Post on 13-Apr-2017
Sumo Logic Confidential
QuickStart WebinarGetting Started with Sumo Logic
Mario SánchezFebruary 2016
Welcome.To give everyone a
chance to successfully connect, we’ll start at
10:05 AM Pacific.
Sumo Logic Confidential
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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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 SECURITYAutomate and demonstrate complianceAudit all systems
Think beyond rules
Sumo Logic Cloud Analytics Service
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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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Sumo Logic Data Flow
Data Collection Search & Analyze Visualize & Monitor
Alerts
Dashboards
Collectors
Sources
Operators
Detect
1 2 3
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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 featuresRole-Based Access Control (Data Provisioning)
Partitioning (Search Optimization Tool)
Adopt a Robust Naming Convention EarlyEx: Prod/Sumo/Apache/Access à Env/Customer/Device/MessageType
Ex: OS/Windows/2012/Messages à Device/Vendor/Version/MessageType
Blog Post: Good SourceCategory, Bad SourceCategory
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Search Basics OverviewTime Range
Histogram
Search Bar
Search Results
Display Options
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Field Browser - Metadata fields
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Field Browser
Metadata Fields
Parsed Fields
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Search Structure
Keywords and operators (separated by pipes) that build on top of each other
Syntax:
metadata tags + keywords | parse | filter | aggregate | sort | limit
Example Search:
Results
where
metadatakeyword
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Keyword Search
Case Insensitive
Wildcard Support (e.g. ERR*)
Boolean Logic SupportAND
OR
!(A OR B)
Combine these keywords with metadata fields
Bloom filtersUsing 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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• 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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• 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 TypeLarge screen displays with streaming updates
Shared Screens for NOC, Operations, Developers, etc.
Live Dashboards
Template for Exploring Data
Operational Investigations Interactive Dashboards
Historical Reportingand Investigation
Audits, Failed/successful logins for certain groups
Interactive Dashboards
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Dashboards - Adding a Panel
1. Perform your 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 Search2. Schedule the Search
3. Specify frequency and time range
4. Specify Alert condition & threshold
5. Specify Alert Type and details
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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
PartitionsIndex data for searching over a smaller data set
Scheduled ViewsPre-aggregating data for fast counts/sums over longer time ranges
Field Extraction RulesParse the data on ingest rather than run-time; simplifies searches
Take advantage of interactive dashboard filters
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Questions?
Additional ResourcesSearch Video Library and Documentation
Search/Post to Community ForumsSearch, post, respond
Submit/vote for feature requests
Submit Tips & Tricks
Open a Support Case
Sumo Logic ServicesCustomer Success, Professional Services, Training
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Helpful Links
Hands-on Lab: Sumo Logic QuickStart Tutorialhttps://service.sumologic.com/help/Default.htm#Tutorial.htm?Highlight=tutorial
Sumo Logic Traininghttps://www.sumologic.com/training
Support Portal, Documentation, Community Forums, Feature Requestshttps://support.sumologic.com/home
Servicescustomer-success@sumologic.com