. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 1.11 Performance Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 particularly if you use SQLite. • apsw: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. • gevent the Tweet model itself: print(tweet.content, tweet.timestamp, tweet.username) For additional performance gains, consider using dicts(), tuples() or namedtuples() when iterating large and/or complex result-sets

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 1.11 Performance Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 particularly if you use SQLite. • apsw: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. • pycrypto join_query.c.id # Becomes: (t1."parent_id" = "jq"."id") 1.11 Performance Techniques This section outlines some techniques for improving performance when using peewee. 1.11.1 Avoiding N+1 queries The term

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 1.11 Performance Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 particularly if you use SQLite. • apsw: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. • gevent the Tweet model itself: print(tweet.content, tweet.timestamp, tweet.username) For additional performance gains, consider using dicts(), tuples() or namedtuples() when iterating large and/or complex result-sets

Expressions Foreign Keys Traversing foreign keys Joining tables Implementing Many to Many Self-joins Performance Techniques Avoiding N+1 queries Iterating over lots of rows Speeding up Bulk Inserts Transactions apsw [https://github.com/rogerbinns/apsw]: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. sweepea the Tweet model itself: print(tweet.content, tweet.timestamp, tweet.username) For additional performance gains, consider using dicts(), tuples() or namedtuples() when iterating large and/or complex result-sets

dictionaries / namedtuples Returning Clause Foreign Keys and Joins Implementing Many to Many Self-joins Performance Techniques Query operators Three valued logic Adding user-defined operators Expressions SQL Functions apsw [https://github.com/rogerbinns/apsw]: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. gevent the Tweet model itself: print(tweet.content, tweet.timestamp, tweet.username) For additional performance gains, consider using dicts(), tuples() or namedtuples() when iterating large and/or complex result-sets

Expressions Foreign Keys Traversing foreign keys Joining tables Implementing Many to Many Self-joins Performance Techniques Avoiding N+1 queries Iterating over lots of rows Speeding up Bulk Inserts Transactions apsw [https://github.com/rogerbinns/apsw]: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. gevent the Tweet model itself: print(tweet.content, tweet.timestamp, tweet.username) For additional performance gains, consider using dicts(), tuples() or namedtuples() when iterating large and/or complex result-sets

Expressions Foreign Keys Traversing foreign keys Joining tables Implementing Many to Many Self-joins Performance Techniques Avoiding N+1 queries Iterating over lots of rows Speeding up Bulk Inserts Transactions apsw [https://github.com/rogerbinns/apsw]: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. sweepea the Tweet model itself: print(tweet.content, tweet.timestamp, tweet.username) For additional performance gains, consider using dicts(), tuples() or namedtuples() when iterating large and/or complex result-sets

particularly if you use SQLite. • apsw: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. • gevent the Tweet model itself: print(tweet.content, tweet.timestamp, tweet.username) For additional performance gains, consider using dicts(), tuples() or namedtuples() when iterating large and/or complex result-sets join_query.c.id # Becomes: (t1."parent_id" = "jq"."id") 1.7.22 Performance Techniques This section outlines some techniques for improving performance when using peewee. 1.7. Querying 81 peewee Documentation

Expressions Foreign Keys Traversing foreign keys Joining tables Implementing Many to Many Self-joins Performance Techniques Avoiding N+1 queries Iterating over lots of rows Speeding up Bulk Inserts Transactions apsw [https://github.com/rogerbinns/apsw]: an optional 3rd-party SQLite binding offering greater performance and much, much saner semantics than the standard library pysqlite. Use with APSWDatabase. pycrypto join_query.c.id # Becomes: (t1."parent_id" = "jq"."id") Performance Techniques This section outlines some techniques for improving performance when using peewee. Avoiding N+1 queries The term N+1 queries

apsw [https://github.com/rogerbinns/apsw]: an optional 3rd-party SQLite binding offering greater performance and comprehensive support for SQLite’s C APIs. Use with APSWDatabase. gevent [http://www.gevent instance: for tweet in query.objects(): print(tweet.username, tweet.content) For maximum performance, you can execute queries and then iterate over the results using the underlying database cursor additional query for every tweet to look up the tweet.user foreign-key. For our small table the performance penalty isn’t obvious, but we would find the delays grew as the number of rows increased. If you’re