JDBC backend

This backend supports databases accessed via JDBCConnection created by RJDBC::JDBC() and DBI::dbConnect(). dbplyr automatically detects the underlying database type from the JDBC connection class and uses the appropriate SQL dialect.

The following databases are recognized via their JDBC connection class:

• PostgreSQL (org.postgresql.*): dialect_postgres()

• MySQL (com.mysql.*): dialect_mysql()

• MariaDB (org.mariadb.*): dialect_mariadb()

• SQLite (org.sqlite.*): dialect_sqlite()

• Oracle (oracle.*): dialect_oracle()

• SQL Server (com.microsoft.sqlserver.*): dialect_mssql()

• SAP HANA (com.sap.db.*): dialect_hana()

• Teradata (com.teradata.*): dialect_teradata()

• Apache Hive (org.apache.hive.*): dialect_hive()

• Apache Spark (org.apache.spark.* or com.simba.spark.*): dialect_spark_sql()

• Snowflake (net.snowflake.*): dialect_snowflake()

• Impala (com.cloudera.impala.*): dialect_impala()

• Amazon Redshift (com.amazon.redshift.*): dialect_redshift()

If your database is not recognized, dbplyr will fall back to a generic ODBC dialect. Please file an issue if you'd like support for additional databases.

See vignette("translation-function") and vignette("translation-verb") for details of overall translation technology.

See also

with_dialect() to use a different dialect if dbplyr guesses incorrectly, or a more specific translation is available.

Examples

# JDBC connections require the RJDBC package and a JDBC driver JAR file.
# Once connected, dbplyr automatically detects the database type:
#
# library(RJDBC)
# library(dplyr, warn.conflicts = FALSE)
#
# drv <- JDBC("org.postgresql.Driver", "postgresql.jar")
# con <- dbConnect(drv, "jdbc:postgresql://localhost/mydb", "user", "password")
# tbl(con, "my_table") |> filter(x > 1)