Value Members

1.  final def !=(arg0: Any): Boolean

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2.  final def ##: Int

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3.  final def ==(arg0: Any): Boolean

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4.  def appendMonoid[F, T, P](appnd: (T, F) => T, pres: (T) => P)(implicit m: Monoid[T]): MonoidAggregator[F, T, P]

   Obtain a MonoidAggregator that uses an efficient append operation for faster aggregation

   Obtain a MonoidAggregator that uses an efficient append operation for faster aggregation

   F

   Data input type

   T

   Aggregating Monoid type

   P

   Presentation (output) type

   appnd

   Function that appends the Monoid. Defines the MonoidAggregator.append method for this aggregator. Analogous to the 'seqop' function in Scala's sequence 'aggregate' method

   pres

   The presentation function

   m

   The Monoid type class

   Note

   The function 'appnd' is expected to obey the law:

   appnd(t, f) == m.plus(t, appnd(m.zero, f))

5.  def appendMonoid[F, T](appnd: (T, F) => T)(implicit m: Monoid[T]): MonoidAggregator[F, T, T]

   Obtain a MonoidAggregator that uses an efficient append operation for faster aggregation.

   Obtain a MonoidAggregator that uses an efficient append operation for faster aggregation. Equivalent to

   appendMonoid(appnd, identity[T]_)(m)

6.  def appendSemigroup[F, T, P](prep: (F) => T, appnd: (T, F) => T, pres: (T) => P)(implicit sg: Semigroup[T]): Aggregator[F, T, P]

   Obtain an Aggregator that uses an efficient append operation for faster aggregation

   Obtain an Aggregator that uses an efficient append operation for faster aggregation

   F

   Data input type

   T

   Aggregating Semigroup type

   P

   Presentation (output) type

   prep

   The preparation function. Expected to construct an instance of type T from a single data element.

   appnd

   Function that appends the Semigroup. Defines the Aggregator.append method for this aggregator. Analogous to the 'seqop' function in Scala's sequence 'aggregate' method

   pres

   The presentation function

   sg

   The Semigroup type class

   Note

   The functions 'appnd' and 'prep' are expected to obey the law:

   appnd(t, f) == sg.plus(t, prep(f))

7.  def appendSemigroup[F, T](prep: (F) => T, appnd: (T, F) => T)(implicit sg: Semigroup[T]): Aggregator[F, T, T]

   Obtain an Aggregator that uses an efficient append operation for faster aggregation.

   Obtain an Aggregator that uses an efficient append operation for faster aggregation. Equivalent to

   appendSemigroup(prep, appnd, identity[T]_)(sg)

8.  implicit def applicative[I]: Applicative[[O]Aggregator[I, _, O]]
9.  def approximatePercentile[T](percentile: Double, k: Int = QTreeAggregator.DefaultK)(implicit num: Numeric[T]): QTreeAggregatorLowerBound[T]

   Returns the lower bound of a given percentile where the percentile is between (0,1] The items that are iterated over cannot be negative.

10.  def approximatePercentileBounds[T](percentile: Double, k: Int = QTreeAggregator.DefaultK)(implicit num: Numeric[T]): QTreeAggregator[T]

    Returns the intersection of a bounded percentile where the percentile is between (0,1] The items that are iterated over cannot be negative.

11.  def approximateUniqueCount[T](implicit arg0: Hash128[T]): MonoidAggregator[T, Either[HLL, Set[T]], Long]

    Using a constant amount of memory, give an approximate unique count (~ 1% error).

    Using a constant amount of memory, give an approximate unique count (~ 1% error). This uses an exact set for up to 100 items, then HyperLogLog (HLL) with an 1.2% standard error which uses at most 8192 bytes for each HLL. For more control, see HyperLogLogAggregator.

12.  final def asInstanceOf[T0]: T0

    Definition Classes

    Any

13.  def clone(): AnyRef

    Attributes

    protected[lang]

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    @throws(classOf[java.lang.CloneNotSupportedException]) @native()

14.  def const[T](t: T): MonoidAggregator[Any, Unit, T]

    This is a trivial aggregator that always returns a single value

15.  def count[T](pred: (T) => Boolean): MonoidAggregator[T, Long, Long]

    How many items satisfy a predicate

16.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

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17.  def equals(arg0: AnyRef): Boolean

    Definition Classes

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18.  def exists[T](pred: (T) => Boolean): MonoidAggregator[T, Boolean, Boolean]

    Do any items satisfy some predicate

19.  def finalize(): Unit

    Attributes

    protected[lang]

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    @throws(classOf[java.lang.Throwable])

20.  def forall[T](pred: (T) => Boolean): MonoidAggregator[T, Boolean, Boolean]

    Do all items satisfy a predicate

21.  def fromMonoid[F, T](implicit mon: Monoid[T], prep: (F) => T): MonoidAggregator[F, T, T]
22.  def fromMonoid[T](implicit mon: Monoid[T]): MonoidAggregator[T, T, T]
23.  def fromReduce[T](red: (T, T) => T): Aggregator[T, T, T]

    Using Aggregator.prepare,present you can add to this aggregator

24.  def fromRing[F, T](implicit rng: Ring[T], prep: (F) => T): RingAggregator[F, T, T]
25.  def fromRing[T](implicit rng: Ring[T]): RingAggregator[T, T, T]
26.  def fromSemigroup[T](implicit sg: Semigroup[T]): Aggregator[T, T, T]
27.  final def getClass(): Class[_ <: AnyRef]

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    @native()

28.  def hashCode(): Int

    Definition Classes

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    @native()

29.  def head[T]: Aggregator[T, T, T]

    Take the first (left most in reduce order) item found

30.  def immutableSortedReverseTake[T](count: Int)(implicit arg0: Ordering[T]): MonoidAggregator[T, TopK[T], Seq[T]]

    Immutable version of sortedReverseTake, for frameworks that check immutability of reduce functions.

31.  def immutableSortedTake[T](count: Int)(implicit arg0: Ordering[T]): MonoidAggregator[T, TopK[T], Seq[T]]

    Immutable version of sortedTake, for frameworks that check immutability of reduce functions.

32.  final def isInstanceOf[T0]: Boolean

    Definition Classes

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33.  def last[T]: Aggregator[T, T, T]

    Take the last (right most in reduce order) item found

34.  def max[T](implicit arg0: Ordering[T]): Aggregator[T, T, T]

    Get the maximum item

35.  def maxBy[U, T](fn: (U) => T)(implicit arg0: Ordering[T]): Aggregator[U, U, U]
36.  def min[T](implicit arg0: Ordering[T]): Aggregator[T, T, T]

    Get the minimum item

37.  def minBy[U, T](fn: (U) => T)(implicit arg0: Ordering[T]): Aggregator[U, U, U]
38.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

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39.  final def notify(): Unit

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    @native()

40.  final def notifyAll(): Unit

    Definition Classes

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    @native()

41.  def numericSum[T](implicit num: Numeric[T]): MonoidAggregator[T, Double, Double]

    An aggregator that sums Numeric values into Doubles.

    An aggregator that sums Numeric values into Doubles.

    This is really no more than converting to Double and then summing. The conversion to double means we don't have the overflow semantics of integer types on the jvm (e.g. Int.MaxValue + 1 == Int.MinValue).

    Note that if you instead wanted to aggregate Numeric values of a type T into the same type T (e.g. if you want MonoidAggregator[T, T, T] for some Numeric type T), you can directly use Aggregator.fromMonoid[T] after importing the numericRing implicit:

    > import com.twitter.algebird.Ring.numericRing > def numericAggregator[T: Numeric]: MonoidAggregator[T, T, T] = Aggregator.fromMonoid[T]

42.  def prepareMonoid[F, T](prep: (F) => T)(implicit m: Monoid[T]): MonoidAggregator[F, T, T]
43.  def prepareSemigroup[F, T](prep: (F) => T)(implicit sg: Semigroup[T]): Aggregator[F, T, T]
44.  def randomSample[T](prob: Double, seed: Int = DefaultSeed): MonoidAggregator[T, Option[Batched[T]], List[T]]

    Randomly selects input items where each item has an independent probability 'prob' of being selected.

    Randomly selects input items where each item has an independent probability 'prob' of being selected. This assumes that all sampled records can fit in memory, so use this only when the expected number of sampled values is small.

45.  def reservoirSample[T](count: Int, seed: Int = DefaultSeed): MonoidAggregator[T, PriorityQueue[(Double, T)], Seq[T]]

    Selects exactly 'count' of the input records randomly (or all of the records if there are less then 'count' total records).

    Selects exactly 'count' of the input records randomly (or all of the records if there are less then 'count' total records). This assumes that all 'count' of the records can fit in memory, so use this only for small values of 'count'.

46.  def size: MonoidAggregator[Any, Long, Long]

    This returns the number of items we find

47.  def sortByReverseTake[T, U](count: Int)(fn: (T) => U)(implicit arg0: Ordering[U]): MonoidAggregator[T, PriorityQueue[T], Seq[T]]

    Same as sortedReverseTake, but using a function that returns a value that has an Ordering.

    Same as sortedReverseTake, but using a function that returns a value that has an Ordering.

    This function is like writing list.sortBy(fn).reverse.take(count).

48.  def sortByTake[T, U](count: Int)(fn: (T) => U)(implicit arg0: Ordering[U]): MonoidAggregator[T, PriorityQueue[T], Seq[T]]

    Same as sortedTake, but using a function that returns a value that has an Ordering.

    Same as sortedTake, but using a function that returns a value that has an Ordering.

    This function is like writing list.sortBy(fn).take(count).

49.  def sortedReverseTake[T](count: Int)(implicit arg0: Ordering[T]): MonoidAggregator[T, PriorityQueue[T], Seq[T]]

    Take the largest count items using a heap

50.  def sortedTake[T](count: Int)(implicit arg0: Ordering[T]): MonoidAggregator[T, PriorityQueue[T], Seq[T]]

    Take the smallest count items using a heap

51.  final def synchronized[T0](arg0: => T0): T0

    Definition Classes

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52.  def toList[T]: MonoidAggregator[T, Option[Batched[T]], List[T]]

    Put everything in a List.

    Put everything in a List. Note, this could fill the memory if the List is very large.

53.  def toSet[T]: MonoidAggregator[T, Set[T], Set[T]]

    Put everything in a Set.

    Put everything in a Set. Note, this could fill the memory if the Set is very large.

54.  def toString(): String

    Definition Classes

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55.  def uniqueCount[T]: MonoidAggregator[T, Set[T], Int]

    This builds an in-memory Set, and then finally gets the size of that set.

    This builds an in-memory Set, and then finally gets the size of that set. This may not be scalable if the Uniques are very large. You might check the approximateUniqueCount or HyperLogLog Aggregator to get an approximate version of this that is scalable.

56.  final def wait(): Unit

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    @throws(classOf[java.lang.InterruptedException])

57.  final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

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    @throws(classOf[java.lang.InterruptedException])

58.  final def wait(arg0: Long): Unit

    Definition Classes

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    @throws(classOf[java.lang.InterruptedException]) @native()