Connect to 'AWS Athena' using R 'AWS SDK' 'paws' ('DBI' Interface).
noctua
The goal of the noctua
package is to provide a DBI-compliant interface to Amazon’s Athena (https://aws.amazon.com/athena/) using paws
SDK. This allows for an efficient, easy setup connection to Athena using the paws
SDK as a driver.
NOTE:Before using noctua
you must have an aws account or have access to aws account with permissions allowing you to use Athena.
Why is the package called noctua
Athena/Minerva is the Greek/Roman god of wisdom, handicraft, and warfare. One of the main symbols for Athena is the Owl. Noctua
is the latin word for Owl.
Installation:
To install noctua
you can get it from CRAN with:
install.packages("noctua")
Or to get the development version from Github with:
remotes::install_github("dyfanjones/noctua")
Connection Methods
Hard Coding
The most basic way to connect to AWS Athena is to hard-code your access key and secret access key. However this method is not recommended as your credentials are hard-coded.
library(DBI)
con <- dbConnect(noctua::athena(),
aws_access_key_id='YOUR_ACCESS_KEY_ID',
aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',
s3_staging_dir='s3://path/to/query/bucket/',
region_name='eu-west-1')
AWS Profile Name
The next method is to use profile names set up by AWS CLI or created manually in the ~/.aws
directory. To create the profile names manually please refer to: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html.
Setting up AWS CLI
noctua
is compatible with AWS CLI. This allows your aws credentials to be stored and not be hard coded in your connection.
To install AWS CLI please refer to: https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html, to configure AWS CLI please refer to: https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html
Once AWS CLI has been set up you will be able to connect to Athena by only putting the s3_staging_dir
.
Using default profile name:
library(DBI)
con <- dbConnect(noctua::athena(),
s3_staging_dir = 's3://path/to/query/bucket/')
Connecting to Athena using profile name other than default
.
library(DBI)
con <- dbConnect(noctua::athena(),
profile_name = "your_profile",
s3_staging_dir = 's3://path/to/query/bucket/')
Assuming ARN Role for connection
Another method in connecting to Athena is to use Amazon Resource Name (ARN) role.
Setting credentials in environmental variables:
library(noctua)
assume_role(profile_name = "YOUR_PROFILE_NAME",
role_arn = "arn:aws:sts::123456789012:assumed-role/role_name/role_session_name",
set_env = TRUE)
# Connect to Athena using temporary credentials
con <- dbConnect(athena(),
s3_staging_dir = 's3://path/to/query/bucket/')
Connecting to Athena directly using ARN role:
library(DBI)
con <- dbConnect(athena(),
profile_name = "YOUR_PROFILE_NAME",
role_arn = "arn:aws:sts::123456789012:assumed-role/role_name/role_session_name",
s3_staging_dir = 's3://path/to/query/bucket/')
To change the duration of ARN role session please change the parameter duration_seconds
. By default duration_seconds
is set to 3600 seconds (1 hour).
Usage
Basic Usage
Connect to athena, and send a query and return results back to R.
library(DBI)
# using default profile to connect
con <- dbConnect(noctua::athena(),
s3_staging_dir = 's3://path/to/query/bucket/')
res <- dbExecute(con, "SELECT * FROM one_row")
dbFetch(res)
dbClearResult(res)
To retrieve query in 1 step.
dbGetQuery(con, "SELECT * FROM one_row")
Intermediate Usage
To create a tables in athena, dbExecute
will send the query to athena and wait until query has been executed. This makes it and idea method to create tables within athena.
query <-
"CREATE EXTERNAL TABLE impressions (
requestBeginTime string,
adId string,
impressionId string,
referrer string,
userAgent string,
userCookie string,
ip string,
number string,
processId string,
browserCookie string,
requestEndTime string,
timers struct<modelLookup:string, requestTime:string>,
threadId string,
hostname string,
sessionId string)
PARTITIONED BY (dt string)
ROW FORMAT serde 'org.apache.hive.hcatalog.data.JsonSerDe'
with serdeproperties ( 'paths'='requestBeginTime, adId, impressionId, referrer, userAgent, userCookie, ip' )
LOCATION 's3://elasticmapreduce/samples/hive-ads/tables/impressions/' ;"
dbExecute(con, query)
noctua has 2 extra function to return extra information around Athena tables: dbGetParitiions
and dbShow
dbGetPartitions
will return all the partitions (returns data.frame):
noctua::dbGetPartition(con, "impressions")
dbShow
will return the table’s ddl, so you will able to see how the table was constructed in Athena (returns SQL character):
noctua::dbShow(con, "impressions")
Advanced Usage
library(DBI)
con <- dbConnect(noctua::athena(),
s3_staging_dir = 's3://path/to/query/bucket/')
Sending data to Athena
noctua has created a method to send data.frame from R to Athena.
# Check existing tables
dbListTables(con)
# Upload iris to Athena
dbWriteTable(con, "iris", iris,
partition=c("TIMESTAMP" = format(Sys.Date(), "%Y%m%d")))
# Read in iris from Athena
dbReadTable(con, "iris")
# Check new existing tables in Athena
dbListTables(con)
# Check if iris exists in Athena
dbExistsTable(con, "iris")
Please check out noctua
method for dbWriteTable
for more information in how to upload data to AWS Athena and AWS S3.
For more information around how to get the most out of AWS Athena when uploading data please check out: Top 10 Performance Tuning Tips for Amazon Athena
Tidyverse Usage
Creating a connection to Athena and query and already existing table iris
that was created in previous example.
library(DBI)
library(dplyr)
con <- dbConnect(noctua::athena(),
aws_access_key_id='YOUR_ACCESS_KEY_ID',
aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',
s3_staging_dir='s3://path/to/query/bucket/',
region_name='eu-west-1')
tbl(con, sql("SELECT * FROM iris"))
# Source: SQL [?? x 5]
# Database: Athena 0.1.4 [eu-west-1/default]
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 more rows
dplyr provides lazy querying with allows to short hand tbl(con, sql("SELECT * FROM iris"))
to tbl(con, "iris")
. For more information please look at https://solutions.posit.co/connections/db/r-packages/dplyr/
tbl(con, "iris")
# Source: table<iris> [?? x 5]
# Database: Athena 0.1.4 [eu-west-1/default]
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 more rows
Querying Athena with profile_name
instead of hard coding aws_access_key_id
and aws_secret_access_key
. By using profile_name
extra Meta Data is returned in the query to give users extra information.
con <- dbConnect(noctua::athena(),
profile_name = "your_profile",
s3_staging_dir='s3://path/to/query/bucket/')
tbl(con, "iris")) %>%
filter(petal_length < 1.3)
# Source: lazy query [?? x 5]
# Database: Athena 0.1.4 [your_profile@eu-west-1/default]
sepal_length sepal_width petal_length petal_width species
<dbl> <dbl> <dbl> <dbl> <chr>
1 4.7 3.2 1.3 0.2 setosa
2 4.3 3 1.1 0.1 setosa
3 5.8 4 1.2 0.2 setosa
4 5.4 3.9 1.3 0.4 setosa
5 4.6 3.6 1 0.2 setosa
6 5 3.2 1.2 0.2 setosa
7 5.5 3.5 1.3 0.2 setosa
8 4.4 3 1.3 0.2 setosa
9 5 3.5 1.3 0.3 setosa
10 4.5 2.3 1.3 0.3 setosa
# … with more rows
tbl(con, "iris") %>%
select(contains("sepal"), contains("petal"))
# Source: lazy query [?? x 4]
# Database: Athena 0.1.4 [your_profile@eu-west-1/default]
sepal_length sepal_width petal_length petal_width
<dbl> <dbl> <dbl> <dbl>
1 5.1 3.5 1.4 0.2
2 4.9 3 1.4 0.2
3 4.7 3.2 1.3 0.2
4 4.6 3.1 1.5 0.2
5 5 3.6 1.4 0.2
6 5.4 3.9 1.7 0.4
7 4.6 3.4 1.4 0.3
8 5 3.4 1.5 0.2
9 4.4 2.9 1.4 0.2
10 4.9 3.1 1.5 0.1
# … with more rows
Upload data using dplyr
function copy_to
and compute
.
library(DBI)
library(dplyr)
con <- dbConnect(noctua::athena(),
profile_name = "your_profile",
s3_staging_dir='s3://path/to/query/bucket/')
Write data.frame to Athena table
copy_to(con, mtcars,
s3_location = "s3://mybucket/data/")
Write Athena table from tbl_sql
athena_mtcars <- tbl(con, "mtcars")
mtcars_filter <- athena_mtcars %>% filter(gear >=4)
Create athena with unique table name
mtcars_filer %>% compute()
Create athena with specified name and s3 location
mtcars_filer %>%
compute("mtcars_filer",
s3_location = "s3://mybucket/mtcars_filer/")
# Disconnect from Athena
dbDisconnect(con)
Work Groups
Creating work group:
library(noctua)
library(DBI)
con <- dbConnect(noctua::athena(),
profile_name = "your_profile",
encryption_option = "SSE_S3",
s3_staging_dir='s3://path/to/query/bucket/')
create_work_group(con, "demo_work_group", description = "This is a demo work group",
tags = tag_options(key= "demo_work_group", value = "demo_01"))
List work groups:
list_work_groups(con)
[[1]]
[[1]]$Name
[1] "demo_work_group"
[[1]]$State
[1] "ENABLED"
[[1]]$Description
[1] "This is a demo work group"
[[1]]$CreationTime
2019-09-06 18:51:28.902000+01:00
[[2]]
[[2]]$Name
[1] "primary"
[[2]]$State
[1] "ENABLED"
[[2]]$Description
[1] ""
[[2]]$CreationTime
2019-08-22 16:14:47.902000+01:00
Update work group:
update_work_group(con, "demo_work_group", description = "This is a demo work group update")
Return work group meta data:
get_work_group(con, "demo_work_group")
$Name
[1] "demo_work_group"
$State
[1] "ENABLED"
$Configuration
$Configuration$ResultConfiguration
$Configuration$ResultConfiguration$OutputLocation
[1] "s3://path/to/query/bucket/"
$Configuration$ResultConfiguration$EncryptionConfiguration
$Configuration$ResultConfiguration$EncryptionConfiguration$EncryptionOption
[1] "SSE_S3"
$Configuration$EnforceWorkGroupConfiguration
[1] FALSE
$Configuration$PublishCloudWatchMetricsEnabled
[1] FALSE
$Configuration$BytesScannedCutoffPerQuery
[1] 10000000
$Configuration$RequesterPaysEnabled
[1] FALSE
$Description
[1] "This is a demo work group update"
$CreationTime
2019-09-06 18:51:28.902000+01:00
Connect to Athena using work group:
con <- dbConnect(noctua::athena(),
work_group = "demo_work_group")
Delete work group:
delete_work_group(con, "demo_work_group")
Similar Projects
Python:
pyAthena
- A python wrapper of the python packageBoto3
using the sqlAlchemy framework: https://github.com/laughingman7743/PyAthena
R:
AWR.Athena
- A R wrapper of RJDBC for the AWS Athena’s JDBC drivers: https://github.com/nfultz/AWR.AthenaRAthena
- A R wrapper of the python packageBoto3
using DBI as the framework: https://github.com/DyfanJones/RAthenaawsathena
- rJava Interface to AWS Athena SDK https://github.com/hrbrmstr/awsathenametis
- Helpers for Accessing and Querying Amazon Athena using R, Including a lightweight RJDBC shim https://github.com/hrbrmstr/metismetisjars
- JARs formetis
https://github.com/hrbrmstr/metis-jarsmetis.tidy
- Access and Query Amazon Athena via the Tidyverse https://github.com/hrbrmstr/metis-tidy
Comparison:
noctua
is basically the same as RAthena
however it utilises the R AWS SDK paws
to achieve the same goal.