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Storage Format Lock-in: The Constraint Limiting Query Performance

Processing engines require rigid storage formats, so queries run slower than the data allows. Where Parquet falls short for selective analytics, and why.
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November 17, 2025
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Modern distributed processing engines achieve remarkable scale and reliability, yet they share a fundamental architectural constraint: rigid storage format requirements. This constraint forces organizations to accept significant performance penalties, sometimes 10-100x slower queries than technically possible, simply because their processing engine cannot adapt to optimized storage formats.

Why Parquet Falls Short for Selective Analytics

Parquet excels as a universal columnar format, but its design prioritizes compatibility and compression over query performance. Understanding its limitations requires examining how analytical queries actually execute.

The Row Group Problem

Parquet organizes data into row groups, typically 100,000 to 1 million rows each. This coarse granularity creates a fundamental problem: if even a single row in a row group matches your query filter, you must read ALL requested columns for the ENTIRE row group.

Consider a query filtering for a specific customer ID in a billion-row table. That customer's 100 transactions might be scattered across 100 different row groups. Parquet must read 100 row groups × 100,000 rows × all requested columns, processing 10 million rows to return 100.

Single-Phase Execution

Parquet readers process queries in a single phase:
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1. Read all requested columns for relevant row groups

2. Decompress the data

3. Apply filters

4. Return matching rows

This means reading and decompressing massive amounts of data that will immediately be discarded. There's no mechanism to filter first, then read only what's needed.

Limited Predicate Pushdown

While Parquet stores min/max statistics per row group and optional Bloom filters, these only help skip entire row groups. For analytical queries where some matching rows exist in many row groups, this provides minimal benefit. You still read entire 100,000-row chunks to extract perhaps 10 rows from each.

How Query-Optimized Formats Solve These Problems

Formats like ClickHouse's MergeTree take a fundamentally different approach:

Granular Storage

Instead of 100,000-row groups, data is organized into 8,192-row granules. This 12x finer granularity means reading much less unnecessary data when matches are sparse. Finding those same 100 customer transactions requires reading ~12x less data just from granularity alone.

Two-Phase Execution with PREWHERE

Query-optimized formats implement two-phase execution:

1. Phase 1: Read ONLY filter columns for candidate granules

2. Phase 2: For rows that pass filters, read the remaining requested columns

This seemingly simple change has a profound impact. Instead of reading 20 columns for millions of rows, you read 1-2 filter columns first, identify the 100 matching rows, then read the other 18 columns for just those 100 rows.

Sparse Indexing

A sparse primary index stores one entry per granule (every 8,192 rows), creating a tiny index that fits entirely in memory even for billion-row tables. Binary search on this index instantly identifies which granules to read, eliminating 99%+ of data before any I/O occurs.

Lazy Materialization

Column reads are deferred until absolutely necessary. If a query has multiple filters, the engine applies them progressively, reading additional columns only for rows that survive each filter. This minimizes decompression work and memory bandwidth usage.

The Performance Gap: A First-Principles Calculation

Let's calculate the actual performance difference using a realistic analytical query on data stored in S3:

Scenario:

  • 100 million rows, 100 columns
  • Query with 0.01% selectivity returning 10,000 rows
  • Projects 20 columns
  • All data in S3 (network I/O is the same for both formats)

Parquet Execution:

  • Data organized in 100,000-row groups
  • 10,000 matching rows spread across ~100 row groups
  • Must read all 20 requested columns for entire row groups containing any match
  • Compressed data transferred from S3: 160 MB (with 10:1 compression)
  • Data decompressed and processed: 1.6 GB

Query-Optimized Format (like ClickHouse MergeTree):

  • Data organized in 8,192-row granules with sparse index
  • Two-phase execution: read filter columns first, identify matches, then read other columns
  • Compressed data transferred from S3: 46 MB (with 3.5:1 compression)
  • Data decompressed and processed: 162 MB

Despite storing data 2.9x larger on S3 due to less aggressive compression, the query-optimized format transfers 3.5x less data over the network and processes 10x less decompressed data. Processing 1.6 GB vs 162 MB of decompressed data is the difference between 100ms and 10ms query time

Why Processing Engines Don't Support Alternative Formats

While Spark and Ray technically could support arbitrary storage formats through extension APIs, in practice they don't. The ecosystem has converged on Parquet, ORC, and Avro, leaving significant performance opportunities unexplored.

The Spark Reality

Spark provides a DataSource V2 API for custom formats, yet virtually no production deployments use them. The practical barriers:

  • Format implementations must handle Spark's complex internal row representation
  • Custom formats cannot leverage Spark's vectorized execution optimizations
  • The Catalyst optimizer cannot reason about custom format capabilities
  • Maintaining custom format readers requires deep Spark internals knowledge

Ray's Format Limitations

Ray delegates data loading to Pandas or PyArrow:

python

@ray.remote

def process_partition(file_path):

    # Limited to formats Pandas supports

    df = pd.read_parquet(file_path)

    return df.groupby('device_id').mean()

Adding optimized formats would require writing C++ extensions, ensuring serialization compatibility, and maintaining format-specific optimizations Ray doesn't understand.

The Architectural Impedance Mismatch

Even when custom format support exists, fundamental architectural assumptions prevent leveraging format-specific optimizations. Standard engines read all requested columns before filtering and lack the execution primitives for two-phase execution. The granularity mismatch between coarse row groups and fine granules cannot be bridged through format plugins.

Real-World Impact on S3-Based Data Lakes

Production systems using S3 data lakes demonstrate concrete impact:

E-commerce Recommendation Engine

  • Dataset in S3: 500M products × 800 attributes
  • Parquet in Spark: 8.2 seconds average latency
  • Possible with optimized format: 100ms
  • Impact: Real-time recommendations impossible

Financial Risk Analytics

  • Dataset in S3: 10 years of trades, 2,000 columns
  • Parquet in Spark: 45 seconds per portfolio
  • Possible with optimized format: 500ms
  • Impact: Risk managers wait minutes for updates that could take seconds

IoT Monitoring

  • Dataset in S3: 100,000 devices × 1,000 metrics
  • Parquet in Ray: 12 seconds per device group
  • Possible with optimized format: 50ms
  • Impact: Alert latency prevents real-time response

The Compression-Performance Trade-off

Parquet achieves superior compression, typically 2-3x better than query-optimized formats. A 100GB dataset might compress to 10GB in Parquet versus 30GB in an optimized format, translating to $2.30/month versus $6.90/month on S3.

However, compute costs dominate this equation. When queries run 10-100x faster with optimized formats, the required infrastructure shrinks proportionally. A workload requiring a 16-node Spark cluster at $8,000/month might run on 2 nodes at $1,000/month with format optimization. The $7,000/month compute savings overwhelms the $4.60/month additional S3 storage cost by a factor of 1,500x.

Performance Patterns by Query Type

Different query patterns show varying sensitivity to format selection:

Highly Selective Queries (<0.1% of rows returned)

  • Performance difference: 20-100x
  • Parquet's coarse row groups create massive read amplification

Wide Table Projections (many columns, few rows)

  • Performance difference: 10-50x
  • Single-phase execution forces reading all columns upfront

Aggregation Queries

  • Performance difference: 5-20x
  • Processing unnecessary rows dominates computation

Full Table Scans

  • Performance difference: 0.7-1.4x (Parquet often faster)
  • Parquet's superior compression provides advantage when reading everything

The Hidden Costs of Format Lock-in

Beyond direct performance impact, format rigidity creates cascading inefficiencies:

Infrastructure Over-provisioning: Organizations scale clusters to compensate for format inefficiency. A workload naturally requiring 2 nodes might run on 20 nodes to achieve acceptable latency.

Architectural Workarounds: Teams implement pre-aggregation pipelines, materialized views, and caching layers, each adding operational complexity without addressing the root cause.

Opportunity Costs: When queries take 30 seconds instead of 300ms, entire categories of applications become infeasible.

The Path Forward

Parquet remains excellent for data interchange, archival storage, and full-scan workloads. The opportunity lies in enabling processing engines to leverage format diversity based on workload requirements.

The ideal architecture would support understanding the profile of the workloads, adjusting compaction accordingly, and using the best data format for the job. Current processing engines cannot implement this strategy due to their format rigidity. They lack the flexibility to transparently read different formats while applying format-specific optimizations.

This limitation is beginning to change. Next-generation execution engines recognize that format flexibility is essential for modern analytical workloads. By decoupling query execution from storage assumptions, systems like Flarion can deliver the performance that has always been technically possible but practically unreachable.

Organizations no longer need to accept 10x performance penalties as the price of distributed processing. The ability to match storage formats to query patterns represents the difference between queries that frustrate users and analytics that drive real-time decisions.

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Why the World Needs Flarion

Why we built Flarion: the cost of processing large data volumes, the pressure for faster results, and the approach we took to both.

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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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The Data Processing Honey Badger: Why Spark Persists

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September 10, 2026

Development on Apache Spark started at Berkeley in 2009, and the first production release shipped on May 30, 2014. In the twelve years since, it has become the analytics workhorse for most of the large corporations in the world, across industries and scale, from seed-stage startups to Fortune 10 enterprises. Every year or so someone declares it old, past its peak, saddled with the JVM, and generally "legacy." And every year there is more of it. What accounts for the disconnect? In this post we'll walk through what we see across customer deployments and why we expect that in ten years there will still be a whole lot of Spark, and probably much more than there is today.

Infinite Scale

Spark scales very well. It’s not rare to see customers running workloads reading dozens of TB, while at the same time other customers process a few GB per workload. The result: for data engineering teams who don’t know how much data they’ll need to process, it’s a clean and easy decision to adopt Spark.

Network Effects

While it’s quite easy to use Spark, especially with PySpark, it’s not easy to deploy it and maintain it. But once the data platform adopts the tooling and learns how to maintain Spark, it is rarely motivated to migrate a piece of critical infrastructure to an unproven alternative, and instead are motivated to push more people to use Spark.

The Challenge of Migrating

Large companies can have thousands of jobs running at any given time, spread across the entire organization. The idea of pushing the various teams to migrate to a new platform is usually a complete non-starter. Oftentimes even gradually moving to systems like Ray is unwelcome due to the cost of maintaining multipledata platforms.

A First Class Citizen in the Data Lake

Delta Lake, Iceberg, and Hudi were each born with Spark as the reference implementation. The result is that Spark works well out-of-the-box with all three, while other systems are gradually adding support. Engineering teams want the best and most recent lakehouse technology and generally Spark supports it. .

Extensibility

Spark is easy to extend without forking. Catalyst exposes optimizer rules, planning strategies, and catalog plugins. DataSource V2 lets anyone teach Spark to read a new system. User defined functions (UDFs) let teams introduce Python or Scala logic into the middle of a pipeline without leaving the framework. Plug-ins allow the introduction of new libraries into the system. The result is that the thing people would otherwise leave Spark to get, a new connector, a custom optimization, a domain-specific function library, usually shows up inside Spark instead.

The Competition

Flink is used for some streaming use cases, Ray for AI use cases, Trino for interactive SQL, DuckDB and Polars for data that fits on one machine. Data warehouses with proprietary engines are taking some share. But at this point nobody is really trying to invent a new full-fledged system to replace Spark. The competition is either specializing in a lane or building underneath it.

Improved Engines

In Spark, the underlying engine is not static. The API hasn’t changed much since DataFrames arrived, but adding Tungsten improved performance with whole stage code generation that’s close to the hardware, while query optimizations, fast paths, and new operators also make the same workload faster without code changes. Databricks added Photon, we produced Flarion, and open source brought Gluten and Comet. It’s possible to stay on Spark and get modern performance, similar to how PostgreSQL keeps getting better and adding functionality without the API changing.

Summing Up

The Spark API is likely going to be with us for a long time, but under the hood a lot is going to change. Piece by piece the engine is being replaced, and it's plausible that in ten years none of the original execution code will be left, while every job still runs and every DataFrame still looks the same. It's the Ship of Theseus, except in this version the ship gets faster with every plank. 

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The Hard Part of Upgrading Spark Versions Isn’t the Code

AI agents can now rewrite Spark jobs for upgrades. Slack, Uber, and Facebook's migrations show the real work: proving the output data didn't change.

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August 18, 2026

A few weeks ago AWS shipped the Spark Upgrade Agent, an AI agent that migrates Spark jobs to Spark 4.0. You point it at a repo, it rewrites deprecated APIs, adjusts for behavioral changes, updates the build for Scala 2.13, submits the result to an EMR cluster, and iterates on failures until the job runs. It handles both Scala and PySpark, and it works the way you'd hope an agent would: plan, transform, validate, repeat. The potential payoff is large - newer Spark versions have better performance and years of accumulated bug fixes.

It looks like a good tool and data teams looking into a Spark migration should consider it, but what we’ve found is that in the enterprise, rewriting code isn’t the main impediment to upgrading Spark workloads. Spark programs are part of complex pipelines, parts of which are poorly understood or maintained, and making changes to a sensitive system is inherently risky. There’s no guarantee that the output data will actually remain the same, and data integrity is the fundamental challenge of completing such a migration.

Several companies have written in detail about major Spark upgrades, and the data integrity challenge is a recurring theme.

Slack

Slack's migration from Spark 2 to Spark 3 took about a year across 60+ EMR clusters and 40+ teams. They saw some code-level breakage: `RAND()` in join keys became an `AnalysisException`, some casts that Spark 2 tolerated started failing, the `Greatest` function handled NULLs differently than its Hive counterpart. Validation was a much larger effort. For billing pipelines, Slack required exact matches, building test tables from production data on Spark 3 and running `EXCEPT` and `COUNT` comparisons in Trino against the Spark 2 outputs, with a Python framework for digging into every discrepancy. The discrepancies weren't all bugs. Non-deterministic row ordering, timestamp variations, and genuine semantic differences between Hive and Spark implementations all produce diffs that need to be investigated. Some are noise, some are real regressions.

Uber

Uber's version of this is bigger and more instructive. They migrated from Spark 2.4 to 3.3 with over two million Spark applications running daily. The code transformation was automated with Polyglot Piranha, their structural rewrite tool. It parses the source code into an AST, matches patterns, and applies transformation rules, including inserting legacy flags like `spark.sql.legacy.allowUntypedScalaUDF` where old behavior had to be preserved. This scaled well. The problem that shaped the whole project was stated plainly: "We had over 40,000 Spark apps, so we couldn't decentralize the data validation." No staging environment, no test cases, no way to ask every team to eyeball their own outputs.

So the flagship engineering artifact of Uber's Spark upgrade wasn't actually a code migrator but Iron Dome: a shadow-testing framework which runs the migrated job against production inputs, rewrites output paths at runtime so results land in staging instead of production, puts guardrails at the Hadoop FileSystem interface so a misrouted write can't touch real data, then compares the shadow output against the production run and only marks the job migrated when they agree. 

Facebook

None of this is specific to the Spark 2-to-3 transition, or even to Spark versions. When Facebook moved Hive workloads onto Spark SQL back in 2017, they ran shadow pipelines writing to tables suffixed `_spark_shadow` so downstream jobs were never exposed, used count checks as a cheap first filter, and reached for full hash validation of outputs only reluctantly (because, as they put it, the hash validation was "sometimes even heavier than the query itself.") Funny enough, proving the new engine produced the same answer could cost more compute than producing the answer. They note that non-deterministic UDFs made validation hard, the same diff-adjudication problem Slack hit eight years later.

Takeaway

In the enterprise, migrations are rightfully considered risky projects that take time and incur risk. This is especially true in the age of AI given that the things that AI doesn’t necessarily deliver are also the riskiest parts of the migration - edge cases, data integrity, the long tail, etc. This isn’t to say that AI can’t help with building tooling for a migration, it clearly can, but usually an agent isn’t going to do the trick alone.

At Flarion, a major goal of ours is to give users the best possible performance and access to modern features without requiring a code migration. If you can get the benefits of Spark 4.2 while staying on Spark 3.4 then that’s a huge time save and reduction in risk. Of course, the data integrity problem doesn’t disappear, it’s now Flarion’s responsibility. One we’re happy to shoulder.

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