Implement basic aggregation, group_by and window (ibis-project#3)

ibis/pyspark/compiler.py

@@ -1,11 +1,16 @@
 import ibis.common as com
 import ibis.sql.compiler as comp
+import ibis.expr.window as window
 import ibis.expr.operations as ops
 
 
 from ibis.pyspark.operations import PysparkTable
+
 from ibis.sql.compiler import Dialect
 
+import pyspark.sql.functions as F
+from pyspark.sql.window import Window
+
 _operation_registry = {
 }
 
@@ -45,6 +50,7 @@ def compile_datasource(t, expr):
     name, _, client = op.args
     return client._session.table(name)
 
+
 @compiles(ops.Selection)
 def compile_selection(t, expr):
     op = expr.op()
@@ -63,6 +69,64 @@ def compile_column(t, expr):
     return t.translate(op.table)[op.name]
 
 
+@compiles(ops.Multiply)
+def compile_multiply(t, expr):
+    op = expr.op()
+    return t.translate(op.left) * t.translate(op.right)
+
+
+@compiles(ops.Subtract)
+def compile_subtract(t, expr):
+    op = expr.op()
+    return t.translate(op.left) - t.translate(op.right)
+
+
+@compiles(ops.Aggregation)
+def compile_aggregation(t, expr):
+    op = expr.op()
+
+    src_table = t.translate(op.table)
+    aggs = [t.translate(m) for m in op.metrics]
+
+    if op.by:
+        bys = [t.translate(b) for b in op.by]
+        return src_table.groupby(*bys).agg(*aggs)
+    else:
+        return src_table.agg(*aggs)
+
+
+@compiles(ops.Max)
+def compile_max(t, expr):
+    op = expr.op()
+
+    # TODO: Derive the UDF output type from schema
+    @F.pandas_udf('long', F.PandasUDFType.GROUPED_AGG)
+    def max(v):
+        return v.max()
+
+    src_column = t.translate(op.arg)
+    return max(src_column)
+
+@compiles(ops.Mean)
+def compile_mean(t, expr):
+    op = expr.op()
+    src_column = t.translate(op.arg)
+
+    return F.mean(src_column)
+
+@compiles(ops.WindowOp)
+def compile_window_op(t, expr):
+    op = expr.op()
+    return t.translate(op.expr).over(compile_window(op.window))
+
+# Cannot register with @compiles because window doesn't have an
+# op() object
+def compile_window(expr):
+    window = expr
+    spark_window = Window.partitionBy()
+    return spark_window
+
+
 t = PysparkExprTranslator()
 
 def translate(expr):

ibis/pyspark/tests/test_basic.py

@@ -3,6 +3,8 @@
 import pytest
 
 import ibis
+import pyspark.sql.functions as F
+from pyspark.sql.window import Window
 
 @pytest.fixture(scope='session')
 def client():
@@ -21,7 +23,6 @@ def test_basic(client):
 
 
 def test_projection(client):
-    import ipdb; ipdb.set_trace()
     table = client.table('table1')
     result1 = table.mutate(v=table['id']).compile().toPandas()
 
@@ -46,5 +47,34 @@ def test_projection(client):
     tm.assert_frame_equal(result2, expected2)
 
 
-def test_udf(client):
+def test_aggregation(client):
     table = client.table('table1')
+    result = table.aggregate(table['id'].max()).compile()
+    expected = table.compile().agg(F.max('id'))
+
+    tm.assert_frame_equal(result.toPandas(), expected.toPandas())
+
+
+def test_groupby(client):
+    table = client.table('table1')
+    result = table.groupby('id').aggregate(table['id'].max()).compile()
+    expected = table.compile().groupby('id').agg(F.max('id'))
+
+    tm.assert_frame_equal(result.toPandas(), expected.toPandas())
+
+
+def test_window(client):
+    table = client.table('table1')
+    w = ibis.window()
+    result = table.mutate(grouped_demeaned = table['id'] - table['id'].mean().over(w)).compile()
+    result2 = table.groupby('id').mutate(grouped_demeaned = table['id'] - table['id'].mean()).compile()
+
+    spark_window = Window.partitionBy()
+    spark_table = table.compile()
+    expected = spark_table.withColumn(
+        'grouped_demeaned',
+        spark_table['id'] - F.mean(spark_table['id']).over(spark_window)
+    )
+
+    tm.assert_frame_equal(result.toPandas(), expected.toPandas())
+    tm.assert_frame_equal(result2.toPandas(), expected.toPandas())