cloud-fan commented on code in PR #48852: URL: https://github.com/apache/spark/pull/48852#discussion_r1843508769
########## docs/sql-pipe-syntax.md: ########## @@ -0,0 +1,540 @@ +--- +layout: global +title: SQL Pipe Syntax +displayTitle: SQL Pipe Syntax +license: | + Licensed to the Apache Software Foundation (ASF) under one or more + contributor license agreements. See the NOTICE file distributed with + this work for additional information regarding copyright ownership. + The ASF licenses this file to You under the Apache License, Version 2.0 + (the "License"); you may not use this file except in compliance with + the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +--- + +### Syntax + +#### Overview + +Apache Spark supports SQL pipe syntax which allows composing queries from combinations of operators. + +* Any query can have zero or more pipe operators as a suffix, delineated by the pipe character `|>`. +* Each pipe operator starts with one or more SQL keywords followed by its own grammar as described + in the table below. +* Most of these operators reuse existing grammar for standard SQL clauses. +* Operators can apply in any order, any number of times. + +`FROM <tableName>` is now a supported standalone query which behaves the same as +`TABLE <tableName>`. This provides a convenient starting place to begin a chained pipe SQL query, +although it is possible to add one or more pipe operators to the end of any valid Spark SQL query +with the same consistent behavior as written here. + +Please refer to the table at the end of this document for a full list of all supported operators +and their semantics. + +#### Example + +For example, this is query 13 from the TPC-H benchmark: + +```sql +SELECT c_count, COUNT(*) AS custdist +FROM + (SELECT c_custkey, COUNT(o_orderkey) c_count + FROM customer + LEFT OUTER JOIN orders ON c_custkey = o_custkey + AND o_comment NOT LIKE '%unusual%packages%' GROUP BY c_custkey + ) AS c_orders +GROUP BY c_count +ORDER BY custdist DESC, c_count DESC; +``` + +To write the same logic using SQL pipe operators, we express it like this: + +```sql +FROM customer +|> LEFT OUTER JOIN orders ON c_custkey = o_custkey + AND o_comment NOT LIKE '%unusual%packages%' +|> AGGREGATE COUNT(o_orderkey) c_count + GROUP BY c_custkey +|> AGGREGATE COUNT(*) AS custdist + GROUP BY c_count +|> ORDER BY custdist DESC, c_count DESC; +``` + +#### Source Tables + +To start a new query using SQL pipe syntax, use the `FROM <tableName>` or `TABLE <tableName>` +clause, which creates a relation comprising all rows from the source table. Then append one or more +pipe operators to the end of this clause to perform further transformations. + +#### Projections + +SQL pipe syntax supports composable ways to evaluate expressions. A major advantage of these +projection features is that they support computing new expressions based on previous ones in an +incremental way. No lateral column references are needed here since each operator applies +independently on its input table, regardless of the order in which the operators appear. Each of +these computed columns then becomes visible to use with the following operator. + +`SELECT` produces a new table by evaluating the provided expressions.<br> +It is possible to use `DISTINCT` and `*` as needed.<br> +This works like the outermost `SELECT` in a table subquery in regular Spark SQL. + +`EXTEND` adds new columns to the input table by evaluating the provided expressions.<br> +This also preserves table aliases.<br> +This works like `SELECT *, new_column` in regular Spark SQL. + +`DROP` removes columns from the input table.<br> +This is similar to `SELECT * EXCEPT (column)` in regular Spark SQL. + +`SET` replaces column values from the input table.<br> +This is similar to `SELECT * REPLACE (expression AS column)` in regular Spark SQL. + +`AS` forwards the input table and introduces a new alias for each row. + +#### Aggregations + +In general, aggregation takes place differently using SQL pipe syntax as opposed to regular Spark +SQL. + +To perform full-table aggregation, use the `AGGREGATE` operator with a list of aggregate +expressions to evaluate. This returns one single row in the output table. + +To perform aggregation with grouping, use the `AGGREGATE` operator with a `GROUP BY` clause. +This returns one row for each unique combination of values of the grouping expressions. The output +table contains the evaluated grouping expressions followed by the evaluated aggregate functions. +Grouping expressions support assigning aliases for purposes of referring to them in future +operators. In this way, it is not necessary to repeat entire expressions between `GROUP BY` and +`SELECT`, since `AGGREGATE` is a single operator that performs both. + +#### Other Transformations + +The remaining operators are used for other transformations, such as filtering, joining, sorting, +sampling, and set operations. These operators generally work in the same way as in regular Spark +SQL, as described in the table below. + +### Independence and Interoperability + +SQL pipe syntax works in Spark without any backwards-compatibility concerns with existing SQL +queries; it is possible to write any query using regular Spark SQL, pipe syntax, or a combination of +the two. As a consequence, the following invariants always hold: + +* Each pipe operator receives an input table and operates the same way on its rows regardless of how + it was computed. +* For any valid chain of N SQL pipe operators, any subset of the first M <= N operators also +represents a valid query.<br> + This property can be useful for introspection and debugging, such as by selected a subset of + lines and using the "run highlighted text" feature of SQL editors like Jupyter notebooks. +* It is possible to append pipe operators to any valid query written in regular Spark SQL.<br> + The canonical way of starting pipe syntax queries is with the `FROM <tableName>` clause.<br> + Note that this is a valid standalone query and may be replaced with any other Spark SQL query + without loss of generality. +* Table subqueries can be written using either regular Spark SQL syntax or pipe syntax.<br> + They may appear inside enclosing queries written in either syntax. +* Other Spark SQL statements such as views and DDL and DML commands may include queries written + using either syntax. + +### Supported Operators + +| Operator | Output rows | +|---------------------------------------------------|---------------------------------------------------------------------------------------------------------------------| +| [FROM](#from-or-table) or [TABLE](#from-or-table) | Returns all the output rows from the source table unmodified. | +| [SELECT](#select) | Evaluates the provided expressions over each of the rows of the input table. | +| [EXTEND](#extend) | Appends new columns to the input table by evaluating the specified expressions over each of the input rows. | +| [SET](#set) | Updates columns of the input table by replacing them with the result of evaluating the provided expressions. | +| [DROP](#drop) | Drops columns of the input table by name. | +| [AS](#as) | Retains the same rows and column names of the input table but with a new table alias. | +| [WHERE](#where) | Returns the subset of input rows passing the condition. | +| [LIMIT](#limit) | Returns the specified number of input rows, preserving ordering (if any). | +| [AGGREGATE](#aggregate) | Performs aggregation with or without grouping. | +| [JOIN](#join) | Joins rows from both inputs, returning a filtered cross-product of the input table and the table argument. | +| [ORDER BY](#order-by) | Returns the input rows after sorting as indicated. | +| [UNION ALL](#union-intersect-except) | Performs the union or other set operation over the combined rows from the input table plus other table argument(s). | +| [TABLESAMPLE](#tablesample) | Returns the subset of rows chosen by the provided sampling algorithm. | +| [PIVOT](#pivot) | Returns a new table with the input rows pivoted to become columns. | +| [UNPIVOT](#unpivot) | Returns a new table with the input columns pivoted to become rows. | + +This table lists each of the supported pipe operators and describes the output rows they produce. +Note that each operator accepts an input relation comprising the rows generated by the query +preceding the `|>` symbol. + +#### FROM or TABLE + +```sql +FROM <tableName> +``` + +```sql +TABLE <tableName> +``` + +Returns all the output rows from the source table unmodified. + +For example: + +```sql +CREATE TABLE t(a INT, b INT) AS VALUES (1, 2), (3, 4); +TABLE t; + ++---+---+ +| a| b| ++---+---+ +| 1| 2| +| 3| 4| ++---+---+ +``` + +#### SELECT + +```sql +|> SELECT <expr> [[AS] alias], ... +``` + +Evaluates the provided expressions over each of the rows of the input table. + +It is possible to use `DISTINCT` and `*` as needed.<br> +This works like the outermost `SELECT` in a table subquery in regular Spark SQL. + +Window functions are supported in the `SELECT` list as well. To use them, the `OVER` clause must be +provided. You may provide the window specification in the `WINDOW` clause. + +For example: + +```sql +CREATE TABLE t(col INT) AS VALUES (0), (1); + +FROM t +|> SELECT col * 2 AS result; + ++------+ +|result| ++------+ +| 0| +| 2| ++------+ +``` + +#### EXTEND + +```sql +|> EXTEND <expr> [[AS] alias], ... +``` + +Appends new columns to the input table by evaluating the specified expressions over each of the input rows. + +For example: + +```sql +VALUES (0), (1) tab(col) +|> EXTEND col * 2 AS result; + ++---+------+ +|col|result| ++---+------+ +| 0| 0| +| 1| 2| ++---+------+ +``` + +#### SET + +```sql +|> SET <column> = <expression>, ... +``` + +Updates columns of the input table by replacing them with the result of evaluating the provided expressions. + +For example: + +```sql +VALUES (0), (1) tab(col) +|> SET col = col * 2; + ++---+ +|col| ++---+ +| 0| +| 2| ++---+ +``` + +#### DROP + +```sql +|> DROP <column>, ... +``` + +Drops columns of the input table by name. + +For example: + +```sql +VALUES (0, 1) tab(col1, col2) +|> DROP col1; + ++----+ +|col2| ++----+ +| 1| ++----+ +``` + +#### AS + +```sql +|> AS <alias> +``` + +Retains the same rows and column names of the input table but with a new table alias. + +For example: + +```sql +VALUES (0, 1) tab(col1, col2) +|> AS new_tab; +|> SELECT * FROM new_tab; + ++----+----+ +|col1|col2| ++----+----+ +| 0| 1| ++----+----+ +``` + +#### WHERE + +```sql +|> WHERE <condition> +``` + +Returns the subset of input rows passing the condition. + +Since this operator may appear anywhere, no separate `HAVING` or `QUALIFY` syntax is needed. + +For example: + +```sql +VALUES (0), (1) tab(col) +|> WHERE col = 1; + ++---+ +|col| ++---+ +| 1| ++---+ +``` + +#### LIMIT + +```sql +|> LIMIT <n> [OFFSET <m>] Review Comment: and `OFFSET` only? -- This is an automated message from the Apache Git Service. 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