How to Chain GDAL Tasks in Prefect

To chain GDAL tasks in Prefect, wrap each CLI or Python API operation as an independent @task, return concrete file paths or URIs, and let Prefect’s execution engine resolve dependencies automatically. Returning an output path from one task and passing it as an input to the next creates an implicit data dependency. For execution-order constraints without data transfer, use the wait_for parameter. This stateless, idempotent pattern ensures reliable, retryable geospatial pipelines that scale cleanly across distributed workers.

Core Dependency Patterns

Prefect 2.x tracks task state and data lineage natively. When designing spatial workflows, you can rely on two primary dependency mechanisms:

Implicit Data Dependencies: When Task A returns a value (e.g., a file path) and Task B consumes it, Prefect automatically schedules B after A completes successfully. This is the cleanest approach for linear GDAL chains like ogr2ogr → gdalwarp → ogrinfo.
Explicit Execution Dependencies: Use wait_for=[task_future] when tasks must run sequentially but don’t share data. This is common for parallel branches that converge on a final merge step, or when you need to enforce cleanup/ordering without passing artifacts.

Treating GDAL as a stateless transformation step aligns with modern Spatial Task Design & Dependency Mapping principles. Each task should:

Accept only explicit inputs (paths, CRS strings, driver names)
Write outputs to deterministic locations
Return the output path for downstream consumption
Fail loudly on non-zero exit codes

Production-Ready Implementation

The following Prefect 2.x flow demonstrates a complete vector ETL chain: format conversion → reprojection → metadata validation. It uses Python’s subprocess module to invoke GDAL CLI utilities, which avoids known memory-leak issues in the Python bindings and matches official GDAL command-line documentation.

from prefect import flow, task, get_run_logger
import subprocess
from pathlib import Path
from typing import List

@task(retries=2, retry_delay_seconds=30, timeout_seconds=600)
def convert_vector(input_path: str, output_path: str, driver: str = "GeoJSON") -> str:
    logger = get_run_logger()
    cmd = ["ogr2ogr", "-f", driver, "-lco", "ENCODING=UTF-8", output_path, input_path]
    logger.info(f"Executing: {' '.join(cmd)}")

    try:
        subprocess.run(cmd, check=True, capture_output=True, text=True)
    except subprocess.CalledProcessError as e:
        raise RuntimeError(f"ogr2ogr conversion failed: {e.stderr.strip()}")
    return output_path

@task(retries=1, retry_delay_seconds=15, timeout_seconds=900)
def reproject_vector(input_path: str, output_path: str, target_crs: str = "EPSG:4326") -> str:
    logger = get_run_logger()
    cmd = ["ogr2ogr", "-t_srs", target_crs, "-lco", "ENCODING=UTF-8", output_path, input_path]
    logger.info(f"Executing: {' '.join(cmd)}")

    try:
        subprocess.run(cmd, check=True, capture_output=True, text=True)
    except subprocess.CalledProcessError as e:
        raise RuntimeError(f"Reprojection failed: {e.stderr.strip()}")
    return output_path

@task(timeout_seconds=120)
def validate_vector(path: str) -> bool:
    logger = get_run_logger()
    cmd = ["ogrinfo", "-ro", "-so", path]
    result = subprocess.run(cmd, capture_output=True, text=True)

    if result.returncode != 0:
        logger.warning(f"Validation returned non-zero: {result.stderr.strip()}")
        return False
    logger.info(f"Schema validation passed for {path}")
    return True

@task(timeout_seconds=60)
def cleanup_staging(paths: List[str]) -> None:
    logger = get_run_logger()
    for p in paths:
        Path(p).unlink(missing_ok=True)
    logger.info("Staging files cleaned up.")

@flow(name="gdal-vector-etl-chain", log_prints=True)
def gdal_chained_flow(input_file: str, staging_dir: str = "/tmp/gdal_staging"):
    Path(staging_dir).mkdir(parents=True, exist_ok=True)

    converted_path = str(Path(staging_dir) / "converted.geojson")
    reprojected_path = str(Path(staging_dir) / "reprojected.geojson")

    # Implicit dependency: output of convert_vector feeds into reproject_vector
    converted = convert_vector(input_file, converted_path)
    reprojected = reproject_vector(converted, reprojected_path)

    # Validation runs after reprojection completes
    is_valid = validate_vector(reprojected)

    if not is_valid:
        raise ValueError("Final validation failed. Pipeline halted.")

    # Resolving `is_valid` above forces `validate_vector` to complete before
    # this line, so cleanup runs only after validation succeeds.
    cleanup_staging([converted_path, reprojected_path])

    return reprojected_path

Execution & Orchestration Best Practices

Retry & Timeout Strategy

GDAL operations on large vector datasets can stall or fail due to I/O contention, malformed geometries, or transient network issues (e.g., cloud storage URIs). Configure retries and retry_delay_seconds at the task level, not the flow level, to isolate failures. Use timeout_seconds to prevent hung processes from consuming worker resources indefinitely.

Path Management & Idempotency

Always resolve paths to absolute strings before passing them to subprocess. Relative paths can break when Prefect agents run tasks in isolated working directories. To guarantee idempotency, overwrite existing staging files or append a run ID to filenames. Prefect’s task dependency resolution ensures that retried tasks re-execute cleanly without duplicating work.

Using `wait_for` for Branching Pipelines

When parallelizing independent GDAL operations (e.g., clipping, dissolving, and attribute filtering), you can fan out tasks and converge them later:

clip_task = clip_geometry(input_path, clip_extent)
dissolve_task = dissolve_features(input_path, group_field)
# Both run in parallel
merge_task = merge_datasets([clip_task, dissolve_task], wait_for=[clip_task, dissolve_task])

This pattern prevents race conditions while maximizing worker utilization.

Scaling & Maintenance

As your geospatial pipelines mature, shift from local staging directories to cloud object storage (S3, GCS, Azure Blob). Prefect’s built-in result handlers and cloud storage blocks can automatically upload GDAL outputs and register them in the artifact registry. For teams standardizing on Building ETL Chains for Vector Data, consider:

Caching expensive transforms: Use cache_key_fn and cache_expiration to skip reprojection or topology validation when input checksums haven’t changed.
Structured logging: Parse ogrinfo or gdalinfo JSON output (-json flag) and emit it as Prefect artifacts for downstream auditing.
Worker isolation: Run GDAL tasks on dedicated infrastructure workers with pre-installed system dependencies (libgdal, proj, geos) to avoid container bloat and version drift.

By treating each GDAL call as a discrete, observable unit, you gain full lineage tracking, automatic retries, and seamless integration with modern orchestration platforms. The pattern scales from single-file conversions to multi-terabyte spatial data lakes without architectural rewrites.

Core Dependency Patterns#

Production-Ready Implementation#

Execution & Orchestration Best Practices#

Retry & Timeout Strategy#

Path Management & Idempotency#

Using wait_for for Branching Pipelines#

Scaling & Maintenance#