nodbi

nodbi provides a single UI for interacting with many NoSQL databases.

So far we support the following DBs:

MongoDB
Redis (server and serverless)
CouchDB
Elasticsearch
etcd
Riak

Currently we have support for data.frame's for the following operations

Create - all DBs
Get - all DBs
Delete - all DBs
Update - just CouchDB (others coming)

sofa, mongolite, elastic, and etseed are on CRAN

RedisAPI, rrlite, reeack are not on CRAN

Install

install.packages("devtools")
devtools::install_github("ropensci/nodbi")

library("nodbi")

Initialize connections

Start CouchDB in your shell of choice by, e.g.: couchdb

src_couchdb()
#> src: couchdb 2.0.0 [127.0.0.1/5984]
#> databases: cab859b90-020a-418b-80fc-b7492378e92, df, mtcars, mtcars2, omdb,
#>      testing123, z85a07642-9f49-408c-a16f-f71135d9450f

Start Elaticsearch in your shell of choice by, e.g.:

cd /usr/local/elasticsearch && bin/elasticsearch

src_elasticsearch()
#> src: elasticsearch 5.1.2 [127.0.0.1:9200]
#> databases: gbifgeo, shakespeare, plos, geoshape, gbifgeopoint, gbif

Start etcd in your shell of choice after installing etcd (https://github.com/coreos/etcd/releases/tag/v2.2.0) by, e.g.: etcd

src_etcd()
#> src:
#>   etcd server: 3.1.1
#>   etcd cluster: 3.1.0

Start MongoDB in your shell of choice by: mongod

src_mongo()
#> MongoDB 3.4.2 (uptime: 502s)
#> URL: MacBook-Pro-10.local/test

If you want to use classic Redis server, we do that through the RedisAPi package, and you'll need to start up Redis by e.g,. redis-server in your shell.

src_redis()
#> $type
#> [1] "redis"
#> 
#> $version
#> [1] '0.4.0'
#> 
#> $con
#> <redis_api>
#>   Redis commands:
#>     APPEND: function
...

But if you want to use serverless Redis via rlite, we do that through using with the rrlite R package - and no need to start a server, of course.

src_rlite()
#> $type
#> [1] "redis"
#> 
#> $version
#> [1] '0.4.0'
#> 
#> $con
#> <redis_api>
#>   Redis commands:
#>     APPEND: function
...

Start your Riak server, then:

src_riak()
#> src: riak 2.1.6-0-gb00d1b4 [127.0.0.1/8098]
#> databases: test

CouchDB

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    1

etcd

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    1

Elasticsearch

Put the iris dataset into ES

src <- src_elasticsearch()
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  setosa

MongoDB

library("ggplot2")
src <- src_mongo(verbose = FALSE)
ff <- docdb_create(src, "diamonds", diamonds)
docdb_get(src, "diamonds")
#>        carat       cut color clarity depth table price     x     y     z
#> 1       0.23     Ideal     E     SI2  61.5  55.0   326  3.95  3.98  2.43
#> 2       0.21   Premium     E     SI1  59.8  61.0   326  3.89  3.84  2.31
#> 3       0.23      Good     E     VS1  56.9  65.0   327  4.05  4.07  2.31
#> 4       0.29   Premium     I     VS2  62.4  58.0   334  4.20  4.23  2.63
#> 5       0.31      Good     J     SI2  63.3  58.0   335  4.34  4.35  2.75
#> 6       0.24 Very Good     J    VVS2  62.8  57.0   336  3.94  3.96  2.48
#> 7       0.24 Very Good     I    VVS1  62.3  57.0   336  3.95  3.98  2.47
#> 8       0.26 Very Good     H     SI1  61.9  55.0   337  4.07  4.11  2.53
#> 9       0.22      Fair     E     VS2  65.1  61.0   337  3.87  3.78  2.49
...

Redis

src <- src_rlite()
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
...

Riak

src <- src_riak()
docdb_create(src, "iris", iris)
#> $success
#> [1] TRUE
#> 
#> $location
#> NULL
#> 
#> $key
#> [1] "iris"

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: 5 × 3
#>         cut mean_depth mean_price
#>       <chr>      <dbl>      <dbl>
#> 1      Fair   64.04168   4358.758
#> 2      Good   62.36588   3928.864
#> 3     Ideal   61.70940   3457.542
#> 4   Premium   61.26467   4584.258
#> 5 Very Good   61.81828   3981.760

skame/nodbi