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Changes are persisted by Cosmos DB, which makes it possible to request changes from any point in time since the creation of the container. A "Time to Live" (or TTL) can be specified at the container level to let Cosmos DB automatically delete items after a certain amount of time expressed in seconds. This countdown starts after the last update ...
The following examples of Gremlin queries and responses in a Gremlin-Groovy environment are relative to a graph representation of the MovieLens dataset. [4] The dataset includes users who rate movies. Users each have one occupation, and each movie has one or more categories associated with it. The MovieLens graph schema is detailed below.
Azure Data Lake service was released on November 16, 2016. It is based on COSMOS, [2] which is used to store and process data for applications such as Azure, AdCenter, Bing, MSN, Skype and Windows Live. COSMOS features a SQL-like query engine called SCOPE upon which U-SQL was built. [2]
A search string can be specified as one of the query parameters to retrieve matching documents. Azure Search supports search strings using simple query syntax. [6] Supported features include logical operators, the suffix operator, and query with Lucene query syntax. [7] (currently in preview) As an example, white+house
In Azure Cosmos DB, connection pooling is managed at the SDK level rather than by the database service itself. SDKs such as those for .NET, Java, and Python implement connection pooling to reuse HTTP connections to the database endpoint, optimizing resource usage and performance.
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 ...
Bitemporal modeling is a specific case of temporal database information modeling technique designed to handle historical data along two different timelines. [1] This makes it possible to rewind the information to "as it actually was" in combination with "as it was recorded" at some point in time.
Given two queries and and a database schema, the query containment problem is the problem of deciding whether for all possible database instances over the input database schema, () (). The main application of query containment is in query optimization: Deciding whether two queries are equivalent is possible by simply checking mutual containment.