X-Git-Url: https://git.jsancho.org/?p=datasette-pytables.git;a=blobdiff_plain;f=DEVELOPERS.md;h=a8eceb2f029eadde2d3639b72986df1ddb9edb68;hp=e0f8db1eea6e084b1dcc6fdeae3f1337b31bcf1a;hb=f2e02a0f692b0312c7e9e1051ff0cca6205b4972;hpb=3b69fc3150e21922485816efbeed22cb561f2af9 diff --git a/DEVELOPERS.md b/DEVELOPERS.md index e0f8db1..a8eceb2 100644 --- a/DEVELOPERS.md +++ b/DEVELOPERS.md @@ -1,16 +1,10 @@ -# How is datasette-pytables made? +# How is datasette-pytables implemented? -Datasette-PyTables is an external connector for [Datasette](https://github.com/simonw/datasette). Datasette publish data in SQLite files to the Internet with a JSON API, and this connector provides a way to do the same with PyTables files. - -Using a modified version of Datasette, [Datasette-Core](https://github.com/PyTables/datasette-core), we can load external connectors that allow us to access to any data container. For this, the connectors need a certain structure. - -Reviewing datasette-pytables code, you will see how to make other connectors for your needs. +Datasette-PyTables is an external connector for [Datasette](https://github.com/simonw/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 a fork of Datasette, [Datasette-Core](https://github.com/PyTables/datasette-core), 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. ## Tables inspection -First of all, we need to export a special method called `inspect` that receives the path of the file as an argument and returns a tuple formed by a dictionary with tables info, a list with views name and a string identifying the connector. - -Each entry in the dictionary for tables info has the next structure: +First of all, we need to implement a special method called `inspect` that receives the path of the file as an argument and returns a tuple formed by a dictionary with tables info, a list with views name and a string identifying the connector. Each entry in the dictionary for tables info has the next structure: tables['table_name'] = { 'name': 'table_name', @@ -22,11 +16,11 @@ Each entry in the dictionary for tables info has the next structure: 'fts_table': None, 'foreign_keys': {'incoming': [], 'outgoing': []} -This structure is used for PyTables. Maybe, in your case, you will need things like primary keys or foreign keys. +This structure is used for PyTables. In your case, you may need additional entries like primary keys or foreign keys. ## Returning results -Datasette runs through SQL queries, so your connector has to accept these queries and execute them. The next class and methods are needed: +Datasette uses SQL for specifying the queries, so your connector has to accept SQL and execute it. The next class and methods are needed: class Connection: def __init__(self, path): @@ -35,23 +29,23 @@ Datasette runs through SQL queries, so your connector has to accept these querie def execute(self, sql, params=None, truncate=False, page_size=None, max_returned_rows=None): ... -The `execute` method receives: +The `Connection.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 -* page_size: the number of rows a page can contain -* max_returned_rows: the maximum number of rows Datasette expects +* **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 +* **page_size**: the number of rows a page can contain +* **max_returned_rows**: the maximum number of rows Datasette expects -We need to parse the query because PyTables has his own style for queries, but other databases could work with the SQL queries without requiring any parsing. +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. -Sometimes, Datasette make queries to `sqlite_master`; you need to keep it in mind. +Note: Sometimes, Datasette make queries to `sqlite_master`; you need to keep it in mind. -The `execute` method has to return a tuple with: +The `Connection.execute()` method has to return a tuple with: -* a list of rows (Datasette expects something like SQLite rows) +* a list of rows (Datasette expects something similar to SQLite rows) * 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',), ...) +* a tuple with the description of the columns in the form `(('c1',), ('c2',), ...)` ## Rows format @@ -61,11 +55,11 @@ For example, if we have the next query: SELECT name FROM persons -we need to return an object that allows to do things that: +we need to return an object that allows to do things like: row[0] == 'Susan' row['name'] == 'Susan' [c for c in row] == ['Susan'] json.dumps(row) -We extend `list` class to get it, but if you respect the requirements for rows, you can develop your own implementation. +In our case, we extend the `list` object to get it, but as long as you implement a similar interface, you can develop your own implementation too.