Examples with TableScan org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableScan used on opensource projects

Example 31 with TableScan

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableScan in project hive by apache.

the class HivePreFilteringRule method matches.

@Override
public boolean matches(RelOptRuleCall call) {
    final Filter filter = call.rel(0);
    final RelNode filterChild = call.rel(1);
    // we can bail out
    if (filterChild instanceof TableScan) {
        return false;
    }
    HiveRulesRegistry registry = call.getPlanner().getContext().unwrap(HiveRulesRegistry.class);
    // we do not need to apply the optimization
    if (registry != null && registry.getVisited(this).contains(filter)) {
        return false;
    }
    return true;
}

Example 32 with TableScan

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableScan in project hive by apache.

the class HiveMaterializedViewsRegistry method createMaterializedViewScan.

private static RelNode createMaterializedViewScan(HiveConf conf, Table viewTable) {
    // 0. Recreate cluster
    final RelOptPlanner planner = CalcitePlanner.createPlanner(conf);
    final RexBuilder rexBuilder = new RexBuilder(new JavaTypeFactoryImpl(new HiveTypeSystemImpl()));
    final RelOptCluster cluster = RelOptCluster.create(planner, rexBuilder);
    // 1. Create column schema
    final RowResolver rr = new RowResolver();
    // 1.1 Add Column info for non partion cols (Object Inspector fields)
    StructObjectInspector rowObjectInspector;
    try {
        rowObjectInspector = (StructObjectInspector) viewTable.getDeserializer().getObjectInspector();
    } catch (SerDeException e) {
        // Bail out
        return null;
    }
    List<? extends StructField> fields = rowObjectInspector.getAllStructFieldRefs();
    ColumnInfo colInfo;
    String colName;
    ArrayList<ColumnInfo> cInfoLst = new ArrayList<>();
    for (StructField structField : fields) {
        colName = structField.getFieldName();
        colInfo = new ColumnInfo(structField.getFieldName(), TypeInfoUtils.getTypeInfoFromObjectInspector(structField.getFieldObjectInspector()), null, false);
        rr.put(null, colName, colInfo);
        cInfoLst.add(colInfo);
    }
    ArrayList<ColumnInfo> nonPartitionColumns = new ArrayList<ColumnInfo>(cInfoLst);
    // 1.2 Add column info corresponding to partition columns
    ArrayList<ColumnInfo> partitionColumns = new ArrayList<ColumnInfo>();
    for (FieldSchema part_col : viewTable.getPartCols()) {
        colName = part_col.getName();
        colInfo = new ColumnInfo(colName, TypeInfoFactory.getPrimitiveTypeInfo(part_col.getType()), null, true);
        rr.put(null, colName, colInfo);
        cInfoLst.add(colInfo);
        partitionColumns.add(colInfo);
    }
    // 1.3 Build row type from field <type, name>
    RelDataType rowType;
    try {
        rowType = TypeConverter.getType(cluster, rr, null);
    } catch (CalciteSemanticException e) {
        // Bail out
        return null;
    }
    // 2. Build RelOptAbstractTable
    List<String> fullyQualifiedTabName = new ArrayList<>();
    if (viewTable.getDbName() != null && !viewTable.getDbName().isEmpty()) {
        fullyQualifiedTabName.add(viewTable.getDbName());
    }
    fullyQualifiedTabName.add(viewTable.getTableName());
    RelNode tableRel;
    // 3. Build operator
    if (obtainTableType(viewTable) == TableType.DRUID) {
        // Build Druid query
        String address = HiveConf.getVar(conf, HiveConf.ConfVars.HIVE_DRUID_BROKER_DEFAULT_ADDRESS);
        String dataSource = viewTable.getParameters().get(Constants.DRUID_DATA_SOURCE);
        Set<String> metrics = new HashSet<>();
        List<RelDataType> druidColTypes = new ArrayList<>();
        List<String> druidColNames = new ArrayList<>();
        // @NOTE this code is very similar to the code at org/apache/hadoop/hive/ql/parse/CalcitePlanner.java:2362
        // @TODO it will be nice to refactor it
        RelDataTypeFactory dtFactory = cluster.getRexBuilder().getTypeFactory();
        for (RelDataTypeField field : rowType.getFieldList()) {
            if (DruidTable.DEFAULT_TIMESTAMP_COLUMN.equals(field.getName())) {
                // Druid's time column is always not null.
                druidColTypes.add(dtFactory.createTypeWithNullability(field.getType(), false));
            } else {
                druidColTypes.add(field.getType());
            }
            druidColNames.add(field.getName());
            if (field.getName().equals(DruidTable.DEFAULT_TIMESTAMP_COLUMN)) {
                // timestamp
                continue;
            }
            if (field.getType().getSqlTypeName() == SqlTypeName.VARCHAR) {
                // dimension
                continue;
            }
            metrics.add(field.getName());
        }
        List<Interval> intervals = Collections.singletonList(DruidTable.DEFAULT_INTERVAL);
        rowType = dtFactory.createStructType(druidColTypes, druidColNames);
        // We can pass null for Hive object because it is only used to retrieve tables
        // if constraints on a table object are existing, but constraints cannot be defined
        // for materialized views.
        RelOptHiveTable optTable = new RelOptHiveTable(null, cluster.getTypeFactory(), fullyQualifiedTabName, rowType, viewTable, nonPartitionColumns, partitionColumns, new ArrayList<>(), conf, null, new QueryTables(true), new HashMap<>(), new HashMap<>(), new AtomicInteger());
        DruidTable druidTable = new DruidTable(new DruidSchema(address, address, false), dataSource, RelDataTypeImpl.proto(rowType), metrics, DruidTable.DEFAULT_TIMESTAMP_COLUMN, intervals, null, null);
        final TableScan scan = new HiveTableScan(cluster, cluster.traitSetOf(HiveRelNode.CONVENTION), optTable, viewTable.getTableName(), null, false, false);
        tableRel = DruidQuery.create(cluster, cluster.traitSetOf(BindableConvention.INSTANCE), optTable, druidTable, ImmutableList.<RelNode>of(scan), ImmutableMap.of());
    } else {
        // Build Hive Table Scan Rel.
        // We can pass null for Hive object because it is only used to retrieve tables
        // if constraints on a table object are existing, but constraints cannot be defined
        // for materialized views.
        RelOptHiveTable optTable = new RelOptHiveTable(null, cluster.getTypeFactory(), fullyQualifiedTabName, rowType, viewTable, nonPartitionColumns, partitionColumns, new ArrayList<>(), conf, null, new QueryTables(true), new HashMap<>(), new HashMap<>(), new AtomicInteger());
        tableRel = new HiveTableScan(cluster, cluster.traitSetOf(HiveRelNode.CONVENTION), optTable, viewTable.getTableName(), null, false, false);
    }
    return tableRel;
}

Example 33 with TableScan

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableScan in project hazelcast by hazelcast.

the class CalciteSqlOptimizer method extractPermissions.

private List<Permission> extractPermissions(PhysicalRel physicalRel) {
    List<Permission> permissions = new ArrayList<>();
    physicalRel.accept(new RelShuttleImpl() {

        @Override
        public RelNode visit(TableScan scan) {
            addPermissionForTable(scan.getTable(), ActionConstants.ACTION_READ);
            return super.visit(scan);
        }

        @Override
        public RelNode visit(RelNode other) {
            addPermissionForTable(other.getTable(), ActionConstants.ACTION_PUT);
            return super.visit(other);
        }

        private void addPermissionForTable(RelOptTable t, String action) {
            if (t == null) {
                return;
            }
            HazelcastTable table = t.unwrap(HazelcastTable.class);
            if (table != null && table.getTarget() instanceof AbstractMapTable) {
                String mapName = ((AbstractMapTable) table.getTarget()).getMapName();
                permissions.add(new MapPermission(mapName, action));
            }
        }
    });
    return permissions;
}

Example 34 with TableScan

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableScan in project hazelcast by hazelcast.

the class DeleteByKeyMapLogicalRule method onMatch.

@Override
public void onMatch(RelOptRuleCall call) {
    TableModify delete = call.rel(0);
    TableScan scan = call.rel(1);
    RelOptTable table = scan.getTable();
    RexNode keyCondition = OptUtils.extractKeyConstantExpression(table, delete.getCluster().getRexBuilder());
    if (keyCondition != null) {
        DeleteByKeyMapLogicalRel rel = new DeleteByKeyMapLogicalRel(delete.getCluster(), OptUtils.toLogicalConvention(delete.getTraitSet()), table, keyCondition);
        call.transformTo(rel);
    }
}

Example 35 with TableScan

use of org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableScan in project beam by apache.

the class AggregateScanConverter method convertAggregateScanInputScanToLogicalProject.

private LogicalProject convertAggregateScanInputScanToLogicalProject(ResolvedAggregateScan node, RelNode input) {
    // AggregateScan's input is the source of data (e.g. TableScan), which is different from the
    // design of CalciteSQL, in which the LogicalAggregate's input is a LogicalProject, whose input
    // is a LogicalTableScan. When AggregateScan's input is WithRefScan, the WithRefScan is
    // ebullient to a LogicalTableScan. So it's still required to build another LogicalProject as
    // the input of LogicalAggregate.
    List<RexNode> projects = new ArrayList<>();
    List<String> fieldNames = new ArrayList<>();
    // LogicalAggregate.
    for (ResolvedComputedColumn computedColumn : node.getGroupByList()) {
        projects.add(getExpressionConverter().convertRexNodeFromResolvedExpr(computedColumn.getExpr(), node.getInputScan().getColumnList(), input.getRowType().getFieldList(), ImmutableMap.of()));
        fieldNames.add(getTrait().resolveAlias(computedColumn.getColumn()));
    }
    // TODO: remove duplicate columns in projects.
    for (ResolvedComputedColumn resolvedComputedColumn : node.getAggregateList()) {
        // Should create Calcite's RexInputRef from ResolvedColumn from ResolvedComputedColumn.
        // TODO: handle aggregate function with more than one argument and handle OVER
        // TODO: is there is general way for column reference tracking and deduplication for
        // aggregation?
        ResolvedAggregateFunctionCall aggregateFunctionCall = ((ResolvedAggregateFunctionCall) resolvedComputedColumn.getExpr());
        if (aggregateFunctionCall.getArgumentList() != null && aggregateFunctionCall.getArgumentList().size() >= 1) {
            ResolvedExpr resolvedExpr = aggregateFunctionCall.getArgumentList().get(0);
            for (int i = 0; i < aggregateFunctionCall.getArgumentList().size(); i++) {
                if (i == 0) {
                    // TODO: assume aggregate function's input is either a ColumnRef or a cast(ColumnRef).
                    // TODO: user might use multiple CAST so we need to handle this rare case.
                    projects.add(getExpressionConverter().convertRexNodeFromResolvedExpr(resolvedExpr, node.getInputScan().getColumnList(), input.getRowType().getFieldList(), ImmutableMap.of()));
                } else {
                    projects.add(getExpressionConverter().convertRexNodeFromResolvedExpr(aggregateFunctionCall.getArgumentList().get(i)));
                }
                fieldNames.add(getTrait().resolveAlias(resolvedComputedColumn.getColumn()));
            }
        }
    }
    return LogicalProject.create(input, ImmutableList.of(), projects, fieldNames);
}