magic-rdds

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Miscellaneous functionality for manipulating Apache Spark RDDs.

magic-rdds 최신버젼 다운로드

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# Magic RDDs
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Enrichment methods for [Apache Spark RDDs](http://spark.apache.org/docs/latest/programming-guide.html#resilient-distributed-datasets-rdds):

```scala
$ spark-shell --packages org.hammerlab:magic-rdds_2.11:4.3.0
…
scala> import magic_rdds._
scala> sc.parallelize(List(1, 1, 1, 2, 2, 2, 2, 2, 2, 10)).runLengthEncode.collect()
res0: Array[(Int, Int)] = Array((1,3), (2,6), (10,1))
```

## Using

Use these Maven coordinates to depend on `magic-rdds`' latest Scala 2.11 build:

```
<dependency>
  <groupId>org.hammerlab</groupId>
  <artifactId>magic-rdds_2.11</artifactId>
  <version>4.3.0</version>
</dependency>
```

In SBT, use:

```
"org.hammerlab" %% "magic-rdds" % "4.3.0"
```

## Overview
Following are explanations of some of the RDDs provided by this repo and the functionality they provide:

### RDDs
RDD-helpers found in [the `org.hammerlab.magic.rdd` package](https://github.com/hammerlab/magic-rdds/tree/master/src/main/scala/org/hammerlab/magic/rdd).

#### [Run-length encoding](https://github.com/hammerlab/magic-rdds/blob/master/src/main/scala/org/hammerlab/magic/rdd/run_length.scala)
Exposes a `runLengthEncode` method on RDDs, per the example above.

#### [Scans](https://github.com/hammerlab/magic-rdds/blob/master/src/main/scala/org/hammerlab/magic/rdd/scan)

##### Basic `.scanLeft` / `.scanRight`

```scala
val rdd = sc.parallelize(1 to 10)

rdd.scanLeft(0)(_ + _).collect
// Array(0, 1, 3, 6, 10, 15, 21, 28, 36, 45)

rdd.scanRight(0)(_ + _).collect
// Array(54, 52, 49, 45, 40, 34, 27, 19, 10, 0)
```

Before the `.collect`, these each return a [`ScanRDD`](src/main/scala/org/hammerlab/magic/rdd/scan/ScanRDD.scala), which is a wrapper around the post-scan `RDD`, the total sum, and an array with the first element in each partition, and is automatically implicitly-convertible to its contained `RDD`.

##### Include each element in the partial-sum that replaces it

```scala
rdd.scanLeftInclusive(0)(_ + _).collect
// Array[Int] = Array(1, 3, 6, 10, 15, 21, 28, 36, 45, 55)

rdd.scanRightInclusive(0)(_ + _).collect
// Array(55, 54, 52, 49, 45, 40, 34, 27, 19, 10)
```

##### Use [`cats.Monoid`s](https://typelevel.org/cats/typeclasses/monoid.html)

```scala
import cats.instances.int._

rdd.scanLeft
rdd.scanRight
rdd.scanLeftInclusive
rdd.scanRightInclusive
```

##### Operate on "value"s of key-values pairs

```scala
val pairRDD = sc.parallelize('a' to 'j' zip (1 to 10))

pairRDD.scanLeftValues.collect
// Array((a,0), (b,1), (c,3), (d,6), (e,10), (f,15), (g,21), (h,28), (i,36), (j,45))

pairRDD.scanLeftValuesInclusive.collect
// Array((a,1), (b,3), (c,6), (d,10), (e,15), (f,21), (g,28), (h,36), (i,45), (j,55))

pairRDD.scanRightValues.collect
// Array((a,54), (b,52), (c,49), (d,45), (e,40), (f,34), (g,27), (h,19), (i,10), (j,0))

pairRDD.scanRightValuesInclusive.collect
// Array((a,55), (b,54), (c,52), (d,49), (e,45), (f,40), (g,34), (h,27), (i,19), (j,10))
```

Additionally, `.scanRight` and `.scanRightValues` expose two implementations with performance tradeoffs:

- the default implementation achieves a `scanRight` by sequencing the following operations:
  - `reverse`
  - `scanLeft`
  - `reverse`

- an alternate implementation, enabled by setting `useRDDReversal = false` in the first parameter list, calls `Iterator.scanRight` on each partition at one point, which materializes the entire partition into memory:

```scala
  pairRDD.scanRightValues(useRDDReversal = false).collect
  // Array((a,54), (b,52), (c,49), (d,45), (e,40), (f,34), (g,27), (h,19), (i,10), (j,0))
  ```

This is generally dangerous with Spark RDDs, where the assumption is typically that a partition is larger than the available executor memory, meaning such an operation is likely to cause an OOM.

#### `.reverse`
Reverse the elements in an RDD, optionally preserving (though still inverting) their partitioning:

```scala
sc.parallelize(1 to 10).reverse().collect
res2: Array[Int] = Array(10, 9, 8, 7, 6, 5, 4, 3, 2, 1)
```

#### `reduceByKey` extensions
Self-explanatory: `.maxByKey`, `.minByKey`

#### RDD Comparisons / Diffs

Example setup:

```scala
val rdd1 = sc.parallelize(1 to 10)
val rdd2 = sc.parallelize((1 to 5) ++ (12 to 7 by -1))

import cmp._
```

##### `unorderedCmp`: compare, disregarding order/position of elements:

```scala
val Unordered.Stats(both, onlyA, onlyB) = rdd1.unorderedCmp(rdd2).stats
both = 9   (1 to 5, 7 to 10)
onlyA = 1  (6)
onlyB = 2  (11, 12)
```

##### `orderedCmp`: distinguish between common elements at the same vs. different indices:

```scala
val Ordered.Stats(eq, reordered, onlyA, onlyB) = rdd1.orderedCmp(rdd2).stats
// eq = 6        (1, 2, 3, 4, 5, 9)
// reordered = 3 (7, 8, 10)
// onlyA = 1     (6)
// onlyB = 2     (11, 12)
```

##### `compareByKey`: unordered comparison of values for each key of paired-RDDs:

```scala
import hammerlab.iterator._

val chars10  = ('a' to 'j') zipWithIndex
val chars4   = chars10.take(4)
val chars5_2 = chars10.mapValues(_ + 10).takeEager(5)

val rdd1 = sc.parallelize(chars10  ++ chars4)
val rdd2 = sc.parallelize(chars5_2 ++ chars4)

val Keyed.Stats(eq, extraA, extraB, onlyA, onlyB) = rdd1.compareByKey(rdd2).stats
// eq     = 4 (a →  1, b →  2, c →  3, d →  4)
// extraA = 4 (a →  1, b →  2, c →  3, d →  4; second copy of each)
// extraB = 0 
// onlyA  = 6 (e →  5, f →  6, g →  7, h →  8, i →  9, j →  10)
// onlyB  = 5 (a → 11, b → 12, c → 13, d → 14, e → 15)
```

#### `collectParts`
`collect` an `RDD` while keeping elements in their respective partitions:

```scala
sc.parallelize(1 to 12).collectParts
// Array(Array(1, 2, 3), Array(4, 5, 6), Array(7, 8, 9), Array(10, 11, 12))
```

#### `.size`

A smarter version of `RDD.count`:

- results are cached; subsequent calls don't recompute
- `size` of a [UnionRDDs](https://github.com/apache/spark/blob/v2.2.0/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala) also computes and caches sizes of its constituent RDDs
- `.sizes`, `.total`: compute sizes of sequences / tuples of RDDs in one Spark job. For example, instead of:

```scala
	val count1 = rdd1.count
	val count2 = rdd2.count
	```

which runs two Spark jobs, you can instead write:

```scala
	val (count1, count2) = (rdd1, rdd2).sizes
	````

and save a job.

#### `.lazyZipWithIndex`

Functionally equivalent to [`RDD.zipWithIndex`](https://github.com/apache/spark/blob/v2.2.0/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L1280-L1295), but runs the first of the two necessary jobs (computing per-partition sizes and cumulative offsets) lazily, in a manner truer to the spirit of the lazy-wherever-possible RDD API than the `.zipWithIndex` implementation.

- `.saveAsSequenceFile`, `.saveCompressed` for non-paired RDDs.

#### Sliding / Windowed Traversals

Exposes several methods in the spirit of [Scala collections' similar API](https://github.com/scala/scala/blob/v2.11.12/src/library/scala/collection/IterableLike.scala#L180-L208).

`Tuple2` / `Tuple3`:

```scala
sc.parallelize(1 to 6).sliding2.collect
// Array((1,2), (2,3), (3,4), (4,5), (5,6))

sc.parallelize(1 to 6).sliding3.collect
// Array((1,2,3), (2,3,4), (3,4,5), (4,5,6))
```

2- and 3-element windows with `Option` contexts; input and output RDDs have same number of eleemnts:

```scala
sc.parallelize(1 to 6).sliding2Next.collect
// Array((1,Some(2)), (2,Some(3)), (3,Some(4)), (4,Some(5)), (5,Some(6)), (6,None))

sc.parallelize(1 to 6).sliding2Prev.collect
// Array((None,1), (Some(1),2), (Some(2),3), (Some(3),4), (Some(4),5), (Some(5),6))

sc.parallelize(1 to 6).sliding3Opt.collect
// Array((None,1,Some(2)), (Some(1),2,Some(3)), (Some(2),3,Some(4)), (Some(3),4,Some(5)), (Some(4),5,Some(6)), (Some(5),6,None))

sc.parallelize(1 to 6).sliding3Next.collect
// Array((1,Some(2),Some(3)), (2,Some(3),Some(4)), (3,Some(4),Some(5)), (4,Some(5),Some(6)), (5,Some(6),None), (6,None,None))
```

Arbitrary number of elements in `Seq`s, optionally preserving total number of elements by keeping partial entries:

```scala
sc.parallelize(1 to 6).sliding(4).collect
// Array(List(1, 2, 3, 4), List(2, 3, 4, 5), List(3, 4, 5, 6))

scala> sc.parallelize(1 to 6).sliding(4, includePartial = true).collect
// Array(List(1, 2, 3, 4), List(2, 3, 4, 5), List(3, 4, 5, 6), List(4, 5, 6), List(5, 6), List(6))
```

#### `.cappedGroupByKey`

Like [`RDD.groupByKey`](https://github.com/apache/spark/blob/v2.2.0/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala#L643) but takes only the first `maxPerKey` elements for each key:

```scala
sc.parallelize(1 to 10).keyBy(_ % 2).cappedGroupByKey(3).collect
// Array((0,Vector(2, 4, 6)), (1,Vector(1, 3, 5)))
```

#### `.sampleByKey`

Similar to `.cappedGroupByKey`, but samples elements from each key in an unbiased manner:

```scala
sc.parallelize(1 to 100).keyBy(_ % 2).sampleByKey(3).collect
// Array((0,ArrayBuffer(24, 78, 98)), (1,ArrayBuffer(17, 47, 49)))
```

#### `.splitByKey`

Split an `RDD[(K, V)]` into a `Map[K, RDD[V]]`, i.e. multiple RDDs each containing the values corresponding to one key.

This is generally a questionable thing to want to do, as subsequent operations on each RDD lose out on Spark's ability to parallelize things.

However, if you are going to do it, this implementation is much better than what you might do naively, i.e. using `.filter` N times on the original RDD.

Instead, this shuffles the full RDD once, into a partitioning where each key's pairs occupy a contiguous range of partitions; it then partition-slices views over those ranges and exposes them as standalone, per-key RDDs.

#### 2-D prefix sum

Given an RDD of elements that each have a logical "row", "column", and "summable" value (an `RDD[((Int, Int), V)]`), generate an RDD that replaces each value with the sum of all values at greater (or equal) (row,col) positions:

```scala
val n = 4
val rdd = sc.parallelize(1 to n flatMap { r ⇒ 1 to n map { c ⇒ r -> c -> r*c } })
def printGrid(rdd: RDD[((Int, Int), Int)]) = {
  val map = rdd.collectAsMap.toMap
  for {
    r ← n to 1 by -1
    c ← 1 to n
  } {
    print("% 4d".format(map((r, c))))
    if (c == n) print("\n")
  }
}

printGrid(rdd)
//   4   8  12  16
//   3   6   9  12
//   2   4   6   8
//   1   2   3   4

val prefix_sum.Result(_, cdf, _, _) = rdd.prefixSum2D()

printGrid(cdf)
//  40  36  28  16
//  70  63  49  28
//  90  81  63  36
// 100  90  70  40
```

[Examples from the tests](https://github.com/hammerlab/magic-rdds/blob/master/src/test/scala/org/hammerlab/magic/rdd/grid) may clarify further.

### ["Batch" execution](src/main/scala/org/apache/spark/batch)
This feature exposes a coarse avenue for forcing fewer than `spark.executor.cores` tasks to run concurrently on each 
executor, during a given stage.

The stage in question is split up into multiple Spark stages, each comprised of a set
number of partitions from the upstream RDD (the "batch size").

If this size is chosen to be ≤ the number of executors, then in general a maximum of one task will be assigned to each 
executor.

This can be useful when some stages in an app are very memory-expensive, while others are not: the memory-expensive ones 
can be "batched" in this way, each task availing itself of a full executor's-worth of memory.

The implementation splits the upstream partitions into (N/batch size) batches and executes each batch as a single stage 
before combining the results into single RDD:

```scala
scala> import org.hammerlab.magic.rdd.batch.implicits._
// create RDD of 10 partitions
val rdd = sc.parallelize(0 until 100, 10)
val res = rdd.batch(numPartitionsPerBatch = 4)

// print operations graph to see how many batches are selected
res.toDebugString
// (10) ReduceRDD[4] at RDD at ReduceRDD.scala:19 []
//  +-(2) MapRDD[3] at RDD at MapRDD.scala:21 []
//     +-(4) MapRDD[2] at RDD at MapRDD.scala:21 []
//        +-(4) MapRDD[1] at RDD at MapRDD.scala:21 []
//           |  ParallelCollectionRDD[0] at parallelize at <console>:27 []
```

See [the package README](src/main/scala/org/apache/spark/batch) for more info!

### And more!
Browse the code and tests, file an issue, or drop by [Gitter](https://gitter.im/hammerlab/magic-rdds) for more info.

## Building
Typical SBT commands will build/test/package the project:

```bash
sbt test
sbt assembly
```

## Releasing
While set to a `-SNAPSHOT` version:

```bash
sbt publish
```

To release a non-`-SNAPSHOT` version:

```bash
sbt publishSigned sonatypeRelease
```

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