Runbook #15 – Keys in a Cassandra Cluster, Common Spark Errors

Apache Spark is a distributed computing framework that provides a fast, scalable, and fault-tolerant way to process large datasets. It addresses the challenges of big data processing by providing an in-memory computing model that allows for faster data processing and more efficient use of resources. However, users may encounter some common errors while using Apache Spark in a Cassandra context. These errors include Spark driver out of memory, job failure as Application Master driver exceeds memory limits, executor out of memory (OOM), total size of results being greater than the Spark Driver Max Result Size value, serialization issues, long-running jobs, and broadcasting large data. This article provides solutions for each of these errors.

Excerpted from the text – “Apache Spark is a distributed computing framework designed to process large datasets in a fast, scalable, and fault-tolerant manner. With the exponential growth of data into big data in recent years, traditional data processing systems like Hadoop MapReduce have become increasingly slow and cumbersome to use. Apache Spark addresses these challenges by providing an in-memory computing model that allows for faster data processing and more efficient use of resources.”