marcin-krystianc opened a new issue, #43891:
URL: https://github.com/apache/arrow/issues/43891
### Describe the enhancement requested
We want to start using fp16 data for our Ml workflows. We hoped for disk
space savings, reduced RAM consumption, and doubled reading performance.
Parquet files with fp16 columns are indeed smaller, but the reading performance
is worse than that of fp32 files.
I think that the reason for that non-optimal performance is the usage of the
`FIXED_LEN_BYTE_ARRAY` physical type for fp16 values. It makes the reader to
memcpy a single fp16 value at a time.
<details>
<summary>Test code</summary>
```
import pyarrow.parquet as pq
import pyarrow as pa
import numpy as np
import humanize
import time
import os
row_groups = 1
n_columns = 7_000
chunk_size = 64_000
n_rows = row_groups * chunk_size
work_items = 2
parquet_path = "/tmp/my.parquet"
def get_table(n_rows, n_columns, data_type = pa.float32()):
# Generate a random 2D array of floats using NumPy
# Each column in the array represents a column in the final table
data = np.random.rand(n_rows, n_columns).astype(np.float32)
# Convert the NumPy array to a list of PyArrow Arrays, one for each
column
pa_arrays = [pa.array(data[:, i]).cast(data_type, safe = False) for i in
range(n_columns)]
schema = pa.schema([(f'column_{i}', data_type) for i in
range(n_columns)])
# Create a PyArrow Table from the Arrays
return pa.Table.from_arrays(pa_arrays, schema=schema)
def worker_arrow_row_group():
pr = pq.ParquetReader()
pr.open(parquet_path, pre_buffer=True)
pr.read_row_groups(range(row_groups), use_threads=False)
def genrate_data(n_rows, n_columns, path, compression, dtype):
table = get_table(n_rows, n_columns, dtype)
t = time.time()
print(f"writing parquet file:{path}, columns={n_columns},
row_groups={row_groups}, rows={n_rows}, compression={compression},
dtype={dtype}")
pq.write_table(table, path, row_group_size=chunk_size,
use_dictionary=False, write_statistics=False, compression=compression,
store_schema=False)
parquet_size = os.stat(path).st_size
print(f"Parquet size={humanize.naturalsize(parquet_size)}")
dt = time.time() - t
print(f"finished writing parquet file in {dt:.2f} seconds")
def measure_reading(worker):
tt = []
# measure multiple times and take the fastest run
for _ in range(0, 11):
t = time.time()
worker()
tt.append(time.time() - t)
return min(tt)
for dtype in [pa.float32(), pa.float16()]:
print(f".")
genrate_data(n_rows, n_columns, path = parquet_path, compression = None,
dtype = dtype)
print(f"`ParquetReader.read_row_groups`, dtype:{dtype},
duration:{measure_reading(worker_arrow_row_group):.2f} seconds")
```
</details>
Results:
```
writing parquet file:/tmp/my.parquet, columns=7000, row_groups=1,
rows=64000, compression=None, dtype=float
Parquet size=1.8 GB
finished writing parquet file in 2.00 seconds
`ParquetReader.read_row_groups`, dtype:float, duration:0.88 seconds
writing parquet file:/tmp/my.parquet, columns=7000, row_groups=1,
rows=64000, compression=None, dtype=halffloat
Parquet size=897.2 MB
finished writing parquet file in 2.20 seconds
`ParquetReader.read_row_groups`, dtype:halffloat, duration:2.49 seconds
```
### Component(s)
C++, Python
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