Azure Cosmos DB Consistency Levels Understanding to manage availability and performance

Azure Cosmos DB Consistency Levels
Understanding Consistency Models in Azure Cosmos DB to manage availability and performance
Cosmos DB: Consistency Levels

Azure Cosmos DB - Consistency levels in Cosmos DB
Understanding consistency levels in Azure cosmos DB | Choose the right one

Distributed databases that rely on replication for high availability, low latency, or both, must make a fundamental tradeoff between the read consistency, availability, latency, and throughput as defined by the PACLC theorem. The linearizability of the strong consistency model is the gold standard of data programmability. But it adds a steep price from higher write latencies due to data having to replicate and commit across large distances. Strong consistency may also suffer from reduced availability (during failures) because data cannot replicate and commit in every region. Eventual consistency offers higher availability and better performance, but its more difficult to program applications because data may not be completely consistent across all regions.

Most commercially available distributed NoSQL databases available in the market today provide only strong and eventual consistency. Azure Cosmos DB offers five well-defined levels. From strongest to weakest, the levels are:

Strong
Bounded staleness
Session
Consistent prefix
Eventual
Each level provides availability and performance tradeoffs. The following image shows the different consistency levels as a spectrum.

Consistency levels and Azure Cosmos DB APIs
Azure Cosmos DB provides native support for wire protocol-compatible APIs for popular databases. These include MongoDB, Apache Cassandra, Gremlin, and Azure Table storage. When using Gremlin API and Table API, the default consistency level configured on the Azure Cosmos account is used.

Guarantees associated with consistency levels
Azure Cosmos DB guarantees that 100 percent of read requests meet the consistency guarantee for the consistency level chosen. The precise definitions of the five consistency levels in Azure Cosmos DB using the TLA+ specification language are provided in the azure-cosmos-tla GitHub repo.

Consistency guarantees in practice
In practice, you may often get stronger consistency guarantees. Consistency guarantees for a read operation correspond to the freshness and ordering of the database state that you request. Read-consistency is tied to the ordering and propagation of the write/update operations.

If there are no write operations on the database, a read operation with eventual, session, or consistent prefix consistency levels is likely to yield the same results as a read operation with strong consistency level.

If your Azure Cosmos account is configured with a consistency level other than the strong consistency, you can find out the probability that your clients may get strong and consistent reads for your workloads by looking at the Probabilistically Bounded Staleness (PBS) metric. This metric is exposed in the Azure portal, to learn more, see Monitor Probabilistically Bounded Staleness (PBS) metric.

Probabilistic bounded staleness shows how eventual is your eventual consistency. This metric provides an insight into how often you can get a stronger consistency than the consistency level that you have currently configured on your Azure Cosmos account. In other words, you can see the probability (measured in milliseconds) of getting strongly consistent reads for a combination of write and read regions.
Strong consistency and multiple write regions
Cosmos accounts configured with multiple write regions cannot be configured for strong consistency as it is not possible for a distributed system to provide an RPO of zero and an RTO of zero. Additionally, there are no write latency benefits on using strong consistency with multiple write regions because a write to any region must be replicated and committed to all configured regions within the account. This results in the same write latency as a single write region account.

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[PaddyMaddy]

Azure Cosmos DB Consistency Levels
Understanding Consistency Models in Azure Cosmos DB to manage availability and performance
Cosmos DB: Consistency Levels

[vanillatrivedi]

I was expecting some practical examples when and where each those consistency levels can be used. The presentation is very theoretical. :(