---
title: "storr"
author: "Rich FitzJohn"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{storr}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

``` {r }
library(storr)
```

`storr` provides very simple key/value stores for R.  They attempt
to provide the most basic set of key/value lookup functionality
that is completely consistent across a range of different
underlying storage drivers (in memory storage, filesystem and
proper databases).  All the storage is _content addressable_, so
keys map onto hashes and hashes map onto data.

The `rds` driver stores contents at some path by saving out to rds
files.  Here I'm using a temporary directory for the path; the
driver will create a number of subdirectories here.
``` {r }
path <- tempfile("storr_")
st <- storr::storr_rds(path)
```

Alternatively you can create the driver explicitly:
``` {r eval=FALSE}
dr <- storr::driver_rds(path)
```

With this driver object we can create the `storr` object which is
what we actually interact with:
``` {r eval=FALSE}
st <- storr::storr(dr)
```

## Key-value store

The main way of interacting with a `storr` object is
`get`/`set`/`del` for getting, setting and deleting data stored at
some key.  To store data:
``` {r }
st$set("mykey", mtcars)
```

To get the data back
``` {r }
head(st$get("mykey"))
```

What is in the `storr`?
``` {r }
st$list()
```

Or, much faster, test for existence of a particular key:
``` {r }
st$exists("mykey")

st$exists("another_key")
```

To delete a key:
``` {r }
st$del("mykey")
```

It's gone!
``` {r }
st$list()
```

though the actual data is still stored in the database:
``` {r }
h <- st$list_hashes()
h
```

The hash of an object is computed using the `digest` package, and
can be done using the `hash_object` method of the storr.
``` {r }
st$hash_object(mtcars)
```

An object can be retrieved directly given its hash:
``` {r }
head(st$get_value(h))
```

similarly, we can test to see if an object is present in the
database using its hash:
``` {r }
st$exists_object(h)
```

though now that there are no keys pointing at the data it is
subject to garbage collection:
``` {r }
del <- st$gc()
del

st$list_hashes()
```

## Namespaces

At some point having everything stored in a great big bucket may
become too unstructured.  To help with this storr implements a very
simple "namespace" system that may help provide some structure.  It
is a single layer of hierarchy above keys; so every key belongs to
a namespace.  The default namespace is "objects" but this can be
configured when the storr is created.
``` {r }
st$default_namespace
```

The `list_namespaces()` method lists all known namespaces
``` {r }
st$list_namespaces()
```

To create a new namespace, simply assign an object into it:
``` {r }
st$set("a", runif(5), namespace = "other_things")
st$list_namespaces()
```

The `list()` method lists the contents of a single namespace
``` {r }
st$list()
st$list("other_things")
```

To get an object, you must use the correct namespace:
``` {r error = TRUE}
st$get("a")
st$get("a", "other_things")
```

## Bulk get/set

If you have many values to get or set, for some databases it will
be much more efficient to get and set them in bulk; this is
particularly the case with high-latency databases (e.g., anything
over a network connection, especially an internet connection).  To
help with this, storr implements `mget` and `mset` methods that
allow multiple values to retrieved or set.

The `mset` function allows multiple keys (and/or multiple
namespaces) and multiple data elements.  The data must have the
same `length()` as the number of keys being set.
``` {r }
st$mset(c("a", "b", "c"), list(1, 2, 3))
st$get("a")
```

The `mget` function fetches zero or more elements.
``` {r }
st$mget(c("a", "b", "c"))
```

`mget` *always* returns a list with the same number of elements as
the number of keys
``` {r }
st$mget("a")
st$mget(character(0))
```

With both `mset` and `mget`, both key and namespace can be vectors;
if either non-scalar, they must have the same length so the logic
is fairly predictable
``` {r }
st$mset("x", list("a", "b"), namespace = c("ns1", "ns2"))
st$mget("x", c("ns1", "ns2"))

st$mget(c("a", "b", "x"), c("objects", "objects", "ns1"))
```

## Import / export

Objects can be imported in and exported out of a `storr`:

Import from a list, environment or another `storr`
``` {r }
st$import(list(a = 1, b = 2))
st$list()
st$get("a")
```

Export to an environment (or another `storr`)
``` {r }
e <- st$export(new.env(parent = emptyenv()))
ls(e)
e$a

st_copy <- st$export(storr_environment())
st_copy$list()
st$get("a")

st2 <- storr::storr(driver = storr::driver_rds(tempfile("storr_")))
st2$list()
st2$import(st)
st2$list()
```

## Supported backends

* environments (`driver_environment`) - mostly for debugging and
  transient storage, but by far the fastest.
* on disk with rds (`driver_rds`) - zero dependencies, quite fast,
  will suffer under high concurrency because there is no file
  locking.
* DBI (`driver_dbi`) - uses (abuses?) a relational database to
  store the data.  This is not the fastest interface but allows for
  interprocess key/value stores where a relational database is
  supported.  All databases supported by DBI are supported (so at
  least `SQLite`, `MySQL` and `Postgres`).
* Redis (`driver_redis`) - uses
  [`redux`](https://github.com/richfitz/redux) to store the data in a
  Redis (`http://redis.io`) database.  About the same speed as rds
  (faster write, slower read at present), but can allow multiple R
  processes to share the same set of objects.
* `rlite` (`driver_rlite`) - stores data in an
  [`rlite`](https://github.com/seppo0010/rlite) database using
  [`rrlite`](https://github.com/ropensci/rrlite).  This is quite
  quick, but is stalled for general release because `rrlite` does not
  support windows.
* The in-development package
  [`thor`](https://github.com/richfitz/thor) provides an alternative
  on-disk storr that can handle multiple processes on a single
  machine.

## Implementation details

`storr` includes a few useful features that are common to all
drivers.

### Content addressable lookup

The only thing that is stored against a key is the hash of some
object.  Each driver does this a different way, but for the rds
driver it stores small text files that list the hash in them.  So:
``` {r }
dir(file.path(path, "keys", "objects"))
readLines(file.path(path, "keys", "objects", "a"))
st$get_hash("a")
```

Then there is one big pool of hash / value pairs:
``` {r }
st$list_hashes()
```

in the rds driver these are stored like so:
``` {r }
dir(file.path(path, "data"))
```

### Environment-based caching

Every time data passes across a `get` or `set` method, `storr`
stores the data in an environment within the `storr` object.
Because we store the content against its hash, it's always in sync
with what is saved to disk.  That means that the look-up process
goes like this:

1. Ask for a key, get returned the hash of the content
2. Check in the caching environment for that hash and return that
if present
3. If not present, read content from disk/db/wherever the driver
stores it and save it into the caching environment

Because looking up data in the environment is likely to be orders
of magnitude faster than reading from disks or databases, this
means that commonly accessed data will be accessed at a similar
speed to native R objects, while still immediately reflecting
changes to the content (because that would mean the hash changes)

To demonstrate:
``` {r }
st <- storr::storr(driver = storr::driver_rds(tempfile("storr_")))
```

This is the caching environment; currently empty
``` {r }
ls(st$envir)
```

Set some key to some data:
``` {r }
set.seed(2)
st$set("mykey", runif(100))
```

The environment now includes an object with a *name* that is the
same as the *hash* of its contents:
``` {r }
ls(st$envir)
```

Extract the object from the environment and hash it
``` {r }
st$hash_object(st$envir[[ls(st$envir)]])
```

When we look up the value stored against key `mykey`, the first
step is to check the key/hash map; this returns the key above (this
step *does* involve reading from disk)
``` {r }
st$get_hash("mykey")
```

It then calls `$get_value` to extract the value associated with
that hash - the first thing that function does is try to locate the
hash in the environment, otherwise it reads the data from wherever
the driver stores it.
``` {r }
st$get_value
```

The speed up is going to be fairly context dependent, but 5-10x
seems pretty good in this case (some of the overhead is simply a
longer code path as we call out to the driver).  For big bits of
data and slow network connections the difference will be much more
pronounced.
``` {r }
hash <- st$get_hash("mykey")
if (requireNamespace("rbenchmark")) {
  rbenchmark::benchmark(st$get_value(hash, use_cache = TRUE),
                        st$get_value(hash, use_cache = FALSE),
                        replications = 1000, order = NULL)[1:4]
}
```

### Classed exceptions

storr uses R's exception handling system and errors inspired from
Python to make it easy to program with `tryCatch`.

If a key is not in the database, storr will return a `KeyError`
(not `NULL` because storing a `NULL` value is a perfectly
reasonable thing to do).

If you _did_ want to return `NULL` when a key is requested but not
present, use `tryCatch` in this way:
``` {r }
tryCatch(st$get("no_such_key"),
         KeyError = function(e) NULL)
```

See `?tryCatch` for details.  The idea is that key lookup errors
will have the class `KeyError` so will be caught here and run the
given function (the argument `e` is the actual error object).
Other errors will not be caught and will still throw.

`HashErrors` will be rarer, but could happen (they might occur if
your driver supports expiry of objects).  We can simulate that by
setting a hash and deleting it:

``` {r }
st$set("foo", letters)
ok <- st$driver$del_object(st$get_hash("foo"))
st$flush_cache()
tryCatch(st$get("foo"),
         KeyError = function(e) NULL,
         HashError = function(e) message("Data is deleted"))
```

Here the `HashError` is triggered.

`KeyError` objects include `key` and `namespace` elements,
`HashError` objects include a `hash` element.  They both inherit
from `c("error", "condition")`.

Finally, when using an external storr (see ?driver_external) storr
will throw a `KeyErrorExternal` if the `fetch_hook` function errors
while trying to retrieve an external resource.
