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

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💂 sergeant

Tools to Transform and Query Data with ‘Apache’ ‘Drill’

** IMPORTANT **

Version 0.7.0 (a.k.a. the main branch) splits off the JDBC interface into a separate package sergeant.caffeinated (GitLab; GitHub).

I# Description

Drill + sergeant is (IMO) a streamlined 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.

Using Drill SQL queries that reference parquet files on a local linux or macOS workstation can often be more performant than doing the same data ingestion & wrangling work with R (especially for large or disperate data sets). Drill can often help further streamline workflows that involve wrangling many tiny JSON files on a daily basis.

Drill can be obtained from https://drill.apache.org/download/ (use “Direct File Download”). Drill can also be installed via Docker. For local installs on Unix-like systems, a common/suggestion location for the Drill directory is /usr/local/drill as the install directory.

Drill embedded (started using the $DRILL_BASE_DIR/bin/drill-embedded script) is a super-easy way to get started playing with Drill on a single workstation and most of many workflows can “get by” using Drill this way.

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

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

The following functions are implemented:

DBI (REST)

  • 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.

dplyr: (REST)

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

Note that a number of Drill SQL functions have been mapped to R functions (e.g. grepl) to make it easier to transition from non-database-backed SQL ops to Drill. See the help on drill_custom_functions for more info on these helper Drill custom function mappings.

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_functions: Show all the available Drill built-in functions & UDFs (Apache Drill 1.15.0+ required)
  • 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_popts: Show all the available Drill options (1.15.0+)
  • drill_rofile: 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

Helpers

  • ctas_profile: Generate a Drill CTAS Statement from a Query
  • drill_up: sart a Dockerized Drill Instance # sdrill_down: stop a Dockerized Drill Instance by container id
  • howall_drill: Show all dead and running Drill Docker containers
  • stopall_drill: Prune all dead and running Drill Docker containers

Installation

install.packages("sergeant", repos = "https://cinc.rud.is")
# or
devtools::install_git("https://git.rud.is/hrbrmstr/sergeant.git")
# or
devtools::install_git("https://git.sr.ht/~hrbrmstr/sergeant")
# or
devtools::install_gitlab("hrbrmstr/sergeant")
# or
devtools::install_bitbucket("hrbrmstr/sergeant")
# or
devtools::install_github("hrbrmstr/sergeant")

Usage

dplyr interface

library(sergeant)
library(tidyverse)

# use localhost if running standalone on same system otherwise the host or IP of your Drill server
ds <- src_drill("localhost")  #ds
db <- tbl(ds, "cp.`employee.json`") 

# without `collect()`:
count(db, gender, marital_status)
##  # Source:   lazy query [?? x 3]
##  # Database: DrillConnection
##  # Groups:   gender
##    gender marital_status     n
##    <chr>  <chr>          <dbl>
##  1 F      S                297
##  2 M      M                278
##  3 M      S                276
##  4 F      M                304

count(db, gender, marital_status) %>% collect()
##  # A tibble: 4 x 3
##  # Groups:   gender [2]
##    gender marital_status     n
##    <chr>  <chr>          <dbl>
##  1 F      S                297
##  2 M      M                278
##  3 M      S                276
##  4 F      M                304

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>  <dbl>
##   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

arrange(db, desc(employee_id)) %>% print(n = 20)
##  # Source:     table<cp.`employee.json`> [?? x 20]
##  # Database:   DrillConnection
##  # Ordered by: desc(employee_id)
##     employee_id full_name first_name last_name position_id position_title store_id department_id birth_date hire_date
##     <chr>       <chr>     <chr>      <chr>     <chr>       <chr>          <chr>    <chr>         <chr>      <chr>    
##   1 999         Beverly … Beverly    Dittmar   17          Store Permane… 8        17            1914-02-02 1998-01-…
##   2 998         Elizabet… Elizabeth  Jantzer   17          Store Permane… 8        17            1914-02-02 1998-01-…
##   3 997         John Swe… John       Sweet     17          Store Permane… 8        17            1914-02-02 1998-01-…
##   4 996         William … William    Murphy    17          Store Permane… 8        17            1914-02-02 1998-01-…
##   5 995         Carol Li… Carol      Lindsay   17          Store Permane… 8        17            1914-02-02 1998-01-…
##   6 994         Richard … Richard    Burke     17          Store Permane… 8        17            1914-02-02 1998-01-…
##   7 993         Ethan Bu… Ethan      Bunosky   17          Store Permane… 8        17            1914-02-02 1998-01-…
##   8 992         Claudett… Claudette  Cabrera   17          Store Permane… 8        17            1914-02-02 1998-01-…
##   9 991         Maria Te… Maria      Terry     17          Store Permane… 8        17            1914-02-02 1998-01-…
##  10 990         Stacey C… Stacey     Case      17          Store Permane… 8        17            1914-02-02 1998-01-…
##  11 99          Elizabet… Elizabeth  Horne     18          Store Tempora… 6        18            1976-10-05 1997-01-…
##  12 989         Dominick… Dominick   Nutter    17          Store Permane… 8        17            1914-02-02 1998-01-…
##  13 988         Brian Wi… Brian      Willeford 17          Store Permane… 8        17            1914-02-02 1998-01-…
##  14 987         Margaret… Margaret   Clendenen 17          Store Permane… 8        17            1914-02-02 1998-01-…
##  15 986         Maeve Wa… Maeve      Wall      17          Store Permane… 8        17            1914-02-02 1998-01-…
##  16 985         Mildred … Mildred    Morrow    16          Store Tempora… 8        16            1914-02-02 1998-01-…
##  17 984         French W… French     Wilson    16          Store Tempora… 8        16            1914-02-02 1998-01-…
##  18 983         Elisabet… Elisabeth  Duncan    16          Store Tempora… 8        16            1914-02-02 1998-01-…
##  19 982         Linda An… Linda      Anderson  16          Store Tempora… 8        16            1914-02-02 1998-01-…
##  20 981         Selene W… Selene     Watson    16          Store Tempora… 8        16            1914-02-02 1998-01-…
##  # … with more rows, and 6 more variables: salary <chr>, supervisor_id <chr>, education_level <chr>,
##  #   marital_status <chr>, gender <chr>, management_role <chr>

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
##     <chr>                    <dbl>
##   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

REST API

dc <- drill_connection("localhost") 

drill_active(dc)
##  [1] TRUE

drill_version(dc)
##  [1] "1.15.0"

drill_storage(dc)$name
##   [1] "cp"       "dfs"      "drilldat" "hbase"    "hdfs"     "hive"     "kudu"     "mongo"    "my"       "s3"

drill_query(dc, "SELECT * FROM cp.`employee.json` limit 100")
##  # A tibble: 100 x 16
##     employee_id full_name first_name last_name position_id position_title store_id department_id birth_date hire_date
##     <chr>       <chr>     <chr>      <chr>     <chr>       <chr>          <chr>    <chr>         <chr>      <chr>    
##   1 1           Sheri No… Sheri      Nowmer    1           President      0        1             1961-08-26 1994-12-…
##   2 2           Derrick … Derrick    Whelply   2           VP Country Ma… 0        1             1915-07-03 1994-12-…
##   3 4           Michael … Michael    Spence    2           VP Country Ma… 0        1             1969-06-20 1998-01-…
##   4 5           Maya Gut… Maya       Gutierrez 2           VP Country Ma… 0        1             1951-05-10 1998-01-…
##   5 6           Roberta … Roberta    Damstra   3           VP Informatio… 0        2             1942-10-08 1994-12-…
##   6 7           Rebecca … Rebecca    Kanagaki  4           VP Human Reso… 0        3             1949-03-27 1994-12-…
##   7 8           Kim Brun… Kim        Brunner   11          Store Manager  9        11            1922-08-10 1998-01-…
##   8 9           Brenda B… Brenda     Blumberg  11          Store Manager  21       11            1979-06-23 1998-01-…
##   9 10          Darren S… Darren     Stanz     5           VP Finance     0        5             1949-08-26 1994-12-…
##  10 11          Jonathan… Jonathan   Murraiin  11          Store Manager  1        11            1967-06-20 1998-01-…
##  # … with 90 more rows, and 6 more variables: salary <chr>, supervisor_id <chr>, education_level <chr>,
##  #   marital_status <chr>, gender <chr>, management_role <chr>

drill_query(dc, "SELECT COUNT(gender) AS gctFROM cp.`employee.json` GROUP BY gender")

drill_options(dc)
##  # A tibble: 179 x 6
##     name                                                        value    defaultValue accessibleScopes kind   optionScope
##     <chr>                                                       <chr>    <chr>        <chr>            <chr>  <chr>      
##   1 debug.validate_iterators                                    FALSE    false        ALL              BOOLE… BOOT       
##   2 debug.validate_vectors                                      FALSE    false        ALL              BOOLE… BOOT       
##   3 drill.exec.functions.cast_empty_string_to_null              FALSE    false        ALL              BOOLE… BOOT       
##   4 drill.exec.hashagg.fallback.enabled                         FALSE    false        ALL              BOOLE… BOOT       
##   5 drill.exec.hashjoin.fallback.enabled                        FALSE    false        ALL              BOOLE… BOOT       
##   6 drill.exec.memory.operator.output_batch_size                16777216 16777216     SYSTEM           LONG   BOOT       
##   7 drill.exec.memory.operator.output_batch_size_avail_mem_fac… 0.1      0.1          SYSTEM           DOUBLE BOOT       
##   8 drill.exec.storage.file.partition.column.label              dir      dir          ALL              STRING BOOT       
##   9 drill.exec.storage.implicit.filename.column.label           filename filename     ALL              STRING BOOT       
##  10 drill.exec.storage.implicit.filepath.column.label           filepath filepath     ALL              STRING BOOT       
##  # … with 169 more rows

drill_options(dc, "json")
##  # A tibble: 10 x 6
##     name                                                    value defaultValue accessibleScopes kind    optionScope
##     <chr>                                                   <chr> <chr>        <chr>            <chr>   <chr>      
##   1 store.hive.maprdb_json.optimize_scan_with_native_reader FALSE false        ALL              BOOLEAN BOOT       
##   2 store.json.all_text_mode                                TRUE  false        ALL              BOOLEAN SYSTEM     
##   3 store.json.extended_types                               TRUE  false        ALL              BOOLEAN SYSTEM     
##   4 store.json.read_numbers_as_double                       FALSE false        ALL              BOOLEAN BOOT       
##   5 store.json.reader.allow_nan_inf                         TRUE  true         ALL              BOOLEAN BOOT       
##   6 store.json.reader.print_skipped_invalid_record_number   TRUE  false        ALL              BOOLEAN SYSTEM     
##   7 store.json.reader.skip_invalid_records                  TRUE  false        ALL              BOOLEAN SYSTEM     
##   8 store.json.writer.allow_nan_inf                         TRUE  true         ALL              BOOLEAN BOOT       
##   9 store.json.writer.skip_null_fields                      TRUE  true         ALL              BOOLEAN BOOT       
##  10 store.json.writer.uglify                                TRUE  false        ALL              BOOLEAN SYSTEM

Working with parquet files

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

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")
##  # A tibble: 5 x 5
##    dir0      N_NATIONKEY N_NAME    N_REGIONKEY N_COMMENT           
##    <chr>           <dbl> <chr>           <dbl> <chr>               
##  1 nationsSF           0 ALGERIA             0 haggle. carefully f 
##  2 nationsSF           1 ARGENTINA           1 al foxes promise sly
##  3 nationsSF           2 BRAZIL              1 y alongside of the p
##  4 nationsSF           3 CANADA              1 eas hang ironic, sil
##  5 nationsSF           4 EGYPT               4 y above the carefull

Drill has 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))'
                )
            )
")
##  # A tibble: 7 x 3
##    city        lon          lat       
##    <chr>       <chr>        <chr>     
##  1 Burbank     -121.9316233 37.3232752
##  2 San Jose    -121.8949555 37.3393857
##  3 Lick        -121.8457863 37.2871647
##  4 Willow Glen -121.8896771 37.3085532
##  5 Buena Vista -121.9166227 37.3213308
##  6 Parkmoor    -121.9307898 37.3210531
##  7 Fruitdale   -121.932746  37.31086

sergeant Metrics

Lang # Files (%) LoC (%) Blank lines (%) # Lines (%)
Rmd 1 1 55 1 54 1 89 1

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.