1. Copyright © 2013 Splunk Inc.
Data Models 101

2. Legal Notices
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
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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
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may not contain current or accurate information. We do not assume any obligation to update any forward-looking
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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 or to include any such feature or functionality in a future release.
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©2013 Splunk Inc. All rights reserved.

3. Search is hard.

4. Analytics Big Picture
Pivot
Build complex reports without the
search language
Data
Model
Provides more meaningful representation
of underlying raw machine data
Analytics
Store
Acceleration technology delivers up to
1000x faster analytics over Splunk 5
4

5. Operational Intelligence Across the Enterprise
[10/11/12
18:57:04
000000b0
UTC]
Raw
Data
IT professional
Create and share data models
Accelerate data models and custom
searches with the analytics store
Create reports with pivot
Analytics
Store
Developer
Leverage data models to
abstract data
Leverage pivot in custom apps
Data
Model
Pivot
Analyst
Create reports using pivot based on
data models created by IT

6. Pivot is a query builder.

7. Data Models 101

8. Source
Data set
Source
Source

9. Success
Sourcetype
Failure
Warning

10. Source
Business division
Source
Data set
Source
Business division
Source

11. Technology 1
Common model
Technology 2
Technology 3

12. Context

13. Splunk Search Language
search and filter | munge | report | clean-up
sourcetype=access_combined source = "/home/ssorkin/banner_access.log.2013.6.gz"
| eval unique=(uid + useragent) | stats dc(unique) by os_name
| rename dc(unique) as "Unique Visitors" os_name as "Operating System"

14. Hurdles
index=main source=*/banner_access* uri_path=/js/*/*/login/* guid=* useragent!=*KTXN* useragent!=*GomezAgent* clientip!=206.80.3.67
clientip!=198.144.207.62 clientip!=97.65.63.66 clientip!=175.45.37.78 clientip!=209.119.210.194 clientip!=212.36.37.138 clientip!=204.156.84.0/24
clientip!=216.221.226.0/24 clientip!=207.87.200.162 | rex field=uri_path "/js/(?<t>[^/]*)/(?<v>[^/]*)/login/(?<l>[^/]*)” | eval license = case(l LIKE "prod%" AND
t="pro", "enterprise", l LIKE "trial%" AND t="pro", "trial", t="free", "free”) | rex field=v "^(?<vers>d.d)” | bin span=1d _time as day | stats values(vers) as vers
min(day) as min_day min(eval(if(vers=="5.0", _time, null()))) as min_day_50 dc(day) as days values(license) as license by guid | eval type =
if(match(vers,"4.*"), "upgrade", "not upgrade") + "/" + if(days > 1, "repeat", "not repeat")| search license=enterprise | eval _time = min_day_50| timechart
count by type| streamstats sum(*) as *
•
Simple searches easy… Multi-stage munging/reporting is hard!
•
Need to understand data’s structure to construct search
•
Non-technical users may not have data source domain knowledge
•
Splunk admins do not have end-user search context

15. Data Model Goals
•
Make it easy to share/reuse domain knowledge
•
Admins/power users build data models
•
Non-technical users interact with data via pivot UI

16. Data Models 101

17. What is a Data Model?
A data model is a search-time mapping of data onto a hierarchical structure
Encapsulate the knowledge
needed to build a search
Pivot reports are build on top
of data models
Data-independent
Screenshot here

18. A Data Model is a Collection of Objects
Screenshot here

19. Objects Have Constraints and Attributes
Screenshot here

20. Child Objects Inherit Constraints and Attributes
Screenshot here

21. Child Objects Inherit Constraints and Attributes

22. Building Data Models

23. Three Root Object Types
Event
– MapstoSplunkevents
– Requiresconstraints
andattributes

24. Three Root Object Types
Event
– MapstoSplunkevents
– Requiresconstraints
andattributes
Search
– MapstoarbitrarySplunksearch(may
includegenerating,transformingand
reportingsearchcommands)
– Requiressearchstringattributes
•
Transaction
– Mapsto groupsof Splunkeventsor
groupsof Splunksearchresults
– Requiresobjectsto group,fields/
conditionstogroupby,andattributes

25. Three Root Object Types
Event
– MapstoSplunkevents
– Requiresconstraints
andattributes
Search
– MapstoarbitrarySplunksearch(may
includegenerating,transformingand
reportingsearchcommands)
Requiressearchstringattributes
Transaction
– Mapsto groupsof Splunkeventsor
groupsof Splunksearchresults
– Requiresobjectsto group,fields/
conditionstogroupby,andattributes

26. Object Attributes
Auto-extracted – default and predefined fields
Eval expression – a new field based
on an expression that you define
Lookup – leverage an existing lookup
table
Regular expression – extract a new
field based on regex
Geo IP – add geolocation fields such
as latitude, longitude, country, etc.

27. Object Attributes
Set field types
Configure various flags
Note: Child object configuration can differ from parent

28. Best Practices
Use event objects as often as possible
– Benefit from data model acceleration
Resist the urge to use search objects instead of event objects!!
– Event based searches can be optimized better
Minimize object hierarchy depth when possible
– Constraint based filtering is less efficient deeper down the tree
Event object with deepest tree (and most matching results) first
– Model-wide acceleration only for first event object and its
descendants

29. Warnings!
Object constraints and attributes cannot contain pipes or subsearches
A transaction object requires at least one event or search object in the data model
Lookups used in attributes must be globally visible (or at least visible to the app
using the data model)
No versioning on data models (and objects)!

30. From Data Models to Reports

31. Using the UI
Subhead
Count of http_success
events, split by useragent
events
fields

32. Under the Hood: Object Search String Generation
Event Object
Syntax:
<constraints search> | <my attribute
definitions>
Example:
sourcetype=access_* OR sourcetype=iis*
uri=* uri_path=* status=* clientip=* referer=*
useragent=*

33. Under the Hood: Object Search String Generation
Search Object
Syntax:
<base search> | <my attribute definitions>
Example:
_time=* host=* source=* sourcetype=* uri=*
status<600 clientip=* referer=* useragent=*
(sourcetype=access_* OR source=*.log) | eval
userid=clientip | stats first(_time) as
earliest, last(_time) as latest, list(uri_path) as
uri_list by userid
| earliest=* latest=* uri_list=*

34. Under the Hood: Object Search String Generation
Transaction Object
Syntax:
<objects to group search> | transaction <group
by fields> <group by params>
| <my attribute definitions>
Example:
sourcetype=access_* uri=* uri_path=* status=*
clientip=* referer=* useragent=* | transaction
clientip useragent | eval
landingpage=mvindex(uri_path,1) | eval
exitpage=mvindex(uri_path,-1)

35. Under the Hood: Object Search String Generation
Child Object
Syntax:
<parent object search> | search <my constraints> |
<my attribute definitions>
Example:
sourcetype=access_* uri=* uri_path=* status=*
clientip=* referer=* useragent=* status=2* | <my
attribute definitions>

36. Using the Splunk Search Language
Object Search String
| datamodel <modelname> <objectID> search
Example:
| datamodel WebIntelligence HTTP_Request search
Behind the scenes:
sourcetype=access_* OR sourcetype=iis* uri=* uri_path=* status=* clientip=* referer=*
useragent=*

37. Under the hood: Pivot Search String Generation
Pivot search = object search + filters + reporting + formatting
Example:
(sourcetype=access_* OR sourcetype=iis*) status=2*
uri=* uri_path=* status=* clientip=* referer=* useragent=*
| stats count AS "Count of HTTP_Sucess" by ”useragent"
| sort limit=0 "useragent" | fields - _span
| fields "useragent" "Count of HTTP_Success"
| fillnull "Count of HTTP_Success"
| fields "useragent" *

38. Using the Splunk Search Language
Pivot Search String
| pivot <modelname> <objectID> [statsfns, rowsplit, colsplit, filters, …]
Example:
| pivot WebIntelligence HTTP_Request count(HTTP_Request) AS "Count of HTTP_Request" SPLITROW status
AS "status" SORT 0 status
Behind the scenes:
sourcetype=access_* OR sourcetype=iis* uri=* uri_path=* status=* clientip=* referer=* useragent=*
| stats count AS "Count of HTTP_Request" by "status"
| sort limit=0 "status" | fields - _span
| fields "status", "Count of HTTP_Request"
| fillnull "Count of HTTP_Request"
| fields "status" *

39. Warnings
• | datamodel and | pivot are generating commands
– They must be at the beginning of the search string
•
Use objectIDs NOT user-visible object names

40. Managing Data
Models

41. Data Model on Disk
Each data model is a separate JSON file
Lives in <myapp>/local/data/models
(or <myapp>/default/data/models for
pre-installed models)
Has associated conf stanzas
and metadata

42. Editing Data Model JSON
At your own risk!
Models edited via the UI are validated
Manually edited data models: NOT SUPPORTED
Exception: installing a new model by adding the file to
<myapp>/<local OR default>/data/models is probably okay

43. Deleting a Data Model
Use the UI for appropriate cleanup
Potential for bad state if manually deleting model on disk

44. Interacting With a Data Model
Use data model builder and pivot UI – safest option!
Use REST API – for developers (see docs for details)
Use | datamodel and | pivot Splunk search commands

45. Permissions
Data models have
permissions just like
other Splunk objects
Edit permissions
through the UI

46. Data Model Acceleration
Admin or power user
Backend magic
Acceleration
Non-technical user
Run search using on-disk acceleration
Run a pivot report
No acceleration
Kick off ad-hoc acceleration and run search

47. Model-Wide Acceleration
Only accelerates first eventbased object and descendants
Does not accelerate search and
transaction-based objects
Pivot search:
| tstats count AS "Count of HTTP_Success" from datamodel="WebIntelligence" where
(nodename="HTTP_Request") (nodename="HTTP_Request.HTTP_Success") prestats=true | stats count AS
"Count of HTTP_Success”

48. Ad-Hoc Object Acceleration
Kick off acceleration on pivot page (re) load for non-accelerated models
and search/transaction objects
Amortize cost of ad-hoc acceleration over repeated pivoting on
same object
Pivot search:
| tstats count AS "Count of HTTP_Success" from sid=1379116434.663 prestats=true | stats count AS
"Count of HTTP_Success”

49. Acceleration Disclaimers
Works with search-head pooling – we collect on indexers
Cannot edit accelerated models

50. Thank You