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How is datasette-pytables implemented?

Datasette-PyTables is an external connector for Datasette. Datasette publish data in SQLite files to the Internet with a JSON API, and this connector provides a way to do the same thing with PyTables files. By using Datasette-Connectors, we can load external connectors that allow us to access to any data container. For this, the connectors need the interface that is described here. By following these interface, you will can make connectors for other data sources too.

Starting from scratch

For making a Datasette connector for your favorite database files, you need to inherit from datasette_connectors.Connector. Then, you can specify your connector type in the class property connector_type and, very important, you should set connection_class property with a class that inherits from datasette_connectors.Connection and implements a method for opening your database files.

For example, for Pytables the next class definition is used:

import tables
import datasette_connectors as dc

class PyTablesConnection(dc.Connection):
    def __init__(self, path, connector):
        super().__init__(path, connector)
        self.h5file = tables.open_file(path)

class PyTablesConnector(dc.Connector):
    connector_type = 'pytables'
    connection_class = PyTablesConnection

Tables inspection

Datasette needs some data about your database so you have to provide it overwriting some methods in your custom connector. For that, the connector stores and instance of the class set in connection_class in the property conn, so you can use self.conn to access to your database in order to retrieve that data.

The methods that must be overwritten are:

  • table_names(self): a list of table names
  • hidden_table_names(self): a list of hidden table names
  • detect_spatialite(self): a boolean indicating if geometry_columns exists
  • view_names(self): a list of view names
  • table_count(self, table_name): an integer with the rows count of the table
  • table_info(self, table_name): a list of dictionaries with columns description
  • foreign_keys(self, table_name): a list of dictionaries with foreign keys description
  • table_exists(self, table_name): a boolean indicating if table exists in the database
  • table_definition(self, table_type, table_name): a string with a 'CREATE TABLE' sql definition
  • indices_definition(self, table_name): a list of strings with 'CREATE INDEX' sql definitions

Returning results

Datasette uses SQL for specifying the queries, so your connector has to accept SQL and execute it. Overwriting execute method you can receive the query in SQL format and return some results.

The Connector.execute() method receives:

  • sql: the query
  • params: a dictionary with the params used in the query
  • truncate: a boolean saying if the returned data can be separated in pages or not
  • custom_time_limit: an integer with a time limit for the execution of the query in seconds
  • page_size: the number of rows a page can contain
  • log_sql_errors: a boolean saying if errors has to be logged

In our case, we need to parse the SQL query because PyTables has its own style for queries, but other databases could work with the SQL queries without requiring any parsing.

Note: Sometimes, Datasette make queries to sqlite_master; you need to keep it in mind.

The Connector.execute() method has to return a tuple with:

  • a list of rows; each row is a dictionary with the field name as key and the field value as value
  • a boolean saying if the data is truncated, i.e., if we return all the rows or there are more rows than the maximum indicated in max_returned_rows
  • a tuple with the description of the columns in the form (('c1',), ('c2',), ...)