Support for LIMIT clause with DataFusion

dask_planner/Cargo.toml

@@ -12,7 +12,7 @@ rust-version = "1.59"
 tokio = { version = "1.0", features = ["macros", "rt", "rt-multi-thread", "sync", "fs", "parking_lot"] }
 rand = "0.7"
 pyo3 = { version = "0.16", features = ["extension-module", "abi3", "abi3-py38"] }
-datafusion = { git="https://github.com/apache/arrow-datafusion/", rev = "8.0.0" }
+datafusion = { git="https://github.com/apache/arrow-datafusion/", rev = "78207f5092fc5204ecd791278d403dcb6f0ae683" }
 uuid = { version = "0.8", features = ["v4"] }
 mimalloc = { version = "*", default-features = false }
 parking_lot = "0.12"

dask_planner/src/expression.rs

@@ -11,7 +11,7 @@ use datafusion::logical_expr::{lit, BuiltinScalarFunction, Expr};
 
 use datafusion::scalar::ScalarValue;
 
-pub use datafusion::logical_expr::LogicalPlan;
+use datafusion::logical_expr::LogicalPlan;
 
 use datafusion::prelude::Column;
 

dask_planner/src/sql.rs

@@ -11,6 +11,7 @@ use crate::sql::exceptions::ParsingException;
 
 use datafusion::arrow::datatypes::{Field, Schema};
 use datafusion::catalog::{ResolvedTableReference, TableReference};
+use datafusion::datasource::TableProvider;
 use datafusion::error::DataFusionError;
 use datafusion::logical_expr::ScalarFunctionImplementation;
 use datafusion::physical_plan::udaf::AggregateUDF;
@@ -55,7 +56,7 @@ impl ContextProvider for DaskSQLContext {
     fn get_table_provider(
         &self,
         name: TableReference,
-    ) -> Result<Arc<dyn table::TableProvider>, DataFusionError> {
+    ) -> Result<Arc<dyn TableProvider>, DataFusionError> {
         let reference: ResolvedTableReference =
             name.resolve(&self.default_catalog_name, &self.default_schema_name);
         match self.schemas.get(&self.default_schema_name) {

dask_planner/src/sql/logical.rs

@@ -6,10 +6,12 @@ mod aggregate;
 mod explain;
 mod filter;
 mod join;
+mod limit;
+mod offset;
 pub mod projection;
 mod sort;
 
-pub use datafusion::logical_expr::LogicalPlan;
+use datafusion::logical_expr::LogicalPlan;
 
 use datafusion::common::Result;
 use datafusion::prelude::Column;
@@ -78,6 +80,16 @@ impl PyLogicalPlan {
         to_py_plan(self.current_node.as_ref())
     }
 
+    /// LogicalPlan::Limit as PyLimit
+    pub fn limit(&self) -> PyResult<limit::PyLimit> {
+        to_py_plan(self.current_node.as_ref())
+    }
+
+    /// LogicalPlan::Offset as PyOffset
+    pub fn offset(&self) -> PyResult<offset::PyOffset> {
+        to_py_plan(self.current_node.as_ref())
+    }
+
     /// LogicalPlan::Projection as PyProjection
     pub fn projection(&self) -> PyResult<projection::PyProjection> {
         to_py_plan(self.current_node.as_ref())
@@ -138,6 +150,7 @@ impl PyLogicalPlan {
             LogicalPlan::TableScan(_table_scan) => "TableScan",
             LogicalPlan::EmptyRelation(_empty_relation) => "EmptyRelation",
             LogicalPlan::Limit(_limit) => "Limit",
+            LogicalPlan::Offset(_offset) => "Offset",
             LogicalPlan::CreateExternalTable(_create_external_table) => "CreateExternalTable",
             LogicalPlan::CreateMemoryTable(_create_memory_table) => "CreateMemoryTable",
             LogicalPlan::DropTable(_drop_table) => "DropTable",
@@ -149,7 +162,7 @@ impl PyLogicalPlan {
             LogicalPlan::SubqueryAlias(_sqalias) => "SubqueryAlias",
             LogicalPlan::CreateCatalogSchema(_create) => "CreateCatalogSchema",
             LogicalPlan::CreateCatalog(_create_catalog) => "CreateCatalog",
-            LogicalPlan::CreateView(_) => "CreateView",
+            LogicalPlan::CreateView(_create_view) => "CreateView",
         })
     }
 

dask_planner/src/sql/logical/aggregate.rs

@@ -1,7 +1,7 @@
 use crate::expression::PyExpr;
 
+use datafusion::logical_expr::LogicalPlan;
 use datafusion::logical_expr::{logical_plan::Aggregate, Expr};
-pub use datafusion::logical_expr::{logical_plan::JoinType, LogicalPlan};
 
 use pyo3::prelude::*;
 

dask_planner/src/sql/logical/filter.rs

@@ -1,7 +1,7 @@
 use crate::expression::PyExpr;
 
 use datafusion::logical_expr::logical_plan::Filter;
-pub use datafusion::logical_expr::LogicalPlan;
+use datafusion::logical_expr::LogicalPlan;
 
 use pyo3::prelude::*;
 

dask_planner/src/sql/logical/join.rs

@@ -1,7 +1,7 @@
 use crate::sql::column;
 
 use datafusion::logical_expr::logical_plan::Join;
-pub use datafusion::logical_expr::{logical_plan::JoinType, LogicalPlan};
+use datafusion::logical_expr::{logical_plan::JoinType, LogicalPlan};
 
 use pyo3::prelude::*;
 

dask_planner/src/sql/logical/limit.rs

@@ -0,0 +1,32 @@
+use crate::expression::PyExpr;
+
+use datafusion::scalar::ScalarValue;
+use pyo3::prelude::*;
+
+use datafusion::logical_expr::{logical_plan::Limit, Expr, LogicalPlan};
+
+#[pyclass(name = "Limit", module = "dask_planner", subclass)]
+#[derive(Clone)]
+pub struct PyLimit {
+    limit: Limit,
+}
+
+#[pymethods]
+impl PyLimit {
+    #[pyo3(name = "getLimitN")]
+    pub fn limit_n(&self) -> PyResult<PyExpr> {
+        Ok(PyExpr::from(
+            Expr::Literal(ScalarValue::UInt64(Some(self.limit.n.try_into().unwrap()))),
+            Some(self.limit.input.clone()),
+        ))
+    }
+}
+
+impl From<LogicalPlan> for PyLimit {
+    fn from(logical_plan: LogicalPlan) -> PyLimit {
+        match logical_plan {
+            LogicalPlan::Limit(limit) => PyLimit { limit: limit },
+            _ => panic!("something went wrong here!!!????"),
+        }
+    }
+}

dask_planner/src/sql/logical/offset.rs

@@ -0,0 +1,42 @@
+use crate::expression::PyExpr;
+
+use datafusion::scalar::ScalarValue;
+use pyo3::prelude::*;
+
+use datafusion::logical_expr::{logical_plan::Offset, Expr, LogicalPlan};
+
+#[pyclass(name = "Offset", module = "dask_planner", subclass)]
+#[derive(Clone)]
+pub struct PyOffset {
+    offset: Offset,
+}
+
+#[pymethods]
+impl PyOffset {
+    #[pyo3(name = "getOffset")]
+    pub fn offset(&self) -> PyResult<PyExpr> {
+        // TODO: Waiting on DataFusion issue: https://github.com/apache/arrow-datafusion/issues/2377
+        Ok(PyExpr::from(
+            Expr::Literal(ScalarValue::UInt64(Some(self.offset.offset as u64))),
+            Some(self.offset.input.clone()),
+        ))
+    }
+
+    #[pyo3(name = "getFetch")]
+    pub fn offset_fetch(&self) -> PyResult<PyExpr> {
+        // TODO: Still need to implement fetch size! For now get everything from offset on with '0'
+        Ok(PyExpr::from(
+            Expr::Literal(ScalarValue::UInt64(Some(0))),
+            Some(self.offset.input.clone()),
+        ))
+    }
+}
+
+impl From<LogicalPlan> for PyOffset {
+    fn from(logical_plan: LogicalPlan) -> PyOffset {
+        match logical_plan {
+            LogicalPlan::Offset(offset) => PyOffset { offset: offset },
+            _ => panic!("Issue #501"),
+        }
+    }
+}

dask_planner/src/sql/logical/projection.rs

@@ -1,6 +1,6 @@
 use crate::expression::PyExpr;
 
-pub use datafusion::logical_expr::LogicalPlan;
+use datafusion::logical_expr::LogicalPlan;
 use datafusion::logical_expr::{logical_plan::Projection, Expr};
 
 use pyo3::prelude::*;

dask_planner/src/sql/table.rs

@@ -7,14 +7,13 @@ use crate::sql::types::SqlTypeName;
 use async_trait::async_trait;
 
 use datafusion::arrow::datatypes::{DataType, Field, SchemaRef};
-pub use datafusion::datasource::TableProvider;
+use datafusion::datasource::{TableProvider, TableType};
 use datafusion::error::DataFusionError;
 use datafusion::logical_expr::{Expr, LogicalPlan, TableSource};
 use datafusion::physical_plan::{empty::EmptyExec, project_schema, ExecutionPlan};
 
 use pyo3::prelude::*;
 
-use datafusion::datasource::TableType;
 use std::any::Any;
 use std::sync::Arc;
 

dask_sql/context.py

@@ -98,6 +98,7 @@ def __init__(self, logging_level=logging.INFO):
         RelConverter.add_plugin_class(logical.DaskFilterPlugin, replace=False)
         RelConverter.add_plugin_class(logical.DaskJoinPlugin, replace=False)
         RelConverter.add_plugin_class(logical.DaskLimitPlugin, replace=False)
+        RelConverter.add_plugin_class(logical.DaskOffsetPlugin, replace=False)
         RelConverter.add_plugin_class(logical.DaskProjectPlugin, replace=False)
         RelConverter.add_plugin_class(logical.DaskSortPlugin, replace=False)
         RelConverter.add_plugin_class(logical.DaskTableScanPlugin, replace=False)

dask_sql/mappings.py

@@ -40,7 +40,9 @@
 }
 
 if FLOAT_NAN_IMPLEMENTED:  # pragma: no cover
-    _PYTHON_TO_SQL.update({pd.Float32Dtype(): "FLOAT", pd.Float64Dtype(): "FLOAT"})
+    _PYTHON_TO_SQL.update(
+        {pd.Float32Dtype(): SqlTypeName.FLOAT, pd.Float64Dtype(): SqlTypeName.DOUBLE}
+    )
 
 # Default mapping between SQL types and python types
 # for values

dask_sql/physical/rel/logical/__init__.py

@@ -3,6 +3,7 @@
 from .filter import DaskFilterPlugin
 from .join import DaskJoinPlugin
 from .limit import DaskLimitPlugin
+from .offset import DaskOffsetPlugin
 from .project import DaskProjectPlugin
 from .sample import SamplePlugin
 from .sort import DaskSortPlugin
@@ -16,6 +17,7 @@
     DaskFilterPlugin,
     DaskJoinPlugin,
     DaskLimitPlugin,
+    DaskOffsetPlugin,
     DaskProjectPlugin,
     DaskSortPlugin,
     DaskTableScanPlugin,

dask_sql/physical/rel/logical/limit.py

@@ -1,11 +1,8 @@
 from typing import TYPE_CHECKING
 
-import dask.dataframe as dd
-
 from dask_sql.datacontainer import DataContainer
 from dask_sql.physical.rel.base import BaseRelPlugin
 from dask_sql.physical.rex import RexConverter
-from dask_sql.physical.utils.map import map_on_partition_index
 
 if TYPE_CHECKING:
     import dask_sql
@@ -25,82 +22,12 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai
         df = dc.df
         cc = dc.column_container
 
-        offset = rel.getOffset()
-        if offset:
-            offset = RexConverter.convert(offset, df, context=context)
-
-        end = rel.getFetch()
-        if end:
-            end = RexConverter.convert(end, df, context=context)
-
-            if offset:
-                end += offset
+        limit = RexConverter.convert(rel, rel.limit().getLimitN(), df, context=context)
 
-        df = self._apply_offset(df, offset, end)
+        # If an offset was present it would have already been processed at this point.
+        # Therefore it is always safe to start at 0 when applying the limit
+        df = df.head(limit, npartitions=-1, compute=False)
 
         cc = self.fix_column_to_row_type(cc, rel.getRowType())
         # No column type has changed, so no need to cast again
         return DataContainer(df, cc)
-
-    def _apply_offset(self, df: dd.DataFrame, offset: int, end: int) -> dd.DataFrame:
-        """
-        Limit the dataframe to the window [offset, end].
-        That is unfortunately, not so simple as we do not know how many
-        items we have in each partition. We have therefore no other way than to
-        calculate (!!!) the sizes of each partition.
-
-        After that, we can create a new dataframe from the old
-        dataframe by calculating for each partition if and how much
-        it should be used.
-        We do this via generating our own dask computation graph as
-        we need to pass the partition number to the selection
-        function, which is not possible with normal "map_partitions".
-        """
-        if not offset:
-            # We do a (hopefully) very quick check: if the first partition
-            # is already enough, we will just use this
-            first_partition_length = len(df.partitions[0])
-            if first_partition_length >= end:
-                return df.head(end, compute=False)
-
-        # First, we need to find out which partitions we want to use.
-        # Therefore we count the total number of entries
-        partition_borders = df.map_partitions(lambda x: len(x))
-
-        # Now we let each of the partitions figure out, how much it needs to return
-        # using these partition borders
-        # For this, we generate out own dask computation graph (as it does not really
-        # fit well with one of the already present methods).
-
-        # (a) we define a method to be calculated on each partition
-        # This method returns the part of the partition, which falls between [offset, fetch]
-        # Please note that the dask object "partition_borders", will be turned into
-        # its pandas representation at this point and we can calculate the cumsum
-        # (which is not possible on the dask object). Recalculating it should not cost
-        # us much, as we assume the number of partitions is rather small.
-        def select_from_to(df, partition_index, partition_borders):
-            partition_borders = partition_borders.cumsum().to_dict()
-            this_partition_border_left = (
-                partition_borders[partition_index - 1] if partition_index > 0 else 0
-            )
-            this_partition_border_right = partition_borders[partition_index]
-
-            if (end and end < this_partition_border_left) or (
-                offset and offset >= this_partition_border_right
-            ):
-                return df.iloc[0:0]
-
-            from_index = max(offset - this_partition_border_left, 0) if offset else 0
-            to_index = (
-                min(end, this_partition_border_right)
-                if end
-                else this_partition_border_right
-            ) - this_partition_border_left
-
-            return df.iloc[from_index:to_index]
-
-        # (b) Now we just need to apply the function on every partition
-        # We do this via the delayed interface, which seems the easiest one.
-        return map_on_partition_index(
-            df, select_from_to, partition_borders, meta=df._meta
-        )