AzureKusto is the R interface to Azure Data Explorer (internally codenamed “Kusto”), a fast, fully managed data analytics service from Microsoft.
AzureKusto provides an interface (including DBI compliant methods) for connecting to Kusto clusters and submitting Kusto Query Language (KQL) statements, as well as a dbplyr style backend that translates dplyr queries into KQL statements.
library(AzureKusto)
## The first time you import AzureKusto, you'll be asked if you'd like to create a directory to cache OAuth2 tokens.
## Connect to an AzureKusto database with (default) device code authentication:
Samples <- kusto_database_endpoint(server="https://help.kusto.windows.net", database="Samples")
## To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code ######### to authenticate.
## Waiting for device code in browser...
## Press Esc/Ctrl + C to abort
## Authentication complete.Now you can issue KQL queries to the Kusto database with run_query() and get the results back as a data.frame object.
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 188run_query() also supports query parameters, to allow you to call parameterized Kusto functions. Simply 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)
head(res)
## StartTime EndTime EpisodeId EventId State
## 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 MISSISSIPPIrun_query() can also handle command statements, which begin with a ‘.’ character. Command statements do not accept parameters and cannot be combined together with query statements in the same request.
Command statements return a list where the first element is the table returned by the command (if any) and the other elements contain command metadata.
res <- run_query(Samples, ".show tables | count")
res[[1]]
## Count
## 1 5The 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 rowstbl_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 AzureKusto implements a subset of the DBI specification for interfacing with databases in R.
The following methods are supported:
dbConnect, dbDisconnect, dbCanConnectdbExistsTable, dbCreateTable, dbRemoveTable, dbReadTable, dbWriteTabledbGetQuery, dbSendQuery, dbFetch, dbSendStatement, dbExecute, dbListFields, dbColumnInfoAzure Data Explorer is quite different to the SQL databases that DBI targets, which affects the behaviour of certain DBI methods and renders other moot.
dbConnect simply wraps a database endpoint object, created with [kusto_database_endpoint]. Similarly, dbDisconnect always returns TRUE. dbCanConnect attempts to check if querying the database will succeed, but this may not be accurate.dbCreateTable(*, temporary=TRUE) will throw an error.dbSendQuery and dbSendStatement will wait for the query to execute, rather than returning immediately. The object returned contains the full result of the query, which dbFetch extracts.library(DBI)
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