Relational Database Access with Python ‘sans’ ORM

Editor's Notes

• #3: For some Python programmers, their only exposure to accessing relational data is via a object relational mapper (ORM). As powerful is the concept of mapping objects to data, sometimes it is much simpler to manipulate your relational data using SQL. This talk will be about using the DB-API, Python’s standard mechanism for accessing relational databases.
• #4: Or maybe you prefer sqlalchemy to abstract away the database. This talk will be about using the DB-API, Python’s standard mechanism for accessing relational databases.
• #5: SQL (Structured Query Language) is a DSL and we can achieve the same results as the previous two slides. This what DBA’s program in. 
• #7: This diagram no longer seems toexist on Travis’s site
• #11: Always use parameter binding. Why? * you normally get better performance from some database engines due to to SQL query caching * reduce the chance of SQL injection
• #12: Always use parameter binding. Why? * you normally get better performance from some database engines due to to SQL query caching * reduce the chance of SQL injection
• #24: Gerald is a general purpose database schema toolkit written in Python. It can be used for cataloguing, managing and deploying database schemas. It is designed to allow you to easily identify the differences between databases.
• #25: SQLPython is a command-line interface to relational databases written in Python. It was created as an alternative to Oracle’s SQL\\*Plus, and can likewise be used instead of postgres’ psql or mysql’smysql text clients. In addition, it offers several extra features inspired by other command-line clients: Neatened output, smart prompt, tab completion, history, scripting, output to file, paste buffer & os command, unix like commands – ls cat grep, data dictionary exploration. Another feature is special output formats. By replacing the ; that terminates a SELECT statement with a backslash-character sequence, you can get output in a number of useful formats like xml, json, csvetc
• #26: One of the most powerful features is the py command. The py command allows the user to execute Python commands, either one-at-a-time (with py {command}) or in an interactive environment (beginning with a bare py statement, and continuing until Ctrl-D, quit(), or exit() is entered). A history of result sets from each query is exposed to the python session as the list r; the most recent result set is r[-1]. Each row can be references as a tuple, or as an object with an attribute for each column.
• #27: Spring Python takes the concepts of the Spring Framework and Spring Security, and brings them to the world of Python. It isn't a simple line-by-line port of the code. Instead, it takes some powerful ideas that were discovered in the realm of Java, and pragmatically applies them in the world of Python.One of these paradigms is a Portable Service Abstraction called DatabaseTemplate. * It is portable because it uses Python's standardized API, not tying us to any database vendor. Instead, in our example, we injected in an instance of Sqlite3ConnectionFactory* It provides the useful service of easily accessing information stored in a relational database, but letting us focus on the query, not the plumbing code* It offers a nice abstraction over Python's low level database API with reduced code noise. This allows us to avoid the cost and risk of writing code to manage cursors and exception handlingDatabaseTemplate handles exceptions by catching and holding them, then properly closing the cursor. It then raises it wrapped inside a Spring Python DataAccessException. This way, database resources are properly disposed of without losing the exception stack trace.The Database Template can be used in isolation from the SpringPython framework.