Importing CSV Data into Datagrip

When working with large datasets, it is often necessary to import data from various sources into your database management system. One common way to do this is by importing CSV (Comma Separated Values) files, which are widely used for exchanging data between different systems. In this article, we will discuss how to import CSV data into Datagrip.

Using PCRE Dialect

Datagrip uses the Perl Compatible Regular Expressions (PCRE) dialect, which is a powerful syntax that exceeds the functionality of other regular expression dialects such as POSIX BRE or POSIX ERE. PCRE dialect can be used in various functions and predicates to filter or replace strings with certain patterns.

Importing CSV Data

To import CSV data into Datagrip, you need to first create a new database connection. Once connected, navigate to the 'Data' tab and click on the 'Import' button. Select 'CSV' as the file type and choose the CSV file you want to import.

Schema Qualified Names

When importing CSV data, it is often necessary to specify the schema name in order to access objects that are not located in the current schema. This can be done by using schema qualified names, which consist of the schema name and the object name separated by a dot. For example, 'ab.c' indicates the name ab.c.

Delimited Identifiers

Datagrip also supports delimited identifiers, which are quoted strings that can contain any characters. Delimited identifiers are case-sensitive when stored in the database, except for users and roles. To use double quotation marks within a name, you must include two successive double quotation marks.

Regular Identifiers

Regular identifiers, on the other hand, are not case-sensitive but are always stored in uppercase letters in the database. Regular identifiers can start with letters of the simple ASCII character set or Unicode classes such as Mn, Mc, Nd, Pc, Cf, and U+00B7 (middle dot).

Importing CSV data into Datagrip is a straightforward process that involves creating a new database connection, selecting the CSV file, and specifying any necessary schema qualified names. By using PCRE dialect and regular expressions, you can filter or replace strings with certain patterns. Additionally, Datagrip supports delimited identifiers and regular identifiers, which provide more flexibility when working with data.

References

For more information about Datagrip's import functionality, refer to the official documentation. For details about the Unicode standard, see 'Unicode.org'(https://unicode.org/).

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