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JellyFisher is a Splunk custom search command that leverage the excellent jellyfish python's library to do approximate and phonetic strings matching.

String comparison algorithms:
- Levenshtein Distance
- Damerau-Levenshtein Distance
- Jaro Distance
- Jaro-Winkler Distance
- Match Rating Approach Comparison
- Hamming Distance

Phonetic encoding algortihms:
- American Soundex
- Metaphone
- NYSIIS (New York State Identification and Intelligence System)
- Match Rating Codex

- The Levenshtein distance between "kitten" and "sitting" is 3.
- The soundex representation of both "Robert" and "Rupert" is "R163"


... | jellyfisher algorithm_name(fields_to_read_words_from)

Results will be written into a new field of the name of the chosen algorithm (ex: levensthein_distance)


... | jellyfisher levensthein_distance(sourcetype,source)
... | jellyfisher jaro_distance(user,sourcetype)
... | jellyfisher soundex(sourcetype)

Algorithms list

distance alogrithms:

  • levenshtein_distance
  • damerau_levenshtein_distance
  • jaro_distance
  • jaro_winkler
  • match_rating_comparison
  • hamming_distance

phonetic alogrithms:

  • soundex
  • nysiis
  • match_rating_codex
  • metaphone
  • porter_stem


The following search is given as demonstration purpose to load a bunch of words (process names) from a CSV lookup file and then compute their distances using the map() command. This is not the perfect implementation, but a good example to start from :)

| inputlookup processNames.csv  
| map search="search index=windows | head 10 | stats count by process | eval pName = $processName$ | jellyfisher levenshtein_distance($processName$, process)" 
| table process, pName, levenshtein_distance

Release Notes

Version 1.0.1
Sept. 6, 2021
  • Restructure to use Splunk SDK
  • Python 3 Compatibility
Version 0.1.0
June 21, 2017

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