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https://github.com/ropensci/nodbi

Document DBI connector for R
https://github.com/ropensci/nodbi
couchdb database elasticsearch mongodb nosql postgresql r r-package rstats sqlite
Last synced: 3 months ago
JSON representation
Document DBI connector for R
Host: GitHub
URL: https://github.com/ropensci/nodbi
Owner: ropensci
License: other
Created: 2015-03-26T21:48:45.000Z (over 9 years ago)
Default Branch: master
Last Pushed: 2024-07-26T04:55:46.000Z (3 months ago)
Last Synced: 2024-07-28T02:18:55.788Z (3 months ago)
Topics: couchdb, database, elasticsearch, mongodb, nosql, postgresql, r, r-package, rstats, sqlite
Language: R
Homepage: https://docs.ropensci.org/nodbi
Size: 2.62 MB
Stars: 76
Watchers: 9
Forks: 6
Open Issues: 0
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Codemeta: codemeta.json
Awesome Lists containing this project

jimsghstars - ropensci/nodbi - Document DBI connector for R (R)
README

        ---

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%"

)

```

⁠

[![R-CMD-check](https://github.com/ropensci/nodbi/workflows/R-CMD-check/badge.svg)](https://github.com/ropensci/nodbi/actions?query=workflow%3AR-CMD-check)

[![codecov](https://codecov.io/gh/rfhb/nodbi/branch/master/graph/badge.svg)](https://app.codecov.io/gh/rfhb/nodbi)

[![CRAN status](https://www.r-pkg.org/badges/version/nodbi)](https://CRAN.R-project.org/package=nodbi)

[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)

⁠

`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 2024-07-25.

| 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                           [email protected]

# 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 <- "test"

query <- '{"clarity": {"$in": ["SI1", "VS1"]}}'

fields <- '{"cut": 1, "_id": 1, "clarity": "1"}'

value <- '{"clarity": "XYZ", "new": ["ABC", "DEF"]}'

data <- as.data.frame(diamonds)[1:1000, ]

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))

  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 = 10L,

  columns = c('test', 'replications', 'elapsed')

)

# 2024-07-24 with 2015 mobile hardware, databases via homebrew, R 4.4.1

result[rev(order(result$elapsed)), ]

#         test replications elapsed

# 4    CouchDB           10   642.2

# 3    Elastic           10    41.5

# 5 PostgreSQL           10     4.3

# 6     DuckDB           10     4.0

# 1    MongoDB           10     3.3

# 2     SQLite           10     2.9

```

## 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}

# 2024-07-24

suppressMessages(testthat::test_local())

# ✔ | F W  S  OK | Context

# ✔ |      2 174 | couchdb [119.5s]                                                                        

# ✔ |      1 173 | duckdb [7.6s]                                                                           

# ✔ |      2 172 | elastic [87.3s]                                                                         

# ✔ |      2 172 | mongodb [8.0s]                                                                          

# ✔ |        175 | postgres [13.6s]                                                                        

# ✔ |        176 | sqlite [9.7s]                                                                           

# 

# ══ Results ══════════════════════════════════════════════════════════════════════════════════════════════

# Duration: 246.2 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 1042 ]

# 2024-07-24

covr::package_coverage(path = ".", type = "tests")

# nodbi Coverage: 94.91%

# R/src_duckdb.R: 76.92%

# R/src_mongo.R: 92.31%

# R/update.R: 92.95%

# R/zzz.R: 93.59%

# R/query.R: 94.81%

# R/src_postgres.R: 95.65%

# R/create.R: 96.12%

# R/get.R: 98.77%

# 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](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.