Apache Spark offers many benefits to enterprises in the big data industry. However, like any tech solution, it comes with its share of challenges and hangups. By prioritizing tools that emphasize observability, you can optimize performance in Spark jobs and begin to get insights into the why of performance issues, not only the what.
Why Apache Spark?
There are several reasons why the Apache Spark architecture provides a very useful tool for the big data industry:
- Speed – Compared to traditional Hadoop ETL-type batch workloads, Apache Spark performs a hundred times faster for in-memory work and 10x faster on disk. This is because Spark automatically allocates heap memory for its data and process, which makes repetitive access for iterative algorithms much faster.
- Ease of use – The Spark architecture provides a more straightforward and accessible experience for their users. Moreover, it’s available in many programming languages, most notably Python, now considered the most popular language used to interface with the platform. Spark also caters to Java, Scala, R, and SQL.
- Generality – Spark has access to a wide range of libraries that it can combine within a single application. Most common of these libraries include DataFrames, SQL, Spark Streaming, and Graphx, along with some more specific ones like MLlib for machine learning.
- Flexibility – Spark architecture has the flexibility to run on various platforms, from Hadoop YARN scheduler to Kubernetes, as well as standalone. It also has access to various databases, relational or otherwise. Most notable are Aluxio, Cassandra HBase, HDFS, Hive, and S3.
The Challenges of Spark
No matter how powerful Spark may be, it still presents its own set of challenges. As a result: According to our 2020 Big Data Performance Report, Spark jobs have been observed to fail about four to seven times more often than other jobs.
- Within a span of 7 days, maximum memory utilization has a median of merely 42.3% across clusters.
- Comparing jobs wastage versus job usage, the average wastage across 40 clusters is higher than 60%. In some cases, this wastage exceeds 75%.
- This underutilization of resources stems from two possible reasons: Either the cluster do