by Mike O’Brien, MongoDB Kernel Tools Lead and maintainer of Mongo-Hadoop, the Hadoop Adapter for MongoDB Hadoop is a powerful, JVM-based platform for running Map/Reduce jobs on clusters of many machines, and it excels at doing analytics
by Mike O’Brien, MongoDB Kernel Tools Lead and maintainer of Mongo-Hadoop, the Hadoop Adapter for MongoDB
Hadoop is a powerful, JVM-based platform for running Map/Reduce jobs on clusters of many machines, and it excels at doing analytics and processing tasks on very large data sets.
Since MongoDB excels at storing large operational data sets for applications, it makes sense to explore using these together - MongoDB for storage and querying, and Hadoop for batch processing.
The MongoDB Connector for Hadoop
We recently released the 1.1 release of the MongoDB Connector for Hadoop. The MongoDB Connector for Hadoop makes it easy to use Mongo databases, or MongoDB backup files in .bson format, as the input source or output destination for Hadoop Map/Reduce jobs. By inspecting the data and computing input splits, Hadoop can process the data in parallel so that very large datasets can be processed quickly.
The MongoDB Connector for Hadoop also includes support for Pig and Hive, which allow very sophisticated MapReduce workflows to be executed just by writing very simple scripts.
- Pig is a high-level scripting language for data analysis and building map/reduce workflows
- Hive is a SQL-like language for ad-hoc queries and analysis of data sets on Hadoop-compatible file systems.
Hadoop streaming is also supported, so map/reduce functions can be written in any language besides Java. Right now the MongoDB Connector for Hadoop supports streaming in Ruby, Node.js and Python.
How it Works
How the Hadoop connector works
- The adapter examines the MongoDB Collection and calculates a set of splits from the data
- Each of the splits gets assigned to a node in Hadoop cluster
- In parallel, Hadoop nodes pull data for their splits from MongoDB (or BSON) and process them locally
- Hadoop merges results and streams output back to MongoDB or BSON
I’ll be giving an hour-long webinar on What’s New with the Mongo-Hadoop integration. The webinar will cover
- Using Java MapReduce with the MongoDB Connector for Hadoop
- Using Hadoop Streaming for other non-JVM languages
- Writing Pig Scripts with the MongoDB Connector for Hadoop
-
MongoDB and Hadoop usage with Elastic MapReduce to easily kick off your Hadoop jobs
-
Overview of MongoUpdateWriteable: Using the result output from Hadoop to modify an existing output collection
The webinar will be offered twice on August 8:
- 8 am PDT / 11 am EDT / 3pm UTC
- 11am PDT / 2pm EDT / 6pm UTC
Register for the Webinar on August 8
Update: Watch the webinar recording
原文地址:MongoDB Connector for Hadoop, 感谢原作者分享。

MySQL通过异步、半同步和组复制三种模式处理数据复制。1)异步复制性能高但可能丢失数据。2)半同步复制提高数据安全性但增加延迟。3)组复制支持多主复制和故障转移,适用于高可用性需求。

EXPLAIN语句可用于分析和提升SQL查询性能。1.执行EXPLAIN语句查看查询计划。2.分析输出结果,关注访问类型、索引使用情况和JOIN顺序。3.根据分析结果,创建或调整索引,优化JOIN操作,避免全表扫描,以提升查询效率。

使用mysqldump进行逻辑备份和MySQLEnterpriseBackup进行热备份是备份MySQL数据库的有效方法。1.使用mysqldump备份数据库:mysqldump-uroot-pmydatabase>mydatabase_backup.sql。2.使用MySQLEnterpriseBackup进行热备份:mysqlbackup--user=root--password=password--backup-dir=/path/to/backupbackup。恢复时,使用相应的命

MySQL慢查询的主要原因包括索引缺失或不当使用、查询复杂度、数据量过大和硬件资源不足。优化建议包括:1.创建合适的索引;2.优化查询语句;3.使用分表分区技术;4.适当升级硬件。

MySQL视图是基于SQL查询结果的虚拟表,不存储数据。1)视图简化复杂查询,2)增强数据安全性,3)维护数据一致性。视图是数据库中的存储查询,可像表一样使用,但数据动态生成。

mysqldiffersfromothersqldialectsinsyntaxforlimit,自动启动,弦乐范围,子征服和表面上分析。1)MySqluessLipslimit,whilesqlserverusestopopandoraclesrontersrontsrontsrontsronnum.2)

MySQL分区能提升性能和简化维护。1)通过按特定标准(如日期范围)将大表分成小块,2)物理上将数据分成独立文件,3)查询时MySQL可专注于相关分区,4)查询优化器可跳过不相关分区,5)选择合适的分区策略并定期维护是关键。

在MySQL中,如何授予和撤销权限?1.使用GRANT语句授予权限,如GRANTALLPRIVILEGESONdatabase_name.TO'username'@'host';2.使用REVOKE语句撤销权限,如REVOKEALLPRIVILEGESONdatabase_name.FROM'username'@'host',确保及时沟通权限变更。


热AI工具

Undresser.AI Undress
人工智能驱动的应用程序,用于创建逼真的裸体照片

AI Clothes Remover
用于从照片中去除衣服的在线人工智能工具。

Undress AI Tool
免费脱衣服图片

Clothoff.io
AI脱衣机

Video Face Swap
使用我们完全免费的人工智能换脸工具轻松在任何视频中换脸!

热门文章

热工具

DVWA
Damn Vulnerable Web App (DVWA) 是一个PHP/MySQL的Web应用程序,非常容易受到攻击。它的主要目标是成为安全专业人员在合法环境中测试自己的技能和工具的辅助工具,帮助Web开发人员更好地理解保护Web应用程序的过程,并帮助教师/学生在课堂环境中教授/学习Web应用程序安全。DVWA的目标是通过简单直接的界面练习一些最常见的Web漏洞,难度各不相同。请注意,该软件中

EditPlus 中文破解版
体积小,语法高亮,不支持代码提示功能

MinGW - 适用于 Windows 的极简 GNU
这个项目正在迁移到osdn.net/projects/mingw的过程中,你可以继续在那里关注我们。MinGW:GNU编译器集合(GCC)的本地Windows移植版本,可自由分发的导入库和用于构建本地Windows应用程序的头文件;包括对MSVC运行时的扩展,以支持C99功能。MinGW的所有软件都可以在64位Windows平台上运行。

SecLists
SecLists是最终安全测试人员的伙伴。它是一个包含各种类型列表的集合,这些列表在安全评估过程中经常使用,都在一个地方。SecLists通过方便地提供安全测试人员可能需要的所有列表,帮助提高安全测试的效率和生产力。列表类型包括用户名、密码、URL、模糊测试有效载荷、敏感数据模式、Web shell等等。测试人员只需将此存储库拉到新的测试机上,他就可以访问到所需的每种类型的列表。

记事本++7.3.1
好用且免费的代码编辑器