API Documentation¶

class dask_sql.Context¶

Main object to communicate with dask_sql. It holds a store of all registered data frames (= tables) and can convert SQL queries to dask data frames. The tables in these queries are referenced by the name, which is given when registering a dask dataframe.

Example

from dask_sql import Context
c = Context()

# Register a table
c.create_table("my_table", df)

# Now execute an SQL query. The result is a dask dataframe
result = c.sql("SELECT a, b FROM my_table")

# Trigger the computation (or use the data frame for something else)
result.compute()

Usually, you will only ever have a single context in your program.

See also

sql() create_table()

create_table(table_name, input_table, format=None, persist=True, **kwargs)¶

Registering a (dask/pandas) table makes it usable in SQL queries. The name you give here can be used as table name in the SQL later.

Please note, that the table is stored as it is now. If you change the table later, you need to re-register.

Instead of passing an already loaded table, it is also possible to pass a string to a storage location. The library will then try to load the data using one of dask’s read methods. If the file format can not be deduced automatically, it is also possible to specify it via the format parameter. Typical file formats are csv or parquet. Any additional parameters will get passed on to the read method. Please note that some file formats require additional libraries. By default, the data will be loaded directly into the memory of the nodes. If you do not want that, set persist to False.

See Data Loading and Input for more information.

Example

This code registers a data frame as table “data” and then uses it in a query.

c.create_table("data", df)
df_result = c.sql("SELECT a, b FROM data")

This code reads a file from disk. Please note that we assume that the file(s) are reachable under this path from every node in the cluster

c.create_table("data", "/home/user/data.csv")
df_result = c.sql("SELECT a, b FROM data")

This example reads from a hive table.

from pyhive.hive import connect

cursor = connect("localhost", 10000).cursor()
c.create_table("data", cursor, hive_table_name="the_name_in_hive")
df_result = c.sql("SELECT a, b FROM data")

Parameters

table_name – (str): Under which name should the new table be addressable
input_table (dask.dataframe.DataFrame or pandas.DataFrame or str or hive.Cursor) – The data frame/location/hive connection to register.
format (str) – Only used when passing a string into the input parameter. Specify the file format directly here if it can not be deduced from the extension. If set to “memory”, load the data from a published dataset in the dask cluster.
persist (bool) – Only used when passing a string into the input parameter. Set to false to turn off loading the file data directly into memory.
**kwargs – Additional arguments for specific formats. See Data Loading and Input for more information.

drop_table(table_name)¶

Remove a table with the given name from the registered tables. This will also delete the dataframe.

Parameters: table_name – (str): Which table to remove.

explain(sql, dataframes=None)¶

Return the stringified relational algebra that this query will produce once triggered (with sql()). Helpful to understand the inner workings of dask-sql, but typically not needed to query your data.

If the query is of DDL type (e.g. CREATE TABLE or DESCRIBE SCHEMA), no relational algebra plan is created and therefore nothing returned.

Parameters

sql (str) – The query string to use
dataframes (Dict[str, dask.dataframe.DataFrame]) – additional Dask or pandas dataframes to register before executing this query

Returns

a description of the created relational algebra.

Return type

str

ipython_magic(auto_include=False)¶

Register a new ipython/jupyter magic function “sql” which sends its input as string to the sql() function. After calling this magic function in a Jupyter notebook or an IPython shell, you can write

%sql SELECT * from data

or

%%sql
SELECT * from data

instead of

c.sql("SELECT * from data")

Parameters

auto_include (bool) –

If set to true, automatically create a table for every pandas or Dask dataframe in the calling context. That means, if you define a dataframe in your jupyter notebook you can use it with the same name in your sql call. Use this setting with care as any defined dataframe can easily override tables created via CREATE TABLE.

df = ...

# Later, without any calls to create_table

%%sql
SELECT * FROM df

register_aggregation(f, name, parameters, return_type, replace=False)¶

Register a custom aggregation with the given name. The aggregation can be used (with this name) in every SQL queries from now on - but only for aggregation operations (no scalar function calls). This means, if you register a aggregation “fagg”, you can now call

SELECT fagg(y)
FROM df
GROUP BY x

Please note that you can always only have one function with the same name; no matter if it is an aggregation or scalar function.

For the registration, you need to supply both the list of parameter and parameter types as well as the return type. Use numpy dtypes if possible.

More information: Custom Functions and Aggregations

Example

The following code registers a new aggregation “fagg”, which computes the sum of a column and uses it on the y column.

fagg = dd.Aggregation("fagg", lambda x: x.sum(), lambda x: x.sum())
c.register_aggregation(fagg, "fagg", [("x", np.float64)], np.float64)

sql = "SELECT fagg(y) FROM df GROUP BY x"
df_result = c.sql(sql)

Parameters

f (dask.dataframe.Aggregate) – The aggregate to register. See the dask documentation for more information.
name (str) – Under which name should the new aggregate be addressable in SQL
parameters (List[Tuple[str, type]]) –
A list ot tuples of parameter name and parameter type. Use numpy dtypes if possible.
return_type (type) – The return type of the function
replace (bool) – Do not raise an error if the function is already present

See also

register_aggregation()

register_model(model_name, model, training_columns)¶

Add a model to the model registry. A model can be anything which has a .predict function that transforms a Dask dataframe into predicted labels (as a Dask series). After model registration, the model can be used in calls to SELECT … FROM PREDICT with the given name. Instead of creating your own model and register it, you can also train a model directly in dask-sql. See the SQL command CrEATE MODEL.

Parameters

model_name (str) – The name of the model
model – The model to store
training_columns – (list of str): The names of the columns which were used during the training.

run_server(client=None, host='0.0.0.0', port=8080, log_level=None, blocking=True)¶

Run a HTTP server for answering SQL queries using dask-sql.

See SQL Server for more information.

Parameters

client (dask.distributed.Client) – If set, use this dask client instead of a new one.
host (str) – The host interface to listen on (defaults to all interfaces)
port (int) – The port to listen on (defaults to 8080)
log_level – (str): The log level of the server and dask-sql

sql(sql, return_futures=True, dataframes=None)¶

Query the registered tables with the given SQL. The SQL follows approximately the postgreSQL standard - however, not all operations are already implemented. In general, only select statements (no data manipulation) works.

For more information, see SQL Syntax.

Example

In this example, a query is called using the registered tables and then executed using dask.

result = c.sql("SELECT a, b FROM my_table")
print(result.compute())

Parameters

sql (str) – The query string to execute
return_futures (bool) – Return the unexecuted dask dataframe or the data itself. Defaults to returning the dask dataframe.
dataframes (Dict[str, dask.dataframe.DataFrame]) – additional Dask or pandas dataframes to register before executing this query

Returns

the created data frame of this query.

Return type

dask.dataframe.DataFrame

stop_server()¶: Stop a SQL server started by ``run_server`.

visualize(sql, filename='mydask.png')¶: Visualize the computation of the given SQL into the png

dask_sql.run_server(context=None, client=None, host='0.0.0.0', port=8080, startup=False, log_level=None, blocking=True)¶

Run a HTTP server for answering SQL queries using dask-sql. It uses the Presto Wire Protocol for communication. This means, it has a single POST endpoint /v1/statement, which answers SQL queries (as string in the body) with the output as a JSON (in the format described in the documentation above). Every SQL expression that dask-sql understands can be used here.

See SQL Server for more information.

Note

The presto protocol also includes some statistics on the query in the response. These statistics are currently only filled with placeholder variables.

Parameters

context (dask_sql.Context) – If set, use this context instead of an empty one.
client (dask.distributed.Client) – If set, use this dask client instead of a new one.
host (str) – The host interface to listen on (defaults to all interfaces)
port (int) – The port to listen on (defaults to 8080)
startup (bool) – Whether to wait until Apache Calcite was loaded
log_level – (str): The log level of the server and dask-sql
blocking – (bool): If running in an environment with an event loop (e.g. a jupyter notebook), do not block. The server can be stopped with context.stop_server() afterwards.

Example

It is possible to run an SQL server by using the CLI script dask-sql-server or by calling this function directly in your user code:

from dask_sql import run_server

# Create your pre-filled context
c = Context()
...

run_server(context=c)

After starting the server, it is possible to send queries to it, e.g. with the presto CLI or via sqlalchemy (e.g. using the PyHive package):

from sqlalchemy.engine import create_engine
engine = create_engine('presto://localhost:8080/')

import pandas as pd
pd.read_sql_query("SELECT 1 + 1", con=engine)

Of course, it is also possible to call the usual CREATE TABLE commands.

If in a jupyter notebook, you should run the following code

from dask_sql import Context

c = Context()
c.run_server(blocking=False)

...

c.stop_server()

Note:: When running in a jupyter notebook without blocking, it is not possible to access the SQL server from within the notebook, e.g. using sqlalchemy. Doing so will deadlock infinitely.

dask_sql.cmd_loop(context=None, client=None, startup=False, log_level=None)¶

Run a REPL for answering SQL queries using dask-sql. Every SQL expression that dask-sql understands can be used here.

Parameters

context (dask_sql.Context) – If set, use this context instead of an empty one.
client (dask.distributed.Client) – If set, use this dask client instead of a new one.
startup (bool) – Whether to wait until Apache Calcite was loaded
log_level – (str): The log level of the server and dask-sql

Example

It is possible to run a REPL by using the CLI script in dask-sql or by calling this function directly in your user code:

from dask_sql import cmd_loop

# Create your pre-filled context
c = Context()
...

cmd_loop(context=c)

Of course, it is also possible to call the usual CREATE TABLE commands.