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Apache Spark Resource Configuration

From Theory to Practice
By
Ran Reichman
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February 5, 2025
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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:

  1. Analyze growth patterns in your data volume and complexity.
  2. 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:

  1. Monitor partition sizes through the Spark UI
  2. Track duration variation across tasks within the same stage
  3. 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.

  1. Late stages in complex DAGs struggle to get resources
  2. Shuffle operations become bottlenecks
  3. Some executors are overwhelmed while others sit idle
  4. 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:

  1. Dividing compute-intensive jobs into smaller jobs that use resources more predictably.
  2. 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)
  3. 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.

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Why the World Needs Flarion

Unlocking Data's Full Potential

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November 27, 2024

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.

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AI Agents Are the New Users of Data Infrastructure

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March 30, 2026

The consumer of data infrastructure is changing. For two decades, the primary user of platforms like Spark, Ray, and Trino has been a human engineer - someone who writes queries, tunes configurations, debugs failures, and builds intuition about their cluster over months of operation. Now, AI agents are increasingly the ones writing SQL, submitting Spark jobs, orchestrating ETL pipelines, and running analytical workloads. Enterprises are increasingly adopting agentic data engineering workflows. At Flarion, we see a few key dynamics at play.

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Tuning Will Converge - Then What?

Configuration tuning is getting automated. Agents can already follow runbooks, detect common failure patterns, and iterate on Spark configurations across runs. Auto-tuning systems are getting smarter. Over time, the combination of agents, automated tooling, and better platform defaults will squeeze most of the available performance out of configuration space. Executor sizing, partition counts, memory fractions, shuffle parameters - these will increasingly be handled by software rather than tribal knowledge.

Once configurations are well-tuned, the remaining performance gap is bounded by the execution engine itself. Spark's JVM-based, row-oriented architecture has fundamental overhead - garbage collection pauses, serialization costs, per-row function call overhead - that no amount of configuration tuning can eliminate. You can tune a Spark job perfectly and still leave 30-60% of potential performance on the table because of the engine's underlying execution model.

Flarion operates below this ceiling. Its native execution engine, built in Rust on Apache Arrow and DataFusion, replaces Spark's physical operators with vectorized execution. The gains come from a fundamentally different execution model. This means Flarion's acceleration stacks on top of whatever tuning an agent or auto-tuner has already done. Even the most optimized jobs get meaningfully faster.

For agents, this is the critical property. After exhausting configuration changes, an agent has two levers: continue building workload-specific tuning rules (low marginal benefit), or enable Flarion in a single configuration line and get 30-60% improvement immediately. In practice, it will do both - but the plugin delivers value on day one while tuning accumulates value over months.

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Separation of Concerns

There's also a trust dimension. Even as agents become capable of modifying code and infrastructure, most organizations won't want them to. Separation of concerns applies to agents the same way it applies to engineering teams: the system writing analytical queries should not also be the system reconfiguring the cluster, rewriting pipeline code, or modifying deployment infrastructure. Giving an agent broad permissions to change Spark configurations, refactor ETL logic, or resize clusters introduces risk that most platform teams won't accept.

This means agents will operate within boundaries. They'll submit workloads, not redesign them. They'll use tools, not modify the toolchain. The performance improvements available to an agent are limited to what can be achieved without crossing those boundaries.

Flarion fits precisely within these boundaries - a single configuration change that delivers material performance gains without touching application code, cluster settings, or deployment infrastructure. The agent stays in its lane. The acceleration happens underneath.

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Agents Are Measurement-Driven

Agents evaluate tools the way engineers wish they could: purely on observed results. An agent running a Spark job with Flarion enabled will observe that the job completed 40% faster at the same resource cost. It will observe this consistently across runs. Same input, same output, less time, less money. The signal is clean and unambiguous.

This matters because agents will increasingly be the ones deciding which tools to use within their permitted boundaries. They'll A/B test configurations, track cost-per-query over time, and converge on whatever delivers the best results. Tools that provide clear, repeatable value survive this selection process. Tools that require persuasion or organizational buy-in to demonstrate value don't - because agents aren't amenable to persuasion.

Flarion's 30-60% acceleration on real production workloads, validated across companies processing billions of records daily, is exactly the kind of signal that agents optimize for.

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The Zero-Config Threshold

The combination of bounded permissions and measurement-driven evaluation creates clear selection pressure on infrastructure. The platforms that agents will adopt are the ones that cross what might be called the zero-config threshold: the point where a tool can be activated and deliver value without requiring expertise.

Consider what an agent requires of data infrastructure. First, activation must be trivial - a single parameter, a plugin, a flag. Something that's easily testable and verifiable. Second, failure modes must be graceful. If something isn't supported, the system should fall back silently rather than throw an error the agent must handle. Agents are poor at diagnosing infrastructure-specific failures; they need systems that degrade predictably rather than fail unexpectedly. Third, the cost model must be transparent. In cloud environments, wall-clock time is cost. An agent optimizing for efficiency needs tools where faster execution directly equals lower spend, without requiring hardware-specific provisioning decisions.

There's a broader principle at work here: infrastructure designed to be usable by an agent will also be easier for a human. Every property that makes a tool agent-friendly - trivial activation, graceful fallback, transparent cost - also makes it friendlier to the human engineer who doesn't have time to read a tuning guide. Building for agents raises the floor for everyone.

Flarion crosses this threshold by design. Its native execution engine intercepts Spark's physical execution plan and replaces supported operators with vectorized native execution. Unsupported operations fall back transparently to Spark. The agent never sees a Flarion-specific error. It never needs to know which operations are accelerated and which aren't. The entire acceleration layer is invisible to the caller, which is precisely what makes it usable by an agent.

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Why Battle-Tested Ecosystems Win

Agents will prefer established ecosystems for the same reasons enterprises do: proven reliability at scale, broad connector support, extensive documentation that language models can reason about, and operational patterns that are well-understood. Spark processes petabytes daily across thousands of organizations. Ray orchestrates ML workloads at companies pushing the boundaries of model training. These platforms have accumulated years of production hardening that no new system can replicate quickly.

Making these ecosystems perform better without requiring expertise is where the real leverage lies. Flarion takes this approach across engines. Today it accelerates Spark workloads across every major deployment - open-source Spark, EMR, Dataproc, Databricks, and Spark on Kubernetes. The same Rust-based execution engine extends to Ray Data pipelines and Trino. An agent building a multi-engine analytical workflow gets consistent acceleration everywhere, through the same mechanism: enable the plugin, get faster results. No engine-specific optimization logic. No architectural trade-offs to evaluate.

This also means no new attack surface. Flarion runs as an in-process plugin inside the existing environment. No data leaves the perimeter. An agent can enable acceleration without triggering security reviews or compliance concerns - a friction reducer that matters enormously for enterprise adoption of agentic workflows.

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Where This Goes

The logical endpoint of this trend is outcome-oriented infrastructure - systems where agents submit workloads with constraints like "as cheap as possible, under 20 minutes" and the platform figures out the rest. The infrastructure handles resource allocation, configuration tuning, hardware routing, and failure recovery autonomously.

Flarion is building toward this future. The vision is autonomous execution where workloads are submitted with SLA targets and the system handles everything else - auto-tuning, auto-scaling, auto-recovery. The interface an agent actually wants: declare the outcome, let the infrastructure deliver it.

The building blocks are here today. A native execution engine that eliminates JVM overhead. Vectorized processing that leverages modern hardware. Transparent fallback that guarantees compatibility. Cross-engine support that works wherever the workload runs.

The agents are already here. The question is which platforms are ready for them.

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How OpenAI Runs Spark: A Case Study in Hybrid Data Infrastructure

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February 2, 2026

At the recent Open Lakehouse + AI Summit, OpenAI's data platform team gave a detailed account of how they run Spark internally. It's a revealing look at the operational reality of serving over a thousand internal customers across model training, analytics, safety research, and finance.

Their setup is representative of large-scale data platforms. They run both Databricks and a self-hosted "OpenAI Spark" on Kubernetes, unified through a shared Unity Catalog. Users switch between engines by changing a single configuration parameter. This hybrid pattern has become the norm for organizations processing data at serious volume, and OpenAI's experience illuminates why.

The Hybrid Reality

Three forces push enterprises toward running their own Spark alongside managed services. First, data security requirements often mandate that sensitive workloads stay within controlled infrastructure - no amount of compliance certifications fully satisfies some internal security teams. Second, the economics shift at scale: organizations processing petabytes daily often find that self-hosted deployments dramatically reduce costs for predictable, high-volume workloads. Third, operating your own stack means you can debug it. Full source code access and the ability to implement workload-specific optimizations matter when you're troubleshooting production incidents.

Building the Infrastructure Layer

The OpenAI team's account of scaling self-hosted Spark follows a familiar trajectory. Initial deployment is straightforward - Spark on Kubernetes, Airflow integration, jobs start flowing - and then usage grows.

Kubernetes control plane limits surface first - API servers buckling under listing operations from thousands of concurrent jobs. The response is multiple clusters, which immediately creates routing problems. Static routing (annotating jobs with target clusters) proves operationally painful. The solution is a gateway service that handles dynamic routing, access control, quota tracking, and auto-tuning based on historical patterns. This is infrastructure that managed services provide invisibly, and that self-hosted deployments must build explicitly.

Catalog integration across both managed and self-hosted environments requires careful coordination: permission verification, scoped credentials, distribution to executors. These are solved problems, but solving them yourself takes engineering time.

Performance at Petabyte Scale

OpenAI's talk gets more interesting when it turns to optimizations that don't appear in Spark documentation. Their CDC ingestion example is illustrative: at petabyte scale, Spark's default merge operation breaks down because mixed event types require outer joins that can't be broadcast. Their solution - splitting merges into separate operations for updates/deletes versus inserts - is the kind of pattern that emerges only from production experience.

Cloud storage API limits create another class of problems. Transaction-per-second caps become bottlenecks when scanning tables with extensive metadata. The optimizations are straightforward once you know to look: listing only from the last known commit, caching metadata, eliminating redundant status checks.

The most impactful optimization they described involves recognizing what data doesn't need to be read at all. Merge operations that update rows based on key matching don't need to scan target table columns if the CDC payload already contains the necessary data. This column pruning yielded 98% reductions in data scanned for some of their workloads.

The Architectural Ceiling

Even with these optimizations, OpenAI's team acknowledged limitations that configuration tuning can't address. PySpark's architecture creates both performance overhead and debugging complexity. JSON processing remains expensive. These are consequences of Spark's JVM-based architecture, and the industry is responding.

Remote shuffle services decouple shuffle data from executor lifecycles. Native acceleration engines process data in columnar format with SIMD instructions. This is the problem Flarion addresses directly - accelerating Spark workloads natively without requiring pipeline changes, targeting exactly the architectural constraints OpenAI describes. Organizations facing similar ceilings can evaluate whether native acceleration closes the gap before committing to the engineering investment of building their own optimization layer.

OpenAI's scale is unique, but its challenges are common. Hybrid deployments, control plane scaling, storage API limits, the performance ceiling of JVM-based processing - these are what enterprises running Spark at scale consistently encounter. Their solutions represent current best practice. The question for most organizations is when they'll face these problems, and whether they'll be ready.

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