big refactoring to how to test works

- new context manager to measure code's performance
- now creates and fill arrays by memory size and not number of rows anymore
- only 1 row from mariadb is broadcasted to the entire array (doesnt spend a lot of time waiting mysql anymore)
- removed useless profile functions (just from this repository)
- added docstrings

performance_test.py

@@ -23,21 +23,23 @@ def bigger_than_memory_write(self, out_of_core_index=False):
   message_list = []
   message_list.append('Write start: ' + get_process_memory_usage())
 
-  #ram_nbytes = psutil.virtual_memory().total
-  #big_array_len = (1*ram_nbytes) // big_array_dtype.itemsize
   _, array_schema = get_field_names_and_schema()
+  float_dtype = dtype(float64)
                 
-  big_array = create_zbigarray((360000,), dtype=array_schema)
-  big_index = create_zbigarray((1, 360000), dtype(float64)) if out_of_core_index else np.arange(360000)
+  array_memory_size = 1*GB
+  array_number_of_items = array_memory_size // array_schema.itemsize
   
+  big_array = create_zbigarray(memory_size=array_memory_size, dtype=array_schema)
+
   root = self.getPhysicalRoot()
-  
   if out_of_core_index:
+    big_index = create_zbigarray(memory_size=array_number_of_items, dtype=float_dtype)
     big_array, big_index = store_arrays(root, [big_array, 'big_array'], [big_index, 'big_index'])
   else:
+    big_index = np.arange(array_number_of_items)
     big_array = store_arrays(root, [big_array, 'big_array'])[0]
   
-  response = populate_array(self, 30000, 12, big_array, big_index, array_schema)
+  response = populate_array(self, big_array, big_index, array_schema)
   message_list.extend(response['messages'])
   
   message_list.append('Write end: ' + get_process_memory_usage())
@@ -57,42 +59,35 @@ def bigger_than_memory_read(self, out_of_core_index=False):
   ob = root.big_index
   message_list.append(len(ob._p_jar.db().storage.load(ob._p_oid, '')[0])+42)
   
-  big_index = root.big_index[:] if out_of_core_index else np.arange(360000)
+  number_of_items = len(root.big_array)
+  item_size = root.big_array.dtype.itemsize
+  message_list.append('Processing %s items with %s bytes each' % (number_of_items, item_size))
   
-  response = process_data(root.big_array[:], big_index[:], columns)
-  message_list.extend(response['messages'])
-  message_list.append(response['result'])
-
+  big_index = root.big_index[:] if out_of_core_index else np.arange(number_of_items)
+  
+  messages = process_data(root.big_array[:], big_index[:], columns)
+  message_list.extend(messages)
   message_list.append('Read end: ' + get_process_memory_usage())
   return message_list
 
-def bigger_than_memory_profile(self):
-  profiles = [bigger_than_memory_read_profile, bigger_than_memory_write_profile]
-  return [profile(self) for profile in profiles]
-
-def bigger_than_memory_read_profile(self):
-  profile_path = '/tmp/profile_read'
-  cProfile.runctx('bigger_than_memory_read(self)', globals(), locals(), profile_path)
-  return "To see the profile start a console on server and: import pstats; pstats.Stats('%s').print_stats()" % profile_path
-  
-def bigger_than_memory_write_profile(self):
-  profile_path = '/tmp/profile_write'
-  cProfile.runctx('bigger_than_memory_write(self)', globals(), locals(), profile_path)
-  return "To see the profile start a console on server and: import pstats; pstats.Stats('%s').print_stats()" % profile_path
-
 @contextmanager
 def timer(name, message_list):
+  '''A context manager to time function calls and add the result to
+  message_list.
+  '''
   start_time = time()
   yield
   elapsed_time = time() - start_time
   message_list.append('[%s] finished in % seconds' % (name, elapsed_time))
 
 def get_process_memory_usage():
+  '''Return the runnign process' memory usage.'''
   process = psutil.Process(os.getpid())
   m = process.memory_info()
   return 'VIRT: %i MB\tRSS: %iMB' % (m.vms//MB, m.rss//MB)
 
 def get_field_names_and_schema():
+  '''Returns the array's dtype and its field names.'''
   field_names = ['f%s' % number if not number == 13 else 'quantity' for number in range(23)]
   array_schema = np.dtype({
     'names' : field_names,
@@ -100,58 +95,51 @@ def get_field_names_and_schema():
       np.dtype,
       ['i8', 'i8', 'i8', 'i8', 'i8', 'i8', 'i8', 'i8', 'i8', 'i8',
         'i8', 'i8', 'i8', 'f8', 'i8', 'i8', 'f8', 'f8', 'f8',
-        'a50', 'a50', 'a50', 'a50']
-        #'f8', 'f8', 'f8', 'f8']
+        # 'a50', 'a50', 'a50', 'a50']
+        'f8', 'f8', 'f8', 'f8']
     )
   })
   return field_names, array_schema
 
-def create_zbigarray(dimension, dtype):
-  return ZBigArray(dimension, dtype)
+def create_zbigarray(memory_size=None, dtype=None):
+  '''Create a ZBigArray with memory_size bytes with the defined dtype.'''
+  if memory_size and dtype:
+    element_number = memory_size // dtype.itemsize
+    return ZBigArray((element_number,), dtype)
+  else:
+    raise Exception('You need to provide memory_size and dtype')
   
 def store_arrays(root, *arrays_filenames):
+  '''Store all [array, filename] pair in arrays_filenames in root and return an
+  ordered list with all store arrays.
+  '''
   for array, filename in arrays_filenames:
     setattr(root, filename, None)
     setattr(root, filename, array)
   transaction.commit()
   return [getattr(root, filename) for _, filename in arrays_filenames]
-  
-def populate_array(root, chunk_size, number_of_chunks, big_array, big_index, schema):
-  offset = 0
-  start_populate_wendelin = mysql_time = time()
-  # ZSQL Method to fetch data from stocks table, chunk by chunk, and put it in
-  # the ZBigArray. Implemented using DTML's sqlvar instruction, LIMIT andd OFFSET.
-  data = root.stock_offset(my_offset=0, my_limit=chunk_size)
-  mysql_time = time() - mysql_time
-  columns = data._names
-  tuples = data.tuples()
-  counter = 0
+
+def populate_array(root, big_array, big_index, schema):
+  '''Fill big_array with copies of a MySQL row until it is completely full'''
   message_list = []
-  message_list.append('Running with %s chunks of %s rows.' % (number_of_chunks, chunk_size))
-  
-  chunk_byte_size = sys.getsizeof(tuples)
-  message_list.append('Memory (in bytes) for each chunk: %s' % chunk_byte_size)
-  for i in range(number_of_chunks):
-    for index, row in enumerate(tuples):
-      tuples[index] = tuple([filter_item(item) for item in row])
-    if len(tuples) < chunk_size:
-      chunk_end = chunk_size*(i) + len(tuples)
-    else:
-      chunk_end = chunk_size*(i+1)
-    big_array[offset:chunk_end] = np.array(tuples, schema)
-    offset += chunk_size
-    temp_mysql_time = time()
-    data = root.stock_offset(my_offset=offset, my_limit=chunk_size)
-    mysql_time += time()-temp_mysql_time
-    tuples = data.tuples()
-  total_time = time() - start_populate_wendelin
-  message_list.append('%s seconds to load & write %s records into wendelin array. %s seconds on MySQL loads and %s writting to disk.' % (total_time, chunk_end, mysql_time, total_time-mysql_time))
+  data = root.stock_offset(my_offset=0, my_limit=1)
+  columns = data._names
+  with timer('Time to prepare data for assignment', message_list):
+    row = tuple([filter_item(item, normalize=True) for item in data.tuples()[0]])
+  with timer('Time to fill ZBigArray', message_list):
+    big_array[:] = row
   transaction.commit()
-  message_list.append('Total size of out-of-core array: %s megabytes.' % (big_array.nbytes // MB))
   return {'messages': message_list, 'columns': columns}
 
-def filter_item(item):
-  if not item or isinstance(item, type(None)): #or isinstance(item, (str, unicode)):
+# method to cast Zope's DateTime objects, falsy-values and strings to floats
+def filter_item(item, normalize=True):
+  '''Typecast item to numeric values if it is a: DateTime or falsy to help
+  pandas/numpy deal with them. If normalize is True it will typecast strings
+  and unicodes to floats too, thus reducing that size dramatically.
+  '''
+  if not item or isinstance(item, type(None)):
+    return 0
+  if normalize and isinstance(item, (str, unicode)):
     return 0
   elif isinstance(item, DateTime):
     return 0
@@ -159,16 +147,22 @@ def filter_item(item):
     return item
 
 def process_data(big_array, big_index, columns):
+  '''Process all data in big_array. Currently, does a complete sum of the
+  quantity column both with wendein.core/numpy and pandas.
+  '''
   message_list = []
   result = None
   
-  with timer('pandas sum', message_list):
-    df = pd.DataFrame.from_records(big_array, index=big_index)
-    result = df.quantity.sum()
+  try:
+    with timer('pandas sum', message_list):
+      df = pd.DataFrame.from_records(big_array, index=big_index)
+      result = df.quantity.sum()
+    message_list.append('Pandas result: %s' % result) if result else None
+  except MemoryError:
+    message_list.append('MemoryError while creating pandas.Dataframe!')
   
   with timer('numpy sum', message_list):
-    big_array['quantity'].sum()
-  
-  # message_list.append('%s seconds to read the wendelin array with %s rows.' % (finish-start, len(big_array)))
-  # message_list.append('Total size of out-of-core array: %s megabytes.' % (big_array.nbytes // MB))
-  return { 'messages': message_list, 'result': result }
+    result = big_array['quantity'].sum()
+  message_list.append('NumPy result: %s' % result)
+
+  return message_list
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