Adding upstream version 6.0.4.upstream/6.0.4

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

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+# SQLGlot
+
+SQLGlot is a no dependency Python SQL parser, transpiler, and optimizer. It can be used to format SQL or translate between different dialects like [DuckDB](https://duckdb.org/), [Presto](https://prestodb.io/), [Spark](https://spark.apache.org/), and [BigQuery](https://cloud.google.com/bigquery/). It aims to read a wide variety of SQL inputs and output syntactically correct SQL in the targeted dialects.
+
+It is a very comprehensive generic SQL parser with a robust [test suite](tests). It is also quite [performant](#benchmarks) while being written purely in Python.
+
+You can easily [customize](#custom-dialects) the parser, [analyze](#metadata) queries, traverse expression trees, and programmatically [build](#build-and-modify-sql) SQL.
+
+Syntax [errors](#parser-errors) are highlighted and dialect incompatibilities can warn or raise depending on configurations.
+
+## Install
+From PyPI
+
+```
+pip3 install sqlglot
+```
+
+Or with a local checkout
+
+```
+pip3 install -e .
+```
+
+## Examples
+Easily translate from one dialect to another. For example, date/time functions vary from dialects and can be hard to deal with.
+
+```python
+import sqlglot
+sqlglot.transpile("SELECT EPOCH_MS(1618088028295)", read='duckdb', write='hive')
+```
+
+```sql
+SELECT TO_UTC_TIMESTAMP(FROM_UNIXTIME(1618088028295 / 1000, 'yyyy-MM-dd HH:mm:ss'), 'UTC')
+```
+
+SQLGlot can even translate custom time formats.
+```python
+import sqlglot
+sqlglot.transpile("SELECT STRFTIME(x, '%y-%-m-%S')", read='duckdb', write='hive')
+```
+
+```sql
+SELECT DATE_FORMAT(x, 'yy-M-ss')"
+```
+
+## Formatting and Transpiling
+Read in a SQL statement with a CTE and CASTING to a REAL and then transpiling to Spark.
+
+Spark uses backticks as identifiers and the REAL type is transpiled to FLOAT.
+
+```python
+import sqlglot
+
+sql = """WITH baz AS (SELECT a, c FROM foo WHERE a = 1) SELECT f.a, b.b, baz.c, CAST("b"."a" AS REAL) d FROM foo f JOIN bar b ON f.a = b.a LEFT JOIN baz ON f.a = baz.a"""
+sqlglot.transpile(sql, write='spark', identify=True, pretty=True)[0]
+```
+
+```sql
+WITH `baz` AS (
+  SELECT
+    `a`,
+    `c`
+  FROM `foo`
+  WHERE
+    `a` = 1
+)
+SELECT
+  `f`.`a`,
+  `b`.`b`,
+  `baz`.`c`,
+  CAST(`b`.`a` AS FLOAT) AS `d`
+FROM `foo` AS `f`
+JOIN `bar` AS `b`
+  ON `f`.`a` = `b`.`a`
+LEFT JOIN `baz`
+  ON `f`.`a` = `baz`.`a`
+```
+
+## Metadata
+
+You can explore SQL with expression helpers to do things like find columns and tables.
+
+```python
+from sqlglot import parse_one, exp
+
+# print all column references (a and b)
+for column in parse_one("SELECT a, b + 1 AS c FROM d").find_all(exp.Column):
+  print(column.alias_or_name)
+
+# find all projections in select statements (a and c)
+for select in parse_one("SELECT a, b + 1 AS c FROM d").find_all(exp.Select):
+  for projection in select.expressions:
+    print(projection.alias_or_name)
+
+# find all tables (x, y, z)
+for table in parse_one("SELECT * FROM x JOIN y JOIN z").find_all(exp.Table):
+  print(table.name)
+```
+
+## Parser Errors
+A syntax error will result in a parser error.
+```python
+transpile("SELECT foo( FROM bar")
+```
+
+sqlglot.errors.ParseError: Expecting ). Line 1, Col: 13.
+  select foo( __FROM__ bar
+
+## Unsupported Errors
+Presto APPROX_DISTINCT supports the accuracy argument which is not supported in Spark.
+
+```python
+transpile(
+    'SELECT APPROX_DISTINCT(a, 0.1) FROM foo',
+    read='presto',
+    write='spark',
+)
+```
+
+```sql
+WARNING:root:APPROX_COUNT_DISTINCT does not support accuracy
+
+SELECT APPROX_COUNT_DISTINCT(a) FROM foo
+```
+
+## Build and Modify SQL
+SQLGlot supports incrementally building sql expressions.
+
+```python
+from sqlglot import select, condition
+
+where = condition("x=1").and_("y=1")
+select("*").from_("y").where(where).sql()
+```
+Which outputs:
+```sql
+SELECT * FROM y WHERE x = 1 AND y = 1
+```
+
+You can also modify a parsed tree:
+
+```python
+from sqlglot import parse_one
+
+parse_one("SELECT x FROM y").from_("z").sql()
+```
+Which outputs:
+```sql
+SELECT x FROM y, z
+```
+
+There is also a way to recursively transform the parsed tree by applying a mapping function to each tree node:
+
+```python
+from sqlglot import exp, parse_one
+
+expression_tree = parse_one("SELECT a FROM x")
+
+def transformer(node):
+    if isinstance(node, exp.Column) and node.name == "a":
+        return parse_one("FUN(a)")
+    return node
+
+transformed_tree = expression_tree.transform(transformer)
+transformed_tree.sql()
+```
+Which outputs:
+```sql
+SELECT FUN(a) FROM x
+```
+
+## SQL Optimizer
+
+SQLGlot can rewrite queries into an "optimized" form. It performs a variety of [techniques](sqlglot/optimizer/optimizer.py) to create a new canonical AST. This AST can be used to standardize queries or provide the foundations for implementing an actual engine.
+
+```python
+import sqlglot
+from sqlglot.optimizer import optimize
+
+>>>
+optimize(
+    sqlglot.parse_one("""
+    SELECT A OR (B OR (C AND D))
+    FROM x
+    WHERE Z = date '2021-01-01' + INTERVAL '1' month OR 1 = 0
+    """),
+    schema={"x": {"A": "INT", "B": "INT", "C": "INT", "D": "INT", "Z": "STRING"}}
+).sql(pretty=True)
+
+"""
+SELECT
+  (
+    "x"."A"
+    OR "x"."B"
+    OR "x"."C"
+  )
+  AND (
+    "x"."A"
+    OR "x"."B"
+    OR "x"."D"
+  ) AS "_col_0"
+FROM "x" AS "x"
+WHERE
+  "x"."Z" = CAST('2021-02-01' AS DATE)
+"""
+```
+
+## SQL Annotations
+
+SQLGlot supports annotations in the sql expression. This is an experimental feature that is not part of any of the SQL standards but it can be useful when needing to annotate what a selected field is supposed to be. Below is an example:
+
+```sql
+SELECT
+  user #primary_key,
+  country
+FROM users
+```
+
+SQL annotations are currently incompatible with MySQL, which uses the `#` character to introduce comments.
+
+## AST Introspection
+
+You can see the AST version of the sql by calling repr.
+
+```python
+from sqlglot import parse_one
+repr(parse_one("SELECT a + 1 AS z"))
+
+(SELECT expressions:
+  (ALIAS this:
+    (ADD this:
+      (COLUMN this:
+        (IDENTIFIER this: a, quoted: False)), expression:
+      (LITERAL this: 1, is_string: False)), alias:
+    (IDENTIFIER this: z, quoted: False)))
+```
+
+## AST Diff
+
+SQLGlot can calculate the difference between two expressions and output changes in a form of a sequence of actions needed to transform a source expression into a target one.
+
+```python
+from sqlglot import diff, parse_one
+diff(parse_one("SELECT a + b, c, d"), parse_one("SELECT c, a - b, d"))
+
+[
+  Remove(expression=(ADD this:
+    (COLUMN this:
+      (IDENTIFIER this: a, quoted: False)), expression:
+    (COLUMN this:
+      (IDENTIFIER this: b, quoted: False)))),
+  Insert(expression=(SUB this:
+    (COLUMN this:
+      (IDENTIFIER this: a, quoted: False)), expression:
+    (COLUMN this:
+      (IDENTIFIER this: b, quoted: False)))),
+  Move(expression=(COLUMN this:
+    (IDENTIFIER this: c, quoted: False))),
+  Keep(source=(IDENTIFIER this: b, quoted: False), target=(IDENTIFIER this: b, quoted: False)),
+  ...
+]
+```
+
+## Custom Dialects
+
+[Dialects](sqlglot/dialects) can be added by subclassing Dialect.
+
+```python
+from sqlglot import exp
+from sqlglot.dialects.dialect import Dialect
+from sqlglot.generator import Generator
+from sqlglot.tokens import Tokenizer, TokenType
+
+
+class Custom(Dialect):
+    class Tokenizer(Tokenizer):
+        QUOTES = ["'", '"']
+        IDENTIFIERS = ["`"]
+
+        KEYWORDS = {
+            **Tokenizer.KEYWORDS,
+            "INT64": TokenType.BIGINT,
+            "FLOAT64": TokenType.DOUBLE,
+        }
+
+    class Generator(Generator):
+        TRANSFORMS = {exp.Array: lambda self, e: f"[{self.expressions(e)}]"}
+
+        TYPE_MAPPING = {
+            exp.DataType.Type.TINYINT: "INT64",
+            exp.DataType.Type.SMALLINT: "INT64",
+            exp.DataType.Type.INT: "INT64",
+            exp.DataType.Type.BIGINT: "INT64",
+            exp.DataType.Type.DECIMAL: "NUMERIC",
+            exp.DataType.Type.FLOAT: "FLOAT64",
+            exp.DataType.Type.DOUBLE: "FLOAT64",
+            exp.DataType.Type.BOOLEAN: "BOOL",
+            exp.DataType.Type.TEXT: "STRING",
+        }
+
+
+Dialects["custom"]
+```
+
+## Benchmarks
+
+[Benchmarks](benchmarks) run on Python 3.10.5 in seconds.
+
+|           Query |         sqlglot |         sqltree |        sqlparse |  moz_sql_parser |        sqloxide |
+| --------------- | --------------- | --------------- | --------------- | --------------- | --------------- |
+|            tpch |   0.01178 (1.0) | 0.01173 (0.995) | 0.04676 (3.966) | 0.06800 (5.768) | 0.00094 (0.080) |
+|           short |   0.00084 (1.0) | 0.00079 (0.948) | 0.00296 (3.524) | 0.00443 (5.266) | 0.00006 (0.072) |
+|            long |   0.01102 (1.0) | 0.01044 (0.947) | 0.04349 (3.945) | 0.05998 (5.440) | 0.00084 (0.077) |
+|           crazy |   0.03751 (1.0) | 0.03471 (0.925) | 11.0796 (295.3) | 1.03355 (27.55) | 0.00529 (0.141) |
+
+
+## Run Tests and Lint
+```
+pip install -r requirements.txt
+./run_checks.sh
+```
+
+## Optional Dependencies
+SQLGlot uses [dateutil](https://github.com/dateutil/dateutil) to simplify literal timedelta expressions. The optimizer will not simplify expressions like
+
+```sql
+x + interval '1' month
+```
+
+if the module cannot be found.