Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/2093#discussion_r16633886
--- Diff: python/pyspark/rdd.py ---
@@ -1777,10 +1698,28 @@ def _defaultReducePartitions(self):
else:
return self.getNumPartitions()
- # TODO: `lookup` is disabled because we can't make direct comparisons
based
- # on the key; we need to compare the hash of the key to the hash of the
- # keys in the pairs. This could be an expensive operation, since those
- # hashes aren't retained.
+ def lookup(self, key):
+ """
+ Return the list of values in the RDD for key `key`. This operation
+ is done efficiently if the RDD has a known partitioner by only
+ searching the partition that the key maps to.
+
+ >>> l = range(1000)
+ >>> rdd = sc.parallelize(zip(l, l), 10)
+ >>> rdd.lookup(42) # slow
+ [42]
+ >>> sorted = rdd.sortByKey()
+ >>> sorted.lookup(42) # fast
+ [42]
+ >>> sorted.lookup(1024)
+ []
+ """
+ self = self.filter(lambda (k, v): k == key).values()
--- End diff --
This reassignment to `self` seems potentially confusing. Could you use a
different name for the filtered RDD, such as `filtered`? I think even a
one-letter variable name, like `f`, is preferable to re-assigning `self`.
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