def test_cc_rename():
c = ColumnContainer(["a", "b", "c"])
c2 = c.rename({"a": "A", "b": "a"})
assert c2.columns == ["A", "a", "c"]
assert c2.mapping() == [("a", "b"), ("b", "b"), ("c", "c"), ("A", "a")]
assert c.columns == ["a", "b", "c"]
assert c.mapping() == [("a", "a"), ("b", "b"), ("c", "c")]
def fix_column_to_row_type(
cc: ColumnContainer,
row_type: "org.apache.calcite.rel.type.RelDataType"
) -> ColumnContainer:
"""
Make sure that the given column container
has the column names specified by the row type.
We assume that the column order is already correct
and will just "blindly" rename the columns.
"""
field_names = [str(x) for x in row_type.getFieldNames()]
cc = cc.rename(columns=dict(zip(cc.columns, field_names)))
# TODO: We can also check for the types here and do any conversions if needed
return cc.limit_to(field_names)
def convert(self, rel: "org.apache.calcite.rel.RelNode",
context: "dask_sql.Context") -> DataContainer:
# Joining is a bit more complicated, so lets do it in steps:
# 1. We now have two inputs (from left and right), so we fetch them both
dc_lhs, dc_rhs = self.assert_inputs(rel, 2, context)
cc_lhs = dc_lhs.column_container
cc_rhs = dc_rhs.column_container
# 2. dask's merge will do some smart things with columns, which have the same name
# on lhs an rhs (which also includes reordering).
# However, that will confuse our column numbering in SQL.
# So we make our life easier by converting the column names into unique names
# We will convert back in the end
cc_lhs_renamed = cc_lhs.make_unique("lhs")
cc_rhs_renamed = cc_rhs.make_unique("rhs")
dc_lhs_renamed = DataContainer(dc_lhs.df, cc_lhs_renamed)
dc_rhs_renamed = DataContainer(dc_rhs.df, cc_rhs_renamed)
df_lhs_renamed = dc_lhs_renamed.assign()
df_rhs_renamed = dc_rhs_renamed.assign()
join_type = rel.getJoinType()
join_type = self.JOIN_TYPE_MAPPING[str(join_type)]
# 3. The join condition can have two forms, that we can understand
# (a) a = b
# (b) X AND Y AND a = b AND Z ... (can also be multiple a = b)
# The first case is very simple and we do not need any additional filter
# In the second case we do a merge on all the a = b,
# and then apply a filter using the other expressions.
# In all other cases, we need to do a full table cross join and filter afterwards.
# As this is probably non-sense for large tables, but there is no other
# known solution so far.
join_condition = rel.getCondition()
lhs_on, rhs_on, filter_condition = self._split_join_condition(
join_condition)
logger.debug(
f"Joining with type {join_type} on columns {lhs_on}, {rhs_on}.")
# lhs_on and rhs_on are the indices of the columns to merge on.
# The given column indices are for the full, merged table which consists
# of lhs and rhs put side-by-side (in this order)
# We therefore need to normalize the rhs indices relative to the rhs table.
rhs_on = [index - len(df_lhs_renamed.columns) for index in rhs_on]
# 4. dask can only merge on the same column names.
# We therefore create new columns on purpose, which have a distinct name.
assert len(lhs_on) == len(rhs_on)
if lhs_on:
# 5. Now we can finally merge on these columns
# The resulting dataframe will contain all (renamed) columns from the lhs and rhs
# plus the added columns
df = self._join_on_columns(
df_lhs_renamed,
df_rhs_renamed,
lhs_on,
rhs_on,
join_type,
)
else:
# 5. We are in the complex join case
# where we have no column to merge on
# This means we have no other chance than to merge
# everything with everything...
# TODO: we should implement a shortcut
# for filter conditions that are always false
def merge_single_partitions(lhs_partition, rhs_partition):
# Do a cross join with the two partitions
# TODO: it would be nice to apply the filter already here
# problem: this would mean we need to ship the rex to the
# workers (as this is executed on the workers),
# which is definitely not possible (java dependency, JVM start...)
lhs_partition = lhs_partition.assign(common=1)
rhs_partition = rhs_partition.assign(common=1)
return lhs_partition.merge(rhs_partition,
on="common").drop(columns="common")
# Iterate nested over all partitions from lhs and rhs and merge them
name = "cross-join-" + tokenize(df_lhs_renamed, df_rhs_renamed)
dsk = {(name, i * df_rhs_renamed.npartitions + j): (
merge_single_partitions,
(df_lhs_renamed._name, i),
(df_rhs_renamed._name, j),
)
for i in range(df_lhs_renamed.npartitions)
for j in range(df_rhs_renamed.npartitions)}
graph = HighLevelGraph.from_collections(
name, dsk, dependencies=[df_lhs_renamed, df_rhs_renamed])
meta = dd.dispatch.concat(
[df_lhs_renamed._meta_nonempty, df_rhs_renamed._meta_nonempty],
axis=1)
# TODO: Do we know the divisions in any way here?
divisions = [None] * (len(dsk) + 1)
df = dd.DataFrame(graph, name, meta=meta, divisions=divisions)
warnings.warn(
"Need to do a cross-join, which is typically very resource heavy",
ResourceWarning,
)
# 6. So the next step is to make sure
# we have the correct column order (and to remove the temporary join columns)
correct_column_order = list(df_lhs_renamed.columns) + list(
df_rhs_renamed.columns)
cc = ColumnContainer(df.columns).limit_to(correct_column_order)
# and to rename them like the rel specifies
row_type = rel.getRowType()
field_specifications = [str(f) for f in row_type.getFieldNames()]
cc = cc.rename({
from_col: to_col
for from_col, to_col in zip(cc.columns, field_specifications)
})
cc = self.fix_column_to_row_type(cc, row_type)
dc = DataContainer(df, cc)
# 7. Last but not least we apply any filters by and-chaining together the filters
if filter_condition:
# This line is a bit of code duplication with RexCallPlugin - but I guess it is worth to keep it separate
filter_condition = reduce(
operator.and_,
[
RexConverter.convert(rex, dc, context=context)
for rex in filter_condition
],
)
logger.debug(f"Additionally applying filter {filter_condition}")
df = filter_or_scalar(df, filter_condition)
dc = DataContainer(df, cc)
dc = self.fix_dtype_to_row_type(dc, rel.getRowType())
return dc
def convert(self, rel: "org.apache.calcite.rel.RelNode",
context: "dask_sql.Context") -> DataContainer:
# Joining is a bit more complicated, so lets do it in steps:
# 1. We now have two inputs (from left and right), so we fetch them both
dc_lhs, dc_rhs = self.assert_inputs(rel, 2, context)
cc_lhs = dc_lhs.column_container
cc_rhs = dc_rhs.column_container
# 2. dask's merge will do some smart things with columns, which have the same name
# on lhs an rhs (which also includes reordering).
# However, that will confuse our column numbering in SQL.
# So we make our life easier by converting the column names into unique names
# We will convert back in the end
cc_lhs_renamed = cc_lhs.make_unique("lhs")
cc_rhs_renamed = cc_rhs.make_unique("rhs")
dc_lhs_renamed = DataContainer(dc_lhs.df, cc_lhs_renamed)
dc_rhs_renamed = DataContainer(dc_rhs.df, cc_rhs_renamed)
df_lhs_renamed = dc_lhs_renamed.assign()
df_rhs_renamed = dc_rhs_renamed.assign()
join_type = rel.getJoinType()
join_type = self.JOIN_TYPE_MAPPING[str(join_type)]
# 3. The join condition can have two forms, that we can understand
# (a) a = b
# (b) X AND Y AND a = b AND Z ... (can also be multiple a = b)
# The first case is very simple and we do not need any additional filter
# In the second case we do a merge on all the a = b,
# and then apply a filter using the other expressions.
# In all other cases, we need to do a full table cross join and filter afterwards.
# As this is probably non-sense for large tables, but there is no other
# known solution so far.
join_condition = rel.getCondition()
lhs_on, rhs_on, filter_condition = self._split_join_condition(
join_condition)
logger.debug(
f"Joining with type {join_type} on columns {lhs_on}, {rhs_on}.")
# lhs_on and rhs_on are the indices of the columns to merge on.
# The given column indices are for the full, merged table which consists
# of lhs and rhs put side-by-side (in this order)
# We therefore need to normalize the rhs indices relative to the rhs table.
rhs_on = [index - len(df_lhs_renamed.columns) for index in rhs_on]
# 4. dask can only merge on the same column names.
# We therefore create new columns on purpose, which have a distinct name.
assert len(lhs_on) == len(rhs_on)
if lhs_on:
lhs_columns_to_add = {
f"common_{i}": df_lhs_renamed.iloc[:, index]
for i, index in enumerate(lhs_on)
}
rhs_columns_to_add = {
f"common_{i}": df_rhs_renamed.iloc[:, index]
for i, index in enumerate(rhs_on)
}
else:
# We are in the complex join case
# where we have no column to merge on
# This means we have no other chance than to merge
# everything with everything...
# We add a 1-column to merge on
lhs_columns_to_add = {"common": 1}
rhs_columns_to_add = {"common": 1}
warnings.warn(
"Need to do a cross-join, which is typically very resource heavy",
ResourceWarning,
)
df_lhs_with_tmp = df_lhs_renamed.assign(**lhs_columns_to_add)
df_rhs_with_tmp = df_rhs_renamed.assign(**rhs_columns_to_add)
added_columns = list(lhs_columns_to_add.keys())
# 5. Now we can finally merge on these columns
# The resulting dataframe will contain all (renamed) columns from the lhs and rhs
# plus the added columns
df = dd.merge(df_lhs_with_tmp,
df_rhs_with_tmp,
on=added_columns,
how=join_type)
# 6. So the next step is to make sure
# we have the correct column order (and to remove the temporary join columns)
correct_column_order = list(df_lhs_renamed.columns) + list(
df_rhs_renamed.columns)
cc = ColumnContainer(df.columns).limit_to(correct_column_order)
# and to rename them like the rel specifies
row_type = rel.getRowType()
field_specifications = [str(f) for f in row_type.getFieldNames()]
cc = cc.rename({
from_col: to_col
for from_col, to_col in zip(cc.columns, field_specifications)
})
cc = self.fix_column_to_row_type(cc, rel.getRowType())
dc = DataContainer(df, cc)
# 7. Last but not least we apply any filters by and-chaining together the filters
if filter_condition:
# This line is a bit of code duplication with RexCallPlugin - but I guess it is worth to keep it separate
filter_condition = reduce(
operator.and_,
[
RexConverter.convert(rex, dc, context=context)
for rex in filter_condition
],
)
logger.debug(f"Additionally applying filter {filter_condition}")
df = filter_or_scalar(df, filter_condition)
dc = DataContainer(df, cc)
dc = self.fix_dtype_to_row_type(dc, rel.getRowType())
return dc
