BUILDING Serverless apps with MongoDB AtLAS, AWS Lambda and Step Functions

#MDBW17
Overview, best practices
BUILDING SERVERLESS APPS
WITH MONGODB ATLAS, AWS
LAMBDA AND STEP FUNCTIONS

#MDBW17
RAPHAEL LONDNER
Developer Advocate,
MongoDB
PAUL SEARS
Solutions Architect,
AWS
paulsear@amazon.com
@rlondner

#MDBW17
AGENDA
• Lambda overview and common use cases
• Lambda code deep dive
• Lambda demo with MongoDB Atlas
• Lambda best practices
• Step Functions
• Step Functions demo

#MDBW17
SERVERLESS COMPUTING - AWS LAMBDA
Run code without provisioning or managing servers – pay only for the
compute time you consume.

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Continuous ScalingNo Servers to
Manage
Subsecond
Metering
BENEFITS OF AWS LAMBDA
AWS Lambda handles:
• Operations and
management
• Provisioning and utilization
• Scaling
• Availability and fault
tolerance
Automatically scales your
application, running code in
response to each trigger
Your code runs in parallel and
processes each trigger
individually, scaling precisely
with the size of the workload
Pricing
• CPU and Network
scaled based on
RAM (128 MB to
1500 MB)
• $0.20 per
1M requests
• Price per 100ms

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AWS LAMBDA USE CASES
• Web applications
• Data processing (real-time streaming analytics)
• Scalable back ends (mobile apps, IoT devices)
• Amazon Alexa
• Chatbots

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Web
Applications
• Static
websites
• Complex web
apps
• Packages for
Flask and
Express
Data
Processing
• Real time
• MapReduce
• Batch
Chatbots
• Powering
chatbot logic
Backends
• Apps &
services
• Mobile
• IoT
</></>
Amazon
Alexa
• Powering
voice-enabled
apps
• Alexa Skills Kit
Autonomous
IT
• Policy engines
• Extending
AWS services
• Infrastructure
management
Data processing: Lambda + S3
COMMON USE CASES

#MDBW17
Data
Processing
• Real time
• MapReduce
• Batch
Data processing: Lambda + Kinesis
COMMON USE CASES

#MDBW17
Web
Applications
• Static
websites
• Complex web
apps
• Packages for
Flask and
Express
Data
Processing
• Real time
• MapReduce
• Batch
Chatbots
• Powering
chatbot logic
Backends
• Apps &
services
• Mobile
• IoT
</></>
Amazon
Alexa
• Powering
voice-enabled
apps
• Alexa Skills Kit
Autonomous
IT
• Policy engines
• Extending
AWS services
• Infrastructure
management
COMMON USE CASES

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COMPONENTS OF A SERVERLESS APP
EVENT SOURCE
Requests to
endpoints
Changes in
resource state
FUNCTION SERVICES

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ANATOMY OF A LAMBDA FUNCTION
• Handler function (or method)
• An input parameter
‒ Integer, double, string, JSON…
• A context parameter
‒ Provides information such as
o Remaining time until timeout, log group and stream, request ID, etc…
‒ Exposes public properties
• A return value (optional)

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ANATOMY OF A LAMBDA FUNCTION (NODE.JS)
• Handler function in index.js
exports.myHandler = function(event, context, callback)
=> {…}
• Handler format in AWS Lambda: [moduleName].[functionName]
index.myHandler

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ANATOMY OF A LAMBDA FUNCTION (JAVA)
• Handler function in example.Hello class
package example;
public class Hello {
public String myHandler(type inputVal, Context context) {...}
• Handler format in Lambda: [package].[className]::[functionName]

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ANATOMY OF A LAMBDA FUNCTION (PYTHON)
• Handler function in hello_python.py file
def my_handler(event, context):
…
return some_value
• Handler format in Lambda: [fileName].[functionName]

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ANATOMY OF A LAMBDA FUNCTION (C#)
• Handler function in Example.Hello
namespace Example;
public class Hello {
public Stream MyHandler(type inputVal, ILambdaContext context) {...}
• Handler forma in Lambda:
[assemblyName]::[namespace].[className]::[methodName]

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HOW TO DEVELOP FOR LAMBDA & ATLAS
IN NODE.JS
Sign up for
Atlas
Create
a
cluster
Copy cluster
URI to a safe
location
Create
a DB
user
Init a
Node
project
Write code!
Import
MongoDB
Node driver

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HOW TO TEST AND DEPLOY YOUR LAMBDA
Upload your
package to
AWS Lambda
Use lambda-
local to test
locally
Zip your
Node.js
code
Create
your
Lambda
function
Set env.
variables,
memory,
security…
Test in AWS
(Console,
API
Gateway)

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BEST PRACTICES: LAMBDA FUNCTION
ü Use the right “timeout”
ü Utilize the functions local storage which is 500MB in size in the /tmp
ü Lower costs and improve performance by minimizing the use of startup code
not directly related to processing the current event
ü Use the built-in CloudWatch monitoring of your Lambda functions to view and
optimize request latencies

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THINGS TO REMEMBER: LAMBDA FUNCTION
ü Memory = “Power level”
q Higher levels offer more memory and more CPU power
ü Functions don’t have a notion of state
q Use MongoDB Atlas, S3, or Elasticache, or AWS Step Functions
q Wrap your config in a function and call it from your published code
ü Use the right access control for downstream services
q IAM roles and permissions for AWS services
q VPC for private endpoints
q KMS for storing credentials for downstream endpoints

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THINGS TO REMEMBER: LAMBDA
APPLICATION
ü Lambda scales by events/requests
q Plan for concurrent request rate on downstream services
ü Shared scaling responsibility for VPC enabled functions
q Sufficient IPs to match your expected concurrency
q at least one subnet in each availability zone
ü Retries are built in for asynchronous and Stream invokes
q Plan for retries for synchronous applications

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WHERE NOT TO CONSIDER LAMBDA (TODAY)
• Large software dependencies: Custom software applications with
licensing agreements such as MS-Office document processing, EDA
tools, Oracle databases, etc.
• OS dependencies: Software packages or applications which rely on
calling underlying Windows RPCs
• Custom hardware: GPU acceleration, hardware affinity

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AWS LAMBDA WITH MONGODB ATLAS
• Store database connection string in an environment variable
‒ Use –E parameter with lambda-local
‒ Reference it with process.env['MONGODB_ATLAS_URI']
• Encrypt database connection string in AWS Lambda
‒ Use AWS.KMS() to decrypt() the connection string

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PERFORMANCE BEST PRACTICES WITH NODE.JS
• Declare the db object outside the handler method
• Do NOT close the db object!
• Set context.callbackWaitsForEmptyEventLoop to
‘false’
• Try to re-use the db object if
db.serverConfig.isConnected() returns true

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λλ
λ
DBMS
λ
λ
λ
λ
λ
λ λ
λ
λ
Queue
MODERN
APP

#MDBW17
“I WANT TO SEQUENCE FUNCTIONS”
“I want to select functions based on data”
“I want to retry functions”
“I want try/catch/finally”
Turning functions into apps
“I have code that runs for hours”
“I want to run functions in parallel”

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BENEFITS OF AWS STEP FUNCTIONS
Diagnose and debug
problems faster
Adapt to change
Easy to connect and
coordinate
distributed components and
microservices to quickly
create apps
Manages the operations
and infrastructure of
service coordination to
ensure availability at
scale, and
under failure
Productivity Agility Resilience

#MDBW17
RUN EACH STEP IN SEQUENCE
Vendor A
Vendor B
Vendor C

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…AND UNWIND IF MY PLANS FAIL AT ANY POINT
Vendor A
Vendor B
Vendor C

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COORDINATION MUST-HAVES
• Scales out
• Doesn’t lose state
• Deals with errors/timeouts
• Easy to build & operate
• Auditable

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APPLICATION LIFECYCLE IN AWS STEP FUNCTIONS
Visualize in the
Console
Define in JSON Monitor
Executions

DEFINE IN JSON AND THEN VISUALIZE IN THE
CONSOLE• {
• ”Comment”: “Hello World Example",
• "StartAt” : "HelloWorld”,
• "States” : {
• "HelloWorld” : {
• "Type” : "Task",
• "Resource” :
"arn:aws:lambda:REGION:ACCOUNT_ID:function:FUNCTI
ON_NAME”,
• "End” : true
• }
• }

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EXECUTE ONE OR ONE MILLION
Start
End
HelloWorld
Start
End
HelloWorld
Start
End
HelloWorld
Start
End
HelloWorld
Start
End
HelloWorld
Start
End
HelloWorld
Start
End
HelloWorld
Start
End
HelloWorld

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MONITOR EXECUTIONS FROM THE CONSOLE

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SEVEN STATE TYPES
Task A single unit of work
Choice Adds branching logic
Parallel Fork and join the data across tasks
Wait Delay for a specified time
Fail
Stops an execution and marks it as a
failure
Succeed Stops an execution successfully
Pass Passes its input to its output

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BUILD VISUAL WORKFLOWS FROM STATE TYPES
Task
Choice
Fail
Parallel

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STEP FUNCTIONS: CUSTOMER USE-CASES
• Workflow/Order management
• Batch processing
• Shell-script replacement
• Enterprise application workflows
• Data gathering and processing

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INTEGRATE WITH OTHER AWS SERVICES
• Create state machines and Activities with AWS CloudFormation
• Call Step Functions with Amazon API Gateway
• Start state machines in response to events or on a schedule with
CloudWatch Events
• Monitor state machine executions with CloudWatch
• Log API calls with CloudTrail

#MDBW17
FOLLOW-ON SESSION
• What: MongoDB Atlas and Serverless Architectures
• When: Wednesday, June 21 at 4.30PM
• Where: Crystal A
• Who: Tosin Ajayi, Sr. Solutions Architect

#MDBW17
REFERENCE LINKS
• Serverless development with Node.js, Lambda and MongoDB Atlas
• Optimizing AWS Lambda performance with MongoDB Atlas and
Node.js
• Step Functions with MongoDB Atlas Part 1
• Step Functions with MongoDB Atlas Part 2
• Lambda with MongoDB GitHub repository
• Step Functions with MongoDB GitHub repository

BUILDING Serverless apps with MongoDB AtLAS, AWS Lambda and Step Functions

BUILDING Serverless apps with MongoDB AtLAS, AWS Lambda and Step Functions

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