nodbi

Document DBI connector for R

https://github.com/ropensci/nodbi

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Document DBI connector for R

README.Rmd

---
output: github_document
editor_options: 
 chunk_output_type: console
---

# nodbi

```{r echo=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  eval = FALSE,
  comment = "#",
  out.width = "100%"
)
```

⁠
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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 2025-07-12.

| 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](#benchmark) and [testing](#testing) below.

## API overview

Parameters for `docdb_*()` functions are the same across all database backends. See [walk-through](#walk-through) below and the canonical testing in [core-nodbi.R](./tests/testthat/core-nodbi.R). "Container" is used as term to indicate where conceptually the backend holds the data, see [Database connections](#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}()` |
| 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

```{r eval=FALSE}
install.packages("nodbi")
```

Development version

```{r eval=FALSE}
remotes::install_github("ropensci/nodbi")
```

Load package from library

```{r}
library("nodbi")
```

## Database connections {#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](https://CRAN.R-project.org/package=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 . "Container" refers to a DuckDB table, with columns `_id` and `json` created and used by package `nodbi`, applying SQL functions and functions as per  to the `json` column. Each row in the table represents a `JSON` document. 

```{r}
src <- nodbi::src_duckdb(dbdir = ":memory:", ...)
```

### MongoDB

"Container" refers to a MongoDB collection, in which `nodbi` creates JSON documents. See also . 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. 

```{r}
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  to the `json` column. Each row in the table represents a `JSON` document. The table is indexed on `_id`. See also . 

```{r}
src <- nodbi::src_sqlite(dbname = ":memory:", ...)
```

### CouchDB

"Container" refers to a CouchDB database, in which `nodbi` creates JSON documents. See also . With CouchDB, function `docdb_update()` uses [jqr](https://cran.r-project.org/package=jqr) to implement patching JSON, in analogy to functions available for the other databases. 

```{r}
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 . Only lowercase is accepted for container names (in parameter `key` of `docdb_*` functions). 

```{r}
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  to the `json` column. With PostgreSQL, a custom `plpgsql` function [jsonb_merge_patch()](https://github.com/ropensci/nodbi/blob/master/R/src_postgres.R#L60) 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()`. 

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

## Walk-through {#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. 

```{r}
# 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
#                           
#  1 5cd6… TRUE     $2,412…    20 blue     Kris… kris… "Sin… 2017-07-1…   
#  2 5cd6… FALSE    $3,400…    20 brown    Rae … raec… "Nis… 2018-12-1…   
#  3 5cd6… TRUE     $1,161…    22 brown    Pace… pace… "Eiu… 2018-08-1…   
#  4 5cd6… FALSE    $2,579…    30 brown    Will… will… "Nul… 2018-02-1…   
#  5 5cd6… FALSE    $3,808…    23 green    Lacy… lacy… "Sun… 2014-08-0…   
#  6 69bc… NA       NA         NA NA       NA    NA     NA   NA          
#  7 69bc… NA       NA         NA NA       NA    NA     NA   NA          
#  8 69bc… NA       NA         NA NA       NA    NA     NA   NA          
#  9 69bc… NA       NA         NA NA       NA    NA     NA   NA          
# 10 69bc… NA       NA         NA NA       NA    NA     NA   NA          
# # ℹ 125 more rows
# # ℹ 16 more variables: url , args , headers , origin ,
# #   id , mpg , cyl , disp , hp , drat , wt ,
# #   qsec , vs , am , gear , carb 
# # 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                           pacebell@conjurica.com
# 109                     Dodge Challenger NULL                                             
# 130                     Pontiac Firebird NULL                                             
# 101 69bcd195-a59c-11ee-bfb9-acbc328130bb NULL https://httpbin.org/stream/98                   

# 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
#       
# 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 {#benchmark}

```{r}
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 <- "test2"
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
)

# 2025-07-12 with M3 hardware, databases via homebrew
result[rev(order(result$elapsed)), c("test", "replications", "elapsed")]
#         test replications elapsed
# 4    CouchDB            3   48.93
# 3    Elastic            3   25.00
# 1    MongoDB            3    2.11
# 5 PostgreSQL            3    1.51
# 6     DuckDB            3    0.92
# 2     SQLite            3    0.65

message(R.version$version.string)
# R version 4.5.1 RC (2025-06-05 r88281)

pkgs <- c("nodbi", "RSQLite", "duckdb", "RPostgres", "mongolite", "elastic", "sofa")
for (pkg in pkgs) message(pkg, ": ", packageVersion(pkg))
# nodbi: 0.13.1
# RSQLite: 2.4.1
# duckdb: 1.3.2
# RPostgres: 1.4.8
# mongolite: 4.0.0
# elastic: 1.2.0
# sofa: 0.4.0
```

## Testing {#testing}

Every database backend is subjected to identical tests, see [core-nodbi.R](https://github.com/ropensci/nodbi/blob/master/tests/testthat/core-nodbi.R). 

```{r testing_and_coverage}
# 2025-07-12

suppressMessages(testthat::test_local())
# ✔ | F W  S  OK | Context
# ✔ |      2 175 | couchdb [46.0s]
# ✔ |      1 174 | duckdb [3.8s]
# ✔ |      2 173 | elastic [37.9s]
# ✔ |      2 173 | mongodb [3.9s]
# ✔ |        176 | postgres [4.8s]
# ✔ |        177 | sqlite [3.7s]
# ══ Results ═══════════════════════════════
# Duration: 100.2 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 ]

covr::package_coverage(path = ".", type = "tests")
# nodbi Coverage: 93.42%
# R/src_postgres.R: 82.19%
# R/src_duckdb.R: 84.00%
# R/zzz.R: 86.67%
# R/query.R: 92.92%
# R/update.R: 94.40%
# R/src_mongo.R: 95.00%
# R/get.R: 95.79%
# R/create.R: 96.25%
# R/delete.R: 98.96%
# R/exists.R: 100.00%
# R/list.R: 100.00%
# R/src_couchdb.R: 100.00%
# R/src_elastic.R: 100.00%
# R/src_sqlite.R: 100.00%
```

## Notes

-   Please [report any issues or bugs](https://github.com/ropensci/nodbi/issues).
-   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](https://ropensci.org/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.

Owner

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