mirror of
https://github.com/PostHog/posthog.git
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254 lines
8.0 KiB
Python
254 lines
8.0 KiB
Python
#!/usr/bin/env python3
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import logging
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import os
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import socket
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import struct
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import sys
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import threading
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import time
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import structlog
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from prometheus_client import CollectorRegistry, Gauge, multiprocess, start_http_server
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loglevel = "error"
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keepalive = 120
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# Set the timeout to something lower than any downstreams, such that if the
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# timeout is hit, then the worker will be killed and respawned, which will then
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# we able to pick up any connections that were previously pending on the socket
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# and serve the requests before the downstream timeout.
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timeout = 15
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grateful_timeout = 120
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METRICS_UPDATE_INTERVAL_SECONDS = int(os.getenv("GUNICORN_METRICS_UPDATE_SECONDS", 5))
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def when_ready(server):
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"""
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To ease being able to hide the /metrics endpoint when running in production,
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we serve the metrics on a separate port, using the
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prometheus_client.multiprocess Collector to pull in data from the worker
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processes.
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"""
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registry = CollectorRegistry()
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multiprocess.MultiProcessCollector(registry)
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port = int(os.environ.get("PROMETHEUS_METRICS_EXPORT_PORT", 8001))
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start_http_server(port=port, registry=registry)
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# Start a thread in the Arbiter that will monitor the backlog on the sockets
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# Gunicorn is listening on.
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socket_monitor = SocketMonitor(server=server, registry=registry)
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socket_monitor.start()
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def post_fork(server, worker):
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"""
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Within each worker process, start a thread that will monitor the thread and
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connection pool.
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"""
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worker_monitor = WorkerMonitor(worker=worker)
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worker_monitor.start()
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def worker_exit(server, worker):
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"""
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Ensure that we mark workers as dead with the prometheus_client such that
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any cleanup can happen.
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"""
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multiprocess.mark_process_dead(worker.pid)
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class SocketMonitor(threading.Thread):
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"""
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We have enabled the statsd collector for Gunicorn, but this doesn't include
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the backlog due to concerns over portability, see
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https://github.com/benoitc/gunicorn/pull/2407
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Instead, we expose to Prometheus a gauge that will report the backlog size.
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We can then:
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1. use this to monitor how well the Gunicorn instances are keeping up with
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requests.
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2. use this metric to handle HPA scaling e.g. in Kubernetes
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"""
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def __init__(self, server, registry):
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super().__init__()
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self.daemon = True
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self.server = server
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self.registry = registry
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def run(self):
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"""
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Every X seconds, check to see how many connections are pending for each
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server socket.
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We label each individually, as limits such as `--backlog` will apply to
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each individually.
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"""
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if sys.platform != "linux":
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# We use the assumption that we are on Linux to be able to get the
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# socket backlog, so if we're not on Linux, we return immediately.
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return
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backlog_gauge = Gauge(
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"gunicorn_pending_connections",
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"The number of pending connections on all sockets. Linux only.",
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registry=self.registry,
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labelnames=["listener"],
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)
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while True:
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for sock in self.server.LISTENERS:
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backlog = self.get_backlog(sock=sock)
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backlog_gauge.labels(listener=str(sock)).set(backlog)
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time.sleep(METRICS_UPDATE_INTERVAL_SECONDS)
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def get_backlog(self, sock):
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# tcp_info struct from include/uapi/linux/tcp.h
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fmt = "B" * 8 + "I" * 24
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tcp_info_struct = sock.getsockopt(socket.IPPROTO_TCP, socket.TCP_INFO, 104)
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# 12 is tcpi_unacked
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return struct.unpack(fmt, tcp_info_struct)[12]
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class WorkerMonitor(threading.Thread):
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"""
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There is a statsd logger support in Gunicorn that allows us to gather
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metrics e.g. on the number of workers, requests, request duration etc. See
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https://docs.gunicorn.org/en/stable/instrumentation.html for details.
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To get a better understanding of the pool utilization, number of accepted
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connections, we start a thread in head worker to report these via prometheus
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metrics.
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"""
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def __init__(self, worker):
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super().__init__()
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self.daemon = True
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self.worker = worker
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def run(self):
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"""
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Every X seconds, check the status of the Thread pool, as well as the
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"""
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active_worker_connections = Gauge(
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"gunicorn_active_worker_connections",
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"Number of active connections.",
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labelnames=["pid"],
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)
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max_worker_connections = Gauge(
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"gunicorn_max_worker_connections",
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"Maximum worker connections.",
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labelnames=["pid"],
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)
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total_threads = Gauge(
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"gunicorn_max_worker_threads",
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"Size of the thread pool per worker.",
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labelnames=["pid"],
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)
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active_threads = Gauge(
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"gunicorn_active_worker_threads",
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"Number of threads actively processing requests.",
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labelnames=["pid"],
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)
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pending_requests = Gauge(
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"gunicorn_pending_requests",
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"Number of requests that have been read from a connection but have not completed yet",
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labelnames=["pid"],
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)
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max_worker_connections.labels(pid=self.worker.pid).set(self.worker.cfg.worker_connections)
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total_threads.labels(pid=self.worker.pid).set(self.worker.cfg.threads)
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while True:
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active_worker_connections.labels(pid=self.worker.pid).set(self.worker.nr_conns)
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active_threads.labels(pid=self.worker.pid).set(min(self.worker.cfg.threads, len(self.worker.futures)))
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pending_requests.labels(pid=self.worker.pid).set(len(self.worker.futures))
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time.sleep(METRICS_UPDATE_INTERVAL_SECONDS)
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LOGGING_FORMATTER_NAME = os.getenv("LOGGING_FORMATTER_NAME", "default")
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# Setup stdlib logging to be handled by Structlog
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def add_pid_and_tid(
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logger: logging.Logger, method_name: str, event_dict: structlog.types.EventDict
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) -> structlog.types.EventDict:
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event_dict["pid"] = os.getpid()
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event_dict["tid"] = threading.get_ident()
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return event_dict
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pre_chain = [
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# Add the log level and a timestamp to the event_dict if the log entry
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# is not from structlog.
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structlog.stdlib.add_log_level,
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structlog.stdlib.add_logger_name,
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add_pid_and_tid,
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structlog.processors.TimeStamper(fmt="iso"),
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]
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# This is a copy the default logging config for gunicorn but with additions to:
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#
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# 1. non propagate loggers to the root handlers (otherwise we get duplicate log
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# lines)
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# 2. use structlog for processing of log records
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#
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# See
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# https://github.com/benoitc/gunicorn/blob/0b953b803786997d633d66c0f7c7b290df75e07c/gunicorn/glogging.py#L48
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# for the default log settings.
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logconfig_dict = {
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"version": 1,
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"disable_existing_loggers": True,
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"formatters": {
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"default": {
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"()": structlog.stdlib.ProcessorFormatter,
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"processor": structlog.dev.ConsoleRenderer(colors=True),
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"foreign_pre_chain": pre_chain,
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},
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"json": {
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"()": structlog.stdlib.ProcessorFormatter,
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"processor": structlog.processors.JSONRenderer(),
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"foreign_pre_chain": pre_chain,
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},
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},
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"root": {"level": "INFO", "handlers": ["console"]},
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"loggers": {
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"gunicorn.error": {
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"level": "INFO",
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"handlers": ["error_console"],
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"propagate": False,
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"qualname": "gunicorn.error",
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},
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"gunicorn.access": {
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"level": "INFO",
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"handlers": ["console"],
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"propagate": False,
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"qualname": "gunicorn.access",
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},
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},
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"handlers": {
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"error_console": {
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"class": "logging.StreamHandler",
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"formatter": LOGGING_FORMATTER_NAME,
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"stream": "ext://sys.stderr",
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},
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"console": {
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"class": "logging.StreamHandler",
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"formatter": LOGGING_FORMATTER_NAME,
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"stream": "ext://sys.stdout",
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},
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},
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}
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