SCHJonathan opened a new pull request, #52154:
URL: https://github.com/apache/spark/pull/52154
<!--
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.
-->
**Please refer to this design doc for more detail: [[design doc
link](https://docs.google.com/document/d/1Bee2xMMD7r8w9i1hq-ikuMEAWg5RQkiBnifIgwPo4xM/edit?tab=t.0#bookmark=id.621wvum20j7w)]**
Introduces a mechanism for lazy execution of Declarative Pipelines query
functions. A query function is something like the `mv1` in this example:
```python
@materialized_view
def mv1():
return spark.table("upstream_table").filter(some_condition)
```
Currently, query functions are always executed eagerly. I.e. the
implementation of the `materialized_view` decorator immediately invokes the
function that it decorates and then registers the resulting DataFrame with the
server.
This PR introduces Spark Connect proto changes that enable executing query
functions later on, initiated by the server during graph resolution. After all
datasets and flows have been registered with the server, the server can tell
the client to execute the query functions for flows that haven't yet
successfully been executed. The way this works is that the client initiates an
RPC with the server, and then the server streams back responses that indicate
to the client when it's time to execute a query function for one of its flows.
Relevant changes:
- New `QueryFunctionFailure` message
- New `QueryFunctionResult` message
- Replace relation field in `DefineFlow` with `query_function_result` field
- New `DefineFlowQueryFunctionResult` message
- New `GetQueryFunctionExecutionSignalStream` message
- New `PipelineQueryFunctionExecutionSignal` message
### 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.
-->
There are some situations where we can't resolve the relation immediately at
the time we're registering a flow.
E.g. consider this situation:
file 1:
```python
@materialized_view
def mv1():
data = [("Alice", 10), ("Bob", 15), ("Alice", 5)]
return spark.createDataFrame(data, ["name", "amount"])
```
file 2:
```python
@materialized_view
def mv2():
return
spark.table("mv1").groupBy("name").agg(sum("amount").alias("total_amount"))
```
Unlike some other transformations, which get analyzed lazily, `groupBy` can
trigger an `AnalyzePlan` Spark Connect request immediately. If the query
function for `mv2` gets executed before `mv1`, then it will hit an error,
because `mv1` doesn't exist yet. `groupBy` isn't the only example here
(`df.schema`, etc).
Other examples of these kinds of situations:
- The set of columns for a downstream table is determined from the set of
columns in an upstream table.
- When `spark.sql` is used.
### 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'.
-->
No
### 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.
-->
It is a proto only changes. Will followup with unit tests and E2E tests once
we add implementation.
### 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.
-->
No
--
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]
