Data warehouses have evolved drastically over the past 30 years—from BI-driven legacy systems to big data-powered modern platforms. Now, with the explosion of GenAI and LLM applications, we're entering a new era where data warehouses must seamlessly integrate with AI workflows, support real-time agent interactions, and deliver extreme performance at scale. Apache Doris 4.0 emerges as the game-changer, combining enterprise-grade analytics with AI-native capabilities to meet the demands of today's intelligent applications.
The Evolution of Data Warehouses: From Legacy to AI-Native
Let's trace the journey of data warehouses and understand how AI is reshaping their core requirements.
Legacy Data Warehouses (BI-Driven)
The first generation of data warehouses separated analytical data from transactional systems to handle large volumes of historical data (e.g., daily trading reports for stockbrokers). However, they quickly hit walls in the big data era:
Scalability: Expensive hardware upgrades with limited horizontal scaling
Cost Efficiency: On-premise deployments required specialized hardware and high maintenance costs
Advanced Analytics: Poor support for real-time insights, AI, and ML
Flexibility: Rigid architectures unable to adapt to new use cases or diverse data sources
Modern Data Warehouses (Big Data-Driven)
Post-2000, the mobile internet and e-commerce boom drove the need for more agile analytics. Modern data warehouses addressed legacy limitations with:
Stateless Compute/Storage: Lower overhead for scaling resources
Low-Latency: Sub-second response times for user queries
High-Concurrency: Effortlessly handles thousands of concurrent workloads
Hybrid Workloads: Supports ad-hoc queries, ETL, and batch processing
Federated Queries: Breaks data silos by unifying access to data lakes, transactional DBs, and more
Data Warehouses in the AI Era
ISG Research predicts: "Through 2027, almost all enterprises developing GenAI applications will invest in data platforms with vector search and retrieval-augmented generation (RAG) to complement foundation models with proprietary data."
LLMs thrive on high-quality data—for both training and inference. AI-driven applications require data warehouses to:
Balance Volume & Quality: High-quality data directly impacts model performance
Dual-Purpose Data: Support both model training and real-time inference
Dynamic Freshness: Handle continuous data read/write with near-zero latency
Agent-Friendly: Enable autonomous AI agents to interact without human intervention
AI-First Design: Natively support LLM functions, vector storage, and high-performance vector I/O
The Paradigm Shift: Agentic-Facing Analytics
Traditional BI and OLAP systems are built for passive, historical reporting—few users running heavy queries with slow tolerance. AI changes this with agentic-facing analytics:
Proactive, autonomous AI agents that reason, analyze in real-time, and trigger actions
Workloads shift to: "Massive users (agents), light/iterative queries, zero latency tolerance"
Requires millisecond response times for thousands of concurrent queries
Legacy OLAP systems can't keep up—their pre-aggregated data cubes, batch processing, and data silos create bottlenecks for agentic workflows. The solution? A semantics-and-response-centric architecture that prioritizes flexibility, real-time access, and unified data context.
Apache Doris Outperforms Competitors: Benchmark Results
Apache Doris (and its commercial distribution VeloDB) sets a new standard for performance across key analytics benchmarks. We compared it against Snowflake and ClickHouse Cloud with equivalent compute resources (128 cores for VeloDB/ClickHouse, XL-size cluster for Snowflake) using Apache JMeter to measure QPS at 10/30/50 parallelisms.
Benchmark Overview
| Benchmark | Focus | Key Findings |
|---|---|---|
| SSB-FLAT | Single wide-table queries (no joins) | VeloDB outperforms Snowflake 4.76–7.39x, ClickHouse 4.76–6.92x |
| SSB (Star Schema) | Join-heavy analytics | VeloDB outperforms Snowflake 5.17–6.37x; ClickHouse failed most join queries |
| TPC-H | Complex ad-hoc decision support | VeloDB outperforms Snowflake 1.71–3.10x; ClickHouse couldn’t run all queries (Q20/Q21/Q22 failed) |
Key Takeaways
Complex Joins: Doris excels at join-heavy workloads (SSB/TPC-H) thanks to its advanced optimizer and execution engine
High Concurrency: Maintains performance at scale (50 parallelisms) while competitors struggle with memory or parsing errors
Wide-Table Performance: Even in single-table scans (SSB-FLAT), outperforms purpose-built systems like ClickHouse
Cost-Efficiency: Delivers more throughput per compute unit than Snowflake’s elastic architecture
Deep Dive: Apache Doris Core Technologies
Apache Doris’s performance and AI readiness stem from its innovative architecture. Let’s explore the key features powering its success.
1. Data Pruning: "Don’t Process Unnecessary Data"
The most efficient way to process data is to avoid processing it entirely. Doris uses two types of pruning:
Static Filters (Pre-Execution)
Partition Pruning: FE uses metadata to skip irrelevant partitions (e.g., time-based partitions outside a date range)
Key Column Pruning: Data is sorted by key columns—binary search narrows down the row range to scan
Value Column Pruning: Column files store min/max metadata to skip files that can’t match predicates
Dynamic Filters (Post-Execution)
For joins, filters are generated after building hash tables on the build side. This prunes irrelevant data on the probe side before joining, reducing join overhead.
2. Advanced Pruning Optimizations
LIMIT Pruning
Pushes LIMIT clauses down to data scanning—stops processing once the required number of rows is retrieved.
TopK Pruning
Optimizes TopK queries (e.g., "top 10 highest-grossing products") with:
Local truncation in scanning threads
Global merge sort via a coordinator
Two-phase execution: first sort key columns to get row indices, then fetch required columns—avoids full data scans
Join Pruning
Reduces probe-side data for hash joins:
Uses build-side hash table values to filter probe-side data
Minimizes data transfer and join computation (O(M+N) complexity vs. O(M*N) for Cartesian product)
3. Pipeline Engine: Efficient Execution at Scale
Doris uses a coroutine-like pipeline engine to maximize CPU utilization:
Yields CPU during blocking operations (disk I/O, network I/O in joins/exchanges)
Eliminates thread switching overhead with task scheduling triggered by external events (e.g., RPC completion)
Independent parallelism per pipeline (not constrained by tablet count)
Even data distribution to minimize skewing via local exchange optimization
Shared states across pipeline tasks (reduces initialization overhead)
4. Vectorized Query Execution
Processes data in batches (vectors) instead of row-by-row, leveraging:
SIMD (Single Instruction, Multiple Data) CPU instructions
Loop unrolling to reduce branch mispredictions
Accelerated compression, computation, and data processing
Delivers 2–10x performance gains for analytical queries
AI-Native Capabilities in Apache Doris 4.0
Apache Doris 4.0 is built for the AI era with native support for:
Vector Search: High-performance storage and retrieval of feature vectors for LLM inference
RAG Integration: Seamlessly connects with LLMs to augment generation with proprietary data
AI Functions: Built-in UDFs for ML/LLM workflows (e.g., embedding generation, text processing)
MCP Server: Native support for Model-as-a-Service integration
Agent Compatibility: Designed for programmatic access by AI agents with low-latency responses
Conclusion
The AI revolution demands data warehouses that are fast, flexible, and AI-native. Apache Doris 4.0 delivers on all fronts:
Outperforms competitors in complex joins, high concurrency, and wide-table analytics
Features like data pruning, pipeline engine, and vectorized execution enable millisecond response times
AI-native capabilities (vector search, RAG, agent support) integrate seamlessly with GenAI workflows
For teams building AI-driven applications, Apache Doris isn’t just a data warehouse—it’s the foundation for intelligent, real-time analytics that powers the next generation of products and decision-making.
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