Use MurmurHash2 for key partition hashing

kafka/partitioner/__init__.py

@@ -1,6 +1,7 @@
 from .roundrobin import RoundRobinPartitioner
-from .hashed import HashedPartitioner
+from .hashed import HashedPartitioner, Murmur2Partitioner, LegacyPartitioner
 
 __all__ = [
-    'RoundRobinPartitioner', 'HashedPartitioner'
+    'RoundRobinPartitioner', 'HashedPartitioner', 'Murmur2Partitioner',
+    'LegacyPartitioner'
 ]

kafka/partitioner/hashed.py

@@ -1,7 +1,25 @@
 from .base import Partitioner
 
-class HashedPartitioner(Partitioner):
+
+class Murmur2Partitioner(Partitioner):
     """
+    Implements a partitioner which selects the target partition based on
+    the hash of the key. Attempts to apply the same hashing
+    function as mainline java client.
+    """
+    def partition(self, key, partitions=None):
+        if not partitions:
+            partitions = self.partitions
+
+        # https://github.com/apache/kafka/blob/0.8.2/clients/src/main/java/org/apache/kafka/clients/producer/internals/Partitioner.java#L69
+        idx = (murmur2(key) & 0x7fffffff) % len(partitions)
+
+        return partitions[idx]
+
+
+class LegacyPartitioner(Partitioner):
+    """DEPRECATED -- See Issue 374
+
     Implements a partitioner which selects the target partition based on
     the hash of the key
     """
@@ -12,3 +30,79 @@ class HashedPartitioner(Partitioner):
         idx = hash(key) % size
 
         return partitions[idx]
+
+
+# Default will change to Murmur2 in 0.10 release
+HashedPartitioner = LegacyPartitioner
+
+
+# https://github.com/apache/kafka/blob/0.8.2/clients/src/main/java/org/apache/kafka/common/utils/Utils.java#L244
+def murmur2(key):
+    """Pure-python Murmur2 implementation.
+
+    Based on java client, see org.apache.kafka.common.utils.Utils.murmur2
+
+    Args:
+        key: if not a bytearray, converted via bytearray(str(key))
+
+    Returns: MurmurHash2 of key bytearray
+    """
+
+    # Convert key to a bytearray
+    if not isinstance(key, bytearray):
+        data = bytearray(str(key))
+
+    length = len(data)
+    seed = 0x9747b28c
+    # 'm' and 'r' are mixing constants generated offline.
+    # They're not really 'magic', they just happen to work well.
+    m = 0x5bd1e995
+    r = 24
+
+    # Initialize the hash to a random value
+    h = seed ^ length
+    length4 = length / 4
+
+    for i in range(length4):
+        i4 = i * 4
+        k = ((data[i4 + 0] & 0xff) +
+            ((data[i4 + 1] & 0xff) << 8) +
+            ((data[i4 + 2] & 0xff) << 16) +
+            ((data[i4 + 3] & 0xff) << 24))
+        k &= 0xffffffff
+        k *= m
+        k &= 0xffffffff
+        k ^= (k % 0x100000000) >> r # k ^= k >>> r
+        k &= 0xffffffff
+        k *= m
+        k &= 0xffffffff
+
+        h *= m
+        h &= 0xffffffff
+        h ^= k
+        h &= 0xffffffff
+
+    # Handle the last few bytes of the input array
+    extra_bytes = length % 4
+    if extra_bytes == 3:
+        h ^= (data[(length & ~3) + 2] & 0xff) << 16
+        h &= 0xffffffff
+
+    if extra_bytes == 2:
+        h ^= (data[(length & ~3) + 1] & 0xff) << 8
+        h &= 0xffffffff
+
+    if extra_bytes == 1:
+        h ^= (data[length & ~3] & 0xff)
+        h &= 0xffffffff
+        h *= m
+        h &= 0xffffffff
+
+    h ^= (h % 0x100000000) >> 13 # h >>> 13;
+    h &= 0xffffffff
+    h *= m
+    h &= 0xffffffff
+    h ^= (h % 0x100000000) >> 15 # h >>> 15;
+    h &= 0xffffffff
+
+    return h