Splunk to Axiom Processing Language (APL) Conversion Reference

Splunk and Axiom data Explorer are powerful tools for log analysis and data exploration. The data explorer interface uses Axiom Processing Language (APL). There are some differeces between the query languages for Splunk and Axiom. When transitioning from Splunk to APL, you will need to understand how to convert your Splunk SPL queries into APL.

This guide provides a high-level mapping from Splunk to APL.

Basic Searching

Splunk uses a search command for basic searching, while in APL, simply specify the dataset name followed by a filter.

Splunk:

search index="myIndex" error

APL:

['myDatasaet']
| where FieldName contains “error”

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Filtering

In Splunk, perform filtering using the search command, usually specifying field names and their desired values. In APL, perform filtering by using the where operator.

Splunk:

Search index=”myIndex” error 
| stats count

APL:

['myDataset']
| where fieldName contains “error”
| count

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Aggregation

In Splunk, the stats command is used for aggregation. In APL, perform aggregation using the summarize operator.

Splunk:

search index="myIndex" 
| stats count by status

APL:

['myDataset'] 
| summarize count() by status

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Time Frames

In Splunk, select a time range for a search in the time picker on the search page. In APL, filter by a time range using the where operator and the timespan field of the dataset.

Splunk:

search index="myIndex" earliest=-1d@d latest=now

APL:

['myDataset']
| where _time >= ago(1d) and _time <= now()

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Sorting

In Splunk, the sort command is used to order the results of a search. In APL, perform sorting by using the sort by operator.

Splunk:

search index="myIndex" 
| sort - content_type

APL:

['myDataset'] 
| sort by countent_type desc

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Selecting Fields

In Splunk, use the fields command to specify which fields to include or exclude in the search results. In APL, use the project operator, project-away operator, or the project-keep operator to specify which fields to include in the query results.

Splunk:

index=main sourcetype=mySourceType
| fields status, responseTime

APL:

['myDataset'] 
| extend newName = oldName
| project-away oldName

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Renaming Fields

In Splunk, rename fields using the rename command, while in APL rename fields using the extend, and project operator. Here is the general syntax:

Splunk:

index="myIndex" sourcetype="mySourceType"
| rename oldFieldName AS newFieldName

APL:

['myDataset']
| where method == "GET"
| extend new_field_name = content_type
| project-away content_type

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Calculated Fields

In Splunk, use the eval command to create calculated fields based on the values of other fields, while in APL use the extend operator to create calculated fields based on the values of other fields.

Splunk

search index="myIndex" 
| eval newField=field1+field2

APL:

['myDataset'] 
| extend newField = field1 + field2

Run in Playground↗

Structure and Concepts

The following table compares concepts and data structures between Splunk and APL logs.

Concept	Splunk	APL	Comment
data caches	buckets	caching and retention policies	Controls the period and caching level for the data.This setting directly affects the performance of queries.
logical partition of data	index	dataset	Allows logical separation of the data.
structured event metadata	N/A	dataset	Splunk doesn’t expose the concept of metadata to the search language. APL logs have the concept of a dataset, which has fields and columns. Each event instance is mapped to a row.
data record	event	row	Terminology change only.
types	datatype	datatype	APL data types are more explicit because they are set on the fields. Both have the ability to work dynamically with data types and roughly equivalent sets of data types.
query and search	search	query	Concepts essentially are the same between APL and Splunk

Functions

The following table specifies functions in APL that are equivalent to Splunk Functions.

Splunk	APL
strcat	strcat()
split	split()
if	iff()
tonumber	todouble(), tolong(), toint()
upper, lower	toupper(), tolower()
replace	replace_string() or replace_regex()
substr	substring()
tolower	tolower()
toupper	toupper()
match	matches regex
regex	matches regex (in splunk, regex is an operator. In APL, it’s a relational operator.)
searchmatch	== (In splunk, `searchmatch` allows searching the exact string.)
random	rand(), rand(n) (Splunk’s function returns a number between zero to 231 -1. APL returns a number between 0.0 and 1.0, or if a parameter is provided, between 0 and n-1.)
now	now()

In Splunk, the function is invoked by using the eval operator. In APL, it’s used as part of the extend or project.

In Splunk, the function is invoked by using the eval operator. In APL, it can be used with the where operator.

Filter

APL log queries start from a tabular result set in which a filter is applied. In Splunk, filtering is the default operation on the current index. You may also use the where operator in Splunk, but we don't recommend it.

Product	Operator	Example
Splunk	search	Sample.Logs="330009.2" method="GET" _indextime>-24h
APL	where	['sample-http-logs'] \| where method == "GET" and _time > ago(24h)

Get n events or rows for inspection

APL log queries also support take as an alias to limit. In Splunk, if the results are ordered, head returns the first n results. In APL, limit isn’t ordered, but it returns the first n rows that are found.

Product	Operator	Example
Splunk	head	Sample.Logs=330009.2 \| head 100
APL	limit	['sample-htto-logs'] \| limit 100

Get the first n events or rows ordered by a field or column

For the bottom results, in Splunk, use tail. In APL, specify ordering direction by using asc.

Product	Operator	Example
Splunk	head	Sample.Logs="33009.2" \| sort Event.Sequence \| head 20
APL	top	['sample-http-logs'] \| top 20 by method

Extend the result set with new fields or columns

Splunk has an eval function, but it's not comparable to the eval operator in APL. Both the eval operator in Splunk and the extend operator in APL support only scalar functions and arithmetic operators.

Product	Operator	Example
Splunk	eval	Sample.Logs=330009.2 \| eval state= if(Data.Exception = "0", "success", "error")
APL	extend	['sample-http-logs'] \| extend Grade = iff(req_duration_ms >= 80, "A", "B")

Rename

APL uses the project operator to rename a field. In the project operator, a query can take advantage of any indexes that are prebuilt for a field. Splunk has a rename operator that does the same.

Product	Operator	Example
Splunk	rename	Sample.Logs=330009.2 \| rename Date.Exception as execption
APL	project	['sample-http-logs'] \| project updated_status = status

Format results and projection

Splunk uses the table command to select which columns to include in the results. APL has a project operator that does the same and more.

Product	Operator	Example
Splunk	table	Event.Rule=330009.2 \| table rule, state
APL	project	['sample-http-logs'] \| project status, method

Splunk uses the field - command to select which columns to exclude from the results. APL has a project-away operator that does the same.

Product	Operator	Example
Splunk	fields -	Sample.Logs=330009.2` \| fields - quota, hightest_seller
APL	project-away	['sample-http-logs'] \| project-away method, status

Aggregation

See the list of summarize aggregations functions that are available.

Splunk operator	Splunk example	APL operator	APL example
stats	search (Rule=120502.*) \| stats count by OSEnv, Audience	summarize	['sample-http-logs'] \| summarize count() by content_type, status

Sort

In Splunk, to sort in ascending order, you must use the reverse operator. APL also supports defining where to put nulls, either at the beginning or at the end.

Product	Operator	Example
Splunk	sort	Sample.logs=120103 \| sort Data.Hresult \| reverse
APL	order by	['sample-http-logs'] \| order by status desc

Whether you're just starting your transition or you're in the thick of it, this guide can serve as a helpful roadmap to assist you in your journey from Splunk to Axiom Processing Language.

Dive into the Axiom Processing Language, start converting your Splunk queries to APL, and explore the rich capabilities of Axiom Data Explorer. Embrace the learning curve, and remember, every complex query you master is another step forward in your data analytics journey.

Good luck and happy data exploring!

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