How to Optimize Spark Performance and Curb Data Platform Cost

Apache Spark is a powerful query engine for high-scale batch analytics. But as data volumes swell and pipeline complexities mount, even seasoned data engineers find themselves grappling with Spark performance pitfalls and inefficiencies. 

Skew, Spill, inefficient queries, suboptimal Spark configurations… How do you spot these performance gremlins before they wreak havoc on your data platform? 

These issues aren't just frustrating – they're costly, burning through platform resources and jeopardizing pipeline SLAs; meaning – increasing platform costs and affecting business outcome.

‍But it doesn’t have to be that way. Spark performance optimization can be a much simpler project. With definity, Spark performance is easily monitored and contextualized, so optimization is simplified and automated, empowering data engineers to optimize their Spark jobs instantly and saving your organization hundreds of thousands of dollars! 

Want to learn more? Let’s dive in!

The Core Performance Issues in Spark

Spark is a distributed data processing framework system, so performance issues often arise from inefficient resource utilization across the cluster. The main causes of these come in the guise of the 5 S’s.

The 5 S’s

Skew

Skew occurs when data is unevenly distributed across partitions, causing some executors to process significantly more data than others.

This imbalance can result in:

• Increased job execution time: most executors finish quickly, but the entire job waits for the overloaded executors.
• Inefficient resource utilization: many executors allocated, but most sit idle for long periods.
• Memory issues: heavily skewed partitions may require more RAM, leading to Spill.

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Spill & Garbage Collection

Spill happens when data is moved from RAM to disk and back again for processing. This typically happens during unoptimized data shuffles; when handling partitions too large for available memory, forcing Spark to write data to disk to avoid OOM errors; when using collect() on large datasets; or with poorly tuned broadcast joins. 

The frequent I/O between RAM and disk significantly burdens Spark's performance, leading to slower job execution times. Similarly, excessive Garbage Collection (GC) is a common result of running Spark with low or unoptimized memory.

Shuffle

Shuffle is the movement of data across executors as part of the required data analysis. With heavy data volumes & unoptimized queries, this can lead to inefficient job execution from increased I/O operations, slowing down job execution and causing OOM errors. 

Storage

Storage issues in Spark arise when data is stored on disk in a non-optimal way, causing excessive shuffling and performance degradation. Key issues include:

• Small files: handling partition files smaller than 128 MB can lead to inefficiencies.
• Scanning: long lists of files in a single directory or multiple levels of folders in highly partitioned datasets can slow down data access.
• Schemas: file format impacts schema inference and reading efficiency.

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Poor storage decisions can lead to increased I/O, longer processing times, and inefficient use of cluster resources.

Serialization

Serialization in Spark involves the distribution of code across clusters, which can impact performance. This can happen when code is serialized, sent to executors, and then deserialized for execution, or when there are language-specific challenges such as code pickling and allocation of Python interpreter instances to each executor.

Inefficient serialization can slow down data transfer between nodes and increase memory usage. 

Why does it matter?

Altogether, these common Spark performance issues typically result in the two most dreadful outcomes to data engineering and platform teams:

• Longer run durations and potential job failures, which can breach pipeline SLAs and affect downstream data consumers
• Lower CPU (vCore) & memory utilization, translating directly to increased platform costs

Optimizing Performance with definity

While understanding overall Spark performance and the 5 S's is crucial, detecting and resolving these problems in complex data pipelines can be quite challenging. 

For instance, Spark UI monitors performance at executor and Spark stage, but not coupled to the business-logic Spark jobs and queries, making it hard to use for actual optimizations. Similarly, cloud native tools often provide overall cost and consumption metrics, but not coupled to specific jobs and not showing the Spark intrinsic characteristics that will help identify waste and potential optimization.

Moreover, optimization is not a one-off exercise of ‘better coding’. As data, pipelines’ code, and platform versions evolve over time, so does performance. So, first, it is a continuous exercise of tracking performance over time and identifying degradations, inefficiencies, and waste. Then, it’s a matter of prioritizing opportunities for optimizations across (potentially thousands of) Spark jobs and understanding how to actually optimize their individual performance.

This is where definity comes in, offering a comprehensive solution to monitor, analyze, and optimize Spark performance. The beauty? It’s done via a central instrumentation, with zero code changes or API calls  – completely seamless for data engineers.

1. Holistic Performance, Costs, and Opportunities Analysis

definity starts by providing a bird's-eye view of resource consumption, utilization, and waste across the platform, in various logical layers - from a single job run, through pipeline, to entire environments. 

This high-level insight helps data engineers quickly identify:

• Highest ROI opportunities for performance optimization
• Overall resource consumption, utilization, cost, and waste
• Pipelines and runs with low resource utilization and high waste over time
• Jobs with high rate of SLA breaches, increased durations, failures, and re-runs
• Jobs with excessive data skew, spill, and GC

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This view is already generated on day-1, allowing teams to start focusing their optimization efforts where they'll have the most significant impact – i.e., immediately capturing cost savings quick-wins ("low-hanging fruits") with direct dollar value.

2. Pipeline Performance Drill-Downs

Once priority pipelines are identified, definity enables engineers to drill down into specific Spark jobs – for a specific run and over time – to understand performance behavior and root cause of issues at the job level.

This analysis covers:

• Resource utilization: monitoring job-level CPU & memory allocation and consumption throughout the run, by query (transformation). 
• Data skew detection: identifying executors processing significantly more data than others, often the culprit behind uneven cluster utilization.
• Configuration analysis: highlighting suboptimal configurations leading to resource waste, such as oversized executor memory or inefficient parallelism settings.
• "Dead Zone" identification: pinpointing periods of low cluster utilization, often caused by poor job scheduling or dependency management.
• Memory usage patterns: analyzing memory-related issues like frequent garbage collection or excessive disk spills, which can significantly impact job runtime.

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definity exposes the exact query (data transformation) causing the inefficient behavior, allowing for and easy & spot on fix. 

3. Monitoring and Alerting Over Time

Once the behavior of an individual run is understood, data engineers need to track pipeline performance over time in order to proactively optimize it or detect degradations. This is especially significant when analytical teams make frequent changes to the pipeline, when data is quickly evolving, or when inputs constantly vary.

definity makes this easy, tracking all resources usage, efficiency, and utilization metrics over time, in a unified view alongside other pipeline health and data quality metrics, so data engineers have more insights into performance optimization. 

Furthermore, definity’s anomaly detection engine automatically detects performance degradations in-motion, and alerts data engineers in real-time, so they can quickly analyze the issue and tune the pipeline, before costs or SLAs are affected.

4. Intelligent Optimization Recommendations

Based on this comprehensive analysis, definity can now provide data engineers with clear, actionable recommendations to optimize their Spark jobs.

definity might suggest executor and driver memory or CPU configurations based on observed usage patterns and job characteristics, or recommend optimal partitioning strategies to minimize data shuffling and improve query performance.

The platform might also propose optimal shuffle partition counts to balance parallelism and reduce unnecessary data movement, skew mitigation strategies to balance data distribution, or opportunities for broadcast joins and suggest appropriate broadcast thresholds.

5. Automated Performance Tuning

definity goes beyond just providing recommendations, and offers capabilities to automatically apply and test optimizations. 

Since the definity agent runs in the pipeline driver, in run-time, the agent can auto-tune jobs – taking into account historical performance data and definity’s optimization recommendation, the agent can automatically adjust job configuration parameters and resources allocation to optimize its performance.

Moreover, definity’s CI testing capabilities allows data engineers to systematically test configuration changes in a controlled environment, before applying them to production pipelines, to detect performance improvements or degradations with a detailed comparative analysis. 

What does it mean for me?

By addressing the core performance issues in Spark, definity provides data engineers with a powerful toolkit to seamlessly monitor platform performance, identify waste and concrete opportunities, and optimize their Spark pipelines. This approach not only improves job runtime and resource utilization but also significantly reduces data platform costs.

With definity, data engineering teams can move from reactive firefighting of performance degradations to proactive performance management, ensuring their Spark pipelines run at peak efficiency in the face of ever-growing data volumes and complexity.

Want to learn more about how you can get day-1 performance insights and concrete cost savings?

‍Book a demo with us today, and see how definity can transform your data pipeline performance observability & optimization.