Using Greenplum to Build a Massively Parallel Processing (MPP) Data Warehouse on OpenMetal

Data architects today face an increasingly complex challenge: building data warehouses that can handle massive volumes while maintaining query performance and cost predictability. Traditional single-server databases reach their limits as data volumes grow into the terabyte and petabyte ranges. This is where massively parallel processing (MPP) architectures like Greenplum become indispensable.

Greenplum represents a mature, battle-tested approach to distributed data warehousing that combines the familiarity of PostgreSQL with the scalability of MPP architecture. When deployed on the right infrastructure, Greenplum can deliver the performance and cost advantages that data teams need for their most demanding analytical workloads.

The Rise of MPP Data Warehouses and Greenplum’s Position

The data warehouse landscape has evolved from the days when organizations could rely on scaling up single-server systems. According to VLDB Solutions, “MPP’s track record running analytics at the world’s biggest companies dates back to the 1980s. No other analytic architecture can make this bold claim.” This longevity demonstrates the proven nature of the MPP approach.

Greenplum is a big data technology based on MPP architecture and the Postgres open source database technology. The platform was originally created around 2005 and has since evolved through various ownership changes, including EMC Corporation, Pivotal Software, and currently VMware (now Broadcom). Despite these transitions, the core technology has remained focused on solving large-scale analytical challenges.

What sets Greenplum apart from newer cloud-native solutions is its foundation in proven PostgreSQL technology. Greenplum Database is based on PostgreSQL open source technology. It is essentially several PostgreSQL disk-oriented database instances acting together as one cohesive database management system (DBMS). This means data teams can leverage existing PostgreSQL skills while gaining the benefits of horizontal scalability.

The competitive landscape includes other MPP systems like Teradata, Amazon Redshift, and Microsoft Azure SQL Data Warehouse. However, according to VLDB Solutions, a Gartner vendor analysis published in March ’19 for the ‘Traditional Data Warehouse’ use case ranked Pivotal (now VMware Tanzu) Greenplum as a very close 3rd behind Oracle Exadata in 2nd place, ranking higher than SAP HANA, Google BigQuery, IBM DB2, Snowflake, Amazon Redshift, and Microsoft Azure SQL Data Warehouse.

For organizations evaluating their options, Greenplum provides several advantages: it runs on standard hardware rather than proprietary appliances, supports both on-premises and cloud deployments, and offers the flexibility of open source licensing. These characteristics make it particularly well-suited for deployment on OpenMetal’s bare metal infrastructure.

Understanding Greenplum’s Massively Parallel Processing Architecture

To appreciate why Greenplum performs so well on OpenMetal’s infrastructure, you need to understand its architectural components and how they work together to process queries in parallel across multiple servers.

The Coordinator-Segment Model

The Greenplum Database coordinator is the entry to the Greenplum Database system, accepting client connections and SQL queries, and distributing work to the segment instances. This coordinator acts as the central point for query planning and result coordination, while the actual data processing happens on distributed segment nodes.

Greenplum Database segment instances are independent PostgreSQL databases that each store a portion of the data and perform the majority of query processing. Each segment operates as a complete PostgreSQL instance, handling its portion of tables and participating in distributed query execution.

The beauty of this architecture lies in its simplicity and proven PostgreSQL foundation. When you submit a query to Greenplum, the coordinator parses and plans the query, then distributes the work to segments that process their data portions in parallel. Results are collected and aggregated before being returned to the client.

Storage and Processing Distribution

Greenplum’s approach to data distribution provides both performance and scalability benefits. User-defined tables and their indexes are distributed across the available segments in a Greenplum Database system; each segment contains a distinct portion of data. This distribution happens automatically based on distribution keys you specify when creating tables.

The system supports multiple storage formats optimized for different use cases. Greenplum Database can use the append-optimized (AO) storage format for bulk loading and reading of data, and provides performance advantages over HEAP tables. Append-optimized storage provides checksums for data protection, compression and row/column orientation.

For analytical workloads, the columnar storage option becomes particularly valuable. Greenplum Database has the option to use column storage, data that is logically organized as a table, using rows and columns that are physically stored in a column-oriented format, rather than as rows. This columnar approach significantly improves performance for analytical queries that scan large datasets but only need specific columns.

Network Architecture Requirements

The interconnect between Greenplum nodes plays a crucial role in overall system performance. The interconnect refers to the inter-process communication between segments and the network infrastructure on which this communication relies. The Greenplum interconnect uses a standard Ethernet switching fabric. For performance reasons, a 10-Gigabit system, or faster, is recommended.

This network requirement aligns perfectly with OpenMetal’s infrastructure design. Our servers feature dual 10Gbps NICs (20Gbps total) with unmetered intra-cluster traffic and dedicated VLANs that ensure Greenplum segment-to-segment communication avoids noisy-neighbor effects. This dedicated network infrastructure is crucial for query distribution and parallelism.

Why OpenMetal’s Infrastructure Excels for Greenplum Deployments

MPP databases like Greenplum have specific infrastructure requirements that differ significantly from traditional single-server databases. The distributed nature of the workload demands consistent performance across multiple nodes, substantial memory capacity, fast storage, and reliable high-bandwidth interconnects.

Bare Metal Performance Advantages

Our bare metal approach eliminates the “hypervisor tax” that virtualized environments impose on memory-intensive MPP workloads. In public cloud environments, hypervisor overhead can consume 10-15% of system resources that could otherwise be available to your Greenplum segments. This overhead becomes particularly problematic for memory-intensive analytical queries that need to process large datasets in RAM.

When you deploy Greenplum on OpenMetal, you get full root access to tune JVM settings, configure NUMA topology, and optimize Linux kernel parameters specifically for Greenplum’s PostgreSQL-based architecture. This level of control is essential for extracting maximum performance from your MPP deployment.

The consistent performance characteristics of bare metal also eliminate the unpredictable performance variations common in public cloud environments. Greenplum’s query optimizer relies on consistent performance characteristics to make optimal execution decisions. When segment performance varies unpredictably due to noisy neighbors, query plans become less effective.

Hardware Configurations Optimized for MPP Workloads

Our current V4 generation hardware provides multiple configurations optimized for different Greenplum deployment scenarios:

Medium V4 servers (2x12C/24T Intel Xeon Scalable 4510, 256GB RAM, 6.4TB Micron 7450 MAX NVMe) serve well for smaller segment deployments or development environments where you need to test Greenplum functionality without the overhead of a full production cluster.

Large V4 configurations (2x16C/32T Xeon Gold 6526Y, 512GB RAM, 2×6.4TB Micron 7450 MAX NVMe expandable) provide balanced options for Greenplum segment hosts. The combination of substantial memory and fast NVMe storage supports both the memory requirements of complex analytical queries and the I/O demands of distributed data processing.

XL V4 servers (2x32C/64T Xeon Gold 6530, 1TB RAM, 4×6.4TB Micron 7450 MAX NVMe expandable to 8) excel for high-memory processing scenarios. These configurations handle memory-intensive operations like large joins and aggregations that are common in data warehouse workloads.

XXL V4 configurations (2x32C/64T Xeon Gold 6530, 2TB DDR5 RAM, 6×6.4TB Micron 7450 MAX NVMe totaling 38.4TB) provide massive capacity for large-scale data warehouse workloads. These servers can handle the most demanding segment workloads while maintaining the memory ratios needed for optimal Greenplum performance.

Storage Architecture for Data Warehouses

For organizations building petabyte-scale deployments, our Storage Large configurations with spinning drives and NVMe caching provide the massive storage capacity needed while maintaining performance through intelligent caching layers. This tiered storage approach allows you to store historical data on cost-effective spinning drives while keeping frequently accessed data on high-performance NVMe.

The storage architecture also supports Greenplum’s external table functionality. We’ve deployed complete ETL pipelines for customers using Apache Spark for data processing, Delta Lake for storage, and Ceph object storage clusters that serve as S3-compatible storage tier for Greenplum external tables. This integrated approach creates end-to-end analytics workflows that span ingestion, processing, and analysis.

Deployment Architecture: Building Your Greenplum Cluster on OpenMetal

Deploying Greenplum effectively requires careful planning of your cluster topology, hardware allocation, and network configuration. The distributed nature of MPP systems means that proper initial setup significantly impacts long-term performance and operational efficiency.

Cluster Topology Planning

A typical production Greenplum deployment on OpenMetal follows a multi-tier architecture that separates different functional roles across dedicated servers. This separation allows for independent scaling and performance optimization of each component.

Coordinator Nodes: Deploy your Greenplum coordinator on dedicated hardware, typically using our Medium V4 or Large V4 configurations. The coordinator is where the global system catalog resides. The global system catalog is the set of system tables that contain metadata about the Greenplum Database system itself. The coordinator does not contain any user data; data resides only on the segments. Since the coordinator primarily handles query planning and coordination rather than data processing, it doesn’t require the same level of resources as segment nodes.

For high availability, you may optionally deploy a backup or mirror of the coordinator instance. A backup coordinator host serves as a warm standby if the primary coordinator host becomes nonoperational. The standby coordinator should be deployed on a separate server to ensure availability during hardware maintenance or failures.

Segment Host Configuration: A segment host typically runs from two to eight Greenplum segments, depending on the CPU cores, RAM, storage, network interfaces, and workloads. Our Large V4 and XL V4 configurations provide the ideal balance of CPU cores, memory, and storage for segment hosts.

The key principle for segment deployment is maintaining consistent hardware across all segment hosts. Segment hosts are expected to be identically configured. The key to obtaining the best performance from Greenplum Database is to distribute data and workloads evenly across a large number of equally capable segments so that all segments begin working on a task simultaneously and complete their work at the same time.

Network Configuration and Interconnect Setup

Proper network configuration is crucial for Greenplum performance. Depending on the number of interfaces available, you will want to distribute interconnect network traffic across the number of available interfaces. This is done by assigning segment instances to a particular network interface and ensuring that the primary segments are evenly balanced over the number of available interfaces.

Our dual 10Gbps NIC configuration on each server enables this multi-interface approach. You can configure separate host address names for each network interface, allowing the operating system to automatically select the best path for interconnect traffic while maintaining load balance across all available bandwidth.

The dedicated VLAN configuration we provide ensures that Greenplum interconnect traffic remains isolated from other network activity. This isolation prevents performance degradation from other applications and provides predictable network performance for distributed query processing.

Storage Layout and File System Optimization

Each CPU is typically mapped to a logical disk. A logical disk consists of one primary file system (and optionally a mirror file system) accessing a pool of physical disks through an I/O channel or disk controller. Our NVMe storage configuration aligns perfectly with this architecture, providing high-performance storage directly attached to each segment host.

The file system configuration should account for Greenplum’s I/O patterns, which include both sequential scans for analytical queries and random I/O for transactional operations. Our Micron 7450 and 7500 MAX NVMe drives provide excellent performance for both access patterns while maintaining the capacity needed for large data warehouse deployments.

Performance Advantages: Bare Metal vs. Virtualized Environments

The performance characteristics of your underlying infrastructure directly impact Greenplum’s ability to execute complex analytical queries efficiently. Understanding these performance differences helps explain why bare metal infrastructure provides significant advantages for MPP deployments.

Memory Performance and NUMA Optimization

Greenplum’s query execution engine relies heavily on memory for operations like hash joins, aggregations, and sorting. In virtualized environments, memory access patterns become unpredictable due to hypervisor overhead and memory balloon drivers that can dynamically relocate memory pages.

On bare metal, you can configure NUMA (Non-Uniform Memory Access) topology to optimize memory access patterns for Greenplum segments. This involves binding segment processes to specific CPU cores and their local memory banks, reducing memory access latency and improving overall query performance.

The consistent memory performance also benefits Greenplum’s shared buffer management. The internals of PostgreSQL have been modified or supplemented to support the parallel structure of Greenplum Database. For example, the system catalog, optimizer, query executor, and transaction manager components have been modified and enhanced to be able to run queries simultaneously across all of the parallel PostgreSQL database instances. These modifications work most effectively when memory performance is predictable and consistent.

Storage I/O Performance and Latency

Analytical queries often require scanning large portions of tables, making storage performance a critical factor in overall query response times. Our NVMe storage provides sub-millisecond latency for both sequential and random I/O operations, significantly outperforming the network-attached storage typically used in public cloud environments.

The direct-attached nature of our NVMe storage also eliminates network bottlenecks that can occur with cloud storage services. When Greenplum segments need to scan large tables or perform sort operations that spill to disk, the local NVMe storage provides consistent, high-bandwidth access without competing with network traffic from other tenants.

CPU Performance and Clock Speed Consistency

Modern analytical workloads benefit significantly from high CPU clock speeds, especially for operations like complex joins and aggregations that don’t parallelize perfectly. Public cloud instances often use CPU models optimized for density rather than peak performance, and they may implement CPU throttling during high utilization periods.

Our Xeon Gold 6530 processors in the XL V4 and XXL V4 configurations provide consistent high clock speeds without throttling. This consistency is particularly important for Greenplum’s cost-based optimizer, which makes decisions based on expected CPU costs for different operations.

Cost Predictability: Fixed Pricing vs. Cloud Usage Spikes

One of the most significant challenges data teams face with public cloud deployments is cost predictability. Greenplum’s continuous query workloads and large data volumes can trigger massive usage spikes that are difficult to predict and budget for.

The Challenge of Public Cloud Pricing for MPP Workloads

Public cloud pricing models work well for applications with predictable, steady-state resource usage. However, MPP data warehouses like Greenplum create several cost challenges in public cloud environments:

Continuous Resource Utilization: Unlike web applications that may have clear usage patterns, data warehouses often run continuous ETL processes and analytical queries. This steady-state utilization eliminates the cost benefits of public cloud’s pay-as-you-go model while subjecting you to premium hourly rates.

Data Egress Costs: Greenplum deployments often involve moving large datasets between staging areas, processing nodes, and external systems. Public cloud providers charge significant fees for data egress, and these costs can quickly exceed the infrastructure costs themselves.

Storage Costs for Large Datasets: Data warehouses require substantial storage capacity, and public cloud storage costs scale linearly with capacity. For petabyte-scale deployments, storage costs can become prohibitive.

OpenMetal’s Fixed-Cost Advantage

Our fixed-cost model eliminates the unpredictable expenses that affect data warehouse projects in public clouds. You pay a predictable monthly price for dedicated hardware, regardless of utilization levels. This predictability allows for accurate budget planning and eliminates the risk of cost spikes during periods of heavy analytical activity.

Our monthly pricing with 95th percentile egress billing provides budget predictability that data teams need for long-term projects handling constant data ingestion and export. This model is much more predictable than hyperscalers’ per-GB charges, especially for workloads that involve frequent data movement.

The cost advantages become more pronounced as deployments scale. While public cloud costs increase linearly (or sometimes exponentially) with resource usage, our bare metal pricing scales more efficiently, particularly for large, stable workloads like data warehouses.

Total Cost of Ownership Considerations

When evaluating the total cost of ownership for Greenplum deployments, consider factors beyond just infrastructure costs:

Software Licensing: Greenplum’s open source licensing eliminates the database licensing costs that can be substantial with proprietary solutions. This advantage is particularly significant for large deployments with many CPU cores.

Operational Efficiency: The predictable performance characteristics of bare metal infrastructure reduce the operational overhead of performance tuning and troubleshooting. Your data engineering team spends less time managing infrastructure performance issues and more time developing analytics solutions.

Data Transfer Costs: For organizations that need to frequently move data between systems or export large datasets for processing, our inclusive bandwidth model provides significant cost savings compared to public cloud egress fees.

Scale-Out Capabilities and Hardware Optimization

One of Greenplum’s core strengths is its ability to scale horizontally by adding additional segment hosts to a cluster. This scale-out capability aligns perfectly with OpenMetal’s flexible provisioning model, allowing you to start with a smaller cluster and expand as your data volumes and query loads grow.

Horizontal Scaling Architecture

The scalability of Greenplum Database is provided by its MPP architecture. Users can add segment hosts to the array to increase processing power and storage capacity. This approach provides several advantages over traditional scale-up approaches:

Linear Performance Scaling: When properly configured, adding segment hosts provides nearly linear improvements in query performance for scan-intensive analytical workloads.

Independent Component Scaling: You can add storage capacity, processing power, or both, depending on your specific bottlenecks.

Online Scaling: New segment hosts can be added to a running cluster without requiring downtime for existing operations.

Hardware Right-Sizing for Different Roles

Our fast scale-out capabilities allow adding segment hosts in approximately 20 minutes, and custom hardware configurations are available for specific requirements. This flexibility enables you to optimize hardware configurations for different roles within your Greenplum cluster:

Compute-Intensive Segments: For workloads involving complex calculations, machine learning algorithms, or intensive data transformations, deploy XL V4 or XXL V4 configurations with high core counts and substantial memory.

Storage-Intensive Segments: For workloads primarily involving large table scans and data archival, consider configurations optimized for storage capacity and sequential I/O performance.

Balanced Segments: For mixed workloads, Large V4 configurations provide an optimal balance of compute, memory, and storage resources.

Greenplum-Specific Optimization Techniques

The flexibility of bare metal infrastructure allows for Greenplum-specific optimizations that aren’t possible in virtualized environments:

Segment-to-Core Mapping: Deploy the optimal number of Greenplum segments per server based on the specific CPU architecture and core count, typically following the recommendation of one segment per CPU core.

Memory Allocation Tuning: Configure PostgreSQL shared buffers, work memory, and other memory-related parameters based on the physical memory available and the number of segments per host.

I/O Scheduler Optimization: Tune Linux I/O schedulers and kernel parameters specifically for Greenplum’s I/O patterns, which differ significantly from general-purpose database workloads.

Production-Ready: ETL Integration and Operational Excellence

Deploying Greenplum successfully requires more than just the database itself. Production deployments need robust ETL capabilities, monitoring solutions, and operational procedures that ensure reliable performance and availability.

ETL Infrastructure and Data Loading

Greenplum supports fast, parallel data loading with its external tables feature. By using external tables in conjunction with Greenplum Database’s parallel file server (gpfdist), administrators can achieve maximum parallelism and load bandwidth from their Greenplum Database system.

We’ve deployed complete ETL pipelines for customers using Apache Spark for data processing, Delta Lake for storage, and Ceph object storage clusters that serve as S3-compatible storage tier for Greenplum external tables, all integrating for end-to-end analytics workflows. This integrated approach provides several benefits:

Unified Storage Layer: Using Ceph object storage as an S3-compatible tier allows Greenplum external tables to access data processed by Spark and other tools without requiring data movement.

Parallel Data Loading: The gpfdist program can serve data to the segment instances at an average rate of about 350 MB/s for delimited text formatted files and 200 MB/s for CSV formatted files. By deploying multiple gpfdist instances across multiple NICs, you can achieve aggregate loading rates that saturate even high-bandwidth network connections.

Scalable Processing: Apache Spark clusters deployed on OpenMetal can handle data transformation and preparation at massive scale before loading into Greenplum, creating efficient end-to-end data pipelines.

Operational Excellence and Support

Our engineer-to-engineer support through dedicated Slack channels means data architects get direct access to infrastructure experts who understand both the hardware requirements and operational challenges of running production MPP clusters at scale. This support model provides several advantages:

Faster Issue Resolution: Direct access to engineers who understand both the infrastructure and the specific requirements of MPP workloads reduces troubleshooting time.

Proactive Optimization: Our team can provide recommendations for infrastructure optimizations based on your specific Greenplum usage patterns and performance requirements.

Architecture Consultation: Beyond basic support, our engineers can help design optimal cluster topologies and hardware configurations for your specific use cases.

High Availability and Disaster Recovery

Production Greenplum deployments require robust high availability and disaster recovery capabilities. When mirroring is enabled in a Greenplum Database system, the system automatically fails over to the mirror copy if a primary copy becomes unavailable.

Our infrastructure supports multiple high availability configurations:

Segment Mirroring: Deploy primary and mirror segments across different physical hosts to ensure availability during hardware failures.

Coordinator Standby: Configure standby coordinator instances on separate hardware to provide coordinator-level redundancy.

Cross-Data Center Replication: For disaster recovery, our multiple data center locations allow for cross-site replication and failover capabilities.

The infrastructure is backed by Tier III data centers with uptime SLAs, providing the foundation reliability that production MPP clusters require.

Getting Started With Your Greenplum MPP Deployment

Building a production-ready Greenplum cluster on OpenMetal involves several planning and implementation phases. The following approach provides a roadmap for successful deployment that scales from initial proof-of-concept through production deployment.

Planning Your Initial Deployment

Start with a clear assessment of your current data volumes, query patterns, and growth projections. This assessment should include:

Current Data Warehouse Requirements: Analyze your existing data volumes, query complexity, and performance requirements. Understanding current pain points helps size the initial cluster appropriately.

Growth Projections: Plan for both data volume growth and increased user adoption. Greenplum’s scale-out architecture makes it easier to accommodate growth, but initial planning ensures efficient resource utilization.

Integration Requirements: Identify existing ETL tools, BI platforms, and data sources that need to integrate with Greenplum. This helps determine network requirements and storage configurations.

Proof of Concept Implementation

Begin with a smaller cluster that allows you to validate Greenplum’s suitability for your specific workloads:

Development Cluster: Deploy a 3-node cluster using Medium V4 or Large V4 configurations. This provides enough capacity to test query patterns and performance characteristics without the cost of a full production deployment.

Data Migration Testing: Use Greenplum’s external tables and gpfdist capabilities to test data loading from your existing sources. This validates ETL processes and identifies potential performance bottlenecks.

Query Performance Validation: Run representative analytical queries against realistic data volumes to validate that Greenplum meets your performance requirements.

Production Deployment Strategy

Based on proof-of-concept results, design your production cluster topology:

Cluster Sizing: Use insights from testing to determine the appropriate number and configuration of segment hosts. Consider both current requirements and near-term growth projections.

Hardware Configuration: Select hardware configurations that match your workload characteristics. Memory-intensive analytical workloads benefit from XL V4 or XXL V4 configurations, while balanced workloads may perform well on Large V4 servers.

Network and Storage Design: Configure multi-NIC networking for optimal interconnect performance and plan storage layout based on your data retention and performance requirements.

The combination of Greenplum’s proven MPP architecture and OpenMetal’s optimized bare metal infrastructure provides a powerful foundation for next-generation data warehouse deployments. To explore how this approach can address your specific data warehouse requirements, contact our team for a detailed consultation.

For more information about building modern data platforms, explore our resources on big data infrastructure options and data warehouse modernization strategies.