metaqueue 0.1.1

Metaqueue enables simple and efficient metadata collection

metaqueue

Goal

Efficiently run tasks concurrently and write metadata to a repository and write metainformation in a PostgresSQL database.

How to install it

1. pip install metaqueue

How to use it

1. Create tasks with any signature you like but this function has to fulfill two requirements, one is that you have to pass a metadataengine into the task and the other is that you have to declare it as async.
2. In order to push metadata to the metadataengine, you can use the following:

metadataengine.publish_to_topic(Metadata(data, name, location, context))

The metadata is associated to a topic which defines what kind of metadata is collected by the metadataengine. Metadata consists of 4 attributes, the data itself, the name, the location and a context. Note that a metadataengine is defined as

MetadataEngine(topic, queue)

where topic is an enum element and queue is a metaqueue. A metaqueue is like a queue but with extra functionality. You don't have to worry about it but it is useful to know how a metaqueue is defined.

MetaQueue(buffer_size = 3, dtype = int)

You can provide a buffer_size which is the maximum capacity of the queue and dtype specifies which kind of data can be stored in it. 3. Now, you are ready to kickstart the TaskRunner which is running the defined tasks concurrently. You can use it in the following way

await TaskRunner.run(async_funcs = [task1, task2], args = [task1_args, task2_args])

where the arguments are given as tuples into the list args. In this step, the tasks are not only run concurrently but also the metadata are collected via the metadataengine. 4. Afterwards, we can define a Metabroker which will then push the metadata to a repository and push the information associated to the metadata into the PostgreSQL database.

connector  = StoreToLocalhost(path = "./log.txt")
metastore  = MetaStore(**db_info)
metabroker = MetaBroker(metadataengines = [engines[0], engines[1]], metastore = metastore, connector = connector)
metabroker.run(timeout = 10)

Up until now, only one connector is supported and that is a local file where the metadata gets written to. Since the MetaStore is using PostgreSQL as a database, you have to provide a running instance of that database. The easiest way is to spin up a docker container and pass the connection information to the MetaStore as db_info. db_info is a dict which contains the following keys: host, database, user, password, port. On the run method of the metabroker you can define a timeout. This should prevent running the metabroker for too long. 5. View the result, inside of your PostgreSQL database you should find your database with a table metadata in which you can find all the information associated to your metadata. Also, the log file should be created and contain all the metadata in a format which resembles the OpenMetrics format.