DataFiller processes a Postgres database schema file augmented with directives in comments, and generates pseudo-random data matching this schema, taking into account constraints such as types, but also primary key, unique, foreign keys, not null…
Version 2.0.0 introduces the following new features:
This tutorial introduces how to use DataFiller to fill a Postgres database, say for testing functionalities and performances.
I have just released version 1.1.3 of DataFiller.
The Python script processes a Postgres database schema file augmented with directives in comments, and generates random data matching this schema, taking into account constraints such as types, but also primary key, unique, foreign keys, not null…
This is the last of five posts on this subject.
In a previous post I have shown how to build a Turing Machine (TM) in SQL with Postgres using a recursive SQL function. I claimed that this would work with versions of Postgres older than 8.4. In this post, I actually investigate this claim, porting the script down to version 7.3 on a Debian Linux. Testing the relatively simple SQL script with older versions reminds us of all the goodies that have been added over the years.
This is the fourth of five posts on this subject.
In previous posts,
I have presented different ways of implementing a Turing Machine (TM) in SQL
with Postgres.
All three techniques rely on WITH RECURSIVE to iterate till the TM stops, so
as to provide some kind of while construct.
In this post, I get rid of this construct, so that the solution does not require
Postgres 8.4 or later.
Obviously there is a trick: I will use a recursive SQL function with side
effects on a TABLE to execute the TM.
This is the third of five posts on this subject.
In previous posts, I have presented how
to implement a Turing Machine (TM) with the tape stored as an ARRAY or in a
separate TABLE accessed through SQL functions.
In this post the solution is more cleanly relational, with the tape contents
stored in a column of the recursive query, very like
Andrew Gierth’s CTS implementation.
This is the second of five posts on this subject.
In a previous post I presented how to implement a Turing Machine (TM) with an ARRAY to store the tape contents. This solution is not really relational, so in this post I’ll show how to build a TM with SQL-only functions.