Datastax - Why Your RDBMS fails at scale
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The document discusses the limitations of traditional relational databases in scaling to meet the demands of modern cloud applications. It outlines some of the issues faced, such as databases not being built for horizontal scaling, single points of failure, and complex replication needs across data centers. The document then introduces DataStax as a distributed, masterless NoSQL database that provides linear scalability, continuous availability, and a platform to support globally distributed cloud applications.
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Big iron 2 (published)Ben Stopford The document discusses the evolution of database technologies from relational databases to NoSQL databases. It argues that NoSQL databases better fit the needs of modern software development by supporting iterative development, fast feedback, and frequent releases. While early NoSQL technologies faced criticisms regarding lack of features like transactions and integrity checks, they proved useful for scaling applications to large data volumes. The document also advocates for an approach that balances flexibility with complexity by using schemaless stores at the front-end and more rigid structures at the back-end.
Top 5 Considerations for a Big Data Solution
Top 5 Considerations for a Big Data SolutionDataStax The document outlines the key factors to consider when selecting a big data solution, emphasizing the importance of handling high velocity, variety, and volume of data. It discusses the capabilities of NoSQL databases like Apache Cassandra and DataStax Enterprise, which are designed to meet the complex challenges associated with big data. Additionally, it highlights the implications of improper data management and the value that businesses can extract from effectively utilizing their data assets.
Scaling RDBMS on AWS- ClustrixDB @AWS Meetup 20160711
Scaling RDBMS on AWS- ClustrixDB @AWS Meetup 20160711Dave Anselmi The document discusses the challenges and strategies for scaling relational database management systems (RDBMS) in cloud environments, particularly focusing on ClustrixDB as a solution. It outlines various scaling techniques such as scaling up, master/slave configurations, master/master setups, and different forms of sharding, each with their respective pros and cons. ClustrixDB is presented as a MySQL-compatible, fault-tolerant system designed for high transaction workloads with linear scalability, targeting both capacity and elasticity in cloud deployments.
Database Architecture & Scaling Strategies, in the Cloud & on the Rack
Database Architecture & Scaling Strategies, in the Cloud & on the Rack Clustrix The document provides an overview of ClustrixDB, emphasizing its capabilities in database scaling within cloud environments and on-premises. It highlights ClustrixDB's features such as resiliency, elasticity, and capacity management, along with various scaling strategies like master/slave configurations, sharding, and its shared-nothing architecture. Additionally, the document discusses the advantages and disadvantages of different database scaling methods and compares ClustrixDB to other solutions in terms of performance and data consistency.
Webinar: The Performance Challenge: Providing an Amazing Customer Experience ...
Webinar: The Performance Challenge: Providing an Amazing Customer Experience ...DataStax The document discusses challenges with cloud applications and provides an overview of DataStax Enterprise (DSE) as a solution. Key points include: DSE is based on Apache Cassandra and provides multiple data models, extensions for production use, and management tools. It addresses challenges like performance, scalability, and availability. The latest DSE 5.0 release adds support for graph and improves development and management experiences. Real-world customer examples needing massive scale are also presented.
Scaling data on public clouds
Scaling data on public cloudsLiran Zelkha ScaleBase is a startup offering a database-as-a-service that provides unlimited database scalability and availability. It uses a Database Load Balancer to elastically scale any SQL database across multiple database instances in a shared-nothing architecture. This allows linear scaling of the database to meet increasing user demands and volumes of data while maintaining high availability.
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四.办理国外各大学文凭(一对一专业服务,可全程监控跟踪进度)
Shifting Focus on AI: How it Can Make a Positive Difference
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Artigo - Playing to Win.planejamento docx
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一比一原版(TUC毕业证书)开姆尼茨工业大学毕业证如何办理
一比一原版(TUC毕业证书)开姆尼茨工业大学毕业证如何办理taqyed 鉴于此，办理TUC大学毕业证开姆尼茨工业大学毕业证书【q薇1954292140】留学一站式办理学历文凭直通车（开姆尼茨工业大学毕业证TUC成绩单原版开姆尼茨工业大学学位证假文凭）未能正常毕业？【q薇1954292140】办理开姆尼茨工业大学毕业证成绩单/留信学历认证/学历文凭/使馆认证/留学回国人员证明/录取通知书/Offer/在读证明/成绩单/网上存档永久可查！
如果您处于以下几种情况：
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【办理开姆尼茨工业大学成绩单Buy Technische Universität Chemnitz Transcripts】
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Starbucks in the Indian market through its joint venture.
Starbucks in the Indian market through its joint venture.sales480687 This topic focuses on the growth and challenges of Starbucks in the Indian market through its joint venture with Tata. It covers localization strategies, menu adaptations, expansion goals, and financial performance. The topic also examines consumer perceptions, market competition, and how Starbucks navigates economic and cultural factors in one of its most promising international markets.
624753984-Annex-A3-RPMS-Tool-for-Proficient-Teachers-SY-2024-2025.pdf
624753984-Annex-A3-RPMS-Tool-for-Proficient-Teachers-SY-2024-2025.pdfCristineGraceAcuyan The little toys
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Datastax - Why Your RDBMS fails at scale
- 1. Why your RDBMS’s fails at scale And one built for it…..
- 2. DataStax: from validation to momentum. 400+ Employees $190M Funding 500+ Customers Founded in April 2010 Santa Clara • San Francisco • Austin • London • Paris • Berlin • Tokyo • Sydney (Series E – Sept. 2014) 30% + 2016, 2017 World’s Best 100 Cloud Companies Ranked #1 in multiple operational database categories © 2017 DataStax, All Rights Reserved. Company Confidential
- 3. Let’s take a moment Your business interacts with people, processes and things all the time © 2017 DataStax, All Rights Reserved. Company Confidential
- 4. CONTEXTUAL Real-time, globally distributed cloud applications must meet expectations. ALWAYS-ON DISTRIBUTED SCALABLEREAL-TIME © 2017 DataStax, All Rights Reserved. Company Confidential
- 5. Netflix disrupted video distribution and creation with a cloud application 70 million Customers 400 Cities 125 million Hours Watched per Day © DataStax, All Rights Reserved.5
- 6. Microsoft remains a leader in collaboration with a cloud application #1 Deployed App in Enterprises 5 Million Events Per Organization a Month 60 Million Monthly Active Users 6
- 7. © DataStax, All Rights Reserved.7 No Downtime: 4 Black Fridays in a row
- 8. Potted history of the Database Database and the Internet 1970 Invented by E.F. Codd at IBM 1979 First commercial RDBMS available (Oracle V2) 1983 official birth of internet or TCP/IP 1986 SQL becomes international standard 1993 WWW finally available 1995 First internet based applications arrive
- 9. Explosion of Cloud Applications
- 10. Some of the issues faced • How do you scale the database? • Add more RAM • Add more CPU • Add faster and more disks • How do you do this? • Bring the database OFFLINE • Vertical scaling has a finite limit
- 11. Some of the issues faced • How do you scale client connections? • Add a connection pool • But this has a finite limit • Adds complexity Listener Connection Pool
- 12. Single Points of Failure • With a single database we have a SPOF • Use replication • Problem solved • But now • Single Master • Scales for Reads not Writes • Action needed if Master goes down • Only suitable for LAN deployments Master Read Only Subscriber
- 13. But How to Horizontally Scale • Shard your data across databases • Each shard needs a replica • Need a load balancer • Just showing 2 shards • Things get more complicated • Could have multiple read only subscribers A-M N-Z Master Read Only Subscriber L o a d b a l a n c e r D
- 14. What about multiple Data Centres? • Extremely complicated • Difficult to support Active Active • Need to consider conflicts • More Disaster Recovery than Disaster Avoidance
- 15. Traditional Data Models don’t help • Normalised Data Model • Random seeks result in high volume of I/O operations • Joins extremely expensive • Won’t scale horizontally • De-Normalised Data Model • Sequential seek to return results • Joins eliminated • Scales indefinitely 1:M M:N
- 16. Summary • Traditional Databases developed before the web and cloud based applications • Scaling up results in downtime • Single node is a single point of failure • Number of client connections finite • Add a read only replica for high availability • Shard to horizontally scale • Data Center support extremely difficult • Data model not built for horizontal scale
- 17. A new approach is required 17 Client/Server 1990s Cloud Today Web 2000s © DataStax, All Rights Reserved.
- 18. Scaling out solves the distributed problem 18 © DataStax, All Rights Reserved. SCALE-OUT APP LAYER SCALE-OUT DATA LAYER MASTER-SLAVE DATABASE
- 19. San Francisco New York London So What’s the answer? • Distributed masterless NoSQL Database • Continuous Availability • Disaster Avoidance • Linear Scale Performance • Add nodes to scale • Runs on Commodity Hardware • Cloud or on Premise or Hybrid
- 20. Linear Scalability • Have More Data? Add more nodes. • Need More Throughput? Add more nodes. http://techblog.netflix.com/2011/11/benchmarking-cassandra-scalability-on.html 9000 Nodes 700 Nodes 400 Nodes
- 21. Continuous Availability • Nodes Down != Database Down • Datacenter Down != Database Down • Upgrade != Database Down
- 22. Platform for Cloud Applications DataStax is a registered trademark of DataStax, Inc. and its subsidiaries in the United States and/or
- 23. The most innovative companies use DataStax © DataStax, All Rights Reserved.23 2010 2012 2014 2016 2017
- 24. Key takeaways • Why your RDBMS fails at scale • Fundamentally not built for cloud based applications • World’s leading brands rely on DataStax for globally distributed data management • Next steps: Download today at www.datastax.com and register for your DataStax Academy account for free online training DataStax: The power behind the moment 24
- 25. Backup Slides 25
- 26. ACID is a lie with data replication Scenario: client with read-heavy workload decides to add asynchronous replication, so there is lag for propagating data from master to the slave. • Consistency: If a client decides to do a read to the slave before the data is replicated, it’s going to get the old data back, which means loss of consistency • Atomicity: not having the correct data results in the failure of entire transaction • Isolation: receiving the old data means loss of isolation • Durability: client will receive the old data and not the data it had written to the master node When applying RDBMS to Big Data replication, ACID collapses 26
- 27. CAP tradeoffs • Relational databases choose strong consistency over high availability • Latency between data centers makes consistency impractical • NoSQL databases like Cassandra choose high availability and partition tolerance over consistency. • Data is replicated asynchronously across multiple data centers. We are LIMITED by the speed of light making consistency impossible. • Lets you specify consistency level (one replica vs majority of replicas) suitable for your application Can’t be both consistent and highly available during a network partition 27
- 28. Replication Complexity in RDBMS © 2015 DataStax, All Rights Reserved.28 *Source: Oracle Database 12c New Features, Slide 17. (http://bit.ly/1MIxKc1)
- 29. HTTP Application Message Queue Streaming Analytics Batch Analytics Real-time DSE Real-time Analytics IoT Reference Architecture © 2015 DataStax, All Rights Reserved.29