Greenplum vs Apache Doris: Features, Performance, and Advantages Compared

As organizations increasingly rely on data for real-time decision-making, the demand for scalable, high-performance analytical databases has never been higher. Among the contenders in the modern analytics space, Greenplum and Apache Doris stand out for their MPP (Massively Parallel Processing) architectures and ability to handle large-scale data workloads. While both are designed for analytics, they differ significantly in architecture, performance, and ease of management. This article provides a side-by-side comparison to help data teams evaluate which solution better aligns with their technical and business needs.

1. Overview and Architecture

Greenplum

Greenplum is an open-source distributed relational database based on PostgreSQL. It adopts an MPP (Massively Parallel Processing) architecture, designed specifically for large-scale data analytics. Its architecture consists of three main components:

• Master Node: The control center of the system. It receives SQL requests, generates query plans, and coordinates task execution on Segment nodes. Typically, one primary and one standby master node are deployed.
• Segment Nodes: Responsible for parallel computation and data storage. The system scales linearly in performance and capacity by adding nodes, supporting anywhere from 1 to 10,000 nodes.
• Interconnect: A high-speed network (e.g., gigabit or 10-gigabit switches) enabling data transmission between the Master and Segment nodes. It supports parallel data loading.

Greenplum is designed for distributed computation to handle large-scale data analytics but introduces architectural complexity. Its Master node can become a performance bottleneck due to its single-point nature.

Apache Doris

Apache Doris is a high-performance, real-time analytical database that also uses an MPP architecture. It is known for being efficient, simple, and unified. The architecture uses a tightly coupled compute-storage design and mainly includes:

• Frontend (FE): Handles user requests, query parsing, metadata management, and node scheduling. It supports multi-node deployment for high availability.
• Backend (BE): Manages data storage and query execution. Data is stored in shards with multiple replicas.

Doris offers a streamlined architecture that is easy to deploy and maintain. It supports the MySQL protocol and standard SQL, providing excellent compatibility. It can process large-scale queries in sub-second latency and supports both high-concurrency point queries and high-throughput analytical workloads.

2. Feature Comparison

The table below compares key features of Greenplum and Apache Doris. 

Summary

• Greenplum: Based on PostgreSQL, excels in bulk analytics and traditional data warehousing, offering rich SQL support and parallel processing. However, its complex architecture, high maintenance cost, and weak real-time and concurrency capabilities make it unsuitable for modern analytics.
• Apache Doris: With a streamlined MPP design, Doris excels in real-time analytics, concurrency, and lakehouse integration. Features like incremental aggregation, compute-storage separation, vectorized engines, and cost-based optimization make it performant, easy to manage, and ecosystem-friendly.

3. Use Cases and Ecosystem

Greenplum

Greenplum mainly targets bulk data analytics scenarios like traditional data warehousing and ETL tasks. Its ecosystem is relatively closed, lacking integration with modern data lakes and real-time data streams.

Apache Doris

Apache Doris supports a wide range of modern analytics scenarios, including:

• Real-Time Analytics:

  • Real-time dashboards and decision-making

  • Interactive ad hoc analysis

  • User behavior analytics and profiling

• Lakehouse Analytics:

  • Accelerated querying of Iceberg, Hudi, etc.

  • Cross-source federation to eliminate data silos

• Semi-Structured Data Analytics:

  • Log and event analysis from distributed systems

Ecosystem Compatibility: Doris supports the MySQL protocol and standard SQL. It integrates seamlessly with BI tools such as Tableau, Power BI, and Apache Superset, offering low migration costs.

4. Technical Innovations

Greenplum

Greenplum builds on PostgreSQL’s technology, using row-based storage and MPP. However, it lacks advanced feature extensions and modern innovation.

Apache Doris

Doris introduces multiple technical innovations:

• Columnar Storage: Combines sorted compound keys, bloom filters, and inverted indexes to optimize performance and compression.
• Flexible Storage Models: Supports detail, primary key, and aggregation models for various scenarios.
• Materialized Views: Includes strong-consistency single-table and async multi-table materialized views for simplified modeling.
• Execution Engine: Implements vectorized and pipeline engines for sub-second querying.
  

5. Conclusion

In summary, Apache Doris outperforms Greenplum in features, performance, and applicability:

• More Comprehensive Features: Doris supports incremental aggregation, high concurrency, and data lake integration.
• Superior Performance: Benchmark tests show Doris can be several times faster in multi-table joins and complex queries.
• Broader Use Cases: Covers real-time, lakehouse, and semi-structured data analytics with strong ecosystem compatibility.

Greenplum remains suitable for traditional batch analytics, but its complex architecture, limited scalability, and closed ecosystem hinder its use in modern data platforms. Apache Doris offers high availability, compatibility, real-time capabilities, and technical innovations, making it a future-proof choice for building unified and efficient analytical platforms.