Snowflake bounded source + Flink runner fix

[turb]

SnowflakeIO has several steps:

1. Run a COPY that outputs partitioned gzipped CSV files in a directory
2. List the directory
3. Read the files
4. Parse CSV

While 1. and 2. are done by one worker, 3. and 4. can be parallelized.

It appears that Google Dataflow is able to do that (using work stealing?), but Apache Flink (with --useDataStreamForBatch=true) propagates the scale of 1. / 2. to 3. 4., leading to very long processing when it can be fully scalable.

This change creates a SnowflakeBoundedSource instead of a simple DoFn to execute the COPY and then read the splits.

When doing that, a bug appears: a race between the apparition of those splits and the reading. It is solved by a change in LazyFlinkSourceSplitEnumerator to make subtasks wait for the splits to be ready.

I tested it still works on Google Dataflow.

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[gemini-code-assist]

Summary of Changes

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This pull request improves the scalability of SnowflakeIO by introducing a BoundedSource, which allows for more efficient parallel processing of staged CSV files. Additionally, it addresses a race condition in the Flink runner's source split enumeration, ensuring that subtasks correctly wait for splits to become available.

Highlights

• SnowflakeIO Parallelization: Replaced the DoFn-based SnowflakeIO implementation with a BoundedSource to enable better parallelization of file reading after the initial COPY INTO stage.
• Flink Runner Fix: Updated LazyFlinkSourceSplitEnumerator to correctly handle split requests that arrive before the source is fully initialized, preventing race conditions.

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R: @ahmedabu98 for label java.

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