The slides were created for one University Program on Apache Hadoop + Apache Apex workshop.
It explains almost all the hdfs related commands in details along with the examples.
The document starts with the introduction for Hadoop and covers the Hadoop 1.x / 2.x services (HDFS / MapReduce / YARN).
It also explains the architecture of Hadoop, the working of Hadoop distributed file system and MapReduce programming model.
Storage Systems for big data - HDFS, HBase, and intro to KV Store - RedisSameer Tiwari
There is a plethora of storage solutions for big data, each having its own pros and cons. The objective of this talk is to delve deeper into specific classes of storage types like Distributed File Systems, in-memory Key Value Stores, Big Table Stores and provide insights on how to choose the right storage solution for a specific class of problems. For instance, running large analytic workloads, iterative machine learning algorithms, and real time analytics.
The talk will cover HDFS, HBase and brief introduction to Redis
Hadoop is a well-known framework used for big data processing now-a-days. It implements MapReduce for processing and utilizes distributed file system known as Hadoop Distributed File System (HDFS) to store data. HDFS provides fault tolerant, distributed and scalable storage for big data so that MapReduce can easily perform jobs on this data. Knowledge and understanding of data storage over HDFS is very important for a researcher working on Hadoop for big data storage and processing optimization. The aim of this presentation is to describe the architecture and process flow of HDFS. This presentation highlights prominent features of this file system implemented by Hadoop to execute MapReduce jobs. Moreover the presentation provides the description of process flow for achieving the design objectives of HDFS. Future research directions to explore and improve HDFS performance are also elaborated on.
Coordinating Metadata Replication: Survival Strategy for Distributed SystemsKonstantin V. Shvachko
Hadoop Summit, April 2014
Amsterdam, Netherlands
Just as the survival of living species depends on the transfer of essential knowledge within the community and between generations, the availability and reliability of a distributed computer system relies upon consistent replication of core metadata between its components. This presentation will highlight the implementation of a replication technique for the namespace of the Hadoop Distributed File System (HDFS). In HDFS, the namespace represented by the NameNode is decoupled from the data storage layer. While the data layer is conventionally replicated via block replication, the namespace remains a performance and availability bottleneck. Our replication technique relies on quorum-based consensus algorithms and provides an active-active model of high availability for HDFS where metadata requests (reads and writes) can be load-balanced between multiple instances of the NameNode. This session will also cover how the same techniques are extended to provide replication of metadata and data between geographically distributed data centers, providing global disaster recovery and continuous availability. Finally, we will review how consistent replication can be applied to advance other systems in the Apache Hadoop stack; e.g., how in HBase coordinated updates of regions selectively replicated on multiple RegionServers improve availability and overall cluster throughput.
In this session you will learn:
History of Hadoop
Hadoop Ecosystem
Hadoop Animal Planet
What is Hadoop?
Distinctions of Hadoop
Hadoop Components
The Hadoop Distributed Filesystem
Design of HDFS
When Not to use Hadoop?
HDFS Concepts
Anatomy of a File Read
Anatomy of a File Write
Replication & Rack awareness
Mapreduce Components
Typical Mapreduce Job
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
There are different dimensions for scalability of a distributed storage system: more data, more stored objects, more nodes, more load, additional data centers, etc. This presentation addresses the geographic scalability of HDFS. It describes unique techniques implemented at WANdisco, which allow scaling HDFS over multiple geographically distributed data centers for continuous availability. The distinguished principle of our approach is that metadata is replicated synchronously between data centers using a coordination engine, while the data is copied over the WAN asynchronously. This allows strict consistency of the namespace on the one hand and fast LAN-speed data ingestion on the other. In this approach geographically separated parts of the system operate as a single HDFS cluster, where data can be actively accessed and updated from any data center. The presentation also cover advanced features such as selective data replication.
Extended version of presentation at Strata + Hadoop World. November 20, 2014. Barcelona, Spain.
http://strataconf.com/strataeu2014/public/schedule/detail/39174
The document starts with the introduction for Hadoop and covers the Hadoop 1.x / 2.x services (HDFS / MapReduce / YARN).
It also explains the architecture of Hadoop, the working of Hadoop distributed file system and MapReduce programming model.
Storage Systems for big data - HDFS, HBase, and intro to KV Store - RedisSameer Tiwari
There is a plethora of storage solutions for big data, each having its own pros and cons. The objective of this talk is to delve deeper into specific classes of storage types like Distributed File Systems, in-memory Key Value Stores, Big Table Stores and provide insights on how to choose the right storage solution for a specific class of problems. For instance, running large analytic workloads, iterative machine learning algorithms, and real time analytics.
The talk will cover HDFS, HBase and brief introduction to Redis
Hadoop is a well-known framework used for big data processing now-a-days. It implements MapReduce for processing and utilizes distributed file system known as Hadoop Distributed File System (HDFS) to store data. HDFS provides fault tolerant, distributed and scalable storage for big data so that MapReduce can easily perform jobs on this data. Knowledge and understanding of data storage over HDFS is very important for a researcher working on Hadoop for big data storage and processing optimization. The aim of this presentation is to describe the architecture and process flow of HDFS. This presentation highlights prominent features of this file system implemented by Hadoop to execute MapReduce jobs. Moreover the presentation provides the description of process flow for achieving the design objectives of HDFS. Future research directions to explore and improve HDFS performance are also elaborated on.
Coordinating Metadata Replication: Survival Strategy for Distributed SystemsKonstantin V. Shvachko
Hadoop Summit, April 2014
Amsterdam, Netherlands
Just as the survival of living species depends on the transfer of essential knowledge within the community and between generations, the availability and reliability of a distributed computer system relies upon consistent replication of core metadata between its components. This presentation will highlight the implementation of a replication technique for the namespace of the Hadoop Distributed File System (HDFS). In HDFS, the namespace represented by the NameNode is decoupled from the data storage layer. While the data layer is conventionally replicated via block replication, the namespace remains a performance and availability bottleneck. Our replication technique relies on quorum-based consensus algorithms and provides an active-active model of high availability for HDFS where metadata requests (reads and writes) can be load-balanced between multiple instances of the NameNode. This session will also cover how the same techniques are extended to provide replication of metadata and data between geographically distributed data centers, providing global disaster recovery and continuous availability. Finally, we will review how consistent replication can be applied to advance other systems in the Apache Hadoop stack; e.g., how in HBase coordinated updates of regions selectively replicated on multiple RegionServers improve availability and overall cluster throughput.
In this session you will learn:
History of Hadoop
Hadoop Ecosystem
Hadoop Animal Planet
What is Hadoop?
Distinctions of Hadoop
Hadoop Components
The Hadoop Distributed Filesystem
Design of HDFS
When Not to use Hadoop?
HDFS Concepts
Anatomy of a File Read
Anatomy of a File Write
Replication & Rack awareness
Mapreduce Components
Typical Mapreduce Job
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
There are different dimensions for scalability of a distributed storage system: more data, more stored objects, more nodes, more load, additional data centers, etc. This presentation addresses the geographic scalability of HDFS. It describes unique techniques implemented at WANdisco, which allow scaling HDFS over multiple geographically distributed data centers for continuous availability. The distinguished principle of our approach is that metadata is replicated synchronously between data centers using a coordination engine, while the data is copied over the WAN asynchronously. This allows strict consistency of the namespace on the one hand and fast LAN-speed data ingestion on the other. In this approach geographically separated parts of the system operate as a single HDFS cluster, where data can be actively accessed and updated from any data center. The presentation also cover advanced features such as selective data replication.
Extended version of presentation at Strata + Hadoop World. November 20, 2014. Barcelona, Spain.
http://strataconf.com/strataeu2014/public/schedule/detail/39174
HDFS Tiered Storage: Mounting Object Stores in HDFSDataWorks Summit
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Azure HDInsight and Amazon EMR. In these settings- but also in more traditional, on premise deployments- applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems to achieve goals for durability, performance, and coordination.
Building on existing heterogeneous storage support, we add a storage tier to HDFS to work with external stores, allowing remote namespaces to be "mounted" in HDFS. This capability not only supports transparent caching of remote data as HDFS blocks, it also supports synchronous writes to remote clusters for business continuity planning (BCP) and supports hybrid cloud architectures.
This idea was presented at last year’s Summit in San Jose. Lots of progress has been made since then and the feature is in active development at the Apache Software Foundation on branch HDFS-9806, driven by Microsoft and Western Digital. We will discuss the refined design & implementation and present how end-users and admins will be able to use this powerful functionality.
Hadoop Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Hadoop Cluster Configuration and Data Loading - Module 2Rohit Agrawal
Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture and Setup, Important Configuration files in a Hadoop Cluster, Data Loading Techniques.
Hadoop Institutes in Bangalore: Kelly Technologies is the best Hadoop Training Institute in Bangalore and providing Hadoop Training classes by real-time faculty with course material and 24x7 Lab Facility.
Apache Hadoop is primarily used to bolster information escalated web applications. Essentially it can isolate programming applications identifying with immense information clusters in hadoop training in hyderabad, into little sections for simple understanding, recording and rehashed utilization
http://www.kellytechno.com/Hyderabad/Course/Hadoop-Training
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Microsoft Azure or Amazon S3, and on-premises object stores, such as Western Digital’s ActiveScale. In these settings, applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems for business continuity planning (BCP) and/or supporting hybrid cloud architectures to achieve the required business goals for durability, performance, and coordination.
To resolve this complexity, HDFS-9806 has added a PROVIDED storage tier to mount external storage systems in the HDFS NameNode. Building on this functionality, we can now allow remote namespaces to be synchronized with HDFS, enabling asynchronous writes to the remote storage and the possibility to synchronously and transparently read data back to a local application wanting to access file data which is stored remotely. In this talk, which corresponds to the work in progress under HDFS-12090, we will present how the Hadoop admin can manage storage tiering between clusters and how that is then handled inside HDFS through the snapshotting mechanism and asynchronously satisfying the storage policy.
Speakers
Chris Douglas, Microsoft, Principal Research Software Engineer
Thomas Denmoor, Western Digital, Object Storage Architect
"An Elephan can't jump. But can carry heavy load".
Besides Facebook and Yahoo!, many other organizations are using Hadoop to run large distributed computations: Amazon.com, Apple, eBay, IBM, ImageShack, LinkedIn, Microsoft, Twitter, The New York Times...
With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
Know how to setup a Hadoop Cluster With HDFS High Availability here : www.edureka.co/blog/how-to-set-up-hadoop-cluster-with-hdfs-high-availability/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. → What's the “Need” ? ←
❏ Big data Ocean
❏ Expensive hardware
❏ Frequent Failures and Difficult recovery
❏ Scaling up with more machines
2
3. → Hadoop ←
❏ Open source software
- a Java framework
- initial release: December 10, 2011
❏ It provides both,
❏ Storage → [HDFS]
❏ Processing → [MapReduce]
❏ HDFS: Hadoop Distributed File System
3
4. → How Hadoop addresses the need? ←
❏ Big data Ocean
■ Have multiple machines. Each will store some portion of data, not the entire data.
❏ Expensive hardware
■ Use commodity hardware. Simple and cheap.
❏ Frequent Failures and Difficult recovery
■ Have multiple copies of data. Have the copies in different machines.
❏ Scaling up with more machines
■ If more processing is needed, add new machines on the fly
4
5. → HDFS ←
❏ Runs on Commodity hardware: Doesn't require expensive machines
❏ Large Files; Write-once, Read-many (WORM)
❏ Files are split into blocks
❏ Actual blocks go to DataNodes
❏ The metadata is stored at NameNode
❏ Replicate blocks to different node
❏ Default configuration:
■ Block size = 128MB
■ Replication Factor = 3
5
9. → Where NOT TO use HDFS ←
❏ Low latency data access
■ HDFS is optimized for high throughput of data at the expense of latency.
❏ Large number of small files
■ Namenode has the entire file-system metadata in memory.
■ Too much metadata as compared to actual data.
❏ Multiple writers / Arbitrary file modifications
■ No support for multiple writers for a file
■ Always append to end of a file
9
11. → NameNode & DataNodes ←
❏ NameNode:
■ Centerpiece of HDFS: The Master
■ Only stores the block metadata: block-name, block-location etc.
■ Critical component; When down, whole cluster is considered down; Single point of failure
■ Should be configured with higher RAM
❏ DataNode:
■ Stores the actual data: The Slave
■ In constant communication with NameNode
■ When down, it does not affect the availability of data/cluster
■ Should be configured with higher disk space
❏ SecondaryNameNode:
■ Doesn't actually act as a NameNode
■ Stores the image of primary NameNode at certain checkpoint
■ Used as backup to restore NameNode
11
13. → JobTracker & TaskTrackers ←
❏ JobTracker:
■ Talks to the NameNode to determine location of the data
■ Monitors all TaskTrackers and submits status of the job back to the client
■ When down, HDFS is still functional; no new MR job; existing jobs halted
■ Replaced by ResourceManager/ApplicationMaster in MRv2
❏ TaskTracker:
■ Runs on all DataNodes
■ TaskTracker communicates with JobTracker signaling the task progress
■ TaskTracker failure is not considered fatal
■ Replaced by NodeManager in MRv2
13
14. → ResourceManager & NodeManager ←
❏ Present in Hadoop v2.0
❏ Equivalent of JobTracker & TaskTracker in v1.0
❏ ResourceManager (RM):
■ Runs usually at NameNode; Distributes resources among applications.
■ Two main components: Scheduler and ApplicationsManager (AM)
❏ NodeManager (NM):
■ Per-node framework agent
■ Responsible for containers
■ Monitors their resource usage
■ Reports the stats to RM
Central ResourceManager and Node specific Manager together is called YARN
14
18. → Interacting with HDFS ←
❏ Command prompt:
■ Similar to Linux terminal commands
■ Unix is the model, POSIX is the API
❏ Web Interface:
■ Similar to browsing a FTP site on web
18
20. → Notes ←
File Paths on HDFS:
■ hdfs://127.0.0.1:8020/user/USERNAME/demo/data/file.txt
■ hdfs://localhost:8020/user/USERNAME/demo/data/file.txt
■ /user/USERNAME/demo/file.txt
■ demo/file.txt
File System:
■ Local: local file system (linux)
■ HDFS: hadoop file system
At some places:
The terms “file” and “directory” has the same meaning.
20
27. 3. Create a file on local & put it on HDFS
❏ Syntax:
■ vi filename.txt
■ hdfs dfs -put [options] <local-file-path> <hdfs-dir-path>
❏ Example:
■ vi file-copy-to-hdfs.txt
■ hdfs dfs -put file-copy-to-hdfs.txt /user/USERNAME/demo/put-
example/
27
28. 4. Get a file from HDFS to local
❏ Syntax:
■ hdfs dfs -get <hdfs-file-path> [local-dir-path]
❏ Example:
■ hdfs dfs -get /user/USERNAME/demo/get-example/file-copy-from-
hdfs.txt ~/demo/
28
29. 5. Copy From LOCAL To HDFS
❏ Syntax:
■ hdfs dfs -copyFromLocal <local-file-path> <hdfs-file-path>
❏ Example:
■ hdfs dfs -copyFromLocal file-copy-to-hdfs.txt
/user/USERNAME/demo/copyFromLocal-example/
29
30. 6. Copy To LOCAL From HDFS
❏ Syntax:
■ hdfs dfs -copyToLocal <hdfs-file-path> <local-file-path>
❏ Example:
■ hdfs dfs -copyToLocal /user/USERNAME/demo/copyToLocal-
example/file-copy-from-hdfs.txt ~/demo/
30
31. 7. Move a file from local to HDFS
❏ Syntax:
■ hdfs dfs -moveFromLocal <local-file-path> <hdfs-dir-path>
❏ Example:
■ hdfs dfs -moveFromLocal /path/to/file.txt
/user/USERNAME/demo/moveFromLocal-example/
31