Apache Spark is one of the most popular engines for distributed data processing on Big Data clusters. Spark jobs come in all shapes, sizes and cluster form factors. Ranging from 10’s to 1000’s of nodes and executors, seconds to hours or even days for job duration, megabytes to petabytes of data and simple data scans to complicated analytical workloads. Throw in a growing number of streaming workloads to huge body of batch and machine learning jobs —
When picking a storage option for an application it is common to pick a single storage option which has the most applicable features to your use case. For mutability and real-time analytics workloads you may want to use Apache Kudu, but for massive scalability at a low cost you may want to use HDFS. For that reason, there is a need for a solution that allows you to leverage the best features of multiple storage options.
Spark ML is one of the dominant frameworks for many major machine learning algorithms, such as the Alternating Least Squares (ALS) algorithm for recommendation systems, the Principal Component Analysis algorithm, and the Random Forest algorithm.
Apache Hive is a widely adopted data warehouse engine that runs on Apache Hadoop. Features that improve Hive performance can significantly improve the overall utilization of resources on the cluster. Hive processes data using a chain of operators within the Hive execution engine. These operators are scheduled in the various tasks (for example, MapTask, ReduceTask, or SparkTask) of the query execution plan. Traditionally, these operators are designed to process one row at a time.
For a user-facing system like Apache Impala, bad performance and downtime can have serious negative impacts on your business. Given the complexity of the system and all the moving parts, troubleshooting can be time-consuming and overwhelming.
In this blog post series, we are going to show how the charts and metrics on Cloudera Manager (CM) can help troubleshoot Impala performance issues. They can also help to monitor the system to predict and prevent future outages.