I use pySpark and set my configuration like following:

spark = (SparkSession.builder.master("local[*]")
        .config("spark.driver.memory", "20g")
        .config("spark.executor.memory", "10g")
        .config("spark.driver.cores", "30")
        .config("spark.num.executors", "8")
        .config("spark.executor.cores", "4")
sc = spark.sparkContext

If I then run PCA:

from pyspark.ml.feature import PCA

pca = PCA(k=50, inputCol="features", outputCol="pcaFeatures")
model = pca.fit(train)

Only one thread is active and therefore the computation takes a long time.

How can I parallelize PCA in Spark?

I run on a local machine and did not configure a cluster in the configs.

Also I did not install the recommended ml packages, since the warning

WARN LAPACK: Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK



1 Answer 1


According to the MLlib Linear Algebra Acceleration Guide documentation, LAPACK and related libraries need to be installed and configured corrected to get the full speed-up of Spark.

Additionally, the documentation mentions that sometimes there might not be a speed-up. That could be a result in your case because of running on a local machine compared to running on a cluster.


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