thoth-storages 0.15.0

Storage and database adapters available in project Thoth

This library provides a library called thoth-storages used in project Thoth. The library exposes core queries and methods for Dgraph database as well as adapters for manipulating with Ceph via its S3 compatible API.

Installation and Usage

The library can be installed via pip or Pipenv from PyPI:

pipenv install thoth-storages

The library does not provide any CLI, it is rather a low level library supporting other parts of Thoth.

You can run prepared testsuite via the following command:

pipenv install --dev
pipenv run python3 setup.py test

# To generate docs:
pipenv run python3 setup.py build_sphinx

Automatically generate schema for Graph database

To automatically generate schema for the graph database from models defined in this module, run:

PYTHONPATH=. pipenv run python3 ./create_schema.py --output thoth/storages/graph/schema.rdf

After running this command, the RDF file describing schema will be updated based on changes in model.

from thoth.storages import GraphDatabase

# Also provide configuration if needed.
graph = GraphDatabase()
graph.connect()
graph.initialize_schema()

Running Dgraph locally

You can use docker-compose present in this repository to run a local Dgraph instance. It does not use TLS certificates (so you must not to provide GRAPH_TLS_PATH environment variable).

$ docker-compose up

After running the command above (make sure your big fat daemon is up using systemctl start docker), you should be able to access a local Dgraph instance at localhost:9080. This is also the default configuration for Dgraph’s adapter - you don’t need to provide GRAPH_SERVICE_HOST explicitly.

The provided docker-compose has also Ratel enabled for to have an UI for graph database content. To access it visit http://localhost:8000/.

The provided docker-compose uses volume mounted from /tmp. After you computer restart, the content will not be available anymore.

If you would like to experiment with Dgraph programatically, you can use the following code snippet as a starting point:

from thoth.storages import GraphDatabase

graph = GraphDatabase()
graph.connect()
# To clear database:
# graph.drop_all()
# To initialize schema in the graph database:
# graph.initialize_schema()

Schema adjustment in deployment

It’s possible to perform adjustments of schema in a deployemnt. It’s important that there are no open transactions (simply retry schema creation until it succeeds). You can use relevant endpoint on Management API for this purpose.

If there are changes in types, Dgraph tries to automatically perform conversion from an old type to the new one as described in the new schema (e.g. a float to string). Invalid schema changes (e.g. parsing string into a float, but the string cannot be parsed as a float) result in schema change errors. These errors need to be handled programatically by deployment administrator (ideally avoid such conversions).

Creating own performance indicators

You can create your own performance indicators. To create own performance indicator, create a script which tests desired functionality of a library. An example can be matrix multiplication script present in performance repository. This script can be supplied to Dependency Monkey to validate certain combination of libraries in desired runtime and buildtime environment or directly on Amun API which will run the given script using desired software and hardware configuration. Please follow instructions on how to create a performance script shown in the README of performance repo.

To create relevant models, adjust thoth/storages/graph/performance.py file and add your model. Describe parameters (reported in @parameters section of performance indicator result) and result (reported in @result). The name of class should match name which is reported by performance indicator run.

@attr.s(slots=True)
class PiMatmul(PerformanceIndicatorBase):
    """A class for representing a matrix multiplication micro-performance test."""

    SCHEMA_PARAMETERS = Schema({
        Required("matrix_size"): int,
        Required("dtype"): str,
        Required("reps"): int,
        Required("device"): str,
    })

    SCHEMA_RESULT = Schema({
        Required("elapsed"): float,
        Required("rate"): float,
    })

    # Device used during performance indicator run - CPU/GPU/TPU/...
    device = model_property(type=str, index="exact")
    matrix_size = model_property(type=int, index="int")
    dtype = model_property(type=str, index="exact")
    reps = model_property(type=int, index="int")
    elapsed = model_property(type=float)
    rate = model_property(type=float)

After you have created relevant model, register your model to ALL_PERFORMANCE_MODELS and re-generate graph database schema (as discussed above).