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A relational database management system uses SQL MERGE (also called upsert) statements to INSERT new records or UPDATE or DELETE existing records depending on whether condition matches. It was officially introduced in the SQL:2003 standard, and expanded [citation needed] in the SQL:2008 standard.
As such, a DataFrame can be thought of as having two indices: one column-based and one row-based. Because column names are stored as an index, these are not required to be unique. [9]: 103–105 If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in ...
The name of a column becomes the name of a "binding variable", whose value is a specific graph element reference for each row of the table. For example, a pattern MATCH (p:Person)-[:LIVES_IN]->(c:City) will generate a two-column output table. The first column named p will contain references to nodes with a label Person .
An embeddable, in-process, column-oriented SQL OLAP RDBMS Databend Rust An elastic and reliable Serverless Data Warehouse InfluxDB: Rust Time series database: Greenplum Database C Support and extensions available from VMware. MapD: C++ MariaDB ColumnStore C & C++ Formerly Calpont InfiniDB: Metakit: C++ MonetDB: C
SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Edgar F. Codd [12] in the early 1970s. [13] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM's original quasirelational database management system, System R, which a group at IBM San ...
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. The key idea of the sort-merge algorithm is ...
Tabular data is two dimensional — data is modeled as rows and columns. However, computer systems represent data in a linear memory model, both in-disk and in-memory. [7] [8] [9] Therefore, a table in a linear memory model requires mapping its two-dimensional scheme into a one-dimensional space.
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.