Skip to content

四火的唠叨

一个纯正程序员的啰嗦

Menu
  • 所有文章
  • About Me
  • 关于四火
  • 旅行映像
  • 独立游戏
  • 资源链接
Menu

Notes: Hadoop-based open source projects

Posted on 04/27/201306/23/2019 by 四火

Here's my notes about introduction and some hints for Hadoop-based open source projects. Hope it's useful to you.

Management Tool

Ambari: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.

Ambari enables System Administrators to:

  • Provision a Hadoop Cluster
    • Ambari handles configuration of Hadoop services for the cluster.
    • Ambari provides a step-by-step wizard for installing Hadoop services across any number of hosts.
  • Manage a Hadoop Cluster
    • Ambari provides central management for starting, stopping, and reconfiguring Hadoop services across the entire cluster.
  • Monitor a Hadoop Cluster
    • Ambari provides a dashboard for monitoring health and status of the Hadoop cluster.
    • Ambari leverages Ganglia for metrics collection.
    • Ambari leverages Nagios for system alerting and will send emails when your attention is needed (e.g., a node goes down, remaining disk space is low, etc).
  • Ambari enables Application Developers and System Integrators to:
    • Easily integrate Hadoop provisioning, management, and monitoring capabilities to their own applications with the Ambari REST APIs.

Chukwa: Chukwa is an open source data collection system for monitoring large distributed systems. Chukwa is built on top of the Hadoop Distributed File System (HDFS) and Map/Reduce framework and inherits Hadoop’s scalability and robustness. Chukwa also includes a flexible and powerful toolkit for displaying, monitoring and analyzing results to make the best use of the collected data.

Data Storage

Avro: A data serialization system.

Avro provides:

  • Rich data structures.
  • A compact, fast, binary data format.
  • A container file, to store persistent data.
  • Remote procedure call (RPC).
  • Simple integration with dynamic languages. Code generation is not required to read or write data files nor to use or implement RPC protocols. Code generation as an optional optimization, only worth implementing for statically typed languages.

Avro provides functionality similar to systems such as Thrift, Protocol Buffers, etc. Avro differs from these systems in the following fundamental aspects.

  • Dynamic typing: Avro does not require that code be generated. Data is always accompanied by a schema that permits full processing of that data without code generation, static datatypes, etc. This facilitates construction of generic data-processing systems and languages.
  • Untagged data: Since the schema is present when data is read, considerably less type information need be encoded with data, resulting in smaller serialization size.
  • No manually-assigned field IDs: When a schema changes, both the old and new schema are always present when processing data, so differences may be resolved symbolically, using field names.

HBase: Use Apache HBase when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables — billions of rows X millions of columns — atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.

Features

  • Linear and modular scalability.
  • Strictly consistent reads and writes.
  • Automatic and configurable sharding of tables
  • Automatic failover support between RegionServers.
  • Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables.
  • Easy to use Java API for client access.
  • Block cache and Bloom Filters for real-time queries.
  • Query predicate push down via server side Filters
  • Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options
  • Extensible jruby-based (JIRB) shell
  • Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX

Hive: Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.

Accumulo: The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell-based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process.

Gora: The Apache Gora open source framework provides an in-memory data model and persistence for big data. Gora supports persisting to column stores, key value stores, document stores and RDBMSs, and analyzing the data with extensive Apache Hadoop MapReduce support.

Although there are various excellent ORM frameworks for relational databases, data modeling in NoSQL data stores differ profoundly from their relational cousins. Moreover, data-model agnostic frameworks such as JDO are not sufficient for use cases, where one needs to use the full power of the data models in column stores. Gora fills this gap by giving the user an easy-to-use in-memory data model and persistence for big data framework with data store specific mappings and built in Apache Hadoop support.

The overall goal for Gora is to become the standard data representation and persistence framework for big data. The roadmap of Gora can be grouped as follows.

  • Data Persistence : Persisting objects to Column stores such as HBase, Cassandra, Hypertable; key-value stores such as Voldermort, Redis, etc; SQL databases, such as MySQL, HSQLDB, flat files in local file system of Hadoop HDFS.
  • Data Access : An easy to use Java-friendly common API for accessing the data regardless of its location.
  • Indexing : Persisting objects to Lucene and Solr indexes, accessing/querying the data with Gora API.
  • Analysis : Accesing the data and making analysis through adapters for Apache Pig, Apache Hive and Cascading
  • MapReduce support : Out-of-the-box and extensive MapReduce (Apache Hadoop) support for data in the data store.

ORM stands for Object Relation Mapping. It is a technology which abstacts the persistency layer (mostly Relational Databases) so that plain domain level objects can be used, without the cumbersome effort to save/load the data to and from the database. Gora differs from current solutions in that:

  • Gora is specially focussed at NoSQL data stores, but also has limited support for SQL databases.
  • The main use case for Gora is to access/analyze big data using Hadoop.
  • Gora uses Avro for bean definition, not byte code enhancement or annotations.
  • Object-to-data store mappings are backend specific, so that full data model can be utilized.
  • Gora is simple since it ignores complex SQL mappings.
  • Gora will support persistence, indexing and anaysis of data, using Pig, Lucene, Hive, etc.

HCatalog: Apache HCatalog is a table and storage management service for data created using Apache Hadoop.

This includes:

  • Providing a shared schema and data type mechanism.
  • Providing a table abstraction so that users need not be concerned with where or how their data is stored.
  • Providing interoperability across data processing tools such as Pig, Map Reduce, and Hive.

Development Platform

Pig: Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.

At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop subproject). Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties:

  • Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.
  • Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency.
  • Extensibility. Users can create their own functions to do special-purpose processing.

Bigtop: Bigtop is a project for the development of packaging and tests of the Apache Hadoop ecosystem.

The primary goal of Bigtop is to build a community around the packaging and interoperability testing of Hadoop-related projects. This includes testing at various levels (packaging, platform, runtime, upgrade, etc…) developed by a community with a focus on the system as a whole, rather than individual projects.

RHIPE: RHIPE (hree-pay') is the R and Hadoop Integrated Programming Environment. It means "in a moment" in Greek. RHIPE is a merger of R and Hadoop. R is the widely used, highly acclaimed interactive language and environment for data analysis. Hadoop consists of the Hadoop Distributed File System (HDFS) and the MapReduce distributed compute engine. RHIPE allows an analyst to carry out D&R analysis of complex big data wholly from within R. RHIPE communicates with Hadoop to carry out the big, parallel computations.

R/Hadoop: The aim of this project is to provide easy to use R interfaces to the open source distributed computing environment Hadoop including Hadoop Streaming and the Hadoop Distributed File System.

Data Transferring Tool

Sqoop: Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases. You can use Sqoop to import data from external structured datastores into Hadoop Distributed File System or related systems like Hive and HBase. Conversely, Sqoop can be used to extract data from Hadoop and export it to external structured datastores such as relational databases and enterprise data warehouses.

Flume: Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.

Workflow & Pipeline

Oozie:

  • Oozie is a workflow scheduler system to manage Apache Hadoop jobs.
  • Oozie Workflow jobs are Directed Acyclical Graphs (DAGs) of actions.
  • Oozie Coordinator jobs are recurrent Oozie Workflow jobs triggered by time (frequency) and data availabilty.
  • Oozie is integrated with the rest of the Hadoop stack supporting several types of Hadoop jobs out of the box (such as Java map-reduce, Streaming map-reduce, Pig, Hive, Sqoop and Distcp) as well as system specific jobs (such as Java programs and shell scripts).
  • Oozie is a scalable, reliable and extensible system.

Crunch: The Apache Crunch Java library provides a framework for writing, testing, and running MapReduce pipelines. Its goal is to make pipelines that are composed of many user-defined functions simple to write, easy to test, and efficient to run.

Running on top of Hadoop MapReduce, the Apache Crunch library is a simple Java API for tasks like joining and data aggregation that are tedious to implement on plain MapReduce. The APIs are especially useful when processing data that does not fit naturally into relational model, such as time series, serialized object formats like protocol buffers or Avro records, and HBase rows and columns. For Scala users, there is the Scrunch API, which is built on top of the Java APIs and includes a REPL (read-eval-print loop) for creating MapReduce pipelines.

文章未经特殊标明皆为本人原创,未经许可不得用于任何商业用途,转载请保持完整性并注明来源链接 《四火的唠叨》

×Scan to share with WeChat

你可能也喜欢看:

  1. Notes: Spark metrics
  2. Study Notes Of Mason
  3. 不适合 Hadoop 解决的问题
  4. Hadoop 的 Secondary Sorting
  5. Hadoop 的 Map-side join 和 Reduce-side join

1 thought on “Notes: Hadoop-based open source projects”

  1. ODOO implementation says:
    04/10/2015 at 7:38 PM

    Great Job!

    Reply

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

订阅·联系

四火,啰嗦的程序员一枚,现居西雅图

Amazon Google Groovy Hadoop Haskell Java JavaScript LeetCode Oracle Python Spark 互联网 前端 华为 历史 同步 团队 图解笔记 基础设施 工作 工作流 工具 工程师 应用系统 异步 微博 思考 技术 数据库 曼联 测试 生活 程序员 管理 系统设计 缓存 编码 编程范型 英语 西雅图 设计 评审 问题 面试 项目

分类

  • Algorithm and Data Structure (30)
  • Concurrency and Asynchronization (6)
  • System Architecture and Design (43)
  • Distributed System (18)
  • Tools Frameworks and Libs (13)
  • Storage and Data Access (8)
  • Front-end Development (33)
  • Programming Languages and Paradigms (55)
  • Testing and Quality Assurance (4)
  • Network and Communication (6)
  • Authentication and Authorization (6)
  • Automation and Operation Excellence (13)
  • Big Data and Machine Learning (5)
  • Product Design (7)
  • Hiring and Interviews (14)
  • Project and Team Management (14)
  • Engineering Culture (17)
  • Critical Thinking (25)
  • Career Growth (57)
  • Life Experience and Thoughts (45)

推荐文章

  • 谈谈分布式锁
  • 常见分布式系统设计图解(汇总)
  • 系统设计中的快速估算技巧
  • 从链表存在环的问题说起
  • 技术面试中,什么样的问题才是好问题?
  • 从物理时钟到逻辑时钟
  • 近期面试观摩的一些思考
  • RSA 背后的算法
  • 谈谈 Ops(汇总 + 最终篇):工具和实践
  • 不要让业务牵着鼻子走
  • 倔强的程序员
  • 谈谈微信的信息流
  • 评审的艺术——谈谈现实中的代码评审
  • Blog 安全问题小记
  • 求第 K 个数的问题
  • 一些前端框架的比较(下)——Ember.js 和 React
  • 一些前端框架的比较(上)——GWT、AngularJS 和 Backbone.js
  • 工作流系统的设计
  • Spark 的性能调优
  • “残酷” 的事实
  • 七年工作,几个故事
  • 从 Java 和 JavaScript 来学习 Haskell 和 Groovy(汇总)
  • 一道随机数题目的求解
  • 层次
  • Dynamo 的实现技术和去中心化
  • 也谈谈全栈工程师
  • 多重继承的演变
  • 编程范型:工具的选择
  • GWT 初体验
  • java.util.concurrent 并发包诸类概览
  • 从 DCL 的对象安全发布谈起
  • 不同团队的困惑
  • 不适合 Hadoop 解决的问题
  • 留心那些潜在的系统设计问题
  • 再谈大楼扔鸡蛋的问题
  • 几种华丽无比的开发方式
  • 我眼中的工程师文化
  • 观点的碰撞
  • 谈谈盗版软件问题
  • 对几个软件开发传统观点的质疑和反驳
  • MVC 框架的映射和解耦
  • 编程的未来
  • DAO 的演进
  • 致那些自嘲码农的苦逼程序员
  • Java 多线程发展简史
  • 珍爱生命,远离微博
  • 网站性能优化的三重境界
  • OSCache 框架源码解析
  • “ 你不适合做程序员”
  • 画圆画方的故事

近期评论

  • + 1.943624 BTC.NEXT - https://graph.org/Ticket--58146-05-02?hs=9a9c6f8dfe3cdbe0074006e3e640b19b& on 所有文章
  • Anonymous on 闲聊投资:亲自体验和护城河
  • 四火 on 关于近期求职的近况和思考
  • YC on 关于近期求职的近况和思考
  • mafulong on 常见分布式基础设施系统设计图解(四):分布式工作流系统
  • 四火 on 常见分布式基础设施系统设计图解(八):分布式键值存储系统
  • Anonymous on 我裸辞了
  • https://umlcn.com on 资源链接
  • Anonymous on 我裸辞了
  • Dylan on 我裸辞了
© 2025 四火的唠叨 | Powered by Minimalist Blog WordPress Theme