QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights. More info: sumologic.com/training

1. Sumo Logic Confidential
QuickStart Webinar
Getting Started with Sumo Logic
Mario Sánchez
February 2016
Welcome.
To give everyone a
chance to successfully
connect, we’ll start at
10:05 AM Pacific.

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Agenda
What is Sumo Logic?
Key Points Regarding Data Collection (Admin Topic)
Searching, Parsing and Analyzing Data
Visualizing and Monitoring – Dashboards and Alerts
Introduction to Library and Apps
Introduction to Optimization Tools(Admin Topic)

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What is Sumo Logic?

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Continuous Intelligence
DEVOPS IT INFRASTRUCTURE
AND OPERATIONS
COMPLIANCE AND
SECURITY
DEVOPS
Streamline
continuous delivery
Monitor KPI’s and
Metrics
Accelerate
Troubleshooting
IT INFRASTRUCTURE
AND OPERATIONS
Monitor all workloads
Troubleshoot and
increase uptime
Simplify, Modernize,
and save costs
COMPLIANCE AND
SECURITY
Automate and
demonstrate
compliance
Audit all systems
Think beyond rules
Sumo Logic Cloud Analytics Service

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sumologic.com/compliance

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Enterprise Logs are Everywhere
Custom App
Code
Server / OS
Virtual
Databases
Network
Open Source
Middleware
Content
Delivery
IaaS,
PaaS
SaaS Security

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High-Level Data Flow

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Sumo Logic Data Flow
Data Collection Search & Analyze Visualize & Monitor
Alerts
Dashboards
Collectors
Sources
Operators
Detect
1 2 3

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Data Collection

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Host A
Collectors and Sources
Apache Access
Apache Error
Collector
A
Host B
Collector
B
Host C
Collector
C
Apache Access
Apache Error
IIS Logs
IIS W3C Logs

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Metadata Fields
Name Description
_collector Name of the collector this data came from
_source Name of the source this data came through
_sourceHost Hostname of the server this data came from
_sourceName Name of the log file (including path)
_sourceCategory Category designation of source data
Tags added to your messages when data is collected
Host A
Apache Access
Apache Error
Collector
A

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Host A
Metadata Field: Source Category
Apache Access
_sourceCategory =
WS/Apache/Access
Apache Error
_sourceCategory =
WS/Apache/Error
Collector
A
Host B
Collector
B
Host C
Collector
C
Apache Access
_sourceCategory =
WS/Apache/Access
Apache Error
_sourceCategory =
WS/Apache/Error
IIS Logs
_sourceCategory =
WS/IIS
IIS W3C Logs
_sourceCategory =
WS/IIS/W3C
Sample Searches for
_sourceCategory:
= WS/Apache/Access
= WS/Apache/*
= WS/*

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Source Category Naming Convention
Simplifies Search Syntax and Scope Definitions
Used for other Sumo Logic features
Role-BasedAccess Control (Data Provisioning)
Partitioning (Search Optimization Tool)
Adopt a Robust Naming Convention Early
Ex: Prod/Sumo/Apache/Access à Env/Customer/Device/MessageType
Ex: OS/Windows/2012/Messages à Device/Vendor/Version/MessageType
Blog Post: GoodSourceCategory, Bad SourceCategory

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Search and Analyze

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Set your
Preferences
Set your Session
Timeout
Query Editing
versus Running

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Search Basics Overview
Time Range
Histogram
Search Bar
Search Results
Display Options

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Field Browser - Metadata fields
Sumo Logic Confidential
Field Browser
Metadata Fields
Parsed Fields

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Search Structure
Keywordsand operators (separated by pipes) that build on top of each other
Syntax:
metadata tags + keywords| parse | filter | aggregate | sort | limit
Example Search:
Results
where
metadata
keyword

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Keyword Search
Case Insensitive
Wildcard Support (e.g. ERR*)
Boolean Logic Support
AND
OR
!(A OR B)
Combine these keywords with metadata fields
Bloom filters
Using keywords helps bloom filters locate data very quickly

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• Determine the data available through your search.
• Pre-populated Dropdown
– Last 15 min, Today
• Absolute
– 12:25PM 12:30PM
– 8/11/2015 13:00AM 8/11/2015 14:00AM
• Relative
– -5m
– -2h
– -2d -1d
Time Range

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Develop Good Search Habits
Use metadata and keyword combinations to reduce scope
Add line breaks after each operation
Limit result sets before aggregating data à user=a | count by user
Use parse anchor instead of parse regex for structured messages
Avoid the use of expensive parse regex tokens like .* à d{2,10}
Narrow your time-range down as much as possible

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Refining Results by Surrounding Messages

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• LogReduce uses fuzzy logic and soft matching to cluster messages providing quick
investigation view into your environment.
Operators: Looking for the Unknown

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• Identify unexpectedly high or low values within determined thresholds
|timeslice 1m
|count by _timeslice
|outlier _count
Operators: Finding Outliers

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• Parsing enables a user to extract parts of a message and classify them as
fields.
– Enables you to perform additional operations
• Logical/conditional – based on values
• Mathematical – operations on value sets
• Parsing Options
– parse anchor: Leverages beginning and ending anchors
– parse regex: Extracts nested information via regex
Extracting and Labeling Additional Fields

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Parse Anchor - Using the UI
Highlighting
strings in the
result allow
you to launch
the UI parser
UI Parser allows
you to select fields
and label them
Results now show
your parsed fields

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• Extracts nested information via regular expressions
• Use if the construct of the messages is inconsistent
_sourceCategory=Apache/Access
| parse regex "[A-Z]+s(?<url>/S*)sHTTP/1.d+"s(?<status_code>d+)s"
Parse Regex

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Regular Expressions – References and Resources
Regular Expressions use JRE
Online Resources:
• regex101.com
• Regular-expressions.info/refadv.html
• en.wikipedia.org/wiki/Regular_expression
• regexr.com
• Book
– Mastering Regular Expressions by Jeffrey E.F. Friedl

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Evaluates messages and places them into groups
• Produces aggregates in a separate tab
• Must come after basic operators such as parse. Cannot be used with summarize.
• The count Operator enables you to group messages that match a classification
– Ex: _sourceCategory=Apache* | count as mycount
– Ex: GET | count by _sourceCategory
Grouping your Data

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• Dissecting your result sets using Metadata Fields
– Ability to aggregate results sets and grouping them by metadata fields
• EX: _collector=*apache* | count by _sourceCategory
– Get a count of grouped result sets
• Ex: (Error OR fail*)| count by _sourcecategory , _sourcehost
– Organize Results by Count
• Ex: _collector=*apache*| count by _sourceCategory | sort by _count
Leveraging Metadata for Grouping

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Timeslice operator enables you to segment your results
by time buckets
– Minute (timeslice by 5m)
– Hour (timeslice by 1h)
– Day (timeslice by 1d)
Example:
_sourceCategory=Apache/Access GET
|timeslice 1m
| count by _timeslice
| sort by _timeslice asc
Time-based Grouping

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Saving Your Searches
Click Save As
under the Query
Window
Description is searchable,
so a include detail to allow
searching at a future time
You can save the search in
your own Personal folder, or
create a sub-folder

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Visualize and Monitor

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• Collection of Panels that provide graphical representation of data
– Each Panel processes results of a search
– Drilldown for additional analysis
• Drill into the query behind the dashboard
• Drill to another dashboard
Introduction to Dashboards

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• Chart Types
– Table
– Bar
– Column
– Line
– Area
– Pie
– Box Plot
– Google Maps
– Single Value
Providing Context through Visualization

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– Live Dashboards
• Provides a live stream of data
• No back filling of data
– Interactive Dashboards
• Search based (On-Demand)
• Backfilling of data
• Support Filtering
Dashboard Types
No
Interaction
Ability to use
Pre-defined
filters

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Live Dashboards versus Interactive Dashboards
Use Case Examples Dashboard Type
Large screen
displays with
streaming updates
Shared Screens for NOC, Operations,
Developers, etc.
Live Dashboards
Template for
Exploring Data
Operational Investigations Interactive
Dashboards
Historical Reporting
and Investigation
Audits, Failed/successful logins for
certain groups
Interactive
Dashboards

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Dashboards - Adding a Panel
1. Performyour
Search
2. Format your
Results
3. Create a Panel

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Alerting
Using a Scheduled Search, you can set Alerts to trigger whenever the search completes
or when a certain condition is met.
Alert types include:
• Save to Index
• Script Action
• Email
• Webhooks
Blog Post: 2 Key Principles for Creating Meaningful Alerts

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Saving and Scheduling an Alert
1. Save your Search
2. Schedule the
Search
3. Specify frequency and time range
4. Specify Alert condition &
threshold
5. Specify Alert Type and details

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Jumpstart with Apps

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Installing Applications

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Optimizing Your Search
Experience

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Factors in Search Performance
Query Structure
Time range
Data Selectivity (keywords, metadata, where statements)
Heavy Operations (join, transaction, summarize)
Overall Data Volume
System load
Improve search experience using Optimization Tools

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Search Optimization Tools
How-To Webinar Recording: https://youtu.be/JNWbtws-sns
Partitions
Index data for searching over a smaller data set
Scheduled Views
Pre-aggregating data for fast counts/sums over longer time ranges
Field Extraction Rules
Parse the data on ingest rather than run-time; simplifies searches
Take advantage of interactive dashboard filters

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Questions?
Additional Resources
Search Video Library and Documentation
Search/Post to Community Forums
Search, post, respond
Submit/vote for feature requests
Submit Tips & Tricks
Open a Support Case
Sumo Logic Services
Customer Success,Professional Services,Training

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Helpful Links
Hands-on Lab: Sumo Logic QuickStart Tutorial
https://service.sumologic.com/help/Default.htm#Tutorial.htm?Highlight=tutorial
Sumo Logic Training
https://www.sumologic.com/training
Support Portal, Documentation, Community Forums, Feature Requests
https://support.sumologic.com/home
Services
customer-success@sumologic.com

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Thank you!