EXPLAIN

Shows generated Intermediate Representation (IR) code, identifying whether it is executed on GPU or CPU. This is primarily used internally by HEAVY.AI to monitor behavior.

EXPLAIN <STMT>

For example, when you use the EXPLAIN command on a basic statement, the utility returns 90 lines of IR code that is not meant to be human readable. However, at the top of the listing, a heading indicates whether it is IR for the CPU or IR for the GPU, which can be useful to know in some situations.

EXPLAIN CALCITE

Returns a relational algebra tree describing the high-level plan to execute the statement.

EXPLAIN CALCITE <STMT>

The table below lists the relational algebra classes used to describe the execution plan for a SQL statement.

Method	Description
LogicalAggregate	Operator that eliminates duplicates and computes totals.
LogicalCalc	Expression that computes project expressions and also filters.
LogicalChi	Operator that converts a stream to a relation.
LogicalCorrelate	Operator that performs nested-loop joins.
LogicalDelta	Operator that converts a relation to a stream.
LogicalExchange	Expression that imposes a particular distribution on its input without otherwise changing its content.
LogicalFilter	Expression that iterates over its input and returns elements for which a condition evaluates to true.
LogicalIntersect	Expression that returns the intersection of the rows of its inputs.
LogicalJoin	Expression that combines two relational expressions according to some condition.
LogicalMatch	Expression that represents a MATCH_RECOGNIZE node.
LogicalMinus	Expression that returns the rows of its first input minus any matching rows from its other inputs. Corresponds to the SQL EXCEPT operator.
LogicalProject	Expression that computes a set of ‘select expressions’ from its input relational expression.
LogicalSort	Expression that imposes a particular sort order on its input without otherwise changing its content.
LogicalTableFunctionScan	Expression that calls a table-valued function.
LogicalTableModify	Expression that modifies a table. Similar to TableScan, but represents a request to modify a table instead of read from it.
LogicalTableScan	Reads all the rows from a RelOptTable.
LogicalUnion	Expression that returns the union of the rows of its inputs, optionally eliminating duplicates.
LogicalValues	Expression for which the value is a sequence of zero or more literal row values.
LogicalWindow	Expression representing a set of window aggregates. See Window Functions

For example, a SELECT statement is described as a table scan and projection.

heavysql> EXPLAIN CALCITE (SELECT * FROM movies);
Explanation
LogicalProject(movieId=[$0], title=[$1], genres=[$2])
   LogicalTableScan(TABLE=[[CATALOG, heavyai, MOVIES]])

If you add a sort order, the table projection is folded under a LogicalSort procedure.

heavysql> EXPLAIN calcite (SELECT * FROM movies ORDER BY title);
Explanation
LogicalSort(sort0=[$1], dir0=[ASC])
   LogicalProject(movieId=[$0], title=[$1], genres=[$2])
      LogicalTableScan(TABLE=[[CATALOG, omnisci, MOVIES]])

When the SQL statement is simple, the EXPLAIN CALCITE version is actually less “human readable.” EXPLAIN CALCITE is more useful when you work with more complex SQL statements, like the one that follows. This query performs a scan on the BOOK table before scanning the BOOK_ORDER table.

heavysql> EXPLAIN calcite SELECT bc.firstname, bc.lastname, b.title, bo.orderdate, s.name
FROM book b, book_customer bc, book_order bo, shipper s
WHERE bo.cust_id = bc.cust_id AND b.book_id = bo.book_id AND bo.shipper_id = s.shipper_id
AND s.name = 'UPS';
Explanation
LogicalProject(firstname=[$5], lastname=[$6], title=[$2], orderdate=[$11], name=[$14])
    LogicalFilter(condition=[AND(=($9, $4), =($0, $8), =($10, $13), =($14, 'UPS'))])
        LogicalJoin(condition=[true], joinType=[INNER])
            LogicalJoin(condition=[true], joinType=[INNER])
                LogicalJoin(condition=[true], joinType=[INNER])
                    LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK]])
                    LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK_CUSTOMER]])
                LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK_ORDER]])
            LogicalTableScan(TABLE=[[CATALOG, omnisci, SHIPPER]])

Revising the original SQL command results in a more natural selection order and a more performant query.

heavysql> EXPLAIN calcite SELECT bc.firstname, bc.lastname, b.title, bo.orderdate, s.name
FROM book_order bo, book_customer bc, book b, shipper s
WHERE bo.cust_id = bc.cust_id AND bo.book_id = b.book_id AND bo.shipper_id = s.shipper_id
AND s.name = 'UPS';
Explanation
LogicalProject(firstname=[$10], lastname=[$11], title=[$7], orderdate=[$3], name=[$14])
    LogicalFilter(condition=[AND(=($1, $9), =($5, $0), =($2, $13), =($14, 'UPS'))])
        LogicalJoin(condition=[true], joinType=[INNER])
            LogicalJoin(condition=[true], joinType=[INNER])
                LogicalJoin(condition=[true], joinType=[INNER])
                  LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK_ORDER]])
                  LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK_CUSTOMER]])
                LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK]])
            LogicalTableScan(TABLE=[[CATALOG, omnisci, SHIPPER]])

EXPLAIN CALCITE DETAILED

Augments the EXPLAIN CALCITE command by adding details about referenced columns in the query plan.

For example, for the following EXPLAIN CALCITE command execution:

heavysql> EXPLAIN CALCITE SELECT x, SUM(y) FROM test GROUP BY x;
Explanation
LogicalAggregate(group=[{0}], EXPR$1=[SUM($1)])
  LogicalProject(x=[$0], y=[$2])
    LogicalTableScan(table=[[testDB, test]])

EXPLAIN CALCITE DETAILED adds more column details as seen below:

heavysql> EXPLAIN CALCITE DETAILED SELECT x, SUM(y) FROM test GROUP BY x;
Explanation
LogicalAggregate(group=[{0}], EXPR$1=[SUM($1)])	{[$1->db:testDB,tableName:test,colName:y]}
  LogicalProject(x=[$0], y=[$2])	{[$2->db:testDB,tableName:test,colName:y], [$0->db:testDB,tableName:test,colName:x]}
    LogicalTableScan(table=[[testDB, test]])