Horizontal scaling

Q: How deeply does Fast Python: High performance techniques for large datasets cover horizontal scaling?

Defines scale-out and contrasts it with scale-up, then identifies Dask’s scheduler and larger-than-memory data as central topics.

As explained in Fast Python: High performance techniques for large datasets

Horizontal scaling is adjusting system capacity by adding or removing computing nodes or equivalent instances that share or distribute the workload.

Horizontal scaling, or scaling out, adds computers to process data or computation that exceeds a single machine’s capacity. Dask supports this approach within the Python analytics ecosystem and can also use a single computer more efficiently. section 10.0

Dask provides a path from local execution to multi-machine computation:

Its distributed scheduler can schedule tasks across machines, with implementations for HPC clusters, SSH connections, and cloud providers. section 10.3
A local configuration offers the same building blocks without requiring a cluster or cloud access; Dask’s main added value comes when using more than one machine. section 10.3
The Mandelbrot image-generation task illustrates Dask’s array interface; combining Dask with Cython or Numba is identified as a more efficient option for very large images. section 10.3

How Fast Python: High performance techniques for large datasets covers it

Cover of Fast Python: High performance techniques for large datasets

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Also explained in

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Amazon Web Services in Action, Third Edition: An in-depth guide to AWSHorizontal scaling is increasing or decreasing system capacity by adding or removing equivalent computing instances.
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MongoDB 8.0 in Action, Third EditionHorizontal scaling is the practice of increasing capacity by distributing workloads across additional machines or partitions.
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Elasticsearch in Action, Second EditionHorizontal scaling increases system capacity by adding more nodes or machines rather than increasing the resources of an existing node.
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Distributed Machine Learning PatternsA scaling technique that increases capacity by adding equivalent service instances or machines rather than enlarging one instance.
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Building Web APIs with ASP.NET CoreHorizontal scaling increases capacity by adding or removing service instances rather than enlarging one instance.
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ScyllaDB in ActionCluster scaling is the operation of increasing a cluster's capacity by adding nodes while retaining its existing nodes.
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Refactoring to RustHorizontal scaling is increasing service capacity by running additional instances of a system.
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Microsoft Azure in ActionA scaling technique that adds or removes database or virtual-machine instances to change aggregate computing capacity.
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Kubernetes in Action, Second EditionScaling out is increasing application capacity by running additional application instances.
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Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and TerraformHorizontal scaling is the technique of increasing capacity by running multiple instances of a component and distributing its workload among them.
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Build AI-Enhanced Web AppsHorizontal scaling is the addition or removal of application instances to change processing capacity.
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Grokking ConcurrencyA scaling approach that increases performance by distributing load across existing and additional processing resources.

Compare how 13 books explain horizontal scaling →

Related concepts

vertical scalingalternative to · Vertical scaling is increasing or decreasing a system’s capacity by changing the resources available to an existing machine, node, or instance.
replicauses · A replica is a copy of data or state, or an equivalent running instance of a component, maintained alongside others in a replicated system.
scalabilitykind of · Scalability is the ability of a system, process, or method to accommodate increasing workload, size, or demand while maintaining acceptable operation.
load balancinguses · Load balancing is the distribution of requests or computational work across multiple resources to manage demand, reduce workload imbalance, and improve utilization or availability.
autoscalingkind of · Autoscaling is the automatic adjustment of computing resource capacity or running instances in response to workload demand or measured system conditions.
dataset splitimplemented by · A dataset split is the division of a dataset into distinct subsets assigned different purposes, such as training, validation, or testing.

This explainer was generated by Manning's knowledge graph from the text of Fast Python: High performance techniques for large datasets. The section links point to the parts of the book it draws on.