Webinar: Proven Approaches to Hive Query Tuning

Webinar: Proven Approaches to Hive Query Tuning

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:

  1. 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.
  2. Ease of useThe 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.
  3. 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.
  4. FlexibilitySpark 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.

Moreover, the report further reveals that without Spark performance tuning and optimization:

  • 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 does not have enough jobs to fully use the available resources, or the jobs themselves are wasting resources.

This is why companies need to optimize performance in Spark. Without applying Spark optimization techniques, clusters will continue to overprovision and underutilize resources. Globally, idle resources alone incur about $8.8 billion year on year, according to an analyst

How to Optimize Performance in Spark

When it comes to optimizing Spark workloads and jobs, the key is observability.

As Alex Pierce, a veteran Pepperdata field engineer, says: “In order for you to understand what needs to be optimized, you need to understand where the opportunities for optimization are and what needs to be changed.”

Take memory utilization. Maybe the users need to allocate more memory to avoid garbage collection. Or, in the case of multi-tenant environments, the users may have allocated too much memory, causing queueing and other problems between tenants. Without the right optimization solution, Spark users remain in the dark about how to properly allocate memory for their clusters.

Another opportunity for Spark performance tuning is to reduce, if not avoid, data skew. Spark is sensitive to data skew, and for a highly distributed and paralyzed application, it can be very damaging. Data skew causes certain application elements to work longer than they should, while other compute resources sit idly, underutilized. A tool that helps optimize performance in Spark should track data skew and make effective recommendations to correct it.

Observability is Key

So how does one optimize performance in Spark and measure success? Again, through observability.

Spark users need to eventually say, “Hey, my applications now run without failures, and I’m meeting my SLAs consistently.” For that, they need the right observability tool to help them determine their memory utilization, data skew, and other issues that might arise from a multi-tenant environment that most companies work in.

The Pepperdata Analytics Stack Performance Suite provides observability and automatic optimization, whether you’re in the cloud or on-prem. The Pepperdata Suite collects hundreds of metrics from your infrastructure and applications to provide detailed and correlated insights. It then uses these to optimize system resources and performance. This level of visibility and automation enables you to run more applications, track and manage spend, and reduce cost. 

Forget manual monitoring and trial-and-error Spark optimization techniques. With Pepperdata you can get the best performance from your Spark jobs, and get the most value from your data. To learn more about how to optimize Spark, check out our recent video covering the topic.