Evolution of large scale websites
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Top level overview of the growth of website system architecture. The most suitable solution to the company is the best.
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¿Cómo conseguir que los datos estén disponibles y poder utilizarlos en tiempo real?
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DataAquitaine February 2022
This talk is about data-driven transformation and its contribution to Digital transformation. The first part shows the necessity to adopt the "software revolution" to adapt constantly to the customer’s environment. I then speak about " Exponential Information Systems" that the the foundation for the data-driven ambitions : Enterprise-wide flows, Customer-time data freshness, Future-proof unified semantics, etc.
The last part talks about Exponential Technologies, such as Artificial intelligence and machine learning, to drive more value from data
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Matinée - Dix conseils pour supporter la croissance de votre Startup de 0 à 10 millions d'utilisateurs
Cette session vous a plû ? Si c'est le cas, n'hésitez pas à vous inscrire à notre Summit !
Roman Pavlyuk, Yaroslav Ravlinko, Intellias. Enterprise IT Transformation and...
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Co-speaker – Yaroslav Ravlinko, Head of IT Advisory and DevOps Group, Technology Office at Intellias.
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Speech Overview:
Why distributed SQL databases will dominate the Big Data world and how technologies like Kubernetes can help in achieving that.
Part 1: IT Organization Transformation
The main pillars of IT organization transformation;
The main trends and finding the right balance between hype and actual usefulness;
Technologies and platforms that will have the biggest impact on our lives in the future
Part 2: Big Data is dead, long live big data?
“Did Google Send the Big Data Industry on a 10-Year Head Fake?”
Spanned vs Hadoop
Spanner: Google’s Globally-Distributed Database;
Spanner and BigQuery (design and architecture);
Why distributed SQL databases will dominate the “big” data world;
So, what’s next?
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Evolution of large scale websites
- 3. 3 billions PVGoogle 24 x 7 - 100%Usability 1 billionimagesFacebook
- 4. They all begin as ...
- 6. SPLIT APPLICATION AND DATABASE
- 7. “For many events, roughly 80% of the effects come from 20% of the causes. ▸Vilfredo Pareto, Italian economist
- 8. CACHE
- 9. APPLICATION CLUSTER AND CACHE
- 10. DATABASE READ AND WRITE SEPARATION
- 11. Place your screenshot here But, it is not done yet ...
- 12. CDN and REVERSE PROXY
- 13. THERE ARE MORE NoSQL and Search Engine Techniques to speed up data storage and data search Business split For examples, facebook’s home page and message page are developed and maintained by different teams Database split Based on business transaction. Single table horizontal split. Single table vertical split.
- 15. WHAT I HAVE LEARNT ▸Evolution ▸Business achieves technology ▸Core value of the system architecture
- 16. THANKS!
Editor's Notes
- I thought it’d be good to do a talk about large scale websites/applications What do they do when developing from small to large
- Instinct is website like Google, Facebook, Twitter. Convert concept to data ...
- High Concurrency, high volume. Google PageViews is 3.5 billions per day Massive data High usability, 24 X 7 - Poor security environment, almost everyday it’s attacked
- But they all begin as a small project, in other word, small website. Facebook began in Harvard’s accommodation Google was first deployed in Stanford University So, imagine that maybe 10 years ago, Steve and Bill wanted to start an e-commerce website
- At the beginning Application, database, file are all in one server, simple Steve and Bill started to promote the website, asked the users the right question to improve user experiences, marketing They have more and more users. Soon they realised that they ran out of storage and website became slow
- Application server handles all the business logic so it needs powerful CPU Database server stores and searches data, so it needs fast hard disk and big RAM File server stores all documents, so it needs bigger hard disk So the website is technically ready for further development, in which Steve and Bill did The website now needs further optimisation
- Apply to the e-commerce, it means that 80% of the transactions are related to 20% of all products. Customers prefer to purchase few good review products Google, Twitter searches are focusing on a few hot hashtags
- Based on 80/20 rule, Steve and Bill had the idea to cache 20% data to memory Local cache is faster but limited In theory, remote distributed cache servers are unlimited Peak time, single application server is the bottleneck
- Use cluster, increase the number of application servers Apply load balance server, distributed requests evenly Application load will no longer be the bottleneck But … How about the database
- Most of the website activities are READ SPLIT database Write to the master database then replicate the slaves database Read from slaves database Perfect! System should be fast enough!
- As Steve and Bill learn from research, website delay and user churn rate have positive correlation To keep the users, they decided to add more JS, pictures to give better visualisation
- In fact, they are all caches CDN cache data in the closest ISP data centre to user Reverse proxy caches data in company’s data centre The purposes are to return data to user as quickly as possible, and to reduce backend servers’ load
- No website/application was borned as large-scale “This is how Google/Facebook did it”, may not work for your own company. Core value of the system architecture is not to build a large scale website right at the beginning. It’s to develop with the business.