Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For Sparse Data - Databricks

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For Sparse Data

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From purchase history to movie ratings, data sparsity has always been one of the primary characteristics of big data. Powerful as Apache Spark is on parallel processing for the partitioned data, many of the algorithms in MLlib are implemented based on the assumption of certain degree of data density, such like the gradients of logistic regression, or cluster centers of KMeans. Yet during collaboration with some internet companies, we often find their feature number at the dimension of millions or even billions, which far exceeds the capacity of some important algorithms in MLlib, or become impractical due to enormous memory consumption even with great sparsity in the training data. To fill the gap, we present a Spark package containing some major improvements we have conducted to support the sparse data at large scope. Through optimization on data structure, network communication and arithmetic operation, we can extensively compress the memory consumption and reduce computation cost for sparse data, thus to enable the algorithms on larger feature dimensions and scope. Two of the examples are the successful support of our implementation on logistic regression with 1 billion features and KMeans with 10M features and hundreds of clusters. We’ll also share some work we are contributing to Spark and some best practices we have accumulated in the context of sparse data support on Spark MLlib.

About Ding Ding

Ding Ding is a software engineer on Intel's big data technology team, where she works on developing and optimizing distributed machine learning and deep learning algorithms on Apache Spark, focusing particularly on large-scale analytical applications and infrastructure on Spark.