Configuration tuning is vital to optimize the performance of big data analysis platforms like
Spark. Existing methods (eg auto-tuning relational databases) are not effective for tuning …

Spark SQL has been widely deployed in industry but it is challenging to tune its
performance. Recent studies try to employ machine learning (ML) to solve this problem, but …

Distributed data analytic engines like Spark are common choices to process massive data in
industry. However, the performance of Spark SQL highly depends on the choice of …

Apache Spark is a popular open-source distributed data processing framework that can
efficiently process massive amounts of data. It provides more than 180 configuration …

The distributed data analytic system--Spark is a common choice for processing massive
volumes of heterogeneous data, while it is challenging to tune its parameters to achieve …

Spark tuning with its dozens of parameters for performance improvement is both a challenge
and time consuming effort. Current techniques rely on trial-and-error or best guess utilizing …

LSM-tree-based key-value stores like RocksDB are widely used to support many
applications. However, configuring a RocksDB instance is challenging for the following …

Database and big data analytics systems such as Hadoop and Spark have a large number
of configuration parameters that control memory distribution, I/O optimization, parallelism …

Distributed analytics engines such as Spark are a common choice for processing extremely
large datasets. However, finding good configurations for these systems remains challenging …

Apache Spark provides a large number of configuration settings that may be tuned to
improve the performance of specific applications running on the platform. However, it is non …