diff --git a/src/py/crankshaft/crankshaft/similarity/similarity.py b/src/py/crankshaft/crankshaft/similarity/similarity.py
index 1c10629..cf2db75 100644
--- a/src/py/crankshaft/crankshaft/similarity/similarity.py
+++ b/src/py/crankshaft/crankshaft/similarity/similarity.py
@@ -1,13 +1,16 @@
-from sklearn.neighbors import BallTree
+from sklearn.neighbors import NearestNeighbors
 import  scipy.stats as stats
 import numpy as np
 import plpy
+import time
+import cPickle
+
 
 def query_to_dictionary(result):
     return [ dict(zip(r.keys(), r.values())) for r in result ]
 
 def drop_all_nan_columns(data):
-    return data[ ~np.isnan(data).all(axis=0)]
+    return data[ :, ~np.isnan(data).all(axis=0)]
     
 def fill_missing_na(data,val=None):
     inds = np.where(np.isnan(data))
@@ -19,21 +22,43 @@ def fill_missing_na(data,val=None):
     return data
     
 def similarity_rank(target_cartodb_id, query):
-    data = query_to_dictionary(plpy.execute(query))  
+    start_time  = time.time() 
+    #plpy.notice('converting to dictionary ', start_time) 
+    #data = query_to_dictionary(plpy.execute(query))  
+    plpy.notice('coverted , running query ', time.time() - start_time) 
     
-    features, target = extract_features_target(data,target_cartodb_id)
+    data = plpy.execute(query_only_values(query))
+    plpy.notice('run query  , getting cartodb_idsi', time.time() - start_time)
+    cartodb_ids = plpy.execute(query_cartodb_id(query))[0]['a']
+    target_id  = cartodb_ids.index(target_cartodb_id)
+    plpy.notice('run query  , extracting ', time.time() - start_time)
+    features, target = extract_features_target(data,target_id)
+    plpy.notice('extracted  , cleaning ', time.time() - start_time)
     features = fill_missing_na(drop_all_nan_columns(features))
+    plpy.notice('cleaned , normalizing', start_time - time.time())
     
     normed_features, normed_target  = normalize_features(features,target)
+    plpy.notice('normalized , training ', time.time() - start_time )
     tree = train(normed_features)
-    dist, ind  = tree.query(normed_target, k=len(features))
-    cartodb_ids  = [data[index]['cartodb_id'] for index in ind[0]]
+    plpy.notice('normalized , pickling ', time.time() - start_time )
+    #plpy.notice('tree_dump ',  len(cPickle.dumps(tree, protocol=cPickle.HIGHEST_PROTOCOL)))
+    plpy.notice('pickles, querying ', time.time() - start_time)
+    dist, ind  = tree.kneighbors(normed_target)
+    plpy.notice('queried , rectifying', time.time() - start_time)
     return zip(cartodb_ids, dist[0])
 
+def query_cartodb_id(query):
+    return 'select array_agg(cartodb_id) a from ({0}) b'.format(query)
+
+def query_only_values(query):
+    first_row = plpy.execute('select * from ({query}) a limit 1'.format(query=query))
+    just_values = ','.join([ key for key in  first_row[0].keys()  if key not in ['the_geom', 'the_geom_webmercator','cartodb_id']])
+    return 'select Array[{0}] a from ({1}) b '.format(just_values, query)
+
+
 def most_similar(matches,query):
     data = plpy.execute(query)    
     features, _ = extract_features_target(data)
-    tree = train(features)
     results = []
     for i in features:
         target = features
@@ -44,22 +69,23 @@ def most_similar(matches,query):
     
     
 def train(features):
-    tree = BallTree(features, leaf_size=2)
+    tree = NearestNeighbors( n_neighbors=len(features), algorithm='auto').fit(features)
     return tree
     
 def normalize_features(features, target):
     maxes = features.max(axis=0)
     mins  = features.min(axis=0)
     return (features - mins)/(maxes-mins), (target-mins)/(maxes-mins)
-    
-def extract_features_target(data, target_cartodb_id=None):
-    target = None
-    features = []
-    for row in data:
-        values = [ val for key, val in row.iteritems() if key not in ['the_geom', 'the_geom_webmercator','cartodb_id']]
-        if row['cartodb_id'] == target_cartodb_id:
-            target = values
-        features.append(values)
+ 
+def extract_row(row):
+    keys = row.keys()
+    values = row.values()
+    del values[ keys.index('cartodb_id')]
+    return values
 
+def extract_features_target(data, target_index=None):
+    target   = None
+    features = [row['a'] for row in data]
+    target   = features[target_index]
     return np.array(features, dtype=float), np.array(target, dtype=float)