Distributed SQL on Bare Metal: Why HTAP Databases Benefit from Dedicated Infrastructure

Distributed SQL databases emerged from a very practical frustration. As applications matured, teams found themselves stitching together transactional databases for day-to-day operations and analytical systems for reporting, monitoring, and decision-making. The data duplication, synchronization lag, and operational complexity of this split architecture eventually became harder to manage than the scale problems it was meant to solve.

HTAP systems – databases designed to handle both OLTP and OLAP workloads, promised a cleaner model: one logical database, one consistent view of data, and SQL semantics that developers already understood. That promise has largely been fulfilled. What often comes as a surprise, however, is how infrastructure behavior becomes the limiting factor once a system is both distributed and strongly consistent.

When you choose distributed SQL on bare metal dedicated servers as your database architecture, you’re choosing to unify transactional processing and analytical queries in a single system. That decision promises simplicity at the application layer but transfers all the complexity downward to the infrastructure.

The tradeoff is clear: fewer moving parts in your stack, more demands on your servers. For teams evaluating distributed SQL systems like TiDB, CockroachDB, YugabyteDB, or SingleStore, the infrastructure question isn’t optional. These databases amplify the impact of every microsecond of latency variance, every scheduling hiccup, every layer of abstraction between code and silicon.

This article explains why distributed SQL databases are infrastructure-sensitive by design, how virtualization compounds their pain points, and why bare metal infrastructure changes the performance equation.

What Distributed SQL Is, and What It Is Not

A distributed SQL database is a horizontally scalable database system that maintains both SQL semantics and strong consistency guarantees across multiple nodes. Unlike sharded MySQL deployments where your application manages data distribution, distributed SQL systems handle sharding transparently. Unlike traditional data warehouses that separate reads from writes, these systems process both workload types concurrently.

The architecture is shared-nothing. Each node stores a subset of data and processes queries independently. Coordination happens through consensus protocols (typically Raft or Paxos) which ensure that all nodes agree on the current state before committing transactions.

This is not a traditional warehouse where you batch-load data overnight. This is not a NoSQL system that trades consistency for availability. Distributed SQL databases attempt to preserve the transactional guarantees you expect from PostgreSQL while adding the scale-out capabilities you need for analytical workloads.

The defining characteristic of these systems is HTAP: Hybrid Transactional/Analytical Processing. They handle OLTP queries that modify single rows in milliseconds while simultaneously running OLAP queries that scan millions of rows across multiple nodes.

Why Distributed SQL Systems Are Infrastructure-Sensitive

• The consensus protocols that enable distributed SQL create continuous coordination overhead. Every write requires acknowledgment from a quorum of nodes. Every read must verify it’s viewing the most recent committed state. This coordination doesn’t happen once per query… it happens hundreds or thousands of times during query execution.
• Network latency matters, but latency variance matters more. A consistent 2ms round-trip is manageable. A network that delivers 1ms most of the time but spikes to 20ms occasionally will cause tail latencies that dominate your p99 performance. Distributed databases magnify these tail latencies because a single slow node can stall an entire distributed transaction.
• The read path in these systems doesn’t follow a simple request-response pattern. When you query data distributed across nodes, the coordinator node must fan out requests to multiple storage nodes, wait for responses, and merge results. If one storage node experiences a CPU scheduling delay or disk IO spike, the entire query waits. This pattern—where the slowest participant determines total latency—means infrastructure variance compounds exponentially as cluster size grows.
• Storage latency follows similar rules. Distributed SQL databases use consensus replication, which means every write goes to disk on multiple nodes before the transaction can commit. The commit latency is determined by the slowest replica. A 100-microsecond difference in disk IO between replicas becomes a visible delay in transaction throughput.
• East-west traffic amplification is another infrastructure dependency unique to distributed systems. When you write a row to a distributed SQL database, that write doesn’t stay on one node. It replicates to at least two other nodes immediately, then potentially propagates to additional nodes for analytics purposes. A single 1KB write can generate 10KB of cross-node traffic. Multiply that by thousands of concurrent transactions and you’re pushing serious bandwidth between nodes. Networking that looks adequate on paper often becomes the bottleneck in practice.

The Hidden Cost of Virtualization for Distributed Databases

Virtualization introduces non-determinism into systems that demand predictability. A CPU core in a virtual machine isn’t a dedicated core—it’s a timeshare on physical hardware managed by a hypervisor scheduler. When your database thread needs CPU, it might wait while the hypervisor services another tenant’s workload.

This scheduling jitter appears as occasional millisecond-scale delays in transaction processing. For a single-node database, a 5ms delay once every few seconds is negligible. For a distributed SQL system coordinating across 20 nodes, those delays stack up. If any node in a transaction’s quorum experiences a scheduling delay, the entire transaction waits.

NUMA awareness gets abstracted away in most virtualized environments. Modern servers have multiple CPU sockets, each with its own local memory. Accessing local memory takes 80 nanoseconds. Accessing memory attached to a different socket takes 140 nanoseconds. Physical deployments can pin database processes to specific NUMA nodes and allocate memory from local banks. Virtual machines typically can’t control this placement, leading to unpredictable memory latency patterns that hurt databases doing heavy in-memory processing.

Storage IO in virtualized environments goes through additional software layers. The hypervisor translates guest block device operations into host operations. Cloud providers add further abstraction with network-attached storage that appears as local disks but actually travels across internal networks. Each layer adds variance. A write that completes in 200 microseconds on bare metal might take anywhere from 300 to 3000 microseconds through a virtualized storage stack, depending on what else is happening on the host.

Network overlay systems in virtualized clouds add 10-50 microseconds of latency and introduce packet loss that doesn’t exist on physical networks. For distributed databases making thousands of inter-node RPC calls per second, these microseconds accumulate into meaningful performance degradation.

Why Bare Metal Changes the Equation

Bare metal infrastructure eliminates the abstraction layers that create non-determinism. When you deploy a distributed SQL database on dedicated servers, the CPU cores you see are the cores you control. There’s no hypervisor scheduler introducing unpredictable delays. Database threads run directly on hardware without competing for cycles with other tenants’ workloads.

• NUMA control becomes possible. You can configure your database processes to run on specific sockets and allocate memory from local banks. This eliminates cross-socket memory traffic and stabilizes memory access latency. For databases with large working sets held in memory, this control directly impacts query latency consistency.
• Storage IO becomes deterministic. Writes go directly to NVMe drives without passing through virtualization layers or network-attached storage systems. You get the full performance of the drive—not a time-shared fraction of it. More importantly, you get predictable latency. If your drive completes writes in 100 microseconds, it does so consistently, not just on average.
• Network topology is clean. Your database nodes connect through physical switches with fixed, measurable latency. There are no software-defined networking layers adding variance. No hypervisor packet processing. No virtual network functions inserting themselves into the data path. The network behaves deterministically, which means tail latencies stay bounded.
• Failure domains are explicit. When a node fails on bare metal, it fails cleanly. The database detects the failure and routes around it. In virtualized environments, failures can be ambiguous—is the VM dead or just slow? Is the storage array gone or temporarily overwhelmed? This ambiguity complicates failure detection and recovery, sometimes leading distributed systems to make incorrect assumptions about node health.

OpenMetal’s bare metal dedicated server infrastructure delivers this foundation with cloud-like provisioning. Each dedicated server includes 20Gbps private connectivity for cluster communication, enabling high-bandwidth replication and query execution without network saturation. The physical networking architecture supports up to 40Gbps burst capacity on egress, accommodating the east-west traffic amplification inherent in distributed databases.

Distributed SQL / HTAP Systems That Benefit from Bare Metal Dedicated Servers

• TiDB splits workloads between TiKV for row storage and TiFlash for columnar analytics. The OLTP side demands low-latency key-value operations, typically completing transactions in single-digit milliseconds. The OLAP side runs queries scanning millions of rows, relying on high memory bandwidth and sustained disk throughput. Both workloads benefit from NUMA-aware memory placement and NVMe storage with consistent IO latency. TiDB’s Raft consensus layer makes hundreds of thousands of RPCs per second across nodes, directly exposing any network latency variance.
• CockroachDB emphasizes strong consistency and geo-distribution. Every write propagates through a Raft group before committing, making storage latency and network latency equally important. CPU performance matters because CockroachDB does significant work at read time—merging versions, applying SQL execution plans, and handling distributed query coordination. The system scales horizontally by adding nodes, which increases east-west traffic proportionally. Predictable networking becomes more valuable as cluster size grows.
• YugabyteDB combines PostgreSQL compatibility with distributed consensus. The PostgreSQL query layer means workloads can be complex—joins, aggregations, analytical queries running alongside transactional workloads. The DocDB storage layer replicates data using Raft, creating the same storage and network sensitivity as other consensus-based systems. Teams migrating from PostgreSQL to YugabyteDB often discover their infrastructure assumptions don’t transfer. What worked with read replicas on a single server doesn’t work when queries distribute across 10 nodes with consensus overhead.
• SingleStore takes a different approach, using lock-free data structures and in-memory processing for OLTP while maintaining columnar storage for analytics. The architecture reduces consensus overhead compared to Raft-based systems, but it’s equally sensitive to memory latency and cross-node bandwidth. When SingleStore runs an OLAP query, it distributes work across all nodes and merges results at the coordinator. Any node experiencing CPU contention or memory pressure becomes a bottleneck for the entire query.

All four systems share a pattern: they trade coordination overhead for architectural simplicity. Instead of sharding your application or maintaining separate OLTP and OLAP databases, you get a single system that handles both workloads. The cost of that simplicity is infrastructure sensitivity. Performance becomes a function of your slowest node, your worst-case network latency, and your tail storage IO times.

Reference Architecture: Distributed SQL on Bare Metal

Role-based node pools address the fact that different parts of your cluster have different resource demands. Coordinator nodes handle query planning and result aggregation—they need CPU and memory but not necessarily fast storage. Storage nodes hold data and serve reads—they need NVMe drives and high memory bandwidth. Some teams run mixed-role deployments where every node does everything. Others separate roles, running coordinators on machines with different specs than storage nodes.

Network topology matters more than you’d expect. Your cluster should exist on a private network with dedicated VLANs isolating database traffic from other workloads. Inter-node communication should bypass internet gateways entirely. OpenMetal’s VLAN infrastructure supports this pattern, allowing distributed SQL clusters to communicate at wire speed without sharing bandwidth with public-facing services. Within the private network, VxLAN support enables further segmentation when running multiple clusters or separating production from staging environments.

Scaling patterns differ from cloud-native applications. You don’t scale distributed SQL databases by adding tiny instances. You add full-sized nodes that can independently handle their share of the data and query load. This means your minimum viable cluster might be three nodes of significant size rather than six small instances. Failure planning follows from node size: if each node represents 33% of your cluster’s capacity, losing one node is a major event. Most deployments run at least five or seven nodes to reduce the impact of individual failures.

The separation between control and data planes deserves attention. Client applications connect to coordinator nodes or load balancers fronting coordinators. These connections stay open, sometimes for minutes or hours in transaction processing systems. Storage nodes, meanwhile, only talk to coordinators and other storage nodes. This separation allows different tuning strategies: coordinators optimize for connection handling and query planning, storage nodes optimize for throughput and replication.

OpenMetal’s bare metal infrastructure simplifies this architecture by delivering fixed-cost servers without the per-instance pricing that makes cloud deployments expensive at scale. The included bandwidth allocations support typical distributed SQL traffic patterns, with transparent 95th percentile billing only for traffic exceeding baseline allotments.

When Bare Metal Is — and Isn’t — the Right Choice

Managed cloud databases make sense when operational overhead outweighs cost considerations. If you’re a three-person startup with no infrastructure expertise, paying extra for a managed service eliminates risk. The database provider handles replication, backups, upgrades, and failure recovery while you focus on product development. This tradeoff gets reevaluated as teams mature and workloads scale.

Team maturity determines whether bare metal becomes a constraint or an advantage. Operating databases on dedicated infrastructure requires understanding storage systems, network configuration, and failure scenarios. If your team already runs databases in production, these skills transfer directly. If you’re learning databases and infrastructure simultaneously, managed services reduce the learning curve.

Scale thresholds vary by workload, but a pattern emerges around sustained steady-state load. If you run 10 database nodes continuously for months, bare metal economics favor you. If your load spikes unpredictably from zero to thousands of queries per second and back to zero, elastic cloud autoscaling might justify its cost. Most production databases don’t follow the second pattern—they have predictable baseline load with manageable peaks.

The right choice often involves both approaches. Run production databases on bare metal dedicated servers for cost efficiency and performance predictability. Use managed cloud databases for development environments, proof-of-concepts, and short-lived projects where provisioning speed matters more than cost optimization. This hybrid approach captures the benefits of both models without forcing an all-or-nothing decision.

Why Platforms Like OpenMetal Are a Natural Fit

Bare metal infrastructure removes the performance penalties that distributed SQL databases can’t tolerate, but traditional bare metal procurement introduces operational friction that makes teams default to virtualized clouds despite the cost. OpenMetal addresses this tension by delivering dedicated hardware without the barriers that typically accompany it.

When you provision bare metal dedicated servers through OpenMetal, you’re getting actual physical machines—not VM instances masquerading as dedicated resources. Each server has dedicated CPU cores that never context-switch to serve other tenants’ workloads. The memory is physical RAM attached to specific NUMA nodes that you control. The NVMe drives respond to your database’s write commands without passing through hypervisor storage layers or network-attached storage arrays. This hardware exclusivity is what distributed SQL systems need to maintain consistent transaction latencies.

The networking infrastructure matters as much as the compute hardware. OpenMetal’s bare metal servers connect through 20Gbps private networking on dedicated VLANs isolated from other customers. When your distributed SQL cluster replicates data between nodes, that traffic travels on physical network interfaces connected to physical switches—no software-defined overlays, no shared bandwidth pools with unpredictable contention. The network path between database nodes is as deterministic as the servers themselves.

This private network design directly addresses the east-west traffic patterns that define distributed databases. Your cluster generates continuous replication traffic as Raft consensus protocols synchronize state across nodes. Query coordinators distribute work to storage nodes and aggregate results. These communication patterns create sustained bandwidth demands that benefit from dedicated network capacity rather than shared infrastructure with burst-oriented pricing.

The provisioning speed matters because it removes the procurement delay that makes bare metal impractical for many teams. You can deploy servers in minutes through a self-service interface rather than waiting weeks for hardware delivery and rack installation. This changes the economics of testing and scaling—you can provision a test cluster to validate performance characteristics, then scale to production size without hardware lead times blocking your migration timeline.

IPMI access provides the hardware-level control that separates true bare metal from virtualized “dedicated” instances. You can access the server console directly, modify BIOS settings to tune NUMA configurations, and install operating systems from ISO images. This level of access enables the performance tuning that distributed databases require: pinning database processes to specific CPU cores, configuring memory allocation policies, and optimizing interrupt handling for network-intensive workloads.

The pricing model eliminates the cost uncertainty that comes with usage-based cloud billing. Each bare metal server has a fixed monthly cost regardless of CPU utilization, network traffic within your private VLAN, or storage IO patterns. When you size a distributed SQL cluster for peak load, you pay the same amount whether you’re serving 1,000 queries per second or 100,000. This makes capacity planning straightforward and removes the perverse incentive to under-provision infrastructure to control costs.

The combination of instant provisioning and long-term price locks addresses the planning challenges that make bare metal difficult. You can reserve servers for up to five years at fixed monthly rates, providing budget predictability for multi-year infrastructure plans. But unlike traditional colocation contracts, you’re not committing to physical hardware you can’t modify—you’re committing to pricing while retaining the flexibility to adjust configurations as your database needs evolve.

For teams running distributed SQL databases at scale, the value proposition becomes clear: you get the performance characteristics of owned hardware without the capital expenditure, the provisioning speed of cloud infrastructure without the virtualization overhead, and the cost predictability of fixed contracts without the inflexibility of long-term hardware commitments. This combination makes bare metal accessible to teams that previously couldn’t justify the operational complexity of managing physical infrastructure.

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Aligning Database Architecture with Physical Reality

Distributed SQL databases relocate complexity from the application layer to the infrastructure layer. This architectural decision creates dependencies on network latency, storage consistency, and CPU predictability that affect every transaction. The systems amplify infrastructure problems rather than abstracting them away, making infrastructure quality a direct determinant of application performance.

Virtualization adds layers of indirection that create exactly the variance these databases can’t tolerate. Bare metal removes those layers, delivering the deterministic performance that coordination-heavy systems require. The choice isn’t about raw throughput—it’s about whether your infrastructure can deliver consistent latency at percentiles that matter.

As distributed SQL systems mature and team expertise grows, the infrastructure conversation shifts from “can we run this?” to “where should we run this?”. The answer increasingly involves dedicated infrastructure that prioritizes predictability over flexibility, recognizing that database performance depends more on avoiding worst-case behavior than achieving best-case benchmarks.

Talk to OpenMetal About Your Database Needs

If dedicated infrastructure with long-term stability aligns with your priorities, the OpenMetal team can help you evaluate whether the platform fits your specific requirements.

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