Separate consumers/producers/partitioners

1 files changed, 0 insertions, 58 deletions

diff --git a/kafka/partitioner.py b/kafka/partitioner.py
deleted file mode 100644
index 695dd6f..0000000
--- a/kafka/partitioner.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from itertools import cycle
-
-
-class Partitioner(object):
-    """
-    Base class for a partitioner
-    """
-    def __init__(self, partitions):
-        """
-        Initialize the partitioner
-
-        partitions - A list of available partitions (during startup)
-        """
-        self.partitions = partitions
-
-    def partition(self, key, partitions):
-        """
-        Takes a string key and num_partitions as argument and returns
-        a partition to be used for the message
-
-        partitions - The list of partitions is passed in every call. This
-                     may look like an overhead, but it will be useful
-                     (in future) when we handle cases like rebalancing
-        """
-        raise NotImplementedError('partition function has to be implemented')
-
-
-class RoundRobinPartitioner(Partitioner):
-    """
-    Implements a round robin partitioner which sends data to partitions
-    in a round robin fashion
-    """
-    def __init__(self, partitions):
-        super(RoundRobinPartitioner, self).__init__(partitions)
-        self.iterpart = cycle(partitions)
-
-    def _set_partitions(self, partitions):
-        self.partitions = partitions
-        self.iterpart = cycle(partitions)
-
-    def partition(self, key, partitions):
-        # Refresh the partition list if necessary
-        if self.partitions != partitions:
-            self._set_partitions(partitions)
-
-        return next(self.iterpart)
-
-
-class HashedPartitioner(Partitioner):
-    """
-    Implements a partitioner which selects the target partition based on
-    the hash of the key
-    """
-    def partition(self, key, partitions):
-        size = len(partitions)
-        idx = hash(key) % size
-
-        return partitions[idx]