spl-to-apl

Translates Splunk SPL queries to Axiom APL. Provides command mappings, function equivalents, and syntax transformations. Use when migrating from Splunk, converting SPL queries, or learning APL equivalents of SPL patterns.

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Install skill "spl-to-apl" with this command: npx skills add axiomhq/skills/axiomhq-skills-spl-to-apl

SPL to APL Translator

Type safety: Fields like status are often stored as strings. Always cast before numeric comparison: toint(status) >= 500, not status >= 500.


Critical Differences

  1. Time is explicit in APL: SPL time pickers don't translate — add where _time between (ago(1h) .. now())
  2. Structure: SPL index=... | command → APL ['dataset'] | operator
  3. Join is preview: limited to 50k rows, inner/innerunique/leftouter only
  4. cidrmatch args reversed: SPL cidrmatch(cidr, ip) → APL ipv4_is_in_range(ip, cidr)

Core Command Mappings

SPLAPLNotes
search index=...['dataset']Dataset replaces index
search field=valuewhere field == "value"Explicit where
wherewhereSame
statssummarizeDifferent aggregation syntax
evalextendCreate/modify fields
table / fieldsprojectSelect columns
fields -project-awayRemove columns
rename x as yproject-rename y = xRename
sort / sort -order by ... asc/descSort
head Ntake NLimit rows
top N fieldsummarize count() by field | top N by count_Two-step
dedup fieldsummarize arg_max(_time, *) by fieldKeep latest
rexparse or extract()Regex extraction
joinjoinPreview feature
appendunionCombine datasets
mvexpandmv-expandExpand arrays
timechart span=Xsummarize ... by bin(_time, X)Manual binning
rare N fieldsummarize count() by field | order by count_ asc | take NBottom N
spathparse_json() or json['path']JSON access
transactionNo direct equivalentUse summarize + make_list

Complete mappings: reference/command-mapping.md


Stats → Summarize

# SPL
| stats count by status

# APL  
| summarize count() by status

Key function mappings

SPLAPL
countcount()
count(field)countif(isnotnull(field))
dc(field)dcount(field)
avg/sum/min/maxSame
median(field)percentile(field, 50)
perc95(field)percentile(field, 95)
first/lastarg_min/arg_max(_time, field)
list(field)make_list(field)
values(field)make_set(field)

Conditional count pattern

# SPL
| stats count(eval(status>=500)) as errors by host

# APL
| summarize errors = countif(status >= 500) by host

Complete function list: reference/function-mapping.md


Eval → Extend

# SPL
| eval new_field = old_field * 2

# APL
| extend new_field = old_field * 2

Key function mappings

SPLAPLNotes
if(c, t, f)iff(c, t, f)Double 'f'
case(c1,v1,...)case(c1,v1,...,default)Requires default
len(str)strlen(str)
lower/uppertolower/toupper
substrsubstring0-indexed in APL
replacereplace_string
tonumbertoint/tolong/torealExplicit types
match(s,r)s matches regex "r"Operator
split(s, d)split(s, d)Same
mvjoin(mv, d)strcat_array(arr, d)Join array
mvcount(mv)array_length(arr)Array length

Case statement pattern

# SPL
| eval level = case(
    status >= 500, "error",
    status >= 400, "warning",
    1==1, "ok"
  )

# APL  
| extend level = case(
    status >= 500, "error",
    status >= 400, "warning",
    "ok"
  )

Note: SPL's 1==1 catch-all becomes implicit default in APL.


Rex → Parse/Extract

# SPL
| rex field=message "user=(?<username>\w+)"

# APL - parse with regex
| parse kind=regex message with @"user=(?P<username>\w+)"

# APL - extract function  
| extend username = extract("user=(\\w+)", 1, message)

Simple pattern (non-regex)

# SPL
| rex field=uri "^/api/(?<version>v\d+)/(?<endpoint>\w+)"

# APL
| parse uri with "/api/" version "/" endpoint

Time Handling

SPL time pickers don't translate. Always add explicit time range:

# SPL (time picker: Last 24 hours)
index=logs

# APL
['logs'] | where _time between (ago(24h) .. now())

Timechart translation

# SPL
| timechart span=5m count by status

# APL
| summarize count() by bin(_time, 5m), status

Common Patterns

Error rate calculation

# SPL
| stats count(eval(status>=500)) as errors, count as total by host
| eval error_rate = errors/total*100

# APL
| summarize errors = countif(status >= 500), total = count() by host
| extend error_rate = toreal(errors) / total * 100

Subquery (subsearch)

# SPL
index=logs [search index=errors | fields user_id | format]

# APL
let error_users = ['errors'] | where _time between (ago(1h) .. now()) | distinct user_id;
['logs']
| where _time between (ago(1h) .. now())
| where user_id in (error_users)

Join datasets

# SPL
| join user_id [search index=users | fields user_id, name]

# APL
| join kind=inner (['users'] | project user_id, name) on user_id

Transaction-like grouping

# SPL
| transaction session_id maxspan=30m

# APL (no direct equivalent — reconstruct with summarize)
| summarize 
    start_time = min(_time),
    end_time = max(_time),
    events = make_list(pack("time", _time, "action", action)),
    duration = max(_time) - min(_time)
  by session_id
| where duration <= 30m

String Matching Performance

SPLAPLSpeed
field="value"field == "value"Fastest
field="*value*"field contains "value"Moderate
field="value*"field startswith "value"Fast
match(field, regex)field matches regex "..."Slowest

Prefer has over contains (word-boundary matching is faster). Use _cs variants for case-sensitive (faster).


Reference

  • reference/command-mapping.md — complete command list
  • reference/function-mapping.md — complete function list
  • reference/examples.md — full query translation examples
  • APL docs: https://axiom.co/docs/apl/introduction

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