AzureKusto

R interface to Kusto, also known as Azure Data Explorer, a fast and highly scalable data exploration service.

Installation

You can install the development version from GitHub, via devtools::install_github("cloudyr/AzureKusto").

Example Usage

Kusto Endpoint Interface

Connect to a Kusto cluster by instantiating a kusto_query_endpoint object with the cluster URI, database name, and an AzureRMR::AzureToken object, which you can obtain via the get_kusto_token helper function.

library(AzureKusto)

Samples <- kusto_query_endpoint(server="https://help.kusto.windows.net",
    database="Samples",
    .azure_token=get_kusto_token(clustername="help", tenant="microsoft"))

# To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code FPD8GZPY9 to authenticate.
# Waiting for device code in browser...
# Press Esc/Ctrl + C to abort
# Authentication complete.

Now you can issue queries to the Kusto database with run_query and get the results back as a data.frame.

res <- run_query(Samples, "StormEvents | summarize EventCount = count() by State | order by State asc")

as_tibble(res)

## # A tibble: 67 x 2
##    State          EventCount
##    <chr>               <dbl>
##  1 ALABAMA              1315
##  2 ALASKA                257
##  3 AMERICAN SAMOA         16
##  4 ARIZONA               340
##  5 ARKANSAS             1028
##  6 ATLANTIC NORTH        188
##  7 ATLANTIC SOUTH        193
##  8 CALIFORNIA            898
##  9 COLORADO             1654
## 10 CONNECTICUT           148
## # ... with 57 more rows

dplyr Interface

The package also implements a dplyr-style interface for building a query upon a tbl_kusto object and then running it on the remote Kusto database and returning the result as a regular tibble object with collect().

library(dplyr)

StormEvents <- tbl_kusto(Samples, "StormEvents")

q <- StormEvents %>%
    group_by(State) %>%
    summarize(EventCount=n()) %>%
    arrange(State)

show_query(q)

# <KQL> database('Samples').StormEvents
# | summarize EventCount = count() by State
# | order by State asc

collect(q)

## # A tibble: 67 x 2
##    State          EventCount
##    <chr>               <dbl>
##  1 ALABAMA              1315
##  2 ALASKA                257
##  3 AMERICAN SAMOA         16
##  4 ARIZONA               340
##  5 ARKANSAS             1028
##  6 ATLANTIC NORTH        188
##  7 ATLANTIC SOUTH        193
##  8 CALIFORNIA            898
##  9 COLORADO             1654
## 10 CONNECTICUT           148
## # ... with 57 more rows

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