Julia AbstractDict and AbstractSet data structures persisted (ACID) to disk
Author blenessy
5 Stars
Updated Last
1 Year Ago
Started In
August 2020


Build Status Coverage Status

Julia Dict and Set data structures safely persisted to disk.

All collections are backed by LMDB - a super fast B-Tree based embedded KV database with ACID guaranties. As with other B-Tree based databases reads are faster than writes. However, write performance is still decent (expect 1k-10k TPS).

Care was taken to make the data structures thread-safe. LMDB handles most of the locking well - we just have to exclusively lock the LMDB.Environment when writing to prevent multiple threads opening multile write transactions (deadlock will occur).

Quick Start

  1. Install this package:
    import Pkg
  2. Create an LMDB.Environment in a directory called data (in your current working directory):
    using PersistentCollections
    env = LMDB.Environment("data")
  3. Create an AbstractDict in your LMDB environment:
    dict = PersistentDict{String,String}(env)
  4. Use it as any other dict:
    dict["foo"] = "bar"
    @assert dict["foo"] == "bar"
    @assert collect(keys(dict)) == ["foo"]
    @assert collect(values(dict)) == ["bar"]
  5. (Optional) note the asymetric performance characteristic of LMDB (B-Tree) based database:
    @time dict["bar"] = "baz";  # Writes to LMDB (B-Tree) are relatively slow
    @time dict["bar"];          # Reads are very fast though :)

User Guide

Dynamic types

It is possible to create persistent collection of Any type although some methods will not be able to convert the value to the correct type because no metadata is stored for this in DB. Most notably the getindex method (e.g. dict["foo"]) will not return a converted value. To mitigate this limitation, use the get method, which includes a default value. The type of the default value (if other than nothing) will be used to convert the value to the desired type.

env = LMDB.Environment("data")
dict = PersistentDict{Any,Any}(env)
dict["foo"] == "bar"
dict["foo"]                  # PersistentCollections.LMDB.MDBValue{Nothing}(0x0000000000000003, Ptr{Nothing} @0x000000012c806ffd, nothing)
get(dict, "foo", "")         # "bar"
convert(String, dict["foo"]) # "bar"

Multiple persistent collections in the same LMDB Environment

It is possible if you need transactional consistency between multiple persistent collections:

  1. Create your LMDB.Environment with "named database" support by specifying the number of persistent collections yoy want with the maxdbs keyword argument:
    env = LMDB.Environment("data", maxdbs=2)
  2. Instantiate your persistent collections with a unique (within LMDB env.) id:
    dict1 = PersistentDict{String,String}(env, id="mydict1")
    dict2 = PersistentDict{String,Int}(env, id="mydict2")

Danger Zone: Manual sync writes to disc

Yes, you can expect significant increase with write throughput if you are willing to risk loosing your last written transactions. Please note that database integrity (risk of curruption) is not in danger here.

unsafe_env = LMDB.Environment("data", flags=LMDB.MDB_NOSYNC)
unsafe_dict = PersistentDict{String,String}(unsafe_env)
flush(unsafe_env) do 
    unsafe_dict["foo"] = "bar"
    unsafe_dict["foo"] = "baz"
end # <== data is flushed to disk here

This is equvalent to:

unsafe_env = LMDB.Environment("data", flags=LMDB.MDB_NOSYNC)
unsafe_dict = PersistentDict{String,String}(unsafe_env)
    unsafe_dict["foo"] = "bar"
    unsafe_dict["foo"] = "baz"

Running Tests

make test

Analyzing Code Coverage

make coverage


make bench



  • Travis CI integration
  • Coveralls integration (when public)
  • All platforms supported
  • Part of Julia Registry


  • Optimised implementation
  • Thread Safe
  • MDB_NOSYNC support
  • Named database support
  • Manual flush (sync) to disk


  • Implemented
  • Thread Safe
  • MDB_NOSYNC support
  • Named database support
  • Manual flush (sync) to disk


Lots of LMDB wrapping magic was pinched from wildart/LMDB.jl - who deserves lots of credits.