@Rocky with starrocks 4.0 and python udf, does python need to be installed on both fe and be or just the be nodes? Does a python udf code change require redeploying either fe or be?

Hello! I'm Rocky. Regarding your questions about StarRocks 4.0 and Python UDFs, here are the details: 1. Does Python need to be installed on both FE and BE or just the BE nodes? Python needs to be installed on the BE (Backend) nodes only. * BE Nodes: These nodes are responsible for the actual execution of the UDF. You must set the

python_envs

configuration in your

be.conf

to point to the location of the Python interpreter (e.g.,

/opt/Python-3.8/

) on each BE node. * FE Nodes: You do not need to install Python on the FE nodes. However, you must enable the UDF feature globally by setting

enable_udf = true

in the

fe.conf

file. 2. Does a Python UDF code change require redeploying either FE or BE? No, a code change to the UDF logic itself does not require redeploying or restarting the FE or BE nodes. * Initial Setup: You only need to restart the FE and BE nodes once to apply the initial configuration changes (

enable_udf

on FE and

python_envs

on BE). * Code Updates: To update the logic of an existing UDF, you typically use the following SQL workflow: 1.

DROP FUNCTION <function_name>(<arg_types>);

2.

CREATE FUNCTION ...

with the updated code (either as an inline code block or a link to a new packaged

.py.zip

file). * Packaged UDFs: If you are using external files, StarRocks automatically downloads the updated package to the BE nodes when the function is called or created. In StarRocks 4.0, a new BE parameter

clear_udf_cache_when_start

was introduced to help manage the local cache of these files upon startup, but it is not required for standard code updates. Summary of Requirements: * Python Version: 3.8 or later. * Dependencies: The

pyarrow

package must be installed in the Python environment on the BE nodes. * Configuration: * FE:

enable_udf = true

in

fe.conf

. * BE:

python_envs = <path_to_python_home>

in

be.conf

. If you have more questions about setting up your Python environment for StarRocks, feel free to ask! References • sql-reference/sql-functions/Python_UDF.md • administration/management/BE_configuration.md

@Rocky how does this differ from java udf?

In StarRocks, the term "this" most likely refers to the recently introduced Python UDF (available as an experimental feature since v3.4.0). Here is a comparison between Python UDFs and Java UDFs: 1. Language and Ecosystem * Java UDF: Uses the Java programming language. It is well-suited for high-performance logic and integrates with the JVM ecosystem. It has been available since v2.2.0. * Python UDF: Uses Python 3.8+. It is ideal for data science, machine learning, and rapid prototyping using Python libraries (like

numpy

,

pandas

, etc.). 2. Development Experience * Java UDF: Requires creating a Maven project, compiling a JAR file, and hosting that JAR on an HTTP server accessible by the StarRocks cluster. * Python UDF: Supports inline code using the

AS $$ ... $$

syntax, allowing you to write logic directly in your SQL statement. It also supports packaged

.py.zip

files for more complex dependencies. 3. Function Types * Java UDF: Supports a wide range of types: * Scalar UDFs (one row in, one row out) * UDAFs (User-defined Aggregate Functions) * UDWFs (User-defined Window Functions) * UDTFs (User-defined Table Functions) * Python UDF: Currently only supports Scalar UDFs. 4. Performance and Data Processing * Java UDF: Operates primarily on a row-by-row basis (scalar). * Python UDF: Supports Vectorized Input via Apache Arrow. By setting

input = "arrow"

, StarRocks can pass batches of data to the Python interpreter, which significantly improves performance compared to row-by-row processing. 5. Deployment and Configuration * Java UDF: Relies on the JVM. You usually only need to configure the JVM heap size (

JAVA_OPTS

in

be.conf

). * Python UDF: Requires a Python environment on the BE nodes. You must set the

python_envs

configuration in

be.conf

and ensure

pyarrow

is installed in that environment. Summary Table Feature | Java UDF | Python UDF Introduced | v2.2.0 | v3.4.0 (Experimental) Supported Types | Scalar, UDAF, UDWF, UDTF | Scalar only Code Definition | External JAR file | Inline (

AS $$

) or

.py.zip

Data Format | Native Java types | Scalar or Arrow (Vectorized) Ease of Use | Moderate (requires build/deploy) | High (inline scripts) Primary Use Case | Complex logic, high perf | Data Science, ML, rapid dev If you were referring to Remote UDFs (External UDFs), the main difference is that Remote UDFs run on a separate server (accessed via RPC/HTTP), whereas Java and Python UDFs run locally on the StarRocks BE nodes. References • sql-reference/sql-functions/Python_UDF.md • sql-reference/sql-functions/JAVA_UDF.md • sql-reference/sql-functions/README.md • using_starrocks/jit.md • sql-reference/sql-functions/hive_bitmap_udf.md

@Rocky elaborate on this: Scalar UDFs (one row in, one row out)

In StarRocks, a Scalar UDF is the most common type of user-defined function. The phrase "one row in, one row out" refers to its operational logic: for every single row of input data the function processes, it produces exactly one corresponding value in the output. Here is an elaboration on how they work and how they compare to other types: 1. The "One Row In, One Row Out" Logic When you use a scalar UDF in a SQL query (e.g.,

SELECT my_func(column) FROM table

), StarRocks applies the function logic to each row individually. * Input: A single set of arguments from one row. * Output: A single return value for that same row. * Analogy: Think of it like the built-in

UPPER()

or

ABS()

functions. If you pass 100 rows into

UPPER(name)

, you get 100 uppercase names back. 2. Comparison with Other UDF Types To understand Scalar UDFs better, it helps to see what they are _not_: * UDAF (Aggregate): "Many rows in, one row out." (e.g.,

SUM

or

COUNT

). It takes a whole group of rows and collapses them into a single result. * UDTF (Table): "One row in, many rows out." (e.g.,

split

). It takes one input and can expand it into multiple rows or columns. * UDWF (Window): Functions used with an

OVER

clause, maintaining the row count but allowing access to a frame of rows. 3. Implementation in StarRocks StarRocks supports Scalar UDFs primarily through Java (stable) and Python (experimental as of v3.4). Example: Java Scalar UDF If you wanted a function that adds a prefix to a string, your Java code would look like this:

java
public class AddPrefix {
    // The 'evaluate' method defines the "one row in, one row out" logic
    public final String evaluate(String input) {
        if (input == null) return null;
        return "SR_" + input;
    }
}

SQL Declaration Once compiled into a JAR, you register it in StarRocks:

sql
CREATE FUNCTION add_prefix(string) 
RETURNS string 
PROPERTIES (
    "symbol" = "com.starrocks.udf.sample.AddPrefix", 
    "type" = "StarrocksJar",
    "file" = "http://<your_server>/udf.jar"
);

4. Key Benefits * Row-Level Transformation: Ideal for data masking, custom mathematical formulas, or complex string manipulations that must happen per record. * Performance: Because they operate row-by-row without needing to buffer large sets of data (unlike aggregates), they are generally memory-efficient. * Global Scope: In StarRocks (v3.0+), you can create

GLOBAL

UDFs so they are available across all databases in the cluster. When to use it: Use a Scalar UDF whenever you need a custom calculation that doesn't change the number of rows in your result set. References • sql-reference/sql-functions/JAVA_UDF.md • sql-reference/sql-functions/Python_UDF.md