Visibility
Spark’s data can be shallow and difficult to interpret. Flarion provides detailed, actionable insights to effectively track job performance.
Prevention
Say goodbye to unexpected failures. Detect and address issues with real-time alerts before they impact operations.
Efficiency
Deep insights allow you to maximize Spark efficiency and ensure smooth, reliable performance.
Core Metrics for Enhanced Visibility

Break down jobs to uncover optimization opportunities that basic Spark tools miss.

Monitor data shifts and resource usage with alerts on potential risks.

Spot and prevent performance drops and task failures with proactive alerts.

Quickly resolve issues with clear insights, leveraging historical data and code context.
Core Benefits
Identify and resolve issues before they disrupt operations.
Break down jobs for improved resource management and performance.
Gain clear, actionable explanations to streamline fixes.
Track job metrics for ongoing performance improvements.
Stay informed of performance shifts with timely notifications.
Effortlessly scale as Spark workloads grow, with continuous optimization.
The Latest Data Processing News & Insights

Unlocking Data's Full Potential
Did you know that data-driven organizations spend up to 40% of their IT budgets on data processing alone?
As organizations scale their data processing capabilities, two critical challenges emerge: the mounting costs of processing big data and the pressing need for faster performance. Today, we're sharing our journey and explaining why Flarion is transforming how organizations leverage their data assets while staying competitive in an increasingly data-driven world.
The Journey to Better Data Processing
Through years of experience across diverse industries, Flarion’s co-founders witnessed the universal struggle of escalating data processing costs and performance bottlenecks.
During his years building data processing systems for mass-scale consumer applications and autonomous vehicles, Ran experienced firsthand how organizations struggled with the growing costs and performance demands of expanding datasets. In consumer applications, better insights can help create great experiences for hundreds of millions of people, but the high computational costs and processing limitations often make this prohibitively expensive. In autonomous vehicles, data processing at scale allows us to understand and tackle the toughest "long tail" challenges, but technical limitations can make this slow and cost-inefficient.
Through his extensive work with enterprises across various industries, Udi observed a consistent pattern: organizations were hitting both a performance and cost ceiling in their data processing capabilities. Despite significant investments in infrastructure and talent, companies found themselves constrained by processing limitations that held back their ability to launch new features or products while managing escalating infrastructure costs.
The Evolution of Data Processing Needs
The landscape of data processing has evolved dramatically. What started as simple analytics has transformed into complex data pipelines processing hundreds of terabytes daily. These diverse challenges underline the pressing need for solutions that address both speed and cost at scale.
In automotive, processing speed directly impacts vehicle safety and performance, while processing costs affect vehicle affordability and market competitiveness. In financial services, faster data processing enables real-time decision-making and better risk assessment, but the infrastructure costs of high-frequency trading and real-time analytics can quickly erode profit margins. For e-commerce companies, efficient data processing means better customer recommendations and inventory management, yet the cost of processing massive customer datasets across global markets can be prohibitive. Almost every industry relies heavily on efficient data processing and analytics, making both speed and cost optimization critical factors in maintaining competitive advantage.
A New Approach to Performance
Traditional approaches to improving data processing often involve extensive code changes, specialized expertise, or specific deployment requirements. For enterprises with massive legacy codebases, these solutions are often impractical or impossible to implement, creating additional complexity without solving the fundamental challenges of performance and cost efficiency.
We built Flarion with a different vision: what if organizations could dramatically improve their data processing performance without changing their code or disrupting existing workflows? With new Spark, Hadoop and Ray execution engines, we've created a solution that delivers up to 3x performance improvement while maintaining robust reliability and full compatibility. Most importantly, Flarion can be implemented in just 5 minutes, requiring minimal effort from organizations looking to modernize their data stack.
Enabling Innovation Through Efficiency
The impact of accelerated data processing extends far beyond just faster completion times. When organizations can process their data more efficiently and cost-effectively, they can explore new use cases, launch innovative features, and focus on extracting value from their data rather than managing infrastructure costs.
For AI and machine learning applications, efficient data processing is becoming increasingly crucial. The ability to process large datasets quickly and reliably can mean the difference between a successful model deployment and a missed opportunity. With Flarion, organizations can focus on innovation rather than infrastructure optimization, all while maintaining their existing codebase and operations.
The Future of Data Processing
As we enter an era where data drives competitive advantage, organizations need solutions that enable them to process more data, faster and more cost-effectively. The future of data processing isn't just about handling today's workloads - it's about being ready for tomorrow's challenges while managing costs sustainably.
With Flarion, organizations are not just keeping pace—they’re leading the charge into a data-driven future. Our solution enables organizations to unlock the full potential of their data assets, whether they're running data processing in the cloud or on-premises. By delivering significant performance improvements through advanced optimization techniques, we're helping organizations process their data more efficiently while reducing their infrastructure costs. Most importantly, we're doing this in a way that respects the reality of enterprise systems - with a solution that can be implemented in minutes, not months.
The future of data processing should empower organizations to focus on innovation and value creation without being held back by legacy infrastructure or rising costs.
At Flarion, we're making that future a reality.
From Theory to Practice
Apache Spark's resource configuration remains one of the most challenging aspects of operating data pipelines at scale. Theoretical best practices are widely available, but production deployments often require adjustments to accommodate real-world constraints. This guide bridges that gap, exploring how to properly size Spark resources—from executors to partitions—while identifying common failure patterns and strategies to address them in production.
The Baseline Configuration
Consider a typical Spark job processing 1TB of data. A standard recommended setup might include:
- A cluster of 20 nodes, each with 32 cores and 256GB RAM
- Effective capacity of 28 cores and 240GB RAM per node after system overhead
- 4 executors per node (80 total executors)
- 7 cores per executor (with 1 core reserved for overhead)
- 56GB RAM per executor
- ~128MB partition sizes for optimal parallelism
While this configuration serves as a solid starting point, production workloads rarely conform to such clean boundaries. Let's examine some common failure patterns and mitigation strategies.When Reality Hits: Failure Patterns and Solutions
Failure Pattern #1: Workload Evolution Requiring Infrastructure Changes
A typical scenario: A job that previously ran efficiently on 20 nodes begins to experience increasing memory pressure or extended runtimes, despite configuration adjustments. Signs of resource constraints include:
- Consistently high GC time across executors (>15% of executor runtime)
- Storage fraction frequently dropping below 0.3
- Executor memory usage consistently above 85%
- Stage attempts failing despite conservative memory settings
Root cause analysis approach:
- Analyze growth patterns in your data volume and complexity.
- Profile representative jobs to understand resource bottlenecks.
Key scaling triggers:
- CPU-bound: When average CPU utilization stays above 80% for most of the job duration.
- Memory-bound: When GC time exceeds 15% or OOM errors occur despite tuning.
- I/O-bound: When shuffle spill exceeds 20% of executor memory.
If CPU-bound (high CPU utilization, low wait times):
- First try increasing cores per executor.
- If insufficient, add nodes while maintaining a similar cores/node ratio.
If memory-bound (Out Of Memory - OOM):
- First try reducing executors per node to allocate more memory per executor.
- If insufficient, add nodes with higher memory configurations.
Failure Pattern #2: Memory Exhaustion In Compute Heavy Operations
A typical scenario: Your job runs fine for many days but then suddenly fails with Out Of Memory (OOM) errors. Investigation reveals that during month-end processing, certain joins produce intermediate results 5-10x larger than your input data. The executor memory gets exhausted trying to handle these large shuffles.A possible solution would be to update the configuration to:
- spark.executor.memoryOverhead: 25% (increased from default 10%)
- spark.memory.fraction: 0.75 (decreased from default 0.6)
These settings help because they:- Reserve more memory for off-heap operations (shuffles, network buffers)- Reduce the fraction of memory used for caching, giving more to execution- Allow GC to reclaim memory more aggressively
Failure Pattern #3: Data Skew, The Silent Killer
A typical scenario: Your daily aggregation job suddenly takes 4 hours instead of 1 hour. Investigation shows that 90% of the data is going to 10% of the partitions. Common culprits:- Timestamp-based keys clustering around business hours- Geographic data concentrated in major cities- Business IDs with vastly different activity levelsBefore implementing solutions, quantify your skew:
- Monitor partition sizes through the Spark UI
- Track duration variation across tasks within the same stage
- Look for orders of magnitude differences in partition sizes
A possible solution would be to analyze your key distribution and for known skewed keys, implement pre-processing like so:// For timestamp skewval smoothed_key = concat(date_col, hash(minute_col) % 10)// For business ID skewval salted_key = concat(business_id, hash(row_number) % 5)Using Spark’s built-in skew handling helps, but understanding the specific skew of your data is more robust and lasting. Spark’s skew handling configurations:
- spark.sql.adaptive.enabled: true
- spark.sql.adaptive.skewJoin.enabled: true
Failure Pattern #4: Resource Starvation in Mixed Workloads
A typical scenario: A seemingly well-configured job starts showing erratic behavior—some stages complete quickly while others seem stuck, executors appear underutilized despite high load, and the overall job progress becomes unpredictable. This is a typical case of resource starvation occurring within a single application.
- Late stages in complex DAGs struggle to get resources
- Shuffle operations become bottlenecks
- Some executors are overwhelmed while others sit idle
- Task attempts timeout and retry repeatedly
The root cause often lies in complex transformation chains: sqlCopydata.join(lookup1).groupBy("key1").agg(...).join(lookup2).groupBy("key2").agg(...)Each transformation creates intermediate results that compete for resources. Without proper management, earlier stages can hog resources, starving later stages.Possible solutions include:
- Dividing compute-intensive jobs into smaller jobs that use resources more predictably.
- If splitting a large job isn’t possible, using checkpoints and persist methods to better divide a single job into distinct parts. (expect a future blog post on these methods)
- Applying Spark Shuffle management - setting spark.dynamicAllocation.shuffleTracking.enabled and spark.shuffle.service.enabled to true.
Conclusions & The Path Forward
We've found that most Spark issues manifest first as performance degradation before becoming outright failures. The goal of a data engineering team isn't to prevent all issues but to catch and address them before they impact production stability. While adding resources can sometimes help, precise optimization and proper monitoring often provide more sustainable solutions. Spark offers a robust set of job management tools and settings, but addressing problems through standard Spark configurations alone often proves insufficient.The Flarion platform transforms this landscape in two key ways: through significant workload acceleration that reduces resource requirements and minimizes garbage collection overhead, and by providing enhanced visibility into Spark deployments. This combination of speed and improved observability enables engineering teams to identify potential issues before they escalate into failures, shifting from reactive troubleshooting to proactive optimization. As a result, data engineering teams experience both reduced failure rates and decreased operational burden, creating a more stable and efficient production environment.
Why does it happen? How to avoid it?
Apache Spark is widely used for processing massive datasets, but Out of Memory (OOM) errors are a frequent challenge that affects even the most experienced teams. These errors consistently disrupt production workflows and can be particularly frustrating because they often appear suddenly when scaling up previously working jobs. Below we'll explore what causes these issues and how to handle them effectively.
Causes of OOM and How to Mitigate Them
Resource-Data Volume Mismatch
The primary driver of OOM errors in Spark applications is the fundamental relationship between data volume and allocated executor memory. As datasets grow, they frequently exceed the memory capacity of individual executors, particularly during operations that must materialize significant portions of the data in memory. This occurs because:
- Data volumes typically grow exponentially while memory allocations are adjusted linearly
- Operations like joins and aggregations can create intermediate results that are orders of magnitude larger than the input data
- Memory requirements multiply during complex transformations with multiple stages
- Executors need substantial headroom for both data processing and computational overhead
Mitigations:
- Monitor memory usage patterns across job runs to identify growth trends and establish predictive scaling
- Implement data partitioning strategies to process data in manageable chunks
- Use appropriate executor sizing via the instruction --executor-memory 8g
- Enable dynamic allocation with spark.dynamicAllocation.enabled=true, automatically adjusting the number of executors based on workload
JVM Memory Management
Spark runs on the JVM, which brings several memory management challenges:
- Garbage collection pauses can lead to memory spikes
- Memory fragmentation reduces effective available memory
- JVM overhead requires additional memory allocation beyond your data needs
- Complex management between off-heap and on-heap memory
Mitigations:
- Consider native alternatives for memory-intensive operations. Spark operations implemented in C++ or Rust can provide the same results with less resource usage compared to JVM code.
- Enable off-heap memory with spark.memory.offHeap.enabled=true, allowing Spark to use memory outside the JVM heap and reducing garbage collection overhead
- Optimize garbage collection with -XX:+UseG1GC, enabling the Garbage-First Garbage Collector, which handles large heaps more efficiently
Configuration Mismatch
The default Spark configurations are rarely suitable for production workloads:
- Default executor memory settings assume small-to-medium datasets
- Memory fractions aren't optimized for specific workload patterns
- Shuffle settings often need adjustment for real-world data distributions
Mitigations:
- Monitor executor memory metrics to identify optimal settings
- Set the more efficient Kyro Serializer with spark.serializer=org.apache.spark.serializer.KryoSerializer
Data Skew and Scaling Issues
Memory usage often scales non-linearly with data size due to:
- Uneven key distributions causing certain executors to process disproportionate amounts of data
- Shuffle operations requiring significant temporary storage
- Join operations potentially creating large intermediate results
Mitigations:
- Monitor partition sizes and executor memory distribution
- Implement key salting for skewed joins
- Use broadcast joins for small tables
- Repartition data based on key distribution
- Break down wide transformations into smaller steps
- Leverage structured streaming for very large datasets
Conclusion
Out of Memory errors are an inherent challenge when using Spark, primarily due to its JVM-based architecture and the complexity of distributed computing. The risk of OOM can be significantly reduced through careful management of data and executor sizing, leveraging native processing solutions where appropriate, and implementing comprehensive memory monitoring to detect usage patterns before they become critical issues.