This online Сloud Webinar “Architecture of Highly Loaded Geo-Distributed Applications” was delivered by Andii Antilikatorov (Senior Solution Architect, Technology) on August 19, 2021 During this event, the speaker shared his proven experience in creating solutions for systems that work in different geographic regions all over the world. More details and presentation you can find here: https://bit.ly/3A2JuoK

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Architecture of Highly Loaded Geo-
Distributed Applications
Andrey Antilikatorov
Solutions Architect
19.08.2021

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New Content On-Boarding
SAS / Signed URL

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File Chunker
Content Filtering and
Tagging (ML-based)
Transcoding & Quality
Conversion
Distribution to Content Delivery Network
New Content On-Boarding Process

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New Content :: Transcoding & Quality Conversion

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File Chunker
Content Filtering and
Tagging (ML-based)
Transcoding & Quality
Conversion
Distribution to Content Delivery Network
New Content On-Boarding

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CDN

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CDN :: Content Distribution Optimization

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CDN :: Content Distribution Optimization

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CDN

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Metadata Storage

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Consistency
Partition
Tolerance
Availability
Metadata Storage

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Metadata Storage
CP AP
CP/AP

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Over 50 Clusters
Over 500 Nodes (The largest cluster has 72 nodes)
Over 500K writes/second
Over 30TB per day
Totally Denormalized Data Model
Cassandra Under High Load

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Storage Optimization

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Big Data Storage Approach

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Data Lake vs Data Warehouse
Structured/ Semi-Structured/
Unstructured/Binary
On-Read / Post Processing
Cost-effective Big Data
storage
Analysts, engineers, data
scientists
Big Data analytics, deep learning,
real-time analytics
Type of data
Processing
Purpose
Primary Users
Tasks
Structured and
normalized
On-Write / Pre-Processing
Analytics for business
decisions
Business analysts
Read-only queries for aggregating
and summarizing data insights

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ETL vs ELT
* Picture from https://big2smart.com/data-lake-vs-data-warehouse-whats-the-difference-and-which-is-the-best-data-architecture/

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Data Lake vs Data Warehouse
Education & Science
IoT & Automotive
Healthcare
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3
Finance and Banking
Public sector
Travel

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Monolithic data storage. Data team needs to know
about all services.
High dependency on data engineers and
analysts.
Data team needs to understand entire business
model
Single point of failure. High DevOps & Engineering
effort.
Problem One
Problem
Two
Problem Three
Problem Four
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Common Problems of ‘Traditional Approach’

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Data Mesh Approach

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Service 1
Service 2 Service 3
Service 5
Service 6
Service 4
Caching

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Service 6
Caching at Netflix
https://github.com/Netflix/EVCache

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Caching at Netflix

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Caching at Netflix

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Endpoint
Service Service
Service
A
Handling Failures & Services Degradation
Service Service Service

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Gracefully handle timed-out calls
Prevent execution of requests which cannot be handled
Disconnect failed services
Configures default responses or fallback scenarios
Gathers custom metrics
Cascading Failures Handling

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Cascading Failures Handling
https://github.com/Netflix/Hystrix
https://github.com/alibaba/Sentinel

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Critical Functionality and Analytics

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Use me! I’m free!!!

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Geo-Optimization :: Taxi Map

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Optimization based on Geolocation

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Uber RingPop

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Automatic redistribution of region IDs.
Seamless adding/removal of nes nodes
Automatic redirects to a server which
owns particular region
High reliability and performance due to
possibility to apply various optimizations
Services can communicate to each
other using RPC calls.
RingPop Requests Processing

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Thank You!