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Programming language: Elixir
License: Apache License 2.0
Latest version: v0.9.1

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An Alternative Elixir Driver for MongoDB

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  • supports MongoDB versions 3.2, 3.4, 3.6, 4.x, 5.x
  • connection pooling (through DBConnection 2.x)
  • streaming cursors
  • performant ObjectID generation
  • aggregation pipeline
  • replica sets
  • support for SCRAM-SHA-256 (MongoDB 4.x)
  • support for GridFS (See)
  • support for change streams api (See)
  • support for bulk writes (See)
  • support for driver sessions (See)
  • support for driver transactions (See)
  • support for command monitoring (See)
  • support for retryable reads (See)
  • support for retryable writes (See)
  • support for simple structs using the Mongo.Encoder protocol
  • support for complex and nested documents using the Mongo.Collection macros
  • support for streaming protocol (See)



Add mongodb_driver to your mix.exs deps.

defp deps do
  [{:mongodb_driver, "~> 0.9.0"}]

Then run mix deps.get to fetch dependencies.

Simple Connection to MongoDB

# Starts an unpooled connection
{:ok, conn} = Mongo.start_link(url: "mongodb://localhost:27017/my-database")

# Gets an enumerable cursor for the results
cursor = Mongo.find(conn, "test-collection", %{})

|> Enum.to_list()
|> IO.inspect

To specify a username and password, use the :username, :password, and :auth_source options.

# Starts an unpooled connection
{:ok, conn} =
    Mongo.start_link(url: "mongodb://localhost:27017/db-name",
                     username: "test_user",
                     password: "hunter2",
                     auth_source: "admin_test")

# Gets an enumerable cursor for the results
cursor = Mongo.find(conn, "test-collection", %{})

|> Enum.to_list()
|> IO.inspect

For secure requests, you may need to add some more options; see the "AWS, TLS and Erlang SSL ciphers" section below.

Failing operations return a {:error, error} tuple where error is a Mongo.Error object:

   code: 13435,
   error_labels: [],
   host: nil,
   message: "not master and slaveOk=false",
   resumable: true,
   retryable_reads: true,
   retryable_writes: true



Using $and

Mongo.find(:mongo, "users", %{"$and" => [%{email: "[email protected]"}, %{first_name: "first_name"}]})

Using $or

Mongo.find(:mongo, "users", %{"$or" => [%{email: "[email protected]"}, %{first_name: "first_name"}]})

Using $in

Mongo.find(:mongo, "users", %{email: %{"$in" => ["[email protected]", "[email protected]"]}})


To insert a single document:

Mongo.insert_one(top, "users", %{first_name: "John", last_name: "Smith"})

To insert a list of documents:

Mongo.insert_many(top, "users", [
  %{first_name: "John", last_name: "Smith"},
  %{first_name: "Jane", last_name: "Doe"}

Data Representation

Since BSON documents are ordered Elixir maps cannot be used to fully represent them. This driver chose to accept both maps and lists of key-value pairs when encoding but will only decode documents to lists. This has the side-effect that it's impossible to discern empty arrays from empty documents. Additionally, the driver will accept both atoms and strings for document keys but will only decode to strings. BSON symbols can only be decoded.

BSON                Elixir
----------          ------
double              0.0
string              "Elixir"
document            [{"key", "value"}] | %{"key" => "value"} (1)
binary              %BSON.Binary{binary: <<42, 43>>, subtype: :generic}
UUID                %BSON.Binary{binary: <<42, 43>>, subtype: :uuid}
UUID (old style)    %BSON.Binary{binary: <<42, 43>>, subtype: :uuid_old}
object id           %BSON.ObjectId{value: <<...>>}
boolean             true | false
UTC datetime        %DateTime{}
null                nil
regex               %BSON.Regex{pattern: "..."}
JavaScript          %BSON.JavaScript{code: "..."}
timestamp           #BSON.Timestamp<value:ordinal>"
integer 32          42
integer 64          #BSON.LongNumber<value>
symbol              "foo" (2)
min key             :BSON_min
max key             :BSON_max
decimal128          Decimal{}

Writing your own encoding info

If you want to write a custom struct to your mongo collection - you can do that by implementing Mongo.Encoder protocol for your module. The output should be a map, which will be passed to the Mongo database.


defmodule CustomStruct do
  @fields [:a, :b, :c, :id]
  @enforce_keys @fields
  defstruct @fields
  defimpl Mongo.Encoder do
    def encode(%{a: a, b: b, id: id}) do
        _id: id,
        a: a,
        b: b,
        custom_encoded: true

So, given the struct:

%CustomStruct{a: 10, b: 20, c: 30, id: "5ef27e73d2a57d358f812001"}

it will be written to database, as:

  "a": 10,
  "b": 20,
  "custom_encoded": true,
  "_id": "5ef27e73d2a57d358f812001"


While using the Mongo.Encoder protocol give you the possibility to encode your structs into maps the opposite way to decode those maps into structs is missing. To handle it you can use the Mongo.Collection which provides some boilerplate code for a better support of structs while using the MongoDB driver

  • automatic load and dump function
  • reflection functions
  • type specification
  • support for embedding one and many structs
  • support for after load function
  • support for before dump function
  • support for id generation
  • support for default values
  • support for derived values

When using the MongoDB driver only maps and keyword lists are used to represent documents. If you prefer to use structs instead of the maps to give the document a stronger meaning or to emphasize its importance, you have to create a defstruct and fill it from the map manually:

defmodule Label do
  defstruct name: "warning", color: "red"

iex> label_map = Mongo.find_one(:mongo, "labels", %{})
  %{"name" => "warning", "color" => "red"}
iex> label = %Label{name: label_map["name"], color: label_map["color"]}

We have defined a module Label as defstruct, then we get the first label document the collection labels. The function find_one returns a map. We convert the map manually and get the desired struct. If we want to save a new structure, we have to do the reverse. We convert the struct into a map:

iex> label = %Label{}
iex> label_map = %{"name" => label.name, "color" => label.color}
iex> {:ok, _} = Mongo.insert_one(:mongo, "labels", label_map)

Alternatively, you can also remove the __struct__ key from label. The MongoDB driver automatically converts the atom keys into strings (Or use the Mongo.Encode protocol)

iex>  Map.drop(label, [:__struct__])
%{color: :red, name: "warning"}

If you use nested structures, the work becomes a bit more complex. In this case, you have to use the inner structures convert manually, too. If you take a closer look at the necessary work, two basic functions can be derived:

  • load Conversion of the map into a struct.
  • dump Conversion of the struct into a map.

Mongo.Collection provides the necessary macros to automate this boilerplate code. The above example can be rewritten as follows:

defmodule Label do
    use Mongo.Collection

    document do
      attribute :name, String.t(), default: "warning"
      attribute :color, String.t(), default: :red

This results in the following module:

defmodule Label do

    defstruct [name: "warning", color: "red"]

    @type t() :: %Label{String.t(), String.t()}

    def new()...
    def load(map)...
    def dump(%Label{})...
    def __collection__(:attributes)...
    def __collection__(:types)...
    def __collection__(:collection)...
    def __collection__(:id)...


You can now create new structs with the default values and use the conversion functions between map and structs:

iex(1)> x = Label.new()
%Label{color: :red, name: "warning"}
iex(2)> m = Label.dump(x)
%{color: :red, name: "warning"}
iex(3)> Label.load(m, true)
%Label{color: :red, name: "warning"}

The load/2 function distinguishes between keys of type binarys load(map, false) and keys of type atoms load(map, true). The default is load(map, false):

iex(1)> m = %{"color" => :red, "name" => "warning"}
iex(2)> Label.load(m)
%Label{color: :red, name: "warning"}

If you would now expect atoms as keys, the result of the conversion is not correct in this case:

iex(3)> Label.load(m, true)
%Label{color: nil, name: nil}

The background is that MongoDB always returns binarys as keys and structs use atoms as keys.

For more information look at the module documentation Mongo.Collection.

Of course, using the Mongo.Collection is not free. When loading and saving, the maps are converted into structures, which increases CPU usage somewhat. When it comes to speed, it is better to use the maps directly.

Using the Repo Module

For convenience, you can also use the Mongo.Repo module in your application to configure the MongoDB application.

Simply create a new module and include the use Mongo.Repo macro:

defmodule MyApp.Repo do
  use Mongo.Repo,
    otp_app: :my_app,
    topology: :mongo

To configure the MongoDB add the configuration to your config.exs:

config :my_app, MyApp.Repo,
  url: "mongodb://localhost:27017/my-app-dev",
  timeout: 60_000,
  idle_interval: 10_000,
  queue_target: 5_000

Finally, we can add the Mongo instance to our application supervision tree:

  children = [
    # ...
    {Mongo, MyApp.Repo.config()},
    # ...

In addition, the convenient configuration, the Mongo.Repo module will also include query functions to use with your Mongo.Collection modules.

For more information check out the Mongo.Repo module documentation and the Mongo module documentation.


You config the logging output by adding in your config file this line

config :mongodb_driver, log: true

The attribute log supports true, false or a log level like :info. The default value is false. If you turn logging on, then you will see log output (command, collection, parameters):

[info] CMD find "my-collection" [filter: [name: "Helga"]] db=2.1ms


The driver uses the :telemetry package to emit the execution duration for each command. The event name is [:mongodb_driver, :execution] and the driver uses the following meta data:

metadata = %{
type: :mongodb_driver,
command: command,
params: parameters,
collection: collection,
options: Keyword.get(opts, :telemetry_options, [])

:telemetry.execute([:mongodb_driver, :execution], %{duration: duration}, metadata)

In a Phoenix application with installed Phoenix Dashboard the metrics can be used by defining a metric in the Telemetry module:

        tags: [:collection, :command],
        unit: {:microsecond, :millisecond}

Then you see for each collection the execution time for each different command in the Dashboard metric page.

Connection Pooling

The driver supports pooling by DBConnection (2.x). By default mongodb_driver will start a single connection, but it also supports pooling with the :pool_size option. For 3 connections add the pool_size: 3 option to Mongo.start_link and to all function calls in Mongo using the pool:

# Starts an pooled connection
{:ok, conn} = Mongo.start_link(url: "mongodb://localhost:27017/db-name", pool_size: 3)

# Gets an enumerable cursor for the results
cursor = Mongo.find(conn, "test-collection", %{})

|> Enum.to_list()
|> IO.inspect

If you're using pooling it is recommended to add it to your application supervisor:

def start(_type, _args) do
  import Supervisor.Spec

  children = [
    worker(Mongo, [[name: :mongo, database: "test", pool_size: 3]])

  opts = [strategy: :one_for_one, name: MyApp.Supervisor]
  Supervisor.start_link(children, opts)

Due to the mongodb specification, an additional connection is always set up for the monitor process.

Replica Sets

By default, the driver will discover the deployment's topology and will connect to the replica set automatically, using either the seed list syntax or the URI syntax. Assuming the deployment has nodes at hostname1.net:27017, hostname2.net:27017 and hostname3.net:27017, either of the following invocations will discover the entire deployment:

{:ok, pid} = Mongo.start_link(database: "test", seeds: ["hostname1.net:27017"])

{:ok, pid} = Mongo.start_link(url: "mongodb://hostname1.net:27017/test")

To ensure that the connection succeeds even when some of the nodes are not available, it is recommended to list all nodes in both the seed list and the URI, as follows:

{:ok, pid} = Mongo.start_link(database: "test", seeds: ["hostname1.net:27017", "hostname2.net:27017", "hostname3.net:27017"])

{:ok, pid} = Mongo.start_link(url: "mongodb://hostname1.net:27017,hostname2.net:27017,hostname3.net:27017/test")

Using an SRV URI also discovers all nodes of the deployment automatically.

Auth Mechanisms

For versions of Mongo 3.0 and greater, the auth mechanism defaults to SCRAM. If you'd like to use MONGODB-X509 authentication, you can specify that as a start_link option.

{:ok, pid} = Mongo.start_link(database: "test", auth_mechanism: :x509)

AWS, TLS and Erlang SSL Ciphers

Some MongoDB cloud providers (notably AWS) require a particular TLS cipher that isn't enabled by default in the Erlang SSL module. In order to connect to these services, you'll want to add this cipher to your ssl_opts:

{:ok, pid} = Mongo.start_link(database: "test",
      ssl_opts: [
        ciphers: ['AES256-GCM-SHA384'],
        cacertfile: "...",
        certfile: "...")

Change Streams

Change streams are available in replica set and sharded cluster deployments and tell you about changes of documents in collections. They work like endless cursors.

The special thing about change streams is that they are resumable: in case of a resumable error, no exception is propagated to the application, but instead the cursor is re-scheduled at the last successful location.

The following example will never stop, thus it is a good idea to use a process for reading from change streams:

seeds = ["hostname1.net:27017", "hostname2.net:27017", "hostname3.net:27017"]
{:ok, top} = Mongo.start_link(database: "my-db", seeds: seeds, appname: "getting rich")
cursor =  Mongo.watch_collection(top, "accounts", [], fn doc -> IO.puts "New Token #{inspect doc}" end, max_time: 2_000 )
cursor |> Enum.each(fn doc -> IO.puts inspect doc end)

An example with a spawned process that sends messages to the monitor process:

def for_ever(top, monitor) do
    cursor = Mongo.watch_collection(top, "users", [], fn doc -> send(monitor, {:token, doc}) end)
    cursor |> Enum.each(fn doc -> send(monitor, {:change, doc}) end)

spawn(fn -> for_ever(top, self()) end)

For more information see Mongo.watch_collection/5


To create indexes you can call the function Mongo.create_indexes/4:

indexes =  [[key: [files_id: 1, n: 1], name: "files_n_index", unique: true]]
Mongo.create_indexes(topology_pid, "my_collection", indexes, opts)

You specify the indexes parameter as a keyword list with all options described in the documentation of the createIndex command.

For more information see:

  • Mongo.create_indexes/4
  • Mongo.drop_index/4

Bulk Writes

The motivation for bulk writes lies in the possibility of optimization, the same operations to group. Here, a distinction is made between disordered and ordered bulk writes. In disordered, inserts, updates, and deletes are grouped as individual commands sent to the database. There is no influence on the order of the execution. A good use case is the import of records from one CSV file. The order of the inserts does not matter.

For ordered bulk writers, order compliance is important to keep. In this case, only the same consecutive operations are grouped.

Currently, all bulk writes are optimized in memory. This is unfavorable for large bulk writes. In this case, one can use streaming bulk writes that only have a certain set of group operation in memory and when the maximum number of operations has been reached, operations are written to the database. The size can be specified.

Using ordered bulk writes. In this example we first insert some dog's name, add an attribute kind and change all dogs to cats. After that we delete three cats. This example would not work with unordered bulk writes.

bulk = "bulk"
       |> OrderedBulk.new()
       |> OrderedBulk.insert_one(%{name: "Greta"})
       |> OrderedBulk.insert_one(%{name: "Tom"})
       |> OrderedBulk.insert_one(%{name: "Waldo"})
       |> OrderedBulk.update_one(%{name: "Greta"}, %{"$set": %{kind: "dog"}})
       |> OrderedBulk.update_one(%{name: "Tom"}, %{"$set": %{kind: "dog"}})
       |> OrderedBulk.update_one(%{name: "Waldo"}, %{"$set": %{kind: "dog"}})
       |> OrderedBulk.update_many(%{kind: "dog"}, %{"$set": %{kind: "cat"}})
       |> OrderedBulk.delete_one(%{kind: "cat"})
       |> OrderedBulk.delete_one(%{kind: "cat"})
       |> OrderedBulk.delete_one(%{kind: "cat"})

result = Mongo.BulkWrite.write(:mongo, bulk, w: 1)

In the following example we import 1.000.000 integers into the MongoDB using the stream api:

We need to create an insert operation for each number. Then we call the Mongo.UnorderedBulk.stream function to import it. This function returns a stream function which accumulate all inserts operations until the limit 1000 is reached. In this case the operation group is send to MongoDB. So using the stream api you can reduce the memory using while importing big volume of data.

|> Stream.map(fn i -> Mongo.BulkOps.get_insert_one(%{number: i}) end)
|> Mongo.UnorderedBulk.write(:mongo, "bulk", 1_000)
|> Stream.run()

For more information see:

  • Mongo.UnorderedBulk
  • Mongo.OrderedBulk
  • Mongo.BulkWrite
  • Mongo.BulkOps

and have a look at the test units as well.


The driver supports the GridFS specifications. You create a Mongo.GridFs.Bucket struct and with this struct you can upload and download files. For example:

    bucket = Bucket.new(top)
    upload_stream = Upload.open_upload_stream(bucket, "test.jpg")
    src_filename = "./test/data/test.jpg"
    File.stream!(src_filename, [], 512) |> Stream.into(upload_stream) |> Stream.run()

    file_id = upload_stream.id

In the example a new bucket with default values is used to upload a file from the file system (./test/data/test.jpg) to the MongoDB (using the name test.jpg). The upload_stream struct contains the id of the new file which can be used to download the stored file. The following code fragments downloads the file by using the file_id.

    dest_filename = "/tmp/my-test-file.jps"

    with {:ok, stream} <- Mongo.GridFs.Download.open_download_stream(bucket, file_id) do
      |> Stream.into(File.stream!(dest_filename))
      |> Stream.run

For more information see:


Since MongoDB 4.x, transactions for multiple write operations are possible. Transaction uses sessions, which just contain a transaction number for each transaction. The Mongo.Session is responsible for the details, and you can use a convenient api for transactions:

{:ok, ids} = Mongo.transaction(top, fn ->
{:ok, %InsertOneResult{:inserted_id => id1}} = Mongo.insert_one(top, "dogs", %{name: "Greta"})
{:ok, %InsertOneResult{:inserted_id => id2}} = Mongo.insert_one(top, "dogs", %{name: "Waldo"})
{:ok, %InsertOneResult{:inserted_id => id3}} = Mongo.insert_one(top, "dogs", %{name: "Tom"})
{:ok, [id1, id2, id3]}
end, w: 1)

The Mongo.transaction/3 function supports nesting. This allows the functions to be called from each other and all write operations are still in the same transaction. The session is stored in the process dictionary under the key :session. The surrounding Mongo.transaction/3 call creates the session and starts the transaction, storing the session in the process dictionary, commits or aborts the transaction. All other Mongo.transaction/3 calls just call the function parameter without other actions.

def insert_dog(top, name) do
  Mongo.insert_one(top, "dogs", %{name: name})

def insert_dogs(top) do
  Mongo.transaction(top, fn ->
    insert_dog(top, "Tom")
    insert_dog(top, "Bell")
    insert_dog(top, "Fass")

:ok = Mongo.transaction(top, fn ->
    insert_dog(top, "Greta")

It is also possible to get more control over the progress of the transaction:

alias Mongo.Session

{:ok, session} = Session.start_session(top, :write, [])
:ok = Session.start_transaction(session)

Mongo.insert_one(top, "dogs", %{name: "Greta"}, session: session)
Mongo.insert_one(top, "dogs", %{name: "Waldo"}, session: session)
Mongo.insert_one(top, "dogs", %{name: "Tom"}, session: session)

:ok = Session.commit_transaction(session)
:ok = Session.end_session(top, session)

For more information see Mongo.Session and have a look at the test units as well.

Aborting a transaction

You have some options to abort a transaction. The simplest possibility is to return an :error. For nested function calls, the Mongo.abort_transaction/1 function call that throws an exception is suitable. That means, you can just generate a raise :should_not_happen exception as well.

Command Monitoring

You can watch all events that are triggered while the driver send requests and processes responses. You can use the Mongo.EventHandler as a starting point. It logs the events from the topic :commands (by ignoring the :isMaster command) to Logger.info:

iex> Mongo.EventHandler.start()
iex> {:ok, conn} = Mongo.start_link(url: "mongodb://localhost:27017/test")
{:ok, #PID<0.226.0>}
 iex> Mongo.find_one(conn, "test", %{})
                                      [info] Received command: %Mongo.Events.CommandStartedEvent{command: [find: "test", ...
                                                                                                                 [info] Received command: %Mongo.Events.CommandSucceededEvent{command_name: :find, ...


Latest MongoDB is used while running the tests. Replica set of three nodes is created and runs all test except the socket and ssl test. If you want to run the test cases against other MongoDB deployments or older versions, you can use the mtools for deployment and run the test cases locally:

pyenv global 3.6
pip3 install --upgrade pip
pip3 install 'mtools[all]'
export PATH=to-your-mongodb/bin/:$PATH
ulimit -S -n 2048 ## in case of Mac OS X
mlaunch init --setParameter enableTestCommands=1 --replicaset --name "rs_1"
mix test --exclude ssl --exclude socket

The SSL test suite is disabled by default.

Enable the SSL Tests

mix test --exclude ssl

Enable SSL on Your MongoDB Server

$ openssl req -newkey rsa:2048 -new -x509 -days 365 -nodes -out mongodb-cert.crt -keyout mongodb-cert.key
$ cat mongodb-cert.key mongodb-cert.crt > mongodb.pem
$ mongod --sslMode allowSSL --sslPEMKeyFile /path/to/mongodb.pem
  • For --sslMode you can use one of allowSSL or preferSSL
  • You can enable any other options you want when starting mongod

Special Thanks

Special thanks to JetBrains for providing a free JetBrains Open Source license for their complete toolbox.

Copyright and License

Copyright 2015 Eric Meadows-Jönsson and Justin Wood \ Copyright 2019 - 2022 Michael Maier

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

*Note that all licence references and agreements mentioned in the mongodb_driver README section above are relevant to that project's source code only.