Datadog - Using Metrics to Measure and Understand Your AWS Environment Performance

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Gaining a better understanding of performance metrics is the best way to get a quick read of infrastructure health. In this session, Matt Williams, DevOps Evangelist for Datadog will show how to create a baseline, then collect, aggregate, and use metrics from AWS and other systems to improve application performance. Various customers’ experiences with this process will be highlighted throughout the session. Join this session to understand how you can simplify your use of metrics and use that knowledge to increase performance. Sponsored by Datadog.

Technologie

USING METRICS TO MEASURE
AND UNDERSTAND YOUR AWS
ENVIRONMENT PERFORMANCE
AWS GLOBAL SUMMIT 2015
9 APRIL 2015
Matt Williams
Evangelist, Datadog

BEFORE I GET STARTED...
getting started is easy

BEFORE I GET STARTED...
getting started is easy
getting good is not easy

BEFORE I GET STARTED...
getting started is easy
getting good is not easy
and it will not be quick

WHY MEASURE?
See where you came from
See how you are doing

WHY MEASURE?
See where you came from
See how you are doing
See how you can improve

WHY IS THIS COMPLICATED?
Elasticity makes this complicated
Elasticity is the new normal

SCENARIOS THAT REQUIRE ELASTICITY
Amateur Sports
Shopping Events
Concerts
Marketing Campaigns
What is your scenario?

HOW TO SOLVE THE COMPLEXITY
Tags are one solution and what Datadog uses

TAGS ALLOW FOR AD-HOC AGGREGATION
Monitor all Docker containers running image web
… in region us-west-2 across all availability zones
… and make sure resident set size < 1GB on c3.xl

WHAT METRICS TO LOOK AT?
There isn’t a single source of expertise
This is the hard part

YOU NEED TO FIND / HIRE /TRAIN AN EXPERT
Your context changes the meaning of metrics

3 CATEGORIES OF METRICS
Utilization - percent/time
Saturation - wait queue length
Errors - error count

WHAT IS GOOD VS BAD?
First learn the patterns

ARE THE ANOMALIES THE FOCUS, OR SHOULD THEY BE
IGNORED

SPECIFIC METRICS TO CONSIDER WITH AWS
It's impossible to say what the best metrics are in all cases.
It depends on YOUR workload

AWS EC2
system.cpu.stolen
system.cpu.idle
system.load.norm.5 *
system.mem.pct_usable *
system.disk.pct_usable *
aws.ec2.network_in/out
aws.ec2.disk_read/write_ops

AWS EC2
(https://www.datadoghq.com/2013/08/understanding-aws-stolen-cpu-and-how-it-affects-your-apps/)

AWS EBS PROVISIONED IOPS
aws.ebs.volume_queue_length (should never go over 1/100)
(https://www.datadoghq.com/2013/07/aws-ebs-provisioned-iops-getting-optimal-performance/)

AWS ELASTIC LOAD BALANCER
healthy_host_count, latency
HTTPCode_ELB_5XX
surge_queue_length
spill_over_count
backent_connection_errors
(https://www.datadoghq.com/2013/11/key-aws-elb-monitoring-metrics/)

SPECIFIC SQS METRICS
number_of_messages_sent (NOM_sent), NOM_received
NOM_deleted (throughput)
sent_message_size (cost)
approximate_NOM_visible (backlog)
approximate_NOM_not_visible (worked on)
(https://www.datadoghq.com/2014/08/monitor-amazon-sqs-message-traffic-datadog/)

THANK YOU
Matt Williams
Evangelist, Datadog
matt.williams@datadoghq.com (mailto:matt.williams@datadoghq.com)
http://datadoghq.com (http://datadoghq.com)
@technovangelist (http://twitter.com/technovangelist)

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