:fire: A fast, easy-to-use database library for R

Designed for both research and production environments

Supports Postgres, MySQL, MariaDB, SQLite, SQL Server, and more

Build Status CRAN status


Install dbx


And follow the instructions for your database

To install with Jetpack, use:



Install the R package


And use:


db <- dbxConnect(adapter="postgres", dbname="mydb")

You can also pass user, password, host, port, and url.

Works with RPostgreSQL as well

MySQL & MariaDB

Install the R package


And use:


db <- dbxConnect(adapter="mysql", dbname="mydb")

You can also pass user, password, host, port, and url.

Works with RMariaDB as well


Install the R package


And use:


db <- dbxConnect(adapter="sqlite", dbname=":memory:")

SQL Server

Install the R package


And use:


db <- dbxConnect(adapter=odbc::odbc(), database="mydb")

You can also pass uid, pwd, server, and port.


For Redshift, follow the Postgres instructions.


Install the appropriate R package and use:

db <- dbxConnect(adapter=odbc::odbc(), database="mydb")



Create a data frame of records from a SQL query

records <- dbxSelect(db, "SELECT * FROM forecasts")

Pass parameters

dbxSelect(db, "SELECT * FROM forecasts WHERE period = ? AND temperature > ?", params=list("hour", 27))

Parameters can also be vectors

dbxSelect(db, "SELECT * FROM forecasts WHERE id IN (?)", params=list(1:3))


Insert records

table <- "forecasts"
records <- data.frame(temperature=c(32, 25))
dbxInsert(db, table, records)

If you use auto-incrementing ids in Postgres, you can get the ids of newly inserted rows by passing the column name:

dbxInsert(db, table, records, returning=c("id"))


Update records

records <- data.frame(id=c(1, 2), temperature=c(16, 13))
dbxUpdate(db, table, records, where_cols=c("id"))

Use where_cols to specify the columns used for lookup. Other columns are written to the table.

Updates are batched when possible, but often need to be run as multiple queries. We recommend upsert when possible for better performance, as it can always be run as a single query. Turn on logging to see the difference.


Available for PostgreSQL 9.5+, MySQL 5.5+, SQLite 3.24+, and SQL Server 2008+

Atomically insert if they don’t exist, otherwise update them

records <- data.frame(id=c(2, 3), temperature=c(20, 25))
dbxUpsert(db, table, records, where_cols=c("id"))

Use where_cols to specify the columns used for lookup. There must be a unique index on them, or an error will be thrown.

To skip existing rows instead of updating them, use:

dbxUpsert(db, table, records, where_cols=c("id"), skip_existing=TRUE)

If you use auto-incrementing ids in Postgres, you can get the ids of newly upserted rows by passing the column name:

dbxUpsert(db, table, records, where_cols=c("id"), returning=c("id"))


Delete specific records

bad_records <- data.frame(id=c(1, 2))
dbxDelete(db, table, where=bad_records)

Delete all records (uses TRUNCATE when possible for performance)

dbxDelete(db, table)


Execute a statement

dbxExecute(db, "UPDATE forecasts SET temperature = temperature + 1")

Pass parameters

dbxExecute(db, "UPDATE forecasts SET temperature = ? WHERE id IN (?)", params=list(27, 1:3))


Log all SQL queries with:


Customize logging by passing a function

logQuery <- function(sql) {
  # your logging code


Database Credentials

Environment variables are a convenient way to store database credentials. This keeps them outside your source control. It’s also how platforms like Heroku store them.

Create an .Renviron file in your home directory with:


Install urltools:


And use:

db <- dbxConnect()

If you have multiple databases, use a different variable name, and:

db <- dbxConnect(url=Sys.getenv("OTHER_DATABASE_URL"))

You can also use a package like keyring.


By default, operations are performed in a single statement or transaction. This is better for performance and prevents partial writes on failures. However, when working with large data frames on production systems, it can be better to break writes into batches. Use the batch_size option to do this.

dbxInsert(db, table, records, batch_size=1000)
dbxUpdate(db, table, records, where_cols, batch_size=1000)
dbxUpsert(db, table, records, where_cols, batch_size=1000)
dbxDelete(db, table, records, where, batch_size=1000)

Query Comments

Add comments to queries to make it easier to see where time-consuming queries are coming from.


The comment will be appended to queries, like:

SELECT * FROM users /*script:forecast.R*/

Set a custom comment with:



To perform multiple operations in a single transaction, use:

DBI::dbWithTransaction(db, {
  dbxInsert(db, ...)
  dbxDelete(db, ...)

For updates inside a transaction, use:

dbxUpdate(db, transaction=FALSE)


To specify a schema, use:

table <- DBI::Id(schema="schema", table="table")

Data Type Notes

Dates & Times

Dates are returned as Date objects and times as POSIXct objects. Times are stored in the database in UTC and converted to your local time zone when retrieved.

Times without dates are returned as character vectors since R has no built-in support for this type. If you use hms, you can convert columns with:

records$column <- hms::as_hms(records$column)

SQLite does not have support for TIME columns, so we recommend storing as VARCHAR.


JSON and JSONB columns are returned as character vectors. You can use jsonlite to parse them with:

records$column <- lapply(records$column, jsonlite::fromJSON)

SQLite does not have support for JSON columns, so we recommend storing as TEXT.

Binary Data

BLOB and BYTEA columns are returned as raw vectors.

Data Type Limitations

Dates & Times

RSQLite does not currently provide enough info to automatically typecast dates and times. You can manually typecast date columns with:

records$column <- as.Date(records$column)

And time columns with:

records$column <- as.POSIXct(records$column, tz="Etc/UTC")
attr(records$column, "tzone") <- Sys.timezone()


RMariaDB and RSQLite do not currently provide enough info to automatically typecast booleans. You can manually typecast with:

records$column <- records$column != 0


RMariaDB does not currently support JSON.

Binary Data

RMySQL can write BLOB columns, but can’t retrieve them directly. To workaround this, use:

records <- dbxSelect(db, "SELECT HEX(column) AS column FROM table")

hexToRaw <- function(x) {
  y <- strsplit(x, "")[[1]]
  z <- paste0(y[c(TRUE, FALSE)], y[c(FALSE, TRUE)])

records$column <- lapply(records$column, hexToRaw)


BIGINT columns are returned as numeric vectors. The numeric type in R loses precision above 253. Some libraries (RPostgres, RMariaDB, RSQLite, ODBC) support returning bit64::integer64 vectors instead.


Connection Pooling

Install the pool package


Create a pool


factory <- function() {
  dbxConnect(adapter="postgres", ...)

pool <- poolCreate(factory, maxSize=5)

Run queries

conn <- poolCheckout(pool)

  dbxSelect(conn, "SELECT * FROM forecasts")
}, finally={

In the future, dbx commands may work directly with pools.


When connecting to a database over a network you don’t fully trust, make sure your connection is secure.

With Postgres, use:

db <- dbxConnect(adapter="postgres", sslmode="verify-full", sslrootcert="ca.pem")

With RMariaDB, use:

db <- dbxConnect(adapter="mysql", ssl.ca="ca.pem")

Please let us know if you have a way that works with RMySQL.


Set session variables with:

db <- dbxConnect(variables=list(search_path="archive"))


Set a statement timeout with:

# Postgres
db <- dbxConnect(variables=list(statement_timeout=1000)) # ms

# MySQL 5.7.8+
db <- dbxConnect(variables=list(max_execution_time=1000)) # ms

# MariaDB 10.1.1+
db <- dbxConnect(variables=list(max_statement_time=1)) # sec

With Postgres, set a connect timeout with:

db <- dbxConnect(connect_timeout=3) # sec


All connections are simply DBI connections, so you can use them anywhere you use DBI.

dbCreateTable(db, ...)

Install dbplyr to use data with dplyr.

forecasts <- tbl(db, "forecasts")


To close a connection, use:



View the changelog


Everyone is encouraged to help improve this project. Here are a few ways you can help:

To get started with development:

git clone https://github.com/ankane/dbx.git
cd dbx

# create Postgres database
createdb dbx_test

# create MySQL database
mysqladmin create dbx_test

In R, do:


To test a single file, use:

devtools::install() # to use latest updates