Data type mappings for qmark and numeric bindings
To address this situation, Snowflake provides a third level of caching: Clients can then request the validation status of a given Snowflake certificate from this server cache. The proxy parameters, i. Use the environment variables instead. If you must use your SSL proxy, we strongly recommend that you update the server policy to pass through the Snowflake certificate such that no certificate is altered in the middle of communications.
Specify the database and schema in which you want to create tables. Also specify the warehouse that will provide resources for executing DML statements and queries.
For example, create a table named testtable and insert two rows into the table:. Instead of inserting data into tables using individual INSERT commands, you can bulk load data from files staged in either an internal or external location.
To load data from files on your host machine into a table, first use the PUT command to stage the file in an internal location, then use the COPY INTO table command to copy the data in the files into the table.
To load data from files already staged in an external location i. For example, to fetch values from testtable:. If you need to get a single result, use the fetchone method:. If you need to get the specified number of rows at a time, use the fetchmany method with the number of rows:. Use fetchone or fetchmany if the result set is too large to fit into memory.
If the query exceeds the length of the parameter value, an error is produced and a rollback occurs. In the following code, error means the query was canceled. The timeout parameter starts Timer and cancels if the query does not finish within the specified time. If you want to fetch a value by column name, create a cursor object of type DictCursor.
Cancel a query by query ID:. Occasionally you may want to bind data with a placeholder in a query. If paramstyle is specified as qmark or numeric in the connection parameter, the binding variables should be? N , respectively, and the binding occurs in the server side. N as the placeholder:. If datetime Python data type is bound, specify the Snowflake timestamp data type, i. Unlike client side binding, the server side binding requires the Snowflake data type for the column.
Although most of common Python data types already have implicit mappings to Snowflake datatype, e. Column metadata is stored in the Cursor object in the description attribute. A query ID is assigned to each query executed by Snowflake. In the Snowflake web interface, query IDs are displayed in the History page and when checking the status of a query. The Snowflake Connector for Python provides a special attribute, sfqid , in the Cursor object so that you can associate it with the status in the web interface.
In order to retrieve the Snowflake query ID, execute the query first and then retrieve it through the sfqid attribute:. The application must handle exceptions raised from Snowflake Connector properly and decide to continue or stop running the code. The Snowflake Connector for Python supports a context manager that allocates and releases resources as required. The context manager is useful for committing or rolling back transactions based on the statement status when autocommit is disabled.
In the above example, when the third statement fails, the context manager rolls back the changes in the transaction and closes the connection. If all statements were successful, the context manager would commit the changes and close the connection.
An equivalent code with try and except blocks is as follows:. The Snowflake Connector for Python leverages the standard Python logging module to log status at regular intervals so that the application can trace its activity working behind the scenes. The simplest way to enable logging is call logging. The relational calculus then defines a number of operations that operate on relations, giving rise to the set based, declarative language SQL.
Practically, relational databases are made up of tables. Each table has a number of columns with defined data types and precisions. Each table will contain zero, one or more rows which are something like instances of objects.
But modules complying to this standard are few and far between. The reason for this is that it was then revised and a version 2. But unlike PHP where each database driver implements its own often slightly different commands for interacting with the database in Python there is a level of consistency between modules. If you have a database that you already use the chances are that there is a Python database module for it.
It also acts as the owner of a number of standard exceptions. So, for our SQLite database we would do something like:. To see the available methods and attributes on your connection use Python's introspection features:. Certain methods are available on the connection object returned by this constructor.
They all relate to 'global' operations for transaction control such as commit or rollback and most importantly allow us to create cursor objects. Each connection can have multiple cursors. Generally you'll create one for each series of transactions, although it is perfectly common just to create one per connection. As a rule you should create one cursor for each concurrent transaction or group of transactions. We create cursors with a call to the constructor method on the connection:.
A cursor object is the means by which we issue SQL statements to our database and then get the results. To run a specific SQL statement use the execute method:. Transaction control is effected through our connection object, so to commit this change we use the commit method:. Then we need to be able to get our data back again. We can continue to use our original cursor as we don't need to keep the results of any prior operations around.
Getting our data is a two step process:. They are fetchone , fetchmany and fetchall. They pretty much do what they say fetching one row from the result set, a group of rows or every row that your query will return in one step. Obviously the fetchall method should be avoided when you are likely to have very big result sets as it may take a long time to return any data.
Note that the 'fetch' methods only have to return a sequence. In Python any number of data types are classified as sequences so don't assume that you will always get a tuple or a list. The DB-API provides a basic standard level of functionality enabling Python programs that deal with databases to be quite similar in structure and content. This isn't a as big a problem as it first seems because rarely do two different databases implement the same functionality and when they do it is rarely through exactly the same interface.
The specification authors took the view that the DB-API would be like the SQL standard, specifying a core of standard functionality and recognising that different databases would need different code to support their different extensions. It was better to provide some flexibility in implementation because this reflects the reality that is modern databases. One of the trickiest things people new to databases and Python get into trouble with is bind parameters.
The typical first use scenario of parameters is something like:. Which has two main problems. Proper use of bind variables and parameters addresses both of these problems. Lets try another insert into our table:. In this case the database is more likely to keep the parsed version of stmt around and save a few machine cycles on the second insert.
Because we are passing the values as explicit parameters the DB-API module can properly escape the contents and reduce the likelihood of malicious or accidental damage to our database. The module author is free to support one or more of the five available styles:. The format option provides all kinds of opportunities for trouble.
Consider these two examples:. One is good practice, the other bad but the visible difference is very subtle. To execute these statements you would do something like:. Again, semantically worlds apart but syntactically quite similar. The bad news is that there isn't a target date for its release. From simple helpers like dtuple. There is a tool for every need. For a list of these modules your best bet is the higher level database programming page on the Python Wiki. Of most benefit to the new or casual user are helpers like dtuple.
This module by Greg Stein allows you to deal with the result sets that are returned from cursors as a dictionary or an object rather than a sequence. Programmers coming from an object oriented background often don't want to write SQL. These ORMs enable a table centric view of your database allowing you to describe your tables in code or to read them from the database data dictionary. SQLAlchemy then adds a number of different ways of mapping these objects to your application whereas SQLObject leaves you to define them yourself.
Operations on these DTOs are transparently echoed into your database by the services they provide. And the equivalent operations with SQLObject:. The advantage of these tools is that they can initially make your application code simpler. By letting the application code interact only with Python objects you can worry about solving the problems your application is aimed at and don't have to deal with the object relational impedance mismatch. The drawback is that the compromises they make in transaction management and generalising between different databases may mean that they actually end up making your application code more complex than it needs to be.