cloud-mappings 1.2.0

MutableMapping interfaces for common cloud storage providers

cloud-mappings

MutableMapping implementations for common cloud storage providers

For now Azure Blob Storage, Azure Table Storage, Google Cloud Storage, and AWS S3 are implemented. Contributions of new providers are welcome.

Installation

with pip:

pip install cloud-mappings

By default, cloud-mappings doesn't install any of the required storage providers dependencies. If you would like to install them alongside cloud-mappings you may run any combination of:

pip install cloud-mappings[azureblob,azuretable,gcpstorage,awss3]

Instantiation

AzureBlobMapping:

from cloudmappings import AzureBlobMapping

cm = AzureBlobMapping.with_pickle(
    container_name="CONTAINER_NAME",
    account_url="AZURE_BLOB_STORAGE_URL",
    credential=AZURE_CREDENTIAL_OBJECT,
    connection_string="AZURE_BLOB_STORAGE_CONNECTION_STRING",
)

AzureTableMapping:

from cloudmappings import AzureTableMapping

cm = AzureTableMapping.with_pickle(
    table_name="TABLE_NAME",
    endpoint="AZURE_TABLE_ENDPOINT",
    credential=AZURE_CREDENTIAL_OBJECT,
    connection_string="AZURE_TABLE_CONNECTION_STRING",
)

Note that Azure Table Storage has a 1MB size limit per entity.

GoogleCloudStorageMapping:

from cloudmappings import GoogleCloudStorageMapping

cm = GoogleCloudStorageMapping.with_pickle(
    bucket_name="BUCKET_NAME",
    project="GCP_PROJECT",
    credentials=GCP_CREDENTIALS_OBJECT,
)

AWSS3Mapping:

from cloudmappings import AWSS3Mapping

cm = AWSS3Mapping.with_pickle(
    bucket_name="AWS_BUCKET_NAME",
    silence_warning=False,
)

Note that AWS S3 does not support server-side atomic requests, so it is not recommended for concurrent use. A warning is printed out by default but may be silenced by passing silence_warning=True.

Usage

Use it just like a standard dict()!

cm["key"] = 1000
cm["key"] # returns 1000
del cm["key"]
"key" in cm # returns false

Etags

Each cloud-mapping keeps an internal dict of etags which it uses to ensure it is only reading/overwriting/deleting data it expects to. If the value in storage is not what the cloud-mapping expects, a cloudmappings.errors.KeySyncError() will be thrown.

If you would like to enable read (get) operations without ensuring etags, you can set read_blindly=True. This can be set in the constructor, or dynamically on the cloud-mapping instance. Blindly reading a value that doesn't exist in the cloud will only raise a KeyError if read_blindly_error=True, otherwise it will return the current value of read_blindly_default (which itself defaults to None). All of these can be changed dynamically and set at initialisation.

If you know what you are doing and you want an operation other than get to go through despite etags, you will need to sync your cloud-mapping with the cloud by calling either .sync_with_cloud() to sync all keys or .sync_with_cloud(key_prefix) to sync a specific key or subset of keys. By default .sync_with_cloud() is called on instantiation of a cloud-mapping if the underlying provider storage already exists. You may skip this initial sync by passing an additional sync_initially=False parameter when you instantiate your cloud-mapping.

The etags property on a cloud-mapping can be manually inspected and adjusted for advanced use cases, but it is not recommended if your use case can be accomplished with the above methods.

Serialisation

If you don't call .with_pickle() and instead pass your providers configuration directly to the CloudMapping class, you will get a "raw" cloud-mapping which accepts only byte-likes as values. Along with the .with_pickle() serialisation utility, .with_json() and .with_json_zlib() also exist. You may build your own serialisation by constructing your cloud-mapping with ordered_dumps_funcs=[dumps_1, dumps_2, ..., dumps_N] and ordered_loads_funcs=[loads_1, loads_2, ..., loads_N], where dumps and loads are the ordered functions to serialise and parse your data respectively.

Development

This project uses .devcontainer to describe the environment to use for development. You may use the environment described in this directory (it integrates automatically with vscode's 'remote containers' extension), or you may create your own environment with the same dependencies.

Dependencies

Install development dependencies with:

pip install -e .[azureblob,azuretable,gcpstorage,awss3,tests]

Tests

Set environment variables for each provider:

• Azure Blob: AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, AZURE_BLOB_STORAGE_ACCOUNT_URL, AZURE_BLOB_STORAGE_HIERARCHICAL_ACCOUNT_URL (the tests assume the same secret is used for both)
• Azure Table: AZURE_TABLE_STORAGE_CONNECTION_STRING
• GCP Storage: GOOGLE_APPLICATION_CREDENTIALS (path to credentials file), GOOGLE_CLOUD_STORAGE_PROJECT
• AWS S3: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY

Run tests with:

pytest --test_container_id <container-suffix-to-use-for-tests>

The testing container will be prefixed by "pytest", and the commit sha is used within build & release workflows. Note that if the container specified already exists one test will fail.