Splunk Ninjas Breakout Session
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Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
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Splunk Ninjas Breakout Session
- 1. Copyright © 2015 Splunk Inc. Splunk Ninjas: New Features, Pivot, and Search Dojo
- 2. 2 Safe Harbor Statement During the course of this presentation, we may make forward looking statements regarding future events or the expected performance of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results could differ materially. For important factors that may cause actual results to differ from those contained in our forward-looking statements, please review our filings with the SEC. The forward-looking statements made in this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, this presentation may not contain current or accurate information. We do not assume any obligation to update any forward looking statements we may make. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only and shall not be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionality described orto includeany suchfeatureor functionalityina futurerelease.
- 3. 3 Agenda What’s new in 6.2 – New features and capabilities Harness the power of search – The 5 search commands that can solve most problems
- 4. 4 Introducing Splunk Enterprise 6.2 4 Getting Data In Advanced Field Extractor Instant Pivot Event Pattern Detection Prebuilt Panels Search Head Clustering Distributed Management Console Powerful Analytics for Broader Number of Users Faster Data Onboarding Breakthrough Scalability and Centralized Mgmt.
- 5. 5 Introducing Splunk Enterprise 6.2 5 Getting Data In Advanced Field Extractor Instant Pivot Event Pattern Detection Prebuilt Panels Search Head Clustering Distributed Management Console Powerful Analytics for Broader Number of Users Faster Data Onboarding Breakthrough Scalability and Centralized Mgmt.
- 6. 6 Getting Data In New interface makes it easier and faster to onboard any data Intuitive wizard-style interface Configurable inputs on forwarders Improved data preview Context-specific FAQs 6
- 7. 7 Advanced Field Extractor Simplified field extractor enables rapid data analysis Highlight-to-extract multiple fields at once Apply keyword search filters Specify required text in extractions View diverse and rare events Validate extracted values with field stats 7
- 8. 8 Demo
- 9. 9 Introducing Splunk Enterprise 6.2 9 Getting Data In Advanced Field Extractor Instant Pivot Event Pattern Detection Prebuilt Panels Search Head Clustering Distributed Management Console Powerful Analytics for Broader Number of Users Faster Data Onboarding Breakthrough Scalability and Centralized Mgmt.
- 10. 10 Prebuilt Panels Build dashboards faster using reusable building blocks Enhanced dashboard edit workflow – Browse or search across reports, panels, dashboards and more – Preview before adding to dashboard Personalize your dashboards Collaborate using a library of pre- built panels Convert panels to inline to further customize 10
- 11. 11 Event Pattern Detection Auto-discover meaningful patterns in your data with a single click Search data without having to know specific terms to search on No need to sift through similar events, just select “Patterns” tab Intuitive interface 11 Screenshot or Image suggestion
- 12. 12 Instant Pivot Pivot directly on any search to discover relationships, build reports From any search, simply select the Statistics tab and click on the pivot icon Explore and analyze data from the Pivot interface Quickly discover relationships in the data and build powerful reports 1
- 13. 13 Download the Overview App
- 14. 14 Demo
- 15. Harness the Power of Search
- 16. 16 search and filter | munge | report | cleanup Search Processing Language sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) dc(clientip) | rename sum(KB) AS "Total MB" dc(clientip) AS "Unique Customers"
- 17. 17 Five Commands that will Solve Most Data Questions eval - Modify or Create New Fields and Values stats - Calculate Statistics Based on Field Values eventstats - Add Summary Statistics to Search Results streamstats - Cumulative Statistics for Each Event transaction - Group Related Events Spanning Time
- 18. 18 eval - Modify or Create New Fields and Values Examples • Calculation: sourcetype=access* |eval KB=bytes/1024 • Evaluation: sourcetype=access* | eval http_response = if(status == 200, "OK", "Error”) • Concatenation: sourcetype=access* | eval connection = clientip.":".port
- 19. 19 eval - Modify or Create New Fields and Values Examples • Calculation: sourcetype=access* |eval KB=bytes/1024 • Evaluation: sourcetype=access* | eval http_response = if(status == 200, "OK", "Error”) • Concatenation: sourcetype=access* | eval connection = clientip.":".port
- 20. 20 eval - Modify or Create New Fields and Values Examples • Calculation: sourcetype=access* |eval KB=bytes/1024 • Evaluation: sourcetype=access* | eval http_response = if(status == 200, "OK", "Error”) • Concatenation: sourcetype=access* | eval connection = clientip.":".port
- 21. 21 stats – Calculate Statistics Based on Field Values Examples • Calculate stats and rename sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) AS “Total KB” • Multiple statistics sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) avg(KB) • By another field sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) avg(KB) by clientip
- 22. 22 stats – Calculate Statistics Based on Field Values Examples • Calculate stats and rename sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) as “Total KB” • Multiple statistics sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) avg(KB) • By another field sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) avg(KB) by clientip
- 23. 23 stats – Calculate Statistics Based on Field Values Examples • Calculate statistics sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) AS "Total KB” • Multiple statistics sourcetype=access* | eval KB=bytes/1024 | stats avg(KB) sum(KB) • By another field sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) avg(KB) by clientip
- 24. 24 eventstats – Add Summary Statistics to Search Results Examples • Overlay Average sourcetype=access* | eventstats avg(bytes) AS avg_bytes | timechart latest(avg_bytes) avg(bytes) • Moving Average sourcetype=access* | eventstats avg(bytes) AS avg_bytes by date_hour | timechart latest(avg_bytes) avg(bytes) • By created field sourcetype=access* | eval http_response = if(status == 200, "OK", "Error”) | eventstats avg(bytes) AS avg_bytes by http_response | timechart latest(avg_bytes) avg(bytes) by http_response
- 25. 25 Examples • Overlay Average sourcetype=access* | eventstats avg(bytes) AS avg_bytes | timechart latest(avg_bytes) avg(bytes) • Moving Average sourcetype=access* | eventstats avg(bytes) AS avg_bytes by date_hour | timechart latest(avg_bytes) avg(bytes) • By created field sourcetype=access* | eval http_response = if(status == 200, "OK", "Error”) | eventstats avg(bytes) AS avg_bytes by http_response | timechart latest(avg_bytes) avg(bytes) by http_response eventstats – Add Summary Statistics to Search Results
- 26. 26 eventstats – Add Summary Statistics to Search Results Examples • Overlay Average sourcetype=access* | eventstats avg(bytes) AS avg_bytes | timechart latest(avg_bytes) avg(bytes) • Moving Average sourcetype=access* | eventstats avg(bytes) AS avg_bytes by date_hour | timechart latest(avg_bytes) avg(bytes) • By created field sourcetype=access* | eval http_response = if(status == 200, "OK", "Error”) | eventstats avg(bytes) AS avg_bytes by http_response | timechart latest(avg_bytes) avg(bytes) by http_response
- 27. 27 streamstats – Cumulative Statistics for Each Event Examples • Cumulative Sum sourcetype=access* | reverse | streamstats sum(bytes) as bytes_total | timechart max(bytes_total) • Cumulative Sum by Field sourcetype=access* | reverse | streamstats sum(bytes) as bytes_total by status | timechart max(bytes_total) by status • Moving Average sourcetype=access* | timechart avg(bytes) as avg_bytes | streamstats avg(avg_bytes) AS moving_avg_bytes window=10 | timechart latest(moving_avg_bytes) latest(avg_bytes)
- 28. 28 streamstats – Cumulative Statistics for Each Event Examples • Cumulative Sum sourcetype=access* | timechart sum(bytes) as bytes | streamstats sum(bytes) as cumulative_bytes | timechart max(cumulative_bytes) • Cumulative Sum by Field sourcetype=access* | reverse | streamstats sum(bytes) as bytes_total by status | timechart max(bytes_total) by status • Moving Average sourcetype=access* | timechart avg(bytes) as avg_bytes | streamstats avg(avg_bytes) AS moving_avg_bytes window=10 | timechart latest(moving_avg_bytes) latest(avg_bytes)
- 29. 29 streamstats – Cumulative Statistics for Each Event Examples • Cumulative Sum sourcetype=access* | timechart sum(bytes) as bytes | streamstats sum(bytes) as cumulative_bytes | timechart max(cumulative_bytes) • Cumulative Sum by Field sourcetype=access* | reverse | streamstats sum(bytes) as bytes_total by status | timechart max(bytes_total) by status • Moving Average sourcetype=access* | timechart avg(bytes) as avg_bytes | streamstats avg(avg_bytes) AS moving_avg_bytes window=10 | timechart latest(moving_avg_bytes) latest(avg_bytes)
- 30. 30 transaction – Group Related Events Spanning Time Examples • Group by Session ID sourcetype=access* | transaction JSESSIONID • Calculate Session Durations sourcetype=access* | transaction JSESSIONID | stats min(duration) max(duration) avg(duration) • Stats is Better sourcetype=access* | stats min(_time) AS earliest max(_time) AS latest by JSESSIONID | eval duration=latest-earliest | stats min(duration) max(duration) avg(duration)
- 31. 31 transaction – Group Related Events Spanning Time Examples • Group by Session ID sourcetype=access* | transaction JSESSIONID • Calculate Session Durations sourcetype=access* | transaction JSESSIONID | stats min(duration) max(duration) avg(duration) • Stats is Better sourcetype=access* | stats min(_time) AS earliest max(_time) AS latest by JSESSIONID | eval duration=latest-earliest | stats min(duration) max(duration) avg(duration)
- 32. 32 transaction – Group Related Events Spanning Time Examples • Group by Session ID sourcetype=access* | transaction JSESSIONID • Calculate Session Durations sourcetype=access* | transaction JSESSIONID | stats min(duration) max(duration) avg(duration) • Stats is Better sourcetype=access* | stats min(_time) AS earliest max(_time) AS latest by JSESSIONID | eval duration=latest-earliest | stats min(duration) max(duration) avg(duration)
- 33. 33 Learn Them Well and Become a Ninja eval - Modify or Create New Fields and Values stats - Calculate Statistics Based on Field Values eventstats - Add Summary Statistics to Search Results streamstats - Cumulative Statistics for Each Event transaction - Group Related Events Spanning Time See many more examples and neat tricks at docs.splunk.com and answers.splunk.com
- 34. Closing Thoughts
- 35. The 6th Annual Splunk Worldwide Users’ Conference September 21-24, 2015 The MGM Grand Hotel, Las Vegas • 50+ Customer Speakers • 50+ Splunk Speakers • 35+ Apps in Splunk Apps Showcase • 65 Technology Partners • 4,000+ IT & Business Professionals • 2 Keynote Sessions • 3 days of technical content (150+ Sessions) • 3 days of Splunk University – Get Splunk Certified – Get CPE credits for CISSP, CAP, SSCP, etc. – Save thousands on Splunk education! 35 Register at: conf.splunk.com
- 36. The 6th Annual Splunk Worldwide Users’ Conference September 21-24, 2015 The MGM Grand Hotel, Las Vegas Did you like this session on Splunk Search Ninja? You should check out these sessions at .conf2015? • Search Efficiency Optimization - Andrew Landen, Splunk SME, National Oilwell Varco • Notes on Optimizing Splunk Performance - Dritan Bitincka, Principal Solutions Architect, Splunk • Onboarding data with Splunk - Andrew Duca, Sr. Professional Services Consultant Register at: conf.splunk.com
- 37. 37 www.splunk.com/apptitude July 20th, 2015 Submission deadline
- 38. 38 We Want to Hear your Feedback! After the Breakout Sessions conclude Text Splunk to 878787 And be entered for a chance to win a $100 AMEX gift card!
- 39. Questions?
- 40. Bonus Command
- 41. 41 cluster – Find Common and/or Rare Events Examples • Find the most common events * | cluster showcount=t t=0.1 | table cluster_count, _raw | sort - cluster_count • Select a field to cluster on sourcetype=access* | cluster field=bc_uri showcount=t | table cluster_count bc_uri _raw | sort -cluster_count • Most or least common errors index=_internal source=*splunkd.log* log_level!=info | cluster showcount=t | table cluster_count _raw | sort -cluster_count
- 42. 42 cluster – Find Common and/or Rare Events Examples • Find the most common events * | cluster showcount=t t=0.1 | table cluster_count, _raw | sort - cluster_count • Select a field to cluster on sourcetype=access* | cluster field=bc_uri showcount=t | table cluster_count bc_uri _raw | sort -cluster_count • Most or least common errors index=_internal source=*splunkd.log* log_level!=info | cluster showcount=t | table cluster_count _raw | sort -cluster_count
- 43. 43 cluster – Find Common and/or Rare Events Examples • Find the most common events * | cluster showcount=t t=0.1 | table cluster_count, _raw | sort - cluster_count • Select a field to cluster on sourcetype=access* | cluster field=bc_uri showcount=t | table cluster_count bc_uri _raw | sort -cluster_count • Most or least common errors index=_internal source=*splunkd.log* log_level!=info | cluster showcount=t | table cluster_count _raw | sort -cluster_count
- 44. 44 Splunk Mobile App EMBEDDING OPERATIONAL INTELLIGENCE • Access dashboards and reports • Annotate dashboards and share with others • Receive push notifications Native Mobile Experience
- 45. Thank You
Editor's Notes
- Intro: - Who you are – cover groupon, box among others - Objective of this track Here is what you need for this presentation: Link to videos on box: <coming soon> You should have the following installed: 6.2 Overview OI Demo– Get it from the Technical Enablement Portal under SE tools –> Demos https://splunk--c.na2.visual.force.com/apex/LMS_TechnicalEnablementPortal NOTE: Configure your role to search the oidemo index by default, otherwise you will have to type “index=oidemo” for the examples later on. There is a lot to cover in this presentation! Try to go quickly and at a pretty high level. When you get through the presentation judge the audience’s interest and go deeper in whichever section. For example, if they want to know more about Pivot and Data Models then unhide those slides and walk through them, or if they want to go deeper on the search commands talk through the extra examples. If running locally on 8000, these are the links to have ready in the background: http://127.0.0.1:8000/en-US/app/oidemo/content_dashboard?form.track_name=Headlines&earliest=0&latest= http://127.0.0.1:8000/en-US/app/oidemo/data_model_editor?model=%2FservicesNS%2Fnobody%2Foidemo%2Fdatamodel%2Fmodel%2FOIDemo http://127.0.0.1:8000/en-US/app/oidemo/search
- Splunk safe harbor statement.
- Features we may not have used. Making data accessible to everyone. New ways of looking at old commands.
- The goal of 6.X is to make you experience more fluid, make the product easier to use, and ultimately to make your more effective How do we achieve this? – 3 major focus areas Let me pause – and ask How many people are using 6.2? Quick Poll., How may are using these features?
- Splunk Enterprise is the industry-leading platform for Operational Intelligence. Version 6.2 enables organizations to onboard, enrich and analyze machine data faster than ever before, scale to higher numbers of concurrent users and searches, and spend less time managing their large, distributed deployments. Easier data onboarding and preparation Getting Data In radically simplifies onboarding of any data source Advanced Field Extractor enables better preparation of machine data for further analysis
- Context-specific FAQs In Splunk 6.2, we’ve completely remodeled the pages and workflows for adding data, and added new features like Forwarder Inputs a new Data Preview. Consolidated Workflow: We’ve made it much easier to find your way to the appropriate input configuration. Instead of selecting from a confusing list of sources, start with a simple choice of “upload, monitor, or forward” and you’ll find yourself in a simple wizard-style workflow of defining the appropriate parameters for the data you want to add. Data Preview The new Data Preview will make it easier for you to create the right sourcetype for your data. In the advanced section, you’ll be able to choose a charset from a list, and see how changes you make to your sourcetype are reflected in props.conf. Forwarder Inputs With Forwarder Inputs, you are able to push input configurations to Splunk instances configured as deployment clients. Simply select one or more forwarders and provide a group name, and you’ll be able to create data inputs on them in the same way you create inputs through the UI on your indexers.
- Step by step walkthough of your new fields 1. Easy highlight 2. Extract multiple fields 3. Validate With this enhancement, we’ve made it easier to extract fields from your data with the Advanced Field Extractor (AFX). A replacement of the existing field extraction utility, AFX enables you to easily capture multiple fields in a single extraction and specify required text to filter events for extraction (improving accuracy and efficiency). AFX also provides a number of methods for detecting false positives in order to help you validate your field extractions and improve the accuracy of your field
- Demo GDI and AFE David used access_combined Walk through the options Create a new index on the fly… Example and info here: https://splunk.box.com/s/zg6964cc15nj9kcldd9w
- Purpose of one click pivot enabling anyone to Pivot directly on data, bypassing the Data Model step Splunk Enterprise is the industry-leading platform for Operational Intelligence. Version 6.2 enables organizations to onboard, enrich and analyze machine data faster than ever before, scale to higher numbers of concurrent users and searches, and spend less time managing their large, distributed deployments. More powerful analytics for everyone Instant Pivot makes analytics easier by enabling anyone to Pivot directly on data, bypassing the Data Model step Event Pattern Detection speeds analysis by identifying meaningful patterns in machine data Prebuilt Panels enables faster dashboard creation by providing the ability to create and package re-usable dashboard building blocks
- Reusable building blocks Panels allow users to build custom dashboards faster, leveraging pre-built dashboard panels packaged within apps. A user can select from pre-built reports and dashboards or create their own from the new Add Panel interface.
- -Auto discovery meaningful patters in your data -slide-bar allows you to set the threshold of similarity of the events so you can tune if you want the pattern to be more or less specific which will increase or reduce the number of patterns. - The default grouping method is to break down the events into terms (match=termlist) and compute the vector between events - T= threshold value - higher threshold value for t, if you want the command to be more discriminating about which events are grouped together. - Can save as eventtypes Event Pattern Detection reduces massive sets of data to its essence rather than sifting through all events. This can be used to identify common and rare events quickly or search your data without having to know specific terms to search on. If you already understand the “cluster” command in Splunk then you know what this is capable of. A slide-bar allows you to set the threshold of similarity of the events so you can tune if you want the pattern to be more or less specific which will increase or reduce the number of patterns.
- How do we use it? Why’ to quickly discovery relationships. Flexible. – between search and the data model Instant Pivot enables you to open any query in the Pivot interface, without requiring the creation of a data model. This means that you have the flexibility to choose what interface to explore your data. This also creates another method to construct data models, starting with search. When a user clicks on the Pivot icon, an ephemeral data model is created that collects user specified fields within Pivot as a single, flat object. The user can save their Pivot (additionally prompts user to save data model). Users can choose to instantly Pivot on their data, modify fields, columns, etc in Pivot and then convert it back to a search if they need to use advanced search commands. Instant Pivot allows users to interact with their data faster.
- For more information, or to try out the features yourself. Check out the overview app which explains each of the features and includes code samples and examples where applicable. SF Dept of health food violation report:
- Lets take another interesting data sample: SF Dept of health food violation report: index=* sourcetype="sf_food_violations” neighbourhood description name postal_code risk_category Question we want to ask of our data: number of violations by name? OK now tell me the most starbucks violations by neighbourhood? STARBUCKS* Example and info here: https://splunk.box.com/s/zg6964cc15nj9kcldd9w
- <This section should take ~15 minutes> Search is the most powerful part of Splunk.
- The Splunk search language is very expressive and can perform a wide variety of tasks ranging from filtering to data, to munging, and reporting. The results can be used to answer questions, visualize results, or even send to a third party application in whatever format they require. Although there are 135 documented search commands; however, most questions can be answered by using just a handful.
- These are the five commands you should get very familiar with. If you know how to use these well, you will be able to solve most data questions that come your way. Let’s take a quick look at each of these.
- sourcetype=access*| eval http_response = if(status == 200, "OK", "Error") sourcetype=access* | eval http_response = if(status == 200, "OK", "Error") | eventstats avg(bytes) AS avg_bytes by http_response | timechart latest(avg_bytes) avg(bytes)
- sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) AS "Sum of KB"
- Stats: Average througput Multiple: Average vs max by ip Multiple: by another field sourcetype=access* | stats avg(bytes) max(bytes) by clientip
- sourcetype=access* | stats values(useragent) avg(bytes) max(bytes) by clientip
- Eventstats let’s you add statistics about the entire search results and makes the statistics available as fields on each event. sourcetype=access*| eventstats avg(bytes) as avg_bytes | timechart latest(avg_bytes) avg(bytes) Let’s use eventstats to create a timechart of the average bytes on top of the overall average.
- We can turn this into a moving average simply by adding “by date_hour” to calculate the average per hour instead of the overall average. index=* sourcetype=access* | eventstats avg(bytes) AS avg_bytes by date_hour | timechart latest(avg_bytes) avg(bytes)
- sourcetype=access* | eval http_response = if(status == 200, "OK", "Error”) | eventstats avg(bytes) AS avg_bytes by http_response | timechart latest(avg_bytes) avg(bytes) by http_response
- Download level changes over time Temp variations over time To create a cumulative sum: sourcetype=access* | timechart sum(bytes) as bytes | streamstats sum(bytes) as cumulative_bytes | timechart max(cumulative_bytes)
- sourcetype=access* | reverse | streamstats sum(bytes) as bytes_total by status | timechart max(bytes_total) by status
- sourcetype=access* | timechart avg(bytes) as avg_bytes | streamstats avg(avg_bytes) AS moving_avg_bytes window=10 | timechart latest(moving_avg_bytes) latest(avg_bytes) Bonus: This could also be completed using the trendline command with the simple moving average (sma) parameter: sourcetype=access* | timechart avg(bytes) as avg_bytes | trendline sma10(avg_bytes) as moving_average_bytes | timechart latest(avg_bytes) latest(moving_average_bytes) Double Bonus: Cumulative sum by period sourcetype=access* | timechart span=15m sum(bytes) as cumulative_bytes by status | streamstats global=f sum(cumulative_bytes) as bytes_total
- sourcetype=access* | transaction JSESSIONID
- sourcetype=access*| transaction JSESSIONID | stats min(duration) max(duration) avg(duration)
- NOTE: Many transactions can be re-created using stats. Transaction is easy but stats is way more efficient and it’s a mapable command (more work will be distributed to the indexers). sourcetype=access* | stats min(_time) AS earliest max(_time) AS latest by JSESSIONID | eval duration=latest-earliest | stats min(duration) max(duration) avg(duration)
- There is much more each of these commands can be used for. Check out answers.splunk.com and docs.splunk.com for many more examples.
- And finally, I would like to encourage all of you to attend our user conference in September. The energy level and passion that our customers bring to this event is simply electrifying. Combined with inspirational keynotes and 150+ breakout session across all areas of operational intelligence, It is simply the best forum to bring our Splunk community together, to learn about new and advanced Splunk offerings, and most of all to learn from one another.
- <If you have time, feel free to show one of your favorite commands or a neat use case of a command. The cluster command is provided here as an example > “There are over 135 splunk commands, the five you have just seen are incredibly powerful. Here is another to add to your arsenal.”
- You can use the cluster command to learn more about your data and to find common and/or rare events in your data. For example, if you are investigating an IT problem and you don't know specifically what to look for, use the cluster command to find anomalies. In this case, anomalous events are those that aren't grouped into big clusters or clusters that contain few events. Or, if you are searching for errors, use the cluster command to see approximately how many different types of errors there are and what types of errors are common in your data. index=* sourcetype=linux_secure | cluster t=.9 showcount=t | table cluster_count _raw | sort -cluster_count
- Decrease the threshold of similarity and see the change in results sourcetype=access* | cluster field=bc_uri showcount=t t=0.1| table cluster_count bc_uri _raw | sort -cluster_count
- index=_internal source=*splunkd.log* log_level!=info | cluster showcount=t | table cluster_count _raw | sort -cluster_count
- Android coming soon!