StarRocks 4.0: Zero Compromise, 60% Faster

OLAP databases routinely claim performance advantages, yet industry-recognized benchmarks under production conditions reveal a different reality. Real-world workloads, shifting data shapes, high-volume JSON ingestion,  and AI-driven analytics requiring millisecond latency across billions of rows—expose the gap between marketing claims and operational performance.

The critical question for production environments isn't raw speed. It's consistency: how reliably does performance scale across diverse workloads without manual intervention or query-specific tuning?

StarRocks 4.0 demonstrates a 60% year-over-year performance improvement while addressing workloads that have historically required architectural tradeoffs.

The Performance Foundation: Where Speed Actually Comes From

Fast queries are table stakes. What breaks analytics teams is unpredictability. The query that suddenly takes 30 seconds instead of three. The dashboard that times out after a schema change. The pipeline that can't hit SLA. 

StarRocks 4.0 addresses this through deep improvements in the operators that define query execution:

• JOIN Optimization: Hash joins and merge joins now handle complex multi-table queries with lower memory overhead and better parallelization. The engine automatically selects optimal join strategies. No manual tuning, no query hints.
• Whether you're joining internal tables or querying across Apache Iceberg catalogs, the improvements apply uniformly.
• Aggregation Tuning: COUNT DISTINCT, GROUP BY, and other aggregation operations now leverage global dictionaries and optimized hash tables to reduce CPU cycles. The new Partition-wise Spillable Aggregate/Distinct operators improve performance in complex, high-cardinality GROUP BY scenarios while reducing read/write overhead.
• String-heavy aggregations that once dominated CPU time now execute as lightweight integer operations through dictionary encoding.
• Spill Handling: When queries exceed memory limits, partition-wise spill mechanisms prevent OOM errors without sacrificing throughput.

The optimized approach minimizes disk I/O and maintains query stability even under memory pressure. Large analytical queries remain stable and predictable, regardless of data volume or cardinality.

Memory and Cache Management:  Unified page cache and data cache for backend metadata use adaptive scaling strategies, automatically adjusting to workload demands. In addition, metadata serves from backend memory whenever possible, minimizing cloud API calls. This particularly impacts Iceberg tables where metadata operations can dominate query latency.

Intelligent caching reduces external storage system calls by up to 90%. These improvements compound across complex analytical demands and apply equally whether you're querying internal tables or external catalogs like Apache Iceberg.

Where Else Performance Breaks Down (And How 4.0 Fixes It)

Core operator improvements deliver speed. But production performance breaks in less obvious places: the query that suddenly regresses after a schema change, the JSON logs that force a choice between flexibility and speed, the data lake queries that spend more time parsing metadata than processing data.

StarRocks 4.0 addresses these real-world bottlenecks across query planning, lakehouse integration, and workload expansion.

When Optimizer Decisions Become Performance Risks

Your dashboard runs in three seconds, until it doesn't. A data distribution change triggers the optimizer to pick a different plan, and suddenly the same query takes 30 seconds. Production SLAs break not because of compute limitations, but because query plans aren't stable.

SQL Plan Manager solves this by binding queries to known-good execution plans. Even as data evolves or nodes restart, performance remains predictable. 

For customer-facing dashboards or AI inference endpoints, latency variance has business consequences.

The JSON Performance Paradox

JSON is everywhere in operational systems: event streams, clickstreams, IoT telemetry.

But querying it has always forced a tradeoff. Keep it flexible and accept slow queries, or flatten it into columns and lose agility when schemas change.

FlatJSON V2 eliminates the compromise through four execution-layer optimizations:

• Zone map indexes skip irrelevant data blocks

• Global dictionaries convert string operations into integer comparisons

• Late materialization only decodes rows that survive filtering

• Efficient decoding avoids redundant parsing operations

The result? 3-15x faster queries without flattening. 

Making Apache Iceberg Tables Performant

Writing to Iceberg tables efficiently has always been a time-consuming endeavor: ingestion produces tiny files, partitions fragment, and metadata goes stale. The result is slow queries and an inability to meet your SLAs.

StarRocks 4.0 applies warehouse discipline to Apache Iceberg at the file writing and management level, improving ingestion performance and operational complexity. When it comes to querying your lake, users are closer than ever to native StarRocks table performance.

• Global shuffle allows StarRocks produce appropriately sized files as partitions scale

• Spillable writes avoid OOM errors and keep large-scale ingestion consistent

• A new Compaction API lets users consolidate and merge files on demand, keeping files manageable and efficient to parse at query time

• Local sort generates files optimal for querying by organizing data at the tablet level

Cutting Costs in 4.0

Despite the cost-saving promises object storage champions, frequent file writes inevitably surge API costs at scale.

StarRocks 4.0 reduces the file write tax through internal improvements that lend themselves to a cost-efficient database experience:

• File bundling that merges writes into larger files, so you can eliminate file explosion from incremental writes

• Metadata caching that prioritizes local data from memory first, saving costs and cutting object storage API calls

Through smarter compaction, users can now experience 70 to 90 percent fewer cloud API calls compared to version 3.3, with no latency increase or data freshness loss.

When Workloads Force You to Choose Between Speed and Accuracy

Time-series analytics traditionally meant denormalizing data into application-layer joins: slow and brittle. Financial calculations hit precision limits where 64-bit decimals overflow, forcing expensive workarounds. Complex data pipelines required external transaction coordinators, adding latency at every step.

These workloads didn't just need specialized systems. They needed compromises that slowed everything down.

StarRocks 4.0 eliminates the performance tax:

• ASOF JOIN executes time-series alignment natively—matching trades with quotes, syncing IoT sensors, building point-in-time feature sets—without the overhead of denormalization or application logic
• Decimal256 with 76-bit precision handles financial aggregations at scale without overflow errors or precision loss that force multi-pass calculations
• Multi-statement transactions enable atomic operations across tables directly in the database, removing external coordination overhead

These capabilities matter tenfold for AI workloads. Models need features joined across dozens of tables, aligned to timestamps, and served in milliseconds.

Previously, feature stores became bottlenecks. Either you denormalized everything and lost flexibility, or you accepted seconds of latency. With sub-second performance across billions of rows, inference SLAs shift from aspirational to achievable.

The Bottom Line

StarRocks 4.0 doesn't just move performance incrementally. It delivers 60% faster queries while expanding into workloads that previously required architectural compromises: unpredictable JSON schemas, messy data lakes, time-series alignment, high-precision financial calculations.

The result: consistent performance that scales with your data, not against it.

Ready to see what 60% faster looks like on your workloads? Explore StarRocks 4.0 at StarRocks.io or join the StarRocks Slack community!