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Going Serverless
Sebastian Hesse · K15t · @seeebiii
Tips & tricks for developing a serverless cloud app
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Work on the same issue data,
in different projects, on different
Jira instances.
Keeping your data in sync
Backbone
Issue Sync for Jira
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About Serverless
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Our Use Cases
Scheduled Tasks
Long Running Tasks
Webhooks
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Typical Cloud Architecture Serverless Architecture
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Our Cloud Setup
Other Services
AWS Lambda
Amazon Web Services
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AWS Lambda
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AWS Lambda
module.exports = function (event, context, cb) {
cb(null, "Hello World");
}
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Event-based Architecture
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Development Setup
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Local
Execute, test and debug your
code on your local machine.
Cloud
Upload your code to the cloud
and execute it there.
Development
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Local Cloud
- Serverless Framework
- Serverless Application
Model (SAM) with
SAM CLI
- LocalStack
- AWS Cloud9
- Upload & execute
- No debugging possible*
* bit.ly/debugging-lambda-dotnet
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Considerations
Tests
You should always write tests
which can already identify bugs.
Quick Updates
Local code updates are faster. But
you can optimize your cloud code
updates.
Real Environment
Local code execution always needs
to simplify something: services,
access policies or more.
bit.ly/test-lambda-functions
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Start Coding!
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Manually managing
Lambda functions
is a pain.
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Infrastructure as Code
Use a tool to describe your
infrastructure as code. This
helps you to keep track of all
your Lambda functions.
CI/CD Pipeline
Automate your deployment as
much as possible. Use tools like
Bitbucket Pipelines, AWS
CodePipeline or similar.
Deployment
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Versioning
Not all customers update your app
at the same time. Be prepared!
Preprocess your app descriptor.
"webhooks": [
{
"event": "jira:issue_updated",
"url": "/webhook?v={{version}}"
}
]
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Best Practices
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Best Practices
Function Scope
1.
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Example Tasks
Further Processing
Sync Content
Receive Webhooks
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“Keep it simple, stupid.”
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Focus
Focus on one task and do
it well.
Reusability
Reuse functions within
your app.
Performance
Keep a high performance,
because every millisecond
costs you money.
Considerations
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Response Times
Response Time in ms
0
250
500
750
1000
# Request
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Cold Start
Function is "warm"
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Response Times
Response Time in ms
0
250
500
750
1000
# Request
1 2 3 4 5
Cold Start
Function is "warm"
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The bigger
your artifact, the slower
the startup time*.
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Which
programming language
can I use?
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All
programming languages can be used.
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Can I use
framework X ?
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No!
Keep your function size as small as possible.
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Can I use
AWS Lambda as a
REST API?
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No!
If you are concerned about response times.
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Response Times
Response Time in ms
0
250
500
750
1000
# Request
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Cold Start is always there!
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API Gateway
Built resilient APIs backed by
Lambda functions.
AWS AppSync
Build your own serverless
backend using AWS AppSync
which is powered by GraphQL.
Serverless REST API
bit.ly/aws-appsync-blog
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Best Practices
Communication
2.
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Synchronous Communication
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Synchronous Communication
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Synchronous Communication
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S3 Bucket
Synchronous Communication
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S3 Bucket
API Gateway
DynamoDB
SQS
Asynchronous Communication
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bit.ly/lambda-triggers
Asynchronous Communication
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bit.ly/lambda-triggers
Asynchronous Communication
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Best Practices
Scalability
3.
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Lambda Functions
are scaled automatically,
right?
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1000
webhooks at the same time is no exception.
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Yes No
Other services are not
necessarily scaled.
Lambda Functions are
scaled by AWS.
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Problem: Scaling
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Problem: Scaling
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Solution: Buffer Requests
Kinesis
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Best Practices
Execution Management
4.
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15
minutes execution time limit.
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15 minutes
are enough for
my use case.
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Really?
How can you be sure?
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- Retrieve data: 350 ms
- Process data: 300 ms
- Update data: 350 ms
Example: Synchronize 1000 Issues
1 sec / issue => ~16 min.
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Recursion
Before timing out, store
current state and call
yourself again.
Best Practices
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Lambda Recursion
module.exports = function(e, ctx, cb) {
while (ctx.getRemainingTimeInMillis() > 1000) {
// do something
}
// store state & call yourself again
}
1.) Same instance problem
2.) Threshold uncertainty
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Outsource
Move bigger workloads to
EC2 or other services.
Best Practices
Recursion
Before timing out, store
current state and call
yourself again.
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Serverless
by managing servers?
Without me!
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Step Functions
Manage the execution
workflow and work around
the time limit.
Outsource
Move bigger workloads to
EC2 or other services.
Best Practices
Recursion
Before timing out, store
current state and call
yourself again.
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Step Functions Example
{
"StartAt": "Wait for Timestamp",
"States": {
"Wait for Timestamp": {
"Type": "Wait",
"SecondsPath": "$.seconds",
"Next": "Send SNS Message"
},
"Send SNS Message": {
"Type": "Task",
"Resource": "$LAMBDA_ARN",
"Retry":[...],
"End": true
}
}
}
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Best Practices
Limits
5.
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3 GB
available for memory allocation.
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Memory allocation
and CPU power
correlate.
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50 MB
Maximum artifact size for zipped files.
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Improve
your dependency
management.
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512 MB
available for temporary, local storage.
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Everything else
goes into other
storage services.
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Best Practices
Maintenance
6.
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Clean Up
Clean up data or make sure
your data is still up-to-date.
Log Collection
Collect CloudWatch logs
and store them somewhere
else, e.g. in a file on S3.
Monitoring
Do health checks of your
own services and send
alarms to Slack.
Maintenance
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Examples
- Shut down resources in your
developer stacks
- Clean up Lambda log groups
- Analyze logs for keywords
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Looking Ahead
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Security
bit.ly/secure-aws-lambda
Legacy Code
bit.ly/migrating-to-lambda
Caching
bit.ly/caching-in-lambda
Further Topics
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Serverless
+
Services
=
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Focus
Keep your functions small.
Find a good use case for
your function.
Scale
Automatic scalability
needs responsibility.
Be Flexible
Expand your functions as
necessary.
Takeaways
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Subtitle
Scale beyond.
Sebastian Hesse · K15t · @seeebiii