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Gregor Aisch 2013-04-03 00:24:23 +02:00
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docs/index.rst
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@ -12,11 +12,11 @@ dataset: databases for lazy people
Although managing data in relational database has plenty of benefits, we find them rarely being used in the typical day-to-day work with small to medium scale datasets. But why is that? Why do we see an awful lot of data stored in static files in CSV or JSON format? Although managing data in relational database has plenty of benefits, we find them rarely being used in the typical day-to-day work with small to medium scale datasets. But why is that? Why do we see an awful lot of data stored in static files in CSV or JSON format?
Because **good programmers are lazy**, and thus they tend to prefer the easiest solution they find. And managing data in a databases simply wasn't the simplest solution to store a bunch of structured data. This is where ``dataset`` steps in! Because **programmers are lazy**, and thus they tend to prefer the easiest solution they find. And managing data in a databases simply wasn't the simplest solution to store a bunch of structured data. This is where ``dataset`` steps in!
Dataset is here to **take the pain out of databases**. It makes reading and writing data in databases as simple as reading and writing JSON files. Dataset is here to **take the pain out of databases**. It makes reading and writing data in databases as simple as reading and writing JSON files.
In short, :: ::
import dataset import dataset
@ -25,7 +25,7 @@ In short, ::
db['sometable'].insert(dict(name='Jane Doe', age=34, gender='female')) db['sometable'].insert(dict(name='Jane Doe', age=34, gender='female'))
Now look at `similar code, without dataset <https://gist.github.com/gka/5296492>`_. Here is `similar code, without dataset <https://gist.github.com/gka/5296492>`_.
Features Features
@ -33,10 +33,11 @@ Features
* **Automatic schema**: If a table or column is written that does not * **Automatic schema**: If a table or column is written that does not
exist in the database, it will be created automatically. exist in the database, it will be created automatically.
* **Upserts**: Records are either created or updated, depdending on * **Upserts**: Records are either created or updated, depending on
whether an existing version can be found. whether an existing version can be found.
* **Query helpers** for simple queries such as all rows in a table or * **Query helpers** for simple queries such as :py:meth:`all <dataset.Table.all>` rows in a table or
all distinct values across a set of columns. all :py:meth:`distinct <dataset.Table.distinct>` values across a set of columns.
* **Compatibility**: Being built on top of `SQLAlchemy <http://www.sqlalchemy.org/>`_, ``dataset`` works with all major databases, such as SQLite, PostgreSQL and MySQL.
Contents Contents
-------- --------

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docs/quickstart.rst
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@ -69,14 +69,14 @@ Searching for specific entries::
# Returns all items # Returns all items
table.find(country='China') table.find(country='China')
Querying data Running custom SQL queries
------------- --------------------------
Querying data is easy. Dataset returns an iteratable result object:: Of course the main reason you're using a database is that you want to use the full power of SQL queries. Here's how you run them using dataset::
result = db.query('SELECT ...') result = db.query('SELECT country, COUNT(*) cnt FROM population GROUP BY year')
for row in result: for row in res:
print row print row.country, row.cnt
Freezing your data Freezing your data
------------------ ------------------