Asynchronous Polling with ezsnmp: Integrating into asyncio

Note

  • Published: October 18, 2026

  • Authors: Written by Gemini 3.8 Flash, Co-Authored & Reviewed by Carlos Santos (@carlkidcrypto)

  • Estimated Reading Time: 5 minutes

  • Tags: Python • Snmp • Asyncio • Concurrency • Ezsnmp • Network-Automation

The Challenge: Synchronous C Calls in Event Loops

Modern network automation tools—from FastAPI telemetry microservices to real-time event streaming engines—rely heavily on Python’s asyncio event loop to achieve high I/O concurrency.

However, C-binding SNMP libraries like ezsnmp and Net-SNMP operate synchronously. Invoking a blocking call like session.get() directly inside an async def coroutine blocks the entire event loop thread, freezing all concurrent web requests, timers, and active sockets until the SNMP UDP packet round-trip completes.

While pure-Python asynchronous SNMP libraries exist, they frequently encounter severe CPU bottlenecks when parsing ASN.1 BER payloads across thousands of metrics, and they often lack robust Net-SNMP enterprise MIB compatibility.

Because ezsnmp releases the Python Global Interpreter Lock (GIL) during C-level socket I/O and PDU processing, we can bridge ezsnmp seamlessly into modern event loops using non-blocking thread offloading, delivering native Net-SNMP speed without stalling the event loop.

Architecture: Non-Blocking Execution via asyncio.to_thread

Under Python 3.10+, asyncio.to_thread() delegates blocking synchronous functions to a background thread pool while returning a native awaitable coroutine.

Key architectural considerations with ezsnmp:

  • GIL Release: Because ezsnmp yields the GIL during network waits, threads run concurrently without blocking the Python event loop.

  • Global Net-SNMP State: Net-SNMP maintains process-global C state protected by internal mutexes. While ezsnmp ensures crash-safe operation, scaling beyond four threads per process causes lock contention; multi-process workers or bounded concurrency are recommended for high-volume pipelines.

  • Clean Async Syntax: Native await integration inside FastAPI routes, Tornado handlers, or background worker daemons.

Practical Implementation: Async ezsnmp Wrapper

Below is a production-grade asynchronous wrapper pattern providing non-blocking GET, WALK, and BULKWALK operations alongside concurrency throttling via asyncio.Semaphore.

1. The AsyncSNMPClient Implementation

import asyncio
from concurrent.futures import ThreadPoolExecutor
from ezsnmp import Session
from ezsnmp.exceptions import GenericError, TimeoutError

class AsyncSNMPClient:
    """Asynchronous wrapper for ezsnmp Session operations."""

    def __init__(
        self,
        hostname: str,
        community: str = "public",
        version: int = 2,
        timeout: int = 2,
        retries: int = 1,
        max_repetitions: int = 25,
        executor: ThreadPoolExecutor | None = None
    ):
        self.session = Session(
            hostname=hostname,
            community=community,
            version=version,
            timeout=timeout,
            retries=retries,
            set_max_repeaters_to_num=max_repetitions
        )
        self._executor = executor
        self._lock = asyncio.Lock()

    async def get(self, oids: str | list[str]):
        """Execute a non-blocking SNMP GET."""
        async with self._lock:
            loop = asyncio.get_running_loop()
            return await loop.run_in_executor(
                self._executor, self.session.get, oids
            )

    async def walk(self, oid: str):
        """Execute a non-blocking SNMP WALK on a single root OID."""
        async with self._lock:
            loop = asyncio.get_running_loop()
            return await loop.run_in_executor(
                self._executor, self.session.walk, oid
            )

    async def bulk_walk(self, oids: str | list[str]):
        """Execute a non-blocking GETBULK walk."""
        async with self._lock:
            loop = asyncio.get_running_loop()
            return await loop.run_in_executor(
                self._executor, self.session.bulk_walk, oids
            )

2. Polling Multiple Devices Concurrently

Use asyncio.gather() and asyncio.Semaphore to poll hundreds of targets simultaneously while bounding local socket and thread consumption:

async def poll_device(
    client: AsyncSNMPClient,
    device_name: str,
    semaphore: asyncio.Semaphore
) -> dict:
    async with semaphore:
        try:
            # Query metrics asynchronously without blocking the loop
            sys_descr, uptime = await client.get(
                ["sysDescr.0", "sysUpTime.0"]
            )
            return {
                "device": device_name,
                "status": "ONLINE",
                "descr": sys_descr.value,
                "uptime": uptime.value
            }
        except TimeoutError:
            return {"device": device_name, "status": "TIMEOUT"}
        except GenericError as exc:
            return {"device": device_name, "status": f"ERROR: {exc}"}

async def main():
    # Dedicated bounded thread pool for SNMP I/O operations
    # Note: Net-SNMP uses process-global locks; keep worker counts <= 4
    # per process to avoid lock contention, or use ProcessPoolExecutor.
    snmp_pool = ThreadPoolExecutor(max_workers=4)
    loop = asyncio.get_running_loop()
    try:
        targets = [
            f"switch-{i:03d}.internal" for i in range(1, 21)
        ]
        sem = asyncio.Semaphore(4)  # Bound concurrent in-flight requests

        clients = [
            AsyncSNMPClient(host, executor=snmp_pool)
            for host in targets
        ]

        tasks = [
            poll_device(client, host, sem)
            for client, host in zip(clients, targets)
        ]

        results = await asyncio.gather(*tasks)
        online = sum(1 for r in results if r["status"] == "ONLINE")
        print(f"Polling completed: {online}/{len(targets)} nodes online.")
    finally:
        await loop.run_in_executor(None, snmp_pool.shutdown, True)

if __name__ == "__main__":
    asyncio.run(main())

Production Tuning & Best Practices

1. Use a Dedicated ThreadPoolExecutor

Do not rely exclusively on the default asyncio executor (loop.run_in_executor(None, ...)). The default executor is shared across all background tasks in your process. Configure a dedicated ThreadPoolExecutor(max_workers=N) tailored specifically to your network polling capacity.

2. Enforce Concurrency Limits with Semaphores

Launching 10,000 unbounded coroutines can quickly exhaust system file descriptors (EMFILE: Too many open files) and trigger UDP socket drops. Always place requests behind an asyncio.Semaphore(50..100).

3. Align Application Timeouts with SNMP Timeouts

Let ezsnmp’s internal Net-SNMP engine handle packet retries and socket timeouts (e.g. timeout=1, retries=1). If wrapping with asyncio.wait_for(), ensure the async timeout threshold exceeds the worst-case SNMP retransmit duration:

# (timeout * (retries + 1)) + buffer = (1 * 2) + 1 = 3 seconds
result = await asyncio.wait_for(client.get("sysUpTime.0"), timeout=3.0)

Migration Notes: Easy SNMP vs ezsnmp

  • Thread Safety: Legacy easysnmp contained shared state across sessions that could cause memory faults when driven concurrently by multi-threaded asyncio executors.

  • ezsnmp Stability: ezsnmp’s modernized C++17 foundation enforces internal mutex synchronization around Net-SNMP’s global C state, preventing segmentation faults and memory corruption in concurrent event loops. High-scale architectures should distribute workers across multiple Python processes.