gitzwz opened a new issue, #1479:
URL: https://github.com/apache/iceberg-python/issues/1479
### Question
I encountered a problem with table.scan.plan_files() where there is no
noticeable time difference between single-threaded and multi-threaded
execution. The total time is directly proportional to the number of manifest
entries. The table I used for testing has 6 manifest files, and each manifest
file contains around 70,000 entries. The most time-consuming process is
_open_manifest in the DataScan.plan_files() function, and it performs similarly
whether using a thread pool or not. Could someone help me investigate if there
might be an issue?
Here is my test code:
`from pyiceberg.catalog import load_catalog
from pyspark.sql import SparkSession
from pyiceberg import expressions as pyi_expr
import time
from line_profiler import LineProfiler
catalog = load_catalog("default")
table = catalog.load_table('b_ods.pyiceberg_test2')
def scan_plan_files(key, values):
row_filter=pyi_expr.In(key, values)
files = table.scan(
row_filter=row_filter,
limit=1000
).plan_files()
print(f"total plans {len(files)}")
for file in files:
print(file.file.file_path)
start_time = time.perf_counter()
scan_plan_files("cid", {'844'})
print(f"Time consumed：{time.perf_counter() - start_time:.3f} seconds")
`
I also modified the ~/.pyiceberg.yaml file, changing *max-workers: 1* to
*max-workers: 32*, but the total time is still around 64 seconds with little to
no change.
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