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World Congress 2024 • Aug 20, 2024 • Session details

Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases

Wei Hu

Can your database survive a 'Taylor Swift' traffic spike without sacrificing strong consistency? Discover how distributed SQL resolves extreme data skew and ensures zero-data-loss failovers at internet scale.

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#1 about 2 min

Evolution of distributed SQL database architectures

The transition from simple key-value systems to scalable databases providing strong consistency and standard SQL capabilities.

#2 about 3 min

Core mechanics of distributed database sharding

How partitioned data across physical locations remains entirely hidden from connecting applications.

#3 about 2 min

Primary use cases for widespread geographical data

Driving massive workload survivability and enforcing regional compliance through strict data sovereignty configurations.

#4 about 2 min

Managing a unified logical database entity

Dividing data into individual actively processing shards that map seamlessly to one system.

#5 about 2 min

Query routing and cross-shard parallel execution

Directing queries accurately to specific partitions or parallelizing commands across all remote instances.

#6 about 4 min

Clustered databases versus native distributed databases

Contrasting tightly-coupled single data center clusters using microsecond networking against independently spaced remote nodes.

#7 about 3 min

Mitigating physical latency with targeted geographical subsets

Overcoming baseline networking constraints by keeping operational data close to the regional consumer.

#8 about 5 min

Meeting banking legal requirements using localized sharding

Fulfilling strict regional data laws without rewriting legacy applications via transparent physical isolation.

#9 about 2 min

Sustaining extreme parallel throughput for internet services

Accelerating hyperscale digital activity targeting sub-millisecond response quotas safely via horizontal resource spreading.

#10 about 2 min

Engineering scale-out architectures specifically for relational queries

Avoiding typical degradation by constructing full relational processing directly over the core rather than mapping abstractions onto object stores.

#11 about 3 min

Configuring dynamic range and consistent hash boundaries

Maximizing index efficiency using specific hash implementations, list variables, and multiple layers of sharding categorization.

#12 about 2 min

Isolating skewed variables using directory-based routing

Segregating highly-requested records to specialized environments separating unbalanced noise from uniform performance paths.

#13 about 1 min

Minimizing geographical latency using active duplicate replication

Copying static reference tables onto every remote shard directly removing massive quantities of cross-environment joins.

#14 about 2 min

Maintaining continuous availability during transient network stalls

Establishing robust connection protocols leveraging specific consensus mathematics minimizing total runtime impact during sudden component losses.

#15 about 4 min

Reducing systemic complexity with universal converged databases

Managing varied structures like spatial geometries concurrently within a single deployment rather than fracturing operations across specialized toolchains.

Matching moments

1:52 min

Evolution of distributed SQL database management systems

Akmal Chaudhri Akmal Chaudhri · LIVE

2:12 min

Architectural benefits of single logical database topologies

Wei Hu Wei Hu · World Congress 2025

1:36 min

Evolution from key-value stores to distributed SQL

Wei Hu Wei Hu · World Congress 2025

1:55 min

Evaluating criteria for distributed SQL system adoption

Wei Hu Wei Hu · World Congress 2025

2:25 min

Distributed SQL properties for agentic AI applications

Daniël van Eeden Daniël van Eeden +1 · World Congress 2026 Europe

52 sec

Defining the core mechanics of distributed databases

Wei Hu Wei Hu · World Congress 2025

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Open session

World Congress 2026 North America

September 24, 2026 · 12:50–13:20

Stage 3

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