Backpressure-3: Merge & ObserveOn (Not Production Ready)

Here is a snapshot of working on backpressure. This is NOT production ready and I'm not all that happy with it right now.

- bad performance
- pivot and parallel operators seem broken (concurrency bugs with `merge` I think)
- bad algorithm in `merge`
- need to replace data structures (lists/queues)
- apparent memory leak in `observeOn` with `Schedulers.computation()` that causes JMH tests to fail on it

```
../gradlew benchmarks '-Pjmh=-f 1 -tu s -bm thrpt -wi 5 -i 5 -r 2 -prof GC rx.usecases.*'
```

Master branch (v0.19):

```                                                     
Benchmark                                        (size)   Mode   Samples         Mean   Mean error    Units
r.u.PerfBaseline.forLoopConsumption                   1  thrpt         5 379615485.594 15379754.350    ops/s
r.u.PerfBaseline.forLoopConsumption                1024  thrpt         5   328864.473    18187.666    ops/s
r.u.PerfBaseline.iterableViaForLoopConsumption        1  thrpt         5 253622547.509 27125669.781    ops/s
r.u.PerfBaseline.iterableViaForLoopConsumption     1024  thrpt         5   188193.794    10372.260    ops/s
r.u.PerfBaseline.iterableViaHasNextConsumption        1  thrpt         5 338153673.244 13436554.726    ops/s
r.u.PerfBaseline.iterableViaHasNextConsumption     1024  thrpt         5   299124.445    28327.383    ops/s
r.u.PerfBaseline.observableConsumption                1  thrpt         5 31394747.471   781746.789    ops/s
r.u.PerfBaseline.observableConsumption             1024  thrpt         5   245864.811     6657.154    ops/s
r.u.PerfObserveOn.observeOnComputation                1  thrpt         5   391499.859    18397.563    ops/s
r.u.PerfObserveOn.observeOnComputation             1024  thrpt         5    13328.636      306.077    ops/s
r.u.PerfObserveOn.observeOnImmediate                  1  thrpt         5 21230728.918   892820.007    ops/s
r.u.PerfObserveOn.observeOnImmediate               1024  thrpt         5   251638.525     3525.681    ops/s
r.u.PerfObserveOn.observeOnNewThread                  1  thrpt         5    21527.805     2739.158    ops/s
r.u.PerfObserveOn.observeOnNewThread               1024  thrpt         5    10020.191      188.225    ops/s
r.u.PerfTransforms.flatMapInt                         1  thrpt         5  3564189.441    39217.324    ops/s
r.u.PerfTransforms.flatMapInt                      1024  thrpt         5     7093.382      179.934    ops/s
r.u.PerfTransforms.flatMapNestedMapFilterTake         1  thrpt         5  2292314.964   137546.825    ops/s
r.u.PerfTransforms.flatMapNestedMapFilterTake      1024  thrpt         5       17.375        0.430    ops/s
r.u.PerfTransforms.mapIntStringInt                    1  thrpt         5  9653977.591   122946.457    ops/s
r.u.PerfTransforms.mapIntStringInt                 1024  thrpt         5    19772.989     1649.906    ops/s
r.u.PerfTransforms.mapPassThru                        1  thrpt         5 19220291.170  1408394.928    ops/s
r.u.PerfTransforms.mapPassThru                     1024  thrpt         5   202820.081    27896.934    ops/s
```

With backpressure changes:

```
Benchmark                                        (size)   Mode   Samples         Mean   Mean error    Units
r.u.PerfBaseline.forLoopConsumption                   1  thrpt         5 357110005.600 40841385.201    ops/s
r.u.PerfBaseline.forLoopConsumption                1024  thrpt         5   323485.335    12560.808    ops/s
r.u.PerfBaseline.iterableViaForLoopConsumption        1  thrpt         5 250763283.151 41052831.579    ops/s
r.u.PerfBaseline.iterableViaForLoopConsumption     1024  thrpt         5   184443.933    17732.768    ops/s
r.u.PerfBaseline.iterableViaHasNextConsumption        1  thrpt         5 328354319.566 20021232.226    ops/s
r.u.PerfBaseline.iterableViaHasNextConsumption     1024  thrpt         5   295521.735    60981.106    ops/s
r.u.PerfBaseline.observableConsumption                1  thrpt         5  9523326.320  1946455.684    ops/s		9.5m vs 31.3m
r.u.PerfBaseline.observableConsumption             1024  thrpt         5    84990.974     4275.619    ops/s
r.u.PerfObserveOn.observeOnComputation                1  thrpt         5   367281.334    88162.113    ops/s		367k vs 391k
r.u.PerfObserveOn.observeOnComputation             1024  thrpt         failed (ran out of memory apparently, see below)
r.u.PerfObserveOn.observeOnImmediate                  1  thrpt         5  7411354.828  1380680.011    ops/s		7.4m vs 21.2m
r.u.PerfObserveOn.observeOnImmediate               1024  thrpt         5    83914.883     6175.528    ops/s
r.u.PerfObserveOn.observeOnNewThread                  1  thrpt         5    21795.495      696.339    ops/s		21.7k vs 21.5k
r.u.PerfObserveOn.observeOnNewThread               1024  thrpt         5    15361.160     1052.314    ops/s		15.3k vs 10k
r.u.PerfTransforms.flatMapInt                         1  thrpt         5  1194526.066    91077.184    ops/s		1.2m vs 3.6m
r.u.PerfTransforms.flatMapInt                      1024  thrpt         5     1799.220       70.146    ops/s
r.u.PerfTransforms.flatMapNestedMapFilterTake         1  thrpt         5   858432.448   135990.002    ops/s		858k vs 2292k
r.u.PerfTransforms.flatMapNestedMapFilterTake      1024  thrpt         5       45.345        1.928    ops/s		17 vs 45 (something is wrong here on both)
r.u.PerfTransforms.mapIntStringInt                    1  thrpt         5  4433584.545  1336597.807    ops/s		4.4m vs 9.6m
r.u.PerfTransforms.mapIntStringInt                 1024  thrpt         5    17271.313      881.956    ops/s
r.u.PerfTransforms.mapPassThru                        1  thrpt         5  6098106.967   526635.319    ops/s		6.1m vs 19.2m
r.u.PerfTransforms.mapPassThru                     1024  thrpt         5    81472.209     5419.922    ops/s


r.u.PerfObserveOn.observeOnComputation with size 1024

Iteration   1: 1052.083 ops/s
          GC | wall time = 2.952 secs,  GC time = 2.945 secs, GC% = 99.76%, GC count = +3
             |

Iteration   2: 161.619 ops/s
          GC | wall time = 2.766 secs,  GC time = 2.765 secs, GC% = 99.96%, GC count = +3
             |
```
Parallel may need to be rewritten completely and not use `groupBy`/`observeOn` as backpressure doesn't compose through those.

Instead I think we'll need to have each parallel `Observable` aggregate their requests into a single upstream request to the origin, and then a round-robin from the origin that splits into each `Observable` according to the requested amounts for each. This would compose backpressure through it, and allow for one `Observable` being slower than others and still letting data flow on the fast ones.