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What is lambda?

Lambda, the 11th letter of the Greek alphabet (λ), is used as both a symbol and a concept in various fields of science, mathematics and computing. Although originally used as a symbol for wavelength in mathematical equations, developers and engineers now use lambda in computer programming and cloud computing.

As a symbol, lambda is used primarily in calculus and physics, most notably in the theories of general relativity and quantum mechanics. Programming languages such as Python and Java also use lambda, as well as machine learning (ML), cloud computing and event-driven applications.

In 2014, Amazon Web Services (AWS) launched AWS Lambda. AWS Lambda is a serverless computing service that allows developers to deploy code in the cloud without managing servers.

Lambda in science

As a symbol, lambda plays an important role in the theories of general relativity and quantum mechanics, considered two of the 20th century's most important advancements in physics. In general relativity, physicist Albert Einstein introduced lambda to denote the cosmological constant, or the value of the universe's rate of expansion.

In quantum mechanics, lambda is the symbol for wavelength, which describes how subatomic particles move and interact. Lambda is also used to symbolize wavelength for any wavelike phenomena in science such as the propagation of light in optical fiber networking and electromagnetic waves in electrical engineering.

Lambda in computer programming

In mathematics and computer programming, the lambda symbol introduces "anonymous functions." Lambda notation distinguishes between variables used as mathematical arguments and variables that stand for predefined values. Alonzo Church introduced the use of the lambda symbol in this sense in his theory of lambda calculus in the 1930s. Popular coding languages such as Python and Java use this lambda notation.

Lambda functions in Python

A lambda function in the versatile coding language of Python is a type of function that doesn't have a specific name and is usually used for simple tasks. It can accept multiple arguments, but it can only perform a single expression. This feature is particularly useful when a developer needs to pass a function as an argument to another function, or when a developer must create a function quickly without defining it in a separate line of code.

The Python syntax for a lambda function is as follows:

lambda arguments: expression

The expression is executed and the result is returned. Lambda functions can be used when a simple, anonymous function is required for a short period of time. The following are examples of lambda functions in Python:

# Add 10 to argument 'a' and return the result

x = lambda a: a + 10


# Multiply argument 'a' with argument 'b' and return the result

x = lambda a, b: a * b

print(x(5, 6))

# Summarize arguments 'a', 'b', and 'c' and return the result

x = lambda a, b, c: a + b + c

print(x(5, 6, 2))

Lambda expressions in Java

Lambda expressions in Java allow developers to make their code more efficient and easier to reuse. They let developers pass a piece of code as a special type of variable called a "method argument" in a Java method. This is especially helpful when you want to tell a program what to do when a button is clicked, for example. By using lambda expressions, developers can express their ideas more simply and make their code easier to understand.

The syntax for lambda expressions in Java is as follows:

(parameters) -> expression

An example of a lambda expression is seen in the following:

// Define a lambda expression that takes two integers and returns 
their sum
BiFunction<Integer, Integer, Integer> sum = (a, b) -> a + b;
System.out.println(sum.apply(5, 6));

AWS Lambda

Amazon Web Services offers a service called AWS Lambda that lets customers run code without having to worry about provisioning or managing servers. AWS Lambda has an event-driven architecture that executes code only when needed and scales automatically, one of its many benefits. Subscribers pay only for the compute time they consume and are not charged for time when their code is not running.

Key Features of AWS Lambda

AWS Lambda includes several features that help developers write and run code in the cloud without managing infrastructure. Some of these key features include the following:

  • Serverless infrastructure. With AWS Lambda's serverless infrastructure, users can run code without provisioning or managing servers. Instead, they can simply write and upload code as a .zip file or container image.
  • Automatic scaling. AWS Lambda automatically responds to code execution requests at any scale, from a few events per day to hundreds of thousands per second.
  • Cost optimization. AWS Lambda can save costs by allowing users to pay only for the compute time used down to the millisecond, instead of provisioning infrastructure upfront for peak capacity.
  • Performance optimization. AWS Lambda allows users to optimize code execution time and performance and respond to high demand in double-digit milliseconds with provisioned concurrency.
  • Integration with other tools. AWS Lambda can be integrated with various other services and tools to enhance their functionality and support different workloads, which is beneficial to developers using multiple tools. Some common tools include the Weights & Biases MLOps platform, the Run:ai AI cluster management tool and Nvidia products for graphics processing.

AWS Lambda use cases

AWS Lambda has several use cases beyond simple code development and deployment. Some of the most popular use cases include the following:

  • Processing data at scale. Code can be executed based on the required capacity, scaling automatically to match data volume and supporting custom event triggers.
  • Interactive web and mobile back ends. AWS Lambda can be combined with additional services from AWS to create secure, stable and scalable online experiences.
  • ML insights. Data can be preprocessed before feeding it to ML models. With access to Amazon Elastic File System (EFS), AWS Lambda simplifies scaling by handling infrastructure management and provisioning.
  • Event-driven applications. Event-driven functions can be built to facilitate communication between decoupled services. Running event-driven applications during periods of peak demand can reduce costs without risking crashes or overprovisioning resources.
AWS Lambda use cases list
Automatic backups and file processing are just two of AWS Lambda's use cases.

Getting started with AWS Lambda

AWS Lambda provides users with several services and functions to help them get started. To get started with AWS Lambda, users need to do the following:

  1. Sign up for an AWS account.
  2. Create a Lambda function using the AWS Management Console, AWS software development kits (SDKs) or AWS Command Line Interface (CLI).
  3. Configure an event source, such as an S3 bucket, an API Gateway or a DynamoDB stream to trigger the Lambda function.
  4. Monitor the execution of the Lambda function using AWS CloudWatch.

Optimizing lambda functions and reducing latency

There are several ways to optimize lambda functions and reduce latency, the time it takes for data to travel from its source to its destination. Latency can slow application performance and harm user experience, which is why it's beneficial to reduce it in applications:

  • Choose the right memory size. Selecting the appropriate memory size for lambda functions can help optimize their performance and reduce execution time.
  • Use provisioned concurrency. To reduce latency during periods of high demand, provisioned concurrency can prewarm a specific number of instances. In AWS Lambda, DynamoDB allows developers to use an on-demand option in addition to provisioned concurrency.
  • Design efficient APIs. Efficient API design and utilizing API gateways can improve the performance of lambda functions and reduce latency.
  • Enable caching. Enabling caching for lambda functions can significantly reduce user latency.

AWS Lambda in ML and serverless applications

AWS Lambda functions and serverless computing play essential roles in the development and deployment of modern ML models and serverless applications.

ML. AWS Lambda functions are used to preprocess data before feeding it to ML models, optimizing their performance. AWS Lambda simplifies infrastructure management and provisioning, enabling developers to focus on building and deploying ML models.

Serverless applications. Serverless applications are built on serverless computing platforms like AWS Lambda. These applications eliminate the need for server management, allowing developers to concentrate on feature development. Lambda functions enable the creation of event-driven applications that scale dynamically and are cost-effective, responding to user actions and requests. AWS Lambda includes the Serverless Application Repository feature, which helps developers publish serverless applications and code.

Lambda is a computer programming term that inspired the name of AWS Lambda. Compare AWS Lambda vs. Azure Functions vs. Google Cloud Functions.

Editor's note: The publisher has used AI technology in the creation of part of this content. The final text has been reviewed, fact-checked, edited and approved by TechTarget editors.

This was last updated in June 2023

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