by Martin Schade, Solutions Architect & Ramesh Dwarakanath, Solutions Architect, AWS Cloud is the new normal, and organizations are deploying different types workloads on AWS. Understanding the performance efficiency and overall application performance is critical to ensuring that you can scale your workload to meet the demands of your customers. Understanding how well your application performs over time helps you to continuously improve and innovate your software to get the most out of the AWS platform. If you aren't measuring custom application metrics, you are operating your software blindly and cannot pinpoint areas of improvement. Learn how to use Amazon CloudWatch custom metrics, alerts, dashboards and AWS X-Ray to architect an application monitoring service to provide insight to your workload's performance.

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Application Performance Management
(APM) on AWS
Ramesh Dwarakanath, Solutions Architect
November 2017
A R C 3 1 7

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What to Expect from this session
Look at industry
trends impacting
monitoring
How to use Amazon CloudWatch
custom metrics, logs and AWS
X-Ray to get custom insights
about your application.
Understand some APM
on AWS best practices
See some of those
scenarios in action

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Why APM is important?
Every application is unique
Libraries
Programing
Language
External
Dependencies
Performance
Requirements
Frameworks
Architecture
Synchronous
Asynchronous
Serverless
Containers
Monolithic
Protocols

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Do you really know your application?
Infrastructure
Impacts
Bottlenecks
When/How to
scale
New
Versions
Traffic
Volumes
Error
Rates
How can you manage what you don’t know?

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Trends in Monitoring
Applications are more dynamic
Complexity is increasing
Increasing business impact

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How to perform APM on AWS
Custom
Metrics
Logs
Measurement of
an event
Human-readable
events Amazon CloudWatch Logs
Amazon CloudWatch
Examples
Throughput, error
count, request rate,
request calculations,
queue sizes
Process output,
event correlation,
root cause analysis
AWS Service
AWS X-Ray
Traces
Cross-service
view of requests
Review request
behavior, improve
app performance,
service mapping

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The Challenge
PutMetricData is a
synchronous HTTP
request. It introduces
latency in your code.
The Solution
Use an UDP agent like
collectd* or logstash**.
Amazon CloudWatch Custom Metrics
* CloudWatch Plugin for collectd: https://aws.amazon.com/blogs/aws/new-cloudwatch-plugin-for-collectd/
** CloudWatch output for Logstash: https://github.com/logstash-plugins/logstash-output-cloudwatch

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Custom Metrics Architecture
Your App
EC2 Instance
UDP
Daemon
localhost
UDP
CloudWatch
API
HTTPS
HTTPS
AWS Management Console
EC2 Role
DevOps Team
HTTPS
Your App
UDP
Daemon
localhost
UDP
AWS
Credentials
Server
Fire and forget
Decouples metrics
aggregation and
API calls

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The Challenge
1. Log centralization
2. Log Archive
3. source code access
and instrumentation
The Solution
Use CloudWatch Logs
agent *.
CloudWatch Custom Metrics From Logs
• CloudWatch Logs Agent Reference: http://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/AgentReference.html

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CloudWatch Logs Architecture
Your App
EC2 Instance
CloudWatch
Logs Agent
logs CloudWatch
API
HTTPS
HTTPS
AWS Management Console
EC2 Role
DevOps Team
HTTPS
Your App
CloudWatch
Logs Agent
AWS
Credentials
Server
No need to
change your code
logs

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From CloudWatch Logs to Custom Metrics
CloudWatch
Logs
Metric Filters Custom Metric
Works with space-delimited or
JSON log formats.

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1
2
3

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Filter Pattern - Space Delimited Logs
2017-09-08T22:33:06.877762 INFO 30223 0.001940809
2017-09-08T22:33:07.882269 INFO 73123 0.004267585
2017-09-08T22:33:08.882543 ERROR Random Error
2017-09-08T22:33:09.882809 ERROR Random Error
2017-09-08T22:33:10.887246 INFO 72689 0.004165085
2017-09-08T22:33:11.893134 INFO 98254 0.005619234
2017-09-08T22:33:12.896820 INFO 59771 0.003355367
[recorded_at, severity=INFO, item_count, elapsed_time]

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Filters Pattern - Space Delimited Logs
2017-09-08T22:33:06.877762 INFO 30223 0.001940809
2017-09-08T22:33:07.882269 INFO 73123 0.004267585
2017-09-08T22:33:08.882543 ERROR Random Error
2017-09-08T22:33:09.882809 ERROR Random Error
2017-09-08T22:33:10.887246 INFO 72689 0.004165085
2017-09-08T22:33:11.893134 INFO 98254 0.005619234
2017-09-08T22:33:12.896820 INFO 59771 0.003355367
[recorded_at, severity=ERROR, msg]

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Filter Pattern - JSON Logs
{"recorded_at":"2017-09-08T22:33:06","severity":"INFO","key1":30223, "key2”:809}
{"recorded_at":"2017-09-08T22:33:07","severity":"INFO","key1":73123, "key2":585}
{"recorded_at":"2017-09-08T22:33:08","severity":"ERROR",”msg":"Random Error"}
{"recorded_at":"2017-09-08T22:33:09","severity":"ERROR",”msg":"Random Error"}
{"recorded_at":"2017-09-08T22:33:10","severity":"INFO","key1":72689, "key2":85}
{"recorded_at":"2017-09-08T22:33:11","severity":"INFO","key1":98254, "key2":234}
{"recorded_at":"2017-09-08T22:33:12","severity":"INFO","key1":59771,"key2":367}
{ $.severity = "INFO" }

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CloudWatch Logs Subscriptions
CloudWatch
Logs
Lambda
Function
Amazon ESsubscription Implement complex
parsing logic
Interact with other AWS
services
Anything you can code

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CloudWatch Dashboards
Monitor multiple
resources in a
single view
Even those
resources that
spreads across
multiple regions

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Logging vs UDP Latency
EC2: t2.micro; EBS: 8GB, GP2
EC2: m4.large; EBS: 8GB, GP2
Source code:PUT LINK HERE

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AWS X-Ray Components
user
Amazon API
Gateway
AWS Lambda
Amazon
DynamoDB
Amazon
SQS queue

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AWS X-Ray Graph

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AWS X-Ray Diagram
Your App
EC2 Instance
X-Ray
Daemon
localhost
UDP
X-Ray
API
HTTPS
HTTPS
AWS Management Console
EC2 Role
DevOps Team
HTTPS
Your App
X-Ray
Daemon
localhost
UDP
AWS
Credentials
Server
Fire and forget
Decouples trace
aggregation and
API calls

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//demo: putting-it-all-together
MyApp
collectd
Logs
X-Ray Daemon
CloudWatch
Logs Agent
CloudWatch
API
X-Ray
API
UDP
UDP
HTTPS
HTTPS
EC2 Instance
HTTPS
while true
factorial(n)
fibonacci(n)
log_metrics
udp_send_metrics
udp_send_xray_traces
sleep 1
end

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Demo
https://github.com/awslabs/cloudwatch-xray-apm-demo

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APM on AWS Best Practices
•Design your apps to report custom metrics.
•Use an UDP Daemon to avoid adding latency.
•Plan carefully your logging and metric strategy.
•Use X-Ray for distributed tracing and
troubleshooting.

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APM on AWS Best Practices
•Deliver CloudWatch Dashboards, Alarms and
self-healing automation as part of your app.
•Be aware of service limits: PutLogsEvents,
PutMetricData, PutTraceSegment,
PutTelemetryRecords, etc.
•Leverage AWS service’s metrics together with
your own custom metrics.

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Remember
Everything fails
all the time.
Werner Vogels, Amazon CTO

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Resources
https://aws.amazon.com/answers/logging/centralized-logging/
https://github.com/awslabs/cloudwatch-logs-centralize-logs
https://github.com/awslabs/cloudwatch-logs-customize-alarms
https://github.com/awslabs/cloudwatch-logs-analyze-data
https://github.com/awslabs/cloudwatch-dashboards-cloudformation-sample

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Thank you!
R a m e s h D w a r a k a n a t h
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