Debug your Container and Serverless Applications with AWS X-Ray in 5 Minutes - AWS Online Tech Talks

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Abhishek Singh, Sr. Manager Product Management (AWS)
Ivonne Roberts, Software Architect (Financial Engines)
Debug your Container and
Serverless Applications with
AWS X-Ray in 5 Minutes or less

What to Expect from the Session
 What is X-Ray and how does it help?
 Concepts
 Demo - Debug an app in < 5 mins
 How can you instrument your own app?
 Customer Story
 Q&A

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What is X-Ray and how does it help?

Traditional Debugging
Developer Local Test
Developer
Add
Breakpoints
Add Log
Statements

Log-Centric Approach
service 1 logs
service 2 logs
service 3 logs
user

Challenges for microservices apps
• Cross-service interactions
• Varying log formats across services
• Collecting, aggregating, and collating logs from services

Challenges for serverless apps
• Resources typically only exist during execution.
• Unlike traditional hosts, can’t install agents for monitoring.
• Collecting, aggregating, and collating logs in real-time without latency overhead.
• Events need to be correlated across services.

The traditional process of debugging doesn’t scale well for
production applications or those built using a service-
oriented, microservice, or serverless architecture.
It’s tedious, repetitive, and time consuming.

User-Centric Approach
user
Amazon
DynamoDB
Table
Amazon
SQS Queue
Frontend API

How does AWS X-Ray help?
• Analyze and debug performance of your distributed applications.
• View latency distribution and pinpoint performance bottlenecks.
• Identify specific user impact across your applications.
• Works across different AWS and non-AWS services.
• Ready to use in production with low latency in real-time.
Enables you to get started quickly without having to manually
instrument your application code to log metadata about requests

Visualize service call graph

Identify performance bottlenecks

Pinpoint issues

Identify errors

Identify impact to users

Lambda + X-Ray

Benefits of using AWS X-Ray with Lambda
• X-Ray agent is natively built into Lambda.
• Identify initialization and cold starts in Lambda
• Pinpoint issues in downstream services called from your AWS
Lambda function.
• Happens with low latency in real-time. Can see traces in seconds.

Visualize Service Call Graph (Lambda)

Identify initialization & cold starts (Lambda)

Pinpoint errors (Lambda)

Additional Use Cases
Use Off-
Cloud
Custom
Payload
Deep
Linking
Custom
Apps via
APIs
Filter
Expressions

X-Ray Service

App & X-Ray
SDK
EC2 Instance
X-Ray
Daemon
Localhost
UDP
X-Ray API
HTTPS
HTTPS
X-Ray Console
App & X-Ray
SDK
Server
X-Ray
Daemon
Localhost
UDP
EC2 Role
AWS
Credentials
DevOps Team
HTTPS
X-Ray Workflow

X-Ray Concepts

X-Ray Concepts
user
Amazon
DynamoDB
Table
Amazon
SQS Queue
Frontend API

X-Ray Concepts
Trace End-to-end data related a single request across services
Segments Portions of the trace that correspond to a single service
Sub-segments Remote call or local compute sections within a service
Annotations Business data that can be used to filter traces
Metadata Business data that can be added to the trace but not used for filtering traces
Errors Normalized error message and stack trace
Sampling Percentage of requests to your application to capture as traces

Let’s debug an app in <5 minutes!

How can you instrument your app?

X-Ray SDK
Enables you to get started quickly without having to manually instrument your
application code to log metadata about requests
Source on GitHub under https://www.github.com/aws/
Available for Java, .NET, .NET Core, Ruby, Python, Go, and Node.js.
Adds filters to automatically captures metadata for calls to:
• AWS services using the AWS SDK
• Non-AWS services over HTTP and HTTPS (3rd party APIs)
• Databases (MySQL, PostgreSQL, and Amazon DynamoDB)
• Queues (Amazon SQS)

App Instrumentation (Node.js)
//Add aws-xray-sdk package to package.json
var XRay = require('aws-xray-sdk');
var AWS = captureAWS(require('aws-sdk'));
…
XRay.config([XRay.plugins.EC2]);
XRay.captureHTTPs(http);
XRay.setDefaultName('myfrontend-dev');
…
app.use(XRay.express.openSegment());
app.get('/', function(req, res)
…
app.get('/blog', function(req, res)
…
app.use(XRay.express.closeSegment());

Adding Business Data (Node.js)
//Example showing how to add business data to traces
app.post('/signup', function(req, res) {
var item = {
'email': {'S': req.body.email},
'name': {'S': req.body.name},
'preview': {'S': req.body.previewAccess},
'theme': {'S': req.body.theme}
};
var seg = XRay.getSegment();
seg.addAnnotation('email', req.body.email);
seg.addAnnotation('theme', req.body.theme);
seg.addAnnotation('previewAccess', req.body.previewAccess);
//Store item to DB
//Send sign-up notification to user
}

Sampling Configuration
{
"rules": {
“payment": {
"id": 1,
"service_name": "*",
"http_method": "*",
"url_path": "/api/payment/*",
"fixed_target": 0,
"rate": 1.00
},
"base": {
"id": 2,
"service_name": "*",
"http_method": "*",
"url_path": "*",
"fixed_target": 1,
"rate": 0.1
}
}
}
This example defines two rules:
The first rule applies a 100% sampling rate with
no minimum number of requests to trace to
requests with paths under /api/payment
The second overrides the default sampling rule
with a rule that traces the first request each
second and 10% of additional requests

X-Ray Daemon
Receives data from the SDK over UDP and acts as a local buffer.
Data is flushed to the backend every second or when the local
buffer fills.
Available for Amazon Linux AMI, RHEL, Ubuntu, OS X, and Windows.
Pre-installed on AWS Lambda.
Can be run anywhere as long as AWS credentials are provided (for
example, EC2, ECS, Kubernetes, on-premise, developer machine,
and others).

X-Ray API
X-Ray provides a set of APIs to enable you to send, filter, and
retrieve trace data
You can send trace data directly to the service without having to use
our SDKs (that is, you can write your own SDKs for languages not
currently supported)
Raw trace data is available using batch get APIs
You can build your own data analysis applications on top of the data
collected by X-Ray

X-Ray API
PutTraceSegments Uploads segment documents to AWS X-Ray
BatchGetTraces Retrieves a list of traces specified by ID
GetServiceGraph Retrieves a document that describes services in your application and
their connections
GetTraceSummaries Retrieves IDs and metadata for traces available for a specified time
frame using an optional filter

How-to guides, blogs, and samples
Lambda:
o https://docs.aws.amazon.com/lambda/latest/dg/lambda-x-ray.html
o https://aws.amazon.com/blogs/compute/analyzing-performance-for-amazon-rekognition-apps-written-on-
aws-lambda-using-aws-x-ray/
o https://github.com/aws-samples/aws-xray-rekognition-lambda-sample
o https://github.com/aws-samples/eb-java-scorekeep/tree/lambda
Containers:
o https://docs.aws.amazon.com/xray/latest/devguide/scorekeep-ecs.html
o https://docs.aws.amazon.com/xray/latest/devguide/xray-daemon-ecs.html
o https://aws.amazon.com/blogs/compute/application-tracing-on-kubernetes-with-aws-x-ray/
o https://github.com/aws-samples/aws-xray-kubernetes
o https://github.com/aws-samples/aws-workshop-for-kubernetes

Trace ID & Propagation

Customer Story

Ivonne Roberts
Software Architect, Office of CTO
Financial Engines, Inc.
Twitter: @ivlo11
LinkedIn: https://www.linkedin.com/in/ivonneroberts/
With roots in Silicon Valley, Financial Engines is the nation’s largest independent investment
advisor1. We believe that all Americans—not just the wealthy—should have access to high-quality,
unbiased financial help, and our clients’ best interests should always come first.
We offer financial help to more than 9 million people across over 700
companies
1 For independence methodology and ranking, see InvestmentNews RIA Data Center. (http://data.investmentnews.com/ria/).

Architecture
• Migrating from a monolith codebase on premise
• First with a lift-and-shift hybrid approach
• And then breaking pieces off into microservices in a serverless architecture with a high bar for using
non-managed services
• 15 microservices in production in AWS

Monitoring Journey
Use Cases:
• Measure performance improvement as we move to the cloud
• Ongoing measurement of performance during updates and changes
• Able to identify and resolve issues fast
Monitoring Implementations:
• Instrument code with timing calculations and writing that to the end of the log
• Stress test applications in dev that scraped Lambda’s log using LogType.TAIL
• CloudWatch and Splunk dashboards and alerts
Monitoring Customer Experience:
• AWS X-Ray instrumentation and monitoring

AWS X-Ray + Lambda
Quick Wins:
• Able to instrument our applications with very little effort
• Able to see a sampling of user traffic
• Average transactions per minute
• Average latency
• Response distribution
• Able to quickly find and address performance concerns
• Increased granularity with annotations
• Slice and dice based on type of computation, company, number of accounts, etc.
Couple of Notes:
• You won’t get all errors sampled unless you add: forceSamplingOfCurrentSegment()
• Can’t annotate root segment so you need to add a “root” sub-segment with annotation and metadata
• Inferred Segment based off of sub-segments with services that don’t issue their own segments

Demo

Highlights
Able to instrument our applications with very little effort
Latency across different resources in a microservice
Slice and dice reports with annotations

Additional Information

Quick Recap
• Serverless Apps have built in logging & monitoring capabilities
• Easy to extend with CloudWatch Metrics, Amazon ES, and third-
party tools
• AWS X-Ray is powerful tool to visualize and troubleshoot issues

X-Ray Pricing
Free tier
o The first 100,000 traces recorded are free
o The first 1,000,000 traces retrieved or scanned are free
Additional charges
o Beyond the free tier, traces recorded cost $5.00 per million traces
o Beyond the free tier, traces retrieved or scanned cost $0.50 per million traces

Helpful Links
Go to https://aws.amazon.com/xray to learn more
Documentation: http://docs.aws.amazon.com/xray/latest/devguide/aws-xray.html
Samples:
o .NET: https://github.com/awslabs/aws-xray-dotnet-webapp
o Java: https://github.com/awslabs/eb-java-scorekeep/tree/xray
o Node.js: https://github.com/awslabs/eb-node-express-sample/tree/xray
o Python: https://github.com/awslabs/eb-py-flask-signup/tree/xray
o Lambda: https://github.com/awslabs/aws-xray-rekognition-lambda-sample
o Alarms & Alerts: https://github.com/aws-samples/aws-xray-cloudwatch-event
o Heatmap & Trends: https://github.com/aws-samples/aws-xray-scatter-sample

Debug your Container and Serverless Applications with AWS X-Ray in 5 Minutes - AWS Online Tech Talks

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à Debug your Container and Serverless Applications with AWS X-Ray in 5 Minutes - AWS Online Tech Talks

Similaire à Debug your Container and Serverless Applications with AWS X-Ray in 5 Minutes - AWS Online Tech Talks (20)

Plus de Amazon Web Services

Plus de Amazon Web Services (20)

Debug your Container and Serverless Applications with AWS X-Ray in 5 Minutes - AWS Online Tech Talks

Notes de l'éditeur