Vertical scaling
Vertical scaling is increasing or decreasing a system’s capacity by changing the resources available to an existing machine, node, or instance.
Elasticsearch in Action, Second Edition
Vertical scaling increases system capacity by increasing the resources of existing machines, such as memory or CPU capacity.
- How it is used
- Handles Elasticsearch capacity growth by enlarging existing machines when data or query load increases.
- How deep it goes
- Explains resource increases, same-machine nodes, downtime, and failure concentration, but stops short of a step-by-step upgrade procedure.
- Examples
- Uses five 2-TB servers to meet a 10-TB requirement across five indexes and discusses additional nodes on one machine.
- What you can do after
- Evaluate capacity strategies while anticipating maintenance downtime and the risks of concentrating data on one machine.
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Building Web APIs with ASP.NET Core
Vertical scaling is increasing the processing capacity of a single server or node.
- How it is used
- Frames database capacity planning as a choice between strengthening one node and distributing workload across a cluster.
- How deep it goes
- Defines scale-up and contrasts it with scale-out, but stops short of implementation procedures or capacity calculations.
- Examples
- Uses SQL, NoSQL, cloud-based SQL, and hybrid database services as architectural comparison points.
- What you can do after
- Evaluate database architectures by comparing single-node capacity, cluster complexity, cost, and availability trade-offs.
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Refactoring to Rust
Vertical scaling is increasing the computational resources available to an existing machine or system instance.
- How it is used
- Addresses performance limits by increasing the resources available to an existing machine instance.
- How deep it goes
- Defines resource-based scaling and contrasts it with horizontal scaling, but does not detail implementation procedures or measurements.
- Examples
- Uses faster CPUs, additional cores, and additional memory as examples of vertical scaling.
- What you can do after
- Evaluate vertical scaling as an alternative to rewriting code or adding system instances.
Microsoft Azure in Action
A scaling technique that changes the size of an individual virtual machine or database server to increase or decrease its computing resources.
- How it is used
- Adjusts one VM or database server to match changing application demand and control resource spending.
- How deep it goes
- Defines scaling up and down, contrasts scaling out, and demonstrates Azure SQL sizing without detailing internals.
- Examples
- Uses changing Azure VM sizes, seasonal Banning Books traffic, DTU tiers, and an Azure CLI database update.
- What you can do after
- Operate Azure VMs or SQL databases by selecting larger or smaller resource configurations for changing workloads.
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Machine Learning System Design
A scaling technique that increases the capacity of a single machine by upgrading or replacing its hardware.
- How it is used
- Supports training pipelines when a dataset or workload exceeds current machine capacity.
- How deep it goes
- Defines hardware upgrades, explains simplicity and limits, and stops short of detailed implementation procedures.
- Examples
- Uses doubled or quadrupled RAM, upgraded GPUs, cloud compute resources, and a single training machine as examples.
- What you can do after
- Evaluate whether hardware upgrades can meet training demands before adopting distributed scaling.
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Cloud Observability in Action
Vertical scaling is the practice of increasing the resources of an existing system instance, such as memory or storage.
- How it is used
- Supports changing compute or telemetry load by increasing resources on an existing instance.
- How deep it goes
- Defines the scaling direction and gives Prometheus limits, but defers detailed implementation procedures.
- Examples
- Uses serverless VMs with adjusted CPU or RAM and Prometheus with added RAM or persistent storage.
- What you can do after
- Operate/configure an existing compute or Prometheus instance with greater CPU, RAM, or storage capacity.
Read how Cloud Observability in Action explains vertical scaling →
Grokking Concurrency
Vertical scaling increases system performance by adding capacity to existing processing resources or replacing them with more powerful resources.
- How it is used
- Explains how systems improve performance by upgrading existing processing resources before considering distributed alternatives.
- How deep it goes
- Defines hardware upgrades, performance ceilings, and costs, then contrasts them with horizontal scaling without detailing implementation.
- Examples
- Uses more memory, a more powerful processor, a supercomputer, and top-tier cloud instances as upgrade examples.
- What you can do after
- Evaluate whether upgrading one machine can improve performance before choosing distributed processing resources.
Related concepts
- horizontal scalingalternative to · Horizontal scaling is adjusting system capacity by adding or removing computing nodes or equivalent instances that share or distribute the workload.
- scalabilitycontrasts with · Scalability is the ability of a system, process, or method to accommodate increasing workload, size, or demand while maintaining acceptable operation.
- autoscalingkind of · Autoscaling is the automatic adjustment of computing resource capacity or running instances in response to workload demand or measured system conditions.
- virtual machineconfigures · A virtual machine is a software-based environment that emulates or implements an abstract computer system, providing an interface on which an operating system or programs can execute.
- sequential processingused by · The execution of operations one after another in a defined order, without overlapping their progress.
- Amazon Relational Database Service (RDS)configures · Amazon Relational Database Service is an AWS-managed service for deploying, operating, and scaling relational databases.
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