Copy over datafreeze documentation, adapt toc etc.

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Friedrich Lindenberg 2013-04-22 17:44:55 +02:00
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Example.yaml Normal file
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common:
database: "postgresql://user:password@localhost/operational_database"
prefix: my_project/dumps/
format: json
exports:
- query: "SELECT id, title, date FROM events"
filename: "index.json"
- query: "SELECT id, title, date, country FROM events"
filename: "countries/{{country}}.csv"
format: csv
- query: "SELECT * FROM events"
filename: "events/{{id}}.json"
mode: item
- query: "SELECT * FROM events"
filename: "all.json"
format: tabson

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<ul>
<li><a href="quickstart.html">Quickstart Guide</a></li>
<li><a href="api.html">API-Documentation</a></li>
<li><a href="freezefile.html">Freezefiles</a></li>
<li><a href="api.html">API documentation</a></li>
<li><a href="install.html">Installation</a></li>
</ul>

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background: #EEE;
}
li {
margin-bottom: 0.3em;
}
@media screen and (max-width: 870px) {
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padding: 0;
}
.rtd_doc_footer {
display: none;
}

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Freezefiles and the ``datafreeze`` command
==========================================
``datafreeze`` creates static extracts of SQL databases for use in interactive
web applications. SQL databases are a great way to manage relational data, but
exposing them on the web to drive data apps can be cumbersome. Often, the
capacities of a proper database are not actually required, a few static JSON
files and a bit of JavaScript can have the same effect. Still, exporting JSON
by hand (or with a custom script) can also become a messy process.
With ``datafreeze``, exports are scripted in a Makefile-like description, making them simple to repeat and replicate.
Basic Usage
-----------
Calling DataFreeze is simple, the application is called with a
freeze file as its argument:
.. code-block:: bash
datafreeze Freezefile.yaml
Freeze files can be either written in JSON or in YAML.
Example Freezefile.yaml
-----------------------
A freeze file is composed of a set of scripted queries and
specifications on how their output is to be handled. An example could look
like this:
.. code-block:: yaml
common:
database: "postgresql://user:password@localhost/operational_database"
prefix: my_project/dumps/
format: json
exports:
- query: "SELECT id, title, date FROM events"
filename: "index.json"
- query: "SELECT id, title, date, country FROM events"
filename: "countries/{{country}}.csv"
format: csv
- query: "SELECT * FROM events"
filename: "events/{{id}}.json"
mode: item
- query: "SELECT * FROM events"
filename: "all.json"
format: tabson
An identical JSON configuration can be found in this repository.
Options in detail
-----------------
The freeze file has two main sections, ``common`` and ``exports``. Both
accept many of the same arguments, with ``exports`` specifying a list of
exports while ``common`` defines some shared properties, such as the
database connection string.
The following options are recognized:
* ``database`` is a database URI, including the database type, username
and password, hostname and database name. Valid database types include
``sqlite``, ``mysql`` and ``postgresql`` (requires psycopg2).
* ``prefix`` specifies a common root directory for all extracted files.
* ``format`` identifies the format to be generated, ``csv``, ``json`` and
``tabson`` are supported. ``tabson`` is a condensed JSON
representation in which rows are not represented by objects but by
lists of values.
* ``query`` needs to be a valid SQL statement. All selected fields will
become keys or columns in the output, so it may make sense to define
proper aliases if any overlap is to be expected.
* ``mode`` specifies whether the query output is to be combined into a
single file (``list``) or whether a file should be generated for each
result row (``item``).
* ``filename`` is the output file name, appended to ``prefix``. All
occurences of ``{{field}}`` are expanded to a fields value to allow the
generation of file names e.g. by primary key. In list mode, templating
can be used to group records into several buckets, e.g. by country or
category.
* ``wrap`` can be used to specify whether the output should be wrapped
in a ``results`` hash in JSON output. This defaults to ``true`` for
``list``-mode output and ``false`` for ``item``-mode.

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@ -22,12 +22,12 @@ Python a breeze:
the necessity for a full ORM model - essentially, databases can be
used like a JSON file or NoSQL store.
* Database contents can be exported (*frozen*) using a sophisticated
plain file generator with JSON and CSV support. Exports can be configured
* Database contents can be exported (*frozen*) using a :doc:`sophisticated
plain file generator <freezefile>` with JSON and CSV support. Exports can be configured
to include metadata and dynamic file names depending on the exported
data.
data. The exporter can also be used as a command-line tool, ``datafreeze``.
The exporter can also be used as a command-line tool, ``datafreeze``.
A simple data loading script using **dataset** might look like this:
::
@ -55,6 +55,8 @@ Features
* **Query helpers** for simple queries such as :py:meth:`all <dataset.Table.all>` rows in a table or
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.
* **Scripted exports**: Data can be exported based on a scripted
configuration, making the process easy and replicable.
Contents
--------
@ -63,6 +65,7 @@ Contents
:maxdepth: 2
quickstart
freezefile
api
Contributors

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@ -144,9 +144,8 @@ You can create one file per row by setting ``mode`` to "item"::
# export one JSON file per user
dataset.freeze(result, 'users/{{ id }}.json', format='json', mode='item')
Since this is a common operation we made it available via command line
utility ``datafreeze``. Read more about the `freezefile markup <https://github.com/spiegelonline/datafreeze#example-freezefileyaml>`_.
utility ``datafreeze``. Read more about the :doc:`freezefile markup <freezefile>`.
.. code-block:: bash