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Description:

queryparserpython3 0.7.0

queryparser
Tool for parsing and processing of (MySQL*)/PostgreSQL and translation of
ADQL SELECT-like queries
Designed to be used in conjunction with django-daiquri
as a query processing backend but it can be easily used as a stand-alone tool
or integrated into another project.
*NOTE: Since version 0.7.0 MySQL is not supported (maintained) anymore.

Installation
The easiest way to install the package is by using the pip tool:
python -m pip install queryparser-python3

Alternatively, you can clone the repository and install it from there.
However, this step also requires generating the parser which is a slightly
more elaborate process (see below).
Generating the parser from the git repository
To generate the parsers you need python3 , java above version
7, and antlr4 (antlr-4.*-complete.jar has to be installed inside the
/usr/local/lib/, /usr/local/bin/ or root directory of the project).
The current version of antlr-4.*-complete.jar can be downloaded via
wget http://www.antlr.org/download/antlr-4.13.1-complete.jar

After cloning the project run
make

and a lib directory will be created. After that, run
python -m pip install .

to install the generated parser in your virtual environment.
Additional requirements
The queryparser assumes that the PostgreSQL database has the extension
pg_sphere installed.
Although the pg_sphere is not required for the python module, the PostgreSQL
queries will not run without this extension installed on the database.
Parsing MySQL and PostgreSQL
Since version 0.7, MySQL part of the parser is not maintained anymore.
Thus, the MySQL related functionality cannot be guaranteed!
Parsing and processing of MySQL queries can be done by creating an instance
of the MySQLQueryProcessor class
from queryparser.mysql import MySQLQueryProcessor
qp = MySQLQueryProcessor()

feeding it a MySQL query
sql = "SELECT a FROM db.tab;"
qp.set_query(sql)

and running it with
qp.process_query()

After the processing is completed, the processor object qp will include
tables, columns, functions, and keywords used in the query or will raise a
QuerySyntaxError if there are any syntax errors in the query.
Alternatively, passing the query at initialization automatically processes it.
PostgreSQL parsing is very similar to MySQL, except it requires importing
the PostgreSQLProcessor class:
from queryparser.postgresql import PostgreSQLQueryProcessor
qp = PostgreSQLQueryProcessor()

The rest of the functionality remains the same.
Translating ADQL
Translation of ADQL queries is done similarly by first creating an instance of
the ADQLQueryTranslator class
from queryparser.adql import ADQLQueryTranslator
adql = "SELECT TOP 100 POINT('ICRS', ra, de) FROM db.tab;"
adt = ADQLQueryTranslator(adql)

and calling
adt.to_postgresql()

which returns a translated string representing a valid MySQL query if
the ADQL query had no errors. The PostgreSQL query can then be parsed with the
PostgreSQLQueryProcessor in the same way as shown above.
Testing
First in the root directory of the project, install optional dependencies
(PyYAML and pytest) by running
python -m pip install .[test]

then run the test suite with
python -m pytest lib/