ENH: condense the blaze query

etc/requirements_blaze.txt

@@ -1,3 +1,4 @@
--e git://github.com/quantopian/blaze.git@831116adba808b89f42cef48c7d96cc44603d05a#egg=blaze-dev
--e git://github.com/quantopian/odo.git@4f7f45fb039d89ea101803b95da21fc055901d66#egg=odo-dev
 -e git://github.com/quantopian/datashape.git@9bd8fb970a0fc55e866a0b46b5101c9aa47e24ed#egg=datashape-dev
+-e git://github.com/quantopian/odo.git@4f7f45fb039d89ea101803b95da21fc055901d66#egg=odo-dev
+-e git://github.com/quantopian/blaze.git@0b6e76122a57c7115f18c6fdbd5fbab5501fd486#egg=blaze-dev
+

tests/pipeline/test_blaze.py

@@ -13,6 +13,7 @@
 from nose_parameterized import parameterized
 import numpy as np
 from numpy.testing.utils import assert_array_almost_equal
+from odo import odo
 import pandas as pd
 from pandas.util.testing import assert_frame_equal
 from toolz import keymap, valmap, concatv
@@ -845,11 +846,18 @@ def compute(self, today, assets, out, data):
     @with_extra_sid
     def test_deltas(self, asset_info):
         expr = bz.Data(self.df, name='expr', dshape=self.dshape)
-        deltas = bz.Data(self.df, name='deltas', dshape=self.dshape)
-        deltas = bz.transform(
-            deltas,
-            value=deltas.value + 10,
-            timestamp=deltas.timestamp + timedelta(days=1),
+        deltas = bz.Data(self.df, dshape=self.dshape)
+        deltas = bz.Data(
+            odo(
+                bz.transform(
+                    deltas,
+                    value=deltas.value + 10,
+                    timestamp=deltas.timestamp + timedelta(days=1),
+                ),
+                pd.DataFrame,
+            ),
+            name='delta',
+            dshape=self.dshape,
         )
 
         expected_views = keymap(pd.Timestamp, {
@@ -996,16 +1004,23 @@ def test_novel_deltas(self, asset_info):
         repeated_dates = base_dates.repeat(3)
         baseline = pd.DataFrame({
             'sid': self.sids * 2,
-            'value': (0, 1, 2, 1, 2, 3),
+            'value': (0., 1., 2., 1., 2., 3.),
+            'int_value': (0, 1, 2, 1, 2, 3),
             'asof_date': repeated_dates,
             'timestamp': repeated_dates,
         })
         expr = bz.Data(baseline, name='expr', dshape=self.dshape)
-        deltas = bz.Data(baseline, name='deltas', dshape=self.dshape)
-        deltas = bz.transform(
-            deltas,
-            value=deltas.value + 10,
-            timestamp=deltas.timestamp + timedelta(days=1),
+        deltas = bz.Data(
+            odo(
+                bz.transform(
+                    expr,
+                    value=expr.value + 10,
+                    timestamp=expr.timestamp + timedelta(days=1),
+                ),
+                pd.DataFrame,
+            ),
+            name='delta',
+            dshape=self.dshape,
         )
         expected_views = keymap(pd.Timestamp, {
             '2014-01-03': np.array([[10.0, 11.0, 12.0],

zipline/pipeline/loaders/blaze/core.py

@@ -872,50 +872,54 @@ def _load_dataset(self, dates, assets, mask, columns):
             data_query_tz,
         )
 
-        def where(e, column):
+        def where(e):
             """Create the query to run against the resources.
 
             Parameters
             ----------
             e : Expr
                 The baseline or deltas expression.
-            column : BoundColumn
-                The column to query for.
 
             Returns
             -------
             q : Expr
-                The query to run for the given column.
+                The query to run.
             """
-            colname = column.name
-            pred = e[TS_FIELD_NAME] <= lower_dt
-            schema = e[colname].schema.measure
-            if isinstance(schema, Option):
-                pred &= e[colname].notnull()
-                schema = schema.ty
-            if schema in floating:
-                pred &= ~e[colname].isnan()
-            filtered = e[pred]
-            lower = filtered.timestamp.max()
-
-            if have_sids:
-                # If we have sids, then we need to take the earliest of the
-                # greatest date that has a non-null value by sid.
-                lower = bz.by(
-                    filtered[SID_FIELD_NAME],
-                    timestamp=lower,
-                ).timestamp.min()
-
-            lower = odo(lower, pd.Timestamp)
+            def lower_for_col(column):
+                pred = e[TS_FIELD_NAME] <= lower_dt
+                colname = column.name
+                schema = e[colname].schema.measure
+                if isinstance(schema, Option):
+                    pred &= e[colname].notnull()
+                    schema = schema.ty
+                if schema in floating:
+                    pred &= ~e[colname].isnan()
+
+                filtered = e[pred]
+                lower = filtered[TS_FIELD_NAME].max()
+                if have_sids:
+                    # If we have sids, then we need to take the earliest of the
+                    # greatest date that has a non-null value by sid.
+                    lower = bz.by(
+                        filtered[SID_FIELD_NAME],
+                        timestamp=lower,
+                    ).timestamp.min()
+                return lower
+
+            lower = odo(
+                reduce(
+                    bz.least,
+                    map(lower_for_col, columns),
+                ),
+                pd.Timestamp,
+                **odo_kwargs
+            )
             if lower is pd.NaT:
-                # If there is no lower date, just query for data in he date
-                # range. It must all be null anyways.
                 lower = lower_dt
-
             return e[
                 (e[TS_FIELD_NAME] >= lower) &
                 (e[TS_FIELD_NAME] <= upper_dt)
-            ][added_query_fields + [colname]]
+            ][added_query_fields + list(map(getname, columns))]
 
         def collect_expr(e):
             """Execute and merge all of the per-column subqueries.
@@ -935,13 +939,9 @@ def collect_expr(e):
             This can return more data than needed. The in memory reindex will
             handle this.
             """
-            return sort_values(reduce(
-                partial(pd.merge, on=added_query_fields, how='outer'),
-                (
-                    odo(where(e, column), pd.DataFrame, **odo_kwargs)
-                    for column in columns
-                ),
-            ), TS_FIELD_NAME)  # sort for the groupby later
+            df = odo(where(e), pd.DataFrame, **odo_kwargs)
+            df.sort(TS_FIELD_NAME, inplace=True)  # sort for the groupby later
+            return df
 
         materialized_expr = collect_expr(expr)
         materialized_deltas = (