Cost based optimization

dask_sql/__init__.py

@@ -1,6 +1,9 @@
 from ._version import get_version
 from .cmd import cmd_loop
 from .context import Context
+from .datacontainer import Statistics
 from .server.app import run_server
 
 __version__ = get_version()
+
+__all__ = [__version__, cmd_loop, Context, run_server, Statistics]

dask_sql/context.py

@@ -2,7 +2,7 @@
 import inspect
 import logging
 import warnings
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Union
+from typing import Any, Callable, Dict, List, Tuple, Union
 
 import dask.dataframe as dd
 import pandas as pd
@@ -20,28 +20,16 @@
     DataContainer,
     FunctionDescription,
     SchemaContainer,
+    Statistics,
 )
 from dask_sql.input_utils import InputType, InputUtil
 from dask_sql.integrations.ipython import ipython_integration
-from dask_sql.java import (
-    DaskAggregateFunction,
-    DaskScalarFunction,
-    DaskSchema,
-    DaskTable,
-    RelationalAlgebraGenerator,
-    RelationalAlgebraGeneratorBuilder,
-    SqlParseException,
-    ValidationException,
-    get_java_class,
-)
+from dask_sql.java import com, get_java_class, org
 from dask_sql.mappings import python_to_sql_type
 from dask_sql.physical.rel import RelConverter, custom, logical
 from dask_sql.physical.rex import RexConverter, core
 from dask_sql.utils import ParsingException
 
-if TYPE_CHECKING:
-    from dask_sql.java import org
-
 logger = logging.getLogger(__name__)
 
 
@@ -90,15 +78,16 @@ def __init__(self):
         self.sql_server = None
 
         # Register any default plugins, if nothing was registered before.
-        RelConverter.add_plugin_class(logical.LogicalAggregatePlugin, replace=False)
-        RelConverter.add_plugin_class(logical.LogicalFilterPlugin, replace=False)
-        RelConverter.add_plugin_class(logical.LogicalJoinPlugin, replace=False)
-        RelConverter.add_plugin_class(logical.LogicalProjectPlugin, replace=False)
-        RelConverter.add_plugin_class(logical.LogicalSortPlugin, replace=False)
-        RelConverter.add_plugin_class(logical.LogicalTableScanPlugin, replace=False)
-        RelConverter.add_plugin_class(logical.LogicalUnionPlugin, replace=False)
-        RelConverter.add_plugin_class(logical.LogicalValuesPlugin, replace=False)
-        RelConverter.add_plugin_class(logical.LogicalWindowPlugin, replace=False)
+        RelConverter.add_plugin_class(logical.DaskAggregatePlugin, replace=False)
+        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.DaskProjectPlugin, replace=False)
+        RelConverter.add_plugin_class(logical.DaskSortPlugin, replace=False)
+        RelConverter.add_plugin_class(logical.DaskTableScanPlugin, replace=False)
+        RelConverter.add_plugin_class(logical.DaskUnionPlugin, replace=False)
+        RelConverter.add_plugin_class(logical.DaskValuesPlugin, replace=False)
+        RelConverter.add_plugin_class(logical.DaskWindowPlugin, replace=False)
         RelConverter.add_plugin_class(logical.SamplePlugin, replace=False)
         RelConverter.add_plugin_class(custom.AnalyzeTablePlugin, replace=False)
         RelConverter.add_plugin_class(custom.CreateExperimentPlugin, replace=False)
@@ -138,6 +127,7 @@ def create_table(
         format: str = None,
         persist: bool = False,
         schema_name: str = None,
+        statistics: Statistics = None,
         gpu: bool = False,
         **kwargs,
     ):
@@ -200,6 +190,11 @@ def create_table(
                 If set to "memory", load the data from a published dataset in the dask cluster.
             persist (:obj:`bool`): Only used when passing a string into the ``input`` parameter.
                 Set to true to turn on loading the file data directly into memory.
+            schema_name: (:obj:`str`): in which schema to create the table. By default, will use the currently selected schema.
+            statistics: (:obj:`Statistics`): if given, use these statistics during the cost-based optimization. If no
+                statistics are provided, we will just assume 100 rows.
+            gpu: (:obj:`bool`): if set to true, use dask-cudf to run the data frame calculations on your GPU.
+                Please note that the GPU support is currently not covering all of dask-sql's SQL language.
             **kwargs: Additional arguments for specific formats. See :ref:`data_input` for more information.
 
         """
@@ -218,6 +213,8 @@ def create_table(
             **kwargs,
         )
         self.schema[schema_name].tables[table_name.lower()] = dc
+        if statistics:
+            self.schema[schema_name].statistics[table_name.lower()] = statistics
 
     def register_dask_table(self, df: dd.DataFrame, name: str, *args, **kwargs):
         """
@@ -676,14 +673,26 @@ def _prepare_schemas(self):
         """
         schema_list = []
 
+        DaskTable = com.dask.sql.schema.DaskTable
+        DaskAggregateFunction = com.dask.sql.schema.DaskAggregateFunction
+        DaskScalarFunction = com.dask.sql.schema.DaskScalarFunction
+        DaskSchema = com.dask.sql.schema.DaskSchema
+
         for schema_name, schema in self.schema.items():
             java_schema = DaskSchema(schema_name)
 
             if not schema.tables:
                 logger.warning("No tables are registered.")
 
             for name, dc in schema.tables.items():
-                table = DaskTable(name)
+                row_count = (
+                    schema.statistics[name].row_count
+                    if name in schema.statistics
+                    else None
+                )
+                if row_count is not None:
+                    row_count = float(row_count)
+                table = DaskTable(name, row_count)
                 df = dc.df
                 logger.debug(
                     f"Adding table '{name}' to schema with columns: {list(df.columns)}"
@@ -735,6 +744,10 @@ def _get_ral(self, sql):
         # get the schema of what we currently have registered
         schemas = self._prepare_schemas()
 
+        RelationalAlgebraGeneratorBuilder = (
+            com.dask.sql.application.RelationalAlgebraGeneratorBuilder
+        )
+
         # Now create a relational algebra from that
         generator_builder = RelationalAlgebraGeneratorBuilder(self.schema_name)
         for schema in schemas:
@@ -744,6 +757,10 @@ def _get_ral(self, sql):
 
         logger.debug(f"Using dialect: {get_java_class(default_dialect)}")
 
+        ValidationException = org.apache.calcite.tools.ValidationException
+        SqlParseException = org.apache.calcite.sql.parser.SqlParseException
+        CalciteContextException = org.apache.calcite.runtime.CalciteContextException
+
         try:
             sqlNode = generator.getSqlNode(sql)
             sqlNodeClass = get_java_class(sqlNode)
@@ -753,16 +770,15 @@ def _get_ral(self, sql):
             rel_string = ""
 
             if not sqlNodeClass.startswith("com.dask.sql.parser."):
-                validatedSqlNode = generator.getValidatedNode(sqlNode)
-                nonOptimizedRelNode = generator.getRelationalAlgebra(validatedSqlNode)
+                nonOptimizedRelNode = generator.getRelationalAlgebra(sqlNode)
                 # Optimization might remove some alias projects. Make sure to keep them here.
                 select_names = [
                     str(name)
                     for name in nonOptimizedRelNode.getRowType().getFieldNames()
                 ]
                 rel = generator.getOptimizedRelationalAlgebra(nonOptimizedRelNode)
                 rel_string = str(generator.getRelationalAlgebraString(rel))
-        except (ValidationException, SqlParseException) as e:
+        except (ValidationException, SqlParseException, CalciteContextException) as e:
             logger.debug(f"Original exception raised by Java:\n {e}")
             # We do not want to re-raise an exception here
             # as this would print the full java stack trace
@@ -798,7 +814,9 @@ def _get_ral(self, sql):
 
     def _to_sql_string(self, s: "org.apache.calcite.sql.SqlNode", default_dialect=None):
         if default_dialect is None:
-            default_dialect = RelationalAlgebraGenerator.getDialect()
+            default_dialect = (
+                com.dask.sql.application.RelationalAlgebraGenerator.getDialect()
+            )
 
         try:
             return str(s.toSqlString(default_dialect))

dask_sql/datacontainer.py

@@ -231,10 +231,22 @@ def __hash__(self):
         return (self.func, self.row_udf).__hash__()
 
 
+class Statistics:
+    """
+    Statistics are used during the cost-based optimization.
+    Currently, only the row count is supported, more
+    properties might follow. It needs to be provided by the user.
+    """
+
+    def __init__(self, row_count: int) -> None:
+        self.row_count = row_count
+
+
 class SchemaContainer:
     def __init__(self, name: str):
         self.__name__ = name
         self.tables: Dict[str, DataContainer] = {}
+        self.statistics: Dict[str, Statistics] = {}
         self.experiments: Dict[str, pd.DataFrame] = {}
         self.models: Dict[str, Tuple[Any, List[str]]] = {}
         self.functions: Dict[str, UDF] = {}

dask_sql/java.py

@@ -85,18 +85,6 @@ def _set_or_check_java_home():
 org = jpype.JPackage("org")
 java = jpype.JPackage("java")
 
-DaskTable = com.dask.sql.schema.DaskTable
-DaskAggregateFunction = com.dask.sql.schema.DaskAggregateFunction
-DaskScalarFunction = com.dask.sql.schema.DaskScalarFunction
-DaskSchema = com.dask.sql.schema.DaskSchema
-RelationalAlgebraGenerator = com.dask.sql.application.RelationalAlgebraGenerator
-RelationalAlgebraGeneratorBuilder = (
-    com.dask.sql.application.RelationalAlgebraGeneratorBuilder
-)
-SqlTypeName = org.apache.calcite.sql.type.SqlTypeName
-ValidationException = org.apache.calcite.tools.ValidationException
-SqlParseException = org.apache.calcite.sql.parser.SqlParseException
-
 
 def get_java_class(instance):
     """Get the stringified class name of a java object"""

dask_sql/mappings.py

@@ -8,10 +8,10 @@
 import pandas as pd
 
 from dask_sql._compat import FLOAT_NAN_IMPLEMENTED
-from dask_sql.java import SqlTypeName
+from dask_sql.java import org
 
 logger = logging.getLogger(__name__)
-
+SqlTypeName = org.apache.calcite.sql.type.SqlTypeName
 
 # Default mapping between python types and SQL types
 _PYTHON_TO_SQL = {

dask_sql/physical/rel/base.py

@@ -42,6 +42,8 @@ def fix_column_to_row_type(
         """
         field_names = [str(x) for x in row_type.getFieldNames()]
 
+        logger.debug(f"Renaming {cc.columns} to {field_names}")
+
         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

dask_sql/physical/rel/custom/create_experiment.py

@@ -5,12 +5,12 @@
 import pandas as pd
 
 from dask_sql.datacontainer import ColumnContainer, DataContainer
-from dask_sql.java import org
 from dask_sql.physical.rel.base import BaseRelPlugin
 from dask_sql.utils import convert_sql_kwargs, import_class
 
 if TYPE_CHECKING:
     import dask_sql
+    from dask_sql.java import org
 
 logger = logging.getLogger(__name__)
 

dask_sql/physical/rel/custom/create_model.py

@@ -2,12 +2,12 @@
 from typing import TYPE_CHECKING
 
 from dask_sql.datacontainer import DataContainer
-from dask_sql.java import org
 from dask_sql.physical.rel.base import BaseRelPlugin
 from dask_sql.utils import convert_sql_kwargs, import_class
 
 if TYPE_CHECKING:
     import dask_sql
+    from dask_sql.java import org
 
 logger = logging.getLogger(__name__)
 

dask_sql/physical/rel/logical/__init__.py

@@ -1,23 +1,25 @@
-from .aggregate import LogicalAggregatePlugin
-from .filter import LogicalFilterPlugin
-from .join import LogicalJoinPlugin
-from .project import LogicalProjectPlugin
+from .aggregate import DaskAggregatePlugin
+from .filter import DaskFilterPlugin
+from .join import DaskJoinPlugin
+from .limit import DaskLimitPlugin
+from .project import DaskProjectPlugin
 from .sample import SamplePlugin
-from .sort import LogicalSortPlugin
-from .table_scan import LogicalTableScanPlugin
-from .union import LogicalUnionPlugin
-from .values import LogicalValuesPlugin
-from .window import LogicalWindowPlugin
+from .sort import DaskSortPlugin
+from .table_scan import DaskTableScanPlugin
+from .union import DaskUnionPlugin
+from .values import DaskValuesPlugin
+from .window import DaskWindowPlugin
 
 __all__ = [
-    LogicalAggregatePlugin,
-    LogicalFilterPlugin,
-    LogicalJoinPlugin,
-    LogicalProjectPlugin,
-    LogicalSortPlugin,
-    LogicalTableScanPlugin,
-    LogicalUnionPlugin,
-    LogicalValuesPlugin,
-    LogicalWindowPlugin,
+    DaskAggregatePlugin,
+    DaskFilterPlugin,
+    DaskJoinPlugin,
+    DaskLimitPlugin,
+    DaskProjectPlugin,
+    DaskSortPlugin,
+    DaskTableScanPlugin,
+    DaskUnionPlugin,
+    DaskValuesPlugin,
+    DaskWindowPlugin,
     SamplePlugin,
 ]

dask_sql/physical/rel/logical/aggregate.py

@@ -95,9 +95,9 @@ def get_supported_aggregation(self, series):
         return self.custom_aggregation
 
 
-class LogicalAggregatePlugin(BaseRelPlugin):
+class DaskAggregatePlugin(BaseRelPlugin):
     """
-    A LogicalAggregate is used in GROUP BY clauses, but also
+    A DaskAggregate is used in GROUP BY clauses, but also
     when aggregating a function over the full dataset.
 
     In the first case we need to find out which columns we need to
@@ -119,7 +119,7 @@ class LogicalAggregatePlugin(BaseRelPlugin):
     these things via HINTs.
     """
 
-    class_name = "org.apache.calcite.rel.logical.LogicalAggregate"
+    class_name = "com.dask.sql.nodes.DaskAggregate"
 
     AGGREGATION_MAPPING = {
         "$sum0": AggregationSpecification("sum", AggregationOnPandas("sum")),

dask_sql/physical/rel/logical/filter.py

@@ -34,13 +34,13 @@ def filter_or_scalar(df: dd.DataFrame, filter_condition: Union[np.bool_, dd.Seri
     return df[filter_condition]
 
 
-class LogicalFilterPlugin(BaseRelPlugin):
+class DaskFilterPlugin(BaseRelPlugin):
     """
-    LogicalFilter is used on WHERE clauses.
+    DaskFilter is used on WHERE clauses.
     We just evaluate the filter (which is of type RexNode) and apply it
     """
 
-    class_name = "org.apache.calcite.rel.logical.LogicalFilter"
+    class_name = "com.dask.sql.nodes.DaskFilter"
 
     def convert(
         self, rel: "org.apache.calcite.rel.RelNode", context: "dask_sql.Context"

dask_sql/physical/rel/logical/join.py

@@ -20,9 +20,9 @@
 logger = logging.getLogger(__name__)
 
 
-class LogicalJoinPlugin(BaseRelPlugin):
+class DaskJoinPlugin(BaseRelPlugin):
     """
-    A LogicalJoin is used when (surprise) joining two tables.
+    A DaskJoin is used when (surprise) joining two tables.
     SQL allows for quite complicated joins with difficult conditions.
     dask/pandas only knows about equijoins on a specific column.
 
@@ -36,7 +36,7 @@ class LogicalJoinPlugin(BaseRelPlugin):
     but so far, it is the only solution...
     """
 
-    class_name = "org.apache.calcite.rel.logical.LogicalJoin"
+    class_name = "com.dask.sql.nodes.DaskJoin"
 
     JOIN_TYPE_MAPPING = {
         "INNER": "inner",