Hadoop is an open-source software framework for distributed storage and processing of large datasets using the MapReduce programming model. It includes HDFS for data storage and MapReduce for data processing across clusters of compute nodes. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. It provides reliability through data replication and distributed architecture.
HBase is a distributed, scalable, big data store that is modeled after Google's BigTable. It uses HDFS for storage and is written in Java. HBase provides a key-value data model and allows for fast lookups by row keys. It does not support SQL queries or transactions. Clients can access HBase data via Java APIs, REST, Thrift or MapReduce. The architecture consists of a master server and multiple region servers that host regions and serve client requests.
Hadoop is an open-source software framework for distributed storage and processing of large datasets using the MapReduce programming model. It includes HDFS for data storage and MapReduce for data processing across clusters of compute nodes. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. It provides reliability through data replication and distributed architecture.
HBase is a distributed, scalable, big data store that is modeled after Google's BigTable. It uses HDFS for storage and is written in Java. HBase provides a key-value data model and allows for fast lookups by row keys. It does not support SQL queries or transactions. Clients can access HBase data via Java APIs, REST, Thrift or MapReduce. The architecture consists of a master server and multiple region servers that host regions and serve client requests.
The document discusses the history and concepts of cloud computing including distributed computing, virtualization, different cloud service models (IaaS, PaaS, SaaS), web 2.0, and major cloud platforms. It also describes Trend Micro's Smart Protection Network and how it utilizes the cloud and big data analytics to detect emerging threats.
MapReduce is a programming model and framework developed by Google for processing and generating large datasets in a distributed computing environment. It allows parallel processing of large datasets across clusters of computers using a simple programming model. It works by breaking the processing into many small fragments of work that can be executed in parallel by the different machines, and then combining the results at the end.
How to plan a hadoop cluster for testing and production environmentAnna Yen
Athemaster wants to share our experience to plan Hardware Spec, server initial and role deployment with new Hadoop Users. There are 2 testing environments and 3 production environments for case study.
The document discusses the history and concepts of cloud computing including distributed computing, virtualization, different cloud service models (IaaS, PaaS, SaaS), web 2.0, and major cloud platforms. It also describes Trend Micro's Smart Protection Network and how it utilizes the cloud and big data analytics to detect emerging threats.
MapReduce is a programming model and framework developed by Google for processing and generating large datasets in a distributed computing environment. It allows parallel processing of large datasets across clusters of computers using a simple programming model. It works by breaking the processing into many small fragments of work that can be executed in parallel by the different machines, and then combining the results at the end.
How to plan a hadoop cluster for testing and production environmentAnna Yen
Athemaster wants to share our experience to plan Hardware Spec, server initial and role deployment with new Hadoop Users. There are 2 testing environments and 3 production environments for case study.
The Construction and Practice of Apache Pegasus in Offline and Online Scenari...acelyc1112009
A presentation in Apache Pegasus meetup in 2022 from Wei Wang.
Apache Pegasus is a horizontally scalable, strongly consistent and high-performance key-value store.
Know more about Pegasus https://pegasus.apache.org, https://github.com/apache/incubator-pegasus
Hadoop con 2015 hadoop enables enterprise data lakeJames Chen
Mobile Internet, Social Media 以及 Smart Device 的發展促成資訊的大爆炸,伴隨產生大量的非結構化及半結構化的資料,不但資料的格式多樣,產生的速度極快,對企業的資訊架構帶來了前所未有的挑戰,面對多樣的資料結構及多樣的分析工具,我們應該採用什麼樣的架構互相整合,才能有效的管理資料生命週期,提取資料價值,Hadoop 生態系統,無疑的在這個大架構裡,將扮演最基礎的資料平台的角色,實現企業的 Data Lake。
Apache Cassandra is an open-source distributed database designed to handle large amounts of data across commodity servers in a highly available manner without single points of failure. It uses a gossip protocol for cluster membership and a Dynamo-inspired architecture to provide availability and partition tolerance, while supporting eventual consistency.
This document outlines and compares two NameNode high availability (HA) solutions for HDFS: AvatarNode used by Facebook and BackupNode used by Yahoo. AvatarNode provides a complete hot standby with fast failover times of seconds by using an active-passive pair and ZooKeeper for coordination. BackupNode has limitations including slower restart times of 25+ minutes and supporting only two-machine failures. While it provides hot standby for the namespace, block reports are sent only to the active NameNode, making it a semi-hot standby solution. The document also briefly mentions other experimental HA solutions for HDFS.
This document summarizes a study on FlumeBase, a system for processing streaming data using SQL queries. It describes FlumeBase's architecture, including how it integrates with Flume and uses SQL queries to define streams, flows, and flow elements for aggregating data. The document notes some potential issues with FlumeBase regarding window alignment, deployment integration with Flume, and code maturity.
This document introduces Flume and Flive. It summarizes that Flume is a distributed data collection system that can easily extend to new data formats and scales linearly as new nodes are added. It discusses Flume's core concepts of events, flows, nodes, and reliability features. It then introduces Flive, an enhanced version of Flume developed by Hanborq that provides improved performance, functionality, manageability, and integration with Hugetable.
Hadoop Streaming allows any executable or script to be used as a MapReduce job. It works by launching the executable or script as a separate process and communicating with it via stdin and stdout. The executable or script receives key-value pairs in a predefined format and outputs new key-value pairs that are collected. Hadoop Streaming uses PipeMapper and PipeReducer to adapt the external processes to the MapReduce framework. It provides a simple way to run MapReduce jobs without writing Java code.
HBase is an open source, distributed, sorted key-value store modeled after Google's BigTable. It uses HDFS for storage and provides random read/write access to large datasets. Data is stored in tables with rows sorted by key and columns grouped into column families. The master coordinates region servers that host regions, the distributed units of data. Clients locate data regions and directly communicate with region servers to read and write data.
This document discusses the versioning conventions and history of Hadoop releases. It notes that features were occasionally developed on branches off the trunk codeline and that some releases included features from different branches, causing confusion. It also summarizes the status of Hadoop 1.0, which unified many previously separated features, and the versioning of the Cloudera CDH distribution in relation to Apache Hadoop releases.
Hadoop MapReduce Introduction and Deep InsightHanborq Inc.
Hadoop MapReduce introduces YARN, which separates cluster resource management from application execution. YARN introduces a global ResourceManager and per-node NodeManagers to manage resources. Applications run as ApplicationMasters and containers on the nodes. This improves scalability, fault tolerance, and allows various application paradigms beyond MapReduce. Optimization techniques for MapReduce include tuning buffer sizes, enabling sort avoidance when sorting is unnecessary, and using Netty and batch fetching to improve shuffle performance.
The document provides an overview of the Hadoop Distributed File System (HDFS). It describes HDFS's master-slave architecture with a single NameNode master and multiple DataNode slaves. The NameNode manages filesystem metadata and data placement, while DataNodes store data blocks. The document outlines HDFS components like the SecondaryNameNode, DataNodes, and how files are written and read. It also discusses high availability solutions, operational tools, and the future of HDFS.
Hanborq Optimizations on Hadoop MapReduceHanborq Inc.
A Hanborq optimized Hadoop Distribution, especially with high performance of MapReduce. It's the core part of HDH (Hanborq Distribution with Hadoop for Big Data Engineering).
22. Hadoop生态系统 -
构建完整的解决方案
User, Applications
API/QL API/QL
User Hive/Pig
Big HugeTable
Data
MapReduce
User
Flume/
Big Bigtable HBase Oozie
Data Flive Bigtable
……
……
User
Big file
HDFS
file file
Data
Shared Cluster of Servers
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23. Hadoop生态系统 -
选择合适的工具解决合适的问题
Hive vs. Pig. vs. Java vs. Others
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