U.S. patent application number 14/526380 was filed with the patent office on 2016-04-07 for event segment search drill down.
The applicant listed for this patent is Splunk Inc.. Invention is credited to Cory Eugene Burke, Katherine Kyle Feeney, Divanny I. Lamas, Clara E. Lee, Matthew G. Ness, Marc Vincent Robichaud.
Application Number | 20160098463 14/526380 |
Document ID | / |
Family ID | 55632923 |
Filed Date | 2016-04-07 |
United States Patent
Application |
20160098463 |
Kind Code |
A1 |
Burke; Cory Eugene ; et
al. |
April 7, 2016 |
Event Segment Search Drill Down
Abstract
In embodiments of event segment search drill down, a search
system exposes a search interface that displays multiple events
returned as a search result set. A segment can be emphasized in
event raw data of an event that is one of multiple events displayed
in the search interface, and a menu is displayed with search
options that are selectable to operate on the emphasized segment.
The menu includes the search options to add the emphasized segment
as a keyword to a search command in a search bar of the search
interface, exclude the keyword that represents the emphasized
segment from a search, or create a new data search based on the
highlighted segment. A selection of one of the search options in
the menu can be received, and the search command in the search bar
is updated based on the search option that is selected.
Inventors: |
Burke; Cory Eugene; (San
Bruno, CA) ; Feeney; Katherine Kyle; (Oakland,
CA) ; Lamas; Divanny I.; (San Francisco, CA) ;
Robichaud; Marc Vincent; (San Francisco, CA) ; Ness;
Matthew G.; (Oakland, CA) ; Lee; Clara E.;
(Pacifica, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Splunk Inc. |
San Francisco |
CA |
US |
|
|
Family ID: |
55632923 |
Appl. No.: |
14/526380 |
Filed: |
October 28, 2014 |
Related U.S. Patent Documents
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62059988 |
Oct 5, 2014 |
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62059989 |
Oct 5, 2014 |
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62059993 |
Oct 5, 2014 |
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62060545 |
Oct 6, 2014 |
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62059994 |
Oct 5, 2014 |
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62060551 |
Oct 6, 2014 |
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Current U.S.
Class: |
715/771 |
Current CPC
Class: |
G06F 16/2425 20190101;
G06F 16/221 20190101; G06F 9/451 20180201; G06F 16/242 20190101;
G06F 40/18 20200101; G06F 3/04847 20130101; G06F 3/04842 20130101;
G06F 16/248 20190101; G06F 16/252 20190101; G06F 16/951 20190101;
G06K 9/2054 20130101; G06F 16/2455 20190101; G06F 3/0482
20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06F 3/0484 20060101 G06F003/0484 |
Claims
1. A method, comprising: executing a search system on one or more
computing devices to perform: causing emphasis of a segment in
event raw data of an event that is one of multiple events returned
as a search result set displayed in a search interface; causing
display of a menu of search options that are selectable to operate
on the emphasized segment in the event raw data; receiving a
selection of one of the search options displayed in the menu; and
updating a search command in a search bar of the search interface
based on the search option that is selected from the menu for the
emphasized segment.
2. The method as recited in claim 1, further comprising: said
causing the emphasized segment responsive to detection of an input
pointer over the segment.
3. The method as recited in claim 1, further comprising: receiving
an input associated with the emphasized segment in the event raw
data; and wherein the menu of the search options is displayed
responsive to the received input.
4. The method as recited in claim 1, wherein the menu of the search
options is displayed proximate the emphasized segment in the search
interface.
5. The method as recited in claim 1, wherein the search options
displayed in the menu include an add to search option, an exclude
from search option, and a new search option.
6. The method as recited in claim 1, wherein the search options
displayed in the menu include an add to search option that is
selectable to initiate the emphasized segment added as a keyword to
the search command in the search bar.
7. The method as recited in claim 1, wherein the search options
displayed in the menu include an exclude from search option that is
selectable to add a keyword that represents the emphasized segment
to the search command in the search bar to exclude the keyword from
a search.
8. The method as recited in claim 1, wherein the search options
displayed in the menu include a new search option that is
selectable to create a new data search based on the emphasized
segment.
9. The method as recited in claim 1, wherein the menu includes the
search options to: add the emphasized segment as a keyword to the
search command in the search bar; exclude a keyword that represents
the emphasized segment from a search; or create a new data search
based on the emphasized segment.
10. The method as recited in claim 1, wherein: said receiving the
selection of a search option to add the emphasized segment as a
keyword to a data search; said updating the search command in the
search bar to include the keyword that represents the emphasized
segment; and the method further comprises: performing the data
search based on the updated search command to determine the
multiple events that each include the keyword that represents the
emphasized segment; and causing display of an updated search result
set of the multiple events that each include the emphasized segment
in the search interface.
11. The method as recited in claim 1, wherein: said receiving the
selection of a search option to add the emphasized segment as a
keyword to a data search; said updating the search command in the
search bar to include the keyword that represents the emphasized
segment; and the method further comprises: performing the data
search based on the updated search command to determine the
multiple events that each include the keyword that represents the
emphasized segment; causing display of an updated search result set
of the multiple events that each include the emphasized segment in
the search interface; receiving an input associated with the
emphasized segment in the event raw data of one of the multiple
events displayed as part of the updated search result set; and
causing display of an additional search menu of options to one of:
remove the keyword that represents the emphasized segment from the
search command; or create a new search based on the emphasized
segment.
12. The method as recited in claim 1, wherein: receiving the
selection of a search option includes receiving the selection to
add a keyword that represents the emphasized segment to the search
command to exclude the keyword from a data search; said updating
the search command in the search bar to exclude the keyword that
represents the emphasized segment; and the method further
comprises: performing the data search based on the updated search
command to determine the multiple events that do not include the
keyword that represents the emphasized segment; and causing display
of an updated search result set of the multiple events that do not
include the emphasized segment.
13. The method as recited in claim 1, wherein: said receiving the
selection of a search option to create a new data search based on
the emphasized segment; said updating the search command in the
search bar to include only a keyword that represents the emphasized
segment; and the method further comprises: performing the new data
search based on the updated search command to determine the
multiple events that include the keyword that represents the
emphasized segment; and causing display of an updated search result
set of the multiple events that include the emphasized segment.
14. The method as recited in claim 1, wherein: the menu of the
search options includes selectable interface links, each associated
with a corresponding search option; and a selectable interface link
being selectable to initiate a new search interface.
15. The method as recited in claim 1, wherein the search options
displayed in the menu include an add to search option, an exclude
from search option, a new search option, and a field extraction
option.
16. The method as recited in claim 1, wherein said causing emphasis
of the segment comprises highlighting the segment.
17. The method as recited in claim 1, wherein the multiple events
are initially derived from collected data that comprises at least
one of raw data, machine data, performance data, log data,
diagnostic information, transformed data, or mashup data combined
from multiple sources.
18. The method as recited in claim 1, wherein the multiple events
of the search result are returned as a result of a search performed
using a late-binding schema on data originally collected from one
or more sources.
19. The method as recited in claim 1, wherein the multiple events
each comprise a portion of raw data that is associated with a
timestamp indicating a respective point in time.
20. A system, comprising: one or more data stores configured to
store collected data that is searchable by a search system using a
late-binding schema; a processing system to implement the search
system on one or more computing devices, the search system
configured to: emphasize a segment in event raw data of an event
that is one of multiple events returned as a search result set
displayed in a search interface; initiate display of a menu of
search options that are selectable to operate on the emphasized
segment in the event raw data; receive a selection of one of the
search options displayed in the menu; and update a search command
in a search bar of the search interface based on the search option
that is selected from the menu for the emphasized segment.
21. The system as recited in claim 20, wherein the search system is
configured to: highlight the segment responsive to detection of an
input pointer over the segment; receive an input associated with
the emphasized segment in the event raw data; and wherein the menu
of the search options is displayed proximate the emphasized segment
in the search interface responsive to the received input.
22. The system as recited in claim 20, wherein the search options
displayed in the menu include an add to search option, an exclude
from search option, and a new search option.
23. The system as recited in claim 20, wherein the menu includes
the search options to: add the emphasized segment as a keyword to
the search command in the search bar; exclude the keyword that
represents the emphasized segment from a search; or create a new
data search based on the emphasized segment.
24. The system as recited in claim 20, wherein the search system is
configured to: receive the selection of a search option to add the
emphasized segment as a keyword to a data search; update the search
command in the search bar to include the keyword that represents
the emphasized segment; perform the data search based on the
updated search command to determine the multiple events that each
include the keyword that represents the emphasized segment; and
initiate display of an updated search result set of the multiple
events that each include the emphasized segment in the search
interface.
25. The system as recited in claim 20, wherein the search system is
configured to: receive the selection of a search option to exclude
a keyword that represents the emphasized segment from a data
search; update the search command in the search bar to exclude the
keyword that represents the emphasized segment; perform the data
search based on the updated search command to determine the
multiple events that do not include the keyword that represents the
emphasized segment; and initiate display of an updated search
result set of the multiple events that do not include the
emphasized segment.
26. The system as recited in claim 20, wherein the search system is
configured to: receive the selection of a search option to create a
new data search based on the emphasized segment; update the search
command in the search bar to include only the keyword that
represents the emphasized segment; perform the new data search
based on the updated search command to determine the multiple
events that include the keyword that represents the emphasized
segment; and initiate display of an updated search result set of
the multiple events that include the emphasized segment.
27. One or more computer-readable, non-volatile storage memory
comprising stored instructions that are executable and, responsive
to execution by a computing device, the computing device performs
operations comprising: causing emphasis of a segment in event raw
data of an event that is one of multiple events returned as a
search result set displayed in a search interface; causing display
of a menu of search options that are selectable to operate on the
emphasized segment in the event raw data; receiving a selection of
one of the search options displayed in the menu; and updating a
search command in a search bar of the search interface based on the
search option that is selected from the menu for the emphasized
segment.
28. The one or more computer-readable, non-volatile storage memory
as recited in claim 27, wherein the computing device performs the
operations comprising: said causing the emphasized segment
responsive to detection of an input pointer over the segment;
receiving an input associated with the emphasized segment in the
event raw data; and wherein the menu of the search options is
displayed proximate the emphasized segment in the search interface
responsive to the received input.
29. The one or more computer-readable, non-volatile storage memory
as recited in claim 27, wherein: the search options displayed in
the menu include an add to search option, an exclude from search
option, and a new search option; the add to search option being
selectable to add the emphasized segment as a keyword to the search
command in the search bar; the exclude from search option being
selectable to exclude the keyword that represents the emphasized
segment from a search; and the new search option being selectable
to create a new data search based on the emphasized segment.
30. The one or more computer-readable, non-volatile storage memory
as recited in claim 27, wherein the search options displayed in the
menu include an add to search option, an exclude from search
option, and a new search option.
Description
RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application Ser. No. 62/059,988 filed Oct. 5, 2014 entitled "Event
Segment Search Drill Down", the disclosure of which is incorporated
by reference herein in its entirety. This application also claims
priority to U.S. Provisional Patent Application Ser. No. 62/059,989
filed Oct. 5, 2014 entitled "Field Value Search Drill Down", the
disclosure of which is incorporated by reference herein in its
entirety. This application also claims priority to U.S. Provisional
Patent Application Ser. No. 62/059,993 filed Oct. 5, 2014 and U.S.
Provisional Patent Application Ser. No. 62/060,545 filed Oct. 6,
2014, both entitled "Statistics Value Chart Interface Row Mode
Drill Down", the disclosures of which are incorporated by reference
herein in their entirety. This application also claims priority to
U.S. Provisional Patent Application Ser. No. 62/059,994 filed Oct.
5, 2014 and U.S. Provisional Patent Application Ser. No. 62/060,551
filed Oct. 6, 2014, both entitled "Statistics Time Chart Interface
Row Mode Drill Down", the disclosures of which are incorporated by
reference herein in their entirety. This application also claims
priority to U.S. Provisional Patent Application Ser. No. 62/059,998
filed Oct. 5, 2014 and U.S. Provisional Patent Application Ser. No.
62/060,560 filed Oct. 6, 2014, both entitled "Statistics Value
Chart Interface Cell Mode Drill Down", the disclosures of which are
incorporated by reference herein in their entirety. This
application also claims priority to U.S. Provisional Patent
Application Ser. No. 62/060,001 filed Oct. 5, 2014 and U.S.
Provisional Patent Application Ser. No. 62/060,567 filed Oct. 6,
2014, both entitled "Statistics Time Chart Interface Cell Mode
Drill Down", the disclosures of which are incorporated by reference
herein in their entirety.
BACKGROUND
[0002] Data analysts for many businesses face the challenge of
making sense of and finding patterns in the increasingly large
amounts of data in the many types and formats that such businesses
generate and collect. For example, accessing computer networks and
transmitting electronic communications across the networks
generates massive amounts of data, including such types of data as
machine data and Web logs. Identifying patterns in this data, once
thought relatively useless, has proven to be of great value to the
businesses. In some instances, pattern analysis can indicate which
patterns are normal and which ones are unusual. For example,
detecting unusual patterns can allow a computer system manager to
investigate the circumstances and determine whether a computer
system security threat exists.
[0003] Additionally, analysis of the data allows businesses to
understand how their employees, potential consumers, and/or Web
visitors use the company's online resources. Such analysis can
provide businesses with operational intelligence, business
intelligence, and an ability to better manage their IT resources.
For instance, such analysis may enable a business to better retain
customers, meet customer needs, or improve the efficiency of the
company's IT resources. Despite the value that one can derive from
the underlying data described, making sense of this data to realize
that value takes effort. In particular, patterns in underlying data
may be difficult to identify or understand when analyzing specific
behaviors in isolation, often resulting in the failure of a data
analyst to notice valuable correlations in the data from which a
business can draw strategic insight.
SUMMARY
[0004] This Summary introduces features and concepts of event
segment search drill down, which is further described below in the
Detailed Description and/or shown in the Figures. This Summary
should not be considered to describe essential features of the
claimed subject matter, nor used to determine or limit the scope of
the claimed subject matter.
[0005] Event segment search drill down is described. In
embodiments, a search system exposes a search interface that
displays multiple events returned as a search result set in the
search interface. A segment can be emphasized in event raw data of
an event that is one of multiple events displayed in the search
interface, and a menu is displayed with search options that are
selectable to operate on the emphasized segment. The menu includes
the search options to add the emphasized segment as a keyword to a
search command in a search bar of the search interface, exclude the
keyword that represents the emphasized segment from a search, or
create a new data search based on the emphasized segment. A
selection of one of the search options in the menu can be received,
and the search command in the search bar is updated based on the
search option that is selected for the emphasized segment.
[0006] In implementations, the segment in the event raw data of the
event can be emphasized responsive to detection of an input pointer
over the segment. An input associated with the emphasized segment
in the event raw data can be received, such as when initiated by a
user in the search interface, and the menu of the search options is
displayed proximate the emphasized segment in the search interface.
For example, the menu may pop-up or drop-down just below the
emphasized segment. A received input that is associated with
emphasized segment of the event initiates a display of the menu
with the search options that are selectable to operate on the
emphasized segment. The search options that are displayed in the
menu include an add to search option, an exclude from search
option, and a new search option.
[0007] In embodiments, the search system can receive a selection of
the search option to add the emphasized segment as a keyword to a
data search, and update the search command in the search bar to
include the keyword that represents the emphasized segment. The
search system can then perform the data search based on the updated
search command to determine the multiple events that each include
the keyword that represents the emphasized segment, and display an
updated search result set of the multiple events that each include
the emphasized segment in the search interface. Additionally, the
search system can receive another input associated with the
emphasized segment in one of the multiple events displayed as part
of the updated search result set, and then display an additional
search menu of options to remove the keyword that represents the
emphasized segment from the search command, or create a new search
based on the emphasized segment.
[0008] Alternatively, the search system can receive a selection of
the search option to exclude the keyword that represents the
emphasized segment from a data search, and update the search
command in the search bar to exclude the keyword that represents
the emphasized segment. The search system can then perform the data
search based on the updated search command to determine the
multiple events that do not include the keyword that represents the
emphasized segment, and display an updated search result set of the
multiple events that do not include the emphasized segment.
Alternatively, the search system can receive a selection of the
search option to create a new data search based on the emphasized
segment, and update the search command in the search bar to include
only the keyword that represents the emphasized segment. The search
system can then perform the new data search based on the updated
search command to determine the multiple events that include the
keyword that represents the emphasized segment, and display an
updated search result set of the multiple events that include the
emphasized segment.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] Embodiments of event segment search drill down are described
with reference to the following Figures. The same numbers may be
used throughout to reference like features and components that are
shown in the Figures:
[0010] FIG. 1 illustrates a block diagram of an event-processing
system in accordance with the disclosed implementations of event
segment search drill down.
[0011] FIG. 2 illustrates a flowchart of how indexers process,
index, and store data received from forwarders in accordance with
the disclosed implementations.
[0012] FIG. 3 illustrates a flowchart of how a search head and
indexers perform a search query in accordance with the disclosed
implementations.
[0013] FIG. 4 illustrates a block diagram of a system for
processing search requests that uses extraction rules for field
values in accordance with the disclosed implementations.
[0014] FIG. 5 illustrates an exemplary search query received from a
client and executed by search peers in accordance with the
disclosed implementations.
[0015] FIG. 6A illustrates a search screen in accordance with the
disclosed implementations.
[0016] FIG. 6B illustrates a data summary dialog that enables a
user to select various data sources in accordance with the
disclosed implementations.
[0017] FIG. 7A illustrates a key indicators view in accordance with
the disclosed implementations.
[0018] FIG. 7B illustrates an incident review dashboard in
accordance with the disclosed implementations.
[0019] FIG. 7C illustrates a proactive monitoring tree in
accordance with the disclosed implementations.
[0020] FIG. 7D illustrates a screen displaying both log data and
performance data in accordance with the disclosed
implementations.
[0021] FIGS. 8A-8G illustrate examples of a search interface in
accordance with the disclosed implementations.
[0022] FIG. 9A illustrates an example of a search interface in
accordance with the disclosed implementations.
[0023] FIG. 9B illustrates an example of an extract fields
interface in accordance with the disclosed implementations.
[0024] FIGS. 10A and 10B illustrate examples of a search interface
in accordance with the disclosed implementations.
[0025] FIGS. 11A-11D illustrate example method(s) of event segment
search drill down in accordance with one or more embodiments.
[0026] FIGS. 12A-12D illustrate example method(s) of field value
search drill down in accordance with one or more embodiments.
[0027] FIG. 13 illustrates an example system with an example device
that can implement embodiments of event segment search drill
down.
DETAILED DESCRIPTION
[0028] Embodiments of event segment search drill down and field
value search drill down are described and can be implemented to
facilitate user-initiated search options when performing data
searches in search interfaces that include events, highlighted
segments in event raw data of the events, values of field-value
pairs in the events, and tagged field-value pairs in the events.
Although described in the context of an event segment that is
highlighted or otherwise visually emphasized in event raw data of a
displayed event, the techniques described herein can be implemented
and applied to any text selection, alphanumeric selection, or
searched text and/or alphanumeric string.
[0029] In embodiments, a segment in the event raw data of an event
can be highlighted (or otherwise emphasized) and a contextual
search menu is displayed with search options that are selectable to
operate on the highlighted segment. Similarly, field-value pair in
an event can be emphasized (e.g., highlighted or any other type of
visual emphasis) and a field value contextual menu is displayed
with search options that are selectable to operate on the
emphasized field-value pair.
[0030] The contextual search menu and the field value contextual
menu includes search options, such as to add the highlighted
segment as a new keyword to a search command in a search bar of the
search interface, add the keyword that represents the highlighted
segment to the search command to exclude the highlighted segment
from a search, or create a new data search based on the keyword
that represents the highlighted segment. Similarly, the search
options include an option to add search criteria of the emphasized
field-value pair to the search command in the search bar of the
search interface, add the search criteria of the emphasized
field-value pair to the search command as the search criteria
excluded from events that do not include the emphasized field-value
pair, or create a new data search based on the emphasized
field-value pair, where the search criteria of the emphasized
field-value pair replaces the search command in the search bar. The
user can select one of the search options in the contextual search
menu or field value contextual menu, and the search command in the
search bar of the search interface is updated based on the search
option that is selected for the highlighted segment or emphasized
field-value pair.
[0031] Example Environment
[0032] Modern data centers often comprise thousands of host
computer systems that operate collectively to service requests from
even larger numbers of remote clients. During operation, these data
centers generate significant volumes of performance data and
diagnostic information that can be analyzed to quickly diagnose
performance problems. In order to reduce the size of this
performance data, the data is typically pre-processed prior to
being stored based on anticipated data-analysis needs. For example,
pre-specified data items can be extracted from the performance data
and stored in a database to facilitate efficient retrieval and
analysis at search time. However, the rest of the performance data
is not saved and is essentially discarded during pre-processing. As
storage capacity becomes progressively cheaper and more plentiful,
there are fewer incentives to discard this performance data and
many reasons to keep it.
[0033] This plentiful storage capacity is presently making it
feasible to store massive quantities of minimally processed
performance data at "ingestion time" for later retrieval and
analysis at "search time." Note that performing the analysis
operations at search time provides greater flexibility because it
enables an analyst to search all of the performance data, instead
of searching pre-specified data items that were stored at ingestion
time. This enables the analyst to investigate different aspects of
the performance data instead of being confined to the pre-specified
set of data items that were selected at ingestion time.
[0034] However, analyzing massive quantities of heterogeneous
performance data at search time can be a challenging task. A data
center may generate heterogeneous performance data from thousands
of different components, which can collectively generate tremendous
volumes of performance data that can be time-consuming to analyze.
For example, this performance data can include data from system
logs, network packet data, sensor data, and data generated by
various applications. Also, the unstructured nature of much of this
performance data can pose additional challenges because of the
difficulty of applying semantic meaning to unstructured data, and
the difficulty of indexing and querying unstructured data using
traditional database systems.
[0035] These challenges can be addressed by using an event-based
system, such as the SPLUNK.RTM. ENTERPRISE system produced by
Splunk Inc. of San Francisco, Calif., to store and process
performance data. The SPLUNK.RTM. ENTERPRISE system is the leading
platform for providing real-time operational intelligence that
enables organizations to collect, index, and harness
machine-generated data from various websites, applications,
servers, networks, and mobile devices that power their businesses.
The SPLUNK.RTM. ENTERPRISE system is particularly useful for
analyzing unstructured performance data, which is commonly found in
system log files. Although many of the techniques described herein
are explained with reference to the SPLUNK.RTM. ENTERPRISE system,
the techniques are also applicable to other types of data server
systems.
[0036] In the SPLUNK.RTM. ENTERPRISE system, performance data is
stored as "events," in which each event comprises a collection of
performance data and/or diagnostic information that is generated by
a computer system and is correlated with a specific point in time.
Events can be derived from "time series data," in which time series
data includes a sequence of data points (e.g., performance
measurements from a computer system) that are associated with
successive points in time and are typically spaced at uniform time
intervals. Events can also be derived from "structured" or
"unstructured" data. Structured data has a predefined format, in
which specific data items with specific data formats reside at
predefined locations in the data. For example, structured data can
include data items stored in fields in a database table. In
contrast, unstructured data does not have a predefined format. This
means that unstructured data can include various data items having
different data types that can reside at different locations. For
example, when the data source is an operating system log, an event
can include one or more lines from the operating system log
containing raw data that includes different types of performance
and diagnostic information associated with a specific point in
time.
[0037] Examples of data sources from which an event may be derived
include, but are not limited to web servers, application servers,
databases, firewalls, routers, operating systems, and software
applications that execute on computer systems, mobile devices, and
sensors. The data generated by such data sources can be produced in
various forms including, for example and without limitation, server
log files, activity log files, configuration files, messages,
network packet data, performance measurements and sensor
measurements. An event typically includes a timestamp that may be
derived from the raw data in the event, or may be determined
through interpolation between temporally proximate events having
known timestamps.
[0038] The SPLUNK.RTM. ENTERPRISE system also facilitates using a
flexible schema to specify how to extract information from the
event data, in which the flexible schema may be developed and
redefined as needed. Note that a flexible schema may be applied to
event data "on the fly" as desired (e.g., at search time), rather
than at ingestion time of the data as in traditional database
systems. Because the schema is not applied to event data until it
is desired (e.g., at search time), it is referred to as a
"late-binding schema."
[0039] During operation, the SPLUNK.RTM. ENTERPRISE system starts
with raw data, which can include unstructured data, machine data,
performance measurements or other time-series data, such as data
obtained from weblogs, syslogs, or sensor readings. It divides this
raw data into "portions," and optionally transforms the data to
produce timestamped events. The system stores the timestamped
events in a data store, and enables a user to run queries against
the data store to retrieve events that meet specified criteria,
such as containing certain keywords or having specific values in
defined fields. Note that the term "field" refers to a location in
the event data containing a value for a specific data item.
[0040] As noted above, the SPLUNK.RTM. ENTERPRISE system
facilitates using a late-binding schema while performing queries on
events. A late-binding schema specifies "extraction rules" that are
applied to data in the events to extract values for specific
fields. More specifically, the extraction rules for a field can
include one or more instructions that specify how to extract a
value for the field from the event data. An extraction rule can
generally include any type of instruction for extracting values
from data in events. In some cases, an extraction rule includes a
regular expression, in which case the rule is referred to as a
"regex rule."
[0041] In contrast to a conventional schema for a database system,
a late-binding schema is not defined at data ingestion time.
Instead, the late-binding schema can be developed on an ongoing
basis until the time a query is actually executed. This means that
extraction rules for the fields in a query may be provided in the
query itself, or may be located during execution of the query.
Hence, as an analyst learns more about the data in the events, the
analyst can continue to refine the late-binding schema by adding
new fields, deleting fields, or changing the field extraction rules
until the next time the schema is used by a query. Because the
SPLUNK.RTM. ENTERPRISE system maintains the underlying raw data and
provides a late-binding schema for searching the raw data, it
enables an analyst to investigate questions that arise as the
analyst learns more about the events.
[0042] In the SPLUNK.RTM. ENTERPRISE system, a field extractor may
be configured to automatically generate extraction rules for
certain fields in the events when the events are being created,
indexed, or stored, or possibly at a later time. Alternatively, a
user may manually define extraction rules for fields using a
variety of techniques. Also, a number of "default fields" that
specify metadata about the events, rather than data in the events
themselves, can be created automatically. For example, such default
fields can specify: a timestamp for the event data; a host from
which the event data originated; a source of the event data; and a
source type for the event data. These default fields may be
determined automatically when the events are created, indexed, or
stored.
[0043] In some embodiments, a common field name may be used to
reference two or more fields containing equivalent data items, even
though the fields may be associated with different types of events
that possibly have different data formats and different extraction
rules. By enabling a common field name to be used to identify
equivalent fields from different types of events generated by
different data sources, the system facilitates use of a "common
information model" (CIM) across the different data sources.
[0044] Data Server System
[0045] FIG. 1 illustrates a block diagram of an example
event-processing system 100, similar to the SPLUNK.RTM. ENTERPRISE
system, and in which embodiments of event segment search drill down
can be implemented. The example event-processing system 100
includes one or more forwarders 101 that collect data obtained from
a variety of different data sources 105, and one or more indexers
102 that store, process, and/or perform operations on this data, in
which each indexer operates on data contained in a specific data
store 103. A search head 104 may also be provided that represents
functionality to obtain and process search requests from clients
and provide results of the search back to the clients, additional
details of which are discussed in relation to FIGS. 3 and 4. The
forwarders 101, indexers 102, and/or search head 104 may be
configured as separate computer systems in a data center, or
alternatively may be configured as separate processes implemented
via one or more individual computer systems. Data that is collected
via the forwarders 101 may be obtained from a variety of different
data sources 105.
[0046] As further illustrated, the search head 104 may interact
with a client application module 106 associated with a client
device, such as to obtain search queries and supply search results
or other suitable data back to the client application module 106
that is effective to enable the client application module 106 to
form search user interfaces 108 through which different views of
the data may be exposed. Various examples and details regarding
search interfaces 108, client application modules 106, search
queries, and operation of the various components illustrated in
FIG. 1 are discussed throughout this document.
[0047] During operation, the forwarders 101 identify which indexers
102 will receive the collected data and then forward the data to
the identified indexers. The forwarders 101 can also perform
operations to strip out extraneous data and detect timestamps in
the data. The forwarders next determine which of the indexers 102
will receive each data item and then forward the data items to the
determined indexers 102. Note that distributing data across the
different indexers 102 facilitates parallel processing. This
parallel processing can take place at data ingestion time, because
multiple indexers can process the incoming data in parallel. The
parallel processing can also take place at search time, because
multiple indexers can search through the data in parallel.
[0048] The example event-processing system 100 and the processes
described below with respect to FIGS. 1-5 are further described in
"Exploring Splunk Search Processing Language (SPL) Primer and
Cookbook" by David Carasso, CITO Research, 2012, and in "Optimizing
Data Analysis With a Semi-Structured Time Series Database" by
Ledion Bitincka, Archana Ganapathi, Stephen Sorkin, and Steve
Zhang, SLAML, 2010, each of which is hereby incorporated herein by
reference in its entirety for all purposes.
[0049] Data Ingestion
[0050] FIG. 2 illustrates a flowchart 200 of how an indexer
processes, indexes, and stores data received from forwarders in
accordance with the disclosed embodiments. At block 201, the
indexer receives the data from the forwarder. Next, at block 202,
the indexer apportions the data into events. Note that the data can
include lines of text that are separated by carriage returns or
line breaks and an event may include one or more of these lines.
During the apportioning process, the indexer can use heuristic
rules to automatically determine the boundaries of the events,
which for example coincide with line boundaries. These heuristic
rules may be determined based on the source of the data, in which
the indexer can be explicitly informed about the source of the data
or can infer the source of the data by examining the data. These
heuristic rules can include regular expression-based rules or
delimiter-based rules for determining event boundaries, in which
the event boundaries may be indicated by predefined characters or
character strings. These predefined characters may include
punctuation marks or other special characters including, for
example, carriage returns, tabs, spaces or line breaks. In some
cases, a user can fine-tune or configure the rules that the
indexers use to determine event boundaries in order to adapt the
rules to the user's specific requirements.
[0051] Next, the indexer determines a timestamp for each event at
block 203. As mentioned above, these timestamps can be determined
by extracting the time directly from data in the event, or by
interpolating the time based on timestamps from temporally
proximate events. In some cases, a timestamp can be determined
based on the time the data was received or generated. The indexer
subsequently associates the determined timestamp with each event at
block 204, for example by storing the timestamp as metadata for
each event.
[0052] Then, the system can apply transformations to data to be
included in events at block 205. For log data, such transformations
can include removing a portion of an event (e.g., a portion used to
define event boundaries, extraneous text, characters, etc.) or
removing redundant portions of an event. Note that a user can
specify portions to be removed using a regular expression or any
other possible technique.
[0053] Next, a keyword index can optionally be generated to
facilitate fast keyword searching for events. To build a keyword
index, the indexer first identifies a set of keywords in block 206.
Then, at block 207 the indexer includes the identified keywords in
an index, which associates each stored keyword with references to
events containing that keyword (or to locations within events where
that keyword is located). When an indexer subsequently receives a
keyword-based query, the indexer can access the keyword index to
quickly identify events containing the keyword.
[0054] In some embodiments, the keyword index may include entries
for name-value pairs found in events, wherein a name-value pair can
include a pair of keywords connected by a symbol, such as an equals
sign or colon. In this way, events containing these name-value
pairs can be quickly located. In some embodiments, fields can
automatically be generated for some or all of the name-value pairs
at the time of indexing. For example, if the string "dest=10.0.1.2"
is found in an event, a field named "dest" may be created for the
event, and assigned a value of "10.0.1.2" as a field-value
pair.
[0055] Finally, the indexer stores the events in a data store at
block 208, where a timestamp can be stored with each event to
facilitate searching for events based on a time range. In some
cases, the stored events are organized into a plurality of buckets,
where each bucket stores events associated with a specific time
range. This not only improves time-based searches, but it also
allows events with recent timestamps that may have a higher
likelihood of being accessed to be stored in faster memory to
facilitate faster retrieval. For example, a bucket containing the
most recent events can be stored as flash memory instead of on a
hard disk.
[0056] Each indexer 102 is responsible for storing and searching a
subset of the events contained in a corresponding data store 103.
By distributing events among the indexers and data stores, the
indexers can analyze events for a query in parallel, for example
using map-reduce techniques, in which each indexer returns partial
responses for a subset of events to a search head that combines the
results to produce an answer for the query. By storing events in
buckets for specific time ranges, an indexer may further optimize
searching by looking only in buckets for time ranges that are
relevant to a query. Moreover, events and buckets can also be
replicated across different indexers and data stores to facilitate
high availability and disaster recovery as is described in U.S.
patent application Ser. No. 14/266,812 filed on 30 Apr. 2014, and
in U.S. patent application Ser. No. 14/266,817 also filed on 30
Apr. 2014.
[0057] Query Processing
[0058] FIG. 3 illustrates a flowchart 300 of how a search head and
indexers perform a search query in accordance with the disclosed
embodiments. At the start of this process, a search head receives a
search query from a client (e.g., a client computing device) at
block 301. Next, at block 302, the search head analyzes the search
query to determine what portions can be delegated to indexers and
what portions need to be executed locally by the search head. At
block 303, the search head distributes the determined portions of
the query to the indexers. Note that commands that operate on
single events can be trivially delegated to the indexers, while
commands that involve events from multiple indexers are harder to
delegate.
[0059] Then, at block 304, the indexers to which the query was
distributed search their data stores for events that are responsive
to the query. To determine which events are responsive to the
query, the indexer searches for events that match the criteria
specified in the query. This criteria can include matching keywords
or specific values for certain fields. In a query that uses a
late-binding schema, the searching operations in block 304 may
involve using the late-binding scheme to extract values for
specified fields from events at the time the query is processed.
Next, the indexers can either send the relevant events back to the
search head, or use the events to calculate a partial result, and
send the partial result back to the search head.
[0060] Finally, at block 305, the search head combines the partial
results and/or events received from the indexers to produce a final
result for the query. This final result can comprise different
types of data depending on what the query is asking for. For
example, the final results can include a listing of matching events
returned by the query, or some type of visualization of data from
the returned events. In another example, the final result can
include one or more calculated values derived from the matching
events.
[0061] Moreover, the results generated by system 100 can be
returned to a client using different techniques. For example, one
technique streams results back to a client in real-time as they are
identified. Another technique waits to report results to the client
until a complete set of results is ready to return to the client.
Yet another technique streams interim results back to the client in
real-time until a complete set of results is ready, and then
returns the complete set of results to the client. In another
technique, certain results are stored as "search jobs," and the
client may subsequently retrieve the results by referencing the
search jobs.
[0062] The search head can also perform various operations to make
the search more efficient. For example, before the search head
starts executing a query, the search head can determine a time
range for the query and a set of common keywords that all matching
events must include. Next, the search head can use these parameters
to query the indexers to obtain a superset of the eventual results.
Then, during a filtering stage, the search head can perform
field-extraction operations on the superset to produce a reduced
set of search results.
[0063] Field Extraction
[0064] FIG. 4 illustrates a block diagram 400 of how fields can be
extracted during query processing in accordance with the disclosed
embodiments. At the start of this process, a search query 402 is
received at a query processor 404. The query processor 404 includes
various mechanisms for processing a query, where these mechanisms
can reside in a search head 104 and/or an indexer 102. Note that
the exemplary search query 402 illustrated in FIG. 4 is expressed
in Search Processing Language (SPL), which is used in conjunction
with the SPLUNK.RTM. ENTERPRISE system. The SPL is a pipelined
search language in which a set of inputs is operated on by a first
command in a command line, and then a subsequent command following
the pipe symbol "|" operates on the results produced by the first
command, and so on for additional commands. Search query 402 can
also be expressed in other query languages, such as the Structured
Query Language ("SQL") or any suitable query language.
[0065] Upon receiving the search query 402, the query processor 404
identifies that the search query 402 includes two fields, "IP" and
"target." The query processor 404 also determines that the values
for the "IP" and "target" fields have not already been extracted
from events in a data store 414, and consequently determines that
the query processor 404 needs to use extraction rules to extract
values for the fields. Hence, the query processor 404 performs a
lookup for the extraction rules in a rule base 406, in which rule
base 406 maps field names to corresponding extraction rules and
obtains extraction rules 408 and 409, where extraction rule 408
specifies how to extract a value for the "IP" field from an event,
and extraction rule 409 specifies how to extract a value for the
"target" field from an event.
[0066] As is illustrated in FIG. 4, the extraction rules 408 and
409 can include regular expressions that specify how to extract
values for the relevant fields. Such regular-expression-based
extraction rules are also referred to as "regex rules." In addition
to specifying how to extract field values, the extraction rules may
also include instructions for deriving a field value by performing
a function on a character string or value retrieved by the
extraction rule. For example, a transformation rule may truncate a
character string, or convert the character string into a different
data format. In some cases, the query itself can specify one or
more extraction rules.
[0067] Next, the query processor 404 sends the extraction rules 408
and 409 to a field extractor 412, which applies the extraction
rules 408 and 409 to events 416-418 in the data store 414. Note
that the data store 414 can include one or more data stores, and
the extraction rules 408 and 409 can be applied to large numbers of
events in the data store 414, and are not meant to be limited to
the three events 416-418 illustrated in FIG. 4. Moreover, the query
processor 404 can instruct the field extractor 412 to apply the
extraction rules to all of the events in the data store 414, or to
a subset of the events that have been filtered based on some
criteria.
[0068] Next, the field extractor 412 applies the extraction rule
408 for the first command "Search IP="10*" to events in the data
store 414, including the events 416-418. The extraction rule 408 is
used to extract values for the IP address field from events in the
data store 414 by looking for a pattern of one or more digits,
followed by a period, followed again by one or more digits,
followed by another period, followed again by one or more digits,
followed by another period, and followed again by one or more
digits. Next, the field extractor 412 returns field values 420 to
the query processor 404, which uses the criterion IP="10*" to look
for IP addresses that start with "10". Note that events 416 and 417
match this criterion, but event 418 does not, so the result set for
the first command is events 416 and 417.
[0069] The query processor 404 then sends the events 416 and 417 to
the next command "stats count target." To process this command, the
query processor 404 causes the field extractor 412 to apply the
extraction rule 409 to the events 416 and 417. The extraction rule
409 is used to extract values for the target field for the events
416 and 417 by skipping the first four commas in the events, and
then extracting all of the following characters until a comma or
period is reached. Next, the field extractor 412 returns field
values 421 to the query processor 404, which executes the command
"stats count target" to count the number of unique values contained
in the target fields, which in this example produces the value "2"
that is returned as a final result 422 for the query.
[0070] Note that query results can be returned to a client, a
search head, or any other system component for further processing.
In general, the query results may include: a set of one or more
events; a set of one or more values obtained from the events; a
subset of the values; statistics calculated based on the values; a
report containing the values; or a visualization, such as a graph
or chart, generated from the values.
[0071] Example Search Screen
[0072] FIG. 6A illustrates an example of a search screen 600 in
accordance with the disclosed embodiments. The search screen 600
includes a search bar 602 that accepts user input in the form of a
search string. It also includes a date time range picker 612 that
enables the user to specify a date and/or time range for the
search. For "historical searches" the user can select a specific
time range, or alternatively a relative time range, such as
"today," "yesterday," or "last week." For "real-time searches," the
user can select the size of a preceding time window to search for
real-time events. The search screen 600 also initially displays a
"data summary" dialog 610 as is illustrated in FIG. 6B that enables
the user to select different sources for the event data, such as by
selecting specific hosts and log files.
[0073] After the search is executed, the search screen 600 can
display the results through search results tabs 604, where the
search results tabs 604 include: an "Events" tab that displays
various information about events returned by the search; a
"Patterns" tab that can be selected to display various patterns
about the events returned by the search; a "Statistics" tab that
displays statistics about the search results and events; and a
"Visualization" tab that displays various visualizations of the
search results. The "Events" tab illustrated in FIG. 6A displays a
timeline graph 605 that graphically illustrates the number of
events that occurred in one-hour intervals over the selected time
range. It also displays an events list 608 that enables a user to
view the raw data in each of the returned events. It additionally
displays a fields sidebar 606 that includes statistics about
occurrences of specific fields in the returned events, including
"selected fields" that are pre-selected by the user, and
"interesting fields" that are automatically selected by the system
based on pre-specified criteria.
[0074] Acceleration Techniques
[0075] The above-described system provides significant flexibility
by enabling a user to analyze massive quantities of minimally
processed performance data "on the fly" at search time instead of
storing pre-specified portions of the performance data in a
database at ingestion time. This flexibility enables a user to see
correlations in the performance data and perform subsequent queries
to examine interesting aspects of the performance data that may not
have been apparent at ingestion time.
[0076] However, performing extraction and analysis operations at
search time can involve a large amount of data and require a large
number of computational operations, which can cause considerable
delays while processing the queries. Fortunately, a number of
acceleration techniques have been developed to speed up analysis
operations performed at search time. These techniques include: (1)
performing search operations in parallel by formulating a search as
a map-reduce computation; (2) using a keyword index; (3) using a
high performance analytics store; and (4) accelerating the process
of generating reports. These techniques are described in more
detail below.
[0077] Map-Reduce Technique
[0078] To facilitate faster query processing, a query can be
structured as a map-reduce computation, wherein the "map"
operations are delegated to the indexers, while the corresponding
"reduce" operations are performed locally at the search head. For
example, FIG. 5 illustrates an example 500 of how a search query
501 received from a client at search head 104 can split into two
phases, including: (1) a "map phase" comprising subtasks 502 (e.g.,
data retrieval or simple filtering) that may be performed in
parallel and are "mapped" to indexers 102 for execution, and (2) a
"reduce phase" comprising a merging operation 503 to be executed by
the search head 104 when the results are ultimately collected from
the indexers.
[0079] During operation, upon receiving search query 501, search
head 104 modifies search query 501 by substituting "stats" with
"prestats" to produce search query 502, and then distributes search
query 502 to one or more distributed indexers, which are also
referred to as "search peers." Note that search queries may
generally specify search criteria or operations to be performed on
events that meet the search criteria. Search queries may also
specify field names, as well as search criteria for the values in
the fields or operations to be performed on the values in the
fields. Moreover, the search head may distribute the full search
query to the search peers as is illustrated in FIG. 3, or may
alternatively distribute a modified version (e.g., a more
restricted version) of the search query to the search peers. In
this example, the indexers are responsible for producing the
results and sending them to the search head. After the indexers
return the results to the search head, the search head performs the
merging operations 503 on the results. Note that by executing the
computation in this way, the system effectively distributes the
computational operations while minimizing data transfers.
[0080] Keyword Index
[0081] As described above with reference to the flow charts 200 and
300 shown in respective FIGS. 2 and 3, the event-processing system
100 can construct and maintain one or more keyword indices to
facilitate rapidly identifying events containing specific keywords.
This can greatly speed up the processing of queries involving
specific keywords. As mentioned above, to build a keyword index, an
indexer first identifies a set of keywords. Then, the indexer
includes the identified keywords in an index, which associates each
stored keyword with references to events containing that keyword,
or to locations within events where that keyword is located. When
an indexer subsequently receives a keyword-based query, the indexer
can access the keyword index to quickly identify events containing
the keyword.
[0082] High Performance Analytics Store
[0083] To speed up certain types of queries, some embodiments of
system 100 make use of a high-performance analytics store, which is
referred to as a "summarization table," that contains entries for
specific field-value pairs. Each of these entries keeps track of
instances of a specific value in a specific field in the event data
and includes references to events containing the specific value in
the specific field. For example, an entry in a summarization table
can keep track of occurrences of the value "94107" in a "ZIP code"
field of a set of events, where the entry includes references to
all of the events that contain the value "94107" in the ZIP code
field. This enables the system to quickly process queries that seek
to determine how many events have a particular value for a
particular field, because the system can examine the entry in the
summarization table to count instances of the specific value in the
field without having to go through the individual events or do
extractions at search time. Also, if the system needs to process
each of the events that have a specific field-value combination,
the system can use the references in the summarization table entry
to directly access the events to extract further information
without having to search each of the events to find the specific
field-value combination at search time.
[0084] In some embodiments, the system maintains a separate
summarization table for each of the above-described time-specific
buckets that stores events for a specific time range, where a
bucket-specific summarization table includes entries for specific
field-value combinations that occur in events in the specific
bucket. Alternatively, the system can maintain a separate
summarization table for each indexer, in which the indexer-specific
summarization table only includes entries for the events in a data
store that is managed by the specific indexer.
[0085] The summarization table can be populated by running a
"collection query" that scans a set of events to find instances of
a specific field-value combination, or alternatively instances of
all field-value combinations for a specific field. A collection
query can be initiated by a user, or can be scheduled to occur
automatically at specific time intervals. A collection query can
also be automatically launched in response to a query that asks for
a specific field-value combination.
[0086] In some cases, the summarization tables may not cover each
of the events that are relevant to a query. In this case, the
system can use the summarization tables to obtain partial results
for the events that are covered by summarization tables, but may
also have to search through other events that are not covered by
the summarization tables to produce additional results. These
additional results can then be combined with the partial results to
produce a final set of results for the query. This summarization
table and associated techniques are described in more detail in
U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014.
[0087] Accelerating Report Generation
[0088] In some embodiments, a data server system such as the
SPLUNK.RTM. ENTERPRISE system can accelerate the process of
periodically generating updated reports based on query results. To
accelerate this process, a summarization engine automatically
examines the query to determine whether generation of updated
reports can be accelerated by creating intermediate summaries. This
is possible if results from preceding time periods can be computed
separately and combined to generate an updated report. In some
cases, it is not possible to combine such incremental results, for
example where a value in the report depends on relationships
between events from different time periods. If reports can be
accelerated, the summarization engine periodically generates a
summary covering data obtained during a latest non-overlapping time
period. For example, where the query seeks events meeting a
specified criteria, a summary for the time period includes only the
events within the time period that meet the specified criteria.
Similarly, if the query seeks statistics calculated from the
events, such as the number of events that match the specified
criteria, then the summary for the time period includes the number
of events in the period that match the specified criteria.
[0089] In parallel with the creation of the summaries, the
summarization engine schedules the periodic updating of the report
associated with the query. During each scheduled report update, the
query engine determines whether intermediate summaries have been
generated covering portions of the time period covered by the
report update. If so, then the report is generated based on the
information contained in the summaries. Also, if additional event
data has been received and has not yet been summarized, and is
required to generate the complete report, the query can be run on
this additional event data. Then, the results returned by this
query on the additional event data, along with the partial results
obtained from the intermediate summaries, can be combined to
generate the updated report. This process is repeated each time the
report is updated.
[0090] Alternatively, if the system stores events in buckets
covering specific time ranges, then the summaries can be generated
on a bucket-by-bucket basis. Note that producing intermediate
summaries can save the work involved in re-running the query for
previous time periods, so only the newer event data needs to be
processed while generating an updated report. These report
acceleration techniques are described in more detail in U.S. Pat.
No. 8,589,403, issued on Nov. 19, 2013, and in U.S. Pat. No.
8,412,696, issued on Apr. 2, 2011.
[0091] Security Features
[0092] The SPLUNK.RTM. ENTERPRISE platform provides various
schemas, dashboards, and visualizations that make it easy for
developers to create applications to provide additional
capabilities. One such application is the SPLUNK.RTM. APP FOR
ENTERPRISE SECURITY, which performs monitoring and alerting
operations, and includes analytics to facilitate identifying both
known and unknown security threats based on large volumes of data
stored by the SPLUNK.RTM. ENTERPRISE system. This differs
significantly from conventional Security Information and Event
Management (SIEM) systems that lack the infrastructure to
effectively store and analyze large volumes of security-related
event data. Traditional SIEM systems typically use fixed schemas to
extract data from pre-defined security-related fields at data
ingestion time, where the extracted data is typically stored in a
relational database. This data extraction process (and associated
reduction in data size) that occurs at data ingestion time
inevitably hampers future incident investigations, when all of the
original data may be needed to determine the root cause of a
security issue, or to detect the tiny fingerprints of an impending
security threat.
[0093] In contrast, the SPLUNK.RTM. APP FOR ENTERPRISE SECURITY
system stores large volumes of minimally processed security-related
data at ingestion time for later retrieval and analysis at search
time when a live security threat is being investigated. To
facilitate this data retrieval process, the SPLUNK.RTM. APP FOR
ENTERPRISE SECURITY provides pre-specified schemas for extracting
relevant values from the different types of security-related event
data, and also enables a user to define such schemas.
[0094] The SPLUNK.RTM. APP FOR ENTERPRISE SECURITY can process many
types of security-related information. In general, this
security-related information can include any information that can
be used to identify security threats. For example, the
security-related information can include network-related
information, such as IP addresses, domain names, asset identifiers,
network traffic volume, uniform resource locator strings, and
source addresses. The process of detecting security threats for
network-related information is further described in U.S. patent
application Ser. Nos. 13/956,252, and 13/956,262. Security-related
information can also include endpoint information, such as malware
infection data and system configuration information, as well as
access control information, such as login/logout information and
access failure notifications. The security-related information can
originate from various sources within a data center, such as hosts,
virtual machines, storage devices, and sensors. The
security-related information can also originate from various
sources in a network, such as routers, switches, email servers,
proxy servers, gateways, firewalls and intrusion-detection
systems.
[0095] During operation, the SPLUNK.RTM. APP FOR ENTERPRISE
SECURITY facilitates detecting so-called "notable events" that are
likely to indicate a security threat. These notable events can be
detected in a number of ways: (1) an analyst can notice a
correlation in the data and can manually identify a corresponding
group of one or more events as "notable;" or (2) an analyst can
define a "correlation search" specifying criteria for a notable
event, and every time one or more events satisfy the criteria, the
application can indicate that the one or more events are notable.
An analyst can alternatively select a pre-defined correlation
search provided by the application. Note that correlation searches
can be run continuously or at regular intervals (e.g., every hour)
to search for notable events. Upon detection, notable events can be
stored in a dedicated "notable events index," which can be
subsequently accessed to generate various visualizations containing
security-related information. Also, alerts can be generated to
notify system operators when important notable events are
discovered.
[0096] The SPLUNK.RTM. APP FOR ENTERPRISE SECURITY provides various
visualizations to aid in discovering security threats, such as a
"key indicators view" that enables a user to view security metrics
of interest, such as counts of different types of notable events.
For example, FIG. 7A illustrates an exemplary key indicators view
700 that comprises a dashboard, which can display a value 701, for
various security-related metrics, such as malware infections 702.
It can also display a change in a metric value 703, which indicates
that the number of malware infections increased by sixty-three (63)
during the preceding interval. The key indicators view 700
additionally displays a histogram panel 704 that displays a
histogram of notable events organized by urgency values, and a
histogram panel 705 of notable events organized by time intervals.
This key indicators view is described in further detail in pending
U.S. patent application Ser. No. 13/956,338 filed Jul. 31,
2013.
[0097] These visualizations can also include an "incident review
dashboard" that enables a user to view and act on "notable events."
These notable events can include: (1) a single event of high
importance, such as any activity from a known web attacker; or (2)
multiple events that collectively warrant review, such as a large
number of authentication failures on a host followed by a
successful authentication. For example, FIG. 7B illustrates an
example of an incident review dashboard 710 that includes a set of
incident attribute fields 711 that, for example, enables a user to
specify a time range field 712 for the displayed events. It also
includes a timeline 713 that graphically illustrates the number of
incidents that occurred in one-hour time intervals over the
selected time range. It additionally displays an events list 714
that enables a user to view a list of each of the notable events
that match the criteria in the incident attributes fields 711. To
facilitate identifying patterns among the notable events, each
notable event can be associated with an urgency value (e.g., low,
medium, high, or critical), which is indicated in the incident
review dashboard. The urgency value for a detected event can be
determined based on the severity of the event and the priority of
the system component associated with the event. The incident review
dashboard is described further on-line (e.g., at an HTTP:// site),
"docs.splunk.com/Documentation/PCI/2.1.1/User/IncidentReviewdashboard."
[0098] Data Center Monitoring
[0099] As mentioned above, the SPLUNK.RTM. ENTERPRISE platform
provides various features that make it easy for developers to
create various applications. One such application is the
SPLUNK.RTM. APP FOR VMWARE.RTM., which performs monitoring
operations and includes analytics to facilitate diagnosing the root
cause of performance problems in a data center based on large
volumes of data stored by the SPLUNK.RTM. ENTERPRISE system.
[0100] This differs from conventional data-center-monitoring
systems that lack the infrastructure to effectively store and
analyze large volumes of performance information and log data
obtained from the data center. In conventional
data-center-monitoring systems, this performance data is typically
pre-processed prior to being stored, for example by extracting
pre-specified data items from the performance data and storing them
in a database to facilitate subsequent retrieval and analysis at
search time. However, the rest of the performance data is not saved
and is essentially discarded during pre-processing. In contrast,
the SPLUNK.RTM. APP FOR VMWARE.RTM. stores large volumes of
minimally processed performance information and log data at
ingestion time for later retrieval and analysis at search time when
a live performance issue is being investigated.
[0101] The SPLUNK.RTM. APP FOR VMWARE.RTM. can process many types
of performance-related information. In general, this
performance-related information can include any type of
performance-related data and log data produced by virtual machines
and host computer systems in a data center. In addition to data
obtained from various log files, this performance-related
information can include values for performance metrics obtained
through an application programming interface (API) provided as part
of the vSphere Hypervisor.TM. system distributed by VMware, Inc. of
Palo Alto, Calif. For example, these performance metrics can
include: (1) CPU-related performance metrics; (2) disk-related
performance metrics; (3) memory-related performance metrics; (4)
network-related performance metrics; (5) energy-usage statistics;
(6) data-traffic-related performance metrics; (7) overall system
availability performance metrics; (8) cluster-related performance
metrics; and (9) virtual machine performance statistics. For more
details about such performance metrics, please see U.S. patent Ser.
No. 14/167,316 filed 29 Jan. 2014, which is hereby incorporated
herein by reference. Also, see "vSphere Monitoring and
Performance," Update 1, vSphere 5.5, EN-001357-00 on-line (e.g., at
an HTTP:// site), "pubs.vmware.com/
vsphere-55/topic/com.vmware.ICbase/PDF/vsphere-esxi-vcenter-server-551-mo-
nitoring-performance-guide.pdf."
[0102] To facilitate retrieving information of interest from
performance data and log files, the SPLUNK.RTM. APP FOR VMWARE.RTM.
provides pre-specified schemas for extracting relevant values from
different types of performance-related event data, and also enables
a user to define such schemas. The SPLUNK.RTM. APP FOR VMWARE.RTM.
additionally provides various visualizations to facilitate
detecting and diagnosing the root cause of performance problems.
For example, one such visualization is a "proactive monitoring
tree" that enables a user to easily view and understand
relationships among various factors that affect the performance of
a hierarchically structured computing system. This proactive
monitoring tree enables a user to easily navigate the hierarchy by
selectively expanding nodes representing various entities (e.g.,
virtual centers or computing clusters) to view performance
information for lower-level nodes associated with lower-level
entities (e.g., virtual machines or host systems). Exemplary
node-expansion operations are illustrated in FIG. 7C, where nodes
733 and 734 are selectively expanded. Note that the nodes 731-739
can be displayed using different patterns or colors to represent
different performance states, such as a critical state, a warning
state, a normal state, or an unknown/off-line state. The ease of
navigation provided by selective expansion in combination with the
associated performance-state information enables a user to quickly
diagnose the root cause of a performance problem. The proactive
monitoring tree is described in further detail in U.S. patent
application Ser. No. 14/235,490 filed on 15 Apr. 2014, which is
hereby incorporated herein by reference for all possible
purposes.
[0103] The SPLUNK.RTM. APP FOR VMWARE.RTM. also provides a user
interface that enables a user to select a specific time range and
then view heterogeneous data, comprising events, log data, and
associated performance metrics, for the selected time range. For
example, the interface screen illustrated in FIG. 7D displays a
listing of recent "tasks and events" and a listing of recent "log
entries" for a selected time range above a performance-metric graph
for "average CPU core utilization" for the selected time range.
Note that a user is able to operate pull-down menus 742 to
selectively display different performance metric graphs for the
selected time range. This enables the user to correlate trends in
the performance-metric graph with corresponding event and log data
to quickly determine the root cause of a performance problem. This
user interface is described in more detail in U.S. patent
application Ser. No. 14/167,316 filed on 29 Jan. 2014, which is
hereby incorporated herein by reference for all possible
purposes.
[0104] Event Segment Search Drill Down
[0105] FIG. 8A illustrates an example of a search interface 800
displayed as a graphical user interface in accordance with the
disclosed embodiments for event segment search drill down. The
search interface 800 includes a search bar 802 that displays a
search command 804, which is "sourcetype=access_combined" in this
example. The search interface 800 also displays events 806 that are
each correlated by a date and time 808. As described previously,
the events 806 are a result set of performing the search command
804 that is currently displayed in the search bar 802, and only a
subset of the events are shown in the search interface. A user can
scroll the list of events 806 in the search interface 800 to view
additional events of the search result set that are not
displayed.
[0106] An event 810 (e.g., the first displayed event in the list of
events 806) generally includes displayed event information,
depending on a selected event view from which a user can select a
format to display some or all of the event information for each of
the events 806 in the search interface. In the example search
interface 800, the events 806 are displayed in a list view, in
which case the displayed event information for event 810 includes
event raw data 812 displayed in an upper portion of the event
display area, and includes field-value pairs 814 displayed in a
lower portion of the event display area. The field-value pairs 814
correlate to selected fields 820 that are also displayed in a
fields sidebar 818. In this example, each of the events 806 include
"host=jmiller-mbpr15.sv.splunk.com" as a field-value pair 816. The
search interface 800 includes the fields sidebar 818, which
displays the selected fields 820 that are also displayed as the
fields 816 for each of the events 806, and the fields sidebar 818
also includes other interesting fields 822.
[0107] In this example search interface 800, a user may highlight
any of the segments (e.g., terms or a combination of terms) in the
event raw data 812, such as "Mozilla/5.0" shown as the highlighted
segment 824 in the event raw data 812 of the event 810. In
implementations, the SPLUNK.RTM. ENTERPRISE system includes a
segmenter that is implemented to analyze the event raw data as a
data string and determine which of the terms or combinations of
terms are the contextually interesting segments that users (e.g.,
the data analysts) would most likely be interested in searching on
or otherwise looking into. The segmenter identifies the segments
(also referred to as terms or keywords) in the event raw data, and
when a user moves a mouse pointer or other input device over a
segment that the segmenter has identified, then the segment is
highlighted in the display of the search interface.
[0108] In implementations, a segment may be highlighted or
otherwise emphasized when a pointer that is displayed in the search
interface 800 moves over a particular segment. This feature is also
referred to as highlight with rollover (e.g., detected when a
pointer moves over a segment). For example, a user may move a mouse
pointer over the "Mozilla/5.0" segment, which is then displayed as
the highlighted segment 824. Alternatively, a user can highlight a
segment in the event raw data 812 by initiating a selection of the
segment, such as with a computer mouse, stylus, or other input
device. A highlighted segment can then be selected in response to a
user input, such as with a mouse click or touch input to select a
particular segment.
[0109] The search interface 800 also includes an event field-picker
toggle 826 that a user can select and initiate a transition to an
alternate view of the search interface for the displayed event 810,
which is shown as an event field-picker interface 828 and further
described with reference to FIG. 8B. The event-limited field picker
interface enables user selection of fields associated with
individual events to display in the view of the events in the
search interface.
[0110] FIG. 8B illustrates an example of the event field-picker
interface 828, as an alternate view of the search interface 800
described with reference to FIG. 8A. A user can transition from the
displayed view of the search interface 800 (shown in FIG. 8A) to
the displayed view of the event field-picker interface 828 (shown
in FIG. 8B) by selecting the event field-picker toggle 826 that
corresponds to the displayed event 810. In this example, the event
field-picker interface 828 includes a listing 830 of the field 832
and value 834 pairs in the event 810. The event field-picker
interface 828 is further described with reference to FIG. 10B in
accordance with the disclosed embodiments for field value search
drill down.
[0111] FIG. 8C further illustrates the example of the search
interface 800 described with reference to FIG. 8A in accordance
with the disclosed embodiments for event segment search drill down.
In this example display of the search interface 800, a user has
initiated the segment 824 being highlighted, such as with a mouse
pointer moved over the "Mozilla/5.0" segment, which is displayed as
the highlighted segment 824 in the event raw data 812.
Additionally, the user has selected the highlighted segment 824,
such as with a mouse click or touch input, and a contextual search
menu 836 is displayed responsive to the user input. In
implementations, the contextual search menu 836 is displayed
proximate the highlighted segment 824 in the search interface 800,
such as a pop-up or drop-down menu just below the highlighted
segment. Although described in the context of an event segment that
is highlighted in event raw data of a displayed event, the
techniques described herein can be implemented and applied to any
text selection, alphanumeric selection, or searched text and/or
alphanumeric string.
[0112] The contextual search menu 836 includes search options 838
that are selectable to operate on the highlighted segment 824 in
the event raw data 812 of the displayed event 810. For example, the
search options 838 displayed in the contextual search menu 836
include: an option "Add to search" 840 that a user can select to
add the highlighted segment 824 as a new keyword to the search
command 804 in the search bar 802; an option "Exclude from search"
842 that the user can select to exclude the keyword that represents
the highlighted segment 824 from searches; and an option "New
search" 844 that the user can select to create a new data search
based on the highlighted segment 824 (e.g., replacing the search
command 804 in the search bar 802 with the keyword that represents
the highlighted segment 824). A user selection of one of the search
options 838 in the contextual search menu 836 can be received, and
the search command 804 in the search bar 802 is updated based on
the search option that is selected for the highlighted segment. In
this example, the contextual search menu 836 also includes
selectable interface links 846 that are each associated with a
corresponding search option 838 in the contextual search menu. A
selectable interface link 846 for an associated search option can
be selected by a user to initiate a new search interface.
[0113] FIG. 8D further illustrates the example of the search
interface 800 described with reference to FIGS. 8A and 8C in
accordance with the disclosed embodiments for event segment search
drill down. In this example display of the search interface 800, a
user has selected the option "Add to search" 840 from the
contextual search menu 836 (shown in FIG. 8C) to add the
highlighted segment 824 as a new keyword to a data search, and
update the search command 804 in the search bar 802 to include the
keyword that represents the highlighted segment, which is shown as
"Mozilla/5.0" added to the search command 804. The search system
can then perform the data search based on the updated search
command 804 to determine the multiple events 806 that each include
the keyword that represents the highlighted segment, and display an
updated search result set of the events 806 that each include the
highlighted segment in the search interface 800. For example, each
of the displayed events 806 in the search interface 800 include the
highlighted "Mozilla/5.0" segment, as shown generally at 848. Note
that each of the displayed events 806 in the search interface 800
also include the rest of the search command 804 (e.g.,
"sourcetype=access_combined") as a field-value pair 816.
[0114] FIG. 8E further illustrates the example of the search
interface 800 described with reference to FIGS. 8A, 8C, and 8D, in
which the multiple events 806 that each include the highlighted
"Mozilla/5.0" segment 824 are displayed, as shown generally at 848.
In this example display of the search interface 800, a user has
selected the highlighted segment 824 (or any of the similar
highlighted segments 848), such as with a mouse click or touch
input, and an additional search menu 850 is displayed responsive to
the user input. In implementations, the additional search menu 850
is displayed proximate the highlighted segment 824 in the search
interface 800, such as a pop-up or drop-down menu just below the
highlighted segment.
[0115] The additional search menu 850 includes search options 852
that are selectable to operate on the highlighted segment 824 in
the event raw data 812 of the displayed event 810. For example, the
search options 852 displayed in the additional search menu 850
include: an option "Remove from search" 854 that a user can select
to remove the keyword that represents the highlighted segment 824
from the search command 804 in the search bar 802; and includes an
option "New search" 856 that the user can select to create a new
data search based on the highlighted segment 824 (e.g., replacing
the search command 804 with the keyword that represents the
highlighted segment 824). A user selection of one of the search
options 852 in the additional search menu 850 can be received, and
the search command 804 in the search bar 802 is updated based on
the search option that is selected for the highlighted segment. In
this example, the additional search menu 850 also includes
selectable interface links 858 that are each associated with a
corresponding search option 852 in the additional search menu. A
selectable interface link 858 for an associated search option can
be selected by a user to initiate a new search interface.
[0116] FIG. 8F further illustrates the example of the search
interface 800 described with reference to FIGS. 8A and 8C in
accordance with the disclosed embodiments for event segment search
drill down. In this example display of the search interface 800, a
user has selected the option "Exclude from search" 842 from the
contextual search menu 836 (shown in FIG. 8C) to exclude the
keyword that represents the highlighted segment 824 from a data
search, and update the search command 804 in the search bar 802 to
indicate that the keyword that represents the highlighted segment
is excluded, which is shown as the keywords "NOT "Mozilla/5.0""
added to the search command 804. The search system can then perform
the data search based on the updated search command 804 to
determine the multiple events 806 that do not include the
highlighted segment, and display an updated search result set of
the events 806 that do not include the highlighted segment in the
search interface 800. For example, each of the displayed events 806
in the search interface 800 do not include the highlighted
"Mozilla/5.0" segment, but still do include the rest of the search
command 804 (e.g., "sourcetype=access_combined") as a field-value
pair 816.
[0117] FIG. 8G further illustrates the example of the search
interface 800 described with reference to FIGS. 8A and 8C in
accordance with the disclosed embodiments for event segment search
drill down. In this example display of the search interface 800, a
user has selected the option "New search" 844 from the contextual
search menu 836 (shown in FIG. 8C) to create a new data search
based on the highlighted segment 824, and update the search command
804 in the search bar 802 to include only the keyword that
represents the highlighted segment, which is shown as "Mozilla/5.0"
as the keyword in the search command 804. The search system can
then perform the new data search based on the updated search
command 804 to determine the multiple events 806 that each include
the keyword that represents the highlighted segment, and display an
updated search result set of the events 806 that each include the
highlighted segment in the search interface 800. For example, each
of the displayed events 806 in the search interface 800 include the
highlighted "Mozilla/5.0" segment, as shown generally at 848.
[0118] FIG. 9A further illustrates the example of the search
interface 800 described with reference to FIG. 8A in accordance
with the disclosed embodiments for event segment search drill down.
In this example display of the search interface 800, a user has
initiated the segment 824 being highlighted, such as with a mouse
pointer moved over the "Mozilla/5.0" segment, which is displayed as
the highlighted segment 824 in the event raw data 812.
Additionally, the user has selected the highlighted segment 824,
such as with a mouse click or touch input, and a contextual search
menu 900 is displayed responsive to the user input. In
implementations, the contextual search menu 900 is displayed
proximate the highlighted segment 824 in the search interface 800,
such as a pop-up or drop-down menu just below the highlighted
segment.
[0119] The contextual search menu 900 includes search options 902
that are selectable to operate on the highlighted segment 824 in
the event raw data 812 of the displayed event 810. For example, the
search options 902 displayed in the contextual search menu 900
include: an option "Add to search" 904 that a user can select to
add the highlighted segment 824 as a new keyword to the search
command 804 in the search bar 802; an option "Exclude from search"
906 that a user can select to exclude the keyword that represents
the highlighted segment 824 from searches; an option "New search"
908 that a user can select to create a new data search based on the
highlighted segment 824 (e.g., replacing the search command 804
with the keyword that represents the highlighted segment 824); and
an option "Field Extraction" 910 that a user can select to initiate
an extract fields interface 912 shown in FIG. 9B that is usable to
define a custom event field for an event 806.
[0120] A custom event field is a field that has been extracted from
an event, such as by using a regex rule or other techniques. The
field extraction process creates a regex that is used to extract
fields from an event and the fields that are extracted are custom
event fields. An extract fields menu 916 includes an entry "Field
Name" 918 that a user can enter to name a field that is to be
extracted. The extract fields menu 916 also includes selectable
options to "Extract" 920, "Require" 922, and "Add Extraction" 924.
The user can select the option "Extract" 920 to create a field
extraction (or regex) for the text selected. For example, if an
event includes the text "foobar=baz" and the user clicks to select
the text "baz", then selects the option "Extract", enters the
"Field Name" as "foobarField", and clicks on the option "Add
Extraction", then the user has created a field extraction that has
a regex that determines whether an event has the text "foobar=[any
value]". If an event includes this text, then a field will be
created for this event with the field name "foobarField" and the
value [any value]. If the user selects the option "Require" 922,
then the selected text is not extracted, but rather, the events
that include the selected text are identified for the user while
setting up the field extraction. The option to "Add Extraction" 924
then saves the regex rule for the field extraction.
[0121] A user selection of one of the search options 902 in the
contextual search menu 900 can be received, and the search command
804 in the search bar 802 is updated based on the search option
that is selected for the highlighted segment. In this example, the
contextual search menu 900 also includes selectable interface links
926 that are each associated with a corresponding search option 902
in the contextual search menu. A selectable interface link 926 for
an associated search option can be selected by a user to initiate a
new search interface.
[0122] Field Value Search Drill Down
[0123] FIG. 10A further illustrates an example of the search
interface 800 described with reference to FIG. 8A in accordance
with the disclosed embodiments for field value search drill down.
In this example display of the search interface 800, a user has
initiated a field-value pair 1000 of a field-value pair 814 being
emphasized (e.g., highlighted), such as with a mouse pointer moved
over the "access_combined.log" value, which is displayed as the
emphasized field-value pair 1000 in the field-value pairs of the
event 810. Additionally, the user has selected the emphasized
field-value pair 1000, such as with a mouse click or touch input,
and a field value contextual menu 1002 is displayed responsive to
the user input. In implementations, the field value contextual menu
1002 is displayed proximate the emphasized field-value pair 1000 in
the search interface 800, such as a pop-up or drop-down menu just
below the emphasized field-value pair. The described
implementations of field value search drill down can also be
applied to tagged field-value pairs, where a tag 1003 identifies a
specific field-value pair 814 and any of the events 806 that have
the tagged field-value pair can be displayed with the associated
tag. A tag 1003 is an alias that designates a field-value pair, and
can be selected to add search criteria that represents the tag to
the search command 804 to search for events that have the
field-value pair designated by the tag.
[0124] The field value contextual menu 1002 includes search options
1004 that are selectable to operate on the emphasized field-value
pair 1000 in the displayed event 810. For example, the search
options 1004 displayed in the field value contextual menu 1002
include: an option "Add to search" 1006 that a user can select to
add search criteria of the emphasized field-value pair 1000 to the
search command 804 in the search bar 802; an option "Exclude from
search" 1008 that the user can select to add search criteria of the
emphasized field-value pair 1000 to the search command in the
search bar as the search criteria excluded from events that do not
include the emphasized field-value pair; and an option "New search"
1010 that the user can select to create a new data search based on
the emphasized field-value pair 1000 (e.g., replacing the search
command 804 with the search criteria of the emphasized field-value
pair 1000). A user selection of one of the search options 1004 in
the field value contextual menu 1002 can be received, and the
search command 804 in the search bar 802 is updated based on the
search option that is selected for the emphasized field-value
pair.
[0125] In this example, the field value contextual menu 1002
includes a statistical event count 1012 that is associated with the
option "Add to search" 1006, and that indicates a number of
multiple events that include the search criteria of the emphasized
field-value pair 1000 of the field-value pairs 814. The field value
contextual menu 1002 also includes a statistical event count 1014
that is associated with the option "Exclude from search" 1008, and
that indicates a number of multiple events that exclude the search
criteria of the emphasized field-value pair 1000. The field value
contextual menu 1002 also includes selectable interface links 1016
that are each associated with a corresponding search option 1004 in
the field value contextual menu. A selectable interface link 1016
for an associated search option can be selected by a user to
initiate a new search interface.
[0126] A user may select the option "Add to search" 1006 from the
field value contextual menu 1002 to add search criteria of the
emphasized field-value pair 1000 to a data search, and the search
command 804 in the search bar 802 is updated to include the search
criteria of the emphasized field-value pair (similar to the example
of the highlighted segment being added as a keyword to the search
command as shown in FIG. 8D). The search system can then perform
the data search based on the updated search command 804 to
determine the multiple events 806 that each include the search
criteria of the emphasized field-value pair, and display an updated
search result set of the events 806 that each include the
emphasized field-value pair in the search interface 800.
[0127] Similar to the example of the additional search interface
850 shown and described with reference to FIG. 8E, multiple events
may include the search criteria of the emphasized field-value pair
that has been added to the search. A selection of the emphasized
field-value pair in a displayed event initiates the additional
search menu 850, which includes the option "Remove from search" 854
that a user can select to remove the search criteria of the
emphasized field-value pair from the search command 804 in the
search bar 802; and includes the option "New search" 856 that the
user can select to create a new data search based on the search
criteria of the emphasized field-value pair (e.g., replacing the
search command 804 with the search criteria of the emphasized
field-value pair).
[0128] Alternatively, a user may select the option "Exclude from
search" 1008 from the field value contextual menu 1002 to exclude
the search criteria of the emphasized field-value pair 1000 from a
data search, and update the search command 804 in the search bar
802 to indicate that the emphasized field-value pair is excluded
(similar to the example of the highlighted segment shown following
the "NOT" operator in the search command as shown in FIG. 8F). The
search system can then perform the data search based on the updated
search command 804 to determine the multiple events 806 that do not
include the search criteria of the emphasized field-value pair, and
display an updated search result set of the events 806 that do not
include the emphasized field-value pair in the search interface
800.
[0129] Alternatively, a user may select the option "New search"
1010 from the field value contextual menu 1002 to create a new data
search based on the emphasized field-value pair 1002, and update
the search command 804 in the search bar 802 to replace the search
command in the search bar with search criteria of the emphasized
field-value pair (similar to the example of the keyword that
represents the highlighted segment added as the only search command
804 in the search bar 802 as shown in FIG. 8G). The search system
can then perform the new data search based on the updated search
command 804 to determine the multiple events 806 that each include
the search criteria of the emphasized field-value pair, and display
an updated search result set of the events 806 that each include
the emphasized field-value pair in the search interface 800.
[0130] FIG. 10B further illustrates an example of the event
field-picker interface 828 described with reference to FIG. 8B in
accordance with the disclosed embodiments for field value search
drill down. In this example display of the field-picker interface
828, a user has initiated a field-value pair 1018 of one of the
field-value pairs (832, 834) being emphasized (e.g., highlighted),
such as with a mouse pointer moved over the "splunkid_access"
value, which is displayed as the emphasized field-value pair 1018
in the listing 830 of the field 832 and value 834 pairs in the
event 810. Additionally, the user has selected the emphasized
field-value pair 1018, such as with a mouse click or touch input,
and a field value contextual menu 1020 is displayed responsive to
the user input. In implementations, the field value contextual menu
1020 is displayed proximate the emphasized field-value pair 1018 in
the event field-picker interface 828, such as a pop-up or drop-down
menu just below the emphasized field-value pair. The described
implementations of field value search drill down can also be
applied to tagged field-value pairs, where a tag identifies a
specific field-value pair (832, 834) and any of the events 806 that
have the tagged field-value pair can be displayed with the
associated tag.
[0131] The field value contextual menu 1020 includes search options
1022 that are selectable to operate on the emphasized field-value
pair 1018 in the displayed event 810. For example, the search
options 1022 displayed in the field value contextual menu 1020
include: an option "Add to search" 1024 that a user can select to
add search criteria of the emphasized field-value pair 1018 to the
search command 804 in the search bar 802; an option "Exclude from
search" 1026 that a user can select to add search criteria of the
emphasized field-value pair 1018 to the search command in the
search bar as the search criteria excluded from events that do not
include the emphasized field-value pair; and an option "New search"
1028 that a user can select to create a new data search based on
the emphasized field-value pair 1018 (e.g., replacing the search
command 804 with the search criteria of the emphasized field-value
pair 1018). A user selection of one of the search options 1022 in
the field value contextual menu 1020 can be received, and the
search command 804 in the search bar 802 is updated based on the
search option that is selected for the emphasized field-value
pair.
[0132] In this example, the field value contextual menu 1020
includes a statistical event count 1030 that is associated with the
option "Add to search" 1024, and that indicates a number of
multiple events that include the search criteria of the emphasized
field-value pair 1018. The field value contextual menu 1020 also
includes a statistical event count 1032 that is associated with the
option "Exclude from search" 1026, and that indicates a number of
multiple events that exclude the search criteria of the emphasized
field-value pair 1018. The field value contextual menu 1020 also
includes selectable interface links 1034 that are each associated
with a corresponding search option 1022 in the field value
contextual menu. A selectable interface link 1034 for an associated
search option can be selected by a user to initiate a new search
interface.
[0133] A user may select the option "Add to search" 1024 from the
field value contextual menu 1020 to add search criteria of the
emphasized field-value pair 1018 to a data search, and the search
command 804 in the search bar 802 is updated to include the search
criteria of the emphasized field-value pair (similar to the example
of the highlighted segment being added as a keyword to the search
command as shown in FIG. 8D). The search system can then perform
the data search based on the updated search command 804 to
determine additional events that each include the search criteria
of the emphasized field-value pair, and display an updated search
result set of the events that each include the emphasized
field-value pair, such as in the search interface 800 that lists
the events 806.
[0134] Alternatively, a user may select the option "Exclude from
search" 1026 from the field value contextual menu 1020 to exclude
the search criteria of the emphasized field-value pair 1018 from a
data search, and update the search command 804 in the search bar
802 to indicate that the emphasized field-value pair is excluded
(similar to the example of the highlighted segment shown following
the "NOT" operator in the search command as shown in FIG. 8F). The
search system can then perform the data search based on the updated
search command 804 to determine additional events that do not
include the search criteria of the emphasized field-value pair, and
display an updated search result set of the events that do not
include the emphasized field-value pair, such as in the search
interface 800 that lists the events 806.
[0135] Alternatively, a user may select the option "New search"
1028 from the field value contextual menu 1020 to create a new data
search based on the emphasized field-value pair 1018, and update
the search command 804 in the search bar 802 to replace the search
command in the search bar with search criteria of the emphasized
field-value pair (similar to the example of the keyword that
represents the highlighted segment added as the only search command
804 in the search bar 802 as shown in FIG. 8G). The search system
can then perform the new data search based on the updated search
command 804 to determine additional events that each include the
search criteria of the emphasized field-value pair, and display an
updated search result set of the events that each include the
emphasized field-value pair, such as in the search interface 800
that lists the events 806.
Example Methods
[0136] Example methods 1100 are described with reference to FIGS.
11A-11D in accordance with one or more embodiments of event segment
search drill down, and example methods 1200 are described with
reference to FIGS. 12A-12D in accordance with one or more
embodiments of field value search drill down. Generally, any of the
components, modules, methods, and operations described herein can
be implemented using software, firmware, hardware (e.g., fixed
logic circuitry), manual processing, or any combination thereof.
Some operations of the example methods may be described in the
general context of executable instructions stored on
computer-readable storage memory that is local and/or remote to a
computer processing system, and implementations can include
software applications, programs, functions, and the like.
Alternatively or in addition, any of the functionality described
herein can be performed, at least in part, by one or more hardware
logic components, such as, and without limitation,
Field-programmable Gate Arrays (FPGAs), Application-specific
Integrated Circuits (ASICs), Application-specific Standard Products
(ASSPs), System-on-a-chip systems (SoCs), Complex Programmable
Logic Devices (CPLDs), and the like.
[0137] Computing devices (to include server devices) can be
implemented with various components, such as a processing system
and memory, and with any number and combination of different
components as further described with reference to the example
device shown in FIG. 13. One or more computing devices can
implement the search system, in hardware and at least partially in
software, such as executable software instructions (e.g.,
computer-executable instructions) that are executable with a
processing system (e.g., one or more computer processors)
implemented by the one or more computing devices. The search system
can be stored on computer-readable, non-volatile storage memory,
such as any suitable memory device or electronic data storage
implemented by the computing devices.
[0138] FIGS. 11A-11D illustrate example method(s) 1100 of event
segment search drill down, which may be implemented by a computing
device, a distributed system of computing devices, and/or by one or
more user client devices. The order in which a method is described
is not intended to be construed as a limitation, and any number or
combination of the method operations and/or methods can be
performed in any order to implement a method, or an alternate
method.
[0139] At 1102, a segment is emphasized in event raw data of an
event that is one of multiple events returned as a search result
set displayed in a search interface. For example, the segment 824
in the event raw data 812 of the event 810 is emphasized (e.g.,
highlighted) in the search interface 800 (FIG. 8A). The segment is
emphasized when a pointer that is displayed in the search interface
800 moves over the segment, such as to highlight the segment with
rollover (e.g., detected when a pointer moves over a segment). For
example, a user may move a mouse pointer over the segment, which is
then displayed as the highlighted segment 824. Alternatively, a
user can emphasize a segment in the event raw data 812 by
initiating a selection of the segment, such as with a computer
mouse, stylus, or other input device.
[0140] At 1104, an input associated with the emphasized segment in
the event raw data is received and, at 1106, a contextual search
menu is displayed with search options that are selectable to
operate on the emphasized segment in the event raw data. For
example, the highlighted segment is selected in response to a user
input, such as with a mouse click or touch input to select the
highlighted segment. The contextual search menu 836 (FIG. 8C) is
then displayed with the search options 838 responsive to the
received user input, and the contextual search menu 836 is
displayed proximate the highlighted segment 824 in the search
interface 800. The search options 838 displayed in the contextual
search menu 836 include the option "Add to search" 840 that a user
can select to add the highlighted segment 824 as a new keyword to
the search command 804 in the search bar 802; the option "Exclude
from search" 842 that the user can select to exclude the keyword
that represents the highlighted segment 824 from searches; and an
option "New search" 844 that the user can select to create a new
data search based on the highlighted segment 824 (e.g., replacing
the search command 804 in the search bar 802 with the keyword that
represents the highlighted segment 824). Additionally, the
contextual search menu 836 of the search options 838 includes
selectable interface links 846, each associated with a
corresponding search option 838, and a selectable interface link
846 is selectable to initiate a new search interface.
[0141] At 1108, a selection of one of the search options displayed
in the contextual search menu is received and, at 1110, a search
command is updated in a search bar of the search interface based on
the search option that is selected from the contextual search menu
for the highlighted segment. For example, a user selection of one
of the search options 838 in the contextual search menu 836 is
received, and the search command 804 in the search bar 802 is
updated based on the search option that is selected for the
highlighted segment 824. These features are further described with
reference to FIGS. 11B-11D
[0142] FIG. 11B illustrates an example method of event segment
search drill down, and is generally described with reference to
adding a highlighted segment as a new keyword to a data search.
[0143] At 1112, the selection of a search option is received to add
the emphasized segment as a keyword to a data search and, at 1114,
the search command in the search bar is updated to include the
keyword that represents the emphasized segment. For example, a user
selects the option "Add to search" 840 from the contextual search
menu 836 (FIG. 8C) to add the highlighted segment 824 as a keyword
to a data search, and update the search command 804 in the search
bar 802 to include the keyword that represents the highlighted
segment, which is shown added to the search command 804 (FIG.
8D).
[0144] At 1116, the data search is performed based on the updated
search command to determine the multiple events that each include
the keyword that represents the emphasized segment. Additionally,
at 1118, an updated search result set of the multiple events that
each include the emphasized segment is displayed in the search
interface. For example, the search system performs the data search
based on the updated search command 804 to determine the multiple
events 806 that each include the keyword that represents the
highlighted segment, and an updated search result set of the events
806 that each include the highlighted segment is displayed in the
search interface 800 (FIG. 8D).
[0145] At 1120, an input is received that is associated with the
emphasized segment in the event raw data of one of the multiple
events displayed as part of the updated search result set.
Additionally, at 1122, an additional search menu of options is
displayed. For example, a user selects the highlighted segment 824
(or any of the similar highlighted segments 848), such as with a
mouse click or touch input, and an additional search menu 850 is
displayed proximate the highlighted segment 824 in the search
interface 800 responsive to the user input (FIG. 8E). The
additional search menu 850 includes search options 852 that are
selectable to operate on the highlighted segment 824 in the event
raw data 812 of the displayed event 810. For example, the search
options 852 include: an option "Remove from search" 854 that a user
can select to remove the highlighted segment 824 from a search; and
an option "New search" 856 that the user can select to create a new
data search based on the highlighted segment 824 (e.g., replacing
the search command 804 in the search bar with the keyword that
represents the highlighted segment 824).
[0146] FIG. 11C illustrates an example method of event segment
search drill down, and is generally described with reference to
excluding a highlighted segment from a data search.
[0147] At 1124, the selection of a search option is received to
exclude the keyword that represents the emphasized segment from a
data search and, at 1126, the search command in the search bar is
updated to exclude the keyword that represents the emphasized
segment. For example, a user selects the option "Exclude from
search" 842 from the contextual search menu 836 (FIG. 8C) to
exclude a keyword that represents the highlighted segment 824 from
a data search, and the search command 804 in the search bar 802 is
updated to indicate a keyword that represents the highlighted
segment is excluded, such as with a "NOT" operator (FIG. 8F).
[0148] At 1128, the data search is performed based on the updated
search command to determine the multiple events that do not include
the keyword that represents the emphasized segment. Additionally,
at 1130, an updated search result set of the multiple events that
do not include the emphasized segment is displayed. For example,
the search system performs the data search based on the updated
search command 804 to determine the multiple events 806 that do not
include the keyword that represents the highlighted segment, and an
updated search result set of the events 806 that do not include the
highlighted segment is displayed in the search interface 800 (FIG.
8F).
[0149] FIG. 11D illustrates an example method of event segment
search drill down, and is generally described with reference to
creating a new data search based on a highlighted segment.
[0150] At 1132, the selection of a search option is received to
create a new data search based on the emphasized segment and, at
1134, the search command in the search bar is updated to include
only the keyword that represents the emphasized segment. For
example, a user selects the option "New search" 844 from the
contextual search menu 836 (FIG. 8C) to create a new data search
based on the highlighted segment 824, and the search command 804 in
the search bar 802 is updated to include only the keyword that
represents the highlighted segment (FIG. 8G).
[0151] At 1136, the new data search is performed based on the
updated search command to determine the multiple events that
include the keyword that represents the emphasized segment.
Additionally, at 1138, an updated search result set of the multiple
events that include the emphasized highlighted segment is
displayed. For example, the search system performs the new data
search based on the updated search command 804 to determine the
multiple events 806 that each include the keyword that represents
the highlighted segment, and an updated search result set of the
events 806 that each include the highlighted segment is displayed
in the search interface 800 (FIG. 8G).
[0152] FIGS. 12A-12D illustrate example method(s) 1200 of field
value search drill down, which may be implemented by a computing
device, a distributed system of computing devices, and/or by one or
more user client devices. The order in which a method is described
is not intended to be construed as a limitation, and any number or
combination of the method operations and/or methods can be
performed in any order to implement a method, or an alternate
method.
[0153] At 1202, a field-value pair is emphasized in an event
displayed in a search interface. For example, the field-value pair
1000 of the field-value pairs 814 in the event 810 is emphasized
(e.g., highlighted) in the search interface 800 (FIG. 10A), such as
with a mouse pointer moved over the emphasized field-value pair
1000. For example, a user may move a mouse pointer over the
field-value pair, which is then displayed as the emphasized
field-value pair 1000. Alternatively, a user can emphasize a
field-value pair in the search interface by initiating a selection
of the field-value pair, such as with a computer mouse, stylus, or
other input device. In implementations, the search interface is the
event field-picker interface 828 (FIG. 10B) that displays a listing
830 of multiple field-value pairs of the event. The field-value
pair 1018 is emphasized responsive to detection of an input pointer
over the field-value pair.
[0154] At 1204, an input is received that is associated with the
emphasized field-value pair and, at 1206, a field value contextual
menu is displayed with search options that are selectable to
operate on the emphasized field-value pair of the event. For
example, the emphasized field-value pair is selected in response to
a user input, such as with a mouse click or touch input to select
the emphasized field-value pair. The field value contextual menu
1002 (FIG. 1 OA) is displayed with the search options 1004
proximate the emphasized field-value pair 1000 in the search
interface 800 responsive to the received input. Similarly, the
field value contextual menu 1020 (FIG. 10B) can be displayed with
the search options 1022 proximate the emphasized field-value pair
1018 in the event field-picker interface 828. The field value
contextual menu 1020 includes a first statistical event count 1012
that indicates a number of multiple events 806 that include the
search criteria of the emphasized field-value pair 1000, and
includes a second statistical event count 1014 that indicates a
number of multiple events that exclude the search criteria of the
emphasized field-value pair.
[0155] The search options displayed in the field value contextual
menu include an option "Add to search" 1006 that a user can select
to add search criteria of the emphasized field-value pair 1000 to
the search command 804 in the search bar 802; an option "Exclude
from search" 1008 that the user can select to add search criteria
of the emphasized field-value pair 1000 to the search command in
the search bar as the search criteria excluded from events that do
not include the emphasized field-value pair; and an option "New
search" 1010 that the user can select to create a new data search
based on the emphasized field-value pair 1000 (e.g., replacing the
search command 804 with the search criteria of the emphasized
field-value pair 1000). The field value contextual menu 1002 of the
search options 1004 also includes selectable interface links 1016,
each associated with a corresponding search option 1004, and a
selectable interface link 1016 is selectable to initiate a new
search interface.
[0156] At 1208, a selection of one of the search options displayed
in the field value contextual menu is received and, at 1210, a
search command in a search bar of the search interface is updated
based on the search option that is selected from the field value
contextual menu for the emphasized field-value pair. For example, a
user selection of one of the search options 1004 in the field value
contextual menu 1002 can be received, and the search command 804 in
the search bar 802 is updated based on the search option that is
selected for the emphasized field-value pair. These features are
further described with reference to FIGS. 12B-12D.
[0157] FIG. 12B illustrates an example method of field value search
drill down, and is generally described with reference to adding
search criteria of an emphasized field-value pair to a data search.
A field-value pair search returns events that have the emphasized
field-value pair, and the value of the field for an event matches
the selected value. A value for a field is an extraction from a
specific location in a event (e.g., the location defined by an
extraction rule). If a value of an emphasized field-value pair
appears in an event, but is not the value of the field for that
event because it is in a location not extracted by the extraction
rule defining the field, that event does not meet the search
criteria of the emphasized field-value pair.
[0158] At 1212, the selection of a search option is received to add
search criteria of the emphasized field-value pair to a data
search, and at 1214, the search command in the search bar is
updated to include the search criteria of the emphasized
field-value pair. For example, a user selects the option "Add to
search" 1006 from the field value contextual menu 1002 to add the
search criteria of the emphasized field-value pair 1000 to a data
search, and the search command 804 in the search bar 802 is updated
to include the search criteria of the emphasized field-value pair
(similar to the example of the highlighted segment being added as a
keyword to the search command as shown in FIG. 8D).
[0159] At 1216, the data search is performed based on the updated
search command to determine additional events that each include the
search criteria of the emphasized field-value pair. Additionally,
at 1218, the additional events that each include the search
criteria of the emphasized field-value pair are displayed in the
search interface. For example, the search system performs the data
search based on the updated search command 804 to determine the
multiple events 806 that each include the search criteria of the
emphasized field-value pair 1000, and an updated search result set
of the events 806 that each include the search criteria of the
emphasized field-value pair is displayed in the search interface
800.
[0160] FIG. 12C illustrates an example method of field value search
drill down, and is generally described with reference to adding
search criteria of an emphasized field value pair that matches
events excluding the emphasized field-value pair.
[0161] At 1220, the selection of a search option is received to
exclude the search criteria of the emphasized field-value pair from
a data search and, at 1222, the search command in the search bar is
updated to indicate that the search criteria of the emphasized
field-value pair is excluded. For example, a user selects the
option "Exclude from search" 1008 from the field value contextual
menu 1002 to exclude the search criteria of the emphasized
field-value pair 1000 from a data search, and update the search
command 804 in the search bar 802 to indicate that the search
criteria of the emphasized field-value pair is excluded (similar to
the example of the keyword that represents the highlighted segment
shown following the "NOT" operator in the search command as shown
in FIG. 8F).
[0162] At 1224, the data search is performed based on the updated
search command to determine additional events that do not include
the search criteria of the emphasized field-value pair.
Additionally, at 1226, the additional events that do not include
the search criteria of the emphasized field-value pair are
displayed. For example, the search system performs the data search
based on the updated search command 804 to determine the multiple
events 806 that do not include the search criteria of the
emphasized field-value pair 1000, and an updated search result set
of the events 806 that do not include the search criteria of the
emphasized field-value pair is displayed in the search interface
800.
[0163] FIG. 12D illustrates an example method of field value search
drill down, and is generally described with reference to creating a
new data search based on an emphasized field-value pair.
[0164] At 1228, the selection of a search option is received to
create a new data search based on the emphasized field-value pair
and, at 1230, the search command in the search bar is replaced with
the search criteria of the emphasized field-value pair. For
example, a user selects the option "New search" 1010 from the field
value contextual menu 1002 to create a new data search based on the
emphasized field-value pair 1002, and the search command 804 in the
search bar 802 is replaced with the search criteria of the
emphasized field-value pair (similar to the example of the keyword
that represents the highlighted segment replacing the search
command 804 in the search bar 802 as shown in FIG. 8G).
[0165] At 1232, the new data search is performed based on the
updated search command to determine additional events that include
the search criteria of the emphasized field-value pair.
Additionally, at 1234, the additional events that include the
search criteria of the emphasized field-value pair are displayed.
For example, the search system performs the new data search based
on the updated search command 804 to determine the multiple events
806 that each include the search criteria of the emphasized
field-value pair 1000, and an updated search result set of the
events 806 that each include the search criteria of the emphasized
field-value pair is displayed in the search interface 800.
[0166] Example System and Device
[0167] FIG. 13 illustrates an example system generally at 1300 that
includes an example computing device 1302 that is representative of
one or more computing systems and/or devices that may implement the
various techniques described herein. This is illustrated through
inclusion of the search interface module 1304 that is
representative of functionality to interact with a search service
1306, e.g., to specify and manage searches using a late-binding
schema and events as described above and thus may correspond to the
client application module 106 and system 100 of FIG. 1. The
computing device 1302 may be, for example, a server of a service
provider, a device associated with a client (e.g., a client
device), an on-chip system, and/or any other suitable computing
device or computing system.
[0168] The example computing device 1302 as illustrated includes a
processing system 1308, one or more computer-readable media 1310,
and one or more I/O interface 1312 that are communicatively
coupled, one to another. Although not shown, the computing device
1302 may further include a system bus or other data and command
transfer system that couples the various components, one to
another. A system bus can include any one or combination of
different bus structures, such as a memory bus or memory
controller, a peripheral bus, a universal serial bus, and/or a
processor or local bus that utilizes any of a variety of bus
architectures. A variety of other examples are also contemplated,
such as control and data lines.
[0169] The processing system 1308 is representative of
functionality to perform one or more operations using hardware.
Accordingly, the processing system 1308 is illustrated as including
hardware element 1314 that may be configured as processors,
functional blocks, and so forth. This may include implementation in
hardware as an application specific integrated circuit or other
logic device formed using one or more semiconductors. The hardware
elements 1314 are not limited by the materials from which they are
formed or the processing mechanisms employed therein. For example,
processors may be comprised of semiconductor(s) and/or transistors
(e.g., electronic integrated circuits (ICs)). In such a context,
processor-executable instructions may be electronically-executable
instructions.
[0170] The computer-readable storage media 1310 is illustrated as
including memory/storage 1316. The memory/storage 1316 represents
memory/storage capacity associated with one or more
computer-readable media. The memory/storage component 1316 may
include volatile media (such as random access memory (RAM)) and/or
nonvolatile media (such as read only memory (ROM), Flash memory,
optical disks, magnetic disks, and so forth). The memory/storage
component 1316 may include fixed media (e.g., RAM, ROM, a fixed
hard drive, and so on) as well as removable media (e.g., Flash
memory, a removable hard drive, an optical disc, and so forth). The
computer-readable media 1310 may be configured in a variety of
other ways as further described below.
[0171] Input/output interface(s) 1312 are representative of
functionality to allow a user to enter commands and information to
computing device 1302, and also allow information to be presented
to the user and/or other components or devices using various
input/output devices. Examples of input devices include a keyboard,
a cursor control device (e.g., a mouse), a microphone, a scanner,
touch functionality (e.g., capacitive or other sensors that are
configured to detect physical touch), a camera (e.g., which may
employ visible or non-visible wavelengths such as infrared
frequencies to recognize movement as gestures that do not involve
touch), and so forth. Examples of output devices include a display
device (e.g., a monitor or projector), speakers, a printer, a
network card, tactile-response device, and so forth. Thus, the
computing device 1302 may be configured in a variety of ways as
further described below to support user interaction.
[0172] Various techniques may be described herein in the general
context of software, hardware elements, or program modules.
Generally, such modules include routines, programs, objects,
elements, components, data structures, and so forth that perform
particular tasks or implement particular abstract data types. The
terms "module," "functionality," and "component" as used herein
generally represent software, firmware, hardware, or a combination
thereof. The features of the techniques described herein are
platform-independent, meaning that the techniques may be
implemented on a variety of commercial computing platforms having a
variety of processors.
[0173] An implementation of the described modules and techniques
may be stored on or transmitted across some form of
computer-readable media. The computer-readable media may include a
variety of media that may be accessed by the computing device 1302.
By way of example, and not limitation, computer-readable media may
include "computer-readable storage media" and "computer-readable
signal media."
[0174] "Computer-readable storage media" may refer to media and/or
devices that enable persistent and/or non-transitory storage of
information in contrast to mere signal transmission, carrier waves,
or signals per se. Thus, computer-readable storage media refers to
non-signal bearing media. The computer-readable storage media
includes hardware such as volatile and non-volatile, removable and
non-removable media and/or storage devices implemented in a method
or technology suitable for storage of information such as computer
readable instructions, data structures, program modules, logic
elements/circuits, or other data. Examples of computer-readable
storage media may include, but are not limited to, RAM, ROM,
EEPROM, flash memory or other memory technology, CD-ROM, digital
versatile disks (DVD) or other optical storage, hard disks,
magnetic cassettes, magnetic tape, magnetic disk storage or other
magnetic storage devices, or other storage device, tangible media,
or article of manufacture suitable to store the desired information
and which may be accessed by a computer.
[0175] "Computer-readable signal media" may refer to a
signal-bearing medium that is configured to transmit instructions
to the hardware of the computing device 1302, such as via a
network. Signal media typically may embody computer readable
instructions, data structures, program modules, or other data in a
modulated data signal, such as carrier waves, data signals, or
other transport mechanism. Signal media also include any
information delivery media. The term "modulated data signal" means
a signal that has one or more of its characteristics set or changed
in such a manner as to encode information in the signal. By way of
example, and not limitation, communication media include wired
media such as a wired network or direct-wired connection, and
wireless media such as acoustic, RF, infrared, and other wireless
media.
[0176] As previously described, hardware elements 1314 and
computer-readable media 1310 are representative of modules,
programmable device logic and/or fixed device logic implemented in
a hardware form that may be employed in some embodiments to
implement at least some aspects of the techniques described herein,
such as to perform one or more instructions. Hardware may include
components of an integrated circuit or on-chip system, an
application-specific integrated circuit (ASIC), a
field-programmable gate array (FPGA), a complex programmable logic
device (CPLD), and other implementations in silicon or other
hardware. In this context, hardware may operate as a processing
device that performs program tasks defined by instructions and/or
logic embodied by the hardware as well as a hardware utilized to
store instructions for execution, e.g., the computer-readable
storage media described previously.
[0177] Combinations of the foregoing may also be employed to
implement various techniques described herein. Accordingly,
software, hardware, or executable modules may be implemented as one
or more instructions and/or logic embodied on some form of
computer-readable storage media and/or by one or more hardware
elements 1314. The computing device 1302 may be configured to
implement particular instructions and/or functions corresponding to
the software and/or hardware modules. Accordingly, implementation
of a module that is executable by the computing device 1302 as
software may be achieved at least partially in hardware, e.g.,
through use of computer-readable storage media and/or hardware
elements 1314 of the processing system 1308. The instructions
and/or functions may be executable/operable by one or more articles
of manufacture (for example, one or more computing devices 1302
and/or processing systems 1308) to implement techniques, modules,
and examples described herein.
[0178] The techniques described herein may be supported by various
configurations of the computing device 1302 and are not limited to
the specific examples of the techniques described herein. This
functionality may also be implemented all or in part through use of
a distributed system, such as over a "cloud" 1318 via a platform
1320 as described below.
[0179] The cloud 1318 includes and/or is representative of a
platform 1320 for resources 1322. The platform 1320 abstracts
underlying functionality of hardware (e.g., servers) and software
resources of the cloud 1318. The resources 1322 may include
applications and/or data that can be utilized while computer
processing is executed on servers that are remote from the
computing device 1302. Resources 1322 can also include services
provided over the Internet and/or through a subscriber network,
such as a cellular or Wi-Fi network.
[0180] The platform 1320 may abstract resources and functions to
connect the computing device 1302 with other computing devices. The
platform 1320 may also serve to abstract scaling of resources to
provide a corresponding level of scale to encountered demand for
the resources 1322 that are implemented via the platform 1320.
Accordingly, in an interconnected device embodiment, implementation
of functionality described herein may be distributed throughout the
system 1300. For example, the functionality may be implemented in
part on the computing device 1302 as well as via the platform 1320
that abstracts the functionality of the cloud 1318.
[0181] Although embodiments of event segment search drill down have
been described in language specific to features and/or methods, the
appended claims are not necessarily limited to the specific
features or methods described. Rather, the specific features and
methods are disclosed as example implementations of event segment
search drill down, and other equivalent features and methods are
intended to be within the scope of the appended claims. Further,
various different embodiments are described and it is to be
appreciated that each described embodiment can be implemented
independently or in connection with one or more other described
embodiments.
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