[
https://issues.apache.org/jira/browse/SPARK-40311?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Santosh Pingale updated SPARK-40311:
------------------------------------
Description:
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()
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}
was:
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}
> 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: 3.0.3, 3.1.3, 3.3.0, 3.2.2
> Reporter: Santosh Pingale
> Priority: Minor
>
> 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()
> 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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