Ballista Python Bindings

Ballista provides Python bindings, allowing SQL and DataFrame queries to be executed from the Python shell.

Like PySpark, it allows you to build a plan through SQL or a DataFrame API against Parquet, CSV, JSON, and other popular file formats files, run it in a distributed environment, and obtain the result back in Python.

Connecting to a Cluster

The following code demonstrates how to create a Ballista context and connect to a scheduler.

>>> import ballista
>>> ctx = ballista.BallistaContext("localhost", 50050)


The Python bindings support executing SQL queries as well.

Registering Tables

Before SQL queries can be executed, tables need to be registered with the context.

Tables can be registered against the context by calling one of the register methods, or by executing SQL.

>>> ctx.register_parquet("trips", "/mnt/bigdata/nyctaxi")
>>> ctx.sql("CREATE EXTERNAL TABLE trips STORED AS PARQUET LOCATION '/mnt/bigdata/nyctaxi'")

Executing Queries

The sql method creates a DataFrame. The query is executed when an action such as show or collect is executed.

Showing Query Results

>>> df = ctx.sql("SELECT count(*) FROM trips")
| COUNT(UInt8(1)) |
| 9071244         |

Collecting Query Results

The collect method executes the query and returns the results in PyArrow record batches.

>>> df = ctx.sql("SELECT count(*) FROM trips")
>>> df.collect()
COUNT(UInt8(1)): int64]

Viewing Query Plans

The explain method can be used to show the logical and physical query plans for a query.

>>> df.explain()
| plan_type     | plan                                                        |
| logical_plan  | Projection: #COUNT(UInt8(1))                                |
|               |   Aggregate: groupBy=[[]], aggr=[[COUNT(UInt8(1))]]         |
|               |     TableScan: trips projection=[VendorID]                  |
| physical_plan | ProjectionExec: expr=[COUNT(UInt8(1))@0 as COUNT(UInt8(1))] |
|               |   ProjectionExec: expr=[9071244 as COUNT(UInt8(1))]         |
|               |     EmptyExec: produce_one_row=true                         |
|               |                                                             |


The following example demonstrates creating arrays with PyArrow and then creating a Ballista DataFrame.

import ballista
import pyarrow

# an alias
f = ballista.functions

# create a context
ctx = ballista.BallistaContext("localhost", 50050)

# create a RecordBatch and a new DataFrame from it
batch = pyarrow.RecordBatch.from_arrays(
    [pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
    names=["a", "b"],
df = ctx.create_dataframe([[batch]])

# create a new statement
df =
    f.col("a") + f.col("b"),
    f.col("a") - f.col("b"),

# execute and collect the first (and only) batch
result = df.collect()[0]

assert result.column(0) == pyarrow.array([5, 7, 9])
assert result.column(1) == pyarrow.array([-3, -3, -3])

User Defined Functions

The underlying DataFusion query engine supports Python UDFs but this functionality has not yet been implemented in Ballista. It is planned for a future release. The tracking issue is #173.