MDP-1754 Improve the memory and cpu performance of reading a subset of

columns.

By using the index bitfield masks we can return a sparse dataframe.

This is a behaviour change, as we don't return rows for timestamps where
the field wasn't updated.

Old code:
=========

# All columns
%timeit l.read('3284.JP', date_range=adu.DateRange(20170101, 20170206))
1 loops, best of 3: 1.99 s per loop

# Multiple columns
%timeit l.read('3284.JP', date_range=adu.DateRange(20170101, 20170206),
columns=['DISC_BID1', 'BID'])
10 loops, best of 3: 82.2 ms per loop

# Single very sparse column
%timeit l.read('3284.JP', date_range=adu.DateRange(20170101, 20170206),
columns=['DISC_BID1'])
10 loops, best of 3: 76.4 ms per loop


New code:
=========

# All columns
%timeit l.read('3284.JP', date_range=adu.DateRange(20170101, 20170206))
1 loop, best of 3: 2.29 s per loop

# Multiple columns
%timeit l.read('3284.JP', date_range=adu.DateRange(20170101, 20170206),
columns=['DISC_BID1', 'BID'])
10 loops, best of 3: 75.4 ms per loop

# Single very sparse column
%timeit l.read('3284.JP', date_range=adu.DateRange(20170101, 20170206),
columns=['DISC_BID1'])
10 loops, best of 3: 47.4 ms per loop

Fixes pandas-dev#290

arctic/tickstore/tickstore.py

@@ -414,10 +414,25 @@ def _read_bucket(self, doc, column_set, column_dtypes, include_symbol, include_i
             raise ArcticException("Unhandled document version: %s" % doc[VERSION])
         rtn[INDEX] = np.cumsum(np.fromstring(lz4.decompress(doc[INDEX]), dtype='uint64'))
         doc_length = len(rtn[INDEX])
-        rtn_length = len(rtn[INDEX])
+        column_set.update(doc[COLUMNS].keys())
+
+        # get the mask for the columns we're about to load
+        union_mask = np.zeros((doc_length + 7) // 8, dtype='uint8')
+        for c in column_set:
+            try:
+                coldata = doc[COLUMNS][c]
+                mask = np.fromstring(lz4.decompress(coldata[ROWMASK]), dtype='uint8')
+                union_mask = union_mask | mask
+            except KeyError:
+                rtn[c] = None
+        union_mask = np.unpackbits(union_mask)[:doc_length].astype('bool')
+        rtn_length = np.sum(union_mask)
+
+        rtn[INDEX] = rtn[INDEX][union_mask]
         if include_symbol:
             rtn['SYMBOL'] = [doc[SYMBOL], ] * rtn_length
-        column_set.update(doc[COLUMNS].keys())
+
+        # Unpack each requested column in turn
         for c in column_set:
             try:
                 coldata = doc[COLUMNS][c]
@@ -427,6 +442,7 @@ def _read_bucket(self, doc, column_set, column_dtypes, include_symbol, include_i
                 rtn[c] = self._empty(rtn_length, dtype=column_dtypes[c])
                 rowmask = np.unpackbits(np.fromstring(lz4.decompress(coldata[ROWMASK]),
                                                       dtype='uint8'))[:doc_length].astype('bool')
+                rowmask = rowmask[union_mask]
                 rtn[c][rowmask] = values
             except KeyError:
                 rtn[c] = None

tests/integration/tickstore/test_ts_read.py

@@ -85,12 +85,12 @@ def test_read_allow_secondary(tickstore_lib):
 
 def test_read_symbol_as_column(tickstore_lib):
     data = [{'ASK': 1545.25,
-                  'index': 1185076787070},
-                 {'CUMVOL': 354.0,
-                  'index': 1185141600600}]
+             'index': 1185076787070},
+            {'CUMVOL': 354.0,
+             'index': 1185141600600}]
     tickstore_lib.write('FEED::SYMBOL', data)
 
-    df = tickstore_lib.read('FEED::SYMBOL', columns=['SYMBOL'])
+    df = tickstore_lib.read('FEED::SYMBOL', columns=['SYMBOL', 'CUMVOL'])
     assert all(df['SYMBOL'].values == ['FEED::SYMBOL'])
 
 
@@ -596,15 +596,12 @@ def test_read_with_image(tickstore_lib):
     # Read one column from the updates
     df = tickstore_lib.read('SYM', columns=('a',), date_range=dr, include_images=True)
     assert set(df.columns) == set(('a',))
-    assert_array_equal(df['a'].values, np.array([37, 1, np.nan]))
+    assert_array_equal(df['a'].values, np.array([37, 1]))
     assert df.index[0] == dt(2013, 1, 1, 10, tzinfo=mktz('Europe/London'))
     assert df.index[1] == dt(2013, 1, 1, 11, tzinfo=mktz('Europe/London'))
-    assert df.index[2] == dt(2013, 1, 1, 12, tzinfo=mktz('Europe/London'))
 
     # Read just the image column
     df = tickstore_lib.read('SYM', columns=['c'], date_range=dr, include_images=True)
     assert set(df.columns) == set(['c'])
-    assert_array_equal(df['c'].values, np.array([2, np.nan, np.nan]))
+    assert_array_equal(df['c'].values, np.array([2]))
     assert df.index[0] == dt(2013, 1, 1, 10, tzinfo=mktz('Europe/London'))
-    assert df.index[1] == dt(2013, 1, 1, 11, tzinfo=mktz('Europe/London'))
-    assert df.index[2] == dt(2013, 1, 1, 12, tzinfo=mktz('Europe/London'))