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When provisioned at the database level, the throughput is shared across all the containers within that database, with the additional ability to have dedicated throughput for some containers. The throughput provisioned on an Azure Cosmos container is exclusively reserved for that container. [12]
This functionality applies to all Cosmos DB account types, including provisioned throughput and serverless models. The stateless, HTTP-based architecture of Cosmos DB facilitates scalable and concurrent operations without the limitations typically associated with traditional connection pooling mechanisms.
The tradeoff between availability, consistency and latency, as described by the PACELC theorem. In database theory, the PACELC theorem is an extension to the CAP theorem.It states that in case of network partitioning (P) in a distributed computer system, one has to choose between availability (A) and consistency (C) (as per the CAP theorem), but else (E), even when the system is running ...
A database shard, or simply a shard, is a horizontal partition of data in a database or search engine. Each shard may be held on a separate database server instance, to spread load. Some data in a database remains present in all shards, [a] but some appears only in a single shard. Each shard acts as the single source for this subset of data.
Aerospike Database is a real-time, high performance NoSQL database. Designed for applications that cannot experience any downtime and require high read & write throughput. Aerospike is optimized to run on NVMe SSDs capable of efficiently storing large datasets (Gigabytes to Petabytes). Aerospike can also be deployed as a fully in-memory cache ...
For others, the application can implement an OCC layer outside of the database, and avoid waiting or silently overwriting records. In such cases, the form may include a hidden field with the record's original content, a timestamp, a sequence number, or an opaque token. On submit, this is compared against the database.
This approach to architecture attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data. The two view outputs may be joined before presentation.
This can involve a combination of operating system constraints and web server software limitations. According to the scope of services to be made available and the capabilities of the operating system as well as hardware considerations such as multi-processing capabilities, a multi-threading model or a single threading model can be preferred.