_expr, where_values, where_template=None, sql_kwargs={}, metadata={})[source]¶

SQL expression reader with explicit partitioning

Reads any arbitrary SQL expressions into pa5titioned data-frame, but requires a full specification of the boundaries.

The boundaries are specified as either a set of strings with WHERE clauses to be applied to the main SQL expression, or a string to be formatted with a set of values to produce the comlete SQL expressions.

Note, if not supplying a meta argument, dask will load the first partition in order to determine the schema. If some of the partitions are empty, loading without a meta will likely fail.

Parameters:

uri: str or None: Full connection string in sqlalchemy syntax
sql_expr: str: SQL expression to evaluate
where_values: list of str or list of values/tuples: Either a set of explicit partitioning statements (e.g., “WHERE index_col < 50”…) or pairs of valued to be entered into where_template, if using
where_template: str (optional): Template for generating partition selection clauses, using the values from where_values, e.g., “WHERE index_col >= {} AND index_col < {}”
sql_kwargs: dict: Further arguments to pass to pd.read_sql_query

Attributes:

datashape
description
hvplot: Returns a hvPlot object to provide a high-level plotting API.
plot: Returns a hvPlot object to provide a high-level plotting API.

Methods

`close`()	Close open resources corresponding to this data source.
`discover`()	Open resource and populate the source attributes.
`read`()	Load entire dataset into a container and return it
`read_chunked`()	Return iterator over container fragments of data source
`read_partition`(i)	Return a (offset_tuple, container) corresponding to i-th partition.
`to_dask`()	Return a dask container for this data source
`yaml`()	Return YAML representation of this data-source

read()[source]¶: Load entire dataset into a container and return it

to_dask()[source]¶: Return a dask container for this data source

class intake_sql.SQLCatalog(uri, views=False, **kwargs)[source]¶

Makes data sources out of known tables in the given SQL service

Attributes:	datashape description `hvplot` Returns a hvPlot object to provide a high-level plotting API. `plot` Returns a hvPlot object to provide a high-level plotting API.

Methods

`close`()	Close open resources corresponding to this data source.
`discover`()	Open resource and populate the source attributes.
`force_reload`()	Imperative reload data now
`read`()	Load entire dataset into a container and return it
`read_chunked`()	Return iterator over container fragments of data source
`read_partition`(i)	Return a (offset_tuple, container) corresponding to i-th partition.
`reload`()	Reload catalog if sufficient time has passed
`to_dask`()	Return a dask container for this data source
`walk`([sofar, prefix, depth])	Get all entries in this catalog and sub-catalogs
`yaml`()	Return YAML representation of this data-source