caching - Spark会自动缓存一些结果吗？

Question

我运行一个操作两次，第二次运行所需的时间很少，因此我怀疑 Spark 会自动缓存一些结果。但我确实找到了任何来源。

我使用的是 Spark1.4。

doc = sc.textFile('...')
doc_wc = doc.flatMap(lambda x: re.split('\W', x))\
            .filter(lambda x: x != '') \
            .map(lambda word: (word, 1)) \
            .reduceByKey(lambda x,y: x+y) 
%%time
doc_wc.take(5) # first time
# CPU times: user 10.7 ms, sys: 425 µs, total: 11.1 ms
# Wall time: 4.39 s

%%time
doc_wc.take(5) # second time
# CPU times: user 6.13 ms, sys: 276 µs, total: 6.41 ms
# Wall time: 151 ms

Answer 1

来自the documentation :

Spark also automatically persists some intermediate data in shuffle operations (e.g. reduceByKey), even without users calling persist. This is done to avoid recomputing the entire input if a node fails during the shuffle. We still recommend users call persist on the resulting RDD if they plan to reuse it.

底层文件系统还将缓存对磁盘的访问。