Cloud and Big Data initiatives. Mark O Connell, EMC

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1 Object storage PRESENTATION systems: TITLE GOES the underpinning HERE of Cloud and Big Data initiatives Mark O Connell, EMC

2 SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA unless otherwise noted. Member companies and individual members may use this material in presentations and literature under the following conditions: Any slide or slides used must be reproduced in their entirety without modification The SNIA must be acknowledged as the source of any material used in the body of any document containing material from these presentations. This presentation is a project of the SNIA Education Committee. Neither the author nor the presenter is an attorney and nothing in this presentation is intended to be, or should be construed as legal advice or an opinion of counsel. If you need legal advice or a legal opinion please contact your attorney. The information presented herein represents the author's personal opinion and current understanding of the relevant issues involved. The author, the presenter, and the SNIA do not assume any responsibility or liability for damages arising out of any reliance on or use of this information. NO WARRANTIES, EXPRESS OR IMPLIED. USE AT YOUR OWN RISK. 2

3 Abstract Object storage systems: the underpinning of Cloud and Big Data initiatives Object storage systems have risen to prominence in the storage industry and underlie both public and private cloud offerings. This talk will cover the needs of a cloud based storage system, why traditional approaches are insufficient to meet this challenge, the basic paradigms and architectures of object storage systems, how the architecture of an object storage system addresses the needs of cloud based storage, and challenges of using object storage systems vs traditional IT systems. Additionally this talk will compare and contrast the major object storage systems in use today, including Amazon s S3, Microsoft s Azure, EMC Atmos, the OpenStack Swift initiative, and others. 3

4 This talk will cover What is Cloud? What are Object Storage systems? How do Object Storage systems satisfy cloud use cases? What is Big Data? How do Big Data, Cloud, and Object Storage relate? 4

5 What is cloud? Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics, three service models, and four deployment models. 5

6 5 Essential Cloud Characteristics On-demand self-service. Broad network access. Resource pooling. Rapid elasticity. Measured service 6

7 What storage meets these criteria? On-demand self-service. Broad network access. Resource pooling. Rapid elasticity. Measured service 7

8 Object storage: Evolution of storage Object storage 8

9 At first there was one Block devices Characteristics Disk, LUN, etc Simple, linear block addresses Fixed size elements (512 byte block) Atomic access at the element level Advantages Speed and performance Ability to satisfy many use cases Disadvantages Difficult to use Fixed size LUNs Caveats: may grow, thin LUNs Fragmentation issues If large LUNs are maintained for performance 9

10 Then there were two Filesystems Characteristics Built on block Space management Sharing NFS, CIFS Byte accessible OS caching for performance Advantages Easy to use, human understandable addressing Standard tools Disadvantages Limitations path length, directory size, FS size, inodes, etc. Semantics heavyweight for some use cases Stateful protocols, unsuitable for internet/mobile use 10

11 The Third Amigo Object Storage Characteristics Advantages Disadvantages 11

12 Defining Characteristics Single, Flat namespace Location independent addressing Constant time read performance Unlimited storage, autoconfiguring Policies at object or bucket level (policies at user defined container level) Per-object user definable metadata 12

13 Typical Characteristics Scale out Distributed Eventually consistent 13

14 Common Characteristics REST/HTTP for internet/mobile access Multi-tenancy Self-service Provisioning Metering 14

15 Single, Flat Namespace Key/Value store (Amazon S3, Azure, SWIFT) May looks like a pathname, e.g. /foo/bar/filename Unique within a bucket Ability to list based on common prefix and up to a delimiter Otherwise no directory semantics Typically used with a consistent hashing algorithm Unique Identifier (Atmos, XAM) Opaque character string Must typically be used with an application database to store the identifiers Typically globally unique 15

16 Location independent addressing Location independent addressing No relationship between a name and the location of the object Two objects with similar names are not necessarily colocated Block systems: Reading N sequential blocks faster than reading N random blocks Flash based storage changes this effect Filesystem: Accessing /foo/bar/file1 may speed up access to /foo/bar/file2 (caching effects) Filesystem: Reading from /foo/bar/file1 normally speeds up future reads of that same file Constant time read performance Read of each object completes in the same time Typically achieved via a hash on object name Hash internally translated to object location(s) 16

17 Unlimited storage and policies Unlimited storage, autoconfiguring Adding storage to the system automatically expands capacity available for all users No limits on total storage, total objects, etc Policies at user defined container level Amazon bucket level policies, reduced redundancy at object level Azure storage account level policies Atmos per object policies Amplidata per namespace policy 17

18 Per-object user definable metadata What s in a document beyond the content? 18

19 Per-object user definable metadata What s in a document beyond the content? 19

20 Per-object user definable metadata Applications embed metadata in files in an application specific mechanism What s different in an object storage system? Object systems provide standard ways to store metadata Enables easier processing of the metadata Allows different applications to manipulate or process the metadata 20

21 Object Storage: Typical Characteristics Scale out Distributed 21

22 Object Storage: Typical Characteristics Eventually consistent Write an object with all 1 s Overwrite the object with all 2 s Read from the object should get 2 s (strong consistency) May get 1 s, will eventually get 2 s Will not get 5 s (a value that was never written) Areas which generally show eventual consistency Quorum reads Number of object replicas Asynchronous replication with read allowed List after write eventually consistent 22

23 Common Characteristics REST/HTTP for internet/mobile access Multi-tenancy More than logical data segregation Separation of users, administrators, etc E.g. a per-filesystem administrator, a per-lun administrator Self-service Provisioning Metering Pay-as-you- go services, pay for pure consumption, not allocation 23

24 Object Storage Examples Clouds powered by object storage Underlying technologies 24

25 Summary: Storage Evolution With Block Storage: Data is organized as an array of unrelated blocks Hosts directly access blocks With File/NAS Storage: Data is organized as an array of unrelated blocks Onboard file system places data on disk External systems directly access files within the onboard file system With Object Storage: Application centric data storage, access, and management model Stores virtual containers that encapsulate data, data attributes, metadata, and Object IDs/keys With Cloud Storage: Nearly limitless scalability designed to manage the explosion of unstructured data Capable of scaling across multiple physical locations, regardless of distance Advanced, policy driven data management based on attributes and metadata Flexible access methods that support traditional object storage models and new web based application architectures Simple set it and forget it management with end user self service capabilities 25

26 Object storage satisfies cloud needs On-demand self-service. Unlimited storage, Multi-tenancy, Self provisioning Broad network access. Scale out architecture, REST/HTTP Resource pooling. Single flat namespace, unlimited storage, autoconfiguring, user defined policy scope, scale out, distributed Rapid elasticity. Single flat namespace, autoconfiguring, location independent addressing, scale out Measured service. Multi-tenancy, Metadata, Metering 26

27 What is Big Data? 27

28 Big Data Definition Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Source: Gartner 28

29 Where does Big Data come from? Shopping habits Web surfing Social media and picture sharing sites Car and appliance sensors s 29

30 Leveraging Big Data Massive data collection Collected across horizontal applications What do online sites like Netflix, Pandora, Amazon, and others do? Utilize data across customers Make predictions, recommendations 30

31 Big data needs Broad network access. Scale out architecture, REST/HTTP Resource pooling. Single flat namespace, unlimited storage, autoconfiguring, user defined policy scope, scale out, distributed Rapid elasticity. Single flat namespace, autoconfiguring, location independent addressing, scale out Commonality with cloud Good fit with object storage architectures 31

32 Object systems uniquely suit Big Data Per-object metadata gives context to drive analytics Medical records from patient X Records from patients with asthma diagnosis May add as agile response to real world situations Asthmatic patients using experimental therapy Z 32

33 Summary 33

34 Object storage systems Object storage systems build on block and file storage Automate the management of these devices Object storage systems are optimized for new usage patterns Non-local access: Web, mobile, etc Non-stateful access: REST, HTTP Scale out access across many independent streams Scalable access: Built for multiple independent use cases Needs of cloud systems overlap with object systems Scale, Elasticity, Pooling of resource, Broad network access Big data systems overlap with cloud and object 34

35 Attribution & Feedback The SNIA Education Committee thanks the following individuals for their contributions to this Tutorial. Authorship History Mark O Connell March 2013 Additional Contributors Michelle Scardino March 2013 Joseph White March 2013 Michael Fishman March 2013 Please send any questions or comments regarding this SNIA Tutorial to 35

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