Splunk Fundamentals 2 Flashcards

1
Q

Knowledge Objects

A

Tools to discover and analyse various aspects of the data

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2
Q

Some functions of knowledge objects

A
  1. Data interpretation: fields and field extraction
  2. Data classification: event types
  3. Data enrichment: lookups and workflow action
  4. Normalization: tags and field aliases
  5. Datasets: data models
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3
Q

Characteristics of Knowledge Objects

A
  1. Shareable
  2. Reusable (i.e. macros and reports can be shared by ppl, apps)
  3. Searchable (persistent => can be used in search)
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4
Q

Naming Convention for Knowledge Objects

A

Group_ObjectType_Description

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5
Q

Common Information Model (CIM)

A
  1. methodology for normalizing data
  2. easily correlate data from different sources and source types
  3. leverage to create various objects discussed in this course - field extractions, field aliases, event types, tags
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6
Q

Fields stored prior to search time

A
  1. meta fields: host, source, sourcetype

2. internal fields: _raw, _time

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7
Q

Field Auto-Extraction

A
  1. Splunk automatically discovers many fields based on source type and key/value pairs found in the data
  2. At search time, field discovery discovers fields directly related to the search’s results
  3. Splunk may discover other fields not directly related to search
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8
Q

Use field extractor (FX) to extract fields that are:

A
  1. static
  2. often used in searches:
    • Graphical UI
    • Extract fields from events using reges or delimiter
    • Extracted fields persist as knowledge objects
    • Can be shared and re-used in multiple searches
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9
Q

Field Extraction Methods

A
  1. Regex

2. Delimiter

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10
Q

Field Aliases and Original Fields

A
  1. original field is not affected by creating an alias

2. both fields appear in the All Fields and Interesting Fields lists (if appear in more than 20% of events)

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11
Q

Search Case Sensitivity: Sensitive

A
  1. boolean operators
  2. field names
  3. field values from lookup (default, configurable)
  4. regular expressions
  5. eval and where commands
  6. Tags
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12
Q

Search Case Sensitivity: Insensible

A
  1. command names (Stats, stats)
  2. command clauses (AS vs as)
  3. search terms
  4. statistical functions
  5. field values (unless from lookup)
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13
Q

Splunk Search Buckets

A
  1. index’s hot bucket = only writable bucket
  2. each bucket has own raw data, metadata, index files
  3. admins can add more
  4. search is done through time and indexes
  5. buckets transform from hot (now -8h) to warm (-9h to -48h) and cold state
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14
Q

General search practices

A
  1. time is the most efficient filter
  2. most powerful keywords are host, source and source type
  3. use fields commands to extract (discover) only fields you need
  4. only trailing wildcards make efficient use of index
    • wildcards are tested after all other terms
  5. inclusion is better than exclusion
  6. filter as early as possible (i.e. remove duplicates)
  7. use appropriate search mode
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15
Q

Transforming commands

A
  1. it is required to ‘transform’ search results into visualisations
  2. massages raw data into a data table
  3. examples of transforming commands:
    • top
    • rare
    • chart
    • timechart
    • stats
    • geostats
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16
Q

Fast search mode

A
  1. returns only essential and required data
  2. content of interesting fields sidebar are lost
  3. for non-transforming searches:
    + events, patterns
    • statistics, visualisations
  4. for transforming searches
    • events, patterns
      + statistics, visualisations
17
Q

Smart search mode

A
  1. default
  2. combines fast and verbose modes
  3. for non-transforming searches (verbose)
    + events, patterns
    • statistics, visualisations
  4. for transforming searches
    • events, patterns
      + statistics or visualisations
18
Q

Verbose search mode

A
  1. emphasises completeness by returning all possible fields and events data
  2. much slower than other modes
  3. for non-transforming searches
    + events (fields sidebar displays all fields), patterns
    • statistics, visualisation
  4. for transforming searches
    + events, patterns, statistics, visualisations
19
Q

Types of Searches

A
  1. Dense (CPU bound)
  2. Sparse (CPU bound)
  3. Super Sparse (I/O bound)
    • a small number of results from each index bucket matching the search
  4. Rare (I/O bound)
    • bloom filter eliminates the buckets that do not include search results
20
Q

Search Job Inspector allows to examine:

A
  1. overall search statistics
  2. how search was processed
  3. wheres plunk spent its time

=> troubleshoot issues, see impact of knowledge objects on performance

21
Q

Search Job Inspector Components

A
  1. Header (basic info: time to run, #events scanned)
  2. Execution costs
  3. Search job properties
22
Q

Search Job Inspector Components

A
  1. Header (basic info: time to run, #events scanned)
  2. Execution costs
  3. Search job properties