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MyBatis is a Java persistence framework that couples objects with stored procedures or SQL statements using an XML descriptor or annotations. MyBatis is free software that is distributed under the Apache License 2.0. MyBatis is a fork of iBATIS 3.0 and is maintained by a team that includes the original creators of iBATIS.
For example, assume there is a database table PRODUCT (PROD_ID INTEGER, PROD_DESC VARCHAR(64)) and a Java class com.example.Product (id: int, description: String).To read the product record having the key PROD_ID into a new Product POJO, the following mapping is added into an iBATIS XML mapping file:
Join and meet are dual to one another with respect to order inversion. A partially ordered set in which all pairs have a join is a join-semilattice. Dually, a partially ordered set in which all pairs have a meet is a meet-semilattice. A partially ordered set that is both a join-semilattice and a meet-semilattice is a lattice.
In database design, a lossless join decomposition is a decomposition of a relation into relations , such that a natural join of the two smaller relations yields back the original relation. This is central in removing redundancy safely from databases while preserving the original data. [ 1 ]
An inner join (or join) requires each row in the two joined tables to have matching column values, and is a commonly used join operation in applications but should not be assumed to be the best choice in all situations. Inner join creates a new result table by combining column values of two tables (A and B) based upon the join-predicate.
Longest prefix match (also called Maximum prefix length match) refers to an algorithm used by routers in Internet Protocol (IP) networking to select an entry from a routing table. [ 1 ] Because each entry in a forwarding table may specify a sub-network, one destination address may match more than one forwarding table entry.
In computer science, the count-distinct problem [1] (also known in applied mathematics as the cardinality estimation problem) is the problem of finding the number of distinct elements in a data stream with repeated elements. This is a well-known problem with numerous applications.
A common solution is to combine both the mean and the median: Create hash functions and split them into distinct groups (each of size ). Within each group use the mean for aggregating together the l {\displaystyle l} results, and finally take the median of the k {\displaystyle k} group estimates as the final estimate.