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Scaling web systems ts
1.
© 2010 Ariba,
Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
2.
What is scalability? ©
2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
3.
Some standard definitions The
ability of a system, network, or process to handle a growing amount of work in a capable manner or its ability to be enlarged to accommodate that growth. (Wikipedia) The ability to handle increased workload by repeatedly applying a cost effective strategy for extending a system’s capacity (SEI) © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
4.
Consider a simple
web application © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
5.
Load v/s Performance
of a nonscalable system © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
6.
Scalability Bottlenecks Memory Out
of memory Disk thrashing Fragmentation CPU Overload Context switches I/O waits Others Disk Network © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
7.
Load v/s performance
of a scalable system (Ideal) © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
8.
© 2010 Ariba,
Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
9.
Improve application performance… ©
2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
10.
Improve application performance Identify
and fix performance bottlenecks Algorithms DB queries Thread Deadlocks I/O Why is it important When you use less resources per task (processor time, memory, N/W round trips etc)… …You can handle a lot more load © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
11.
Loose coupling paradigms
- SOA Loosely coupled interactions One-to-one communications Consumer-based trigger Synchronous © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
12.
Loose coupling paradigms
- EDA Decoupled interactions Many-to-many communications Event-based trigger Asynchronous © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
13.
Distribute work, data Motivations: Can
scale independently Failures are isolated Segment Functionality application pools Segment Data Based on functional areas Horizontal split © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
14.
Asynchronous communication Motivations Can scale
components independently Can decouple availability Can spread peak load over time Integrate different services asynchronously Point to point / publish subscribe Staged event driven architecture (SEDA) © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
15.
Point to point messaging Publish-Subscribe messaging ©
2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
16.
Aggressive Caching Motivations: Save processing
cycles Save on network round-trip delays Content caching on CDNs Caching on clients (browsers/mobile devices) Caching at application layer Distributed caching © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
17.
Cache Everywhere Distributed In memory
Cache Proxy App Server Web Browser LRU Cache Browser Cache Page Cache Query Cache CDN Resource Cache © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc. Database Result Cache
18.
Avoid or distribute
state Motivations: Save memory and processing cycles Reduce machine affinity Strive for statelessness Maintain session data in browsers if possible Store session state in distributed cache © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
19.
Database Simplify entity relationships
to aid split Use the right kind of Database lock Avoid distributed transactions Don’t select everything, read only as much data as you can use Consider NoSQL storage © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
20.
© 2010 Ariba,
Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
21.
How do we
scale… Individual community © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
22.
Scaling storage: Multi
SID Goal: To be able to scale out DB storage as required Application Realm-Schema map Persistence Layer DB Instance #1 Schema 1 Schema 2 Schema 20 DB Instance #2 Schema 1 Schema 2 Schema 20 © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
23.
Clustering v/s Sharding Clustering
Oracle RAC, Hbase Automatically scale datastore Rebalances to distribute capacity Nodes communicate with each other Very complicated Cluster manager failure! Sharding Data distributed manually Split database to add capacity Data does not move Nodes are unaware of each other Custom algorithm based on functional / key distribution © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
24.
Scaling search: Arches Goal:
To be able to scale up search/publish activities linearly © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
25.
Arches goals… Elastic
architecture with ability to add capacity on the fly Sub-second search performance Improving indexing performance with customer isolation Eventually be used to build the global search service all across Ariba Arches interfaces… Pub API: Publish endpoint exposed over one way messaging Search API: REST based search endpoint Pull API: REST based data pull endpoint to be implemented by applications Manage API: REST based management endpoint © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
26.
Lightweight Metadata: Overcoming memory
bottleneck Problem: All realms, even realms with no customization and no activity consume lots of system resources Realms with no customization have the same foot print as realms with lots of customization Important for mid-market offering Goals © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
27.
Light weight metadata
solution Shape of a Class is now shared across Variants Sub Types computed dynamically © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
28.
Dynamic capacity realms
project Goal: Remove downtimes for scaling our products. Dynamic scalability Tolerate change to cluster topology Central connection manager Goal: Improve database connection usage. Central connection manager to distribute database connections based on usage Local pools & Global pool © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
29.
ADE (Ariba data
enrichment) scalability Instance 1 Weblogic Managed Server ADE Client Weblogic Administrative Server Load balancing (EJBs & RMI) Weight based sticky session JMS Product Product Engines Engines Instance 2 Weblogic Managed Server JDBC Clustering JMS Product Product Engines Engines Instance 3 Weblogic Managed Server JMS Product Product Engines Engines ADE DB © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc. SDB (Ops)
30.
© 2010 Ariba,
Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
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