thor provides an wrapper around LMDB; the lightning memory-mapped database. This is an embedded key-value store; there is no server (like SQLite) - the database exists purely on disk and uses file locking to manage concurrent access between processes.

Key-value stores are simple systems for persistently storing values against keys. In the case of thor, both the keys and the data can be strings or (raw) data. This provides a low-level building block on which other applications can be built. The complications come from trying to efficiently query the store, or patterns like “add a new value but only if the previous value was foo”.

This package does not provide a faithful 1:1 mapping of the underlying LMDB C API because that requires too much care at the R level not to crash R! Instead, probably at the cost of some performance, thor provides a set of wrappers that try to prevent crashes by invalidating objects in the correct order. The approach taken in is very similar to the python interface to LMDB; py-lmdb.

Because the whole point of interacting with a database is side effects, thor uses R6 for the interface. This has the unfortunate effect of complicating the documentation somewhat because R’s documentation is focussed heavily on functions and the package provides only one function (thor::mdb_env) with everything else happening through methods of this object, and the objects that it creates.

thor tries to expose the underlying LMDB interface in a nested set of objects of increasing power (and complexity). The objects that the package provides are

  • mdb_env: the environment object, which is the interface to the database file. Everything starts here!

  • mdb_dbi: a database handle. Multiple databases may be stored within a single environment and if more than one is used then this object is passed about to control which database things affect.

  • mdb_txn: a transaction object. LMDB is a transactional database and this object is used to carry out actions within a transaction (such as getting and putting data).

  • mdb_cursor: a cursor. To go beyond basic get/put, cursors are required. These can be used to iterate through the ## *database, and to find entries.

  • mdb_proxy: a proxy for a result. This is used to defer copying data from the database into R for as long as possible. It’s a bit of an experiment so we’ll see how useful it turns out to be.

All of these objects have their own help pages, even though only mdb_env has an actual function. On those help pages every public function described (this is the same set that is printed when displaying the objects). There are other functions that can be reached using $ - functions beginning with a . should be considered private; using these can crash R. Other functions (such as format) exist because of the way thor uses R6.

For basic operations, one can just use the mdb_env object and ignore the rest of the package. To do more interesting things, you’ll need transactions (mdb_txn), and then perhaps you’ll need cursors (mdb_cursor). The proxy objects are available if you use transactions.

The environment

The first step is to create an “environment”; this holds one or more “databases” (though in the most simple case you can forget that detail and just treat the environment as a database).

env <- thor::mdb_env(tempfile())

The first argument to thor::mdb_env is the filename - this is a directory where the database files will be kept. Here I am using a temporary file for the database.

As an R6 object, the database environment has a number of methods that can be used to perform actions on the database. The print method groups these by theme:

## <mdb_env>
##   Informational:
##     path()
##     flags()
##     info()
##     stat()
##     maxkeysize()
##     maxreaders()
##   Transactions:
##     begin(db = NULL, write = FALSE, sync = NULL, metasync =  ...
##     with_transaction(fun, db = NULL, write = FALSE)
##   Databases:
##     open_database(key = NULL, reversekey = FALSE, create = TRUE)
##     drop_database(db, delete = TRUE)
##   Management:
##     sync(force = FALSE)
##     copy(path, compact = FALSE)
##     close()
##     destroy()
##     reader_list()
##     reader_check()
##   Helpers:
##     get(key, missing_is_error = TRUE, as_raw = NULL, db = NULL)
##     put(key, value, overwrite = TRUE, append = FALSE, db = NULL)
##     del(key, db = NULL)
##     exists(key, db = NULL)
##     list(starts_with = NULL, as_raw = FALSE, size = NULL, db ...
##     mget(key, as_raw = NULL, db = NULL)
##     mput(key, value, overwrite = TRUE, append = FALSE, db =  ...
##     mdel(key, db = NULL)

The last group Helpers are wrappers that let you ignore the transactional nature of LMDB if you just want to do really simple things.

The database is currently empty:

## character(0)

But we can add some data to it:

for (i in 1:10) {

Now there are 10 keys in the database, each holding a value:

keys <- env$list()
##  [1] "careful_gaur"                    "necessary_hen"                  
##  [3] "preagricultural_icterinewarbler" "rhombohedral_walrus"            
##  [5] "uninspirable_muntjac"            "unterrestrial_oryx"             
##  [7] "upstanding_vixen"                "waiting_xiaosaurus"             
##  [9] "wet_pullet"                      "zealous_illadopsis"

LMDB stores keys in sorted order (not necessarily R’s sorted order - you can see how LMDB sorts things with the cmp method of a transaction - see ?mdb_txn), so list will return things in that order.

Each key has a value (in this case just a hex string)

## [1] "58d2ab26954cfabd74d84197d526b33d"

Delete a key with

## [1] TRUE

and now there are only 9 keys

## [1] 9

Test for existence of a key with exists

## [1] FALSE
## [1] TRUE

The mget method will get multiple keys at once, mset will set multiple key/value pairs at once and mdel will delete multiple keys at once.


For anything more complicated than this you would want to use transactions (see below).

The Informational methods all return information about the state of the LMDB environment;

The path that the data is stored in

## [1] "/tmp/RtmpiHjhjJ/file1dcd2efd98bc"

which will contain two files - the actual data and a lock file (see lmdb’s documentation for more on these).

## [1] "data.mdb" "lock.mdb"

Flags that the environment was opened with (this corresponds to the arguments to the thor::mdb_env function)

##   subdir readonly writemap metasync     sync mapasync     lock  rdahead 
##     TRUE    FALSE    FALSE     TRUE     TRUE    FALSE     TRUE     TRUE 
##  meminit 
##     TRUE

A couple of different forms of (somewhat cryptic) information about the state of the environment

##    mapsize  last_pgno last_txnid maxreaders numreaders 
##    1048576          7         12        126          1
##          psize          depth   branch_pages     leaf_pages overflow_pages 
##           4096              0              0              0              0 
##        entries 
##              0

(Note entries in env$stat() is the number of keys in the database)


LMDB is transactional; everything that happens to the database, read or write, happens as a transaction. For a write transaction either the whole transaction happens or none of it happens. For both read and write transactions, the “view” of the database is consistent from the beginning to the end of a transaction. So if you have a read transaction and while it is doing things a write transaction writes to the database, the read transaction does not “see” these changes. You can only have one write transaction at once, but as many read transactions as you’d like.

txn <- env$begin(write = TRUE)

As for mdb_env, the transaction object prints methods grouped by theme

## <mdb_txn>
##   Informational:
##     id()
##     stat()
##   Finish:
##     commit()
##     abort(cache = TRUE)
##   Cursors:
##     cursor()
##   Data:
##     get(key, missing_is_error = TRUE, as_proxy = FALSE, as_r ...
##     put(key, value, overwrite = TRUE, append = FALSE)
##     del(key)
##     exists(key)
##     list(starts_with = NULL, as_raw = FALSE, size = NULL)
##     mget(key, as_proxy = FALSE, as_raw = NULL)
##     mput(key, value, overwrite = TRUE, append = FALSE)
##     mdel(key)
##     replace(key, value, as_raw = NULL)
##     pop(key, as_raw = NULL)
##   Compare:
##     cmp(a, b)

Simple operations (put, get, del, etc)

To insert data into the database, use the put method

txn$put("key", "value")

…to get it back out again, use the get method

## [1] "value"

…to delete it, use the del method, which returns TRUE if the object was deleted and FALSE if not

## [1] TRUE
## [1] FALSE

To test if an key exists or not, use the exists method (which uses a cursor internally - see below)

## [1] FALSE

The helper functions mget, mput and mdel functions do get / put and del to multiple keys at once, more efficiently than looping in R:

values <- ids::sentence(length(keys), style = "sentence")
txn$mput(keys, values)

To list keys, use list

##  [1] "careful_gaur"                    "necessary_hen"                  
##  [3] "preagricultural_icterinewarbler" "rhombohedral_walrus"            
##  [5] "uninspirable_muntjac"            "unterrestrial_oryx"             
##  [7] "upstanding_vixen"                "waiting_xiaosaurus"             
##  [9] "wet_pullet"                      "zealous_illadopsis"

And to fetch multiple values (as_raw is explained below)

txn$mget(keys[1:3], as_raw = FALSE)
## [1] "21 upbeat walruses assembling madly"  
## [2] "4 eager lizards spurting purposefully"
## [3] "7 unkempt weasels hobbling youthfully"

Or delete multiple values


exists is itself always vectorised


Because the database is transactional, we can now either use txn$commit() to save the changes or txn$abort() to discard the changes.

Multiple transactions at once

As well as being able to roll back a transaction, the other function they serve is that each transaction gets a consistent view of the database. At this point we have one write transaction running, but it’s not committed yet. So if we start another transaction, it will not see any of the uncommitted “changes” that our transaction has made:

txn_new <- env$begin()
## character(0)

(or equivalently, env$list()). Because of the design of LMDB, you cannot have multiple active write transactions at once

env$put("key", "value")
## Error in self$.check_write(): Write transaction is already active for this environment
env$begin(write = TRUE)
## Error in self$.check_write(): Write transaction is already active for this environment

(if a write transaction is made by another process against the same LMDB database, then it will wait for our transaction to complete before its write transaction will start - this will cause R to be unresponsive during this time)

Let’s commit the changes made:


After being committed a transaction cannot be reused:

## Error in mdb_cursor_open(self$.ptr, self$.db$.ptr): txn has been cleaned up; can't use!

New transactions can now see the changes

## [1] "rhombohedral_walrus"  "uninspirable_muntjac" "unterrestrial_oryx"  
## [4] "upstanding_vixen"     "waiting_xiaosaurus"   "wet_pullet"          
## [7] "zealous_illadopsis"

But importantly old ones can’t

## character(0)

This is because the old transaction has a consistent view of the database - from the point that it starts to the point that it ends, a read-only transaction will see the same data and a read-write transaction will only see changes that it has made.

(cleaning things up a little)


Non-string data

thor (and LMDB) can handle two types of data; strings (as above) and raw vectors. Raw vectors can be used to serialise R objects using serialize, which allows storing of arbitrary data. This is the approach taken by redux among other packages.

All strings can be represented in raw vectors but the reverse is not true; character strings may not contain the null byte and the resulting string may not make sense. thor uses the presence of a null byte as a heuristic when it needs to test if a value is raw or not.

So the string “hello” can be converted to raw:

## [1] 68 65 6c 6c 6f

But the set of bytes 2a 00 ff cannot be:

rawToChar(as.raw(c(42, 0, 255)))
## Error in rawToChar(as.raw(c(42, 0, 255))): embedded nul in string: '*\0\xff'

This poses some problems for specifying and predicting return types, which will be explored below. thor tries hard to set the return type predictably; a few boolean arguments to the function determine the type rather than the contents of the data.

txn <- env$begin(write = TRUE)

First, this is why one might want to store raw data in a database. Suppose we want to store the contents of mtcars as a value. It’s not a string so we can’t do

txn$put("mtcars", mtcars)
## Error in mdb_put(self$.ptr, self$.db$.ptr, key, value, overwrite, append): Invalid data type for 'value'; expected string or raw

First we should serialise it to raw:

mtcars_ser <- serialize(mtcars, NULL)

which creates a fairly long string of bytes

##  raw [1:3807] 58 0a 00 00 ...

converting back from this to an R object is easy with unserialize

identical(unserialize(mtcars_ser), mtcars)
## [1] TRUE
txn$put("mtcars", mtcars_ser)
## [1] "mtcars"

When fetching the data, thor will work out that this is raw data and return a raw vector:

## [1] "raw"

So we can now store and retrieve arbitrary R objects into the database.

identical(unserialize(txn$get("mtcars")), mtcars)
## [1] TRUE
## [1] TRUE

Automatic type detection is a mixed blessing (like pitfalls with sapply) and thor provides mechanisms for taming it.

Here are two values as raw vectors - one that can be converted to a string and one that can’t

bytes <- as.raw(c(42, 0, 255))
string <- charToRaw("hello!")

txn$put("bytes", bytes)
txn$put("string", string)

The value of the return type is determined both by the value of the object and by the value of the argument as_raw.

stored as_raw result
string NULL character
string FALSE character
string TRUE raw
bytes NULL character
bytes FALSE error
bytes TRUE raw

for example

## [1] "hello!"

is character because as_raw is NULL and the value can be represented as a string, while

## [1] 2a 00 ff

is raw because the value cannot be represented as a string. Specifying as_raw = TRUE will always return raw because everything can be represented as raw. And specifying as_raw = FALSE will throw an error for a value that cannot be converted into a string.

For mget, it’s a bit trickier because we need to check every value as they come out to see if it’s a string or a character. The rules here are:

stored as_raw container contents
string NULL list character
string FALSE character (character)
string TRUE list raw
bytes NULL list raw
bytes FALSE error (error)
bytes TRUE list raw
mixed NULL list mixed
mixed FALSE error (error)
mixed NULL list raw

That is, if as_raw = FALSE we return a character or error if this is not possible, otherwise (as_raw = TRUE, as_raw = NULL) we always return a list. This should make programming with because the value of as_raw entirely predicts the container type. Within the container, the rule for contents is the same as for get().

So, the default (as_raw = NULL) returns a list with auto-detected types for each element:

txn$mget(c("string", "bytes"))
## [[1]]
## [1] "hello!"
## [[2]]
## [1] 2a 00 ff

Or we could get both as raw

txn$mget(c("string", "bytes"), as_raw = TRUE)
## [[1]]
## [1] 68 65 6c 6c 6f 21
## [[2]]
## [1] 2a 00 ff

But because one of the values is binary, we can’t do this:

txn$mget(c("string", "bytes"), as_raw = FALSE)
## Error in thor_mget(self$.ptr, self$.db$.ptr, key, as_proxy, as_raw): value contains embedded nul bytes; cannot return string

But if we only pull strings it’s ok:

txn$mget(c("string", "string"), as_raw = FALSE)
## [1] "hello!" "hello!"


LMDB will allow multiple process to access the database at the same time, but enforce only one write transaction. However to make that work relies on file locking. The LMDB documentation covers issues around more detail - all the issues there apply to thor, though some of them are ensured by the thor’s design (and because R is single threaded some do not really affect us).

  • crashed processes may leave stale lockfiles that may need to be removed by reader_check()

  • do not use LMDB database on remote systems, even between processes on the same host, as file locking and memory map sync may be unreliable. This may be disappointing, but if you have multiple hosts you really do need a server based solution, not a file based one.

  • avoid long-lived transactions, as they can cause the database size to grow quickly.