Santosh Pingale created SPARK-40311:
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Summary: Introduce withColumnsRenamed
Key: SPARK-40311
URL: https://issues.apache.org/jira/browse/SPARK-40311
Project: Spark
Issue Type: Improvement
Components: PySpark, SparkR, SQL
Affects Versions: 3.2.2, 3.3.0, 3.1.3, 3.0.3
Reporter: Santosh Pingale
Add a scala, pyspark, R dataframe API that can rename multiple columns in a
single command. This is mostly a performance related optimisations where users
iteratively perform `withColumnRenamed`. With 100s columns and multiple
iterations, there are cases where either driver will blow up or users will
receive a StackOverflowError.
{code:java}
import datetime
import numpy as np
import pandas as pd
num_rows = 2
num_columns = 100
data = np.zeros((num_rows, num_columns))
columns = map(str, range(num_columns))
raw = spark.createDataFrame(pd.DataFrame(data, columns=columns))
a = datetime.datetime.now()
{col, f"prefix_{col}" for col in raw.columns}
for col in raw.columns:
raw = raw.withColumnRenamed(col, f"prefix_{col}")
b = datetime.datetime.now()
for col in raw.columns:
raw = raw.withColumnRenamed(col, f"prefix_{col}")
c = datetime.datetime.now()
for col in raw.columns:
raw = raw.withColumnRenamed(col, f"prefix_{col}")
d = datetime.datetime.now()
for col in raw.columns:
raw = raw.withColumnRenamed(col, f"prefix_{col}")
e = datetime.datetime.now()
for col in raw.columns:
raw = raw.withColumnRenamed(col, f"prefix_{col}")
f = datetime.datetime.now()
for col in raw.columns:
raw = raw.withColumnRenamed(col, f"prefix_{col}")
g = datetime.datetime.now()
g-a
datetime.timedelta(seconds=12, microseconds=480021) {code}
{code:java}
import datetime import numpy as np import pandas as pd num_rows = 2 num_columns
= 100 data = np.zeros((num_rows, num_columns)) columns = map(str,
range(num_columns)) raw = spark.createDataFrame(pd.DataFrame(data,
columns=columns)) a = datetime.datetime.now() raw =
DataFrame(raw._jdf.withColumnsRenamed({col: f"prefix_{col}" for col in
raw.columns}), spark) b = datetime.datetime.now() raw =
DataFrame(raw._jdf.withColumnsRenamed({col: f"prefix_{col}" for col in
raw.columns}), spark) c = datetime.datetime.now() raw =
DataFrame(raw._jdf.withColumnsRenamed({col: f"prefix_{col}" for col in
raw.columns}), spark) d = datetime.datetime.now() raw =
DataFrame(raw._jdf.withColumnsRenamed({col: f"prefix_{col}" for col in
raw.columns}), spark) e = datetime.datetime.now() raw =
DataFrame(raw._jdf.withColumnsRenamed({col: f"prefix_{col}" for col in
raw.columns}), spark) f = datetime.datetime.now() raw =
DataFrame(raw._jdf.withColumnsRenamed({col: f"prefix_{col}" for col in
raw.columns}), spark) g = datetime.datetime.now() g-a
datetime.timedelta(microseconds=632116)
{code}
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