CouchDB入门指南：英文原版PDF

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"Getting Started with CouchDB 无水印原版pdf" 本文将深入介绍《Getting Started with CouchDB》这本书的主要内容，以及CouchDB的基本概念和技术要点。CouchDB是一款开源的、基于文档的分布式数据库管理系统，它采用了JSON作为数据格式，JavaScript作为查询语言，并支持多版本控制和实时数据同步。 CouchDB的核心特性之一是它的“面向文档”设计。在CouchDB中，数据以JSON文档的形式存储，这种格式易于理解和操作，同时也适合于Web应用程序的开发。文档可以包含任意结构化的数据，使得CouchDB非常灵活，能适应各种类型的数据模型。另一个关键特性是它的“复制”功能。CouchDB允许数据库在多个服务器之间进行实时同步，这使得构建高可用性和容错性的系统变得简单。这种复制不仅限于同构环境，还可以在不同的CouchDB实例之间或者与其他支持CouchDB协议的数据库之间进行。 CouchDB的查询语言是MapReduce，这是通过JavaScript实现的。用户可以通过编写map和reduce函数来处理和聚合数据。Map函数用于将数据分组，Reduce函数则用于对分组后的数据进行总结和聚合。这种查询方式提供了强大的数据分析能力。本书《Getting Started with CouchDB》由MC Brown撰写，旨在引导读者了解和掌握CouchDB的基础知识和实践应用。书中可能涵盖了安装和配置CouchDB、创建和管理数据库、处理文档、执行MapReduce查询、设置复制和安全性等方面的内容。此外，作者可能还会讨论CouchDB在Web应用、移动应用和物联网(IoT)等领域的实际应用案例。书中还提到了一些出版和编辑的相关信息，如版权、编辑团队、设计者等，这些都是书籍出版过程中不可或缺的部分。同时，读者可以在O'Reilly Media的官方网站上找到更多关于本书的详细信息，包括可能出现的错误和更新。《Getting Started with CouchDB》是初学者和开发者入门CouchDB的理想读物，通过学习本书，读者可以了解到如何利用CouchDB构建高效、可扩展和可靠的数据存储解决方案。

In a traditional database, there are many different ways of modeling this information,

but a common one is to use relations to model the information. There is a core contact

table, another table for each phone number, another for emails and IM, etc., and all

this is then linked together using a unique ID so that you can obtain all the information

you need.

There is nothing inherently wrong with this approach. In fact, in many cases there are

some significant advantages to this approach when working with some types of data.

However, the point here is that your data may not fit an arbitrary (and fixed) data model

such as the one described here. It can even be difficult as the data matures and expands

to know where the right place for information is. Twenty years ago, requirements like

email, website, or Skype addresses won’t have occurred to most designers.

Within CouchDB, the opposite approach is used. Rather than trying to create a struc-

ture into which all the information that you want to store can be shoehorned, CouchDB

stores the data as documents, and worries about how to report and aggregate the in-

formation that is stored. Using our contact example, the information could be recorded

in the database exactly as written it above, with each person’s contact details stored as

a CouchDB document. We can make the decision during the reporting phase on how

to output information, what information to output, and indeed whether to output that

data at all.

Replication

Databases are no longer isolated, single systems. Whether you want a database that

can be shared among multiple devices (your desktop, laptop, and mobile phone), be-

tween multiple offices, or to be used as part of your database scaling operations, copying

and sharing database information has become required functionality.

Different databases have traditionally approached this in a variety of different ways,

including binary logs, data streams, row-based logging, and more complex hashing

techniques. Within CouchDB, a simple but very effective method has been developed

that uses the individual documents as the key to the method of sharing and distributing

the document information between databases.

Note that the distinction is that replication occurs between databases, not necessarily

instances of CouchDB. You can use replication to copy documents between databases

on the same machine, the same database on different machines, and different databases

across multiple machines and devices.

The simplicity and ease with which you can share and exchange information in this

way is a key feature of CouchDB. The replication system uses the same REST API as

the client interface to the database, and it supports the ability to filter and select records

during the replication process.

4 | Chapter 1: Why CouchDB?

Another useful aspect of CouchDB replication is that it operates one way. That is, if

you have a desktop machine and a laptop and you want to replicate your data so that

you can take it with you, you can perform a specific desktop to laptop replication. If

you make changes to the database while you are away, replicate the changes back from

the mobile to the desktop. Better still, you can replicate both ways and keep the two

databases in sync. This approach simplifies the entire replication process and ensures

that you can always replicate the data where you need it.

CouchDB allows you to create both the one-shot replication, and to configure replica-

tion that will continuously replicate changes to your configured database. In CouchDB

1.1 and later, the replication configuration is retained when restarting CouchDB.

The one-way nature of replication also means that you can replicate documents from

multiple databases into a single database. For example, data collection or logging sys-

tems can use multiple CouchDB instances to collect information, then replicate the

data all to one machine for processing and statistics.

CouchDB also handles problems with replication with ease. The Fallacies of Dis-

tributed Computing imply that all of the following are solved in a perfect system:

1. The network is reliable.

2. Latency is zero.

3. Bandwidth is infinite.

4. The network is secure.

5. Topology doesn’t change.

6. There is one administrator.

7. Transport cost is zero.

8. The network is homogeneous.

The reality, of course, is quite different. Rather than expecting everything to work fine,

CouchDB expects there to be a problem and tries to cope with it. Rather than treating

a fault with replication as a serious problem, CouchDB instead tries to recover grace-

fully from the problem and only tells the user when there is a problem that requires

user interaction. The replication process is also incremental, so that if anything goes

wrong, such as a network outage, replication will pick right back up where it stopped.

One other ability is that replication information can also be filtered. For example, if

you wanted to replicate only the contact information from one database into another,

you can apply a filter to the replication process and copy only the documents that are

marked as contacts. Or only replication records created in the last three months, or

only those with an “r” in the month. The filter uses the same JavaScript as the other

systems in CouchDB and is therefore immensely customizable to your needs.

To summarize, replication offers a number of potential scenarios:

• Replicate from database A to database B once

Replication | 5

• Replicate from database A to database B continuously

• Replicate from database A to database B and B to A continuously

• Replicate from database A to B to C to D to E to A

• Replicate between databases A, B, C, D, and E

• Replicate from database A, B, C, and D to database E

You may think that all of this replication introduces some interesting issues when the

same record is edited or modified on multiple machines. CouchDB has a solution for

this, too, called conflict resolution. But to keep things simple, even the default response

in the event of a conflict is consistent so that it doesn’t stop your database from oper-

ating within a cluster.

Eventual Consistency

As you have seen in the previous section discussion on replication, the issue of distrib-

uting your data around different CouchDB instances is one way to take advantage of

the functionality and flexibility that CouchDB offers.

One of the issues in a distributed system is the expectation that your network and

system operate effectively and reliably. In a typical relational database management

system (RDBMS) solution, for example, reliability and consistency, in particular, in a

distributed system can start to be a problem. You rely on global state, timing, forced

delays, and synchronous operations to ensure that your writes are available across your

entire system before your application needs to read it back.

Within the three distinct concerns of consistency, availability, and partition tolerance

of Brewer’s CAP theorem on distributed applications, the RDBMS is relying on the C

and A portions to support the distributed model. Different solutions approach the

problem differently, but a common approach includes using a single database for writes

and multiple for reads, which introduces the problem of synchronizing operations so

that all clients get the right data.

That is, once you scale up your system beyond a single node and you start to distribute

your load across multiple machines, you have to start worrying about how to make the

data available, keep it consistent, and partition the information across the database to

help support the distributed model.

CouchDB approaches the problem differently using what is called eventual consistency.

If the availability of your database is a priority, then CouchDB can be used in a way

that allows a single node to provide read and write support, and therefore consistency

for the immediate user. The other nodes in the distributed system can catch up later,

becoming eventually consistent with the other nodes as the data is updated. This can

be achieved while providing high-availability of the data in question.

6 | Chapter 1: Why CouchDB?

CouchDB employs other tricks to help improve this consistency model on a single node

basis, and to improve the overall performance and throughput. There is no need to go

into detail, but some of the features CouchDB uses include:

Key/value nature of the data store

Key/value nature of the data store enables very quick access to the documents

stored. Using a key to read or write a single document provides a huge advantage

in terms of reading and writing over a row or lookup method.

B-tree storage engine for all internal data, documents, and views

B-tree engines are quick for retrieving single keys and key ranges. Better still, the

view model also allows for key/value data to be written directly into the B-tree

storage engine automatically sorted by the key. This further improves single and

range-based key lookups.

Lock-free database updates

Traditional databases will lock an entire data store (table) or record while data is

inserted or updated. CouchDB uses a Multi-Version Concurrency Control

(MVCC) model. Instead of locking the database, CouchDB writes a new version

of the existing record. This allows different processes to access old versions while

the new version is being inserted, and also means that updating the information is

really just a case of appending the new data, not reading, updating, and writing

back a new version.

Freeform document format

Most databases will enforce strict requirements on the format of the data and check

and invalidate insert and update requests if they are not in the correct format. In

many cases, your application can use the JSON object structure directly without

having to serialize your objects or data into the fixed format required by the data-

base engine.

CouchDB can write the JSON document directly, simplifying the writing/update

process, while allowing you to optionally enfore a structure on your JSON docu-

ments within the database itself if you need it. The enforcement and validation,

though, continues to work with the JSON structure of the data.

By using these features, and the eventual consistency model within a distributed de-

ployment, you can work with CouchDB to help support and improve your performance

and latency, and to scale in a more linear fashion.

Data: Local, Remote, Everywhere

The CouchDB document-based approach solves another of the major issues in the

modern world, which is one of access and ability. Although it is obvious we are moving

to a fully connected world and environment, the reality is that there will always be a

location, device, or situation where network access is unavailable.

Data: Local, Remote, Everywhere | 7

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