Sumo Logic QuickStart - May 2016
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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.
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Sumo Logic QuickStart - May 2016
- 1. Sumo Logic Confidential QuickStart Webinar Getting Started with Sumo Logic Mario Sánchez May 2016 Welcome. To give everyone a chance to successfully connect, we’ll start at 10:05 AM Pacific.
- 2. Sumo Logic Confidential At the completion of this webinar, you will be able to… Understand Data Collection (Admin Topic) Search, Parse and Analyze Data Visualize and Monitor through Dashboards & Alerts Take advantage of the Library and Apps Understand Optimization Tools (Admin Topic)
- 3. Sumo Logic Confidential What is Sumo Logic?
- 4. Sumo Logic Confidential 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
- 6. Sumo Logic Confidential Enterprise Logs are Everywhere Custom App Code Server / OS Virtual Databases Network Open Source Middleware Content Delivery IaaS, PaaS SaaS Security
- 7. Sumo Logic Confidential High-Level Data Flow
- 8. Sumo Logic Confidential Sumo Logic Data Flow Data Collection Search & Analyze Visualize & Monitor Alerts Dashboards Collectors Sources Operators Detect 1 2 3
- 9. Sumo Logic Confidential Data Collection
- 10. Sumo Logic Confidential 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
- 11. Sumo Logic Confidential 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) _sourceCategor y Category designation of source data Tags added to your messages when data is collected Host A Apache Access Apache Error Collector A
- 12. Sumo Logic Confidential 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/*
- 13. Sumo Logic Confidential Source Category Naming Convention Simplifies Search Syntax and Scope Definitions Used for other Sumo Logic features Role-Based Access Control (Data Provisioning) Partitioning (Search Optimization Tool) Adopt a Robust Naming Convention Early Ex: Prod/Sumo/Apache/Access Env/Customer/Device/MessageType Ex: OS/Windows/2012/Messages Device/Vendor/Version/MessageType Blog Post: Good SourceCategory, Bad SourceCategory
- 14. Sumo Logic Confidential Search and Analyze
- 15. Sumo Logic Confidential Set your Preferences Set your Session Timeout Query Editing versus Running
- 16. Sumo Logic Confidential Search Basics Overview Time Range Histogram Search Bar Search Results Display Options
- 17. Sumo Logic Confidential Field Browser - Metadata fields Sumo Logic Confidential Field Browser Metadata Fields Parsed Fields
- 18. Sumo Logic Confidential 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 metadata keyword
- 19. Sumo Logic Confidential 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
- 20. Sumo Logic Confidential • 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
- 21. Sumo Logic Confidential Develop Good Search Habits Use metadata and keyword combinations to reduce scope Add line breaks after each operation Limit result sets before aggregating data user=a | count by user Use parse anchor instead of parse regex for structured messages Avoid the use of expensive parse regex tokens like .* d{2,10} Narrow your time-range down as much as possible
- 22. Sumo Logic Confidential Refining Results by Surrounding Messages
- 23. Sumo Logic Confidential • LogReduce uses fuzzy logic and soft matching to cluster messages providing quick investigation view into your environment. Operators: Looking for the Unknown
- 24. Sumo Logic Confidential • Identify unexpectedly high or low values within determined thresholds |timeslice 1m |count by _timeslice |outlier _count Operators: Finding Outliers
- 25. Sumo Logic Confidential • 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
- 26. Sumo Logic Confidential 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
- 27. Sumo Logic Confidential • 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
- 28. Sumo Logic Confidential 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
- 29. Sumo Logic Confidential 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
- 30. Sumo Logic Confidential • 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
- 31. Sumo Logic Confidential 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
- 32. Sumo Logic Confidential Visualize and Monitor
- 33. Sumo Logic Confidential • 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
- 34. Sumo Logic Confidential • Chart Types – Table – Bar – Column – Line – Area – Pie – Box Plot – Google Maps – Single Value Providing Context through Visualization
- 35. Sumo Logic Confidential – 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
- 36. Sumo Logic Confidential 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
- 37. Sumo Logic Confidential Dashboards - Adding a Panel 1. Perform your Search 2. Format your Results 3. Create a Panel
- 38. Sumo Logic Confidential 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
- 39. Sumo Logic Confidential 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
- 40. Sumo Logic Confidential Jumpstart with Apps
- 41. Sumo Logic Confidential Installing Applications
- 42. Sumo Logic Confidential Optimizing Your Search Experience
- 43. Sumo Logic Confidential 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
- 44. Sumo Logic Confidential 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
- 45. Sumo Logic Confidential 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
- 46. Sumo Logic Confidential Thank you!
Editor's Notes
- Welcome everyone to Sumo Logic’s Quickstart Webinar. My name is …. And I am … My goal today is to provide you with basic understanding of the Sumo logic Service and how it can help you uncover events that were difficult to do with simple searches and greps. Before we get started, some let’s cover some housekeeping items: - To avoid distractions, all Participants are muted - If you want to ask a question, feel free to use the GoToWebinar panel - Slides and a recording of this webinar will be shared
- Given that we have quite a few beginners, we will start with a high-level view and then dive into details. We will start with.. (review entire agenda) - Although we will cover a few slides, most of this webinar will be done through a demo (a specific use case related to web servers) – reviewing the recording will allow you to follow along in your environment with your own data. Note that Tips and Tricks and Best Practices will be scattered throughout the presentation
- Sumo Logic helps you gain insights into the growing pool of data within youe complex environment. We help you gain visibility across DevOps, IT Ops and Compliance and Security environments.
- Most of you are using the Sumo Logic service for at least one of the 3 following use cases: For DevOps –allows DevOps teams to monitor KPI’s to deliver quality software; less time troubleshooting and more time developing code. For IT Ops – Extract valuable information such as latencies, performance metrics, trends and any critical events tied with core systems. For Compliance and Security – Sumo Logic helps organizations simplify and automate compliance & security monitoring across their entire stack, using predictive analytics
- Because we are a cloud-native solution, securing your data is extremely important to us. These are some of the certifications we hold. Note that this are on top of any AWS security certifications. Sumo Logic is the most thoroughly audited SaaS provider in the analytics space. We are more akin to an organization that harbors government data, than a normal technology company. Below is a link that gives you ore detail of each certification achieved.
- What data can we ingest? We can ingest data from just about any source you can imagine - structured or unstructured. Here are just a few of the devices, applications and frameworks you may be using - all of which produce log data that SL can analyze. The left hands side can present you technology stack – from custom application code all the way down to your network devices. Sumo Logic has hired a few people from these organizations to expand our domain expertise in these areas.
- Sumo Logic Data Flow is broken into 3 main areas: Data Collection through configurable Collectors and Sources. Collectors collect, compress, cache and encrypt the data for secure transfer. Search and Analyze – Users can run searches and correlate events in real-time across the entire application stack. We will be spending most of our time in this area during this webinar, as this is most likely what you will first be doing as a new user. Visualize and Monitor- Users have the ability to create custom dashboards to help you easily monitor your data in real-time. Custom alerts notify you when specific events are identified across your stack. I will cover Data Collection at a high-level, and cover the next 2 areas through a demo.
- This is an great example what we see at a typical customer. This customer is sending web server log files to the Sumo Logic service. Host A and Host B are each sending a couple of log files through a locally installed Sumo Logic collector. In the case of Host C, which is sending IIS log files, it’s using a hosted collector where a local script in Host C can send data to an HTTP endpoint (running curl and POST commands). Hosted collectors are also able to load data from AWS S3 buckets. For instructor: Demo how to search help for “collector types” The great news for this customer is that they can centralize all their webserver logs, and start searching across all logs, or just Apache logs, or just Host B logs.
- Great, data is ingested into the Sumo Logic service, but something else is also happening in the background. Every single message ingested gets tagged with metadata that makes it much easier to search for related messages. This table shows the 5 main tags (review them all) In particular, I want to point out the source Category metadata field, as choosing the right naming convention can make a big impact on your searching capabilities and performance.
- This example will highlight the importance of defining the proper source category: Notice I’ve added the desired SourceCategory for each Source: = WS/Apache/Access Searches across Apache Security logs in both Host A and Host B = WS/Apache/* Searches across all Apache sources in both Host A and Host B = WS/* Searches across all Web Servers across all hosts
- Source Categories are very powerful, and adopting a robust naming convention early is key to simplify your searches and scope definitions. Source Category is also used in other areas of our service, for example, for restricting or granting data access to users and roles, or for optimizing searches through partitions (which we will talk about later in this webinar)
- The second area of Data flow is Search and Analyze. I will jump into a demo to show you more detail of how this can be done.
- The combination of metadata and keywords reflect the search of your scope. This is what we’re going to talk a little bit more about in the next few slides. It’s very important that you be very selective about the data you’ll be searching through.
- You’ll also want to include keywords as part of your search scope. Architecture discussions – show how the searches work in the backend
- Plug for regex101.
- Surrounding messages allow you to investigate events surrounding a message from the context of the Host, file name or category identified enabling you to view the activity for the defined time period.
- Introduce LogReduce Here we see the search “ (Error OR fail*)” returns 99 pages of error results, which can be cumbersome to go through. What you can do is click on the LogReduce button to invoke the Sumo Logic log reduce technology via the summarize operator to automatically find patterns in your logs and reduce this result set to a single page.
- Demo the outlier operator Timeslices are required Adjustable variables allow you to get the right sensitivity Threshold Number of rolling stddev above/below the moving average Default: 3 Consecutive Number of consecutive points above/below the threshold to trigger Default: 1 Direction Detect high values, low values or both Default: Both Window Number of trailing timeslices used to calculate Default: 10
- Need Identify Specific value you want to extract It enables you to perform additional operations Logical/Conditional – based on values Mathematical - Ways of defining fields Parse Anchor: is used to extract a string based on start and stop anchor points, and then to alias the extracted string as a user-created field. Parse Regex: or Extract, uses regular expressions to extract more complex or nested information as aliased fields.
- Walk through screenshots for defining one field
- We add the [A-Z]+ to denote all uppercase HTTP methods like GET, POST, CONNECT, PUT, DELETE etc. Also we make sure we are capturing a URL but specifying the starting character is a / and that it has one or more non-white space chars in it followed by a HTTP/1.\d+ word.
- Now that u have your data, whats next. See how your data is segmented – you can do this by grouping / Dissecting your result sets using meta data fields Example: Counting by _sourceCategory Introduce concept of count Example: count on no groups Get a count of grouped result sets Example: Group by one group (category) Many _sourcecategory and host Include the underscore before the field name (sort by _count)
- Now that u have your data, whats next. See how your data is segmented – you can do this by grouping / Dissecting your result sets using meta data fields Example: Counting by _sourceCategory Introduce concept of count Example: count on no groups Get a count of grouped result sets Example: Group by one group (category) Many _sourcecategory and host Include the underscore before the field name (sort by _count)
- Different ways of seeing the same data in Interactive Search