Transformations

A curated guide to Pinot transformation functions used in queries and ingestion configs.

This page groups the transformation functions that show up most often in Pinot ingestion configs and SELECT expressions. It is a navigation guide, not a full function reference: use the linked category pages for complete signatures and examples.

The most important split is whether the function returns a typed value that can be used directly in query output, or whether it is mainly intended for ingestion-time shaping. The typed JSON helpers are especially useful when you need a stable column type in SQL.

Core families

Family	Common functions	Best fit	Notes
Math	ADD, SUB, MULT, DIV, MOD, ABS, CEIL, FLOOR, EXP, LN, SQRT, ROUND	Query projections and ingestion transforms	Use these when you need deterministic numeric shaping before aggregation or filtering.
String	UPPER, LOWER, SUBSTR, CONCAT, TRIM, LTRIM, RTRIM, REGEXPEXTRACT, REGEXPREPLACE, REPLACE, STARTSWITH	Query projections and ingestion transforms	Strong fit for normalization, tokenization, and cleanup.
Date/time	DATETRUNC, FROMDATETIME, TODATETIME, TOEPOCH*, FROMEPOCH*, DATETIMECONVERT	Query projections and ingestion transforms	Use these to align timestamps to consistent buckets and storage formats.
JSON	JSONEXTRACTSCALAR, JSONPATHSTRING, JSONPATHLONG, JSONPATHDOUBLE, JSONFORMAT, TOJSONMAPSTR	Typed query output and ingestion configs	Prefer the typed helpers when the output column needs a stable SQL type.
Binary	SHA, SHA256, SHA512, MD5, TOBASE64, UTF8	Query projections and ingestion transforms	Useful for fingerprinting and encoding.
Array and multi-value	ARRAYLENGTH, VALUEIN, MAP_VALUE, array helpers	Query projections and ingestion transforms	Use these when a source column contains arrays or maps.
Utility	EXTRACT, ISJSON, ISSUBNETOF	Query projections and filtering	These helpers tend to sit at the boundary between shape cleanup and query logic.

JSON helpers worth calling out

Pinot supports both generic and typed JSON access. The typed variants are the safer choice for query output because they keep the result schema explicit.

• Use JSONPATHSTRING, JSONPATHLONG, and JSONPATHDOUBLE when the result type must be fixed.

• Use JSONPATH and JSONPATHARRAY when you are shaping ingestion data and the downstream schema can handle the inferred type.

• Use JSONFORMAT and TOJSONMAPSTR when you need to serialize an object or map back to JSON text.

Multi-value patterns

The most common multi-value patterns are simple normalization and membership checks.

• ARRAYLENGTH gives you the size of a multi-value column.

• VALUEIN filters a multi-value column against a fixed set of values.

• MAP_VALUE extracts a value from a stored map when you know the key.

When to use this guide

Use this page when you are:

• Designing ingestion transforms.

• Normalizing strings or timestamps before storing data.

• Converting semi-structured data into stable typed columns.

• Checking whether a function belongs in a projection, a filter, or an ingestion rule.

What this page covered

• The transformation families that matter most in Pinot docs and ingestion configs.

• Which JSON helpers are safe when you need typed query output.

• Which multi-value helpers are most useful for shaping data before it reaches a table.

Next step

• Open the relevant family page for the exact syntax and examples, or move to the SSE vs MSE guide if the query shape also depends on the execution engine.

Related pages

• Functions

• SSE vs MSE

• Supported Transformations