---
title: "Working with Database Storage Backends in `rdflib`"
author: "Carl Boettiger"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  cache = TRUE
)
```


By default, `rdflib` stores triples in memory, which can be read in from and written out to files on disk using a variety of serializations (`rdfxml`, `json-ld`, `ntriples`, `nquads` or `turtle`).  This is analgous to reading in a `data.frame` object from a `csv`, `tsv`, or other text-delimited file, which requires the full data object to be read into memory before a user can manipulate it.  This approach runs into limitations with very large datasets, which may exceed the memory available.  Just as R packages such as `dplyr` offer the ability to perform manipulations with very large data through a connection to a database backend such as SQLite, MySQL or PostgreSQL, `rdflib` can rely on these and other databases to provide a disk-based backend. 

### Installation

Unfortunately, at this time, support for these storage devices is not included in the prebuild Mac and Windows binaries of the `redland` R package.  Users wanting to take advantage of disk-based storage must instead build and install the `redland` R package from source; and in some cases, also build the `redland` C libraries from source.  This package provides a [Dockerfile](https://github.com/ropensci/rdflib/blob/master/inst/docker/Dockerfile) containing a portable recipe for building the library with support for the 5 main backend storage devices: SQLite, MySQL, PostgreSQL, Virtuoso, and Berkeley DB.  This vignette documents the installation and use of these devices.  

Also see the [official documentation](http://librdf.org/docs/api/redland-storage-modules.html) for the redland C library discussing all supported storage devices.  


# Benchmarks

Here we show examples of reading in a modest set of 190,000 triples from an `nquads` file and executing a simple SPARQL query using each the five backends supported by `rdflib`.  First we load the libraries and prepare an example file by triple-ifying 10,000 rows of the `flights` data.frame:

```{r}
library(rdflib)
library(nycflights13)
```

```{r}
example <- flights[1e4,]

system.time(
write_nquads(example, "example.nq", prefix = "flights")
)

system.time(
write_nquads(flights, "flights.nq", prefix = "flights")
)
```

## In Memory

Because the dataset is small enough to easily fit in memory, the default in-memory option is an obvious choice with excellent performance.  Note that this option will not be possible with larger triplestores (e.g. with millions or more triples).  Our testing has found that even on machines with 100GB+ of RAM, the redland in-memory backend is not able to take advantage of that additional memory and disk-based backends are required. 

```{r}
triplestore <- rdf()
```

```{r}

system.time(
  read_nquads("example.nq", rdf = triplestore) # smaller set
)

```

```{r}
query <- 
  'SELECT  ?carrier ?flight ?origin ?dep_delay
WHERE {
?flight <flights:carrier>  ?carrier .
?flight <flights:dep_delay>  ?dep_delay .
?flight <flights:origin> ?origin
}'
```

```{r}
system.time(
df <- rdf_query(triplestore, query)
)
```

```{r}
rdf_free(triplestore)
```


## BDB

The [Berkeley DB](https://en.wikipedia.org/wiki/Berkeley_DB) is a simple key-value store 

> It is the most mature and primary persistent store and suitable for large models, tested in the 2-3 million range.

Berkeley DB is a simple disk-based storage option.  Install both the redland libraries and the berkeley-db (e.g. `bd-dev` on Debian/Ubuntu) libraries, and then install `redland` from source.  BDB is relatively fast for data too large for memory.


```{r}
triplestore <- rdf(storage="BDB", new_db = TRUE)
system.time(
  read_nquads("flights.nq", rdf = triplestore)
)
```


```{r}
system.time(
df <- rdf_query(triplestore, query)
)
```

```{r}
rdf_free(triplestore)
## Becuase BDB is just a hash table, redland needs three separate files:
unlink("rdflib-po2s.db")
unlink("rdflib-so2p.db")
unlink("rdflib-sp2o.db")
```

## Virtuoso

Unlike the other backends which use general purpose relational databases or key-value stores, [Virtuoso](https://en.wikipedia.org/wiki/Virtuoso_Universal_Server) dedicated to RDF-based data.  Virtuoso is a popular open source database for RDF with a rich set of built-in interfaces and features, but we can also interact with it directly through the `redland` bindings just like we do any other backend in `rdflib`.  Virtuoso setup may be slightly more involved for individuals unfamiliar with it, but will probably provide the best performance in the case of very large triplestores.  The example below shows a new database setup, but `rdflib` can also connect to any existing Virtuoso database. 

```{r}
triplestore <- rdf(storage = "virtuoso", 
                   user = "dba", 
                   password = "dba", 
                   dsn = "Local Virtuoso"
                   )

system.time(
  read_nquads("flights.nq", rdf = triplestore)
)
```


```{r}
system.time(
df <- rdf_query(triplestore, query)
)
df <- rdf_query(triplestore, "SELECT ?s ?p ?o WHERE{ ?s ?p ?o }")
df
```


```{r}
rdf_free(triplestore)
```

Or on remote virtuoso:


```{r}
triplestore <- rdf(storage = "virtuoso", 
                   user = "dba", 
                   password = "dba", 
                   host = "virtuoso:1111"
                   )

system.time(
  read_nquads("flights.nq", rdf = triplestore)
)
```




## POSTGRES

Postgres and MySQL are ubiquitious relational databases.  This backend requires the `redland` binaries are built from source with this support enabled, which is not the case for pre-built Mac or Linux binaries.   

```{r}
triplestore <- rdf(storage = "postgres", 
                   host = "postgres", 
                   user = "postgres", 
                   password = "rdflib", 
                   new_db = TRUE)
system.time(
  read_nquads("flights.nq", rdf = triplestore)
)
```



```{r}
system.time(
df <- rdf_query(triplestore, query)
)
```

```{r}
rdf_free(triplestore)
```  

## MySQL

```{r}
triplestore <- rdf(storage = "mysql", 
                   host = "mariadb", 
                   user = "root", 
                   password = "rdflib", 
                   database = "mysql",
                   new_db = TRUE
                  )
  read_nquads("flights.nq", rdf = triplestore)
 
```


```{r}
system.time(df <- rdf_query(triplestore, query))
```

```{r}
rdf_free(triplestore)

```




## SQLite


[SQLite](https://en.wikipedia.org/wiki/SQLite) is relatively easy to set up, but appears to have rather poor overall performance. Requires SQLite development libraries installed (should work 'out-of-the-box' with Mac binaries for `redland` package).  


```{r}
triplestore <- rdf(storage="sqlite", new_db = TRUE, name="rdflib.sqlite")

system.time(
  read_nquads("flights.nq", rdf = triplestore)
)
```


```{r}
system.time(
df <- rdf_query(triplestore, query)
)
```

```{r}
rdf_free(triplestore)
```

# Building redland with full backend support

Getting full support for the above backend databases through the `redland` R package is not trivial.  The `redland` R package provides bindings to the redland libraries.  Unfortunately, commonly available binary versions of those libraries, such as `librdf0-dev` on Debian, `redland` on Mac OSX brew, and the statically linked versions for Mac and Windows shipping in the R package, do not build those libraries with the optional support for all backends. (NOTE: it is the C library itself which must be built from source with these options, not just the R package source).  Consequently, users must build `librdf` from the original sources, <https://github.com/dajobe/librdf> with all backend linking libraries available, and then also build the `redland` R package from source, to be able to access all of these backends. On a Debian or Ubuntu system this looks like the following:

```bash
apt-get update && apt-get -yq install \
libxml2-dev \
libcurl4-openssl-dev \
libssl-dev \
git \
automake \
libtool \
gtk-doc-tools \
bison \
flex \
libgmp-dev  \
libmhash-dev \
libgcrypt20-dev \
libpcre3-dev \
libv8-dev \
libjq-dev \
libpq-dev \
libdb-dev \
libsqlite3-dev \
libmariadbclient-dev \
librdf-storage-virtuoso \
virtuoso-server \
unixodbc-dev
```

Now we can build `raptor` (parsers), `rasqal` (sparql queries) and `rdflib` from source:

```
git clone git://github.com/dajobe/raptor.git && \
cd raptor && \
./autogen.sh && \
make && \
make install && \
cd .. && \
git clone git://github.com/dajobe/rasqal.git && \
cd rasqal && \
./autogen.sh && \
make && \
make install && \
cd .. && \
git clone git://github.com/dajobe/librdf.git && \
cd librdf && \
./autogen.sh && \
make && \
make install
```

A See the [Dockerfile](https://github.com/ropensci/rdflib/tree/master/inst/docker/Dockerfile) in `inst/docker` for an example of this, or simply use the [Rocker-based](https://rocker-project.org) image `ropensci/rdflib`.  

# Testing

`rdflib` uses Circle-CI to test database backends using `docker-compose`.  `docker-compose` pulls down dedicated docker containers for `postgres` and `mariadb`, along with the `ropensci/rdflib` container, which includes a version of `redland` compiled with support for all five major backend storage systems.  See the [Dockerfile](https://github.com/ropensci/rdflib/tree/master/inst/docker/Dockerfile) in `inst/docker` and associated [docker-compose.yml]((https://github.com/ropensci/rdflib/tree/master/docker-compose.yml) used in testing for an example of this configuration.  You can also pull the Docker image `ropensci/rdflib` from Docker Hub for testing with all these libraries installed.     





The badge below (also on the package README) indicates that these dedicated tests are passing.

[![CircleCI](https://circleci.com/gh/ropensci/rdflib.svg?style=svg)](https://circleci.com/gh/ropensci/rdflib)