The goal of the RAthena package is to provide a DBI-compliant interface
to Amazon’s Athena (https://aws.amazon.com/athena/) using Boto3 SDK.
This allows for an efficient, easy setup connection to Athena using the
Boto3 SDK as a driver.
NOTE: Before using RAthena you must have an aws account or have
access to aws account with permissions allowing you to use Athena.
Before installing RAthena ensure that Python 3+ is installed onto your
machine: https://www.python.org/downloads/. To install Boto3 either it
can installed the pip command or using RAthena installation function:
pip install boto3
RAthena Method (after RAthena has been installed this method can be used)
RAthena::install_boto()To install RAthena you can get it from CRAN with:
install.packages("RAthena")Or to get the development version from Github with:
remotes::install_github("dyfanjones/rathena")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(RAthena::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')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.
RAthena 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(RAthena::athena(),
s3_staging_dir = 's3://path/to/query/bucket/')Connecting to Athena using profile name other than default.
library(DBI)
con <- dbConnect(RAthena::athena(),
profile_name = "your_profile",
s3_staging_dir = 's3://path/to/query/bucket/')library(RAthena)
get_session_token("YOUR_PROFILE_NAME",
serial_number='arn:aws:iam::123456789012:mfa/user',
token_code = "531602",
set_env = TRUE)
# Connect to Athena using temporary credentials
con <- dbConnect(athena(),
s3_staging_dir = 's3://path/to/query/bucket/')Another method in connecting to Athena is to use Amazon Resource Name (ARN) role.
Setting credentials in environmental variables:
library(RAthena)
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).
Connect to athena, and send a query and return results back to R.
library(DBI)
con <- dbConnect(RAthena::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')
res <- dbExecute(con, "SELECT * FROM one_row")
dbFetch(res)
dbClearResult(res)To retrieve query in 1 step.
dbGetQuery(con, "SELECT * FROM one_row")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)RAthena has 2 extra function to return extra information around Athena
tables: dbGetParitiions and dbShow
dbGetPartitions will return all the partitions (returns data.frame):
RAthena::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):
RAthena::dbShow(con, "impressions")library(DBI)
con <- dbConnect(RAthena::athena(),
s3_staging_dir = 's3://path/to/query/bucket/')RAthena 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 RAthena 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
Creating a connection to Athena and query and already existing table
iris that was created in previous example.
library(DBI)
library(dplyr)
con <- dbConnect(RAthena::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 1.9.210 [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 1.9.210 [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(RAthena::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 1.9.210 [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 1.9.210 [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(RAthena::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)Creating work group:
library(RAthena)
library(DBI)
con <- dbConnect(RAthena::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(RAthena::athena(),
profile_name = "your_profile",
work_group = "demo_work_group")Delete work group:
delete_work_group(con, "demo_work_group")pyAthena- A python wrapper of the python packageBoto3using the sqlAlchemy framework: https://github.com/laughingman7743/PyAthenapyAthenaJDBC- A python interface into AWS Athena’s JDBC drivers: https://github.com/laughingman7743/PyAthenaJDBC
AWR.Athena- A R wrapper of RJDBC for the AWS Athena’s JDBC drivers: https://github.com/nfultz/AWR.Athenanoctua- A R wrapper of the R AWS SDKpawsto develop a DBI interface https://github.com/DyfanJones/noctuaawsathena- 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 formetishttps://github.com/hrbrmstr/metis-jarsmetis.tidy- Access and Query Amazon Athena via the Tidyverse https://github.com/hrbrmstr/metis-tidy
awsathena and metis family of packages are currently used in production every day to analyze petabytes of internet scan and honeypot data.
The reason why RAthena stands slightly apart from AWR.Athena is that
AWR.Athena uses the Athena JDBC drivers and RAthena uses the Python
AWS SDK Boto3. The ultimate goal is to provide an extra method for R
users to interface with AWS Athena. As pyAthena is the most similar
project, this project has used an appropriate name to reflect this …
RAthena.