@ancient-wolf-19325 When would I use Binary vs Blob type?

In Flyte, the choice between using

Binary

and

Blob

types depends on the nature and structure of the data you are working with. 1. Binary Type: - Use Case: Suitable for handling raw binary data. - Example: When you need to work with raw bytes, such as reading or writing binary files directly. - Implementation: The

Binary

type is used to represent raw binary data in Flyte. It is typically used when the data does not have a specific structure or format. - Code Example:

func TestMakeBinaryLiteral(t *testing.T) {
         s := MakeBinaryLiteral([]byte{'h'})
         assert.Equal(t, []byte{'h'}, s.GetScalar().GetBinary().GetValue())
     }

- Reference: FlyteIDL literals_test.go 2. Blob Type: - Use Case: Suitable for handling structured data stored as files, such as images, datasets, or any large files. - Example: When you need to work with files that are stored in a blob store (e.g., S3, GCS) and have a specific format or structure. - Implementation: The

Blob

type in Flyte is used to represent files or directories that are stored in a blob store. It can handle both single-part and multi-part blobs. - Code Example:

class MyDatasetTransformer(TypeTransformer[MyDataset]):
         _TYPE_INFO = BlobType(format="binary", dimensionality=BlobType.BlobDimensionality.MULTIPART)

         def __init__(self):
             super(MyDatasetTransformer, self).__init__(name="mydataset-transform", t=MyDataset)

         def get_literal_type(self, t: Type[MyDataset]) -> LiteralType:
             return LiteralType(blob=self._TYPE_INFO)

- Reference: Flytesnacks custom_types.py Would you like more detail on any specific aspect? Sources: - FlyteIDL literals_test.go - Flytesnacks custom_types.py 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 Can I stream the Blob type?

Flyte does not natively support streaming for the

Blob

type. The

Blob

type in Flyte is designed to handle files or directories stored in a blob store, and it typically involves uploading or downloading the entire file or directory as a single operation. If you need to handle large files or datasets that would benefit from streaming, you would need to implement custom logic to manage the streaming process. This could involve breaking the data into smaller chunks and processing each chunk individually. Would you like more detail on how to implement custom streaming logic? Sources: - Flytesnacks custom_types.py 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 Which data types support streaming inputs and outputs?

Flyte currently supports streaming for

FlyteFile

and

FlyteDirectory

types, but this feature is experimental. Streaming support for these types allows for efficient handling of large files and directories by processing data in chunks rather than loading the entire content into memory at once. Supported Streaming Types: 1. FlyteFile: - Use Case: Suitable for handling large files that need to be processed in a streaming fashion. - Example: Reading a large CSV file line by line. - Reference: FlyteFile streaming documentation 2. FlyteDirectory: - Use Case: Suitable for handling large directories containing multiple files that need to be processed in a streaming fashion. - Example: Processing a directory of images one by one. - Reference: FlyteDirectory streaming documentation Example Code Snippet: Here is an example of how you might use

FlyteFile

with streaming:

from flytekit import task, workflow
from flytekit.types.file import FlyteFile

@task
def process_large_file(file: FlyteFile) -> int:
    with file.open() as f:
        for line in f:
            # Process each line
            pass
    return 0

@workflow
def my_workflow(file: FlyteFile) -> int:
    return process_large_file(file=file)

Would you like more detail on implementing streaming for these types? Sources: - FlyteFile streaming documentation - FlyteDirectory streaming documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 Can you show me an example of streaming inputs into a Java task or workflow?

Flyte's streaming support is primarily documented and demonstrated for Python tasks and workflows. As of now, there is no explicit documentation or examples available for streaming inputs into Java tasks or workflows in Flyte. Would you like more detail on how to implement streaming in Python, or do you have any other specific questions about Flyte? Sources: - FlyteFile streaming documentation - FlyteDirectory streaming documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 How does the MULTIPART BlobType.BlobDimensionality work? Can I use it to support a stream?

The

MULTIPART

BlobType.BlobDimensionality

in Flyte is designed to handle large datasets by splitting them into multiple parts, which can be processed in parallel. This is particularly useful for large files or directories that need to be processed in chunks. How MULTIPART BlobType.BlobDimensionality Works: 1. Definition: The

MULTIPART

dimensionality indicates that the data is divided into multiple parts, each of which can be processed independently. 2. Use Case: Suitable for large datasets that are too big to be processed as a single unit. 3. Implementation: When using

MULTIPART

, Flyte can handle the data as a collection of smaller parts, which can be processed in parallel to improve efficiency. Example Code Snippet: Here is an example of how you might define and use a

MULTIPART

BlobType

in Flyte:

from flytekit import task, workflow
from flytekit.types.file import FlyteFile
from flytekit.models.core.types import BlobType

@task
def process_multipart_blob(blob: FlyteFile[BlobType(format="csv", dimensionality=BlobType.BlobDimensionality.MULTIPART)]) -> int:
    with blob.open() as f:
        for part in f:
            # Process each part
            pass
    return 0

@workflow
def my_workflow(blob: FlyteFile[BlobType(format="csv", dimensionality=BlobType.BlobDimensionality.MULTIPART)]) -> int:
    return process_multipart_blob(blob=blob)

Streaming with MULTIPART: While

MULTIPART

BlobType

can handle large datasets by splitting them into parts, it does not inherently support streaming in the sense of processing data as it arrives. However, you can implement custom logic to process each part in a streaming fashion. Would you like more detail on implementing custom streaming logic for

MULTIPART

blobs? Sources: - Flytesnacks custom_types.py - FlyteFile streaming documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.