R package nodbi provides a single interface for several
NoSQL databases and SQL databases with JSON functionality, with the same
function parameters and return values across all of:
Package nodbi has been designed to use any specific SQL
functions a database may have for JSON and has added
functionality tested for performance to enable switching databases
without changing user code.
First, a connection to a database is opened. In the example, no additional parameters are used such as database file or server; see the help page for the respective database.
“Container” is used as term to indicate where conceptually the
database holds the data (e.g. a collection in MongoDB, a table in
DuckDB). The key parameter of nodbi functions
holds the name of the relevant container.
# name of container
key <- "my_container"
# nodbi can connect any of these databases
if (FALSE) {
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")
)
}
# this example is run with
src <- src_sqlite()
# note additional parameters can be specified,
# for example for local or remote MongoDb:
help("src_mongo")Create a container if it does not yet exist and fill with
value. The return value is the number of created documents.
“Documents” refers to the rows in a data frame such as
mtcars, or the number of NDJSON lines, or the
number of list items, or the number of objects in an JSON
array.
The parameter value in any nodbi function
can take a data frame, a list, a JSON string, or a file name or URL
pointing to NDJSON.
# check if container already exists
docdb_exists(src = src, key = key)
#> [1] FALSE
# load data from a data frame with row names into
# the container specified in "key" parameter
docdb_create(src = src, key = key, value = mtcars)
#> [1] 32
# do not run during testing
if (FALSE) {
# load additionally 98 NDJSON records
docdb_create(src, key, "https://httpbin.org/stream/98")
}
# load additionally mapdata as list
docdb_create(src, key, jsonlite::fromJSON(mapdata, simplifyVector = FALSE))
#> Note: container 'my_container' already exists
#> [1] 2
# show JSON structure of contacts
jsonlite::minify(contacts)
#> [{"_id":"5cd67853f841025e65ce0ce2","isActive":false,"balance":"$3,808.45","age":23,"eyeColor":"green","name":"Lacy Chen","email":"[email protected]","about":"Sunt consequat ad dolore.\nExercitation nisi reprehenderit.","registered":"2014-08-03T12:11:54 -02:00","tags":["nulla","nisi","adipisicing","do","ad","ullamco","irure"],"friends":[{"id":0,"name":"Wooten Goodwin"},{"id":1,"name":"Brandie Woodward"},{"id":2,"name":"Angelique Britt"}]},{"_id":"5cd678531b423d5f04cfb0a1","isActive":false,"balance":"$3,400.50","age":20,"eyeColor":"brown","name":"Rae Colon","email":"[email protected]","about":"Nisi excepteur duis duis aliquip qui id consequat consequat.","registered":"2018-12-19T06:23:35 -01:00","tags":["nostrud","eu","consectetur","adipisicing","labore","ut","voluptate"],"friends":[{"id":0,"name":"Yang Yates"},{"id":1,"name":"Lacy Chen"}]},{"_id":"5cd6785335b63cb19dfa8347","isActive":false,"balance":"$2,579.09","age":30,"eyeColor":"brown","name":"Williamson French","email":"[email protected]","about":"Nulla do sunt consectetur officia. Laboris pariatur incididunt.","registered":"2018-02-14T10:59:57 -01:00","tags":["exercitation","do","magna","ut","consectetur","ex","incididunt"],"friends":[{"id":0,"name":"Coleen Dunn"},{"id":1,"name":"Doris Phillips"},{"id":2,"name":"Concetta Turner"}]},{"_id":"5cd6785325ce3a94dfc54096","isActive":true,"balance":"$1,161.52","age":22,"eyeColor":"brown","name":"Pace Bell","email":"[email protected]","about":"Eiusmod sunt laborum ipsum do cupidatat qui id dolore do.","registered":"2018-08-17T12:23:42 -02:00","tags":["aliqua","consectetur","commodo","velit","cupidatat","duis","dolore"],"friends":[{"id":0,"name":"Baird Keller"},{"id":1,"name":"Francesca Reese"},{"id":2,"name":"Dona Bartlett"}]},{"_id":"5cd678530df22d3625ed8375","isActive":true,"balance":"$2,412.67","age":20,"eyeColor":"blue","name":"Krista Baxter","email":"[email protected]","about":"Sint quis nulla ea fugiat. Commodo nisi qui eu sit.","registered":"2017-07-19T05:03:47 -02:00","tags":["sit","cillum","commodo","labore","sint","in","exercitation"],"friends":[{"id":0,"name":"Pace Bell"}]}]
# load additionally contacts JSON data
docdb_create(src, key, contacts)
#> Note: container 'my_container' already exists
#> [1] 5Check and list any other containers exist in the database:
The unique document identifier is its _id, corresponding
to a primary index with a constraint to be unique in SQL databases.
The _id’s of an input value are either the
row names of a data frame (such as mtcars) or top-level
elements with the name _id such as in contacts
shown just above.
Thus, expect a warning when trying to create documents with
_id’s that already exist in the container.
The return value can be 0 when no documents could newly
be created, or the number of the subset of documents in
value that did not yet exist and could newly be
created.
# zero new documents created
docdb_create(src, key, value = mtcars)
#> Note: container 'my_container' already exists
#> Warning: Could not create some documents, reason: UNIQUE constraint failed
#> [1] 0For updating existing documents, see below function
docdb_update().
All documents in a container can now be retrieved with
docdb_get().
# use library for more
# readable print output
if (require(tibble)) {
# get all documents, irrespective of schema
as_tibble(docdb_get(src, key))
# get just 2 documents using limit and note that
# only fields for these documents are returned
as_tibble(docdb_get(src, key, limit = 2L))
}
#> Loading required package: tibble
#> # A tibble: 2 × 5
#> `_id` destination_addresses origin_addresses rows status
#> <chr> <list> <list> <lis> <chr>
#> 1 0535870e-9f90-11ef-945c-0… <chr [3]> <chr [2]> <df> OK
#> 2 05358718-9f90-11ef-945c-0… <chr [3]> <chr [1]> <df> OKOne of the most powerful functions of nodbi is
docdb_query() because it permits to combine a query to
select documents and to filter for fields of interest.
# query for some documents
docdb_query(src = src, key = key, query = '{"mpg": {"$gte": 30}}')
#> _id mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 2 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 3 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 4 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2Both parameters query (obligatory) and
fields (optional) use, across all databases, MongoDB syntax
such as documented for queries and fields.
# 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": {"$gte": 30}}', fields = '{"wt": 1, "mpg": 1}')
#> _id wt mpg
#> 1 Fiat 128 2.200 32.4
#> 2 Honda Civic 1.615 30.4
#> 3 Toyota Corolla 1.835 33.9
#> 4 Lotus Europa 1.513 30.4Unless fields specifies "_id": 0, the
_id field is always included in the output of
docdb_query().
# query some fields from some documents, limit return to one document
docdb_query(src, key, '{"mpg": {"$gte": 30}}', fields = '{"_id": 0, "mpg": 1}', limit = 1L)
#> mpg
#> 1 32.4Queries can be more complex such as in this example, showing a dot notation of a sub-field and an example operator (regular expression).
# 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 "5cd67853f841025e65ce0ce2" "5cd6785335b63cb19dfa8347" "5cd6785325ce3a94dfc54096"
#> $ age : int 23 30 22
#> $ friends.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"Queries work across documents of different structure such as here.
# query with results across documents
docdb_query(
src, key,
query = '{"$or": [{"age": {"$gt": 21}}, {"mpg": {"$gte": 30}}]}',
fields = '{"name": 1, "disp": 1}'
)
#> _id name disp
#> 1 Fiat 128 <NA> 78.7
#> 2 Honda Civic <NA> 75.7
#> 3 Toyota Corolla <NA> 71.1
#> 4 Lotus Europa <NA> 95.1
#> 5 5cd67853f841025e65ce0ce2 Lacy Chen NA
#> 6 5cd6785335b63cb19dfa8347 Williamson French NA
#> 7 5cd6785325ce3a94dfc54096 Pace Bell NAThe JSON data handled by package nodbi may
have a large number of field included nested fields in objects (see for
example name within array friends above).
Thus, an argument is provided for docdb_query() so that the
function returns only the comprehensive list of all field names in
documents selected with a query (or in all documents in the container if
query = "{}" is specified).
docdb_query(src, key, query = '{"_id": {"$regex": "^[0-9]"}}', listfields = TRUE)
#> [1] "about" "age"
#> [3] "balance" "destination_addresses"
#> [5] "email" "eyeColor"
#> [7] "friends" "friends.id"
#> [9] "friends.name" "isActive"
#> [11] "name" "origin_addresses"
#> [13] "registered" "rows"
#> [15] "rows.elements" "rows.elements.distance"
#> [17] "rows.elements.distance.somevalue" "rows.elements.distance.text"
#> [19] "rows.elements.duration" "rows.elements.duration.somevalue"
#> [21] "rows.elements.duration.text" "rows.elements.status"
#> [23] "status" "tags"The dot notation is a path from a root field to the nested field, and
this notation can be used in query and fields
parameters of docdb_query().
Queries can also be used for updating (patching) selected documents
with a new value. The return value of
docdb_update() corresponds to the number of documents that
were updated.
This is another powerful function because value can come
from a data frame, a list, a JSON string, or a file name or URL pointing
to NDJSON, and if value includes row names or
_id’s, these are used to identify the documents to be
updated.
# number of documents corresponding to query
nrow(docdb_query(src, key, query = '{"carb": 3}'))
#> [1] 3
# update all documents using JSON, replacing the previously existing values
docdb_update(src, key, value = '{"vs": 9, "xy": [1, 2]}', query = '{"carb": 3}')
#> [1] 3
# update with value that includes _id's
docdb_update(src, key, value = '{"_id": "Merc 450SLC", "xy": 33}', query = "{}")
#> [1] 1
# show updated values
docdb_query(src, key, query = '{"carb": 3}', fields = '{"xy": 1}')
#> _id xy
#> 1 Merc 450SE 1, 2
#> 2 Merc 450SL 1, 2
#> 3 Merc 450SLC 33Documents and containers can be deleted with
docdb_delete(). Its return value corresponds to the success
of the delete operation.
# number of documents corresponding to query
nrow(docdb_query(src, key, query = '{"age": {"$lte": 23}}'))
#> [1] 4
# to delete selected documents, specify a query parameter
docdb_delete(src, key, query = '{"age": {"$lte": 23}}')
#> [1] TRUE
# this deletes the complete container from database
docdb_delete(src, key)
#> [1] TRUE
# check if still exists
docdb_exists(src, key)
#> [1] FALSEPackage nodbi includes an automatic mechanism for
shutting down, at the time of quit() or session restart,
those databases that require it (SQLite, DuckDB, PostgreSQL).
Nevertheless, it is good practice to manually disconnect and shut down connections as specific to the database, for example for SQLite:
src
#> src: sqlite
#> SQLite library version: 3.46.0
#> size: NA MB
#> dbname: :memory:
#> Warning: Database is only in memory, will not persist after R ends! Consider to copy it with
#> RSQLite::sqliteCopyDatabase(
#> from = <your nodbi::src_sqlite() object>$con,
#> to = <e.g. RSQLite::dbConnect(RSQLite::SQLite(), 'local_file.db')>
#> )
# shutdown
DBI::dbDisconnect(src$con, shutdown = TRUE)
rm(src)