10 KiB
AzureKusto 
R interface to Kusto, also known as Azure Data Explorer, a fast and highly scalable data exploration service.
Installation
AzureKusto is available on CRAN.
options(repos="https://cloud.r-project.org")
install.packages("AzureKusto")
You can install the development version from GitHub. The primary repo is https://github.com/Azure/AzureKusto; please submit issues and pull requests there. AzureKusto is also mirrored at the Cloudyr organisation, at https://github.com/cloudyr/AzureKusto.
devtools::install_github("Azure/AzureKusto")
Example usage
Kusto endpoint interface
Connect to a Kusto cluster by instantiating a kusto_database_endpoint object with the cluster URI and database name.
library(AzureKusto)
Samples <- kusto_database_endpoint(server="https://help.kusto.windows.net", database="Samples")
# (New in 1.1.0) Some other ways to call this that also work:
# Samples <- kusto_database_endpoint(server="help", database="Samples")
# Samples <- kusto_database_endpoint(cluster="help", database="Samples")
# No app ID supplied; using KustoClient app
# Waiting for authentication in browser...
# Press Esc/Ctrl + C to abort
# VSCode WebView only supports showing local http content.
# Opening in external browser...
# Browsing https://login.microsoftonline.com/common/oauth2/v2.0/authorize...
# 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")
head(res)
## State EventCount
## 1 ALABAMA 1315
## 2 ALASKA 257
## 3 AMERICAN SAMOA 16
## 4 ARIZONA 340
## 5 ARKANSAS 1028
## 6 ATLANTIC NORTH 188
run_query() also supports query parameters. Pass your parameters as additional
keyword arguments and they will be escaped and interpolated into the query
string.
res <- run_query(Samples, "MyFunction(lim)", lim=10L)
Command statements work much the same way, except that they do not accept parameters.
res <- run_query(Samples, ".show tables")
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
(New in 1.1.0) The $ operator can be used to access fields in dynamic columns:
q <- StormEvents %>%
slice_sample(10) %>%
mutate(Description = as.character(StormSummary$Details$Description)) %>%
select(EventId, Description)
show_query(q)
# <KQL> cluster('https://help.kusto.windows.net').database('Samples').['StormEvents']
# | sample 10
# | extend ['Description'] = tostring(['StormSummary'] . ['Details'] . ['Description'])
# | project ['EventId'], ['Description']
# # A tibble: 10 × 2
# EventId Description
# <int> <chr>
# 1 61032 A waterspout formed in the Atlantic southeast of Melbourne Beach and briefly moved toward shore.
# 2 60904 As much as 9 inches of rain fell in a 24-hour period across parts of coastal Volusia County.
# 3 60913 A tornado touched down in the Town of Eustis at the northern end of West Crooked Lake. The tornado quickly intensified to EF1 strength as it moved north northwest through Eustis. The track was just under two miles long…
# 4 64588 The county dispatch reported several trees were blown down along Quincey Batten Loop near State Road 206. The cost of tree removal was estimated.
# 5 68796 Numerous large trees were blown down with some down on power lines. Damage occurred in eastern Adams county.
# 6 68814 This tornado began as a small, narrow path of minor damage, including a porch being blown off a house. It reached its maximum intensity as it crossed highway 29. Here, a brick home had all of its roof structure blown o…
# 7 68834 Several trees and power lines were blown down along Zetus Road in the Zetus Community. A few of those trees were down on a mobile home which caused significant damage.
# 8 68846 A swath of penny to quarter sized hail fell from just east of French Camp to about 6 miles north of Weir.
# 9 73241 The heavy rain from an active monsoonal trough that had been nearly stationary just to the south of the islands caused widespread flooding across Tutuila. Flash Flooding was reported from the Malaeimi Valley to the Ba…
# 10 64725 State Route 8 and Rock Run Road were flooded and impassable
tbl_kusto also accepts query parameters, in case the Kusto source table is a
parameterized function:
MyFunctionDate <- tbl_kusto(Samples, "MyFunctionDate(dt)", dt=as.Date("2019-01-01"))
MyFunctionDate %>%
select(StartTime, EndTime, EpisodeId, EventId, State) %>%
head() %>%
collect()
## # A tibble: 6 x 5
## StartTime EndTime EpisodeId EventId State
## <dttm> <dttm> <int> <int> <chr>
## 1 2007-09-29 08:11:00 2007-09-29 08:11:00 11091 61032 ATLANTIC SOUTH
## 2 2007-09-18 20:00:00 2007-09-19 18:00:00 11074 60904 FLORIDA
## 3 2007-09-20 21:57:00 2007-09-20 22:05:00 11078 60913 FLORIDA
## 4 2007-12-30 16:00:00 2007-12-30 16:05:00 11749 64588 GEORGIA
## 5 2007-12-20 07:50:00 2007-12-20 07:53:00 12554 68796 MISSISSIPPI
## 6 2007-12-20 10:32:00 2007-12-20 10:36:00 12554 68814 MISSISSIPPI
Exporting to storage
(New in 1.1.0) The function export() enables you to export a query result to
Azure Storage in one step.
export(
database = Samples,
storage_uri = "https://mystorage.blob.core.windows.net/StormEvents",
query = "StormEvents | summarize EventCount = count() by State | order by State",
name_prefix = "events",
format = "parquet"
)
# Path NumRecords SizeInBytes
# 1 https://mystorage.blob.core.windows.net/StormEvents/events/events_1.snappy.parquet 67 1511
library(dplyr)
StormEvents <- tbl_kusto(Samples, "StormEvents")
q <- StormEvents %>%
group_by(State) %>%
summarize(EventCount=n()) %>%
arrange(State) %>%
export("https://mystorage.blob.core.windows.net/StormEvents")
# # A tibble: 1 × 3
# Path NumRecords SizeInBytes
# <chr> <dbl> <dbl>
# 1 https://mystorage.blob.core.windows.net/StormEvents/export/export_1.snappy.parquet 50 59284
DBI interface
AzureKusto implements a subset of the DBI specification for interacting with databases. It should be noted that Kusto is quite different to the SQL databases that DBI targets, which affects the behaviour of certain DBI methods and renders other moot.
library(DBI)
# connect to the server: basically a wrapper for kusto_database_endpoint()
Samples <- dbConnect(AzureKusto(),
server="https://help.kusto.windows.net",
database="Samples")
dbListTables(Samples)
## [1] "StormEvents" "demo_make_series1" "demo_series2"
## [4] "demo_series3" "demo_many_series1"
dbExistsTable(Samples, "StormEvents")
##[1] TRUE
dbGetQuery(Samples, "StormEvents | summarize ct = count()")
## ct
## 1 59066
Azure Resource Manager interface
On the admin side, AzureKusto extends the framework supplied by the AzureRMR to support Kusto. Methods are provided to create and delete clusters and databases, and manage database principals.
# create a new Kusto cluster
az <- AzureRMR::get_azure_login()
ku <- az$
get_subscription("sub_id")$
get_resource_group("rgname")$
create_kusto_cluster("mykustocluster")
# create a new database
db1 <- ku$create_database("database1")
# add a user
db1$add_principals("myusername", role="User", fqn="aaduser=username@mydomain")
