Shared nothing architecture is a distributed system design where each node operates independently, with its own CPU, memory, and storage, and no shared state between nodes.
This architecture enables systems to scale horizontally, isolate failures, and deliver predictable performance under high load. It is the foundation of modern distributed databases built for real-time analytics and high-ingestion workloads.
By eliminating shared resources, shared nothing architecture allows systems to scale simply by adding nodes, without introducing centralized bottlenecks or coordination overhead.
What Is Shared Nothing Architecture?
Shared nothing architecture is an approach to building distributed systems in which:
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Each node owns its compute, memory, and storage
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No data structures, disks, or memory are shared across nodes
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Nodes communicate only through network-based messaging
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Data is partitioned and distributed across the cluster
Because each node is autonomous, workloads can be processed in parallel, and failures remain isolated to individual nodes rather than cascading across the system.
This design contrasts sharply with architectures that rely on shared storage or shared memory, which often struggle to scale beyond a limited number of nodes.
Benefits of shared-nothing design
| Advantage | Impact |
|---|---|
| No single point of failure | Continuous uptime and data availability |
| Linear scalability | Add nodes without reconfiguring the cluster |
| Workload isolation | Each node operates independently, preventing contention |
| Automatic rebalancing | Data redistributes evenly as nodes join or leave |
| Simplicity | Uniform configuration across all nodes |
Core Principles of Shared Nothing Architecture
Shared nothing systems are built around a few fundamental principles:
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Node Independence: Each node can operate, scale, and fail independently without impacting the rest of the cluster.
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Horizontal Scalability: Capacity and performance increase by adding nodes rather than upgrading hardware.
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Data Partitioning: Data is sharded across nodes, allowing queries and writes to execute in parallel.
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Fault Isolation: A failure affects only the data and workloads handled by the failed node, not the entire system.
These principles make shared nothing architecture especially well suited for distributed databases handling high volumes of continuously arriving data.
Shared Nothing vs Shared Disk and Shared Memory Architectures
Understanding shared nothing architecture is easier when compared to other common distributed designs.
Shared Nothing Architecture:
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No shared disk or memory
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Linear horizontal scaling
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High fault tolerance
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Ideal for large-scale distributed databases
Shared Disk Architecture:
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Centralized storage shared across nodes
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Compute can scale, storage becomes a bottleneck
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Failure domains are larger
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Common in traditional clustered databases
Shared Memory Architecture:
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Nodes access the same memory space
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Limited scalability
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Tight coupling between components
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Typically constrained to small systems
For systems that must scale to many nodes while maintaining consistent performance, shared nothing architecture offers clear advantages.
How CrateDB Implements Shared Nothing Architecture
CrateDB builds on the shared-nothing foundation to deliver real-time analytics and search across massive datasets:
- Automatic replication: Data is copied across independent nodes for redundancy.
- Distributed execution: Queries run where the data lives, minimizing network traffic.
- Self-healing clusters: If a node fails or re-joins, CrateDB automatically rebalances data.
- Rolling upgrades: Updates and maintenance happen without service disruption.
Together, these features make CrateDB an always-on distributed database capable of handling real-time workloads at global scale.
Cluster state management of CrateDB
CrateDB’s cluster state management is the coordination layer that keeps every node in sync.
It ensures that all nodes share a consistent, up-to-date view of the cluster (its configuration, topology, and data distribution), so the system can operate reliably at scale.
Each node maintains a versioned copy of the current cluster state.
However, only one node (the master node) is authorized to make state changes at runtime.
At any point in time, there can be only one active master, elected by a quorum of master-eligible nodes.
Once a master is elected, the cluster becomes fully operational and ready to handle queries.
When the master node updates the cluster state, it publishes the new version to all other nodes and waits for acknowledgments before proceeding with the next update.
This approach guarantees strong consistency and prevents conflicting changes across the cluster.
The cluster state includes all metadata required for coordination and fault-tolerant operation, such as:
- Global cluster configuration and settings
- Discovered nodes and their health status
- Table schemas and mappings
- Locations and status of primary and replica shards
