carfield.com.hk

Idempotence.txt

2023-02-13T14:02:04Z

Sample chater of REST book , which contain a nice discussion of why Idempotence is important - http://www.infoq.com[..]dson-ruby-restful-ws/en/resources/04.pdf

New Acid:
* A – Associative
* C – Commutative
* I – Idempotent
* D - Distributed

http://www.eaipatterns.com/ramblings/68_acid.html

Idempotency patterns - http://jonathan-oliver.blogspot.com[..]ot.com/2010/04/idempotency-patterns.html

Detailed discussion about how to design Idempotence - http://queue.acm.org/detail.cfm?id=2187821

Discuss about clock in distributed environment - http://basho.com[..]e-bad-or-welcome-to-distributed-systems/

Idempotent operations - https://levelup.gitconnected.com[..]d.com/idempotent-operations-b1d4b9b76417

database.txt

2022-04-09T12:18:32Z

Partitioning GitHub’s relational databases to handle scale - https://github.blog[..]ning-githubs-relational-databases-scale/ https://www.infoq.cn/article/L1oIV0GORfNfuysaHfL4

A Distributed System Interview Question: How to Avoid Read Inconsistency During a Transaction - https://betterprogramming.pub[..]stency-during-a-transaction-6ac3b4a8f92a

distribute.txt

2022-01-19T00:24:47Z

In one sentence, here's why: humans are notoriously bad at keeping "self" distinct from "other". Egomania, projection (transference), and enmeshment are well-known symptoms of this problem. OK, so I hear you saying, "yeah, but what does this have to do with programming?" It certainly seems absurd to suggest that if we are bad at something we know the most about (our "selves"), how could we possibly say that we have a good approach for the programming analogues - objects, modules, etc. - http://www.artima.com/weblogs/viewpost.jsp?thread=46706

Argue why space base design is better than n-tier design - http://www.google.com[..]0The%20End%20of%20Tier-based%20Computing

Some key research of google for distributed computation - http://www.infoq.com/news/2007/06/google-scalability

Someone think we are not yet (per Oct 2007) have good language support for distibuted computing - http://kasparov.skife.org/blog/2007/10/11/

A blog contain a lot distributed computing information - http://www.highscalability.com/

How Wikipedia manage their site - http://dammit.lt/uc/workbook2007.pdf

Google tutorial for Design Distributed System - http://code.google.com/edu/parallel/dsd-tutorial.html

http://en.wikipedia.org/wiki/Distributed_hash_table

The Hadoop Distributed File System: Architecture and Design - http://hadoop.apache.org/core/docs/r0.18.0/hdfs_design.html

http://www.metabrew.com[..]-a-list-of-distributed-key-value-stores/

Applying experience from CPU design for distributed solution - http://blog.paralleluniverse.co[..]o/post/26909672264/on-distributed-memory

Distributed systems for fun and profit - http://book.mixu.net/distsys/single-page.html

Monitor and design - http://highscalability.com[..]buted-mission-critical-applications.html

Uber case study - http://highscalability.com[..]les-their-real-time-market-platform.html

Load balancer design - http://www.thegeekstuff.com/2016/01/load-balancer-intro/

Some issues of distributing logic to difference systems - http://blog.takipi.com[..]t-f-up-your-microservices-in-production/

https://blog.acolyer.org[..]lised-solution-to-distributed-consensus/

Two Phase Commit - https://martinfowler.com[..]istributed-systems/two-phase-commit.html

cloud.txt

2021-01-24T12:25:17Z

1. Use Cloud for Scaling
2. Use Cloud for Multi-tenancy
3. Use Cloud for Batch processing
4. Use Cloud for Storage
5. Use Cloud for Communication

http://horicky.blogspot.com/2009/11/cloud-computing-patterns.html

http://horicky.blogspot.com/2009/11/nosql-patterns.html

Database in cloud - http://drdobbs.com[..]int?articleId=218900502&siteSectionName=

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics - http://www.biomedcentral.com/1471-2105/11/S12/S1

The architecture that survived when amazon outage - http://www.infoq.com/news/2011/04/twilio-cloud-architecture

Introduction of few tools for cloud development - http://blog.codecentric.de[..]/01/best-development-tools-in-the-cloud/ http://www.javacodegeeks.com[..]/07/developing-and-testing-in-cloud.html

Google Finds: Centralized Control, Distributed Data Architectures Work Better than Fully Decentralized Architectures - http://highscalability.com[..]control-distributed-data-architectu.html

https://www.thoughtworks.com[..]ticles/understanding-clouds-significance

Failover - https://www.forelse.io[..]rchitectures-for-mitigating-aws-outages/

event.txt

2020-05-07T12:31:36Z

Event Bus Implementation(s) - https://hackernoon.com/event-bus-implementation-s-d2854a9fafd5 http://www.infoq.com[..]2017/08/Summary-event-bus-implementation

http://jakewharton.com/managing-the-reactive-world-with-rxjava/

6 Event-Driven Architecture Patterns — https://medium.com[..]rchitecture-patterns-part-1-93758b253f47

scalability.txt

2020-03-28T12:09:46Z

There are two key primary ways of scaling web applications which is in practice today.
1) “Vertical Scalability” - Adding resource within the same logical unit to increase capacity. An example of this would be to add CPUs to an existing server, or expanding storage by adding hard drive on an existing RAID/SAN storage.
2) “Horizontal Scalability” - Adding multiple logical units of resources and making them work as a single unit. Most clustering solutions, distributed file systems, load-balancers help you with horizontal scalability.

Scalability can be further sub-classified based on the “scalability factor”.
1) If the scalability factor stays constant as you scale. This is called “linear scalability“.
2) But chances are that some components may not scale as well as others. A scalability factor below 1.0 is called “sub-linear scalability“.
3) Though rare, its possible to get better performance (scalability factor) just by adding more components (i/o across multiple disk spindles in a RAID gets better with more spindles). This is called “supra-linear scalability“.
4) If the application is not designed for scalability, its possible that things can actually get worse as it scales. This is called “negative scalability“.

http://www.royans.net/arch/2007/09/22/what-is-scalability/

Report of building web application with 55k pageload with rail - http://shanti.railsblog.com[..]mongrels-handled-a-550k-pageview-digging

XMPP a IM protocol about scalability - http://www.process-one.net[..]icle/the_aol_xmpp_scalability_challenge/

Presentation and resources of making you website more scalable - http://www.scribd.com[..]9/Real-World-Web-Performance-Scalability http://www.theserverside.com[..]lications&asrc=EM_NLN_3990118&uid=703565 http://www.theserverside.com[..]ionsPart2&asrc=EM_NLN_3990119&uid=703565

Brian Zimmer, architect at travel startup Yapta, highlights some worst practices jeopardizing the growth and scalability of a system:
* The Golden Hammer. Forcing a particular technology to work in ways it was not intended is sometimes counter-productive. Using a database to store key-value pairs is one example. Another example is using threads to program for concurrency.
* Resource Abuse. Manage the availability of shared resources because when they fail, by definition, their failure is experienced pervasively rather than in isolation. For example, connection management to the database through a thread pool.
* Big Ball of Mud. Failure to manage dependencies inhibits agility and scalability.
* Everything or Something. In both code and application dependency management, the worst practice is not understanding the relationships and formulating a model to facilitate their management. Failure to enforce diligent control is a contributing scalability inhibiter.
* Forgetting to check the time. To properly scale a system it is imperative to manage the time alloted for requests to be handled.
* Hero Pattern. One popular solution to the operation issue is a Hero who can and often will manage the bulk of the operational needs. For a large system of many components this approach does not scale, yet it is one of the most frequently-deployed solutions.
* Not automating. A system too dependent on human intervention, frequently the result of having a Hero, is dangerously exposed to issues of reproducibility and hit-by-a-bus syndrome.
* Monitoring. Monitoring, like testing, is often one of the first items sacrificed when time is tight.

http://highscalability.com/scalability-worst-practices

Useful Corporate Blogs that Talk About Scalability - http://highscalability.com[..]l-corporate-blogs-talk-about-scalability

Overview of mapreduce and how it compare with other distributed programming model -http://natishalom.typepad.com[..]0/is-mapreduce-going-to-main-stream.html

Paper of data store at amazon http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html

Discuss how haven't sync can cause performance issue - http://www.theserverside.com[..]lications&asrc=EM_NLN_6273194&uid=703565 http://bugs.sun.com/bugdatabase/view_bug.do?bug_id=6423457

Discussion about Cloud Based Memory Architectures - http://highscalability.com[..]ased-memory-architectures-next-big-thing

http://highscalability.com[..]alability-and-performance-best-practices

Interview with google engineer - http://www.zdnet.co.uk[..]gle-at-scale-everything-breaks-40093061/

Surprisingly youtube is blocking - http://highscalability.com[..]e-scalability-lessons-in-30-minutes.html

If we are seeing a sustained arrival rate of requests, greater than our system is capable of processing, then something has to give. Having the entire system degrade is not the ideal service we want to give our customers. A better approach would be to process transactions at our systems maximum possible throughput rate, while maintaining a good response time, and rejecting requests above this arrival rate. - http://mechanical-sympathy.blogspot.com.au[..]apply-back-pressure-when-overloaded.html

How twitter scaling - http://www.infoq.com/presentations/Twitter-Analytics

How Reddit scaling - http://www.infoq.com/presentations/scaling-reddit

How Hotjar scaling - https://www.hotjar.com[..]-while-scaling-hotjars-tech-architecture

How infiniteDB prevent locking and IO - http://highscalability.com[..]-scalable-relational-database-manag.html

http://highscalability.com[..]ard-way-about-scaling-a-million-use.html http://martin.kleppmann.com[..]2014/03/26/six-things-about-scaling.html

The experiences of various big companies, about network issues - http://aphyr.com/posts/288-the-network-is-reliable

Stackoverflow, scale without cloud - http://highscalability.com[..]nth-25-servers-and-i.html?SSLoginOk=true

How netflix scale - http://highscalability.com[..]ix-what-happens-when-you-press-play.html

Scaling to 100k Users - https://alexpareto.com[..]ity/systems/2020/02/03/scaling-100k.html https://www.infoq.cn/article/Tyx5HwaD9OKNX4xzFaFo

performance.txt

2019-02-20T03:22:20Z

Basically, cache as much as you can, limit the bandwidth as much as you can - http://horicky.blogspot.com[..]2009/08/skinny-straw-in-cloud-shake.html

http://www.edwardcapriolo.com[..]ry/cassandra_compression_is_like_getting

Compression usually very useful - http://abdullin.com[..]observations-on-big-data-for-retail.html

Discussion of design of Aeron, a new messaging system - http://highscalability.com[..]eally-need-another-messaging-system.html

How BBG use Hadoop, and tune it - http://highscalability.com[..]2/17/the-big-problem-is-medium-data.html

Benefits of single-threaded design - https://epickrram.blogspot.com/2019/02/recall-design.html

amazon.txt

2016-01-12T07:48:42Z

Tutorial of how to use EC2 - http://www.thediscoblog.com/2011/09/15/working-with-ec2-video/

http://www.dodgycoder.net[..]ability-lessons-from-google-youtube.html

http://www.infoq.com[..]tions/amazon-dynamodb-patterns-practices

http://highscalability.com[..]ling-to-11-million-users-on-amazons.html

mapreduce.txt

2014-04-14T03:43:47Z

MapReduce patterns

Basic MapReduce Patterns
Counting and Summing
Collating
Filtering (“Grepping”), Parsing, and Validation
Distributed Task Execution
Sorting
Not-So-Basic MapReduce Patterns
Iterative Message Passing (Graph Processing)
Distinct Values (Unique Items Counting)
Cross-Correlation
Relational MapReduce Patterns
Selection
Projection
Union
Intersection
Difference
GroupBy and Aggregation
Joining

- http://highlyscalable.wordpress.com[..]press.com/2012/02/01/mapreduce-patterns/

Showing that map reduce can support real time transaction processing - http://googleblog.blogspot.com[..]09/12/relevance-meets-real-time-web.html

Using map-reduce in cloud - http://horicky.blogspot.com/2010/02/cloud-mapreduce-tricks.html

Papers of using mapreduce - http://atbrox.com[..]thms-in-academic-papers-may-2010-update/ http://code.google.com/edu/parallel/mapreduce-tutorial.html

mapreduce experiment - http://www.macs.hw.ac.uk/~rs46/multicore_challenge1/

Pattern and anti-pattern - http://developer.yahoo.com[..]/2010/08/apache_hadoop_best_practices_a/ http://blog.dynatrace.com[..]bout-the-performance-of-map-reduce-jobs/

http://techalpine.com/what-are-the-hadoop-mapreduce-concepts/

overload.txt

2013-12-20T04:17:18Z

Solution #1: Have More Resources than You'll Ever Need
Solution #2: Disable Features During High Loads
Solution #3: Auto Scaling
Solution #4: Use Message Queues

http://blog.iron.io[..]spikability-applications-ability-to.html

http://highscalability.com[..]izing-and-capacity-planning-assumin.html

feed.txt

2013-10-29T09:54:03Z

http://highscalability.com[..]for-scaling-your-high-traffic-feeds.html

nosql.txt

2013-10-18T06:22:38Z

Compare SQL ( RDBMS ) and noSQL ( object base {distributed??} ) data management - http://queue.acm.org/detail.cfm?id=1961297&ref=fullrss

The definition - http://martinfowler.com/bliki/NosqlDefinition.html http://martinfowler.com/articles/nosqlKeyPoints.html

Is Cassandra really that good? - http://www.datastax.com/dev/blog/2012-in-review-performance

http://martinfowler.com/articles/bigData/

NoSQL Options Compared - http://www.drdobbs.com[..]cleId=240151198&siteSectionName=database

A 3 Step Guide to Getting Started with NoSQL - http://jyops.blogspot.com.au[..]-step-guide-to-getting-started-with.html

SAFER THAN RDBMSs – BUILT FOR DISASTER AVOIDANCE - The http://www.datastax.com[..]/10/why-nosql-can-be-safer-than-an-rdbms

realtime.txt

2013-10-17T10:16:02Z

Few real time map reduce framework - http://www.infoq.com/news/2011/09/twitter-storm-real-time-hadoop

How facebook and twitter doing real-time analytic - http://natishalom.typepad.com[..]ers-approach-to-real-time-analytics.html

spanner.txt

2012-10-02T12:34:57Z

Google spanner

https://cloudant.com/blog/cloudant-labs-on-google-spanner/
http://nosql.mypopescu.com[..]ogle-spanners?e57078b0?317bb970?b3b99980

storage.txt

2012-04-16T14:38:34Z

How facbook manage photo storage - http://www.facebook.com/note.php?note_id=76191543919&ref=mf

Promotion letter for oracle coherence, but still a good reading - http://highlyscalable.wordpress.com[..]e-of-high-performance-ecommerce-backend/

GridGain.txt

2011-02-07T16:38:32Z

Implement ping-pong play between two nodes on the cloud using GridGain Distributed Actors - http://gridgaintech.wordpress.com[..]11/01/26/distributed-actors-in-gridgain/

eventual-consistency.txt

2009-11-27T07:56:17Z

A short example to show how eventual-consistency work - http://sbtourist.blogspot.com[..]/11/eventual-consistency-by-example.html