Index
A
- Apigee
- reference / AWS API Gateway introduction
- asynchronous processing, Twitter stream
- about / Asynchronous processing of Twitter streams
- system architecture / System architecture
- data producer / Data producer
- data consumer / Data consumers
- results, viewing / Viewing results
- AWS API Gateway / AWS API Gateway introduction
- AWS Athena / AWS Athena
- AWS Certificate Manager (ACM) / Serverless tooling
- AWS Lambda versions
- trimming / Trimming AWS Lambda versions
- Azure Cosmos DB / Data storage
B
- batch layer
- about / Batch layer
- computation / Computation in the batch layer
- benefits, queuing systems
- durability / Basics of queuing systems
- scalability / Basics of queuing systems
- predictable load / Basics of queuing systems
- best practices, cold starts
- cloud functions, working with / Keeping cloud functions warm
- AWS Lambda functions / AWS Lambda functions and VPCs
- VPCs / AWS Lambda functions and VPCs
- start-up times, for different languages / Start-up times for different languages
- memory allocation / Allocating more memory
- Big Data Benchmark dataset
- URL / AWS Athena
C
- cold starts / Cold starts
- complex integration
- Lambda function, using / Complex integration using a Lambda function
- application code, implementing / Implementing the application code
- new resource and method, setting up / Setting up a new resource and method
- cryptocurrency prices, processing
- lambda architecture, using / Processing cryptocurrency prices using lambda architecture
- system architecture / System architecture
- AWS resources / AWS resources
- data producer / Data producer
- speed layer / Speed layer
- batch layer / Batch layer
- results / Results
D
- data layer, three-tier web application with REST / Data layer
- data producers
- streaming / Streaming data producers
- data storage / Data storage
- data store
- using, for results / Using a data store for results
- dead-letter queue
- using / Using a dead-letter queue
- deployed web application
- viewing / Viewing the deployed web application
- DynamoDB / Data storage
E
- Elastic MapReduce (EMR)
- using / Using Elastic MapReduce
- Enron emails, processing
- with serverless MapReduce / Processing Enron emails with serverless MapReduce
- datasets, URL / Processing Enron emails with serverless MapReduce
- driver function, implementing / Driver function
- mapper, implementing / Mapper implementation
- reducer, implementing / Reducer implementation
- environments
- managing / Managing different environments
- error tracking
- about / Error tracking
- Sentry, integrating into / Integrating Sentry for error tracking
- Rollbar, integrating / Integrating Rollbar
F
- fanout pattern
- using, with messaging pattern / Using the Fan-out and Messaging Patterns together
- Firehose
- reference / Batch layer
G
- Google Bigtable / Data storage
- GraphQL
- about / Introduction to GraphQL
- reference / Organization of the Lambda functions
I
- images, resizing
- about / Resizing images in parallel
- project, setting up / Setting up the project
- trigger, setting up / Setting up trigger and worker functions
- worker functions, setting up / Setting up trigger and worker functions
- permissions, setting up / Setting up permissions
- application code, implementing / Implementing the application code
- code, testing / Testing our code
J
- JsonPlaceholder
- reference / Simple proxy to a legacy API
K
- Kinesis Firehose stream
- reference / AWS resources
- Kong
- reference / AWS API Gateway introduction
L
- lambda architecture
- reference / Introducing the lambda architecture
- about / Introducing the lambda architecture
- examples / Introducing the lambda architecture
- batch layer / Batch layer
- speed layer / Speed layer
- cryptocurrency prices, processing / Processing cryptocurrency prices using lambda architecture
- Lambda function
- used, for complex integration / Complex integration using a Lambda function
- lambda serverless architecture
- about / Lambda serverless architecture
- data producers, streaming / Streaming data producers
- data storage / Data storage
- speed layer, computation / Computation in the speed layer
- batch layer, computation / Computation in the batch layer
- local development / Local development and testing, Local development
- logging systems / Logging
- Loggly
- reference / Digesting structured logs
- logic layer, three-tier web application / Implementing the entry point
- logic layer, three-tier web application with GraphQL
- Lambda functions, organizing / Organization of the Lambda functions
- application code, organizing / Organization of the application code
- function layout / Function layout
- writing / Writing the logic layer
- queries, implementing / Implementing GraphQL queries
- mutations, implementing / Implementing GraphQL mutations
- logic layer, three-tier web application with REST
- application code and function layout / Application code and function layout
- Lambda functions, organizing / Organization of the Lambda functions
- application code, organizing / Organization of the application code
- configuration with environment variables / Configuration with environment variables
- code structure / Code structure
- function layout / Function layout
- writing / Writing our logic layer
- application entrypoint / Application entrypoint
- application logic / Application logic
- andler.py, wiring to Lambda via API Gateway / Wiring handler.py to Lambda via API Gateway
- log messages
- structuring / Structuring log messages
M
- mapper
- role / Role of the mapper
- MapReduce
- benefits / Introduction to MapReduce
- references / Introduction to MapReduce
- example / MapReduce example
- mapper, role / Role of the mapper
- reducer, role / Role of the reducer
- architecture / MapReduce architecture
- serverless architecture / MapReduce serverless architecture
- messaging pattern
- using, with fanout pattern / Using the Fan-out and Messaging Patterns together
- migration
- techniques / Migration techniques
- staged migration / Staged migration
- URLs, migrating / Migrating URLs
N
- notifications
- using, with subscriptions / Using notifications with subscriptions
- using, with queues / Using notifications with queues
P
- pass-through proxy
- setting up / Setting up a pass-through proxy
- deploying / Deploying a pass-through proxy
- points of presence (POPs) / CDN with CloudFront
- Postgres database
- deploying / Deploying the Postgres database
- presentation layer, three-tier web application with REST
- about / Presentation layer
- file storage with S3 / File storage with S3
- CDN with CloudFront / CDN with CloudFront
- proxy
- applying, to legacy API / Simple proxy to a legacy API
- proxy integration / AWS API Gateway introduction
- proxy resource / Staged migration
- pyrollbar
- reference / Integrating Rollbar
Q
- queue
- notifications, using / Using notifications with queues
- using, as rate-limiter / Using a queue as a rate-limiter
- queuing systems
- about / Basics of queuing systems
- benefits / Basics of queuing systems
- queue service, selecting / Choosing a queue service
- queues, versus streams / Queues versus streams
R
- reducer
- role / Role of the reducer
- responses
- transforming, from modern API / Transforming responses from a modern API
- response transformation
- method execution flow / Method execution flow
- example, setting / Setting up example
- resource, setting up / Setting up a new resource and method
- method, setting up / Setting up a new resource and method
- Integration Request, setting up / Setting up Integration Request
- Integration Response, setting up / Setting up Integration Response
- REST API
- deploying / Deploying the REST API
- Rollbar
- integrating / Integrating Rollbar
S
- sensitive configuration
- securing / Securing sensitive configuration
- variables, encrypting / Encrypting variables
- variables, decrypting / Decrypting variables
- Sentry
- integrating, for error tracking / Integrating Sentry for error tracking
- serverless-prune-plugin
- reference / Trimming AWS Lambda versions
- serverless frameworks
- reference / Serverless tooling
- serverless functions
- daemon processes, mimicking / Mimicking daemon processes with serverless functions
- serverless MapReduce
- architecture / MapReduce serverless architecture
- for processing Enron emails / Processing Enron emails with serverless MapReduce
- serverless MapReduce, alternatives
- exploring / Exploring alternate implementations
- AWS Athena / AWS Athena
- data store, using for results / Using a data store for results
- Elastic MapReduce (EMR), using / Using Elastic MapReduce
- serverless MapReduce, limitations
- about / Understanding the limitations of serverless MapReduce
- memory limits / Memory limits
- storage limits / Storage limits
- time limits / Time limits
- serverless tooling / Serverless tooling
- Simple Notification Service (SNS) / Using notifications with subscriptions
- Simple Queuing Service (SQS) / Using notifications with queues
- Simple Storage Service (S3) / File storage with S3
- speed layer
- about / Speed layer
- computation / Computation in the speed layer
- staged migration / Staged migration
- static assets
- setting up / Setting up static assets
- streaming systems
- AWS / Streaming data producers
- Google Compute Cloud / Streaming data producers
- Azure / Streaming data producers
- structured logs
- digesting / Digesting structured logs
- subscriptions
- notifications, using / Using notifications with subscriptions
- system architecture
- about / System architecture
- synchronous invocation, versus asynchronous invocation / Synchronous versus asynchronous invocation
- system architecture, cryptocurrency price processing
- about / System architecture
- data producer / Data producer
- speed layer / Speed layer
- batch layer / Batch layer
T
- testing
- local method / Learning about testing locally
- tests
- executing / Running tests
- three-tier web application, with GraphQL
- system architecture / System architecture
- logic layer / Logic layer
- presentation layer / Presentation layer
- logic layer, working with / Writing the logic layer
- deployment / Deployment
- deployed application, viewing / Viewing the deployed application
- iteration and deployment / Iteration and deployment
- three-tier web application, with REST
- system architecture / System architecture
- presentation layer / Presentation layer
- logic layer / Logic layer
- data layer / Data layer
- about / Logic layer
- iteration and deployment / Iteration and deployment
- entire stack, deploying / Deploying the entire stack
- code, deploying / Deploying the application code
- Twelve-Factor App
- reference / Configuration with environment variables
V
- virtual private cloud (VPC) / Deploying the Postgres database