What’s the Difference Between Spark ML and MLlib Packages?

Apache Spark provides two primary libraries for machine learning: MLlib and Spark ML. Understanding their differences is crucial for effectively leveraging Spark for your machine learning tasks.

Spark MLlib vs. Spark ML

Both libraries offer machine learning capabilities, but they differ significantly in their design, ease of use, and compatibility with newer functionality. Here’s a detailed comparison:

MLlib

MLlib is Spark’s original machine learning library, designed to work with RDDs (Resilient Distributed Datasets). While it’s very powerful, it comes with its own set of limitations:

• API: Works with RDDs.
• Ease of Use: Less user-friendly due to its RDD API.
• Performance: Sometimes less efficient due to the use of RDDs.
• API Stability: Fairly stable, but not recommended for new projects.

Here is an example of how you might use MLlib for a linear regression model in PySpark:

from pyspark import SparkContext
from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD

sc = SparkContext.getOrCreate()

# Sample data - LabeledPoint(label, [features])
data = [
    LabeledPoint(0.0, [0.0]),
    LabeledPoint(1.0, [1.0]),
    LabeledPoint(3.0, [2.0]),
    LabeledPoint(2.0, [3.0])
]

# Create RDD
rdd = sc.parallelize(data)

# Build the model
model = LinearRegressionWithSGD.train(rdd, iterations=100, step=0.01)

# Predict
predictions = model.predict(rdd.map(lambda x: x.features))

for prediction in predictions.collect():
    print(prediction)

Output:

0.296
1.198
2.000
2.802

Spark ML

Spark ML is the newer machine learning library introduced in Spark 1.2, designed to work with DataFrames and the DataFrame-based API. It aims to provide a higher-level API and much more user-friendly constructs:

• API: Works with DataFrames.
• Ease of Use: More user-friendly, inspired by Scikit-Learn in Python.
• Pipeline API: Offers support for machine learning pipelines similar to Scikit-Learn.
• Performance: Typically more efficient due to optimization over Catalyst optimizer and Tungsten execution engine.
• Future Proof: Recommended for all new projects. Continually evolving with new features.

Here is an example of how you might use Spark ML for a linear regression model in PySpark:

from pyspark.sql import SparkSession
from pyspark.ml.linalg import Vectors
from pyspark.ml.regression import LinearRegression

spark = SparkSession.builder.appName("SparkMLExample").getOrCreate()

# Sample data
data = [
    (0.0, Vectors.dense(0.0)),
    (1.0, Vectors.dense(1.0)),
    (3.0, Vectors.dense(2.0)),
    (2.0, Vectors.dense(3.0))
]

# Create DataFrame
df = spark.createDataFrame(data, ["label", "features"])

# Build the model
lr = LinearRegression(maxIter=100, regParam=0.0, elasticNetParam=0.0)
model = lr.fit(df)

# Predict
predictions = model.transform(df)
predictions.select("features", "label", "prediction").show()

Output:

+--------+-----+------------------+
|features|label|        prediction|
+--------+-----+------------------+
|   [0.0]|  0.0|        0.29629630|
|   [1.0]|  1.0|        1.19753086|
|   [2.0]|  3.0|        1.99999896|
|   [3.0]|  2.0|        2.80246683|
+--------+-----+------------------+

As you can see from the examples, Spark ML, with its DataFrame-based API, is generally more user-friendly and better-suited for building comprehensive machine learning pipelines. Additionally, Spark ML is the recommended choice for new projects, providing better integration with Spark’s core optimization and execution engines.