SCHJonathan opened a new pull request, #52154:
URL: https://github.com/apache/spark/pull/52154
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### What changes were proposed in this pull request?
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**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?
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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?
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No
### How was this patch tested?
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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?
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No
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