diff --git a/ccdbknn.py b/ccdbknn.py
new file mode 100644
index 0000000..dd7e86c
--- /dev/null
+++ b/ccdbknn.py
@@ -0,0 +1,89 @@
+#!/usr/bin/python3
+from __future__ import print_function
+import numpy 
+import sys 
+import nmslib 
+import time 
+import math 
+import os.path
+from scipy.sparse import csr_matrix
+
+def eprint(*args, **kwargs):
+    print(*args, file=sys.stderr, **kwargs)
+
+def read_data(filename, max_qty = None): 
+    row = []
+    col = []
+    data = []
+    read_qty = 0
+    row_max = 0
+    with open(filename,'r') as f:
+        read_num_ft = 0
+        for line in f:
+            x = line.strip().split()
+            if (len(x) == 0): continue
+            if (len(x) % 2 != 0):
+                raise(Exception('Poorly formated line %d in file %s' % (read_qty + 1, filename)))
+            for i in range(0, len(x), 2):
+                #row.append(int(x[0])-1) 
+                row.append(read_qty) 
+                feat_id = int(x[i])
+                read_num_ft = max(read_num_ft, feat_id + 1)
+                col.append(feat_id)
+                data.append(float(x[i+1]))
+
+            read_qty = read_qty+1
+            # row_max = max(row_max, int(x[0]))
+            if max_qty != None and read_qty >= max_qty: break
+            if (read_qty % 10) == 0:
+                eprint('Read %d rows' % read_qty)
+    eprint('Read %d rows, # of features %d' %  (read_qty, read_num_ft))
+    ft_mat = csr_matrix((numpy.array(data), (numpy.array(row), numpy.array(col))), 
+                        shape=(read_qty, read_num_ft)) 
+    return (read_qty, ft_mat)
+
+input_file = sys.argv[1]
+
+# This file will contain nearest neighbors, one per line:
+# node [tab char] neighbor_1 neighbor_2 ...
+
+out_file = input_file + ".bin"
+
+(all_qty, data_matrix) = read_data(input_file)
+# Set index parameters
+# These are the most important onese
+M = 30
+efC = 100
+
+num_threads = 70
+index_time_params = {'M': M, 'indexThreadQty': num_threads, 'efConstruction': efC, 'post' : 0}
+K=100
+# Intitialize the library, specify the space, the type of the vector and add data points 
+index = nmslib.init(method='hnsw', space='cosinesimil_sparse', data_type=nmslib.DataType.SPARSE_VECTOR) 
+index.addDataPointBatch(data_matrix)
+eprint('Starting index creation.')
+# Create an index
+start = time.time()
+index.createIndex(index_time_params, print_progress=True) 
+end = time.time() 
+eprint('Index-time parameters', index_time_params)
+eprint('Indexing time = %f' % (end-start))
+
+# Setting query-time parameters
+efS = 100
+query_time_params = {'efSearch': efS}
+eprint('Setting query-time parameters', query_time_params)
+index.setQueryTimeParams(query_time_params)
+# Querying
+query_qty = data_matrix.shape[0]
+start = time.time() 
+nbrs = index.knnQueryBatch(data_matrix, k = K, num_threads = num_threads)
+end = time.time() 
+eprint('kNN time total=%f (sec), per query=%f (sec), per query adjusted for thread number=%f (sec)' % 
+      (end-start, float(end-start)/query_qty, num_threads*float(end-start)/query_qty))
+
+for i in range(0, len(nbrs), 1):
+    for j in range(0, len(nbrs[i][0]), 1):
+        print("%d %f " % (nbrs[i][0][j], nbrs[i][1][j]), end='')
+    print()
+#index.saveIndex('sparse_index.bin')