Uses tokenized query returned by python-sqlparse and generates query metadata
# sql-metadata

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Uses tokenized query returned by [`python-sqlparse`](https://github.com/andialbrecht/sqlparse) and generates query metadata.

**Extracts column names and tables** used by the query. 
Automatically conduct **column alias resolution**, **sub queries aliases resolution** as well as **tables aliases resolving**.

Provides also a helper for **normalization of SQL queries**.

Supported queries syntax:

* MySQL
* PostgreSQL
* Sqlite
* MSSQL
* [Apache Hive](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML)

(note that listed backends can differ quite substantially but should work in regard of query types supported by `sql-metadata`)

You can test the capabilities of `sql-metadata` with an interactive demo: [https://sql-app.infocruncher.com/](https://sql-app.infocruncher.com/)

## Usage

```
pip install sql-metadata
```

### Extracting raw sql-metadata tokens

```python
from sql_metadata import Parser

# extract raw sql-metadata tokens
Parser("SELECT * FROM foo").tokens
# ['SELECT', '*', 'FROM', 'foo']
```

### Extracting columns from query

```python
from sql_metadata import Parser

# get columns from query - for more examples see `tests/test_getting_columns.py`
Parser("SELECT test, id FROM foo, bar").columns
# ['test', 'id']

Parser("INSERT /* VoteHelper::addVote xxx */  INTO `page_vote` (article_id,user_id,`time`) VALUES ('442001','27574631','20180228130846')").columns
# ['article_id', 'user_id', 'time']

parser = Parser("SELECT a.* FROM product_a.users AS a JOIN product_b.users AS b ON a.ip_address = b.ip_address")

# note that aliases are auto-resolved
parser.columns
# ['product_a.*', 'product_a.users.ip_address', 'product_b.users.ip_address']

# note that you can also extract columns with their place in the query
# which will return dict with lists divided into select, where, order_by, group_by, join, insert and update
parser.columns_dict
# {'select': ['product_a.users.*'], 'join': ['product_a.users.ip_address', 'product_b.users.ip_address']}
```

### Extracting columns aliases from query

```python
from sql_metadata import Parser
parser = Parser("SELECT a, (b + c - u) as alias1, custome_func(d) alias2 from aa, bb order by alias1")

# note that columns list do not contain aliases of the columns
parser.columns
# ["a", "b", "c", "u", "d"]

# but you can still extract aliases names
parser.columns_aliases_names
# ["alias1", "alias2"]

# aliases are resolved to the columns which they refer to
parser.columns_aliases
# {"alias1": ["b", "c", "u"], "alias2": "d"}

# you can also extract aliases used by section of the query in which they are used
parser.columns_aliases_dict
# {"order_by": ["alias1"], "select": ["alias1", "alias2"]}

# the same applies to aliases used in queries section when you extract columns_dict
# here only the alias is used in order by but it's resolved to actual columns
assert parser.columns_dict == {'order_by': ['b', 'c', 'u'],
                               'select': ['a', 'b', 'c', 'u', 'd']}
```

### Extracting tables from query

```python
from sql_metadata import Parser

# get tables from query - for more examples see `tests/test_getting_tables.py`
Parser("SELECT a.* FROM product_a.users AS a JOIN product_b.users AS b ON a.ip_address = b.ip_address").tables
# ['product_a.users', 'product_b.users']

Parser("SELECT test, id FROM foo, bar").tables
# ['foo', 'bar']

# you can also extract aliases of the tables as a dictionary
parser = Parser("SELECT f.test FROM foo AS f")

# get table aliases
parser.tables_aliases
# {'f': 'foo'}

# note that aliases are auto-resolved for columns
parser.columns
# ["foo.test"]
```

### Extracting values from insert query
```python
from sql_metadata import Parser

parser = Parser(
    "INSERT /* VoteHelper::addVote xxx */  INTO `page_vote` (article_id,user_id,`time`) " 
    "VALUES ('442001','27574631','20180228130846')"
)
# extract values from query
parser.values
# ["442001", "27574631", "20180228130846"]

# extract a dictionary with column-value pairs
parser.values_dict
#{"article_id": "442001", "user_id": "27574631", "time": "20180228130846"}

# if column names are not set auto-add placeholders
parser = Parser(
    "INSERT IGNORE INTO `table` VALUES (9, 2.15, '123', '2017-01-01');"
)
parser.values
# [9, 2.15, "123", "2017-01-01"]

parser.values_dict
#{"column_1": 9, "column_2": 2.15, "column_3": "123", "column_4": "2017-01-01"}
```


### Extracting limit and offset
```python
from sql_metadata import Parser

Parser('SELECT foo_limit FROM bar_offset LIMIT 50 OFFSET 1000').limit_and_offset
# (50, 1000)

Parser('SELECT foo_limit FROM bar_offset limit 2000,50').limit_and_offset
# (50, 2000)
```

### Extracting with names

```python
from sql_metadata import Parser

parser = Parser(
    """
WITH
    database1.tableFromWith AS (SELECT aa.* FROM table3 as aa 
                                left join table4 on aa.col1=table4.col2),
    test as (SELECT * from table3)
SELECT
  "xxxxx"
FROM
  database1.tableFromWith alias
LEFT JOIN database2.table2 ON ("tt"."ttt"."fff" = "xx"."xxx")
"""
)

# get names/ aliases of with statements
parser.with_names
# ["database1.tableFromWith", "test"]

# get definition of with queries
parser.with_queries
# {"database1.tableFromWith": "SELECT aa.* FROM table3 as aa left join table4 on aa.col1=table4.col2"
#  "test": "SELECT * from table3"}

# note that names of with statements do not appear in tables
parser.tables
# ["table3", "table4", "database2.table2"]
```

### Extracting sub-queries

```python
from sql_metadata import Parser

parser = Parser(
"""
SELECT COUNT(1) FROM
(SELECT std.task_id FROM some_task_detail std WHERE std.STATUS = 1) a
JOIN (SELECT st.task_id FROM some_task st WHERE task_type_id = 80) b
ON a.task_id = b.task_id;
"""
)

# get sub-queries dictionary
parser.subqueries
# {"a": "SELECT std.task_id FROM some_task_detail std WHERE std.STATUS = 1",
#  "b": "SELECT st.task_id FROM some_task st WHERE task_type_id = 80"}


# get names/ aliases of sub-queries / derived tables
parser.subqueries_names
# ["a", "b"]

# note that columns coming from sub-queries are resolved to real columns
parser.columns
#["some_task_detail.task_id", "some_task_detail.STATUS", "some_task.task_id", 
# "task_type_id"]

# same applies for columns_dict, note the join columns are resolved
parser.columns_dict
#{'join': ['some_task_detail.task_id', 'some_task.task_id'],
# 'select': ['some_task_detail.task_id', 'some_task.task_id'],
# 'where': ['some_task_detail.STATUS', 'task_type_id']}

```

See `tests` file for more examples of a bit more complex queries.

### Queries normalization and comments extraction

```python
from sql_metadata import Parser
parser = Parser('SELECT /* Test */ foo FROM bar WHERE id in (1, 2, 56)')

# generalize query
parser.generalize
# 'SELECT foo FROM bar WHERE id in (XYZ)'

# remove comments
parser.without_comments
# 'SELECT foo FROM bar WHERE id in (1, 2, 56)'

# extract comments
parser.comments
# ['/* Test */']
```

See `test/test_normalization.py` file for more examples of a bit more complex queries.

## Migrating from `sql_metadata` 1.x

`sql_metadata.compat` module has been implemented to make the introduction of sql-metadata v2.0 smoother.

You can use it by simply changing the imports in your code from:

```python
from sql_metadata import get_query_columns, get_query_tables
```

into:

```python
from sql_metadata.compat import get_query_columns, get_query_tables
```

The following functions from the old API are available in the `sql_metadata.compat` module:

* `generalize_sql`
* `get_query_columns` (since #131 columns aliases ARE NOT returned by this function)
* `get_query_limit_and_offset`
* `get_query_tables`
* `get_query_tokens`
* `preprocess_query`

## Authors and contributors

Created and maintained by [@macbre](https://github.com/macbre) with a great contributions from [@collerek](https://github.com/collerek) and the others.

* aborecki (https://github.com/aborecki)
* collerek (https://github.com/collerek)
* dylanhogg (https://github.com/dylanhogg)
* macbre (https://github.com/macbre)

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