Log Analytics with AWS: Data Analytics Week at the San Francisco Loft Nearly everything in IT - servers, applications, websites, connected devices, and other things - generate discrete, time-stamped records of events called logs. Processing and analyzing these logs to gain actionable insights is log analytics. We'll look at how to use centralized log analytics across multiple sources with Amazon Elasticsearch Service. Level: Intermediate Speakers: Rajeev Srinivasan - Strategic Solutions Architect, AWS Austin Clark - Technical Account Manager, AWS

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Log Analytics with AWS
Rajeev Srinivasan
Strategic Solutions Architect
srrajeev@amazon.com
Pop-up Loft
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Austin Clark
Technical Account Manager
auclark@amazon.com

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Your pager goes off
at 3:00am.
The servers are
melting!

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You VPN in and quick check the logs
Uh oh

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If you had Amazon Elasticsearch Service

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Transition from IT
to DevOps
Increase in IoT and
Mobile Devices
Cloud-based
architectures
Machine-generated data is growing 10x faster than business data
Source: insideBigData - The Exponential Growth of Data, February 16, 2018
THE EXPLOSION OF MACHINE-GENERATED DATA

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Amazon Elasticsearch Service is a
fully managed service that makes
it easy to deploy, manage, and
scale Elasticsearch and Kibana
A M A Z O N E L A S T I C S E A R C H S E R V I C E
+

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B E N E F I T S O F A MA ZO N E LA ST I C SE A R C H SE R VI C E
Supports Open-Source
APIs and Tools
Drop-in replacement with no
need to learn new APIs or skills
Easy to Use
Deploy a production-ready
Elasticsearch cluster in
minutes
Scalable
Resize your cluster with a few
clicks or a single API call
Secure
Deploy into your VPC and restrict
access using security groups and
IAM policies
Highly Available
Replicate across Availability
Zones, with monitoring and
automated self-healing
Tightly Integrated with
Other AWS Services
Seamless data ingestion, security,
auditing and orchestration

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ELASTICSEARCH LEADING USE CASES
Application Monitoring &
Root-cause Analysis
Security Information and Event
Management (SIEM)
IoT & Mobile Business & Clickstream Analytics
Provides developers with a high
performance, self-service operational
monitoring and analytics platform
Enables security practitioners to centralize
and analyze events from across the entire
organization
Gives developers and lines of business
users real-time location-aware insights
into their device fleets
Provides business users with a real-time view
of the performance of their web content and
e-commerce platforms

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Application Monitoring & Root-cause Analysis
C A S E S T U D Y : E XPE D I A
Logs, lots and lots of logs. How to cost effectively monitor logs?
Require centralized logging infrastructure
Did not have the man power to manage infrastructure
P R O B L E M
Quick insights: Able to identify and troubleshoot issues in real-time
Secure: Integrated w/ AWS IAM
Scalable: Cluster sizes are able to grow to accommodate additional log sources
B E N E F I T S
Streaming AWS CloudTrail logs, application logs, and Docker
startup logs to Elasticsearch
Created centralized logging service for all team members
Using Kibana for visualizations and for Elasticsearch queries
S O L U T I O N
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

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Business and Clickstream Analytics
C A S E S T U D Y : FI NA NC I A L T I ME S
What stories do our readers care about? What’s hot?
Required a custom clickstream analytics solution
Need a solution that delivers analytics in real-time
Did not have a team to manage analytics infrastructure
P R O B L E M
B E N E F I T S
Streaming user data to Amazon ES for analysis. Created their own
custom dashboards for editors and journalists – Lantern.
Lantern - ”shines a light” on reader activity for the editors and
journalists at the FT
Critical tool for making editorial decisions. Daily editorial meetings
start by looking at Lantern dashboard
S O L U T I O N
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Reliability : Lantern is used throughout the day by journalists and editors. Relying on Amazon to manage
their systems for maximum uptime.
Cost savings: Able to easily tune their cluster to meet their needs with minimal management overhead

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Software & Internet Financial ServicesEducation Technology BioTech and Pharma
Media and Entertainment Social Media Telecommunications Travel & Transportation
Real Estate Logistics & Operations Publishing Other
A MA ZO N E LA ST I C SE A R C H SE R VI C E C UST O ME R S

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Elasticsearch is a distributed solution
You run
clusters of
instances

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Data pattern
Amazon ES cluster
logs_01.21.2018
logs_01.22.2018
logs_01.23.2018
logs_01.24.2018
logs_01.25.2018
logs_01.26.2018
logs_01.27.2018
Shard 1
Shard 2
Shard 3
host
request
verb
status
timestamp
etc.
Each index has
multiple shards
Each shard contains
a set of documents
Each document contains
a set of fields and values
One index per day

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Elasticsearch documents: structured JSON
{ "verb": "GET",
"ident": "-",
"bytes": 6245,
"@timestamp": "1995-07-01T00:00:01",
"request":
"GET /history/apollo/ HTTP/1.0",
"host": "199.72.81.55",
"authuser": "-",
"@timestamp_utc":
"1995-07-01T04:00:01+00:00",
"timezone": "-0400",
"response": 200 }
• Documents contain fields –
name/value pairs
• Fields can nest
• Value types include text,
numerics, dates, and geo objects
• Field values can be single or array
• When you send documents to
Elasticsearch they should arrive
as JSON
199.72.81.55 - - [01/Jul/1995:00:00:01 -0400] "GET /history/apollo/ HTTP/1.0" 200 6245

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Documents are the core entity
ID
Field: value
Field: value
Field: value
Field: value

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Amazon ES stores documents in an index
Amazon Elasticsearch Service domain
ID
Field: value
Field: value
Field: value
Field: value
_bulk API
Index / Type

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Indexes have primary shards
Amazon Elasticsearch Service domain
ID
Field: value
Field: value
Field: value
Field: value
_bulk API
Index / Type
Shards
• Shards are instances of
Apache Lucene
• Shards are the units of
storage and compute in
an Amazon ES domain
• The primary shard count
is fixed at index creation
• Each shard’s set of
documents is distinct
• Lucene creates an index
for each field in each
document

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Indexes have replica shards
Amazon Elasticsearch Service domain
ID
Field: value
Field: value
Field: value
Field: value
_bulk API
Index / Type
Shards
• Replica shards are
distributed copies of
their primaries
• The replica shard count
is dynamic
• Replica shards provide
data redundancy and
parallelism

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Shards are distributed across instances
Amazon Elasticsearch Service domain
ID
Field: value
Field: value
Field: value
Field: value
_bulk API
Index / Type
Shards
Instances
• Primary and replica
are distributed to
different instances
• Distribution is initially
by shard count
• Storage available
limits new shard
allocation as well
• Instances with shards
are called data nodes

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Shards (Lucene) store indexes on disk
Amazon Elasticsearch Service domain
ID
Field: value
Field: value
Field: value
Field: value
_bulk API
Index / Type
Shards
Instances Storage

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Service architecture
AWS SDK
AWS CLI
AWS CloudFormation
Elasticsearch
data nodes
Elasticsearch
master nodes
Elastic Load
Balancing
IAM
Amazon
CloudWatch
AWS
CloudTrail
Amazon Elasticsearch Service domain

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How you shard is
how you scale

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How many shards?
• Best practice: shards should be < 50GB
• Divide index size by ~40GB to get initial
shard count
• Active shards per instance ~= vCPUs
• Always use at least 1 replica for production!
Example: 2 TB corpus will need 50 primary shards
2,000 GB / 40GB per shard = 50 shards

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Which instance type?
Instance Max Storage* Workload
T2 3.5TB You want to do dev/QA
M3, M4 150TB Your data and queries are “average”
R3, R4 150TB You have higher request volumes, larger
documents, or are using aggregations heavily
C4 150TB You need to support high concurrency
I2, I3 1.5 PB You have XL storage needs

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How many instances?
• The index size will be about the same as the
corpus of source documents
• Double this if you are deploying an index replica
• Size based on storage requirements
• Either local storage or up to 1.5 TB of Amazon
Elastic Block Store (EBS) per instance
Example: a 2 TB corpus will need 4 instances
Assuming a replica and using EBS
Given 1.5 TB of storage per instance, this gives 6TB of storage

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Load data into
Amazon ES

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The components of ingestion
Data source Collect Transform Buffer Deliver

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AWS-Centric solutions
Amazon Kinesis
Firehose
Amazon CloudWatch Logs
Logstash
AWS IoT
Elasticsearch
data nodes
Elasticsearch
master nodes
Kibana
Data Producers Buffer/
Transform/
Deliver
Amazon ES Cluster Analytics UI

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Amazon Lambda architectures
Files
S3 Events
Amazon S3 AWS Lambda
Function
DynamoDB
streams
Amazon
DynamoDB
Table
AWS Lambda
Function
Amazon Elasticsearch Service
Amazon KinesisData
Producers
AWS Lambda
Function

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Kinesis Firehose delivery architecture
• For public access
domains
• Easily transform
data
• Serverless with
built-in batching,
index rollover, error
handling
S3 bucket
source records
data source
source records
Amazon Elasticsearch
Service
Firehose
delivery stream
transformed
records
delivery failure
Data transformation
function
transformation failure

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Secure your data
• Pub l i c – IA M
• Pr i v ate – e ndp o i nt
• E nc r yp ti o n

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Use IAM for public endpoints
{
"Version": "2012-10-17",
"Statement": [ {
"Effect":...
"Principal": ...
"Action": [...],
"Resource": ...,
"Condition": ...
} ]
}
• Effect: Allow or Deny
• Principal: AWS account ID
• Action
• HTTP verbs
• Service actions
• Resource: Amazon ES
domain/index
• Condition: IP Address

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Use security groups for private endpoints
• Private networking between
your VPC and Amazon
Elasticsearch Service
• Traffic does not
traverse the public
internet
• Use IAM security groups for
authentication and access
control
VPC subnet
security group
VPC subnet
security group
Amazon Elasticsearch Service
Data Master
Data
Master
IAM
IAM
Availability
Zone B
Availability
Zone A

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Encrypt your data
Elasticsearch
data nodes
Elasticsearch
master nodes
AWSKMS
• Use your own KMS keys
• Encrypted data at rest on
Amazon ES instances
• Encrypted automatic
snapshots

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Make your domain
more stable

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Use dedicated masters
Amazon ES cluster
Dedicated master nodes:
cluster state Data nodes: queries and updates

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Master node recommendations
Number of data nodes Master node instance type
< 10 m3.medium+
< 20 m4.large+
<= 50 c4.xlarge+
50-100 c4.2xlarge+
• In production, always use an odd number of masters, >= 3
• Use fewer, smaller than data nodes

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Use zone awareness
Amazon ES cluster
Availability Zone 1 Availability Zone 2

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CloudWatch Alarms
Name Metric Threshold Periods
ClusterStatus.red Maximum >= 1 1
ClusterIndexWritesBlocked Maximum >= 1 1
CPUUtilization/MasterCPUUtilization Average >= 80% 3
JVMMemoryPressure/Master... Maximum >= 80% 3
FreeStorageSpace Minimum <= (25% of
avail space)
1
AutomatedSnapshotFailure Maximum >= 1 1

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Elasticsearch
analysis is
delivered by
Aggregations

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Think buckets and metrics
Buckets – a collection of documents meeting some criterion
Metrics – calculations on the content of buckets
Bucket: time
Metric:count

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Dig in to your data in real time

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Run Elasticsearch in the AWS Cloud with
Amazon Elasticsearch Service
Deploy, scale, ingest, secure, monitor, and
analyze
Start sending your log data today!Amazon
Elasticsearch
Service

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Hands-on Lab: Log Analytics with AWS
https://github.com/wrbaldwin/da-week/

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