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Splunk live! ninjas_break-out
- 1. 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 6.1 – New features and capabilities Data Models and Pivot – Analyze data without using search commands Harness the power of search – The 5 search commands that can solve most problems
- 4. 4 Introducing Splunk Enterprise 6.1 ENABLING THE MISSION-CRITICAL ENTERPRISE ENHANCED INTERACTIVE ANALYTICS EMBEDDING OPERATIONAL INTELLIGENCE
- 5. 5 Introducing Splunk Enterprise 6.1 ENABLING THE MISSION-CRITICAL ENTERPRISE ENHANCED INTERACTIVE ANALYTICS EMBEDDING OPERATIONAL INTELLIGENCE
- 6. 6 Mission-critical Availability New Clustering Features • Location aware replication • Search Head Affinity MISSION CRITICAL ENTERPRISE REPLICATION Portland Datacenter New York Datacenter
- 7. 7 Load and Preview Structured Data Data Preview with Structured Inputs • Easily onboard structured data • Preview the fields before indexing • Configure from the GUI Adjust configurations in the UI • Delimiters, Headers, Time Stamp Preview results before committing MISSION CRITICAL ENTERPRISE
- 8. 8 MISSION CRITICAL ENTERPRISE Integrated Mainframe Insights New Forwarder • Collect data from mainframes • Correlate with the rest of the stack
- 9. 13 Introducing Splunk Enterprise 6.1 ENABLING THE MISSION-CRITICAL ENTERPRISE ENHANCED INTERACTIVE ANALYTICS EMBEDDING OPERATIONAL INTELLIGENCE
- 10. 14 More Actionable Alerting Customized Alerts • Add tokens to the alerts from the search results • Select preferred format and delivery of results Customize Recipients Customize Message Select Delivery Method EMBEDDING OPERATIONAL INTELLIGENCE
- 11. 15 Visualization in Splunk iframe Visualization in non-Splunk UI Add Splunk Insights to Business Apps Embedded Reporting • Embed scheduled reports into web applications • Share with users who don’t have access to Splunk • 1-line copy/paste to embed in external application EMBEDDING OPERATIONAL INTELLIGENCE
- 12. 16 Splunk Mobile App EMBEDDING OPERATIONAL INTELLIGENCE • Access dashboards and reports • Annotate dashboards and share with others • Receive push notifications Native Mobile Experience
- 13. 17 Download the Overview App http://apps.splunk.com/app/1773
- 14. Data Models and Pivot
- 15. 19 Model, Report, and Accelerate Build complex reports without the search language Provides more meaningful representation of underlying raw machine data Pivot Data Model Acceleration technology delivers up to 1000x faster analytics over Splunk 5 Analytics Store
- 16. 20 Creating a Data Model Basic Steps 1. Have a use for a Data Model 2. Write a base search 3. Select the fields to include
- 17. 27 Data Model Acceleration • Automatically collected and maintained • Stored on the indexers • Must share the Data Model • Cost is additional disk space Makes reporting crazy fast
- 18. 28 Pivot • Drag-and-drop interface • No need to understand underlying data • Click to visualize Select fields from data model Time window All chart types available in the chart toolbox Save report to share Build Reports without SPL
- 19. Harness the Power of Search
- 20. 36 search and filter | munge | report | cleanup Search Processing Language sourcetype=access* | eval KB=bytes/1024 | stats sum(MB) dc(clientip) | rename sum(MB) AS "Total MB" dc(clientip) AS "Unique Customers"
- 21. 37 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
- 22. 39 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
- 23. 40 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 eval - Modify or Create New Fields and Values
- 24. 41 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 eval - Modify or Create New Fields and Values
- 25. 43 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
- 26. 44 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
- 27. 45 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
- 28. 47 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
- 29. 48 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
- 30. 49 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
- 31. 51 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)
- 32. 52 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) streamstats – Cumulative Statistics for Each Event
- 33. 53 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) streamstats – Cumulative Statistics for Each Event
- 34. 55 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)
- 35. 56 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) transaction – Group Related Events Spanning Time
- 36. 57 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) transaction – Group Related Events Spanning Time
- 37. 58 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
- 38. Questions?
- 39. Bonus Command
- 40. 61 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
- 41. 62 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. 63 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. Thank You
Notes de l'éditeur
- The Enhanced Dashboard Editor makes it easier to build advanced dashboards, adding visualizations and charts – all without Advanced XML. You can now easily add new inputs and panels to drive a richer experience and create advanced visualizations all in the the UI – without any coding.
- With Contextual Drill-down a primary panel can drive the charts, tables and visualizations on the rest of a dashboard.
- Splunk 6.1 delivers new controls to deliver an even more focused analytics experience with the machine data in Splunk Enterprise. Chart Overlay: Improves data analysis by providing the ability to overlay one chart on top of another. Pan and Zoom Controls: Enables more focused analytics by providing the ability in selecting a range of interest on a chart and zoom in for deeper analysis.
- Alerts are triggered when certain conditions are met – a feature Splunk Enterprise has had for sometime. Now with Splunk Enterprise 6.1 you can deliver alerts with embedded machine data context. This includes fields and values from the result set that triggered the alert as well as the search artifacts such as the time range the search ran over. You can also choose what you include or exclude in the email.
- Alerts are triggered when certain conditions are met – a feature Splunk Enterprise has had for sometime. Now with Splunk Enterprise 6.1 you can deliver alerts with embedded machine data context. This includes fields and values from the result set that triggered the alert as well as the search artifacts such as the time range the search ran over. You can also choose what you include or exclude in the email.
- Embedded Reports, enables any Splunk report or table to be embedded in third-party business application such as salesforce.com, WordPress, Wiki or Microsoft® SharePoint With Embedded Reports users are connected to the critical insights using tools they are already familiar with – all without having access to Splunk. Simply copy the iframe code provided by Splunk and paste it into your webpage. The authentication is handled in the URL.
- 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.
- This section should take ~10 minutes
- Data Model – A data model is just like a map of the underlying data. It defines meaningful relationships in the data Pivot – is an interface to analyze data without using the splunk search language Analytics Store – is an option that can be applied to Data Models to make Pivot searches extremely fast. Think of it like our 3rd generation acceleration technology. Let’s dig into each of these features
- 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)
- Note: Chart is just stats visualized. Timechart is just stats by _time visualized.
- sourcetype=access* | eval KB=bytes/1024 | stats sum(KB) AS "Sum of KB"
- sourcetype=access* | stats values(useragent) 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. <Walk through the examples with a demo. Hidden slides are available as backup>
- Eventstats let’s you add statistics about the entire search results and makes the statistics available as fields on each event. Let’s use eventstats to create a timechart of the average bytes on top of the overall average. index=* sourcetype=access* | eventstats avg(bytes) AS avg_bytes | timechart latest(avg_bytes) avg(bytes)
- 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)
- 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