nodbi
'NoSQL' Database Connector
Simplified document database manipulation and analysis, including support for many 'NoSQL' databases, including document databases ('Elasticsearch', 'CouchDB', 'MongoDB'), 'key-value' databases ('Redis'), and other 'NoSQL' types ('etcd').
nodbi provides a single user interface for interacting with many NoSQL databases.
So far we support the following DBs:
- MongoDB
- Redis (server based)
- CouchDB
- Elasticsearch
- etcd
Currently we have support for data.frame's for the following operations
- Create - all DBs
- Exists - except MongoDB
- Get - all DBs
- Query - except Redis, etcd
- Delete - all DBs
- Update - just CouchDB
cran version
install.packages("nodbi")dev version
install.packages("devtools")devtools::install_github("ropensci/nodbi")library("nodbi")Initialize connections
Start CouchDB on the cli or with the app
src_couchdb()#> src: couchdb 2.3.0 [127.0.0.1/5984]#> databases: cats, df, flights, foobar, geotest, mtcars, mtcars2, sofadb, test,#> testing123Start Elasticsearch, e.g.:
cd /usr/local/elasticsearch && bin/elasticsearchsrc_elastic()#> src: elasticsearch 7.0.0 [127.0.0.1:9200]#> databases: gbifgeo, mtcars, gbif, plos, diamonds_smallStart etcd after installing etcd (https://github.com/coreos/etcd/releases) by, e.g.: etcd
src_etcd()#> src:#> etcd server: 3.3.11#> etcd cluster: 3.3.0If you want to use classic Redis server, we do that through the redux
package, and you'll need to start up Redis by e.g,. redis-server in your shell.
src_redis()#> src: redis 1.1.0 [127.0.0.1:6379]#> keys: diamonds, mtcars, fooStart MongoDB: mongod (may need to do sudo mongod)
src_mongo()#> MongoDB 4.0.5 (uptime: 2300s)#> URL: leothelion.local/testCouchDB
src <- src_couchdb()docout <- docdb_create(src, key = "mtcars", value = mtcars)head( docdb_get(src, "mtcars") )#> mpg cyl disp hp drat wt qsec vs am gear carb#> 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4#> 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4#> 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1#> 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1#> 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2#> 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1etcd
src <- src_etcd()ff <- docdb_create(src, "/mtcars", mtcars)head( docdb_get(src, "/mtcars") )#> mpg cyl disp hp drat wt qsec vs am gear carb#> 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4#> 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4#> 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1#> 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1#> 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2#> 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1Elasticsearch
Put the iris dataset into ES
src <- src_elastic()ff <- docdb_create(src, "iris", iris)head( docdb_get(src, "iris") )#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species#> 5.0 3.6 1.4 0.2 setosa#> 4.9 3.1 1.5 0.1 setosa#> 4.8 3.4 1.6 0.2 setosa#> 5.4 3.9 1.3 0.4 setosa#> 5.1 3.3 1.7 0.5 setosa#> 5.2 3.4 1.4 0.2 setosaRedis
src <- src_redis()docdb_create(src, "mtcars", mtcars)#> [Redis: OK]docdb_get(src, "mtcars")#> mpg cyl disp hp drat wt qsec vs am gear carb#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2...MongoDB
library("ggplot2")src <- src_mongo(verbose = FALSE)ff <- docdb_create(src, "diamonds", diamonds)docdb_get(src, "diamonds")#> mpg cyl disp hp drat wt qsec vs am gear carb#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2...Use with dplyr
library("dplyr")src <- src_mongo(verbose = FALSE)docdb_get(src, "diamonds") %>% group_by(cut) %>% summarise(mean_depth = mean(depth), mean_price = mean(price))#> # A tibble: 6 x 3#> cut mean_depth mean_price#> <chr> <dbl> <dbl>#> 1 <NA> NA NA #> 2 Fair 64.0 4359.#> 3 Good 62.4 3929.#> 4 Ideal 61.7 3458.#> 5 Premium 61.3 4584.#> 6 Very Good 61.8 3982.Meta
- Please report any issues or bugs.
- License: MIT
- Get citation information for
nodbiin R doingcitation(package = 'nodbi') - Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
News
nodbi 0.2.0
NEW FEATURES
docdb_get()gainslimitparameter to do pagination, for CouchDB, Elasticsearch and MongoDB only (#17) (#23)- gains function
docdb_query()to send queries to each backend (#18) (#22) - gains function
docdb_exists()to check if a database or equivalent exists (#21) (#22)
MINOR IMPROVEMENTS
- Updated package for new version of
elastic, which has slightly different setup for connecting to the Elasticsearch instance (#20)
nodbi 0.1.0
NEW FEATURES
- released to CRAN