HBase In Action - Chapter 04: HBase table design
Learning HBase, Real-time Access to Your Big Data, Data Manipulation at Scale, Big Data, Text Mining, HBase, Deploying HBase
Optimization on Key-value Stores in Cloud EnvironmentFei Dong
This document discusses optimizing key-value stores like HBase in cloud environments. It introduces HBase, a distributed, column-oriented database built on HDFS that provides scalable storage and retrieval of large datasets. The document compares rule-based and cost-based optimization strategies, and explores using rule-based optimization to analyze HBase's performance when deployed on Amazon EC2 instances. It describes developing an HBase profiler to measure the costs of using HBase for storage.
The project is focussed on Comparison Between HBASE and CASSANDRA using YCSB. It is a data storage and management project performed at National College Of Ireland
Chicago Data Summit: Apache HBase: An IntroductionCloudera, Inc.
Apache HBase is an open source distributed data-store capable of managing billions of rows of semi-structured data across large clusters of commodity hardware. HBase provides real-time random read-write access as well as integration with Hadoop MapReduce, Hive, and Pig for batch analysis. In this talk, Todd will provide an introduction to the capabilities and characteristics of HBase, comparing and contrasting it with traditional database systems. He will also introduce its architecture and data model, and present some example use cases.
HBase Read High Availability Using Timeline Consistent Region Replicasenissoz
This document summarizes a talk on implementing timeline consistency for HBase region replicas. It introduces the concept of region replicas, where each region has multiple copies hosted on different servers. The primary accepts writes, while secondary replicas are read-only. Reads from secondaries return possibly stale data. The talk outlines the implementation of region replicas in HBase, including updates to the master, region servers, and IPC. It discusses data replication approaches and next steps to implement write replication using the write-ahead log. The goal is to provide high availability for reads in HBase while tolerating single-server failures.
Adding Value to HBase with IBM InfoSphere BigInsights and BigSQLPiotr Pruski
This is the extended deck I used for my presentation at the Information On Demand 2013 conference for Session Number 1687 - Adding Value to HBase with IBM InfoSphere BigInsights and BigSQL.
This presentation covers accessing HBase using Big SQL. It starts by going over general HBase concepts, than delves into how Big SQL adds an SQL layer on top of HBase (via HBase storage handler), secondary index support, queries, etc.
An Introduction to Cloudera Impala, shows how Impala works, and the internal processing of query of Impala, including architecture, frontend, query compilation, backend, code generation, HDFS-related stuff and performance comparison.
HBase is a column-oriented NoSQL database that provides random real-time read/write access to big data stored in Hadoop's HDFS. It is modeled after Google's Bigtable and sits on top of HDFS to allow fast access to large datasets. HBase architecture includes HMaster, HRegionServers, ZooKeeper, and HDFS. HMaster manages metadata and load balancing while HRegionServers serve read/write requests directly from clients. ZooKeeper coordinates the cluster and HDFS provides storage. Data is stored in tables divided into regions hosted by HRegionServers.
Compaction and Splitting in Apache AccumuloHortonworks
The document discusses compaction and splitting in Apache Accumulo distributed key-value stores. It explains that Accumulo tables are divided into non-overlapping ranges called tablets, and that compaction merges sorted files within a tablet into a single file to improve read performance. Splitting divides large tablets into two in order to balance workload. The document provides details on Accumulo's and HBase's compaction algorithms and how they determine when to compact and split tablets.
HBase Read High Availability Using Timeline-Consistent Region ReplicasHBaseCon
Speakers: Enis Soztutar and Devaraj Das (Hortonworks)
HBase has ACID semantics within a row that make it a perfect candidate for a lot of real-time serving workloads. However, single homing a region to a server implies some periods of unavailability for the regions after a server crash. Although the mean time to recovery has improved a lot recently, for some use cases, it is still preferable to do possibly stale reads while the region is recovering. In this talk, you will get an overview of our design and implementation of region replicas in HBase, which provide timeline-consistent reads even when the primary region is unavailable or busy.
HBase Data Modeling and Access Patterns with Kite SDKHBaseCon
This document discusses the Kite SDK and how it provides a higher-level API for developing Hadoop data applications. It introduces the Kite Datasets module, which defines a unified storage interface for datasets. It describes how Kite implements partitioning strategies to map data entities to storage partitions, and column mappings to define how data fields are stored in HBase tables. The document provides examples of using Kite datasets to randomly access and update data stored in HBase.
HadoopDB is a system that combines the performance of parallel database systems with the flexibility and fault tolerance of Hadoop. It uses Hadoop as the communication layer between multiple single-node database instances running on cluster nodes. Benchmark results showed that HadoopDB's performance was close to parallel databases for structured queries and similar to Hadoop for unstructured queries, while also providing Hadoop's ability to operate in heterogeneous environments and tolerate faults.
Harmonizing Multi-tenant HBase Clusters for Managing Workload DiversityHBaseCon
Speakers: Dheeraj Kapur, Rajiv Chittajallu & Anish Mathew (Yahoo!)
In early 2013, Yahoo! introduced multi-tenancy to HBase to offer it as a platform service for all Hadoop users. A certain degree of customization per tenant (a user or a project) was achieved through RegionServer groups, namespaces, and customized configs for each tenant. This talk covers how to accommodate diverse needs to individual tenants on the cluster, as well as operational tips and techniques that allow Yahoo! to automate the management of multi-tenant clusters at petabyte scale without errors.
New features in Oracle Database 12c include the ability to restore tables and partitions using RMAN backups. A table or partition recovery using RMAN will identify required backups, construct an auxiliary database temporarily, export the table/partition to a dump file, and optionally import the table/partition back into the source database. It is also now possible to execute SQL statements directly in RMAN without using a SQL prefix. Additionally, DDL statements can now be logged to XML and log files when DDL logging is enabled. Data files can also be renamed or relocated online using the ALTER DATABASE statement in 12c.
The document discusses Oracle Database 12c Resource Manager capabilities for managing resources in Container Databases (CDBs) and Pluggable Databases (PDBs). The key points are:
1. Resource Manager can manage resources at both the CDB level, allocating resources between PDBs, and at the PDB level, allocating resources between sessions within each PDB.
2. At the CDB level, Resource Manager uses shares to define the proportion of CDB resources allocated to each PDB and limits to control each PDB's resource usage.
3. At the PDB level, Resource Manager uses resource plans to allocate resources between consumer groups, similar to how it manages resources within a non-CDB database.
This document discusses HadoopDB and Apache Hive. HadoopDB aims to combine the scalability of MapReduce with the performance of parallel databases by running Hive queries over data stored in node-local relational databases rather than HDFS. It describes HadoopDB's architecture, which replaces HDFS with local databases, and benchmarks comparing it to MapReduce. It also summarizes Hive's data model, query language and architecture, which provides a SQL interface to MapReduce by translating queries into map and reduce jobs.
1. Introduction to Apache Accumulo provides an overview of the key-value store Accumulo.
2. Accumulo is a sorted, distributed key-value store that enables interactive access to trillions of records across hundreds to thousands of servers. It provides cell-based access control and customizable server-side processing.
3. The document discusses Accumulo's history and architecture, including how it uses Hadoop for storage and Zookeeper for coordination. It also covers Accumulo's features like iterators for server-side programming and cell-level access control labels.
The document is a presentation about using Hadoop for analytic workloads. It discusses how Hadoop has traditionally been used for batch processing but can now also be used for interactive queries and business intelligence workloads using tools like Impala, Parquet, and HDFS. It summarizes performance tests showing Impala can outperform MapReduce for queries and scales linearly with additional nodes. The presentation argues Hadoop provides an effective solution for certain data warehouse workloads while maintaining flexibility, ease of scaling, and cost effectiveness.
Speaker: Jesse Anderson (Cloudera)
As optional pre-conference prep for attendees who are new to HBase, this talk will offer a brief Cliff's Notes-level talk covering architecture, API, and schema design. The architecture section will cover the daemons and their functions, the API section will cover HBase's GET, PUT, and SCAN classes; and the schema design section will cover how HBase differs from an RDBMS and the amount of effort to place on schema and row-key design.
Intro to HBase Internals & Schema Design (for HBase users)alexbaranau
This document provides an introduction to HBase internals and schema design for HBase users. It discusses the logical and physical views of HBase, including how tables are split into regions and stored across region servers. It covers best practices for schema design, such as using row keys efficiently and avoiding redundancy. The document also briefly discusses advanced topics like coprocessors and compression. The overall goal is to help HBase users optimize performance and scalability based on its internal architecture.
HBase is a scalable NoSQL database modeled after Google's Bigtable. It is built on top of HDFS for storage, and uses Zookeeper for distributed coordination and failover. Data in HBase is stored in tables and sorted by row key, with columns grouped into families and cells containing values and timestamps. HBase tables are split into regions for scalability and fault tolerance, with a master server coordinating region locations across multiple region servers.
This document discusses Bronto's use of HBase for their marketing platform. Some key points:
- Bronto uses HBase for high volume scenarios, realtime data access, batch processing, and as a staging area for HDFS.
- HBase tables at Bronto are designed with the read/write patterns and necessary queries in mind. Row keys and column families are structured to optimize for these access patterns.
- Operations of HBase at scale require tuning of JVM settings, monitoring tools, and custom scripts to handle compactions and prevent cascading failures during high load. Table design also impacts operations and needs to account for expected workloads.
This document summarizes an update on OpenTSDB, an open source time series database. It discusses OpenTSDB's ability to store trillions of data points at scale using HBase, Cassandra, or Bigtable as backends. Use cases mentioned include systems monitoring, sensor data, and financial data. The document outlines writing and querying functionality and describes the data model and table schema. It also discusses new features in OpenTSDB 2.2 and 2.3 like downsampling, expressions, and data stores. Community projects using OpenTSDB are highlighted and the future of OpenTSDB is discussed.
This talk delves into the many ways that a user has to use HBase in a project. Lars will look at many practical examples based on real applications in production, for example, on Facebook and eBay and the right approach for those wanting to find their own implementation. He will also discuss advanced concepts, such as counters, coprocessors and schema design.
Hadoop World 2011: Advanced HBase Schema DesignCloudera, Inc.
While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns.
Column-Stores vs. Row-Stores: How Different are they Really?Daniel Abadi
The document compares the performance of row-stores and column-stores for data warehousing workloads. It finds that with certain optimizations, the performance difference can be minimized:
A row-store can match the performance of a column-store by vertically partitioning columns and allowing virtual tuple IDs. Removing optimizations from a column-store, like compression and late materialization, causes its performance to degrade to that of a row-store. While column stores are better suited for data warehousing, row-stores can achieve similar performance with improvements to support vertical partitioning and column-specific optimizations.
This document provides an overview and best practices for operating HBase clusters. It discusses HBase and Hadoop architecture, how to set up an HBase cluster including Zookeeper and region servers, high availability considerations, scaling the cluster, backup and restore processes, and operational best practices around hardware, disks, OS, automation, load balancing, upgrades, monitoring and alerting. It also includes a case study of a 110 node HBase cluster.
Spark Streaming allows real-time processing of live data streams. It works by dividing the streaming data into batches called DStreams, which are then processed using Spark's batch API. Common sources of data include Kafka, files, and sockets. Transformations like map, reduce, join and window can be applied to DStreams. Stateful operations like updateStateByKey allow updating persistent state. Checkpointing to reliable storage like HDFS provides fault-tolerance.
This talk at the Percona Live MySQL Conference and Expo describes open source column stores and compares their capabilities, correctness and performance.
The document summarizes a presentation about HBase schema design. It discusses key aspects of HBase schema design including row keys, column families, and data modeling techniques. It provides an example of modeling user follow relationships in HBase and optimizing the schema to simplify transactions and queries.
Breaking the Sound Barrier with Persistent Memory HBaseCon
Liqi Yi and Shylaja Kokoori (Intel)
A fully optimized HBase cluster could easily hit the limit of the underlying storage device’s capability, which is beyond the reach of software optimization alone. To get around this constraint, we need a new design that brings data processing and data storage closer together. In this presentation, we will look at how persistent memory will change the way large datasets are stored. We will review the hardware characteristics of 3D XPoint™, a new persistent memory technology with low latency and high capacity. We will also discuss opportunities for further improvement within the HBase framework using persistent memory.
Column-oriented databases store data by column rather than by row. This allows fast retrieval of entire columns of data with one read operation. Column-oriented databases are well-suited for analytical queries that retrieve many rows but only a few columns, as only the needed columns are read from disk. Row-oriented databases are better for transactional queries that retrieve or update individual rows. The type of data storage - row-oriented or column-oriented - depends on the types of queries that will be run against the data.
Moderated by Lars Hofhansl (Salesforce), with Matteo Bertozzi (Cloudera), John Leach (Splice Machine), Maxim Lukiyanov (Microsoft), Matt Mullins (Facebook), and Carter Page (Google)
The future of HBase, via a variety of viewpoints.
Apache HBase Improvements and Practices at XiaomiHBaseCon
Duo Zhang and Liangliang He (Xiaomi)
In this session, we’ll discuss the various practices around HBase in use at Xiaomi, including those relating to HA, tiered compaction, multi-tenancy, and failover across data centers.
Design Patterns for Building 360-degree Views with HBase and KijiHBaseCon
Speaker: Jonathan Natkins (WibiData)
Many companies aspire to have 360-degree views of their data. Whether they're concerned about customers, users, accounts, or more abstract things like sensors, organizations are focused on developing capabilities for analyzing all the data they have about these entities. This talk will introduce the concept of entity-centric storage, discuss what it means, what it enables for businesses, and how to develop an entity-centric system using the open-source Kiji framework and HBase. It will also compare and contrast traditional methods of building a 360-degree view on a relational database versus building against a distributed key-value store, and why HBase is a good choice for implementing an entity-centric system.
HBase is a distributed column-oriented database built on top of Hadoop that provides quick random access to large amounts of structured data. It leverages the fault tolerance of HDFS and allows for real-time read/write access to data stored in HDFS. HBase sits above HDFS and provides APIs for reading and writing data randomly. It is a scalable, schema-less database modeled after Google's Bigtable.
HBase is a distributed column-oriented database built on top of HDFS that provides random real-time read/write access to large amounts of structured data stored in HDFS. It uses a column-oriented data model where data is stored in columns that are grouped together into column families and tables are divided into regions distributed across region servers. HBase is part of the Hadoop ecosystem and provides an interface for applications to perform read and write operations on data stored in HDFS.
HBase is an open-source, non-relational, distributed database built on top of Hadoop and HDFS. It is modeled after Google's Bigtable and is written in Java. HBase stores data in tables comprised of rows and columns, with each table divided into regions spread across nodes in the cluster. It provides fast random reads and writes and scales horizontally on commodity hardware.
This document provides an overview of Hive and HBase. It discusses how Hive allows SQL-like queries over data stored in Hadoop files, and how data can be loaded into and manipulated within Hive tables. It also describes HBase as a column-oriented NoSQL database built on Hadoop that allows for fast random reads and updates of large datasets. Key concepts covered include HiveQL, user defined functions, dynamic partitioning, and loading data. For HBase, it discusses tables, rows, columns, and cells as well as its architecture, client APIs, and integration with MapReduce.
This presentation about HBase will help you understand what is HBase, what are the applications of HBase, how is HBase is different from RDBMS, what is HBase Storage, what are the architectural components of HBase and at the end, we will also look at some of the HBase commands using a demo. HBase is an essential part of the Hadoop ecosystem. It is a column-oriented database management system derived from Google’s NoSQL database Bigtable that runs on top of HDFS. After watching this video, you will know how to store and process large datasets using HBase. Now, let us get started and understand HBase and what it is used for.
Below topics are explained in this HBase presentation:
1. What is HBase?
2. HBase Use Case
3. Applications of HBase
4. HBase vs RDBMS
5. HBase Storage
6. HBase Architectural Components
What is this Big Data Hadoop training course about?
Simplilearn’s Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. The Big Data Hadoop and Spark developer course have been designed to impart 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
Introduction to HBase | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2slpJqY
This CloudxLab Introduction to HBase tutorial helps you to understand HBase in detail. Below are the topics covered in this tutorial:
1) HBase - Data Models Examples
2) Bloom Filter
3) HBase - REST APIs
4) HBase - Hands-on Demos on CloudxLab
This document provides an overview of NoSQL databases and the HBase framework. It discusses key aspects of NoSQL including advantages like high scalability and schema flexibility. It then describes the different categories of NoSQL databases including key-value, column-oriented, graph and document oriented. The document proceeds to explain aggregate data models and how key-value and document databases are aggregate-oriented. It provides details on HBase, describing it as a column-oriented database, and its architecture, data model involving tables, rows, column families and cells.
Big Data Frameworks: Introduction to NoSQL – Aggregate Data Models – Hbase: Data Model and Implementations – Hbase Clients – Examples – .Cassandra: Data Model – Examples – Cassandra Clients – Hadoop Integration. Pig – Grunt – Pig Data Model – Pig Latin – developing and testing Pig Latin scripts. Hive – Data Types and File Formats – HiveQL Data Definition – HiveQL Data Manipulation – HiveQL Queries
HBase is a distributed, non-relational database modeled after Google's BigTable. It provides a key-value data store that sits atop HDFS. This tutorial introduces HBase and the rhbase R package for interacting with HBase. It discusses installing HBase and rhbase, loading sample vehicle sensor data into HBase, and designing tables around query patterns to optimize retrieval of related data in one call.
This document provides a quick guide to refresh skills on HBase architecture and concepts. It discusses HBase's limitations in satisfying the CAP theorem, its architecture components including the HMaster, Region Servers and Zookeeper. It also covers best practices for row key design, and differences between minor and major compactions. The HColumnDescriptor class and HBase catalog tables -.META. and -ROOT- are also summarized.
HBase is a distributed, column-oriented database that provides fast random access to large amounts of structured data stored on HDFS. It is modeled after Google's Bigtable and provides real-time read/write access to big data. HBase tables contain rows organized by column families that can scale horizontally on commodity servers.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
CCS334 BIG DATA ANALYTICS UNIT 5 PPT ELECTIVE PAPERKrishnaVeni451953
HBase is an open source, column-oriented database built on top of Hadoop that allows for the storage and retrieval of large amounts of sparse data. It provides random real-time read/write access to this data stored in Hadoop and scales horizontally. HBase features include automatic failover, integration with MapReduce, and storing data as multidimensional sorted maps indexed by row, column, and timestamp. The architecture consists of a master server (HMaster), region servers (HRegionServer), regions (HRegions), and Zookeeper for coordination.
HBase is an open-source, distributed, versioned, key-value database modeled after Google's Bigtable. It is designed to store large volumes of sparse data across commodity hardware. HBase uses Hadoop for storage and provides real-time read and write capabilities. It scales horizontally and is highly fault tolerant through its master-slave architecture and use of Zookeeper for coordination. Data in HBase is stored in tables and indexed by row keys for fast lookup, with columns grouped into families and versions stored by timestamps.
This document provides an overview of HBase, including its architecture and how it compares to relational databases and HDFS. Some key points:
- HBase is a non-relational, distributed, column-oriented database that runs on top of Hadoop. It uses a master-slave architecture with an HMaster and multiple HRegionServers.
- Unlike relational databases, HBase is schema-less, column-oriented, and designed for denormalized data in wide, sparsely populated tables.
- Compared to HDFS, HBase provides low-latency random reads/writes instead of batch processing. Data is accessed via APIs instead of MapReduce.
- HBase uses LSM
Introduction to Apache HBase, MapR Tables and SecurityMapR Technologies
This talk with focus on two key aspects of applications that are using the HBase APIs. The first part will provide a basic overview of how HBase works followed by an introduction to the HBase APIs with a simple example. The second part will extend what we've learned to secure the HBase application running on MapR's industry leading Hadoop.
Keys Botzum is a Senior Principal Technologist with MapR Technologies. He has over 15 years of experience in large scale distributed system design. At MapR his primary responsibility is working with customers as a consultant, but he also teaches classes, contributes to documentation, and works with MapR engineering. Previously he was a Senior Technical Staff Member with IBM and a respected author of many articles on WebSphere Application Server as well as a book. He holds a Masters degree in Computer Science from Stanford University and a B.S. in Applied Mathematics/Computer Science from Carnegie Mellon University.
MyLife with HBase or HBase three flavorsresponseteam
Description:
A HBase is a NoSQL column store. What does that mean functionally to a software developer?
-A conceptional view of HBase
-How to use HBase
-What features HBase has
-Benefits of HBase
How are we using HBase here at MyLife? I will describe three projects here at MyLife that are currently using HBase in production that I was/am involved with.
-Email content storage
-Connection-Identity mappings
-User stream cache backing
Each of these projects uses HBase in a different way.
HBase is a distributed, column-oriented database that runs on top of Hadoop and HDFS, providing Bigtable-like capabilities for massive tables of structured and unstructured data. It is modeled after Google's Bigtable and provides a distributed, scalable, versioned storage system with strong consistency for random read/write access to billions of rows and millions of columns. HBase is well-suited for handling large datasets and providing real-time read/write access across clusters of commodity servers.
Data Storage and Management project ReportTushar Dalvi
This paper aims at evaluating the performance of random reads and random writes the information of HBase and Cassandra and compare the results that we got through various ubuntu operation
Breaking with relational DBMS and dating with Hbase [5th IndicThreads.com Con...IndicThreads
Session Presented at 5th IndicThreads.com Conference On Java held on 10-11 December 2010 in Pune, India
WEB: http://J10.IndicThreads.com
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Hbase is an open-source, non-relational, distributed, sparse, column-oriented data-store modeled after Google’s BigTable and is written in Java.
In this presentation we will talk about how to migrate a RDBMS based Java application to Hbase based application. We will have a discussion on following points:
• Hbase schema design (a paradigm shift from the way we think about data-storage right now) compared to RDBMS based schema design.
• The challenges faced while porting the application with HBase.
• Introduction to HBql to query the data from Hbase.
• Monitoring example application for Hbase (JMX APIs exposed) and Machine’s performance with Gangila.
• Discussion on Thrift interface and how can we used Rest interface to integrate hbase with non java based applications.
• Cluster replication and what is coming in the next major 0.90 release of Hbase.
• We will end up the session, with the demo of ported application.
Takeaways for the Audience 1. When is Hbase appropriate and when not?
2. Hbase architecture and schema design
3. RDBMS vs Hbase
4. Interfacing Hbase with applications using Thrift or REST
5. Hbase cluster and Replication
6. Hbase monitoring
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- Converting to XHTML requires following XML syntax rules like closing all tags, using empty element syntax, proper nesting, and lowercase tags and attribute quotes.
The document discusses various uses of XML including moving legacy data like spreadsheets and databases into XML format, using XML for web pages and print publishing, creating business forms with XML, and incorporating XML into business processes. It also provides an overview of related XML technologies such as XSLT, XPath, XForms, SOAP, and others.
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Lecture 18 - Model-Driven Service Developmentphanleson
This document discusses model-driven service development (MDSD). MDSD involves generating code for both service providers and consumers based on models or descriptions of services. Code generators can be used to produce common code structures for different services, reducing duplication. Models define services and their properties, and may be represented using different notations like UML or XML. Meta-models define the structure of models. MDSD processes involve defining meta-models, creating models, transforming models using generators to produce code, and setting up consumer-driven or provider-driven transformation workflows.
PRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdfnservice241
The University of Ghana has launched a new vision and strategic plan, which will focus on transforming lives and societies through unparalleled scholarship, innovation, and result-oriented discoveries.
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Dear Sakthi Thiru Dr. G. B. Senthil Kumar,
It is with great honor and respect that we extend this formal invitation to you. As a distinguished leader whose presence commands admiration and reverence, we cordially invite you to join us in celebrating the 25th anniversary of our graduation from Adhiparasakthi Engineering College on 27th July, 2024. we would be honored to have you by our side as we reflect on the achievements and memories of the past 25 years.
FINAL MATATAG PE and Health CG 2023 Grades 4-10.pdf
HBase In Action - Chapter 04: HBase table design
1. CHAPTER 04: HBASE TABLE DESIGN
HBase IN ACTION
by Nick Dimiduk et. al.
2. Overview: HBase table design
HBase schema design concepts
Mapping relational modeling knowledge to the
HBase world
Advanced table definition parameters
HBase Filters to optimize read performance
3. 4.1 How to approach schema design
When we say schema, we include the following
considerations:
How many column families should the table have?
What data goes into what column family?
How many columns should be in each column family?
What should the column names be?
What information should go into the cells?
How many versions should be stored for each cell?
What should the rowkey structure be, and what should it
contain?
4. Hbase Course
Data Manipulation at Scale: Systems and
Algorithms
Using HBase for Real-time Access to Your Big
Data
5. 4.1.1 Modeling for the questions
A table store data about what users a particular user
follows, support
read the entire list of users,
and query for the presence of a specific user in that list
7. 4.1.1 Modeling for the questions (cont.)
Thinking further along those lines, you can come up
with the following questions:
1. Whom does TheFakeMT follow?
2. Does TheFakeMT follow TheRealMT?
3. Who follows TheFakeMT?
4. Does TheRealMT follow TheFakeMT?
8. 4.1.2 Defining requirements: more work up front
always pays
From the perspective of TwitBase, you expect data to
be written to HBase when the following things
happen:
A user follows someone
A user unfollows someone they were following
10. 4.1.2 Defining requirements: more work up front
always pays (cont.)
What is different from design tables in relational
systems and tables in HBase?
13. 4.1.4 Targeted data access
Only the keys are indexed in HBase tables.
There are two ways to retrieve data from a table: Get and
Scan.
HBase tables are flexible, and you can store anything in
the form of byte[].
Store everything with similar access patterns in the same
column family.
Indexing is done on the Key portion of the KeyValue
objects, consisting of the rowkey, qualifier, and
timestamp in that order.
Tall tables can potentially allow you to move toward O(1)
operations, but you trade atomicity
14. 4.1.4 Targeted data access (cont.)
De-normalizing is the way to go when designing HBase
schemas.
Think how you can accomplish your access patterns in
single API calls rather than multiple API calls.
Hashing allows for fixed-length keys and better
distribution but takes away ordering.
Column qualifiers can be used to store data, just like
cells.
The length of column qualifiers impacts the storage
footprint because you can put data in them.
The length of the column family name impacts the size of
data sent over the wire to the client (in KeyValue
objects).
15. 4.2 De-normalization is the word in HBase land
One of the key concepts when designing HBase
tables is de-normalization.
16. 4.3 Heterogeneous data in the same table
HBase schemas are flexible, and you’ll use that
flexibility now to avoid doing scans every time you
want a list of followers for a given user.
Isolate different access patterns as much as possible.
The way to improve the load distribution in this case
is to have separate tables for the two types of
relationships you want to store.
17. 4.4 Rowkey design strategies
In designing HBase tables, the rowkey is the single
most important thing.
Your rowkeys determine the performance you get
while interacting with HBase tables.
Unlike relational databases, where you can index on
multiple columns, Hbase indexes only on the key;
18. 4.5 I/O considerations
The sorted nature of HBase tables can turn out to be
a great thing for your application—or not
Optimized for writes
HASHING
SALTING
Optimized for reads
Cardinality and rowkey structure
19. 4.6 From relational to non-relational
There is no simple way to map your relational
database knowledge to HBase. It’s a different
paradigm of thinking
Things don’t necessarily map 1:1, and these concepts
are evolving and being defined as the adoption of
NoSQL systems increases.
20. 4.6.1 Some basic concepts
ENTITIES
These map to tables.
In both relational databases and HBase, the default container
for an entity is a table, and each row in the table should
represent one instance of that entity.
ATTRIBUTES
These map to columns.
Identifying attribute: This is the attribute that uniquely
identifies exactly one instance of an entity (that is, one row).
Non-identifying attribute: Non-identifying attributes are
easier to map.
21. 4.6.1 Some basic concepts (cont.)
RELATIONSHIPS
These map to foreign-key relationships.
There is no direct mapping of these in HBase, and often it
comes down to denormalizing the data.
HBase, not having any built-in joins or constraints, has little
use for explicit relationships.
22. 4.6.2 Nested entities
In Hbase, the columns (also known as column
qualifiers) aren’t predefined at design time.
23. 4.6.2 Nested entities (cont.)
it’s possible to model it in HBase
as a single row.
There are some limitations to
this
this technique only works to one
level deep: your nested entities can’t
themselves have nested entities.
it’s not as efficient to access an
individual value stored as a nested
column qualifier inside a row
24. 4.6.3 Some things don’t map
COLUMN FAMILIES
(LACK OF) INDEXES
VERSIONING
25. 4.7 Advanced column family configurations
HBase has a few advanced features that you can use
when designing your tables.
Configurable block size
hbase(main):002:0> create 'mytable', {NAME => 'colfam1',
BLOCKSIZE => '65536'}
Block cache
hbase(main):002:0> create 'mytable', {NAME => 'colfam1',
BLOCKCACHE => 'false’}
Aggressive caching
hbase(main):002:0> create 'mytable', {NAME => 'colfam1',
IN_MEMORY => 'true'}
27. 4.8 Filtering data
Filters are a powerful feature that can come in handy
in such cases.
HBase provides an API you can use to implement
custom filters.
28. 4.8.1 Implementing a filter
Implement custom filter by extending FilterBase
abstract class
The filtering logic goes in the filterKeyValue(..) method
To install custom filters
have to compile them into a JAR and put them in the HBase
classpath so they get picked up by the RegionServers at startup
time.
To compile the JAR, in the top-level directory of the project, do
the following:
mvn install
cp target/twitbase-1.0.0.jar /my/folder/
30. Hbase Course
Data Manipulation at Scale: Systems and
Algorithms
Using HBase for Real-time Access to Your Big
Data
31. 4.9 Summary
It’s about the questions, not the relationships.
Design is never finished.
Scale is a first-class entity.
Every dimension is an opportunity.
Editor's Notes
http://ouo.io/uaiKO
Spark is a “computational engine” that is responsible for scheduling, distributing, and monitoring applications consisting of many computational tasks across many worker machines, or a computing cluster.
First, all libraries and higher- level components in the stack benefit from improvements at the lower layers.
Second, the costs associated with running the stack are minimized, because instead of running 5–10 independent software systems, an organization needs to run only one.
Finally, one of the largest advantages of tight integration is the ability to build appli‐ cations that seamlessly combine different processing models.
Spark is a “computational engine” that is responsible for scheduling, distributing, and monitoring applications consisting of many computational tasks across many worker machines, or a computing cluster.
First, all libraries and higher- level components in the stack benefit from improvements at the lower layers.
Second, the costs associated with running the stack are minimized, because instead of running 5–10 independent software systems, an organization needs to run only one.
Finally, one of the largest advantages of tight integration is the ability to build appli‐ cations that seamlessly combine different processing models.