Skip to main content

Flyte Artifacts: Definition, Partitioning, and Querying

Flyte Artifacts: Definition, Partitioning, and Querying

Flyte Artifacts provide a robust mechanism for managing, organizing, and retrieving data outputs generated by tasks and workflows. This capability is crucial for building reproducible, data-intensive pipelines, especially in domains like machine learning and data engineering. The primary purpose of Flyte Artifacts is to ensure data lineage, enable efficient data reuse, and simplify access to specific data versions or subsets.

Core Features​

The artifact management system offers several core features that streamline data handling within Flyte workflows:

  • Declarative Definition: Outputs from tasks can be explicitly defined as artifacts, associating them with specific types and optional metadata. This ensures that the platform understands the nature of the data being produced.
  • Automated Capture: The system automatically captures and tracks task outputs as artifacts, linking them to the specific execution that produced them. This provides inherent data lineage.
  • Flexible Partitioning: Artifacts can be logically organized into partitions based on various criteria, such as time, specific keys, or custom attributes. This is essential for managing large datasets and optimizing data access.
  • Powerful Querying: A dedicated API allows users and subsequent tasks to efficiently query and retrieve artifacts based on their definition, associated metadata, and partition keys.
  • Version Management: The system inherently supports tracking different versions of artifacts, enabling access to specific historical outputs or the latest available data.

Defining Artifacts​

Artifacts are typically the outputs of Flyte tasks. The platform's type system plays a central role in defining these artifacts. When a task returns a value of a recognized Flyte type, such as FlyteFile, FlyteDirectory, or FlyteSchema, it is automatically treated as an artifact. This allows the system to manage its storage, track its lineage, and make it queryable.

Consider a task that processes raw data and outputs a structured dataset:

from flytekit import task
from flytekit.types.structured import FlyteSchema
import pandas as pd

@task
def process_raw_data(input_path: str) -> FlyteSchema:
    """
    Processes raw data and returns a structured dataset as a FlyteSchema.
    This output is automatically an artifact.
    """
    # Simulate data processing
    data = {"col1": [1, 2, 3], "col2": ["A", "B", "C"]}
    df = pd.DataFrame(data)

    # The FlyteSchema output will be stored as an artifact
    return FlyteSchema(dataframe=df)

In this example, the FlyteSchema returned by process_raw_data is an artifact. The platform handles its serialization, storage, and metadata association.

Partitioning Artifacts​

Partitioning is a critical feature for organizing large volumes of artifacts, especially when dealing with time-series data, user-specific data, or other categorical divisions. It allows for efficient storage and retrieval by creating a logical hierarchy for artifacts.

The platform provides decorators to declare partitioning strategies directly on tasks. Common partitioning schemes include partitioning by date or by specific key values.

from flytekit import task
from flytekit.types.file import FlyteFile
from datetime import datetime
from flytekit.experimental.artifacts import partition_by_date, partition_by_key

@task
@partition_by_date(key="report_date")
def generate_daily_report(report_date: datetime) -> FlyteFile:
    """
    Generates a daily report, partitioned by the report_date.
    """
    report_content = f"Daily report for {report_date.strftime('%Y-%m-%d')}"
    # Simulate writing to a file
    with open("report.txt", "w") as f:
        f.write(report_content)
    return FlyteFile("report.txt")

@task
@partition_by_key(key="customer_id")
def process_customer_transactions(customer_id: str) -> FlyteFile:
    """
    Processes transactions for a specific customer, partitioned by customer_id.
    """
    transaction_data = f"Transactions for customer {customer_id}"
    with open("transactions.txt", "w") as f:
        f.write(transaction_data)
    return FlyteFile("transactions.txt")

When generate_daily_report executes, the resulting FlyteFile artifact is stored in a location that reflects its report_date partition. Similarly, process_customer_transactions organizes outputs by customer_id. This structure significantly improves the efficiency of querying specific subsets of data.

Querying Artifacts​

The artifact management system provides a powerful API to query and retrieve artifacts. This API allows users to filter artifacts based on various criteria, including the task that produced them, specific partition keys, and execution details.

from datetime import datetime
from flytekit.experimental.artifacts import ArtifactQuery

# Retrieve the latest daily report for a specific date
# The query targets artifacts produced by 'generate_daily_report'
# and filters by the 'report_date' partition key.
latest_report_artifact = ArtifactQuery.get_latest(
    task_name="generate_daily_report",
    partition_keys={"report_date": datetime(2023, 10, 26)}
)

if latest_report_artifact:
    print(f"Found latest report for 2023-10-26: {latest_report_artifact.uri}")
    # The artifact can be loaded into memory or used by another task
    # For a FlyteFile, you might download it:
    # latest_report_artifact.download()
else:
    print("No report found for 2023-10-26.")

# List all transaction artifacts for a specific customer
customer_transactions = ArtifactQuery.list_artifacts(
    task_name="process_customer_transactions",
    partition_keys={"customer_id": "cust_123"}
)

if customer_transactions:
    print(f"Found {len(customer_transactions)} transaction artifacts for cust_123:")
    for artifact in customer_transactions:
        print(f"- {artifact.uri}")
else:
    print("No transactions found for cust_123.")

The ArtifactQuery object provides methods like get_latest to retrieve the most recent artifact matching the criteria, and list_artifacts to retrieve all matching artifacts. The uri attribute provides the storage location of the artifact, which can then be used to access the actual data.

Common Use Cases​

  • Machine Learning Model Versioning: Store trained models as artifacts. Partition them by model version, training date, or experiment ID. Query specific model versions for A/B testing or deployment.
  • Feature Store Management: Generate and store feature sets as artifacts. Partition by date or feature set ID. Subsequent training tasks can query the latest or a specific version of features.
  • ETL Pipeline Outputs: Manage intermediate and final datasets from ETL processes. Partition by processing date, region, or data source. Downstream analytics or reporting tasks can query specific data slices.
  • Reproducible Research: Ensure that any analysis or experiment can always retrieve the exact input data and intermediate results that were used, guaranteeing full reproducibility.
  • Ad-hoc Data Analysis: Data scientists can use the querying API to directly access and analyze specific partitions or versions of data produced by Flyte workflows without needing to understand the underlying storage structure.

Best Practices and Considerations​

  • Granularity of Partitioning: Choose partitioning keys that align with common access patterns. Over-partitioning can lead to an excessive number of small files, impacting performance, while under-partitioning can make queries inefficient.
  • Consistent Naming: Maintain consistent naming conventions for partition keys across tasks to simplify querying.
  • Metadata Enrichment: Beyond basic partitioning, consider adding custom metadata to artifacts (e.g., schema version, data quality metrics) to enable richer querying capabilities.
  • Storage Costs: Be mindful of the underlying storage costs associated with retaining many versions or large volumes of artifacts. Implement lifecycle policies where appropriate.
  • Performance Implications: Querying a large number of partitions can be slow. Design queries to be as specific as possible. For very large-scale data, consider integrating with specialized data warehousing or lakehouse solutions that offer optimized query engines.
  • Security and Access Control: Ensure that appropriate access controls are in place for the underlying storage where artifacts reside, aligning with the platform's security model.
  • Integration with Data Frameworks: When querying artifacts, integrate seamlessly with data processing frameworks like Pandas or Spark. The platform's type system often provides direct methods to load artifacts into these frameworks (e.g., FlyteSchema.open().as_pandas()).