NoSQL Database Connector • nodbi

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nodbi is an R package that provides a single interface for several NoSQL databases and databases with JSON functionality, with the same function parameters and return values across all database backends. Last updated 2024-11-06.

Currently, `nodbi` supports as database backends	for an `R` object of any of these data types	for these operations
MongoDB	data.frame	List, Exists
SQLite	list	Create
PostgreSQL	JSON string	Get
DuckDB	file name of NDJSON records	Query
Elasticsearch	URL of NDJSON records	Update
CouchDB		Delete

For speed comparisons of database backends, see benchmark and testing below.

API overview

Parameters for docdb_*() functions are the same across all database backends. See walk-through below and the canonical testing in core-nodbi.R. “Container” is used as term to indicate where conceptually the backend holds the data, see Database connections below. The key parameter holds the name of a container.

Purpose	Function call
Create database connection (see below)	`src <- nodbi::src_{duckdb, postgres, mongo, sqlite, couchdb, elastic}(<see below for parameters>)`
Load `my_data` (a data frame, list, JSON string, or file name or URL pointing to NDJSON records) into database, container `my_container`	`nodbi::docdb_create(src = src, key = "my_container", value = my_data)`
Get all documents back into a data frame	`nodbi::docdb_get(src = src, key = "my_container")`
Get documents selected with query (as MongoDB-compatible JSON) into a data frame	`nodbi::docdb_query(src = src, key = "my_container", query = '{"age": 20}')`
Get selected fields (in MongoDB compatible JSON) from documents selected by query into a data frame	`nodbi::docdb_query(src = src, key = "my_container", query = '{"age": {"$gt": 20}}', fields = '{"friends.name": 1, "_id": 0, "age": 1}', limit = 2L)`
Update (patch) documents selected by query with new data `my_data` (in a data frame, list, JSON string, or file name or URL pointing to NDJSON records)	`nodbi::docdb_update(src = src, key = "my_container", value = my_data, query = '{"age": 20}')`
Check if container exists	`nodbi::docdb_exists(src = src, key = "my_container")`
List all containers in database	`nodbi::docdb_list(src = src)`
Delete document(s) in container	`nodbi::docdb_delete(src = src, key = "my_container", query = '{"age": 20}')`
Delete container	`nodbi::docdb_delete(src = src, key = "my_container")`
Close and remove database connection manually (when restarting R, connections are automatically closed and removed by `nodbi`)	`rm(src)`

Install

CRAN version

install.packages("nodbi")

Development version

remotes::install_github("ropensci/nodbi")

Load package from library

library("nodbi")

Database connections

Overview on parameters and aspects that are specific to the database backend. These are only needed once, for for src_*() to create a connection object. Any such connection object is subsequently used similarly across the docdb_* functions.

“Container” refers to how conceptually the backend holds the data. Data types are mapped from JSON to R objects by jsonlite. Any root-level _id is extracted from the document(s) and used for an index column _id, otherwise a UUID is created as _id.

DuckDB

See also https://CRAN.R-project.org/package=duckdb. “Container” refers to a DuckDB table, with columns _id and json created and used by package nodbi, applying SQL functions and functions as per https://duckdb.org/docs/extensions/json to the json column. Each row in the table represents a JSON document.

src <- nodbi::src_duckdb(dbdir = ":memory:", ...)

MongoDB

“Container” refers to a MongoDB collection, in which nodbi creates JSON documents. See also https://jeroen.github.io/mongolite/. MongoDB but none of the other databases require to specify the container name already in the src_*() function; use the collection name for parameter key in docdb_* functions.

src <- nodbi::src_mongo(
  collection = "my_container", db = "my_database",
  url = "mongodb://localhost", ...)

SQLite

“Container” refers to an SQLite table, with columns _id and json created and used by package nodbi, applying SQL functions and functions as per https://www.sqlite.org/json1.html to the json column. Each row in the table represents a JSON document. The table is indexed on _id. See also https://CRAN.R-project.org/package=RSQLite.

src <- nodbi::src_sqlite(dbname = ":memory:", ...)

CouchDB

“Container” refers to a CouchDB database, in which nodbi creates JSON documents. See also https://CRAN.R-project.org/package=sofa. With CouchDB, function docdb_update() uses jqr to implement patching JSON, in analogy to functions available for the other databases.

src <- nodbi::src_couchdb(
  host = "127.0.0.1", port = 5984L, path = NULL,
  transport = "http", user = NULL, pwd = NULL, headers = NULL)

Elasticsearch

“Container” refers to an Elasticsearch index, in which nodbi creates JSON documents. Opensearch can equally be used. See also https://CRAN.R-project.org/package=elastic. Only lowercase is accepted for container names (in parameter key of docdb_* functions).

src <- nodbi::src_elastic(
  host = "127.0.0.1", port = 9200L, path = NULL,
  transport_schema = "http", user = NULL, pwd = NULL, ...)

PostgreSQL

“Container” refers to a PostgreSQL table, with columns _id and json created and used by package nodbi, applying SQL functions and functions as per https://www.postgresql.org/docs/current/functions-json.html to the json column. With PostgreSQL, a custom plpgsql function jsonb_merge_patch() is used for docdb_update(). The order of variables in data frames returned by docdb_get() and docdb_query() can differ from their order the input to docdb_create().

src <- nodbi::src_postgres(
  dbname = "my_database", host = "127.0.0.1", port = 5432L, ...)

Walk-through

This example is to show how functional nodbi is at this time: With any of the six database backends, the functions work in the same way and return the same values.

# load nodbi
library(nodbi)

# name of container
key <- "my_container"

# connect any of these database backends
src <- src_duckdb()
src <- src_mongo(collection = key)
src <- src_sqlite()
src <- src_postgres()
src <- src_elastic()
src <- src_couchdb(
  user = Sys.getenv("COUCHDB_TEST_USER"), 
  pwd = Sys.getenv("COUCHDB_TEST_PWD"))

# check if container already exists
docdb_exists(src, key)
# [1] FALSE

# load data (here data frame, alternatively a list, JSON or file with NSJSON)
# into the container "my_container" specified in "key" parameter
docdb_create(src, key, value = mtcars)
# [1] 32

# load additionally 98 NDJSON records
docdb_create(src, key, "https://httpbin.org/stream/98")
# Note: container 'my_container' already exists
# [1] 98

# load additionally contacts JSON data, from package nodbi
docdb_create(src, key, contacts)
# Note: container 'my_container' already exists
# [1] 5

# get all documents, irrespective of schema
dplyr::tibble(docdb_get(src, key))
# # A tibble: 135 × 27
#    `_id` isActive balance   age eyeColor name  email about registered tags   friends
#    <chr> <lgl>    <chr>   <int> <chr>    <chr> <chr> <chr> <chr>      <list> <list> 
#  1 5cd6… TRUE     $2,412…    20 blue     Kris… kris… "Sin… 2017-07-1… <chr>  <df>   
#  2 5cd6… FALSE    $3,400…    20 brown    Rae … raec… "Nis… 2018-12-1… <chr>  <df>   
#  3 5cd6… TRUE     $1,161…    22 brown    Pace… pace… "Eiu… 2018-08-1… <chr>  <df>   
#  4 5cd6… FALSE    $2,579…    30 brown    Will… will… "Nul… 2018-02-1… <chr>  <df>   
#  5 5cd6… FALSE    $3,808…    23 green    Lacy… lacy… "Sun… 2014-08-0… <chr>  <df>   
#  6 69bc… NA       NA         NA NA       NA    NA     NA   NA         <NULL> <NULL> 
#  7 69bc… NA       NA         NA NA       NA    NA     NA   NA         <NULL> <NULL> 
#  8 69bc… NA       NA         NA NA       NA    NA     NA   NA         <NULL> <NULL> 
#  9 69bc… NA       NA         NA NA       NA    NA     NA   NA         <NULL> <NULL> 
# 10 69bc… NA       NA         NA NA       NA    NA     NA   NA         <NULL> <NULL> 
# # ℹ 125 more rows
# # ℹ 16 more variables: url <chr>, args <df[,0]>, headers <df[,4]>, origin <chr>,
# #   id <int>, mpg <dbl>, cyl <int>, disp <dbl>, hp <int>, drat <dbl>, wt <dbl>,
# #   qsec <dbl>, vs <int>, am <int>, gear <int>, carb <int>
# # ℹ Use `print(n = ...)` to see more rows


# query some documents
docdb_query(src, key, query = '{"mpg": {"$gte": 30}}')
#              _id mpg cyl disp  hp drat  wt qsec vs am gear carb
# 1       Fiat 128  32   4   79  66  4.1 2.2   19  1  1    4    1
# 2    Honda Civic  30   4   76  52  4.9 1.6   19  1  1    4    2
# 3 Toyota Corolla  34   4   71  65  4.2 1.8   20  1  1    4    1
# 4   Lotus Europa  30   4   95 113  3.8 1.5   17  1  1    5    2

# query some fields from some documents; 'query' is a mandatory 
# parameter and is used here in its position in the signature
docdb_query(src, key, '{"mpg": {"$gte": 30}}', fields = '{"wt": 1, "mpg": 1}')
#              _id  wt mpg
# 1       Fiat 128 2.2  32
# 2    Honda Civic 1.6  30
# 3   Lotus Europa 1.5  30
# 4 Toyota Corolla 1.8  34

# query some subitem fields from some documents
str(docdb_query(
  src, key, 
  query = '{"$or": [{"age": {"$gt": 21}}, 
           {"friends.name": {"$regex": "^B[a-z]{3,9}.*"}}]}', 
  fields = '{"age": 1, "friends.name": 1}'))
# 'data.frame': 3 obs. of  3 variables:
#  $ _id         : chr  "5cd6785325ce3a94dfc54096" "5cd6785335b63cb19dfa8347" "5cd67853f841025e65ce0ce2"
#  $ age         : int  22 30 23
#  $ friends.name:List of 3
#   ..$ : chr  "Baird Keller" "Francesca Reese" "Dona Bartlett"
#   ..$ : chr  "Coleen Dunn" "Doris Phillips" "Concetta Turner"
#   ..$ : chr  "Wooten Goodwin" "Brandie Woodward" "Angelique Britt"

# such queries can also be used for updating (patching) selected documents 
# with a new 'value'(s) from a JSON string, a data frame a list or a file with NSJSON)
docdb_update(src, key, value = '{"vs": 9, "xy": [1, 2]}', query = '{"carb": 3}')
# [1] 3
docdb_query(src, key, '{"carb": {"$in": [1,3]}}', fields = '{"vs": 1, "_id": 0}')[[1]]
# [1] 1 1 1 9 9 9 1 1 1 1
docdb_get(src, key)[c(3, 109, 130, 101), c("_id", "xy", "url", "email")]
#                                      _id   xy                           url                  email
# 3               5cd6785325ce3a94dfc54096 NULL                          <NA> pacebell@conjurica.com
# 109                     Dodge Challenger NULL                          <NA>                   <NA>
# 130                     Pontiac Firebird NULL                          <NA>                   <NA>
# 101 69bcd195-a59c-11ee-bfb9-acbc328130bb NULL https://httpbin.org/stream/98                   <NA>

# use with dplyr
# *note* that dplyr includes a (deprecated) function src_sqlite
# which would mask nodbi's src_sqlite, so it is excluded here
library("dplyr", exclude = c("src_sqlite", "src_postgres"))
# 
docdb_get(src, key) %>%
  group_by(gear) %>%
  summarise(mean_mpg = mean(mpg))
# # A tibble: 4 × 2
#    gear mean_mpg
#   <int>    <dbl>
# 1     3     16.1
# 2     4     24.5
# 3     5     21.4
# 4    NA     NA 

# delete documents; query is optional parameter and has to be 
# specified for deleting documents instead of deleting the container
dim(docdb_query(src, key, query = '{"$or": [{"age": {"$lte": 20}}, {"age": {"$gte": 25}}]}'))
# [1] 3 11
docdb_delete(src, key, query = '{"$or": [{"age": {"$lte": 20}}, {"age": {"$gte": 25}}]}')
# TRUE
nrow(docdb_get(src, key))
# [1] 132

# delete container from database
docdb_delete(src, key)
# [1] TRUE
# 
# shutdown
DBI::dbDisconnect(src$con, shutdown = TRUE); rm(src)

Benchmark

library("nodbi")

srcMongo <- src_mongo()
srcSqlite <- src_sqlite()
srcPostgres <- src_postgres()
srcDuckdb <- src_duckdb()
srcElastic <- src_elastic()
srcCouchdb <- src_couchdb(
  user = Sys.getenv("COUCHDB_TEST_USER"), 
  pwd = Sys.getenv("COUCHDB_TEST_PWD"))

key <- "test"
query <- '{"clarity": {"$in": ["NOTME", "VS1"]}}'
fields <- '{"cut": 1, "_id": 1, "clarity": 1}'
value <- '{"clarity": "XYZ", "new": ["ABC", "DEF"]}'
data <- diamonds[1:1000, ]
ndjs <- tempfile()
jsonlite::stream_out(diamonds[1:10000, ], con = file(ndjs), verbose = FALSE)

testFunction <- function(src, key, value, query, fields) {
  try(docdb_delete(src, key), silent = TRUE)
  on.exit(docdb_delete(src, key))
  suppressMessages(docdb_create(src, key, data))
  suppressMessages(docdb_create(src, key, ndjs))
  head(docdb_get(src, key))
  docdb_query(src, key, query = query, fields = fields)
  docdb_query(src, key, query = query, listfields = TRUE)
  docdb_update(src, key, value = value, query = query)
}

result <- rbenchmark::benchmark(
  MongoDB = testFunction(src = srcMongo, key, value, query, fields),
  SQLite = testFunction(src = srcSqlite, key, value, query, fields),
  Elastic = testFunction(src = srcElastic, key, value, query, fields),
  CouchDB = testFunction(src = srcCouchdb, key, value, query, fields),
  PostgreSQL = testFunction(src = srcPostgres, key, value, query, fields),
  DuckDB = testFunction(src = srcDuckdb, key, value, query, fields),
  replications = 3L
)

# 2024-10-26 with 2015 hardware, databases via homebrew on localhost, R 4.4.1
result[rev(order(result$elapsed)), c('test', 'replications', 'elapsed')]
#         test replications elapsed
# 4    CouchDB            3   224.6
# 3    Elastic            3    80.7
# 5 PostgreSQL            3     4.6
# 1    MongoDB            3     3.9
# 6     DuckDB            3     3.5
# 2     SQLite            3     2.1

Testing

Every database backend is subjected to identical tests, see core-nodbi.R.

# 2024-10-26
suppressMessages(testthat::test_local())
# ✔ | F W  S  OK | Context
# ✔ |      2 175 | couchdb [236.8s]
# ✔ |      1 174 | duckdb [12.6s]
# ✔ |      2 173 | elastic [114.7s]
# ✔ |      2 173 | mongodb [9.9s]
# ✔ |        176 | postgres [14.9s]
# ✔ |        177 | sqlite [10.4s]
# 
# ══ Results ══════════════════════════════════════════════════════
# Duration: 399.5 s
# 
# ── Skipped tests (7) ────────────────────────────────────────────
# • Testing for auto disconnect and shutdown not relevant (3): 
#   test-couchdb.R:26:3, test-elastic.R:21:3, test-mongodb.R:24:3
# • Testing for parallel writes not possible or implemented (4): 
#   test-couchdb.R:26:3, test-duckdb.R:22:3,
#   test-elastic.R:21:3, test-mongodb.R:24:3
# 
# [ FAIL 0 | WARN 0 | SKIP 7 | PASS 1048 ]

# 2024-10-26
covr::package_coverage(path = ".", type = "tests")
# nodbi Coverage: 94.90%
# R/src_duckdb.R: 76.92%
# R/src_mongo.R: 92.31%
# R/zzz.R: 93.59%
# R/update.R: 94.39%
# R/query.R: 94.69%
# R/get.R: 95.24%
# R/src_postgres.R: 95.65%
# R/create.R: 95.87%
# R/delete.R: 98.96%
# R/exists.R: 100.00%
# R/list.R: 100.00%
# R/src_couchdb.R: 100.00%
# R/src_elasticsearch.R: 100.00%
# R/src_sqlite.R: 100.00%

Notes

Please report any issues or bugs.
License: MIT
Get citation information for nodbi in R doing citation(package = 'nodbi')
Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Support for redis has been removed since version 0.5.

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