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 2023-09-23.
Currently, nodbi supports as database backends |
for an R object of any of these data types |
for the 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 |
Limitations: *For Elasticsearch, only simple queries (e.g. equality for a single field) and for CouchDB only root fields.
For a speed comparisons of database backends, see benchmark 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, a list, a JSON string, or a 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 | nodbi::docdb_query(src = src, key = "my_container", query = '{"age": 20}', fields = '{"name": 1, "_id": 0, "age": 1}') |
Update (patch) documents selected by query with new data my_data in data frame, list, JSON string, a 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 closed and removed automatically by nodbi) |
rm(src) |
Install
CRAN version
install.packages("nodbi")Development version
remotes::install_github("ropensci/nodbi")Load package from library
Database connections
Overview on parameters and aspects that are specific to the database backend. These are only needed once: for src_*() to create a connection object. The connection object is subsequently used with docdb_* functions. “Container” refers to where conceptually the backend holds the data. Data types are mapped from JSON to R objects by jsonlite.
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 as per https://duckdb.org/docs/extensions/json to the json column. Each row in the table represents a JSON document. Any root-level _id is extracted from the document(s) and used for column _id, otherwise a UUID is created as _id. The table is indexed on _id.
src <- nodbi::src_duckdb(dbdir = ":memory:", ...)MongoDB
“Container” refers to a MongoDB collection, in which nodbi creates JSON documents. If documents do not have root-level _id’s, UUID’s are created as _id’s. MongoDB but none of the other databases require to specify the container already in the src_*() function. See also https://jeroen.github.io/mongolite/.
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 as per https://www.sqlite.org/json1.html to the json column. Each row in the table represents a JSON document. Any root-level _id is extracted from the document(s) and used for column _id, otherwise a UUID is created as _id. 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. If documents do not have root-level _id’s, UUID’s are created as _id’s. With CouchDB, function docdb_update() uses jqr to implement patching JSON, in analogy to functions available for the other databases. See also https://CRAN.R-project.org/package=sofa.
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. Any root-level _id is extracted from the document(s) and used as document ID _id, otherwise a UUID is created as document ID _id. Only lowercase is accepted for container names (in parameter key). Opensearch can equally be used. See also https://CRAN.R-project.org/package=elastic.
src <- nodbi::src_elastic(
host = "127.0.0.1", port = 9200L, path = NULL,
transport_schema = "http", user = NULL, pwd = NULL, force = FALSE, ...)PostgreSQL
“Container” refers to a PostgreSQL table, with columns _id and json created and used by package nodbi, applying SQL functions as per https://www.postgresql.org/docs/current/functions-json.html to the json column. Each row in the table represents a JSON document. Any root-level _id is extracted from the document(s) and used for column _id, otherwise a UUID is created as _id. The table is indexed on _id. 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(). See also https://CRAN.R-project.org/package=RPostgreSQL.
src <- nodbi::src_postgres(
dbname = "my_database", host = "127.0.0.1", port = 5432L, ...)Walk-through
This example is meant to show how functional nodbi is at this time.
# load nodbi
library(nodbi)
# name of container
key <- "my_container"
# connect database backend
#
# parts of the example do not yet work with
# these database backends, see *notes* below
src <- src_elastic()
src <- src_couchdb()
#
# this example fully works with any of
src <- src_mongo(collection = key)
src <- src_postgres()
src <- src_sqlite()
src <- src_duckdb()
# check if container already exists
docdb_exists(src, key)
# [1] FALSE
# load data (here data frame, alternatively list or JSON)
# 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` url args headers$Host origin id isActive balance age eyeColor name email
# <chr> <chr> <df[> <chr> <chr> <int> <lgl> <chr> <int> <chr> <chr> <chr>
# 1 006c91d9-… http… httpbin.org 83.13… 87 NA NA NA NA NA NA
# 2 00bfd508-… http… httpbin.org 83.13… 17 NA NA NA NA NA NA
# 3 04f17695-… http… httpbin.org 83.13… 53 NA NA NA NA NA NA
# 4 05d0f2be-… http… httpbin.org 83.13… 80 NA NA NA NA NA NA
# 5 06b6b86d-… http… httpbin.org 83.13… 55 NA NA NA NA NA NA
# 6 0760c273-… http… httpbin.org 83.13… 11 NA NA NA NA NA NA
# 7 08de6593-… http… httpbin.org 83.13… 47 NA NA NA NA NA NA
# 8 0b1cb164-… http… httpbin.org 83.13… 63 NA NA NA NA NA NA
# 9 0bafc40e-… http… httpbin.org 83.13… 60 NA NA NA NA NA NA
# 10 11ba908b-… http… httpbin.org 83.13… 74 NA NA NA NA NA NA
# # ℹ 125 more rows
# # ℹ 18 more variables: headers$`X-Amzn-Trace-Id` <chr>, $`User-Agent` <chr>, $Accept <chr>,
# # about <chr>, registered <chr>, tags <list>, friends <list>, mpg <dbl>, cyl <int>,
# # disp <dbl>, hp <int>, drat <dbl>, wt <dbl>, qsec <dbl>, vs <int>, am <int>, gear <int>,
# # carb <int>
# query some documents
# *note*: such complex queries do not yet work with src_elasticsearch()
docdb_query(src, key, query = '{"mpg": {"$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
# *note*: such complex queries do not yet work with src_elasticsearch()
docdb_query(src, key, '{"mpg": {"$gte": 30}}', fields = '{"wt": 1, "mpg": 1}')
# wt mpg
# 1 2.2 32
# 2 1.6 30
# 3 1.8 34
# 4 1.5 30
# query some subitem fields from some documents
# *note*: such complex queries do not yet work with
# src_couchdb() or src_elasticsearch()
str(docdb_query(
src, key,
query = '{"$or": [{"age": {"$gt": 21}},
{"friends.name": {"$regex": "^B[a-z]{3,9}.*"}}]}',
fields = '{"age": 1, "friends.name": 1}'))
# 'data.frame': 3 obs. of 2 variables:
# $ age : int 23 30 22
# $ friends:'data.frame': 3 obs. of 1 variable:
# ..$ name:List of 3
# .. ..$ : chr "Wooten Goodwin" "Brandie Woodward" "Angelique Britt"
# .. ..$ : chr "Coleen Dunn" "Doris Phillips" "Concetta Turner"
# .. ..$ : chr "Baird Keller" "Francesca Reese" "Dona Bartlett"
# such queries can also be used for updating (patching) selected documents
# with a new 'value'(s) from a JSON string, a data frame or a list
docdb_update(src, key, value = '{"vs": 9, "xy": [1, 2]}', query = '{"carb": 3}')
# [1] 3
# *note*: such queries do not yet work with src_elasticsearch()
docdb_query(src, key, '{"carb": {"$in": [1,3]}}', fields = '{"vs": 1}')[[1]]
# [1] 1 1 1 9 9 9 1 1 1 1
docdb_get(src, key)[c(43, 93, 100, 101), c("_id", "xy", "url", "email")]
# _id xy url email
# 43 5cd6785325ce3a94dfc54096 NULL <NA> pacebell@conjurica.com
# 93 Merc 450SL 1, 2 <NA> <NA>
# 100 Volvo 142E NULL <NA> <NA>
# 101 a12f9901-4edf-4cdd-b3e6-3a9a7ca48c9d NULL https://httpbin.org/stream/98 <NA>
# use with dplyr
# *note* that dplyr includes a (deprecated) function src_sqlite
# which would mask nodbi's src_sqlite, so it is excluded here
library("dplyr", exclude = c("src_sqlite", "src_postgres"))
#
docdb_get(src, key) %>%
group_by(gear) %>%
summarise(mean_mpg = mean(mpg))
# # A tibble: 4 × 2
# gear mean_mpg
# <int> <dbl>
# 1 3 16.1
# 2 4 24.5
# 3 5 21.4
# 4 NA NA
# delete documents; query is optional parameter and has to be
# specified for deleting documents instead of deleting the container
# *note*: such complex queries do not yet work with src_couchdb() or src_elasticsearch()
dim(docdb_query(src, key, query = '{"$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": "SI1"}'
fields <- '{"cut": 1, "_id": 1, "clarity": "1"}'
value <- '{"clarity": "XYZ", "new": ["ABC", "DEF"]}'
data <- as.data.frame(diamonds)[1:2000, ]
ndjs <- tempfile()
jsonlite::stream_out(iris, con = file(ndjs), verbose = FALSE)
testFunction <- function(src, key, value, query, fields) {
on.exit(docdb_delete(src, key))
suppressMessages(docdb_create(src, key, data))
suppressMessages(docdb_create(src, key, ndjs))
# Elasticsearch needs a delay to process the data
if (inherits(src, "src_elastic")) Sys.sleep(1)
head(docdb_get(src, key))
docdb_query(src, key, query = query, fields = fields)
docdb_update(src, key, value = value, query = query)
}
# 2023-09-23 with 2015 mobile hardware, databases via homebrew
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 = 10L,
columns = c('test', 'replications', 'elapsed')
)
# test replications elapsed
# 4 CouchDB 10 303.5
# 3 Elastic 10 69.9 # 10s to be subtracted
# 5 PostgreSQL 10 3.9
# 2 SQLite 10 3.4
# 6 DuckDB 10 3.2
# 1 MongoDB 10 2.5Testing
Every database backend is subjected to identical tests, see core-nodbi.R.
# 2023-09-23
testthat::test_local()
# ✔ | F W S OK | Context
# ✔ | 2 107 | couchdb [118.7s]
# ✔ | 144 | duckdb [5.2s]
# ✔ | 3 72 | elastic [104.2s]
# ✔ | 1 137 | mongodb [5.5s]
# ✔ | 147 | postgres [43.2s]
# ✔ | 146 | sqlite [41.7s]
#
# ══ Results ═══════════════════════════════════════════════════════════════════════════════════════
# Duration: 318.9 s
#
# ── Skipped tests (6) ─────────────────────────────────────────────────────────────────────────────
# • auto disconnect not relevant (3): core-nodbi.R:429:5, core-nodbi.R:429:5, core-nodbi.R:429:5
# • bulk updates not yet implemented (2): core-nodbi.R:287:3, core-nodbi.R:287:3
# • queries need to be translated into elastic syntax (1): core-nodbi.R:207:3
#
# [ FAIL 0 | WARN 0 | SKIP 6 | PASS 753 ]Notes
- Please report any issues or bugs.
- License: MIT
- Get citation information for
nodbiin R doingcitation(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, because no way was found to query and update specific documents in a container.