Gadfly (database)


Gadfly is a relational database management system written in Python. Gadfly is a collection of Python modules that provides relational database functionality entirely implemented in Python. It supports a subset of the standard RDBMS Structured Query Language.
Gadfly runs wherever Python runs and supports client/server on any platform that supports the standard Python socket interface. The file formats used by Gadfly for storage are cross-platform—a gadfly database directory can be moved from Windows 95 to Linux using a binary copying mechanism and gadfly will read and run the database.
It supports persistent databases consisting of a collection of structured tables with indices, and a large subset of SQL for accessing and modifying those tables. It supports a log-based recovery protocol which allows committed operations of a database to be recovered even if the database was not shut down in a proper manner. It also supports a TCP/IP Client/Server mode where remote clients can access a Gadfly database over a TCP/IP network subject to configurable security mechanisms.
Since Gadfly depends intimately on the kwParsing package it is distributed as part of the kwParsing package, under the same copyright.
Gadfly allows Python programs to store, retrieve and query tabular data without having to rely on any external database engine or package. It provides an in-memory relational database style engine for Python programs, complete with a notion of a "committed, recoverable transaction" and "aborts".

Usage

The main "gadfly" module attempts to faithfully adhere to Greg Stein's Python Database API, as discussed and certified by the Python DB-SIG.
Concurrent database updates are not supported. The "databases" are currently designed to be written/modified by one process in isolation. Multiple processes can access a Gadfly database when accesses are arbitrated by a TCP/IP Gadfly server process.

Creating a new database

Unlike most Python/database-engine interfaces Gadfly databases must be created using Python. To accomplish this programmers use:

import gadfly
connection = gadfly.gadfly

with no arguments. Then they startup a database using the startup method:

connection.startup

Here "mydirectory" must be a directory which exists and which can be written to in order to store the database files. The startup creates some files in "mydirectory". This has the effect of clobbering any existing Gadfly database called "mydatabase" in the directory "mydirectory". Gadfly will not allow a start up the same connection twice, however.
The first "import gadfly" reads in and initializes some rather large data structures used for parsing SQL, and thus may take longer than other module imports.
Within the database the user can create tables, populate them, and commit the result when they are happy:

cursor = connection.cursor
cursor.execute
cursor.execute VALUES
cursor.execute
for x in cursor.fetchall:
print x
# prints
connection.commit

Reconnecting to an existing database

Once a database exists it can be reconnected to:

import gadfly
connection = gadfly.gadfly

This will read in the database tables with the most recently committed values. The initialized database is then queried and updated:

cursor = connection.cursor
cursor.execute
cursor.execute
for x in cursor.fetchall:
print x
# prints

If the user does not want to commit updates then the do not execute a commit on the connection object. To restore the old values from the existing database they use:

connection.abort

Updates are only stored upon a connection.commit.

print cursor.pp

to "pretty print" the result of any evaluation.

Features

In this version all tables are read into memory upon "connecting" to the database and "touched" tables are written out upon checkpoint. Each table is represented as a separate file in the destination directory, and there is a "data definition" file as well. During active use a log file appears in the active directory as well, and if the process crashes this log file is used to recover committed operations.

The SELECT statement

At this point Gadfly supports quite a lot of the SQL semantics requested in the ODBC 2.0 specification. SQL statements supported include the SELECT:

SELECT expressions or *
FROM tables






This statement is quite powerful. It reads as follows:
  1. Make all combinations of rows from the tables
  2. Eliminate those combinations not satisfying condition
  3. form aggregate groups that match on group-expressions
  4. eliminate aggregate groups that don't satisfy the aggregate-condition.
  5. compute the columns to keep.
  6. combine the result with the result of another select statement.
  7. if DISTINCT, throw out redundant entries.
  8. order the result by the columns.
The actual implementation in gadfly is much better than the intuitive reading, particularly at steps 1 and 2.
Conditions may include equalities, and inequalities of expressions. Conditions may also be combined using AND, OR, NOT. Expressions include column names, constants, and standard arithmetic operations over them.
Embedded queries supported include subquery expressions, expr IN, quantified comparisons, and the EXISTS predicate.
Aggregate tests and computations can only be applied after the GROUPing and before the columns are selected. Aggregate operations include COUNT, COUNT, AVG, SUM, MAX, MIN, and the non-standard MEDIAN. These may be applied to DISTINCT values. if no GROUPing is present the aggregate computations apply to the entire result after step 2.
There is much more to know about the SELECT statement. The test suite test/test_gadfly.py gives numerous examples of SELECT statements.

Table creation and "data types"

Create tables using the CREATE TABLE statement:

CREATE TABLE name

Data types currently "supported" are integer, float, and varchar. They are ignored by the implementation, anything that is hashable and marshallable can currently go in any column. For example:

CREATE TABLE frequents

At present tuples, complexes, or anything else can be put into a column specified as "VARCHAR".

Other supported statements

Gadfly also supports the searched DELETE and UPDATE; INSERT VALUES and INSERT subselect; CREATE/DROP INDEX, and DROP TABLE. These have the informal syntax:

DELETE FROM table WHERE condition
UPDATE table SET col=expr WHERE condition
INSERT INTO table VALUES
INSERT INTO table subselect
CREATE INDEX name ON table
DROP TABLE table
DROP INDEX name

Multiple statements may be executed in one cursor.execute by separating the statements with semicolons in S, for example S might have the string value:

DROP INDEX tdindex;
DROP TABLE templikes

SQL is case insensitive.

Dynamic values

Expressions also include the special expression '?' as in:

insertstat = "INSERT INTO ph VALUES "
cursor.execute
cursor.execute
cursor.execute

Dynamic values allow the cursor to use the same parsed expression many times for a similar operation. Above the insertstat is parsed and bound to the database only once. Using dynamic attributes should speed up accesses. Thus the above should run much faster than the equivalent:

cursor.execute VALUES
cursor.execute VALUES
cursor.execute VALUES

Dynamic attributes can appear in other statements containing expressions.
For SELECT, UPDATE, and DELETE the dynamic expression substitutions must consist of a single tuple, as in:

stat = "SELECT * FROM ph WHERE nm=?"
cursor.execute
...
cursor.execute
...

Since the dynamic substitution eliminates the need for parsing and binding the above should run faster than the equivalent:

cursor.execute
...
cursor.execute
...

If several similar queries are repeated multiple times, each query "template string" is associated with a unique cursor object so that each template must be parsed and bound only once. Some relatively complex queries from the test suite run 2 to 3 times faster after they have been parsed and bound, even without the kjbuckets builtin. With kjbuckets the same ran 5 to 10 times faster.

Multiple batch inserts and dynamic values

For the special case of INSERT VALUES a list of substitution tuples allows the query engine to perform the inserts in optimized batch mode. Thus the fastest way to perform the three inserts given earlier is:

data =
stat = "INSERT INTO ph VALUES "
cursor.execute

It would be even faster if the cursor had previously executed the stat with different data.

Introspection

By default a gadfly database automatically includes "introspective" tables which allow a gadfly query to "query the shape of the database"—for example to view table names and names of rows in tables:

>>> g = gadfly
>>> g.startup
>>> c = g.cursor
>>> c.execute
>>> print c.pp
IS_VIEW | TABLE_NAME
1 | __TABLE_NAMES__
1 | DUAL
1 | __DATADEFS__
1 | __COLUMNS__
1 | __INDICES__
1 | __INDEXCOLS__

Here DUAL is a standard one row/one column test table and the other tables provide information about the database schema:

>>> c.execute
>>> c.execute
>>> c.execute
>>> c.execute
>>> c.execute
>>> print c.pp
IS_VIEW | TABLE_NAME
0 | T1
1 | __DATADEFS__
1 | __INDICES__
0 | T2
1 | __TABLE_NAMES__
1 | __COLUMNS__
1 | DUAL
1 | __INDEXCOLS__
>>> c.execute
>>> print c.pp
COLUMN_NAME | TABLE_NAME
A | T1
B | T1
NAME | __DATADEFS__
DEFN | __DATADEFS__
INDEX_NAME | __INDICES__
TABLE_NAME | __INDICES__
IS_UNIQUE | __INDICES__
TABLE_NAME | __TABLE_NAMES__
IS_VIEW | __TABLE_NAMES__
B | T2
C | T2
COLUMN1 | DUAL
TABLE_NAME | __COLUMNS__
COLUMN_NAME | __COLUMNS__
INDEX_NAME | __INDEXCOLS__
COLUMN_NAME | __INDEXCOLS__
>>> c.execute
>>> print c.pp
IS_UNIQUE | TABLE_NAME | INDEX_NAME
0 | T1 | T1B
1 | T1 | T1A
>>> c.execute
>>> print c.pp
COLUMN_NAME | INDEX_NAME
B | T1B
A | T1A
>>> c.execute
>>> print c.pp
COLUMN1
=

Interactive testing

After installation, the created database can be interactively tested from the same directory using the interactive interpreter:

Python 2.1.3
on linux2
Type "copyright", "credits" or "license" for more information.
>>>
>>> from gadfly import gadfly
>>> connection = gadfly
>>> cursor = connection.cursor
>>> cursor.execute
>>> cursor.description
,, )
>>> print cursor.pp
DRINKER | PERWEEK | BAR
adam | 1 | lolas
woody | 5 | cheers
sam | 5 | cheers
norm | 3 | cheers
wilt | 2 | joes
norm | 1 | joes
lola | 6 | lolas
norm | 2 | lolas
woody | 1 | lolas
pierre | 0 | frankies
>>>

Architecture

The SQL grammar is described in grammar.py, the binding of the grammar constructs to semantic objects is performed in bindings.py, the semantic objects and their execution strategies is defined in semantics.py. The semantics use a lot of classical and non-classical logic as well as optimization heuristics to define a relatively efficient and correct implementation of SQL.
The most basic data structures of the implementation are given in either kjbuckets0.py or the faster kjbucketsmodule.c, which implement the same data type signatures in Python and in a C extension to Python respectively.
The database.py module is a simple wrapper that provides a standard DBAPI interface to the system.
The test suite test/test_gadfly.py attempts to provide a regression test and a demonstration of the system.
The SQL parser also requires the kwParsing parser generation package, which consists of a number of additional python modules.

Tools

With gfplus a gadfly database can be interactively manipulated with SQL commands.
The tool works similar to Oracle's SQL*Plus.

Concurrency

Because it lacks true concurrency control and file-system based indexing it is not appropriate for very large multiprocess transaction-based systems.
Two applications may access the same database concurrently. However, changes made by one application may not be seen by the other application until after it restarts. This may be because each application loads the database in-memory at startup only.