Jesuino created SPARK-51533:
-------------------------------
Summary: Performance Degradation with mapInPandas in Spark 3.5.*
Key: SPARK-51533
URL: https://issues.apache.org/jira/browse/SPARK-51533
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 3.5.5, 3.5.4, 3.5.3, 3.5.2, 3.5.1, 3.5.0
Reporter: Jesuino
After upgrading to Spark 3.5.*, I noticed a significant performance degradation
when using `mapInPandas` for computationally intensive tasks, in this case
computing SHAP values in parallel. Performance remained consistent across Spark
versions from 3.1 to 3.4. However, after upgrading to Spark 3.5, execution time
has increased substantially.
h1. Minimal Reproducible Example
I've created a [minimal reproducible
example|https://gist.github.com/jesuinovieira/610b28c99b00c108a170c7a276943d3b]
to isolate the issue as much as I could. Below are the execution times per SHAP
iteration using this code:
||Model||Size (MB)||Spark 3.4.4 (s/it)||Spark 3.5.0 (s/it)||
|lgb-s|20|1|5|
|lgb-m|52|2.5|13|
|lgb-l|110|5|40|
As shown, execution time has increased by approximately 5-8x after upgrading to
Spark 3.5.
{code:python}
import time
import os
import sys
import findspark
import pandas as pd
import shap
import lightgbm as lgb
import requests
from typing import Iterable
from sklearn.model_selection import train_test_split
findspark.init()
os.environ["PYSPARK_PYTHON"] = sys.executable
import pyspark.sql
import pyspark.sql.types as T
def explain(df, model, background_data):
def compute_shap(iterable: Iterable[pd.DataFrame]) ->
Iterable[pd.DataFrame]:
for i, batch in enumerate(iterable):
if i > 0:
break
explainer.shap_values(batch, silent=False)
yield pd.DataFrame(columns=["dummy"])
explainer = shap.KernelExplainer(
model=model.predict,
data=background_data,
keep_index=True,
link="identity",
)
print("Computing shap values")
t1 = time.time()
schema = T.StructType([T.StructField("dummy", T.IntegerType())])
shap_values = df.mapInPandas(compute_shap, schema=schema)
shap_values.collect()
t2 = time.time()
print(f"Elapsed time: {round(t2 - t1, 2)} seconds")
conf = pyspark.SparkConf().setAppName("bug")
# Set maxRecordsPerBatch to 1 since we are interested in a single iteration
conf.set("spark.sql.execution.arrow.maxRecordsPerBatch", "1")
spark = pyspark.sql.SparkSession.builder.config(conf=conf).getOrCreate()
# NOTE: set size to train lgb model with different number of estimators
# s: n_estimator=1000, m: n_estimators=2500, l: n_estimators=5000
size = "s"
# Download the dataset if it doesn't exist
url =
"https://raw.githubusercontent.com/saul-chirinos/Electricity-demand-forecasting-in-Panama/master/Data/continuous%20dataset.csv";
filename = "panama.csv"
if not os.path.isfile(filename):
response = requests.get(url)
response.raise_for_status()
with open(filename, "wb") as file:
file.write(response.content)
# Load data
data = pd.read_csv(filename).drop(columns=["datetime", "QV2M_san", "T2M_san",
"T2M_toc"])
X, y = data.drop(columns=["nat_demand"]), data["nat_demand"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
# Train model
params = {"n_estimators": 1000 if size == "s" else 2500 if size == "m" else
5000, "num_leaves": 256}
train, test = lgb.Dataset(X_train, label=y_train), lgb.Dataset(X_test,
label=y_test)
predictor = lgb.train(params=params, train_set=train, valid_sets=[test])
predictor.save_model(f"lgb-{size}.txt")
# NOTE: use this for multiple runs to avoid retraining
#
# Load model
# predictor = lgb.Booster(model_file=f"lgb-{size}.txt")
print(f"lgb-{size}: {os.path.getsize(f'lgb-{size}.txt') / (1024 * 1024):.2f}
MB")
# Select samples for background data and to be explained
background_data = X_train.iloc[:10]
df = spark.createDataFrame(X_test.iloc[:100]).coalesce(1)
print(f"{pyspark.__version__=}")
explain(df, predictor, background_data)
{code}
h1. What I Tried
* Reviewed Spark 3.5 release notes and reverted relevant configuration changes
— no impact
* Checked logical/physical plans - no major differences
* Analyzed execution with sparkmeasure — no notable differences
* Tested with all versions from 3.5.0 to 3.5.5 - the issue persists in every
release
h1. Questions
* Has anyone else experienced similar slowdowns in Spark 3.5.* with
mapInPandas?
* Could this be related to changes in serialization, Arrow, or Pandas UDF
internals?
* Any suggestions on how to further diagnose or work around this issue?
Thanks!
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