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26.0.x: higher client CPU per call for `fetchone()` / `execute()` compared to 4.0.2

Aperta
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#621 3 commenti 0 reazioni 0 assegnatari Vedi su GitHub

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Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
32/100
Tipo di issue
Bug
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
python
Ambito
databases, performance

Direzione di ricerca

Start by running the included repro_overhead.py against 4.0.2 and 26.0.1 in both thin and thick modes to confirm the fetchone() and execute() CPU gap while fetchmany() stays flat. Then compare how the two versions handle the per-row fetch path, since that is where the regression appears. Done means the per-call overhead is traced to a specific change and the numbers come back within a few percent of 4.0.2 in both modes.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

bug
  1. What versions are you using?
  • python-oracledb: 26.0.1, compared with 4.0.2 (stock PyPI wheels)
  • Oracle Database: 12.1.0.2.0
  • Oracle Client (Thick mode): 19.18.0.0.0
  • Python: 3.11.13
  • platform.platform: Linux-6.18.54.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
  • sys.maxsize > 2**32: True
  1. Is it an error or a hang or a crash?

Neither: performance regression.

  1. What error(s) or behavior you are seeing?

fetchone() and execute() use more client CPU in 26.0.1 than in 4.0.2, in both Thin and Thick modes. fetchmany() is unchanged.

Client CPU time measured with the script below (seconds, average of two runs, each run being the median of 5):

Operation Mode 4.0.2 26.0.1 Change
fetchone() loop, 50k rows Thin 0.182 0.237 +31 %
fetchone() loop, 50k rows Thick 0.199 0.303 +52 %
2000 × (execute() + fetchone()) Thin 0.383 0.445 +16 %
2000 × (execute() + fetchone()) Thick 0.397 0.477 +20 %
fetchmany() scan, 50k rows Thin 0.169 0.179 +6 %
fetchmany() scan, 50k rows Thick 0.190 0.190 0 %
  1. Does your application call init_oracle_client()?

Both modes were measured (Thin and Thick).

  1. Include a runnable Python script that shows the problem.

No schema needed. Run once with each oracledb version:

ORA_USER=... ORA_PASSWORD=... ORA_DSN=host:port/service python repro_overhead.py thin
ORA_USER=... ORA_PASSWORD=... ORA_DSN=host:port/service python repro_overhead.py thick

import os
import statistics
import sys
import time

import oracledb

MODE = sys.argv[1] if len(sys.argv) > 1 else "thin"
ROWS = 50_000
N_EXEC = 2_000
REPEAT = 5

if MODE == "thick":
    oracledb.init_oracle_client()
oracledb.defaults.arraysize = 100
oracledb.defaults.prefetchrows = 101

conn = oracledb.connect(user=os.environ["ORA_USER"], password=os.environ["ORA_PASSWORD"],
                        dsn=os.environ["ORA_DSN"])

SCAN = f"""select level, level / 7, date '2026-01-01' + mod(level, 365), 'row_' || level
           from dual connect by level <= {ROWS}"""
ONE = "select :k, 'x' from dual"


def s1_fetchmany():
    with conn.cursor() as cur:
        cur.execute(SCAN)
        while cur.fetchmany():
            pass


def s2_execute_fetchone():
    with conn.cursor() as cur:
        for k in range(N_EXEC):
            cur.execute(ONE, k=k)
            cur.fetchone()


def s3_fetchone():
    with conn.cursor() as cur:
        cur.execute(SCAN)
        while cur.fetchone() is not None:
            pass


def cpu(fn):
    fn()                                    # warm-up
    samples = []
    for _ in range(REPEAT):
        c0 = time.process_time()
        fn()
        samples.append(time.process_time() - c0)
    return statistics.median(samples)


print("oracledb", oracledb.__version__, MODE)
print(f"fetchmany() scan of {ROWS} rows      : CPU {cpu(s1_fetchmany):.3f} s")
print(f"{N_EXEC} x (execute() + fetchone())  : CPU {cpu(s2_execute_fetchone):.3f} s")
print(f"fetchone() loop over {ROWS} rows     : CPU {cpu(s3_fetchone):.3f} s")
Lingua principale
Python
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