PyMuPDF Documentation Release 1.24.2 Artifex Apr 29, 2024 ABOUT 1 Features Comparison 3 2 Performance 5 3 License and Copyright 7 4 Installation 9 5 The Basics 15 6 Tutorial 35 7 PyMuPDF, LLM 477 31 Appendix 4: Performance Comparison Methodology 487 32 Change Log 493 33 Deprecated Names 543 Index 545 ii PyMuPDF Documentation, Release 1.24.2 is a high-performance Python library for data CHAPTER TWO PERFORMANCE To benchmark performance against a range of tasks a test suite with a fixed set of 8 PDFs with a total of 7,031 pages containing text & images is used to obtain performance timings

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 environment variable: $ NO_SQLITE=1 python setup.py build Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: Model Definition Storing data

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 environment variable: $ NO_SQLITE=1 python setup.py build 1.2 Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: • Model Definition 4 Chapter options. from peewee import * # SQLite database using WAL journal mode and 64MB cache. sqlite_db = SqliteDatabase('/path/to/app.db', pragmas={ 'journal_mode': 'wal', 'cache_size': -1024 * 64}) # Connect to

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 framework, there are helper extension modules available. Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: Model Definition Storing data

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 environment variable: $ NO_SQLITE=1 python setup.py build Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: Model Definition Storing data from peewee import * # SQLite database using WAL journal mode and 64MB cache. sqlite_db = SqliteDatabase('/path/to/app.db', pragmas={ 'journal_mode': 'wal', 'cache_size': -1024 * 64}) # Connect

particularly if you use SQLite. • apsw: an optional 3rd-party SQLite binding offering greater performance and comprehensive support for SQLite’s C APIs. Use with APSWDatabase. • gevent is an optional environment variable: $ NO_SQLITE=1 python setup.py build 1.2 Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: • Model Definition 4 Chapter basic, database- specific configuration options. from peewee import * # SQLite database using WAL journal mode and 64MB cache. sqlite_db = SqliteDatabase('/path/to/app.db', pragmas={ (continues on next

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 environment variable: $ NO_SQLITE=1 python setup.py build 1.2 Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: • Model Definition 4 Chapter

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 environment variable: $ NO_SQLITE=1 python setup.py build 1.2 Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: • Model Definition 4 Chapter

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 environment variable: $ NO_SQLITE=1 python setup.py build Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: Model Definition Storing data

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 framework, there are helper extension modules available. Quickstart This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: Model Definition Storing data