Datasketch

* Performance improvement for MinHash's update method.
* Make MinHash updates 4.5X faster by using `update_batch` method for bulk update on MinHash. [See API doc].(http://ekzhu.com/datasketch/documentation.html#datasketch.MinHash.update_batch)
* Further performance gain by using bulk generation of MinHash using `MinHash.bulk` or `MinHash.generator`. See [API doc](http://ekzhu.com/datasketch/documentation.html#datasketch.MinHash.bulk) and [pull request](https://github.com/ekzhu/datasketch/pull/142).
* Optional compression for MinHash LSH index by hashing the bucket key produced by `MinHashLSH._H`. See [pull request](https://github.com/ekzhu/datasketch/pull/143). This leads to saving of memory/storage space used by the index.

Thank you Sinusoidal36!

* Minor bug fixes
* Cassandra storage layer, thank ostefano! Now you can specify the Cassandra config just like the Redis one.

python
from datasketch import MinHashLSH

lsh = MinHashLSH(
threashold=0.5, num_perm=128, storage_config={
'type': 'cassandra',
'cassandra': {
'seeds': ['127.0.0.1'],
'keyspace': 'lsh_test',
'replication': {
'class': 'SimpleStrategy',
'replication_factor': '1',
},
'drop_keyspace': False,
'drop_tables': False,
}
}
)

Now support `hashfunc` parameter for MinHash and HyperLogLog. The old parameter `hashobj` is removed.

python
Let's use MurmurHash3.
import mmh3

We need to define a new hash function that outputs an integer that
can be encoded in 32 bits.
def _hash_func(d):
return mmh3.hash32(d)

Use this function in MinHash constructor.
m = MinHash(hashfunc=_hash_func)


v.1.3.0
Use dynamic programming to create optimal partition, allow LSH Ensemble index to adapt to any set size distribution.

* Adding batch removal functionality for Async MinHashLSH
* Because Redis does not support async operation, removed Redis support from Async MinHashLSH

For details see Pull 70
Thanks aastafiev for the contribution.

Add support for MongoDB replica set