Azure #CosmosDB Lessons learnt from building a globally distributed database from the ground up Dharma Shukla (@dharmashukla), Distinguished Engineer, Microsoft 

Cosmos DB 

2010 Project Florence 2017 Cosmos DB Blood, sweat and tears Requirements (2010) • Turnkey global distribution • Low latency at the 99th percentile, worldwide • Guaranteed high availability • Programmable consistency • Elastically scale throughput and storage, globally, on demand • Operate at the lowest possible cost 

Database hosted in the cloud != Database born in the cloud 

Global Distribution Exploiting the cloud properties to the extreme IaaS hosted managed database offerings cannot match this! Millions of trans/sec Petabytes of data Elastic and unlimited scalability Cost efficiencies with fine grained multi-tenancy 

Typical activity of an application 

 Elastically scaling throughput from 10 to 100s of millions of transactions/sec across multiple regions  Fully resource governed stack  Highly responsive partition management  Modular, resource governed nested consensus  Multiple granularities of throughput (e.g. sec, min, hour) at different price points Elastically scaling throughput, anywhere, anytime 9 PM PST Less throughput More throughput More throughput Less throughput 11 PM PST 

At global scale CREATE INDEX, DROP INDEX, ALTER TABLE 

• Logical index layouts (inverted, tree, columnar, …) • Automatic and synchronous indexing of all ingested content • No schemas or secondary indices ever needed • Resource governed, write optimized database engine with latch free and log structured techniques Schema agnostic database engine locations headquarter exports 0 1 country Germany city Berlin country France city Paris city Moscow city Athens Belgium 0 1 { "locations": [ { "country": "Germany", "city": "Berlin" }, { "country": "France", "city": "Paris" } ], "headquarter": "Belgium", "exports":[{"city":"Moscow"}, {"city":"Athens"}] } 

Real world consistency is not a binary choice 

The wild west of consistency models… 

The state of commercial databases Strong consistency High latency Eventual consistency, Low latency 

Consistency models in Cosmos DB 5 well-defined consistency levels with clear tradeoffs Strong Bounded-stateless Session Consistent prefix Eventual Most real-life applications do not fall into these two extremes 

Insights from production workloads 4 18 73 2 3 Usage (%) Strong Bounded Staleness Session Consistent Prefix Eventual 0 0.2 0.4 0.6 0.8 1 1.2 Throughput Consistency distribution among customers Consistency vs. Throughput 

High availability SLA is not good enough 

Microsoft Azure 

 Global distribution, horizontal partitioning and fine-grained multi-tenancy cannot be an afterthought while building a cloud database  Schema agnostic database engine design is crucial for a globally distributed database  Intermediate consistency models are extremely useful  A globally distributed database must provide comprehensive SLAs beyond just high availability  Throughput, latency at 99th percentile, consistency and high availability Lessons learned 

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