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From Reynold Xin <r...@databricks.com>
Subject Re: DataFrame.withColumn very slow when used iteratively?
Date Wed, 03 Jun 2015 02:43:15 GMT
.select itself is the bulk add right?

On Tue, Jun 2, 2015 at 5:32 PM, Andrew Ash <andrew@andrewash.com> wrote:

> Would it be valuable to create a .withColumns([colName], [ColumnObject])
> method that adds in bulk rather than iteratively?
>
> Alternatively effort might be better spent in making .withColumn()
> singular faster.
>
> On Tue, Jun 2, 2015 at 3:46 PM, Reynold Xin <rxin@databricks.com> wrote:
>
>> We improved this in 1.4. Adding 100 columns took 4s on my laptop.
>> https://issues.apache.org/jira/browse/SPARK-7276
>>
>> Still not the fastest, but much faster.
>>
>> scala> Seq((1, 2)).toDF("a", "b")
>> res6: org.apache.spark.sql.DataFrame = [a: int, b: int]
>>
>> scala>
>>
>> scala> val start = System.nanoTime
>> start: Long = 1433274299441224000
>>
>> scala> for (i <- 1 to 100) {
>>      |   df = df.withColumn("n" + i,
>> org.apache.spark.sql.functions.lit(0))
>>      | }
>>
>> scala> val end = System.nanoTime
>> end: Long = 1433274303250091000
>>
>> scala>
>>
>> scala> println((end - start) / 1000 / 1000 / 1000)
>> 3
>>
>>
>> On Tue, Jun 2, 2015 at 12:34 PM, zsampson <zsampson@palantir.com> wrote:
>>
>>> Hey,
>>>
>>> I'm seeing extreme slowness in withColumn when it's used in a loop. I'm
>>> running this code:
>>>
>>> for (int i = 0; i < NUM_ITERATIONS ++i) {
>>> df = df.withColumn("col"+i, new Column(new Literal(i,
>>> DataTypes.IntegerType)));
>>> }
>>>
>>> where df is initially a trivial dataframe. Here are the results of
>>> running
>>> with different values of NUM_ITERATIONS:
>>>
>>> iterations      time
>>> 25      3s
>>> 50      11s
>>> 75      31s
>>> 100     76s
>>> 125     159s
>>> 150     283s
>>>
>>> When I update the DataFrame by manually copying/appending to the column
>>> array and using DataFrame.select, it runs in about half the time, but
>>> this
>>> is still untenable at any significant number of iterations.
>>>
>>> Any insight?
>>>
>>>
>>>
>>> --
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>>> Nabble.com.
>>>
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>>
>

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