queries. This is enormously advantageous for working with tree and graph-structured data - imagine retrieving all of the relations of a graph node to a given depth, for example. Find the upward recommendation once. Peewee makes this possible, too, but since Peewee models form a graph (via foreign- keys), the selected data is returned as a graph of model instances. To see what I mean, consider this query: SELECT foo.username) For queries with complex joins and selections from several models, constructing this graph can be expensive. If you wish, instead, to have all columns as attributes on a single model, you can

once. Peewee makes this possible, too, but since Peewee models form a graph (via foreign- keys), the selected data is returned as a graph of model instances. To see what I mean, consider this query: SELECT foo.username) For queries with complex joins and selections from several models, constructing this graph can be expensive. If you wish, instead, to have all columns as attributes on a single model, you will reconstruct the model graph for each row returned. This operation can be slow for complex graphs. Ordinarily, when a query contains joins, peewee will reconstruct the graph of joined data returned by

queries. This is enormously advantageous for working with tree and graph-structured data - imagine retrieving all of the relations of a graph node to a given depth, for example. Find the upward recommendation once. Peewee makes this possible, too, but since Peewee models form a graph (via foreign- keys), the selected data is returned as a graph of model instances. To see what I mean, consider this query: SELECT foo.username) For queries with complex joins and selections from several models, constructing this graph can be expensive. If you wish, instead, to have all columns as attributes on a single model, you can

queries. This is enormously advantageous for working with tree and graph-structured data - imagine retrieving all of the relations of a graph node to a given depth, for example. Find the upward recommendation once. Peewee makes this possible, too, but since Peewee models form a graph (via foreign- keys), the selected data is returned as a graph of model instances. To see what I mean, consider this query: SELECT foo.username) For queries with complex joins and selections from several models, constructing this graph can be expensive. If you wish, instead, to have all columns as attributes on a single model, you can

once. Peewee makes this possible, too, but since Peewee models form a graph (via foreign-keys), the selected data is returned as a graph of model instances. To see what I mean, consider this query: 1.7. foo.username) For queries with complex joins and selections from several models, constructing this graph can be expensive. If you wish, instead, to have all columns as attributes on a single model, you can will reconstruct the model graph for each row returned. This operation can be slow for complex graphs. Ordinarily, when a query contains joins, peewee will reconstruct the graph of joined data returned by

queries. This is enormously advantageous for working with tree and graph- structured data - imagine retrieving all of the relations of a graph node to a given depth, for example. Find the upward recommendation once. Peewee makes this possible, too, but since Peewee models form a graph (via foreign-keys), the selected data is returned as a graph of model instances. To see what I mean, consider this query: SELECT foo.username) For queries with complex joins and selections from several models, constructing this graph can be expensive. If you wish, instead, to have all columns as attributes on a single model, you can

queries. This is enormously advantageous for working with tree and graph- structured data - imagine retrieving all of the relations of a graph node to a given depth, for example. Find the upward recommendation once. Peewee makes this possible, too, but since Peewee models form a graph (via foreign-keys), the selected data is returned as a graph of model instances. To see what I mean, consider this query: SELECT foo.username) For queries with complex joins and selections from several models, constructing this graph can be expensive. If you wish, instead, to have all columns as attributes on a single model, you can

large number of rows that contain columns from multiple tables, peewee will reconstruct the model graph for each row returned. This operation can be slow for complex graphs. For example, if we were selecting objects() which will return the rows as model instances, but will not attempt to resolve the model graph. For example: query = (Tweet .select(Tweet, User) # Select tweet and user data. from tweet to user, and we have selected fields from both models, Peewee will reconstruct the model-graph for us: >>> for tweet in Tweet.select(Tweet.content, User.username).join(User): ... print(tweet

large number of rows that contain columns from multiple tables, peewee will reconstruct the model graph for each row returned. This operation can be slow for complex graphs. For example, if we were selecting objects() which will return the rows as model instances, but will not attempt to resolve the model graph. For example: query = (Tweet .select(Tweet, User) # Select tweet and user data. .join(User)) from tweet to user, and we have selected fields from both models, Peewee will reconstruct the model-graph for us: >>> for tweet in Tweet.select(Tweet.content, User.username).join(User): ... print(tweet.user

reconstruct the model graph for each row returned. This operation can be slow for complex graphs. To speed up model creation, you can: Call naive(), which will not construct a graph and simply patch all Foreign keys and Many-to-many relationships The translate() function will recursively traverse the graph of models and return a dictionary populated with everything it finds. Back-references are not searched create it. This method should be used for creating tables as it will resolve the model dependency graph and ensure the tables are created in the correct order. This method will also create any indexes and