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Showing content from http://spark.apache.org/docs/latest/api/R/reference/write.jdbc.html below:

Save the content of SparkDataFrame to an external database table via JDBC. — write.jdbc • SparkR

Save the content of the SparkDataFrame to an external database table via JDBC. Additional JDBC database connection properties can be set (...) You can find the JDBC-specific option and parameter documentation for writing tables via JDBC in https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html#data-source-option Data Source Option in the version you use.

write.jdbc(x, url, tableName, mode = "error", ...)

# S4 method for class 'SparkDataFrame,character,character'
write.jdbc(x, url, tableName, mode = "error", ...)

x

a SparkDataFrame.

url

JDBC database url of the form jdbc:subprotocol:subname.

tableName

the name of the table in the external database.

mode

one of 'append', 'overwrite', 'error', 'errorifexists', 'ignore' save mode (it is 'error' by default)

...

additional JDBC database connection properties.

Also, mode is used to specify the behavior of the save operation when data already exists in the data source. There are four modes:

• 'append': Contents of this SparkDataFrame are expected to be appended to existing data.

• 'overwrite': Existing data is expected to be overwritten by the contents of this SparkDataFrame.

• 'error' or 'errorifexists': An exception is expected to be thrown.

• 'ignore': The save operation is expected to not save the contents of the SparkDataFrame and to not change the existing data.

write.jdbc since 2.0.0

Other SparkDataFrame functions: SparkDataFrame-class, agg(), alias(), arrange(), as.data.frame(), attach,SparkDataFrame-method, broadcast(), cache(), checkpoint(), coalesce(), collect(), colnames(), coltypes(), createOrReplaceTempView(), crossJoin(), cube(), dapplyCollect(), dapply(), describe(), dim(), distinct(), dropDuplicates(), dropna(), drop(), dtypes(), exceptAll(), except(), explain(), filter(), first(), gapplyCollect(), gapply(), getNumPartitions(), group_by(), head(), hint(), histogram(), insertInto(), intersectAll(), intersect(), isLocal(), isStreaming(), join(), limit(), localCheckpoint(), merge(), mutate(), ncol(), nrow(), persist(), printSchema(), randomSplit(), rbind(), rename(), repartitionByRange(), repartition(), rollup(), sample(), saveAsTable(), schema(), selectExpr(), select(), showDF(), show(), storageLevel(), str(), subset(), summary(), take(), toJSON(), unionAll(), unionByName(), union(), unpersist(), unpivot(), withColumn(), withWatermark(), with(), write.df(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

if (FALSE) { # \dontrun{
sparkR.session()
jdbcUrl <- "jdbc:mysql://localhost:3306/databasename"
write.jdbc(df, jdbcUrl, "table", user = "username", password = "password")
} # }

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