from dask_expr import from_legacy_dataframe
[docs]def read_sql(sql, con, index_col, **kwargs):
"""
Read SQL query or database table into a DataFrame.
This function is a convenience wrapper around ``read_sql_table`` and
``read_sql_query``. It will delegate to the specific function depending
on the provided input. A SQL query will be routed to ``read_sql_query``,
while a database table name will be routed to ``read_sql_table``.
Note that the delegated function might have more specific notes about
their functionality not listed here.
Parameters
----------
sql : str or SQLAlchemy Selectable
Name of SQL table in database or SQL query to be executed. TextClause is not supported
con : str
Full sqlalchemy URI for the database connection
index_col : str
Column which becomes the index, and defines the partitioning. Should
be a indexed column in the SQL server, and any orderable type. If the
type is number or time, then partition boundaries can be inferred from
``npartitions`` or ``bytes_per_chunk``; otherwise must supply explicit
``divisions``.
Returns
-------
dask.dataframe
See Also
--------
read_sql_table : Read SQL database table into a DataFrame.
read_sql_query : Read SQL query into a DataFrame.
"""
from dask.dataframe.io.sql import read_sql
df = read_sql(sql, con, index_col, **kwargs)
return from_legacy_dataframe(df)
[docs]def read_sql_table(
table_name,
con,
index_col,
divisions=None,
npartitions=None,
limits=None,
columns=None,
bytes_per_chunk="256 MiB",
head_rows=5,
schema=None,
meta=None,
engine_kwargs=None,
**kwargs,
):
"""
Read SQL database table into a DataFrame.
If neither ``divisions`` or ``npartitions`` is given, the memory footprint of the
first few rows will be determined, and partitions of size ~256MB will
be used.
Parameters
----------
table_name : str
Name of SQL table in database.
con : str
Full sqlalchemy URI for the database connection
index_col : str
Column which becomes the index, and defines the partitioning. Should
be a indexed column in the SQL server, and any orderable type. If the
type is number or time, then partition boundaries can be inferred from
``npartitions`` or ``bytes_per_chunk``; otherwise must supply explicit
``divisions``.
columns : sequence of str or SqlAlchemy column or None
Which columns to select; if None, gets all. Note can be a mix of str and SqlAlchemy columns
schema : str or None
Pass this to sqlalchemy to select which DB schema to use within the
URI connection
divisions: sequence
Values of the index column to split the table by. If given, this will
override ``npartitions`` and ``bytes_per_chunk``. The divisions are the value
boundaries of the index column used to define the partitions. For
example, ``divisions=list('acegikmoqsuwz')`` could be used to partition
a string column lexographically into 12 partitions, with the implicit
assumption that each partition contains similar numbers of records.
npartitions : int
Number of partitions, if ``divisions`` is not given. Will split the values
of the index column linearly between ``limits``, if given, or the column
max/min. The index column must be numeric or time for this to work
limits: 2-tuple or None
Manually give upper and lower range of values for use with ``npartitions``;
if None, first fetches max/min from the DB. Upper limit, if
given, is inclusive.
bytes_per_chunk : str or int
If both ``divisions`` and ``npartitions`` is None, this is the target size of
each partition, in bytes
head_rows : int
How many rows to load for inferring the data-types, and memory per row
meta : empty DataFrame or None
If provided, do not attempt to infer dtypes, but use these, coercing
all chunks on load
engine_kwargs : dict or None
Specific db engine parameters for sqlalchemy
kwargs : dict
Additional parameters to pass to `pd.read_sql()`
Returns
-------
dask.dataframe
See Also
--------
read_sql_query : Read SQL query into a DataFrame.
Examples
--------
>>> df = dd.read_sql_table('accounts', 'sqlite:///path/to/bank.db',
... npartitions=10, index_col='id') # doctest: +SKIP
"""
from dask.dataframe.io.sql import read_sql_table as _read_sql_table
df = _read_sql_table(
table_name,
con,
index_col,
divisions=divisions,
npartitions=npartitions,
limits=limits,
columns=columns,
bytes_per_chunk=bytes_per_chunk,
head_rows=head_rows,
schema=schema,
meta=meta,
engine_kwargs=engine_kwargs,
**kwargs,
)
return from_legacy_dataframe(df)
[docs]def read_sql_query(
sql,
con,
index_col,
divisions=None,
npartitions=None,
limits=None,
bytes_per_chunk="256 MiB",
head_rows=5,
meta=None,
engine_kwargs=None,
**kwargs,
):
"""
Read SQL query into a DataFrame.
If neither ``divisions`` or ``npartitions`` is given, the memory footprint of the
first few rows will be determined, and partitions of size ~256MB will
be used.
Parameters
----------
sql : SQLAlchemy Selectable
SQL query to be executed. TextClause is not supported
con : str
Full sqlalchemy URI for the database connection
index_col : str
Column which becomes the index, and defines the partitioning. Should
be a indexed column in the SQL server, and any orderable type. If the
type is number or time, then partition boundaries can be inferred from
``npartitions`` or ``bytes_per_chunk``; otherwise must supply explicit
``divisions``.
divisions: sequence
Values of the index column to split the table by. If given, this will
override ``npartitions`` and ``bytes_per_chunk``. The divisions are the value
boundaries of the index column used to define the partitions. For
example, ``divisions=list('acegikmoqsuwz')`` could be used to partition
a string column lexographically into 12 partitions, with the implicit
assumption that each partition contains similar numbers of records.
npartitions : int
Number of partitions, if ``divisions`` is not given. Will split the values
of the index column linearly between ``limits``, if given, or the column
max/min. The index column must be numeric or time for this to work
limits: 2-tuple or None
Manually give upper and lower range of values for use with ``npartitions``;
if None, first fetches max/min from the DB. Upper limit, if
given, is inclusive.
bytes_per_chunk : str or int
If both ``divisions`` and ``npartitions`` is None, this is the target size of
each partition, in bytes
head_rows : int
How many rows to load for inferring the data-types, and memory per row
meta : empty DataFrame or None
If provided, do not attempt to infer dtypes, but use these, coercing
all chunks on load
engine_kwargs : dict or None
Specific db engine parameters for sqlalchemy
kwargs : dict
Additional parameters to pass to `pd.read_sql()`
Returns
-------
dask.dataframe
See Also
--------
read_sql_table : Read SQL database table into a DataFrame.
"""
from dask.dataframe.io.sql import read_sql_query as _read_sql_query
df = _read_sql_query(
sql,
con,
index_col,
divisions=divisions,
npartitions=npartitions,
limits=limits,
bytes_per_chunk=bytes_per_chunk,
head_rows=head_rows,
meta=meta,
engine_kwargs=engine_kwargs,
**kwargs,
)
return from_legacy_dataframe(df)
[docs]def to_sql(
df,
name: str,
uri: str,
schema=None,
if_exists: str = "fail",
index: bool = True,
index_label=None,
chunksize=None,
dtype=None,
method=None,
compute=True,
parallel=False,
engine_kwargs=None,
):
"""Store Dask Dataframe to a SQL table
An empty table is created based on the "meta" DataFrame (and conforming to the caller's "if_exists" preference), and
then each block calls pd.DataFrame.to_sql (with `if_exists="append"`).
Databases supported by SQLAlchemy [1]_ are supported. Tables can be
newly created, appended to, or overwritten.
Parameters
----------
name : str
Name of SQL table.
uri : string
Full sqlalchemy URI for the database connection
schema : str, optional
Specify the schema (if database flavor supports this). If None, use
default schema.
if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the table already exists.
* fail: Raise a ValueError.
* replace: Drop the table before inserting new values.
* append: Insert new values to the existing table.
index : bool, default True
Write DataFrame index as a column. Uses `index_label` as the column
name in the table.
index_label : str or sequence, default None
Column label for index column(s). If None is given (default) and
`index` is True, then the index names are used.
A sequence should be given if the DataFrame uses MultiIndex.
chunksize : int, optional
Specify the number of rows in each batch to be written at a time.
By default, all rows will be written at once.
dtype : dict or scalar, optional
Specifying the datatype for columns. If a dictionary is used, the
keys should be the column names and the values should be the
SQLAlchemy types or strings for the sqlite3 legacy mode. If a
scalar is provided, it will be applied to all columns.
method : {None, 'multi', callable}, optional
Controls the SQL insertion clause used:
* None : Uses standard SQL ``INSERT`` clause (one per row).
* 'multi': Pass multiple values in a single ``INSERT`` clause.
* callable with signature ``(pd_table, conn, keys, data_iter)``.
Details and a sample callable implementation can be found in the
section :ref:`insert method <io.sql.method>`.
compute : bool, default True
When true, call dask.compute and perform the load into SQL; otherwise, return a Dask object (or array of
per-block objects when parallel=True)
parallel : bool, default False
When true, have each block append itself to the DB table concurrently. This can result in DB rows being in a
different order than the source DataFrame's corresponding rows. When false, load each block into the SQL DB in
sequence.
engine_kwargs : dict or None
Specific db engine parameters for sqlalchemy
Raises
------
ValueError
When the table already exists and `if_exists` is 'fail' (the
default).
See Also
--------
read_sql : Read a DataFrame from a table.
Notes
-----
Timezone aware datetime columns will be written as
``Timestamp with timezone`` type with SQLAlchemy if supported by the
database. Otherwise, the datetimes will be stored as timezone unaware
timestamps local to the original timezone.
.. versionadded:: 0.24.0
References
----------
.. [1] https://docs.sqlalchemy.org
.. [2] https://www.python.org/dev/peps/pep-0249/
Examples
--------
Create a table from scratch with 4 rows.
>>> import pandas as pd
>>> import dask.dataframe as dd
>>> df = pd.DataFrame([ {'i':i, 's':str(i)*2 } for i in range(4) ])
>>> ddf = dd.from_pandas(df, npartitions=2)
>>> ddf # doctest: +SKIP
Dask DataFrame Structure:
i s
npartitions=2
0 int64 object
2 ... ...
3 ... ...
Dask Name: from_pandas, 2 tasks
>>> from dask.utils import tmpfile
>>> from sqlalchemy import create_engine, text
>>> with tmpfile() as f:
... db = 'sqlite:///%s' %f
... ddf.to_sql('test', db)
... engine = create_engine(db, echo=False)
... with engine.connect() as conn:
... result = conn.execute(text("SELECT * FROM test")).fetchall()
>>> result
[(0, 0, '00'), (1, 1, '11'), (2, 2, '22'), (3, 3, '33')]
"""
from dask.dataframe.io.sql import to_sql as _to_sql
return _to_sql(
df.to_legacy_dataframe(),
name=name,
uri=uri,
schema=schema,
if_exists=if_exists,
index=index,
index_label=index_label,
chunksize=chunksize,
dtype=dtype,
method=method,
compute=compute,
parallel=parallel,
engine_kwargs=engine_kwargs,
)