Tools to Transform and Query Data with 'Apache' 'Drill'

'Apache Drill' is a low-latency distributed query engine designed to enable data exploration and 'analytics' on both relational and non-relational 'datastores', scaling to petabytes of data. Methods are provided that enable working with 'Apache' 'Drill' instances via the 'REST' 'API', 'JDBC' interface (optional), 'DBI' 'methods' and using 'dplyr'/'dbplyr' idioms.


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sergeant : Tools to Transform and Query Data with 'Apache' 'Drill'

Drill + sergeant is (IMO) a nice alternative to Spark + sparklyr if you don't need the ML components of Spark (i.e. just need to query "big data" sources, need to interface with parquet, need to combine disparate data source types — json, csv, parquet, rdbms - for aggregation, etc). Drill also has support for spatial queries.

I find writing SQL queries to parquet files with Drill on a local linux or macOS workstation to be more performant than doing the data ingestion work with R (especially for large or disperate data sets). I also work with many tiny JSON files on a daily basis and Drill makes it much easier to do so. YMMV.

You can download Drill from https://drill.apache.org/download/ (use "Direct File Download"). I use /usr/local/drill as the install directory. drill-embedded is a super-easy way to get started playing with Drill on a single workstation and most of my workflows can get by using Drill this way. If there is sufficient desire for an automated downloader and a way to start the drill-embedded server from within R, please file an issue.

There are a few convenience wrappers for various informational SQL queries (like drill_version()). Please file an PR if you add more.

The package has been written with retrieval of rectangular data sources in mind. If you need/want a version of drill_query() that will enable returning of non-rectangular data (which is possible with Drill) then please file an issue.

Some of the more "controlling vs data ops" REST API functions aren't implemented. Please file a PR if you need those.

Finally, I run most of this locally and at home, so it's all been coded with no authentication or encryption in mind. If you want/need support for that, please file an issue. If there is demand for this, it will change the R API a bit (I've already thought out what to do but have no need for it right now).

The following functions are implemented:

DBI

  • A "just enough" feature complete R DBI driver has been implemented using the Drill REST API, mostly to facilitate the dplyr interface. Use the RJDBC driver interface if you need more DBI functionality.
  • This also means that SQL functions unique to Drill have also been "implemented" (i.e. made accessible to the dplyr interface). If you have custom Drill SQL functions that need to be implemented please file an issue on GitHub. Many should work without it, but some may require a custom interface.

RJDBC

  • drill_jdbc: Connect to Drill using JDBC, enabling use of said idioms. See RJDBC for more info.
  • NOTE: The DRILL JDBC driver fully-qualified path must be placed in the DRILL_JDBC_JAR environment variable. This is best done via ~/.Renviron for interactive work. i.e. DRILL_JDBC_JAR=/usr/local/drill/jars/drill-jdbc-all-1.10.0.jar

dplyr:

  • src_drill: Connect to Drill (using dplyr) + supporting functions

See dplyr for the dplyr operations (light testing shows they work in basic SQL use-cases but Drill's SQL engine has issues with more complex queries).

Drill APIs:

  • drill_connection: Setup parameters for a Drill server/cluster connection
  • drill_active: Test whether Drill HTTP REST API server is up
  • drill_cancel: Cancel the query that has the given queryid
  • drill_jdbc: Connect to Drill using JDBC
  • drill_metrics: Get the current memory metrics
  • drill_options: List the name, default, and data type of the system and session options
  • drill_profile: Get the profile of the query that has the given query id
  • drill_profiles: Get the profiles of running and completed queries
  • drill_query: Submit a query and return results
  • drill_set: Set Drill SYSTEM or SESSION options
  • drill_settings_reset: Changes (optionally, all) session settings back to system defaults
  • drill_show_files: Show files in a file system schema.
  • drill_show_schemas: Returns a list of available schemas.
  • drill_stats: Get Drillbit information, such as ports numbers
  • drill_status: Get the status of Drill
  • drill_storage: Get the list of storage plugin names and configurations
  • drill_system_reset: Changes (optionally, all) system settings back to system defaults
  • drill_threads: Get information about threads
  • drill_uplift: Turn a columnar query results into a type-converted tbl
  • drill_use: Change to a particular schema.
  • drill_version: Identify the version of Drill running
devtools::install_github("hrbrmstr/sergeant")

Experimental dplyr interface

library(sergeant)
ds <- src_drill("localhost")  # use localhost if running standalone on same system otherwise the host or IP of your Drill server
ds
#> src:  DrillConnection
#> tbls: INFORMATION_SCHEMA, cp.default, dfs.d, dfs.default, dfs.h, dfs.natexp, dfs.p, dfs.root, dfs.tmp, sys
db <- tbl(ds, "cp.`employee.json`") 
 
# without `collect()`:
count(db, gender, marital_status)
#> # Source:   lazy query [?? x 3]
#> # Database: DrillConnection
#> # Groups:   gender
#>   marital_status gender     n
#>            <chr>  <chr> <int>
#> 1              S      F   297
#> 2              M      M   278
#> 3              S      M   276
#> 4              M      F   304
 
# ^^ gets translated to:
# SELECT *
# FROM (SELECT  gender ,  marital_status , COUNT(*) AS  n 
#       FROM  cp.`employee.json` 
#       GROUP BY  gender ,  marital_status )  govketbhqb 
# LIMIT 1000
 
count(db, gender, marital_status) %>% collect()
#> # A tibble: 4 x 3
#> # Groups:   gender [2]
#>   marital_status gender     n
#> *          <chr>  <chr> <int>
#> 1              S      F   297
#> 2              M      M   278
#> 3              S      M   276
#> 4              M      F   304
 
# ^^ gets translated to:
# SELECT  gender ,  marital_status , COUNT(*) AS  n 
# FROM  cp.`employee.json` 
# GROUP BY  gender ,  marital_status 
 
group_by(db, position_title) %>% 
  count(gender) -> tmp2
 
group_by(db, position_title) %>% 
  count(gender) %>% 
  ungroup() %>% 
  mutate(full_desc=ifelse(gender=="F", "Female", "Male")) %>% 
  collect() %>% 
  select(Title=position_title, Gender=full_desc, Count=n)
#> # A tibble: 30 x 3
#>                     Title Gender Count
#>  *                  <chr>  <chr> <int>
#>  1              President Female     1
#>  2     VP Country Manager   Male     3
#>  3     VP Country Manager Female     3
#>  4 VP Information Systems Female     1
#>  5     VP Human Resources Female     1
#>  6          Store Manager Female    13
#>  7             VP Finance   Male     1
#>  8          Store Manager   Male    11
#>  9           HQ Marketing Female     2
#> 10 HQ Information Systems Female     4
#> # ... with 20 more rows
 
# ^^ gets translated to:
# SELECT  position_title ,  gender ,  n ,
#         CASE WHEN ( gender  = 'F') THEN ('Female') ELSE ('Male') END AS  full_desc 
# FROM (SELECT  position_title ,  gender , COUNT(*) AS  n 
#       FROM  cp.`employee.json` 
#       GROUP BY  position_title ,  gender )  dcyuypuypb 
 
arrange(db, desc(employee_id)) %>% print(n=20)
#> # Source:     table<cp.`employee.json`> [?? x 16]
#> # Database:   DrillConnection
#> # Ordered by: desc(employee_id)
#>    store_id gender department_id birth_date supervisor_id  last_name          position_title  hire_date
#>       <int>  <chr>         <int>     <date>         <int>      <chr>                   <chr>     <dttm>
#>  1       18      F            18 1914-02-02          1140      Stand Store Temporary Stocker 1998-01-01
#>  2       18      M            18 1914-02-02          1140    Burnham Store Temporary Stocker 1998-01-01
#>  3       18      F            18 1914-02-02          1139  Doolittle Store Temporary Stocker 1998-01-01
#>  4       18      M            18 1914-02-02          1139     Pirnie Store Temporary Stocker 1998-01-01
#>  5       18      M            17 1914-02-02          1140     Younce Store Permanent Stocker 1998-01-01
#>  6       18      F            17 1914-02-02          1140    Biltoft Store Permanent Stocker 1998-01-01
#>  7       18      M            17 1914-02-02          1139   Detwiler Store Permanent Stocker 1998-01-01
#>  8       18      F            17 1914-02-02          1139     Ciruli Store Permanent Stocker 1998-01-01
#>  9       18      F            16 1914-02-02          1140     Bishop Store Temporary Checker 1998-01-01
#> 10       18      F            16 1914-02-02          1140  Cutwright Store Temporary Checker 1998-01-01
#> 11       18      F            16 1914-02-02          1139   Anderson Store Temporary Checker 1998-01-01
#> 12       18      F            16 1914-02-02          1139  Swartwood Store Temporary Checker 1998-01-01
#> 13       18      M            15 1914-02-02          1140 Curtsinger Store Permanent Checker 1998-01-01
#> 14       18      F            15 1914-02-02          1140      Quick Store Permanent Checker 1998-01-01
#> 15       18      M            15 1914-02-02          1139      Souza Store Permanent Checker 1998-01-01
#> 16       18      M            15 1914-02-02          1139   Compagno Store Permanent Checker 1998-01-01
#> 17       18      M            11 1961-09-24          1139  Jaramillo  Store Shift Supervisor 1998-01-01
#> 18       18      M            11 1972-05-12            17     Belsey Store Assistant Manager 1998-01-01
#> 19       12      M            18 1914-02-02          1069    Eichorn Store Temporary Stocker 1998-01-01
#> 20       12      F            18 1914-02-02          1069  Geiermann Store Temporary Stocker 1998-01-01
#> # ... with more rows, and 8 more variables: management_role <chr>, salary <dbl>, marital_status <chr>, full_name <chr>,
#> #   employee_id <int>, education_level <chr>, first_name <chr>, position_id <int>
 
# ^^ gets translated to:
# SELECT *
# FROM (SELECT *
#       FROM  cp.`employee.json` 
#       ORDER BY  employee_id  DESC)  lvpxoaejbc 
# LIMIT 5
 
mutate(db, position_title=tolower(position_title)) %>%
  mutate(salary=as.numeric(salary)) %>% 
  mutate(gender=ifelse(gender=="F", "Female", "Male")) %>%
  mutate(marital_status=ifelse(marital_status=="S", "Single", "Married")) %>% 
  group_by(supervisor_id) %>% 
  summarise(underlings_count=n()) %>% 
  collect()
#> # A tibble: 112 x 2
#>    supervisor_id underlings_count
#>  *         <int>            <int>
#>  1             0                1
#>  2             1                7
#>  3             5                9
#>  4             4                2
#>  5             2                3
#>  6            20                2
#>  7            21                4
#>  8            22                7
#>  9             6                4
#> 10            36                2
#> # ... with 102 more rows
 
# ^^ gets translated to:
# SELECT  supervisor_id , COUNT(*) AS  underlings_count 
# FROM (SELECT  employee_id ,  full_name ,  first_name ,  last_name ,  position_id ,  position_title ,  store_id ,  department_id ,  birth_date ,  hire_date ,  salary ,  supervisor_id ,  education_level ,  gender ,  management_role , CASE WHEN ( marital_status  = 'S') THEN ('Single') ELSE ('Married') END AS  marital_status 
#       FROM (SELECT  employee_id ,  full_name ,  first_name ,  last_name ,  position_id ,  position_title ,  store_id ,  department_id ,  birth_date ,  hire_date ,  salary ,  supervisor_id ,  education_level ,  marital_status ,  management_role , CASE WHEN ( gender  = 'F') THEN ('Female') ELSE ('Male') END AS  gender 
#             FROM (SELECT  employee_id ,  full_name ,  first_name ,  last_name ,  position_id ,  position_title ,  store_id ,  department_id ,  birth_date ,  hire_date ,  supervisor_id ,  education_level ,  marital_status ,  gender ,  management_role , CAST( salary  AS DOUBLE) AS  salary 
#                   FROM (SELECT  employee_id ,  full_name ,  first_name ,  last_name ,  position_id ,  store_id ,  department_id ,  birth_date ,  hire_date ,  salary ,  supervisor_id ,  education_level ,  marital_status ,  gender ,  management_role , LOWER( position_title ) AS  position_title 
#                         FROM  cp.`employee.json` )  cnjsqxeick )  bnbnjrubna )  wavfmhkczv )  zaxeyyicxo 
# GROUP BY  supervisor_id 

Usage

library(sergeant)
 
# current verison
packageVersion("sergeant")
#> [1] '0.5.2'
dc <- drill_connection("localhost") 
drill_active(dc)
#> [1] TRUE
 
drill_version(dc)
#> [1] "1.10.0"
 
drill_storage(dc)$name
#> [1] "cp"    "dfs"   "hbase" "hive"  "kudu"  "mongo" "s3"

Working with the built-in JSON data sets:

drill_query(dc, "SELECT * FROM cp.`employee.json` limit 100")
#> Parsed with column specification:
#> cols(
#>   store_id = col_integer(),
#>   gender = col_character(),
#>   department_id = col_integer(),
#>   birth_date = col_date(format = ""),
#>   supervisor_id = col_integer(),
#>   last_name = col_character(),
#>   position_title = col_character(),
#>   hire_date = col_datetime(format = ""),
#>   management_role = col_character(),
#>   salary = col_double(),
#>   marital_status = col_character(),
#>   full_name = col_character(),
#>   employee_id = col_integer(),
#>   education_level = col_character(),
#>   first_name = col_character(),
#>   position_id = col_integer()
#> )
#> # A tibble: 100 x 16
#>    store_id gender department_id birth_date supervisor_id last_name         position_title  hire_date   management_role
#>  *    <int>  <chr>         <int>     <date>         <int>     <chr>                  <chr>     <dttm>             <chr>
#>  1        0      F             1 1961-08-26             0    Nowmer              President 1994-12-01 Senior Management
#>  2        0      M             1 1915-07-03             1   Whelply     VP Country Manager 1994-12-01 Senior Management
#>  3        0      M             1 1969-06-20             1    Spence     VP Country Manager 1998-01-01 Senior Management
#>  4        0      F             1 1951-05-10             1 Gutierrez     VP Country Manager 1998-01-01 Senior Management
#>  5        0      F             2 1942-10-08             1   Damstra VP Information Systems 1994-12-01 Senior Management
#>  6        0      F             3 1949-03-27             1  Kanagaki     VP Human Resources 1994-12-01 Senior Management
#>  7        9      F            11 1922-08-10             5   Brunner          Store Manager 1998-01-01  Store Management
#>  8       21      F            11 1979-06-23             5  Blumberg          Store Manager 1998-01-01  Store Management
#>  9        0      M             5 1949-08-26             1     Stanz             VP Finance 1994-12-01 Senior Management
#> 10        1      M            11 1967-06-20             5  Murraiin          Store Manager 1998-01-01  Store Management
#> # ... with 90 more rows, and 7 more variables: salary <dbl>, marital_status <chr>, full_name <chr>, employee_id <int>,
#> #   education_level <chr>, first_name <chr>, position_id <int>
 
drill_query(dc, "SELECT COUNT(gender) AS gender FROM cp.`employee.json` GROUP BY gender")
#> Parsed with column specification:
#> cols(
#>   gender = col_integer()
#> )
#> # A tibble: 2 x 1
#>   gender
#> *  <int>
#> 1    601
#> 2    554
 
drill_options(dc)
#> # A tibble: 113 x 4
#>                                              name value   type    kind
#>  *                                          <chr> <chr>  <chr>   <chr>
#>  1                 planner.enable_hash_single_key  TRUE SYSTEM BOOLEAN
#>  2      store.parquet.reader.pagereader.queuesize     2 SYSTEM    LONG
#>  3             planner.enable_limit0_optimization FALSE SYSTEM BOOLEAN
#>  4              store.json.read_numbers_as_double FALSE SYSTEM BOOLEAN
#>  5                planner.enable_constant_folding  TRUE SYSTEM BOOLEAN
#>  6                      store.json.extended_types FALSE SYSTEM BOOLEAN
#>  7   planner.memory.non_blocking_operators_memory    64 SYSTEM    LONG
#>  8                  planner.enable_multiphase_agg  TRUE SYSTEM BOOLEAN
#>  9                  exec.query_profile.debug_mode FALSE SYSTEM BOOLEAN
#> 10 planner.filter.max_selectivity_estimate_factor     1 SYSTEM  DOUBLE
#> # ... with 103 more rows
 
drill_options(dc, "json")
#> # A tibble: 7 x 4
#>                                                    name value   type    kind
#>                                                   <chr> <chr>  <chr>   <chr>
#> 1                     store.json.read_numbers_as_double FALSE SYSTEM BOOLEAN
#> 2                             store.json.extended_types FALSE SYSTEM BOOLEAN
#> 3                              store.json.writer.uglify FALSE SYSTEM BOOLEAN
#> 4                store.json.reader.skip_invalid_records FALSE SYSTEM BOOLEAN
#> 5 store.json.reader.print_skipped_invalid_record_number FALSE SYSTEM BOOLEAN
#> 6                              store.json.all_text_mode FALSE SYSTEM BOOLEAN
#> 7                    store.json.writer.skip_null_fields  TRUE SYSTEM BOOLEAN

Working with parquet files

drill_query(dc, "SELECT * FROM dfs.`/usr/local/drill/sample-data/nation.parquet` LIMIT 5")
#> Parsed with column specification:
#> cols(
#>   N_COMMENT = col_character(),
#>   N_NAME = col_character(),
#>   N_NATIONKEY = col_integer(),
#>   N_REGIONKEY = col_integer()
#> )
#> # A tibble: 5 x 4
#>              N_COMMENT    N_NAME N_NATIONKEY N_REGIONKEY
#> *                <chr>     <chr>       <int>       <int>
#> 1  haggle. carefully f   ALGERIA           0           0
#> 2 al foxes promise sly ARGENTINA           1           1
#> 3 y alongside of the p    BRAZIL           2           1
#> 4 eas hang ironic, sil    CANADA           3           1
#> 5 y above the carefull     EGYPT           4           4

Including multiple parquet files in different directories (note the wildcard support):

drill_query(dc, "SELECT * FROM dfs.`/usr/local/drill/sample-data/nations*/nations*.parquet` LIMIT 5")
#> Parsed with column specification:
#> cols(
#>   N_COMMENT = col_character(),
#>   N_NAME = col_character(),
#>   N_NATIONKEY = col_integer(),
#>   N_REGIONKEY = col_integer(),
#>   dir0 = col_character()
#> )
#> # A tibble: 5 x 5
#>              N_COMMENT    N_NAME N_NATIONKEY N_REGIONKEY      dir0
#> *                <chr>     <chr>       <int>       <int>     <chr>
#> 1  haggle. carefully f   ALGERIA           0           0 nationsSF
#> 2 al foxes promise sly ARGENTINA           1           1 nationsSF
#> 3 y alongside of the p    BRAZIL           2           1 nationsSF
#> 4 eas hang ironic, sil    CANADA           3           1 nationsSF
#> 5 y above the carefull     EGYPT           4           4 nationsSF

A preview of the built-in support for spatial ops

Via: https://github.com/k255/drill-gis

A common use case is to select data within boundary of given polygon:

drill_query(dc, "
select columns[2] as city, columns[4] as lon, columns[3] as lat
    from cp.`sample-data/CA-cities.csv`
    where
        ST_Within(
            ST_Point(columns[4], columns[3]),
            ST_GeomFromText(
                'POLYGON((-121.95 37.28, -121.94 37.35, -121.84 37.35, -121.84 37.28, -121.95 37.28))'
                )
            )
")
#> Parsed with column specification:
#> cols(
#>   city = col_character(),
#>   lon = col_double(),
#>   lat = col_double()
#> )
#> # A tibble: 7 x 3
#>          city       lon      lat
#> *       <chr>     <dbl>    <dbl>
#> 1     Burbank -121.9316 37.32328
#> 2    San Jose -121.8950 37.33939
#> 3        Lick -121.8458 37.28716
#> 4 Willow Glen -121.8897 37.30855
#> 5 Buena Vista -121.9166 37.32133
#> 6    Parkmoor -121.9308 37.32105
#> 7   Fruitdale -121.9327 37.31086

JDBC

library(RJDBC)
#> Loading required package: rJava
 
# Use this if connecting to a cluster with zookeeper
# con <- drill_jdbc("drill-node:2181", "drillbits1") 
 
# Use the following if running drill-embedded
con <- drill_jdbc("localhost:31010", use_zk=FALSE)
#> Using [jdbc:drill:drillbit=bigd:31010]...
drill_query(con, "SELECT * FROM cp.`employee.json`")
#> # A tibble: 1,155 x 16
#>    employee_id         full_name first_name last_name position_id         position_title store_id department_id
#>  *       <dbl>             <chr>      <chr>     <chr>       <dbl>                  <chr>    <dbl>         <dbl>
#>  1           1      Sheri Nowmer      Sheri    Nowmer           1              President        0             1
#>  2           2   Derrick Whelply    Derrick   Whelply           2     VP Country Manager        0             1
#>  3           4    Michael Spence    Michael    Spence           2     VP Country Manager        0             1
#>  4           5    Maya Gutierrez       Maya Gutierrez           2     VP Country Manager        0             1
#>  5           6   Roberta Damstra    Roberta   Damstra           3 VP Information Systems        0             2
#>  6           7  Rebecca Kanagaki    Rebecca  Kanagaki           4     VP Human Resources        0             3
#>  7           8       Kim Brunner        Kim   Brunner          11          Store Manager        9            11
#>  8           9   Brenda Blumberg     Brenda  Blumberg          11          Store Manager       21            11
#>  9          10      Darren Stanz     Darren     Stanz           5             VP Finance        0             5
#> 10          11 Jonathan Murraiin   Jonathan  Murraiin          11          Store Manager        1            11
#> # ... with 1,145 more rows, and 8 more variables: birth_date <chr>, hire_date <chr>, salary <dbl>, supervisor_id <dbl>,
#> #   education_level <chr>, marital_status <chr>, gender <chr>, management_role <chr>
 
# but it can work via JDBC function calls, too
dbGetQuery(con, "SELECT * FROM cp.`employee.json`") %>% 
  tibble::as_tibble()
#> # A tibble: 1,155 x 16
#>    employee_id         full_name first_name last_name position_id         position_title store_id department_id
#>  *       <dbl>             <chr>      <chr>     <chr>       <dbl>                  <chr>    <dbl>         <dbl>
#>  1           1      Sheri Nowmer      Sheri    Nowmer           1              President        0             1
#>  2           2   Derrick Whelply    Derrick   Whelply           2     VP Country Manager        0             1
#>  3           4    Michael Spence    Michael    Spence           2     VP Country Manager        0             1
#>  4           5    Maya Gutierrez       Maya Gutierrez           2     VP Country Manager        0             1
#>  5           6   Roberta Damstra    Roberta   Damstra           3 VP Information Systems        0             2
#>  6           7  Rebecca Kanagaki    Rebecca  Kanagaki           4     VP Human Resources        0             3
#>  7           8       Kim Brunner        Kim   Brunner          11          Store Manager        9            11
#>  8           9   Brenda Blumberg     Brenda  Blumberg          11          Store Manager       21            11
#>  9          10      Darren Stanz     Darren     Stanz           5             VP Finance        0             5
#> 10          11 Jonathan Murraiin   Jonathan  Murraiin          11          Store Manager        1            11
#> # ... with 1,145 more rows, and 8 more variables: birth_date <chr>, hire_date <chr>, salary <dbl>, supervisor_id <dbl>,
#> #   education_level <chr>, marital_status <chr>, gender <chr>, management_role <chr>

Test Results

library(sergeant)
library(testthat)
#> 
#> Attaching package: 'testthat'
#> The following object is masked from 'package:dplyr':
#> 
#>     matches
 
date()
#> [1] "Mon Jul 17 12:35:05 2017"
 
devtools::test()
#> Loading sergeant
#> Testing sergeant
#> dplyr: ...
#> rest: ................
#> 
#> DONE ===================================================================================================================

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

News

sergeant 0.5.0

  • make rJava & RJDBC optional (WIP)

sergeant 0.4.0

  • Getting ready for new dplyr (thx to Edward Visel)
  • Cleaned up roxygen docs so that src_drill is exported now.

sergeant 0.3.2

  • Finally got quoting done. I thought I had before but I guess I hadn't.
  • Added documnentation for built-in and custom Drill function that are supported.

sergeant 0.3.1.9000

  • fixed src_drill() example
  • JDBC driver still in github repo but no longer included in pkg builds. See README.md or drill_jdbc() help for more information on using the JDBC driver with sergeant.

sergeant 0.3.0.9000

  • New DBI interface (to the REST API)
  • dplyr interface now uses the DBI interace to the REST API
  • CRAN checks pass besides size (removing JDBC driver in next dev iteration)

sergeant 0.2.1.9000

sergeant 0.2.0.9000

  • experimental alpha dplyr driver

sergeant 0.1.2.9000

  • can pass RJDBC connections made with drill_jdbc() to drill_query()
  • finally enaled nodes parameter to be a multi-element character vector as it said in the function description

sergeant 0.1.2.9000

  • support embedded drill JDBC connection

sergeant 0.1.1.9000

  • tweaked drill_query() and drill_version()

sergeant 0.1.0.9000

  • Added JDBC connector and included JDBC driver in the package (for now)
  • Changed idiom to piping in a connection object
  • Added a NEWS.md file to track changes to the package.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("sergeant")

0.5.2 by Bob Rudis, a year ago


https://github.com/hrbrmstr/sergeant


Report a bug at https://github.com/hrbrmstr/sergeant/issues


Browse source code at https://github.com/cran/sergeant


Authors: Bob Rudis [aut, cre], Edward Visel [ctb]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports httr, jsonlite, htmltools, readr, purrr, scales, utils, methods

Depends on DBI, dplyr, dbplyr

Suggests RJDBC, rJava, testthat, covr


See at CRAN