The Full-Stack Developer‘s Guide to AWS Lambda Function Triggers

As a veteran full-stack developer, serverless architectures have become second nature. And no service enables more effective serverless workflows than AWS Lambda. One of Lambda‘s most powerful features is its versatile system for triggers – the wide variety of AWS services and third-party tools that can automatically invoke your functions.

In this comprehensive guide, we‘ll explore the available triggers for calling Lambda functions while highlighting realistic use cases relevant to full-stack developers.

In-Depth: How Lambda Function Invocation Works

Before diving into the specific triggers, it‘s helpful to level-set on how Lambda invocation operates under the hood. This will allow us to better understand the types of applications suited to different triggers.

There are two main invocation types:

Synchronous: The invoking service waits for the Lambda function to process the event and return a response before continuing. This is common for use cases like backing APIs or performing data transformations.

Asynchronous: The invoker triggers the function and moves on without waiting for a response. Asynchronous invocations allow the function to run in parallel without slowing down the calling service. This models is useful for things like notifications, backups, etc.

Synchronous invocations wait for responses, while async triggers allow parallel execution.

Lambda supports a few different invocation sources:

• Services: Most triggers come from AWS services that can directly invoke functions.

• API: Functions can be invoked synchronously via an HTTPS endpoint.

• AWS CLI: Manually trigger functions via the CLI for testing purposes.

Now let‘s explore some of the most essential and widely-used service triggers available.

API Gateway

One of the most powerful triggers for Lambda is API Gateway. As full-stack developers, we know that API Gateway handles all the complex parts of running robust, scalable APIs.

The integration allows API Gateway to directly trigger Lambda functions while handling authentication, request validation, throttling limits, CORS and more. This enables you to focus on business logic within the function.

You can enable API Gateway triggers during Lambda creation:

// serverless.yml

functions:
  addUser:
    handler: handlers.addUser
    events:
      - http: 
          path: /users
          method: post  

Or by adding it as a trigger later from the Lambda console.

Two types of API Gateway APIs can invoke Lambda:

HTTP APIs: Simple, low-cost HTTP endpoints routing requests to functions. Easy JSON transformations.

REST APIs: More advanced features supporting additional verbs like PUT, PATCH, websockets and more.

Overall, API Gateway + Lambda form an extremely scalable backend platform for serverless applications. They compliment each other perfectly for any function meant to power APIs.

Real-World Use Cases

• REST API backing a mobile or single-page app
• Internal HTTP API enabling services to communicate
• Webhook receiver processing requests from a third-party app

I utilize API Gateway triggers for Lambda extensively in my full-stack projects. They enable straight-forward routing and preprocessing for any function powering an API endpoint.

S3 Triggers

Allowing S3 buckets to directly invoke Lambda opens up many useful automation workflows. The event-drive architecture lends itself perfectly to reacting to file uploads and modifications.

Some example use cases include:

• Resizing images when uploaded to S3
• Scanning new files for viruses and violations
• Extracting metadata and indexing documents
• Transcoding video uploads
• Triggering data pipelines on dataset changes

S3 can trigger Lambda on 4 types of bucket events:

• OBJECT_CREATED – new file uploaded
• OBJECT_REMOVED – existing file deleted
• OBJECT_RESTORED – restored archived file
• REDUCED_REDUNDANCY_LOST_OBJECT – data lost in lower redundancy

For example, you could trigger a Lambda to create thumbnail images each time a new image is added to an S3 bucket:

const s3Handler = async (event) => {

  // Get uploaded image file from event  
  const srcKey = event.Records[0].s3.object.key;

  // Generate thumbnail
  const thumbFile = await generateThumb(srcKey);

  // Save to S3
  await s3.putObject({
    Bucket: THUMB_BUCKET, 
    Key: thumbFile.name,
    Body: thumbFile.buffer
  })

}

The function would receive the bucket name, key, and other metadata to leverage when processing.

According to AWS, S3 + Lambda can resize over 20 million images per month for under $10 – showcasing the cost benefits.

Real-world Use Cases

• Image processing – compression, resizing, watermarking
• Anti-virus scanning for uploaded documents
• Triggering analytics jobs on dataset updates
• Search indexing on new uploads
• Backing up data to Glacier on modifications

S3 is likely one of the most universal and useful triggers for practical serverless applications. Any file-related workflow can leverage Lambda triggers to react to uploads and changes.

CloudWatch Events & EventBridge

CloudWatch Events and EventBridge give developers the power to trigger Lambda functions in response to almost any event within the AWS environment. They serve as the central nervous system for reacting to changes.

EventBridge builds on CloudWatch Events with more capabilities and integrations. Some examples use cases include:

• Executing cleanup jobs on schedules and cron
• Responding to changes in EC2, DynamoDB, security groups etc
• Processing logs sent to CloudWatch Logs
• Webhook integrations with SaaS apps like GitHub or Shopify
• Cross-region and cross-account integrations

CloudWatch allows you to set basic scheduling and services rules:

rate(1 hour)
service: EC2, state: STOPPED  

While EventBridge provides more flexibility for triggers:

{
  "detail-type": [
    "Generator Started",
    "Generator Stopped"
  ]
}

For EventBridge rules, you can configure Lambda functions as targets, passing event payloads to trigger logic.

I like to use EventBridge to trigger various management and data sync jobs across our environment. The pub/sub architecture keeps things decoupled.

Real-World Use Cases

• Cron jobs – db cleanup, audit reporting
• Security monitoring – scanning VPC changes
• CI/CD pipelines – call pipeline stages
• Social media – process tweets mentioning brand
• CloudWatch log processing
• Third-party integrations – Shopify, GitHub, etc.

Between native AWS service events and third-parties, EventBridge opens immense possibilities for integration. It can replace most cron jobs and glue together environments.

DynamoDB Streams

As a managed NoSQL database, DynamoDB integrates with Lambda functions through DynamoDB Streams.

When table data is modified, a Dynamo stream sends details on the changes made to Lambda for asynchronous processing. This allows you to perform tasks like:

• Send customized notifications
• Maintain derivative tables – indexes, aggregates etc
• Implement business logic – rewards programs
• React to inventory changes

DynamoDB Streams sends item change details to Lambda.

The stream records contain details like the primary keys of changed items and images of the data before and after state.

Triggers can be enabled on DynamoDB tables individually either through CloudFormation or the Dynamo console by checking "Enable triggers":

Resources:
  ItemsTable: 
    Type: AWS::DynamoDB::Table
    Properties:
      TableName: items
      BillingMode: PAY_PER_REQUEST
      AttributeDefinitions:
        - AttributeName: itemId
          AttributeType: S  
        - AttributeName: createdDate
          AttributeType: S
      KeySchema:
        - AttributeName: itemId  
          KeyType: HASH
      StreamSpecification: 
        StreamViewType: NEW_IMAGE # full item data after modification

According to Forbes, DynamoDB streams can transfer over 68,000 records per second to Lambda!

Real-world Use Cases

• Send email/SMS on data changes
• Sync derived tables – ElasticSearch indexes
• Maintain de-normalized tables
• Cache updates
• Business workflow triggers – loyalty programs

DynamoDB streams provide highly scalable data pipelines to allow Lambdas to react to database changes for tasks like indexing, notifications, analytics etc.

SQS Queue Triggers

Simple Queue Service (SQS) offers a reliable, scalable queue for moving data between components.

Like other triggers, SQS queues can directly invoke Lambda functions to consume entries as they arrive. The integration handles batching events, retries, and parallelization.

Because Lambda scales inherently, it can handle SQS queues of any volume without consuming standby resources.

Some common use cases include:

• Distributed processing of jobs
• Asynchronous communications
• Backing APIs with queue buffering
• S3 event processing

A basic SQS setup with Lambda is straightforward:

Resources:

  JobQueue:
    Type: AWS::SQS::Queue  

  ProcessJobFunction:
    Type: AWS::Lambda::Function
    Properties:
      Handler: process.handler
      Events:
        JobQueue:
          Type: SQS  
          Properties:
            Queue: !GetAtt JobQueue.Arn
            BatchSize: 10

Here Lambda will process entries from JobQueue asynchronously in batches.

According to AWS, queues can push over 100,000 msg/sec to Lambda. The combo forms a robust streaming architecture.

Real-world Use Cases

• Scalable processing pipelines
• Distributing requests across apps
• Asynchronous worker tasks
• Backing ingestion for streaming apps
• Glue between services and S3

SQS fills a great niche for ingesting high volumes of events and data with Lambda processing. The queues act as fault-tolerant buffers across services.

Kinesis Data Streams

For high throughput data streams, Kinesis makes an excellent trigger option. It can continuously pass real-time data into Lambda functions.

Kinesis Data Streams allow applications to ingest GBs of data per second – think IOT device events, clicks & impressions, aggregated logs etc.

Triggers then funnel this data to Lambda functions for rapid processing. This enables use cases like:

• Real-time metrics and analytics
• Continuous anomaly detection
• Analytics dataset building
• Log analytics
• Complex stream processing

The trigger can be configured via the CLI:

aws lambda add-event-source-mapping \
    --function-name ProcessKinesisStream \
    --event-source  arn:aws:kinesis:us-east-1:123:stream/page-clicks \
    --starting-position LATEST

Kinesis + Lambda provide tools for extracting value from volume at scale.

According to AWS stats, Kinesis can stream over 7 million events/second to Lambda!

Real-world Use Cases

• Clickstream analysis
• System/app monitoring
• Sensor data analytics
• IT infrastructure telemetry
• Metrics and performance data

For high throughput event streams, Kinesis to Lambda creates extremely powerful pipelines. This drives real-time analytics and reporting for massive datasets.

Third-Party Service Integrations

While AWS services can handle many use cases, SaaS platforms often provide the core of the tech stack. Luckily, Lambda integrations are becoming ubiquitous.

Popular platforms like Segment, Shopify, Salesforce, and Slack enable Lambda triggers through webhooks or custom events:

Segment -> Lambda:

• Stream segmented event data
• Enrich events with external data
• Forward to other tools like Redshift

GitHub -> Lambda:

• React to commits and PRs
• Run automated workflows
• Process changes

Shopify -> Lambda:

• Sync products/inventory/orders
• Generate recommended products
• Submit orders to ERP

And many more! Most SaaS providers have guides for sending events to Lambda.

For example, here is how to stream GitHub activity:

Third party triggers connect SaaS activity directly to your infrastructure.

Real-World Use Cases

• CRM data synchronization
• Manage marketing analytics
• Process payments
• React to social media
• Send mobile push notifications
• Third-party log analysis

External triggers enable building flexible pipelines across products. They unlock new opportunities for optimization and automation.

Recap: Choosing the Right Triggers

With so many options, it can get overwhelming to decide the best triggers for a use case. Here is a recap of the key options:

Trigger	Invocation Type	Pros	Cons
API Gateway	Sync	Simple HTTP API integration	Adding APIs just to enable triggers
S3	Async	React to storage	Only works for file updates
DynamoDB	Sync/Async	Database integration	Coupled to DynamoDB
CloudWatch	Async	Wide variety of events	Configuration complexity
SQS	Async	Decoupled messaging	Additional queue management
Kinesis	Async	High volume data	Complex stream processing

Evaluate the direction of dataflow, invocation style, data formats and volume to choose an optimal approach.

Recurse Final Thoughts

Through its extensive integration with over 70+ AWS service offerings, Lambda provides unlimited options for triggering functions exactly when needed.

Synchronous invocations empower backing scalable APIs and data workflows. Asynchronous triggers enable parallelizing processes or chained callbacks on environment events.

External SaaS integrations form event-driven pipelines across cloud products. Together, these capabilities help optimize costs, enable innovation and automate management.

As full stack developers, we‘re perfectly positioned to take advantage of Lambda‘s versatile triggers within creative architectures. This guide just scratches the surface of the possibilities. But it equips you with a foundation to start building more reactive serverless applications.

Let me know in the comments if you have any other favorite uses cases or tips for Lambda triggers!