Module transfer_manager (3.3.0)

Concurrent media operations.

Modules Functions

download_chunks_concurrently

download_chunks_concurrently(
    blob,
    filename,
    chunk_size=33554432,
    download_kwargs=None,
    deadline=None,
    worker_type="process",
    max_workers=8,
    *,
    crc32c_checksum=True
)

Download a single file in chunks, concurrently.

In some environments, using this feature with mutiple processes will result in faster downloads of large files.

Using this feature with multiple threads is unlikely to improve download performance under normal circumstances due to Python interpreter threading behavior. The default is therefore to use processes instead of threads.

Parameters
Name Description
blob Blob

The blob to be downloaded.
filename str

The destination filename or path.
chunk_size int

The size in bytes of each chunk to send. The optimal chunk size for maximum throughput may vary depending on the exact network environment and size of the blob.
download_kwargs dict

A dictionary of keyword arguments to pass to the download method. Refer to the documentation for blob.download_to_file() or blob.download_to_filename() for more information. The dict is directly passed into the download methods and is not validated by this function. Keyword arguments "start" and "end" which are not supported and will cause a ValueError if present. The key "checksum" is also not supported in download_kwargs, but see the argument crc32c_checksum (which does not go in download_kwargs) below.
deadline int

The number of seconds to wait for all threads to resolve. If the deadline is reached, all threads will be terminated regardless of their progress and concurrent.futures.TimeoutError will be raised. This can be left as the default of None (no deadline) for most use cases.
worker_type str

The worker type to use; one of google.cloud.storage.transfer_manager.PROCESS or google.cloud.storage.transfer_manager.THREAD. Although the exact performance impact depends on the use case, in most situations the PROCESS worker type will use more system resources (both memory and CPU) and result in faster operations than THREAD workers. Because the subprocesses of the PROCESS worker type can't access memory from the main process, Client objects have to be serialized and then recreated in each subprocess. The serialization of the Client object for use in subprocesses is an approximation and may not capture every detail of the Client object, especially if the Client was modified after its initial creation or if Client._http was modified in any way. THREAD worker types are observed to be relatively efficient for operations with many small files, but not for operations with large files. PROCESS workers are recommended for large file operations.
max_workers int

The maximum number of workers to create to handle the workload. With PROCESS workers, a larger number of workers will consume more system resources (memory and CPU) at once. How many workers is optimal depends heavily on the specific use case, and the default is a conservative number that should work okay in most cases without consuming excessive resources.
crc32c_checksum bool

Whether to compute a checksum for the resulting object, using the crc32c algorithm. As the checksums for each chunk must be combined using a feature of crc32c that is not available for md5, md5 is not supported.
Exceptions
Type Description
`concurrent.futures.TimeoutError if deadline is exceeded. DataCorruption if the download's checksum doesn't agree with server-computed checksum. The google.cloud.storage._media exception is used here for consistency with other download methods despite the exception originating elsewhere.

download_many

download_many(
    blob_file_pairs,
    download_kwargs=None,
    threads=None,
    deadline=None,
    raise_exception=False,
    worker_type="process",
    max_workers=8,
    *,
    skip_if_exists=False
)

Download many blobs concurrently via a worker pool.

Exceptions
Type Description
`concurrent.futures.TimeoutError if deadline is exceeded.
Returns
Type Description
list A list of results corresponding to, in order, each item in the input list. If an exception was received, it will be the result for that operation. Otherwise, the return value from the successful download method is used (which will be None).

download_many_to_path

download_many_to_path(
    bucket,
    blob_names,
    destination_directory="",
    blob_name_prefix="",
    download_kwargs=None,
    threads=None,
    deadline=None,
    create_directories=True,
    raise_exception=False,
    worker_type="process",
    max_workers=8,
    *,
    skip_if_exists=False
)

Download many files concurrently by their blob names.

The destination files are automatically created, with paths based on the source blob_names and the destination_directory.

The destination files are not automatically deleted if their downloads fail, so please check the return value of this function for any exceptions, or enable raise_exception=True, and process the files accordingly.

For example, if the blob_names include "icon.jpg", destination_directory is "/home/myuser/", and blob_name_prefix is "images/", then the blob named "images/icon.jpg" will be downloaded to a file named "/home/myuser/icon.jpg".

Exceptions
Type Description
`concurrent.futures.TimeoutError if deadline is exceeded.
Returns
Type Description
list A list of results corresponding to, in order, each item in the input list. If an exception was received, it will be the result for that operation. Otherwise, the return value from the successful download method is used (which will be None).

upload_chunks_concurrently

upload_chunks_concurrently(filename, blob, content_type=None, chunk_size=33554432, deadline=None, worker_type='process', max_workers=8, *, checksum='auto', timeout=60, retry=<google.api_core.retry.retry_unary.Retry object>)

Upload a single file in chunks, concurrently.

This function uses the XML MPU API to initialize an upload and upload a file in chunks, concurrently with a worker pool.

The XML MPU API is significantly different from other uploads; please review the documentation at https://cloud.google.com/storage/docs/multipart-uploads before using this feature.

The library will attempt to cancel uploads that fail due to an exception. If the upload fails in a way that precludes cancellation, such as a hardware failure, process termination, or power outage, then the incomplete upload may persist indefinitely. To mitigate this, set the AbortIncompleteMultipartUpload with a nonzero Age in bucket lifecycle rules, or refer to the XML API documentation linked above to learn more about how to list and delete individual downloads.

Using this feature with multiple threads is unlikely to improve upload performance under normal circumstances due to Python interpreter threading behavior. The default is therefore to use processes instead of threads.

ACL information cannot be sent with this function and should be set separately with ObjectACL methods.

Parameters
Name Description
filename str

The path to the file to upload. File-like objects are not supported.
blob Blob

The blob to which to upload.
content_type str

(Optional) Type of content being uploaded.
chunk_size int

The size in bytes of each chunk to send. The optimal chunk size for maximum throughput may vary depending on the exact network environment and size of the blob. The remote API has restrictions on the minimum and maximum size allowable, see: https://cloud.google.com/storage/quotas#requests
deadline int

The number of seconds to wait for all threads to resolve. If the deadline is reached, all threads will be terminated regardless of their progress and concurrent.futures.TimeoutError will be raised. This can be left as the default of None (no deadline) for most use cases.
worker_type str

The worker type to use; one of google.cloud.storage.transfer_manager.PROCESS or google.cloud.storage.transfer_manager.THREAD. Although the exact performance impact depends on the use case, in most situations the PROCESS worker type will use more system resources (both memory and CPU) and result in faster operations than THREAD workers. Because the subprocesses of the PROCESS worker type can't access memory from the main process, Client objects have to be serialized and then recreated in each subprocess. The serialization of the Client object for use in subprocesses is an approximation and may not capture every detail of the Client object, especially if the Client was modified after its initial creation or if Client._http was modified in any way. THREAD worker types are observed to be relatively efficient for operations with many small files, but not for operations with large files. PROCESS workers are recommended for large file operations.
max_workers int

The maximum number of workers to create to handle the workload. With PROCESS workers, a larger number of workers will consume more system resources (memory and CPU) at once. How many workers is optimal depends heavily on the specific use case, and the default is a conservative number that should work okay in most cases without consuming excessive resources.
checksum str

(Optional) The checksum scheme to use: either "md5", "crc32c", "auto" or None. The default is "auto", which will try to detect if the C extension for crc32c is installed and fall back to md5 otherwise. Each individual part is checksummed. At present, the selected checksum rule is only applied to parts and a separate checksum of the entire resulting blob is not computed. Please compute and compare the checksum of the file to the resulting blob separately if needed, using the "crc32c" algorithm as per the XML MPU documentation.
timeout float or tuple

(Optional) The amount of time, in seconds, to wait for the server response. See: configuring_timeouts
retry google.api_core.retry.Retry

(Optional) How to retry the RPC. A None value will disable retries. A google.api_core.retry.Retry value will enable retries, and the object will configure backoff and timeout options. Custom predicates (customizable error codes) are not supported for media operations such as this one. This function does not accept ConditionalRetryPolicy values because preconditions are not supported by the underlying API call. See the retry.py source code and docstrings in this package (google.cloud.storage.retry) for information on retry types and how to configure them.
Exceptions
Type Description
`concurrent.futures.TimeoutError if deadline is exceeded.

upload_many

upload_many(
    file_blob_pairs,
    skip_if_exists=False,
    upload_kwargs=None,
    threads=None,
    deadline=None,
    raise_exception=False,
    worker_type="process",
    max_workers=8,
)

Upload many files concurrently via a worker pool.

Exceptions
Type Description
`concurrent.futures.TimeoutError if deadline is exceeded.
Returns
Type Description
list A list of results corresponding to, in order, each item in the input list. If an exception was received, it will be the result for that operation. Otherwise, the return value from the successful upload method is used (which will be None).

upload_many_from_filenames

upload_many_from_filenames(
    bucket,
    filenames,
    source_directory="",
    blob_name_prefix="",
    skip_if_exists=False,
    blob_constructor_kwargs=None,
    upload_kwargs=None,
    threads=None,
    deadline=None,
    raise_exception=False,
    worker_type="process",
    max_workers=8,
    *,
    additional_blob_attributes=None
)

Upload many files concurrently by their filenames.

The destination blobs are automatically created, with blob names based on the source filenames and the blob_name_prefix.

For example, if the filenames include "images/icon.jpg", source_directory is "/home/myuser/", and blob_name_prefix is "myfiles/", then the file at "/home/myuser/images/icon.jpg" will be uploaded to a blob named "myfiles/images/icon.jpg".

Exceptions
Type Description
`concurrent.futures.TimeoutError if deadline is exceeded.
Returns
Type Description
list A list of results corresponding to, in order, each item in the input list. If an exception was received, it will be the result for that operation. Otherwise, the return value from the successful upload method is used (which will be None).