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From Anwar AliKhan <anwaralikhan...@gmail.com>
Subject Re: When is a Bigint a long and when is a long a long
Date Sun, 28 Jun 2020 10:52:08 GMT
I wish to draw your attention for your consideration to  this  approach
where the BigInt data type maps to Long without drawing an error.

https://stackoverflow.com/questions/31011797/bug-in-spring-data-jpa-spring-data-returns-listbiginteger-instead-of-listlon

"This is a issue with Spring data JPA. If in DB the datatype is defined as
BigInteger and in JPA query we tries to fetch as Long then it will not give
any error , but it set value as BigInteger in Long datatype."


The use of spark.range(10).map(_.toLong).reduce(_+_)

means extra processing while iterating through each element by map method
making ready a  new array  for reduce function.  I feel the extra
processing  should be avoided.


On Sat, 27 Jun 2020, 17:36 Sean Owen, <srowen@gmail.com> wrote:

> It does not return a DataFrame. It returns Dataset[Long].
> You do not need to collect(). See my email.
>
> On Sat, Jun 27, 2020, 11:33 AM Anwar AliKhan <anwaralikhanuae@gmail.com>
> wrote:
>
>> So the range function actually returns BigInt (Spark SQL type)
>> and the fact Dataset[Long] and printSchema are displaying (toString())
>> Long instead of BigInt needs looking into.
>>
>> Putting that to one side
>>
>> My issue with using collect() to get around the casting of elements
>> returned
>> by range is,  I read some literature which says the collect() returns all
>> the data to the driver
>> and so can likely cause Out Of memory error.
>>
>> Question:
>> Is it correct that collect() behaves that way and can cause Out of memory
>> error ?
>>
>> Obviously it will be better to use  .map for casting because then the
>> work is being done by workers.
>> spark.range(10).map(_.toLong),reduce(_+_)
>> <http://www.backbutton.co.uk/>
>>
>>
>> On Sat, 27 Jun 2020, 15:42 Sean Owen, <srowen@gmail.com> wrote:
>>
>>> There are several confusing things going on here. I think this is part
>>> of the explanation, not 100% sure:
>>>
>>> 'bigint' is the Spark SQL type of an 8-byte long. 'long' is the type
>>> of a JVM primitive. Both are the same, conceptually, but represented
>>> differently internally as they are logically somewhat different ideas.
>>>
>>> The first thing I'm not sure about is why the toString of
>>> Dataset[Long] reports a 'bigint' and printSchema() reports 'long'.
>>> That might be a (cosmetic) bug.
>>>
>>> Second, in Scala 2.12, its SAM support causes calls to reduce() and
>>> other methods, using an Object type, to be ambiguous, because Spark
>>> has long since had Java-friendly overloads that support a SAM
>>> interface for Java callers. Those weren't removed to avoid breakage,
>>> at the cost of having to explicitly tell it what overload you want.
>>> (They are equivalent)
>>>
>>> This is triggered because range() returns java.lang.Longs, not long
>>> primitives (i.e. scala.Long). I assume that is to make it versatile
>>> enough to use in Java too, and because it's hard to write an overload
>>> (would have to rename it)
>>>
>>> But that means you trigger the SAM overload issue.
>>>
>>> Anything you do that makes this a Dataset[scala.Long] resolves it, as
>>> it is no longer ambiguous (Java-friendly Object-friendly overload does
>>> not apply). For example:
>>>
>>> spark.range(10).map(_.toLong).reduce(_+_)
>>>
>>> If you collect(), you still have an Array[java.lang.Long]. But Scala
>>> implicits and conversions make .reduce(_+_) work fine on that; there
>>> is no "Java-friendly" overload in the way.
>>>
>>> Normally all of this just works and you can ignore these differences.
>>> This is a good example of a corner case in which it's inconvenient,
>>> because of the old Java-friendly overloads. This is by design though.
>>>
>>> On Sat, Jun 27, 2020 at 8:29 AM Anwar AliKhan <anwaralikhanuae@gmail.com>
>>> wrote:
>>> >
>>> > As you know I have been puzzling over this issue :
>>> > How come spark.range(100).reduce(_+_)
>>> > worked in earlier spark version but not with the most recent versions.
>>> >
>>> > well,
>>> >
>>> > When you first create a dataset, by default the column "id" datatype
>>> is  [BigInt],
>>> > It is a bit like a coin Long on one side and bigint on the other side.
>>> >
>>> > scala> val myrange = spark.range(1,100)
>>> > myrange: org.apache.spark.sql.Dataset[Long] = [id: bigint]
>>> >
>>> > The Spark framework error message after parsing the reduce(_+_) method
>>> confirms this
>>> > and moreover stresses its constraints of expecting data  type long as
>>> parameter argument(s).
>>> >
>>> > scala> myrange.reduce(_+_)
>>> > <console>:26: error: overloaded method value reduce with alternatives:
>>> >   (func:
>>> org.apache.spark.api.java.function.ReduceFunction[java.lang.Long])java.lang.Long
>>> <and>
>>> >   (func: (java.lang.Long, java.lang.Long) =>
>>> java.lang.Long)java.lang.Long
>>> >  cannot be applied to ((java.lang.Long, java.lang.Long) => scala.Long)
>>> >        myrange.reduce(_+_)
>>> >                ^
>>> >
>>> > But if you ask the printSchema method it disagrees with both of the
>>> above and says the column "id" data is Long.
>>> > scala> range100.printSchema()
>>> > root
>>> >  |-- id: long (nullable = false)
>>> >
>>> > If I ask the collect() method, the collect() method  agrees with
>>> printSchema() that the datatype of column "id" is  Long and not BigInt.
>>> >
>>> > scala> range100.collect()
>>> > res10: Array[Long] = Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
>>> 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
>>> 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
>>> 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
>>> 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
>>> 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99)
>>> >
>>> > To settle the dispute between the methods and get the collect() to
>>> "show me the money" I  called the collect() to pass its return type to
>>> reduce(_+_).
>>> >
>>> > "Here is the money"
>>> > scala> range100.collect().reduce(_+_)
>>> > res11: Long = 4950
>>> >
>>> > The collect() and printSchema methods could be implying  there is no
>>> difference between a Long or  a BingInt.
>>> >
>>> > Questions :  These return type  differentials, are they  by design  or
>>> an oversight  bug ?
>>> > Questions :  Why the change from earlier version to later version ?
>>> > Question   :     Will you be updating the reduce(_+_)  method ?
>>> >
>>> > When it comes to creating a dataset using toDs there is no dispute,
>>> > all the methods agree that it is neither a BigInt or a Long but an int
>>> even integer.
>>> >
>>> > scala> val dataset = Seq(1, 2, 3).toDS()
>>> > dataset: org.apache.spark.sql.Dataset[Int] = [value: int]
>>> >
>>> > scala> dataset.collect()
>>> > res29: Array[Int] = Array(1, 2, 3)
>>> >
>>> > scala> dataset.printSchema()
>>> > root
>>> >  |-- value: integer (nullable = false)
>>> >
>>> > scala> dataset.show()
>>> > +-----+
>>> > |value|
>>> > +-----+
>>> > |    1|
>>> > |    2|
>>> > |    3|
>>> > +-----+
>>> >
>>> > scala> dataset.reduce(_+_)
>>> > res7: Int = 6
>>> >
>>>
>>

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