Fix(jupyter):use non-blocking stdin check to prevent hanging

[kafka1991]

use non-blocking stdin check to prevent hanging
close #260 and #152

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• Bug Fix

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• Documentation is written (mandatory for new features)

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[auxten]

Better to have some tests inserting data with Jupyter Notebook

[auxten]

This changed the behavior of stdin empty check. The orginal one will block wait for data if not eol found.
I still think this mod is risky while processing large chunk of input data with slow IO.

[kafka1991]

Upon confirmation:

• The std_in check here is only used for INSERT statements on the clickhouse-local client side; engines like CSV read do not use it for reading.

• The interaction code between Python and C++ does not use the std_in fd

[auxten]

Maybe gen some bigger CSV to test it is better

[kafka1991]

complete add more tests

[kafka1991]

@auxten

It's worth noting the following code doesn't work correctly (we can't accept and insert data from standard input like clickhouse-local).

    def testStdin(self):
        print("start")
        from chdb import session
        chs = session.Session()
        chs.query("CREATE DATABASE IF NOT EXISTS test ENGINE = Atomic")
        chs.query("USE test")
        chs.query('DROP TABLE IF EXISTS embeddings')
        time.sleep(60)
        chs.query("""CREATE TABLE embeddings
                     (
                         movieId   UInt32 NOT NULL,
                         embedding Array(Float32) NOT NULL
                     ) ENGINE = MergeTree()
          ORDER BY movieId""")
        chs.query("""INSERT INTO embeddings FORMAT CSV""")
        count = chs.query('SELECT COUNT(*) as count FROM embeddings')
        print(f"Records: {count}")

terminal run

cat tests/movie_embeddings.csv | python tests/test_query_py.py testStdin()

Receive exception:

RuntimeError: Code: 108. DB::Exception: Code: 108. DB::Exception: No data to insert. (NO_DATA_TO_INSERT) (version 25.5.2.1). (NO_DATA_TO_INSERT)

This issue has already existed in our main branch, introduced by commit 6123ace. This feature is mutually exclusive with the non-blocking Jupyter Notebook, so we can confirm that it is not supported.