fugue 0.7.3

An abstraction layer for distributed computation

Fugue

Documentation	Tutorials	Chat with us on slack!

Fugue is a unified interface for distributed computing that lets users execute Python, pandas, and SQL code on Spark, Dask and Ray without rewrites.

The most common use cases are:

• Accelerating or scaling existing Python and pandas code by bringing it to Spark or Dask with minimal rewrites.
• Using FugueSQL to define end-to-end workflows on top of pandas, Spark, and Dask DataFrames. FugueSQL is an enhanced SQL interface that can invoke Python code with added keywords.
• Maintaining one codebase for pandas, Spark, Dask and Ray projects. Logic and execution are decoupled through Fugue, enabling users to be focused on their business logic rather than writing framework-specific code.
• Improving iteration speed of big data projects. Fugue seamlessly scales execution to big data after local development and testing. By removing PySpark code, unit tests can be written in Python or pandas and ran locally without spinning up a cluster.

For a more comprehensive overview of Fugue, read this article.

Installation

Fugue can be installed through pip or conda. For example:

pip install fugue

It also has the following extras:

• spark: to support Spark as the ExecutionEngine
• dask: to support Dask as the ExecutionEngine.
• ray: to support Ray as the ExecutionEngine.
• duckdb: to support DuckDB as the ExecutionEngine, read details.
• ibis: to enable Ibis for Fugue workflows, read details.
• cpp_sql_parser: to enable the CPP antlr parser for Fugue SQL. It can be 50+ times faster than the pure Python parser. For the main Python versions and platforms, there is already pre-built binaries, but for the remaining, it needs a C++ compiler to build on the fly.
• all: install everything above

For example a common use case is:

pip install fugue[duckdb,spark]

Notice that installing extras may not be necessary. For example if you already installed Spark or DuckDB independently, Fugue is able to automatically enable the support for them.

Getting Started

The best way to get started with Fugue is to work through the 10 minute tutorials:

• Fugue in 10 minutes
• FugueSQL in 10 minutes

The tutorials can also be run in an interactive notebook environment through binder or Docker:

Using binder

Note it runs slow on binder because the machine on binder isn't powerful enough for a distributed framework such as Spark. Parallel executions can become sequential, so some of the performance comparison examples will not give you the correct numbers.

Using Docker

Alternatively, you should get decent performance by running this Docker image on your own machine:

docker run -p 8888:8888 fugueproject/tutorials:latest

For the API docs, click here

Fugue Transform

The simplest way to use Fugue is the transform() function. This lets users parallelize the execution of a single function by bringing it to Spark, Dask or Ray. In the example below, the map_letter_to_food() function takes in a mapping and applies it on a column. This is just pandas and Python so far (without Fugue).

import pandas as pd
from typing import Dict

input_df = pd.DataFrame({"id":[0,1,2], "value": (["A", "B", "C"])})
map_dict = {"A": "Apple", "B": "Banana", "C": "Carrot"}

def map_letter_to_food(df: pd.DataFrame, mapping: Dict[str, str]) -> pd.DataFrame:
    df["value"] = df["value"].map(mapping)
    return df

Now, the map_letter_to_food() function is brought to the Spark execution engine by invoking the transform function of Fugue. The output schema, params and engine are passed to the transform() call. The schema is needed because it's a requirement on Spark. A schema of "*" below means all input columns are in the output.

from pyspark.sql import SparkSession
from fugue import transform

spark = SparkSession.builder.getOrCreate()

df = transform(input_df,
               map_letter_to_food,
               schema="*",
               params=dict(mapping=map_dict),
               engine=spark
            )
df.show()

+---+------+
| id| value|
+---+------+
|  0| Apple|
|  1|Banana|
|  2|Carrot|
+---+------+

PySpark equivalent of Fugue transform

from typing import Iterator, Union
from pyspark.sql.types import StructType
from pyspark.sql import DataFrame, SparkSession

spark_session = SparkSession.builder.getOrCreate()

def mapping_wrapper(dfs: Iterator[pd.DataFrame], mapping):
  for df in dfs:
      yield map_letter_to_food(df, mapping)

def run_map_letter_to_food(input_df: Union[DataFrame, pd.DataFrame], mapping):
  # conversion
  if isinstance(input_df, pd.DataFrame):
      sdf = spark_session.createDataFrame(input_df.copy())
  else:
      sdf = input_df.copy()

  schema = StructType(list(sdf.schema.fields))
  return sdf.mapInPandas(lambda dfs: mapping_wrapper(dfs, mapping),
                          schema=schema)

result = run_map_letter_to_food(input_df, map_dict)
result.show()

This syntax is simpler, cleaner, and more maintainable than the PySpark equivalent. At the same time, no edits were made to the original pandas-based function to bring it to Spark. It is still usable on pandas DataFrames. Because the Spark execution engine was used, the returned df is now a Spark DataFrame. Fugue transform() also supports Dask, Ray and pandas as execution engines.

FugueSQL

A SQL-based language capable of expressing end-to-end workflows. The map_letter_to_food() function above is used in the SQL expression below. This is how to use a Python-defined transformer along with the standard SQL SELECT statement.

from fugue_sql import fsql
import json

query = """
    SELECT id, value FROM input_df
    TRANSFORM USING map_letter_to_food(mapping={{mapping}}) SCHEMA *
    PRINT
    """
map_dict_str = json.dumps(map_dict)

fsql(query,mapping=map_dict_str).run()

For FugueSQL, we can change the engine by passing it to the run() method: fsql(query,mapping=map_dict_str).run(spark).

Jupyter Notebook Extension

There is an accompanying notebook extension for FugueSQL that lets users use the %%fsql cell magic. The extension also provides syntax highlighting for FugueSQL cells. It works for both classic notebook and Jupyter Lab. More details can be found in the installation instructions.

Ecosystem

By being an abstraction layer, Fugue can be used with a lot of other open-source projects seamlessly.

Fugue can use the following projects as backends:

• Spark
• Dask
• Ray
• Duckdb - in-process SQL OLAP database management
• Ibis - pandas-like interface for SQL engines
• dask-sql - SQL interface for Dask

Fugue is available as a backend or can integrate with the following projects:

• PyCaret - low code machine learning
• Pandera - data validation
• Nixtla - timeseries modelling

Further Resources

View some of our latest conferences presentations and content. For a more complete list, check the Resources page in the tutorials.

Case Studies

• How LyftLearn Democratizes Distributed Compute through Kubernetes Spark and Fugue

Blogs

• Introducing Fugue - Reducing PySpark Developer Friction
• Introducing FugueSQL — SQL for Pandas, Spark, and Dask DataFrames (Towards Data Science by Khuyen Tran)
• Why Pandas-like Interfaces are Sub-optimal for Distributed Computing
• Interoperable Python and SQL in Jupyter Notebooks (Towards Data Science)
• Using Pandera on Spark for Data Validation through Fugue (Towards Data Science)

Conferences

• Large Scale Data Validation with Spark and Dask (PyCon US)
• FugueSQL - The Enhanced SQL Interface for Pandas, Spark, and Dask DataFrames (PyData Global)
• Scaling Machine Learning Workflows to Big Data with Fugue (KubeCon)

Community and Contributing

Feel free to message us on Slack. We also have contributing instructions.