[SPARK-5224] [PySpark] improve performance of parallelize list/ndarray

[davies]

After the default batchSize changed to 0 (batched based on the size of object), but parallelize() still use BatchedSerializer with batchSize=1, this PR will use batchSize=1024 for parallelize by default.

Also, BatchedSerializer did not work well with list and numpy.ndarray, this improve BatchedSerializer by using len and getslice.

Here is the benchmark for parallelize 1 millions int with list or ndarray:

	before	after	improvements
list	11.7 s	0.8 s	14x
numpy.ndarray	32 s	0.7 s	40x

[SparkQA]

Test build #25479 has started for PR 4024 at commit 7618c7c.

• This patch merges cleanly.

[SparkQA]

Test build #25479 has finished for PR 4024 at commit 7618c7c.

• This patch fails Spark unit tests.
• This patch merges cleanly.
• This patch adds no public classes.

[AmplabJenkins]

Test FAILed.
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Test FAILed.

[SparkQA]

Test build #564 has started for PR 4024 at commit 7618c7c.

• This patch merges cleanly.

[SparkQA]

Test build #564 has finished for PR 4024 at commit 7618c7c.

• This patch passes all tests.
• This patch merges cleanly.
• This patch adds the following public classes (experimental):
  • SparkSubmit.printErrorAndExit(s"Cannot load main class from JAR $primaryResource")
  • class BinaryClassificationMetrics(

[davies]

@JoshRosen ping!

[JoshRosen]

LGTM, so I'm going to merge this into master (1.3.0) and branch-1.2 (1.2.1). Thanks!