Storage adapter implementation

Currently, there are available prepared database adapters, see Selinonlib module. In order to use these storages, you have to manually install database adapters using extras as they are not explicitly included by requirements.

SqlStorage - SQLAlchemy adapter for SQL databases

pip3 install selinon[postgresql]

A configuration example:

  - name: 'MySqlStorage'
    classname: 'PostgreSQL'
    import: 'selinon.storages.postgresql'
      connection_string: 'postgres://postgres:postgres@postgres:5432/postgres'
      encoding: 'utf-8'
    echo: false

The implementation is available in selinon.storages.postgresql.

Redis - Redis database adapter

pip3 install selinon[redis]

A configuration example:

  - name: 'MyRedisStorage'
    classname: 'Redis'
    import: 'selinon.storages.redis'
      host: 'redishost'
      port: 6379
      db: 0
      charset: 'utf-8'
    port: 27017

The implementation is available in selinon.storages.redis.

MongoDB - MongoDB database adapter

pip3 install selinon[mongodb]

A configuration example:

  - name: 'MyMongoStorage'
    classname: 'MongoDB'
    import: 'selinon.storages.mongodb'
      db_name: 'database_name'
      collection_name: 'collection_name'
      host: 'mongohost'
    port: 27017

The implementation is available in selinon.storages.mongodb.

S3 - AWS S3 database adapter

`pip3 install selinon[s3]`

A configuration example:

  - name: 'MyS3Storage'
    classname: 'S3Storage'
    import: 'selinon.storages.s3'
      bucket: 'my-bucket-name'
      aws_access_key_id: 'AAAAAAAAAAAAAAAAAAAA'
      region_name: 'us-east-1'

The implementation is available in selinon.storages.s3.

In memory storage

A configuration example:

  - name: 'Memory'
    classname: 'InMemoryStorage'
    import: 'selinon.storages.memory'
      echo: false

No additional requirements are necessary to be installed. This storage adapter stores results in memory. It is suitable for use with Selinon CLI and executor where you just want to run a flow and check results. As results are stored in memory, it is not possible to scale number of workers in many cases as results are stored in memory of a node.

The implementation is available in selinon.storages.memory.

Few notes on using adapters

If you want to you multiple adapters, you can specify multiple adapters in extras when installing:

pip3 install selinon[mongodb,postgresql,s3]

Note that spaces are not allowed in extras (also escape brackets when using zsh).

Using a custom storage adapter

You can define your own storage by inheriting from DataStorage abstract class:

from selinon import DataStorage

class MyStorage(DataStorage):
    def __init__(self, host, port):
        # arguments from YAML file are pasased to constructor as key-value arguments

    def is_connected():
        # predicate used to check connection
        return False

    def connect():
        # define how to connect based on your configuration

    def disconnect():
        # define how to disconnect from storage

    def retrieve(self, flow_name, task_name, task_id):
        # define how to retrieve results

    def store(self, flow_name, task_name, task_id, result):
        # define how to store results

    def store_error(self, node_args, flow_name, task_name, task_id, exc_info):
        # optionally define how to track errors/task failures if you need to

And pass this storage to Selinon in your YAML configuration:

  # from myapp.storages import MyStorage
  - name: 'MyStorage'
    import: 'myapp.storages'
      host: 'localhost'
      port: '5432'

If you create an adapter for some well known storage and you feel that your adapter is generic enough, feel free to share it with community by opening a pull request!

Database connection pool

Each worker is trying to be efficient when it comes to number of connections to a database. There is held only one instance of DataStorage class per whole worker. Selinon transparently takes care of concurrent-safety when calling methods of DataStorage if you plan to run your worker with concurrency level higher than one.


You can also simply share connection across multiple DataStorage classes in inheritance hierarchy and reuse already defined connections. You can also do storage aliasing as described in Best practices.

If you would like to request some storage from your configuration, you can request storage adapter from Selinon StoragePool:

from selinon import StoragePool

# Name of storage was set to MyMongoStorage in nodes.yaml configuration file (section storages).
mongo = StoragePool.get_connected_storage('MyMongoStorage')

Selinon will transparently take care of instantiation, connection and sharing connection pool across the whole process. Check out other useful methods of StoragePool.


If there is anything wrong with storage or storage adapters causing dispatcher failing to determine the next steps in the flow, dispatcher is retried respecting the flow’s retry_countdown configuration option. This way you will not lose messages that cannot be consumed due to storage errors. However if a task cannot write or read from a storage, it is marked as failed.