Running Async Spatial Queries in Python

Concurrent spatial workloads in Python stall due to synchronous I/O blocking, unbounded heap allocation during geometry materialization, and GIL contention during WKB deserialization. Integrating DuckDB’s spatial extension with asyncio requires strict connection isolation, hard memory ceilings, and Arrow batch streaming. Architecting Python & DuckDB Integration Workflows mandates decoupling query execution from result materialization to prevent event-loop starvation and OOM termination.

Connection Lifecycle & Async Dispatch #

DuckDB’s Python client lacks a native async API. Spatial queries must execute in worker threads via asyncio.to_thread or a bounded ThreadPoolExecutor. DuckDB connections are not thread-safe; sharing a single duckdb.Connection across concurrent tasks triggers RuntimeError: DuckDB connection is not thread-safe. Implementing Async Execution Patterns requires connection-per-task isolation:

import asyncio
import duckdb

async def execute_spatial_query(query: str, params: dict | None = None):
    conn = duckdb.connect(":memory:")
    try:
        # Configure limits before execution
        conn.execute("PRAGMA memory_limit='4GB';")
        conn.execute("SET threads=4;")
        conn.execute("SET enable_progress_bar=false;")

        # Dispatch blocking call to thread pool
        result = await asyncio.to_thread(conn.execute, query, params)
        return result
    finally:
        conn.close()

Connections must be instantiated within the async task scope and closed deterministically. FD leaks occur when exceptions bypass conn.close(). Use explicit try/finally or contextlib.closing.

Memory Ceilings & Arrow Streaming #

DuckDB defaults to unbounded memory allocation. Pulling large geometry columns (ST_AsBinary, ST_GeomFromWKB) via fetchall() or fetchdf() materializes the entire result set in CPython memory, bypassing streaming and triggering OOM kills. Replace synchronous fetches with fetch_record_batch() to consume PyArrow RecordBatch objects in fixed-size chunks.

async def stream_spatial_batches(query: str, batch_size: int = 1024):
    conn = duckdb.connect(":memory:")
    try:
        conn.execute("PRAGMA memory_limit='4GB';")
        conn.execute("SET enable_progress_bar=false;")

        # Execute and fetch streaming iterator
        rel = await asyncio.to_thread(conn.execute, query)
        batch_iterator = await asyncio.to_thread(rel.fetch_record_batch, batch_size=batch_size)

        for batch in batch_iterator:
            yield batch  # Backpressure handled by async generator
    finally:
        conn.close()

Peak RSS remains bounded to batch_size * row_width. Configure batch_size based on available heap and geometry complexity. For datasets exceeding 500k rows, never bypass the batch iterator.

Geometry Deserialization & GeoPandas Sync #

DuckDB’s spatial extension outputs geometries as BLOB (WKB) by default. Passing raw WKB to geopandas.GeoDataFrame triggers implicit row-by-row Shapely deserialization on the main thread, causing GIL contention and event-loop starvation. Vectorize parsing using shapely.from_wkb within the async pipeline, then construct the DataFrame via Arrow interop:

import geopandas as gpd
import shapely
import pyarrow as pa

async def build_geodataframe_from_batches(batch_generator):
    batches = []
    async for batch in batch_generator:
        # Vectorized WKB -> Geometry conversion off main thread
        wkb_col = batch.column("geom")
        geometry_arr = await asyncio.to_thread(shapely.from_wkb, wkb_col.to_pylist())

        # Replace WKB column with parsed geometry
        schema = batch.schema
        new_batch = batch.set_column(
            schema.get_field_index("geom"),
            schema.field("geom"),
            pa.array(geometry_arr)
        )
        batches.append(new_batch)

    # Single-pass GeoDataFrame construction
    table = pa.Table.from_batches(batches)
    return await asyncio.to_thread(gpd.GeoDataFrame.from_arrow, table, geometry="geom")

Reference the official DuckDB Spatial Extension documentation for geometry type mappings and WKB compliance. Use shapely.from_wkb for deterministic parsing; avoid geopandas.points_from_xy for complex polygons/linestrings.

Diagnostic Queries & Incident Fallback Routing #

Monitor memory pressure and thread saturation before deployment. Execute diagnostic queries at connection initialization:

-- Verify active memory limit
SELECT current_setting('memory_limit') AS active_limit;
-- Inspect memory allocation by component
SELECT * FROM duckdb_memory() ORDER BY memory_usage_bytes DESC LIMIT 10;
-- Validate thread pool utilization
SELECT current_setting('threads') AS active_threads;

Fallback Routing #

If asyncio.to_thread raises MemoryError, duckdb.IOException, or pyarrow.lib.ArrowInvalid, route to synchronous chunked execution with explicit backpressure and garbage collection:

import gc
from concurrent.futures import ThreadPoolExecutor

def fallback_sync_execution(query: str, chunk_size: int = 2048):
    conn = duckdb.connect(":memory:")
    try:
        conn.execute("PRAGMA memory_limit='2GB';")
        conn.execute("SET threads=2;")
        rel = conn.execute(query)

        with ThreadPoolExecutor(max_workers=1) as executor:
            for batch in rel.fetch_record_batch(batch_size=chunk_size):
                yield batch
                gc.collect()  # Force heap compaction between chunks
    finally:
        conn.close()

Route async failures to fallback_sync_execution when sys.getsizeof() exceeds 80% of resource.RLIMIT_AS. Disable spatial indexes during bulk inserts to prevent write amplification. Validate WKB integrity using ST_IsValid(geom) before materialization to prevent deserialization crashes.