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The sort-merge join (also known as merge join) is a join algorithm and is used in the implementation of a relational database management system. The basic problem of a join algorithm is to find, for each distinct value of the join attribute, the set of tuples in each relation which display that value.
CUBRID supports MERGE INTO [10] statement. And supports the use of INSERT... ON DUPLICATE KEY UPDATE syntax. [11] It also supports REPLACE INTO for compatibility with MySQL. [12] Apache Phoenix supports UPSERT VALUES [13] and UPSERT SELECT [14] syntax. Spark SQL supports UPDATE SET * and INSERT * clauses in actions. [15] Apache Impala supports ...
Note that in this trivial case, the compiler may choose to inline both calls to the function, such that the resulting machine code is identical for both the duplicated and non-duplicated examples above. If the function is not inlined, then the additional overhead of the function calls will probably take longer to run (on the order of 10 ...
Conversely, an inner join can result in disastrously slow performance or even a server crash when used in a large volume query in combination with database functions in an SQL Where clause. [2] [3] [4] A function in an SQL Where clause can result in the database ignoring relatively compact table indexes. The database may read and inner join the ...
One method for deduplicating data relies on the use of cryptographic hash functions to identify duplicate segments of data. If two different pieces of information generate the same hash value, this is known as a collision. The probability of a collision depends mainly on the hash length (see birthday attack).
[2] The three-way merge looks for sections which are the same in only two of the three files. In this case, there are two versions of the section, and the version which is in the common ancestor "C" is discarded, while the version that differs is preserved in the output. If "A" and "B" agree, that is what appears in the output.
Pipeline: allowing the simultaneous running of several components on the same data stream, e.g. looking up a value on record 1 at the same time as adding two fields on record 2 Component: The simultaneous running of multiple processes on different data streams in the same job, e.g. sorting one input file while removing duplicates on another file
Change data capture both increases in complexity and reduces in value if the source system saves metadata changes when the data itself is not modified. For example, some Data models track the user who last looked at but did not change the data in the same structure as the data. This results in noise in the Change Data Capture.