jimmy narang 1. a service in the cloud has to: be able to handle arbitrary node failures be...

30
An Introduction to Windows Azure Jimmy Narang 1

Upload: josephine-york

Post on 28-Dec-2015

214 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

1

An Introduction to Windows AzureJimmy Narang

Page 2: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

2

Cloud Services• A service in the cloud has to:• Be able to handle arbitrary node failures• Be available all the time• Be able to scale up or down on demand without the need

to re-write the code• Handle platform or software upgrades

Page 3: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

3

Cloud Services: Architecture• The service design must be:• Loosely coupled• Such that node failures do not affect functionality• Nodes can be initialized and added easily• State of the service is decoupled from nodes• Scale can be achieved through quantity (scale out)

Page 4: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

4

Azure• Cloud: thousands of connected servers• Azure: an operating system for the cloud• Abstracts away hardware – switches, servers, disks,

routers, load-balancers• Manages deployment, so that developer can upload code

and hit ‘run’• Provides reliable common storage that can be accessed

from any mode• Provides a familiar development platform

Page 5: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

5

Azure Service Architecture• A service boundary• Roles• Each role has a number of identical instances• Two types of roles: web roles and worker role

• Storage• Accessible from any instance• Blobs, tables, queues

• Endpoints• External: communicate outside the service boundary• Internal: communicate within the service boundary

Page 6: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

6

Web Role

Service Architecture continued …

Cloud Storage

LB

n role instances

Worker RoleWeb RoleWeb Role Worker RoleWeb Role Worker Role

External endpoint

Service Boundary

Internal endpointsExternal endpoint

m role instances

Page 7: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

7

Azure: Programming Model• Developers write their code and describe a service

model• Service model includes role definitions, VM Size,

instance counts, endpoints, etc.• code + service model is packed and uploaded to

Azure, which deploys the service in Microsoft Datacenters

Page 8: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

8

Roles and Role Instances• Two types: web roles and worker roles• No Admin access; cannot install applications• Choose a particular VM capacity for each role• Specify number of instances per role• Azure starts a fresh instance if an existing one crashes

• Code: • Extend RoleEntryPoint class for worker roles; optional for

web roles.• Asp.Net for web roles

Page 9: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

9

External Endpoints• Each service runs in an isolated boundary • The service deployment is assigned a Virtual IP address

(VIP)• The service is reachable externally via ‘external endpoints’ on

this VIP

• External endpoints: ports selected to be exposed to the outside world for in-coming connections to the service• Usually http and https on web roles (i.e., port no. 80 and 81)• Can be TCP endpoints on worker roles

• Both web and worker roles can make outbound connections to Internet resources • via HTTP or HTTPS and via Microsoft .NET APIs for TCP/IP sockets.

Page 10: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

10

Internal Endpoints• Azure provides APIs to obtain internal IPs of each

instance in each role• Roles can define ‘internal endpoints’ (ports

exposed within the service) to communicate between instances

Page 11: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

11

Azure Storage• Accessed from anywhere using account name and

storage key• Exposed in the form of URIs:• http://<accntName>.queue.core.windows.net/

<queueName>• http://<accntName>.blob.core.windows.net/

<container>/<blobName>• http://<accntName>.table.core.windows.net/

<tableName>

Page 12: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

12

Azure Storage: Queues• Queues: often the best way to communicate

between roles• Messages can be 8kb max• use messages as pointers to blobs/tables for larger data

• Can create several queues per account• Not guaranteed Fifo; no priority queues either.• Guaranteed each message will be seen at least

once

Page 13: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

13

Queue Operations• Create / Delete queue• Get / Put message• Peek message (queueName, n)• Delete message (queueName, msgId, popreceipt)• ‘get message’ does not lead to deletion!

• Clear Queue

Page 14: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

14

Queue Messages• MessageID: A GUID associated with each msg• VisibilityTimeOut: default 30 seconds, max: 2

hours. Messages not deleted within this interval will return to the queue

• PopReceipt: A string retrieved with every get-msg.• PopReceipt+MsgID required to delete a msg• MessageTTl: (7 days) messages not deleted within

this interval are garbage collected

Page 15: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

15

Queue: example

2 1

C1

C2

123

Producers Consumers

P 3 12

C1: GetMsg (returns 1)C2: GetMsg (returns 2)C2: DeleteMsg #2C1 diesC2: GetMsg (returns 3)Visibility Timeout on Msg#1C2: DeleteMsg #3C2: GetMsg (returns 1)

Page 16: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

16

Azure storage: Blobs• A large chunk of (raw binary) data• Blob Operations:• Create / Delete • Read / Write: byte range (page blob) or blocks (block blob)• Lease the blob• Create a Snapshot • Create a copy• Mount as Drive (page blob)

Page 17: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

17

Blobs: Access control• Hierarchy: accounts, containers, blobs• http://<account>.blob.core.windows.net/<container>/

<blobname>• An account can contain multiple containers• A container can contain blobs or other containers

• Fine grained access control can be granted to containers/blobs (grant permissions for individual operations such as read, write, delete, list, take snapshot etc.)

Page 18: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

18

Block Blobs• A blob as a sequential list of blocks• Each block has an ID • Blocks are immutable• Upload blocks out of order / in parallel• PutBlock to upload block• PutBlockList to stitch uploaded blocks into blob

• Order of upload doesn’t matter; order in Putblocklist matters.

• Putblocklist: First commit wins (all uncommitted blocks are garbage collected)

Page 19: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

19

Blo

ck I

d 1

Blo

ck I

d 3

Blo

ck I

d 2

Blo

ck I

d 4

Blo

ck I

d 2

Blo

ck I

d 3

Blo

ck I

d 4

Blo

ck I

d 4

PutBlob (name);PutBlock(BlockId1);PutBlock(BlockId3);PutBlock(BlockId4);PutBlock(BlockId2);PutBlock(BlockId4);PutBlockList(BlockId2, BlockId3, BlockId4);

Block Blobs: example

Page 20: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

20

Page Blobs• Page blobs: A collection of pages• Specify blob size at creation time.• Entire range initialized to 0 at creation

• Read/Write specific byte ranges, no ‘commit’ required (unlike block blobs)

• 512 Byte alignment required for write operations; not required for read

Page 21: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

21

Blobs: leasing• A lease is a timed (1 min) lock on a block• Acquire lease: create a lease for a blob without one• Renew: request to hold the existing lease• Release• Break: to end the lease but ensure that another instance

cannot acquire it until the current lease has expired

Page 22: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

22

Azure storage: Tables• Can scale up to billions of entries and terabytes of

data• Contain set of ‘entities’ (rows) with ‘properties’

(columns)• (Partition Key, Row Key) defines the primary key• Partition key is used to partition the table into storage

nodes• Row key uniquely identifies an entity within a partition

Page 23: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

23

Azure storage: Tables• No Fixed schema, except for Partition Key, Row

Key, and Timestamp• Properties are stored as <name, typed value>• Two entities can have very different properties

• Common data types – int, string, guid, timestamp etc. – supported.

• Limits on the size of an entity (1MB), and # of properties(255, including keys & timestamp)

Page 24: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

24

Table: Operations• Queries: • always return whole entities, no projections• Only ‘From’, ‘Take’ (max 1000), ‘where’ operators

supported – no select, sort, group-by, join, etc.• Normal Boolean and comparison operators supported.• For good performance, ‘where’ should have the partition

key

• Insert / Delete• Update: Replaces the original entity• Merge: modifies properties in place

Page 25: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

25

Tables: Consistency• ACID guaranteed for transactions involving a

single entity.• Group Transactions have restrictions, such as:• Only possible for entities in the same partition• Entity needs to be identified by primary key• Max 100 operations per ‘batch’

• Snapshot isolation: there will be no dirty reads• Application needs to ensure cross-table

consistency

Page 26: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

26

Tables: Partitioning• A partition (i.e. all entities with the same partition

key) are served by the same ‘node’• ‘node’ here should not be thought of as a single server,

but a single ‘place’.• Entity locality: Entities within the same partition are stored

together

• Tradeoffs in choosing the partition key:• large partitions: efficient group queries• small partitions: spread across more nodes => greater

scalability

Page 27: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

27

Tables: Concurrency• Updating an entity is a multi-step process:• Get the entity from the server• Update it locally, and submit to server

• Entity can get changed in that time • Use E-tags (“version numbers”) stored in the

header associated with each entity• Update only if version number matches with the one you

were expecting• Or use If-Match * to unconditionally update

Page 28: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

28

Azure Diagnostics• Use for debugging, performance monitoring,

traffic analysis etc.• Based on logging: no remote desktop access to

instances• Choose the required Log sources: Azure, IIS logs,

Windows event logs, Perf counters, Crash dumps (and others)

• Then dump the logs locally or store them in Azure storage (at scheduled intervals or on-demand)

Page 29: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

29

Azure: other features• X Drive• Mount a page blob as a VHD (per instance)

• SQL Azure• Complete relational SQL storage in the cloud

• Azure appliance• A container of pre-configured hardware with Azure

installed

• Content Delivery Network• Mark public blobs to be copied to edge locations across a

region

Page 30: Jimmy Narang 1. A service in the cloud has to: Be able to handle arbitrary node failures Be available all the time Be able to scale up or down on demand

30

Azure: SDK and devFabric• <DEMO>