RPresto is a DBI-based adapter for the open source distributed SQL query engine Presto for running interactive analytic queries.
RPresto is both on CRAN and github.
For the CRAN version, you can use
install.packages("RPresto")
You can install the development version of RPresto from GitHub with:
# install.packages("devtools")
::install_github("prestodb/RPresto") devtools
The following examples assume that you have a in-memory Presto server set up locally. It’s the simplest server which stores all data and metadata in RAM on workers and both are discarded when Presto restarts. If you don’t have one set up, please refer to the memory connector documentation.
# Load libaries and connect to Presto
library(RPresto)
library(DBI)
<- DBI::dbConnect(
con drv = RPresto::Presto(),
host = "http://localhost",
port = 8080,
user = Sys.getenv("USER"),
catalog = "memory",
schema = "default"
)
There are two levels of APIs: DBI
and
dplyr
.
DBI
APIsThe easiest and most flexible way of executing a SELECT
query is using a dbGetQuery()
call. It returns the query result in a tibble
.
::dbGetQuery(con, "SELECT CAST(3.14 AS DOUBLE) AS pi")
DBI#> # A tibble: 1 × 1
#> pi
#> <dbl>
#> 1 3.14
dbWriteTable()
can be used to write a small data frame into a Presto table.
# Writing iris data frame into Presto
::dbWriteTable(con, "iris", iris) DBI
dbExistsTable()
checks if a table exists.
::dbExistsTable(con, "iris")
DBI#> [1] TRUE
dbReadTable()
reads the entire table into R. It’s essentially a SELECT *
query on the table.
::dbReadTable(con, "iris")
DBI#> # A tibble: 150 × 5
#> sepal.length sepal.width petal.length petal.width species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # … with 140 more rows
dbRemoveTable()
drops the table from Presto.
::dbRemoveTable(con, "iris") DBI
You can execute a statement and returns the number of rows affected
using dbExecute()
.
# Create an empty table using CREATE TABLE
::dbExecute(
DBI"CREATE TABLE testing_table (field1 BIGINT, field2 VARCHAR)"
con,
)#> [1] 0
dbExecute()
returns the number of rows affected by the
statement. Since a CREATE TABLE
statement creates an empty
table, it returns 0.
::dbExecute(
DBI
con,"INSERT INTO testing_table VALUES (1, 'abc'), (2, 'xyz')"
)#> [1] 2
Since 2 rows are inserted into the table, it returns 2.
# Check the previous INSERT statment works
::dbReadTable(con, "testing_table")
DBI#> # A tibble: 2 × 2
#> field1 field2
#> <int> <chr>
#> 1 1 abc
#> 2 2 xyz
dplyr
APIsWe also include dplyr
database backend integration
(which is mainly implemented using the dbplyr
package).
# Load packages
library(dplyr)
library(dbplyr)
We can use dplyr::copy_to()
to write a local data frame to a PrestoConnection
and
immediately create a remote table on it.
# Add mtcars to Presto
if (DBI::dbExistsTable(con, "mtcars")) {
::dbRemoveTable(con, "mtcars")
DBI
}<- dplyr::copy_to(dest = con, df = mtcars, name = "mtcars")
tbl.mtcars # colnames() gives the column names
%>% colnames()
tbl.mtcars #> [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
#> [11] "carb"
dplyr::tbl()
also work directly on the PrestoConnection
.
# Treat "iris" in Presto as a remote data source that dplyr can now manipulate
if (!DBI::dbExistsTable(con, "iris")) {
::dbWriteTable(con, "iris", iris)
DBI
}<- dplyr::tbl(con, "iris")
tbl.iris
%>% colnames()
tbl.iris #> [1] "sepal.length" "sepal.width" "petal.length" "petal.width" "species"
# dplyr verbs can be applied onto the remote data source
%>%
tbl.iris group_by(species) %>%
summarize(
mean_sepal_length = mean(sepal.length, na.rm = TRUE)
%>%
) arrange(species) %>%
collect()
#> # A tibble: 3 × 2
#> species mean_sepal_length
#> <chr> <dbl>
#> 1 setosa 5.01
#> 2 versicolor 5.94
#> 3 virginica 6.59
BIGINT
handlingRPresto’s handling of BIGINT
(i.e. 64-bit integers) is
similar to other DBI packages (e.g. bigrquery
,
RPostgres
).
We provide a bigint
argument that users can use in multiple
interfaces to specify how they want BIGINT
typed data to be
translated into R.
The bigint
argument takes one of the following 4
possible values.
bigint = "integer"
is the default setting. It
translates BIGINT
to R’s native integer type (i.e. 32-bit
integer). The range of 32-bit integer is
[-2,147,483,648, 2,147,483,647]
which should cover most
integer use cases.
In case that you need to represent integer values outside of the
32-bit integer range, you have 2 options:
bigint = "numeric"
which translates the number into a
double
floating-point type in R; and
bigint = "integer64"
which packages the number using the
bit64::integer64
class. Note that both of those two
approaches actually the same precision-preservation range:
+/-(2^53-1) = +/-9,007,199,254,740,991
, due to the fact
that the Presto REST API uses JSON to encode the number and JSON has a
limit at 53 bits (rather than 64 bits).
bigint = "character"
casts the number into a string.
This is most useful when BIGINT
is used to represent an ID
rather than a real arithmetic number.
bigint
The DBI interface function dbGetQuery()
is the most
fundamental interface whereby bigint
can be specified. All
other interfaces are either built on top of dbGetQuery()
or
only take effect when used with dbGetQuery()
.
# BIGINT within the 32-bit integer range is simply translated into integer
::dbGetQuery(con, "SELECT CAST(1 AS BIGINT) AS small_bigint")
DBI#> # A tibble: 1 × 1
#> small_bigint
#> <int>
#> 1 1
# BIGINT outside of the 32-bit integer range generates a warning and returns NA
# when bigint is not specified
::dbGetQuery(con, "SELECT CAST(POW(2, 31) AS BIGINT) AS overflow_bigint")
DBI#> Warning in as.integer.integer64(x): NAs produced by integer overflow
#> # A tibble: 1 × 1
#> overflow_bigint
#> <int>
#> 1 NA
# Using bigint to specify numeric or integer64 translations
::dbGetQuery(
DBI"SELECT CAST(POW(2, 31) AS BIGINT) AS bigint_numeric",
con, bigint = "numeric"
)#> # A tibble: 1 × 1
#> bigint_numeric
#> <dbl>
#> 1 2147483648
::dbGetQuery(
DBI"SELECT CAST(POW(2, 31) AS BIGINT) AS bigint_integer64",
con, bigint = "integer64"
)#> # A tibble: 1 × 1
#> bigint_integer64
#> <int64>
#> 1 2147483648
When used with the dplyr
interface, bigint
can be specified in two places.
bigint
argument to
dbConnect()
when creating the
PrestoConnection
. All queries that use the connection later
will use the specified bigint
setting.<- DBI::dbConnect(
con.bigint drv = RPresto::Presto(),
host = "http://localhost",
port = 8080,
user = Sys.getenv("USER"),
catalog = "memory",
schema = "default",
# bigint can be specified in dbConnect
bigint = "integer64"
)
# BIGINT outside of the 32-bit integer range is automatically translated to
# integer64, per the connection setting earlier
::dbGetQuery(
DBI"SELECT CAST(POW(2, 31) AS BIGINT) AS bigint_integer64"
con.bigint,
)#> # A tibble: 1 × 1
#> bigint_integer64
#> <int64>
#> 1 2147483648
bigint
for a particular
query when using the dplyr
interface without affecting
other queries, you can pass bigint
to the
collect()
call.<- dplyr::tbl(
tbl.bigint sql("SELECT CAST(POW(2, 31) AS BIGINT) AS bigint")
con,
)
# Default collect() generates a warning and returns NA
::collect(tbl.bigint)
dplyr#> Warning in as.integer.integer64(x): NAs produced by integer overflow
#> # A tibble: 1 × 1
#> bigint
#> <int>
#> 1 NA
# Passing bigint to collect() specifies BIGINT treatment
::collect(tbl.bigint, bigint = "integer64")
dplyr#> # A tibble: 1 × 1
#> bigint
#> <int64>
#> 1 2147483648
To connect to Trino you must set the use.trino.headers
parameter so RPresto
knows to send the correct headers to
the server. Otherwise all the same functionality is supported.
<- DBI::dbConnect(
con.trino ::Presto(),
RPrestouse.trino.headers = TRUE,
host = "http://localhost",
port = 8080,
user = Sys.getenv("USER"),
schema = "<schema>",
catalog = "<catalog>",
source = "<source>"
)
To pass extraCredentials that gets added to the
X-Presto-Extra-Credential
header use the
extra.credentials
parameter so RPresto
will
add that to the header while creating the
PrestoConnection
.
Set use.trino.headers
if you want to pass
extraCredentials through the X-Trino-Extra-Credential
header.
<- DBI::dbConnect(
con ::Presto(),
RPrestohost = "http://localhost",
port = 7777,
user = Sys.getenv("USER"),
schema = "<schema>",
catalog = "<catalog>",
source = "<source>",
extra.credentials = "test.token.foo=bar",
)
Assuming that you have Kerberos already set up with the Presto
coordinator, you can use RPresto
with Kerberos by passing a
Kerberos config header to the request.config
argument of
dbConnect()
. We provide a convenient wrapper
kerberos_configs()
to further simplify the workflow.
<- DBI::dbConnect(
con ::Presto(),
RPrestohost = "http://localhost",
port = 7777,
user = Sys.getenv("USER"),
schema = "<schema>",
catalog = "<catalog>",
source = "<source>",
request.config = kerberos_configs()
)
Presto exposes its interface via a REST based API1. We
utilize the httr package to
make the API calls and use jsonlite to reshape the
data into a tibble
.
RPresto has been tested on Presto 0.100.
RPresto is BSD-licensed.
See https://github.com/prestodb/presto/wiki/HTTP-Protocol for a description of the API.↩︎