1.2 Additional Features and Behaviors
Both the aggregation pipeline and map-reduce can operate on a sharded collection. Map-reduce operations
can also output to a sharded collection. See Aggregation Pipeline and Sharded Collections (page 17) and Map-Reduce
and Sharded Collections (page 17) for details.
The aggregation pipeline can use indexes to improve its performance during some of its stages. In addition, the
aggregation pipeline has an internal optimization phase. See Pipeline Operators and Indexes (page 9) and Aggregation
Pipeline Optimization (page 15) for details.
For a feature comparison of the aggregation pipeline, map-reduce, and the special group functionality, see Aggregation
Commands Comparison (page 34).
2 Aggregation Concepts
MongoDB provides the three approaches to aggregation, each with its own strengths and purposes for a given situation.
This section describes these approaches and also describes behaviors and limitations specific to each approach. See
also the chart (page 34) that compares the approaches.
Aggregation Pipeline (page 7) The aggregation pipeline is a framework for performing aggregation tasks, modeled
on the concept of data processing pipelines. Using this framework, MongoDB passes the documents of a single
collection through a pipeline. The pipeline transforms the documents into aggregated results, and is accessed
through the aggregate database command.
Map-Reduce (page 10) Map-reduce is a generic multi-phase data aggregation modality for processing quantities of
data. MongoDB provides map-reduce with the mapReduce database command.
Single Purpose Aggregation Operations (page 12) MongoDB provides a collection of specific data aggregation op-
erations to support a number of common data aggregation functions. These operations include returning counts
of documents, distinct values of a field, and simple grouping operations.
Aggregation Mechanics (page 15) Details internal optimization operations, limits, support for sharded collections,
and concurrency concerns.
2.1 Aggregation Pipeline
New in version 2.2.
The aggregation pipeline is a framework for data aggregation modeled on the concept of data processing pipelines.
Documents enter a multi-stage pipeline that transforms the documents into an aggregated results.
The aggregation pipeline provides an alternative to map-reduce and may be the preferred solution for many aggregation
tasks where the complexity of map-reduce may be unwarranted.
Aggregation pipeline have some limitations on value types and result size. See Aggregation Pipeline Limits (page 16)
for details on limits and restrictions on the aggregation pipeline.
Pipeline
Conceptually, documents from a collection travel through an aggregation pipeline, which transforms these objects as
they pass through. For those familiar with UNIX-like shells (e.g. bash), the concept is analogous to the pipe (i.e. |).
The MongoDB aggregation pipeline starts with the documents of a collection and streams the documents from one
pipeline operator to the next to process the documents. Each operator in the pipeline transforms the documents as they
pass through the pipeline. Pipeline operators do not need to produce one output document for every input document.
Operators may generate new documents or filter out documents. Pipeline operators can be repeated in the pipeline.
7
剩余37页未读，继续阅读
XMonsterH
- 粉丝: 8
- 资源: 18
我的内容管理
展开
最新资源
- 计算机人脸表情动画技术发展综述
- 关系数据库的关键字搜索技术综述：模型、架构与未来趋势
- 迭代自适应逆滤波在语音情感识别中的应用
- 概念知识树在旅游领域智能分析中的应用
- 构建is-a层次与OWL本体集成：理论与算法
- 基于语义元的相似度计算方法研究：改进与有效性验证
- 网格梯度多密度聚类算法：去噪与高效聚类
- 网格服务工作流动态调度算法PGSWA研究
- 突发事件连锁反应网络模型与应急预警分析
- BA网络上的病毒营销与网站推广仿真研究
- 离散HSMM故障预测模型：有效提升系统状态预测
- 煤矿安全评价：信息融合与可拓理论的应用
- 多维度Petri网工作流模型MD_WFN：统一建模与应用研究
- 面向过程追踪的知识安全描述方法
- 基于收益的软件过程资源调度优化策略
- 多核环境下基于数据流Java的Web服务器优化实现提升性能
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
