zhidongqu-db opened a new pull request, #53924:
URL: https://github.com/apache/spark/pull/53924
<!--
Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, please read our contributor guidelines:
https://spark.apache.org/contributing.html
2. Ensure you have added or run the appropriate tests for your PR:
https://spark.apache.org/developer-tools.html
3. If the PR is unfinished, add '[WIP]' in your PR title, e.g.,
'[WIP][SPARK-XXXX] Your PR title ...'.
4. Be sure to keep the PR description updated to reflect all changes.
5. Please write your PR title to summarize what this PR proposes.
6. If possible, provide a concise example to reproduce the issue for a
faster review.
7. If you want to add a new configuration, please read the guideline first
for naming configurations in
'core/src/main/scala/org/apache/spark/internal/config/ConfigEntry.scala'.
8. If you want to add or modify an error type or message, please read the
guideline first in
'common/utils/src/main/resources/error/README.md'.
-->
### What changes were proposed in this pull request?
<!--
Please clarify what changes you are proposing. The purpose of this section
is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR. See the examples below.
1. If you refactor some codes with changing classes, showing the class
hierarchy will help reviewers.
2. If you fix some SQL features, you can provide some references of other
DBMSes.
3. If there is design documentation, please add the link.
4. If there is a discussion in the mailing list, please add the link.
-->
This PR adds support for vector norm and normalization functions to Spark
SQL, complementing the vector distance/similarity functions added previously.
Norm Function
- vector_norm(vector, degree) - Returns the Lp norm of a float vector using
the specified degree. Degree defaults to 2.0 (Euclidean norm) if unspecified.
Normalization Function
- vector_normalize(vector, degree) - Normalizes a float vector to unit
length using the specified norm degree. Degree defaults to 2.0 (Euclidean norm)
if unspecified.
Supported norm degrees:
- 1.0 - L1 norm (Manhattan norm)
- 2.0 - L2 norm (Euclidean norm)
- float('inf') - Infinity norm (maximum absolute value)
Key implementation details:
- Type Safety: Functions accept only ARRAY<FLOAT> for vector and FLOAT for
degree. No implicit type casting is performed
- passing ARRAY<DOUBLE> or ARRAY<INT> results in a
DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE error.
- Degree Validation: Invalid degree values throw INVALID_VECTOR_NORM_DEGREE
error with clear messaging.
- NULL Handling: NULL array inputs return NULL. Arrays containing NULL
elements also return NULL.
- Edge Cases: Empty vectors return 0.0 for norm functions and an empty array
for normalize. Zero-norm vectors return NULL for normalize (division by zero).
- Code Generation: Optimized codegen with manual loop unrolling (8 elements
at a time) to enable potential JIT SIMD vectorization.
This PR only includes SQL language support; DataFrame API will be added in a
separate PR.
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
Vector norm and normalization functions are fundamental operations for:
- Preprocessing: Normalizing embedding vectors before similarity
computations (many ML models require unit-length vectors)
- Feature scaling: Ensuring vectors have consistent magnitudes across
different sources
- Distance metrics: Computing norm-based distances and preparing vectors for
distance computation
These functions complement the vector distance/similarity functions and are
commonly available in other systems.
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such as
new features, bug fixes, or other behavior changes. Documentation-only updates
are not considered user-facing changes.
If yes, please clarify the previous behavior and the change this PR proposes
- provide the console output, description and/or an example to show the
behavior difference if possible.
If possible, please also clarify if this is a user-facing change compared to
the released Spark versions or within the unreleased branches such as master.
If no, write 'No'.
-->
Yes, this PR introduces 2 new SQL functions
```
-- L2 norm (Euclidean norm) - default
SELECT vector_norm(array(3.0F, 4.0F));
-- Returns: 5.0
-- L1 norm (Manhattan norm)
SELECT vector_norm(array(3.0F, 4.0F), 1.0F);
-- Returns: 7.0
-- Infinity norm (maximum absolute value)
SELECT vector_norm(array(3.0F, -4.0F), float('inf'));
-- Returns: 4.0
-- L2 normalization - default (returns unit vector)
SELECT vector_normalize(array(3.0F, 4.0F));
-- Returns: [0.6, 0.8]
-- L1 normalization
SELECT vector_normalize(array(3.0F, 4.0F), 1.0F);
-- Returns: [0.42857143, 0.5714286]
-- Infinity normalization
SELECT vector_normalize(array(3.0F, 4.0F), float('inf'));
-- Returns: [0.75, 1.0]
```
### How was this patch tested?
<!--
If tests were added, say they were added here. Please make sure to add some
test cases that check the changes thoroughly including negative and positive
cases if possible.
If it was tested in a way different from regular unit tests, please clarify
how you tested step by step, ideally copy and paste-able, so that other
reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why
it was difficult to add.
If benchmark tests were added, please run the benchmarks in GitHub Actions
for the consistent environment, and the instructions could accord to:
https://spark.apache.org/developer-tools.html#github-workflow-benchmarks.
-->
TODO
### Was this patch authored or co-authored using generative AI tooling?
<!--
If generative AI tooling has been used in the process of authoring this
patch, please include the
phrase: 'Generated-by: ' followed by the name of the tool and its version.
If no, write 'No'.
Please refer to the [ASF Generative Tooling
Guidance](https://www.apache.org/legal/generative-tooling.html) for details.
-->
Yes, code assistance with Claude Opus 4.5 in combination with manual editing
by the author.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
