> For the complete documentation index, see [llms.txt](https://docs.pinot.apache.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pinot.apache.org/release-1.2.0/for-users/user-guide-query/multi-stage-query/operator-types/transform.md). # Transform The transform operator is used to apply a transformation to the input data. They may filter out columns or add new ones by applying functions to the existing columns. This operator is generated by the multi-stage query engine when you use a `SELECT` clause in a query, but can also be used to implement other transformations. ## Implementation details Transform operators apply some transformation functions to the input data received from upstream. The cost of the transformation usually depends on the complexity of the functions applied, but comparing to other operators, it is usually not very high. ### Blocking nature The transform operator is a streaming operator. It emits the blocks of rows as soon as they are received from the upstream operator. ## Hints None ## Stats ### executionTimeMs Type: Long The summation of time spent by all threads executing the operator. This means that the wall time spent in the operation may be smaller that this value if the parallelism is larger than 1. ### emittedRows Type: Long The number of groups emitted by the operator. ## Explain attributes The transform operator is represented in the explain plan as a `LogicalProject` explain node. This explain node has a list of attributes that represent the transformations applied to the input data. Each attribute has a name and a value, which is the expression used to generate the column. For example: ``` LogicalProject(userUUID=[$6], deviceOS=[$4], EXPR$2=[SUBSTRING($4, 0, 2)]) LogicalTableScan(table=[[default, userAttributes]]) ``` Is saying that the output of the operator has three columns: * `userUUID` is the 7th column in the virtual row projected by LogicalTableScan, which corresponds to the `userUUID` column in the table. * `deviceOS` is the 5th column in the virtual row projected by LogicalTableScan, which corresponds to the `deviceOS` column in the table. * `EXPR$2` is the result of the `SUBSTRING($4, 0, 2)` expression applied to the 5th column in the virtual row projected by LogicalTableScan. Given we know that the 5th column is `deviceOS`, we can infer that `EXPR$2` is the first two characters of the `deviceOS` column. ## Tips and tricks None --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.pinot.apache.org/release-1.2.0/for-users/user-guide-query/multi-stage-query/operator-types/transform.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.