Python与MongoDB实战指南：2011年流行文档数据库应用

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"《MongoDB与Python模式与过程：2011》是一本由Niall O'Higgins编著的专业书籍，由O'Reilly Media出版，于2011年9月30日发行。这本书专为想要利用Python应用程序操作文档导向数据库MongoDB的开发者设计。书中提供了丰富的实践案例和代码示例，涵盖了如何在Python环境中设置MongoDB、基本操作如读写文档、高效查询、数据完整性维护、嵌入式数据模型设计、并发问题避免（如防止race conditions和deadlocks）等关键技能。作者详细介绍了如何使用MongoDB的查询语言进行复杂操作，包括快速地理定位查询以支持地理位置应用，以及如何为社交网络数据构建索引。此外，书中还探讨了如何在Pyramid、Django和Pylons等流行的Python Web框架中整合MongoDB，以及如何进行数据建模、编写有效查询和编写防错误代码。书中的“配方”部分涵盖了以下核心内容： 1. 基础操作：学习如何读取、写入、计数和排序MongoDB集合中的文档。 2. 查询语言：掌握MongoDB的强大查询能力，包括复杂的聚合和索引优化。 3. 数据一致性：确保在复制或分布式环境中数据的正确性和完整性。 4. 嵌入式设计：利用嵌入避免不必要的JOIN操作，提高数据存储效率。 5. 错误预防：学会编写健壮的代码，防止KeyError和其他常见错误。 6. 原子操作：通过快照更新模式实现游戏得分、计费系统等应用的实时同步。 7. Web框架集成：了解如何在Pylons 1.x、Django和Pyramid等框架中无缝使用MongoDB。《MongoDB与Python模式与过程：2011》不仅适合初学者理解MongoDB的基础概念，也对有经验的开发者提供了实用的技巧和最佳实践，帮助他们提升在实际项目中的工作效率。无论你是MongoDB新手还是寻求技术升级，这本书都是一个宝贵的资源。"

CHAPTER 1

Getting Started

Introduction

First released in 2009, MongoDB is relatively new on the database scene compared to

contemporary giants like Oracle which trace their first releases to the 1970’s. As a

document-oriented database generally grouped into the NoSQL category, it stands out

among distributed key value stores, Amazon Dynamo clones and Google BigTable re-

implementations. With a focus on rich operator support and high performance Online

Transaction Processing (OLTP), MongoDB is in many ways closer to MySQL than to

batch-oriented databases like HBase.

The key differences between MongoDB’s document-oriented approach and a tradi-

tional relational database are:

1. MongoDB does not support joins.

2. MongoDB does not support transactions. It does have some support for atomic

operations, however.

3. MongoDB schemas are flexible. Not all documents in a collection must adhere to

the same schema.

1 and 2 are a direct result of the huge difficulties in making these features scale across

a large distributed system while maintaining acceptable performance. They are trade-

offs made in order to allow for horizontal scalability. Although MongoDB lacks joins,

it does introduce some alternative capabilites, e.g. embedding, which can be used to

solve many of the same data modeling problems as joins. Of course, even if embedding

doesn’t quite work, you can always perform your join in application code, by making

multiple queries.

The lack of transactions can be painful at times, but fortunately MongoDB supports a

fairly decent set of atomic operations. From the basic atomic increment and decrement

operators to the richer “findAndModify”, which is essentially an atomic read-modify-

write operator.

It turns out that a flexible schema can be very beneficial, especially when you expect

to be iterating quickly. While up front schema design—as used in the relational model

—has its place, there is often a heavy cost in terms of maintenance. Handling schema

updates in the relational world is of course doable, but comes with a price.

In MongoDB, you can add new properties at any time, dynamically, without having to

worry about ALTER TABLE statements that can take hours to run and complicated

data migration scripts. However, this approach does come with its own tradeoffs. For

example, type enforcement must be carefully handled by the application code. Custom

document versioning might be desirable to avoid large conditional blocks to handle

heterogeneous documents in the same collection.

The dynamic nature of MongoDB lends itself quite naturally to working with a dynamic

language such as Python. The tradeoffs between a dynamically typed language such as

Python and a statically typed language such as Java in many respects mirror the trade-

offs between the flexible, document-oriented model of MongoDB and the up-front and

statically typed schema definition of SQL databases.

Python allows you to express MongoDB documents and queries natively, through the

use of existing language features like nested dictionaries and lists. If you have worked

with JSON in Python, you will immediately be comfortable with MongoDB documents

and queries.

For these reasons, MongoDB and Python make a powerful combination for rapid, iter-

ative development of horizontally scalable backend applications. For the vast majority

of modern Web and mobile applications, we believe MongoDB is likely a better fit than

RDBMS technology.

Finding Reference Documentation

MongoDB, Python, 10gen’s PyMongo driver and each of the Web frameworks men-

tioned in this book all have good reference documentation online.

For MongoDB, we would strongly suggest bookmarking and at least skimming over

the official MongoDB manual which is available in a few different formats and con-

stantly updated at http://www.mongodb.org/display/DOCS/Manual. While the manual

describes the JavaScript interface via the mongo console utility as opposed to the Python

interface, most of the code snippets should be easily understood by a Python program-

mer and more-or-less portable to PyMongo, albeit sometimes with a little bit of work.

Furthermore, the MongoDB manual goes into greater depth on certain advanced and

technical implementation and database administration topics than is possible in this

book.

2 | Chapter 1: Getting Started

For the Python language and standard library, you can use the help() function in the

interpreter or the pydoc tool on the command line to get API documentation for any

methods or modules. For example:

pydoc string

The latest Python language and API documentation is also available for online browsing

at http://docs.python.org/.

10gen’s PyMongo driver has API documentation available online to go with each re-

lease. You can find this at http://api.mongodb.org/python/. Additionally, once you have

the PyMongo driver package installed on your system, a summary version of the API

documentation should be available to you in the Python interpreter via the help()

function. Due to an issue with the virtualenv tool mentioned in the next section, “py-

doc” does not work inside a virtual environment. You must instead run python -m pydoc

pymongo.

Installing MongoDB

For the purposes of development, it is recommended to run a MongoDB server on your

local machine. This will permit you to iterate quickly and try new things without fear

of destroying a production database. Additionally, you will be able to develop with

MongoDB even without an Internet connection.

Depending on your operating system, you may have multiple options for how to install

MongoDB locally.

Most modern UNIX-like systems will have a version of MongoDB available in their

package management system. This includes FreeBSD, Debian, Ubuntu, Fedora, Cen-

tOS and ArchLinux. Installing one of these packages is likely the most convenient ap-

proach, although the version of MongoDB provided by your packaging vendor may lag

behind the latest release from 10gen. For local development, as long as you have the

latest major release, you are probably fine.

10gen also provides their own MongoDB packages for many systems which they update

very quickly on each release. These can be a little more work to get installed but ensure

you are running the latest-and-greatest. After the initial setup, they are typically trivial

to keep up-to-date. For a production deployment, where you likely want to be able to

update to the most recent stable MongoDB version with a minimum of hassle, this

option probably makes the most sense.

In addition to the system package versions of MongoDB, 10gen provide binary zip and

tar archives. These are independent of your system package manager and are provided

in both 32-bit and 64-bit flavours for OS X, Windows, Linux and Solaris. 10gen also

provide statically-built binary distributions of this kind for Linux, which may be your

best option if you are stuck on an older, legacy Linux system lacking the modern libc

Installing MongoDB | 3

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