MatrixSynapse/docker/configure_workers_and_start.py

1030 lines
41 KiB
Python
Executable File

#!/usr/bin/env python
# Copyright 2021 The Matrix.org Foundation C.I.C.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script reads environment variables and generates a shared Synapse worker,
# nginx and supervisord configs depending on the workers requested.
#
# The environment variables it reads are:
# * SYNAPSE_SERVER_NAME: The desired server_name of the homeserver.
# * SYNAPSE_REPORT_STATS: Whether to report stats.
# * SYNAPSE_WORKER_TYPES: A comma separated list of worker names as specified in WORKERS_CONFIG
# below. Leave empty for no workers. Add a ':' and a number at the end to
# multiply that worker. Append multiple worker types with '+' to merge the
# worker types into a single worker. Add a name and a '=' to the front of a
# worker type to give this instance a name in logs and nginx.
# Examples:
# SYNAPSE_WORKER_TYPES='event_persister, federation_sender, client_reader'
# SYNAPSE_WORKER_TYPES='event_persister:2, federation_sender:2, client_reader'
# SYNAPSE_WORKER_TYPES='stream_writers=account_data+presence+typing'
# * SYNAPSE_AS_REGISTRATION_DIR: If specified, a directory in which .yaml and .yml files
# will be treated as Application Service registration files.
# * SYNAPSE_TLS_CERT: Path to a TLS certificate in PEM format.
# * SYNAPSE_TLS_KEY: Path to a TLS key. If this and SYNAPSE_TLS_CERT are specified,
# Nginx will be configured to serve TLS on port 8448.
# * SYNAPSE_USE_EXPERIMENTAL_FORKING_LAUNCHER: Whether to use the forking launcher,
# only intended for usage in Complement at the moment.
# No stability guarantees are provided.
# * SYNAPSE_LOG_LEVEL: Set this to DEBUG, INFO, WARNING or ERROR to change the
# log level. INFO is the default.
# * SYNAPSE_LOG_SENSITIVE: If unset, SQL and SQL values won't be logged,
# regardless of the SYNAPSE_LOG_LEVEL setting.
#
# NOTE: According to Complement's ENTRYPOINT expectations for a homeserver image (as defined
# in the project's README), this script may be run multiple times, and functionality should
# continue to work if so.
import os
import platform
import re
import subprocess
import sys
from collections import defaultdict
from itertools import chain
from pathlib import Path
from typing import (
Any,
Dict,
List,
Mapping,
MutableMapping,
NoReturn,
Optional,
Set,
SupportsIndex,
)
import yaml
from jinja2 import Environment, FileSystemLoader
MAIN_PROCESS_HTTP_LISTENER_PORT = 8080
MAIN_PROCESS_INSTANCE_NAME = "main"
MAIN_PROCESS_LOCALHOST_ADDRESS = "127.0.0.1"
MAIN_PROCESS_REPLICATION_PORT = 9093
# A simple name used as a placeholder in the WORKERS_CONFIG below. This will be replaced
# during processing with the name of the worker.
WORKER_PLACEHOLDER_NAME = "placeholder_name"
# Workers with exposed endpoints needs either "client", "federation", or "media" listener_resources
# Watching /_matrix/client needs a "client" listener
# Watching /_matrix/federation needs a "federation" listener
# Watching /_matrix/media and related needs a "media" listener
# Stream Writers require "client" and "replication" listeners because they
# have to attach by instance_map to the master process and have client endpoints.
WORKERS_CONFIG: Dict[str, Dict[str, Any]] = {
"pusher": {
"app": "synapse.app.generic_worker",
"listener_resources": [],
"endpoint_patterns": [],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"user_dir": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client"],
"endpoint_patterns": [
"^/_matrix/client/(api/v1|r0|v3|unstable)/user_directory/search$"
],
"shared_extra_conf": {
"update_user_directory_from_worker": WORKER_PLACEHOLDER_NAME
},
"worker_extra_conf": "",
},
"media_repository": {
"app": "synapse.app.generic_worker",
"listener_resources": ["media"],
"endpoint_patterns": [
"^/_matrix/media/",
"^/_synapse/admin/v1/purge_media_cache$",
"^/_synapse/admin/v1/room/.*/media.*$",
"^/_synapse/admin/v1/user/.*/media.*$",
"^/_synapse/admin/v1/media/.*$",
"^/_synapse/admin/v1/quarantine_media/.*$",
],
# The first configured media worker will run the media background jobs
"shared_extra_conf": {
"enable_media_repo": False,
"media_instance_running_background_jobs": WORKER_PLACEHOLDER_NAME,
},
"worker_extra_conf": "enable_media_repo: true",
},
"appservice": {
"app": "synapse.app.generic_worker",
"listener_resources": [],
"endpoint_patterns": [],
"shared_extra_conf": {
"notify_appservices_from_worker": WORKER_PLACEHOLDER_NAME
},
"worker_extra_conf": "",
},
"federation_sender": {
"app": "synapse.app.generic_worker",
"listener_resources": [],
"endpoint_patterns": [],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"synchrotron": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client"],
"endpoint_patterns": [
"^/_matrix/client/(v2_alpha|r0|v3)/sync$",
"^/_matrix/client/(api/v1|v2_alpha|r0|v3)/events$",
"^/_matrix/client/(api/v1|r0|v3)/initialSync$",
"^/_matrix/client/(api/v1|r0|v3)/rooms/[^/]+/initialSync$",
],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"client_reader": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client"],
"endpoint_patterns": [
"^/_matrix/client/(api/v1|r0|v3|unstable)/publicRooms$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/joined_members$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/context/.*$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/members$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/state$",
"^/_matrix/client/v1/rooms/.*/hierarchy$",
"^/_matrix/client/(v1|unstable)/rooms/.*/relations/",
"^/_matrix/client/v1/rooms/.*/threads$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/login$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/account/3pid$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/account/whoami$",
"^/_matrix/client/versions$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/voip/turnServer$",
"^/_matrix/client/(r0|v3|unstable)/register$",
"^/_matrix/client/(r0|v3|unstable)/register/available$",
"^/_matrix/client/(r0|v3|unstable)/auth/.*/fallback/web$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/messages$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/event",
"^/_matrix/client/(api/v1|r0|v3|unstable)/joined_rooms",
"^/_matrix/client/(api/v1|r0|v3|unstable/.*)/rooms/.*/aliases",
"^/_matrix/client/v1/rooms/.*/timestamp_to_event$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/search",
"^/_matrix/client/(r0|v3|unstable)/user/.*/filter(/|$)",
"^/_matrix/client/(r0|v3|unstable)/password_policy$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/directory/room/.*$",
"^/_matrix/client/(r0|v3|unstable)/capabilities$",
],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"federation_reader": {
"app": "synapse.app.generic_worker",
"listener_resources": ["federation"],
"endpoint_patterns": [
"^/_matrix/federation/(v1|v2)/event/",
"^/_matrix/federation/(v1|v2)/state/",
"^/_matrix/federation/(v1|v2)/state_ids/",
"^/_matrix/federation/(v1|v2)/backfill/",
"^/_matrix/federation/(v1|v2)/get_missing_events/",
"^/_matrix/federation/(v1|v2)/publicRooms",
"^/_matrix/federation/(v1|v2)/query/",
"^/_matrix/federation/(v1|v2)/make_join/",
"^/_matrix/federation/(v1|v2)/make_leave/",
"^/_matrix/federation/(v1|v2)/send_join/",
"^/_matrix/federation/(v1|v2)/send_leave/",
"^/_matrix/federation/(v1|v2)/invite/",
"^/_matrix/federation/(v1|v2)/query_auth/",
"^/_matrix/federation/(v1|v2)/event_auth/",
"^/_matrix/federation/v1/timestamp_to_event/",
"^/_matrix/federation/(v1|v2)/exchange_third_party_invite/",
"^/_matrix/federation/(v1|v2)/user/devices/",
"^/_matrix/federation/(v1|v2)/get_groups_publicised$",
"^/_matrix/key/v2/query",
],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"federation_inbound": {
"app": "synapse.app.generic_worker",
"listener_resources": ["federation"],
"endpoint_patterns": ["/_matrix/federation/(v1|v2)/send/"],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"event_persister": {
"app": "synapse.app.generic_worker",
"listener_resources": ["replication"],
"endpoint_patterns": [],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"background_worker": {
"app": "synapse.app.generic_worker",
"listener_resources": [],
"endpoint_patterns": [],
# This worker cannot be sharded. Therefore, there should only ever be one
# background worker. This is enforced for the safety of your database.
"shared_extra_conf": {"run_background_tasks_on": WORKER_PLACEHOLDER_NAME},
"worker_extra_conf": "",
},
"event_creator": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client"],
"endpoint_patterns": [
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/redact",
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/send",
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/(join|invite|leave|ban|unban|kick)$",
"^/_matrix/client/(api/v1|r0|v3|unstable)/join/",
"^/_matrix/client/(api/v1|r0|v3|unstable)/knock/",
"^/_matrix/client/(api/v1|r0|v3|unstable)/profile/",
"^/_matrix/client/(v1|unstable/org.matrix.msc2716)/rooms/.*/batch_send",
],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"frontend_proxy": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client", "replication"],
"endpoint_patterns": ["^/_matrix/client/(api/v1|r0|v3|unstable)/keys/upload"],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"account_data": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client", "replication"],
"endpoint_patterns": [
"^/_matrix/client/(r0|v3|unstable)/.*/tags",
"^/_matrix/client/(r0|v3|unstable)/.*/account_data",
],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"presence": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client", "replication"],
"endpoint_patterns": ["^/_matrix/client/(api/v1|r0|v3|unstable)/presence/"],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"receipts": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client", "replication"],
"endpoint_patterns": [
"^/_matrix/client/(r0|v3|unstable)/rooms/.*/receipt",
"^/_matrix/client/(r0|v3|unstable)/rooms/.*/read_markers",
],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"to_device": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client", "replication"],
"endpoint_patterns": ["^/_matrix/client/(r0|v3|unstable)/sendToDevice/"],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
"typing": {
"app": "synapse.app.generic_worker",
"listener_resources": ["client", "replication"],
"endpoint_patterns": [
"^/_matrix/client/(api/v1|r0|v3|unstable)/rooms/.*/typing"
],
"shared_extra_conf": {},
"worker_extra_conf": "",
},
}
# Templates for sections that may be inserted multiple times in config files
NGINX_LOCATION_CONFIG_BLOCK = """
location ~* {endpoint} {{
proxy_pass {upstream};
proxy_set_header X-Forwarded-For $remote_addr;
proxy_set_header X-Forwarded-Proto $scheme;
proxy_set_header Host $host;
}}
"""
NGINX_UPSTREAM_CONFIG_BLOCK = """
upstream {upstream_worker_base_name} {{
{body}
}}
"""
# Utility functions
def log(txt: str) -> None:
print(txt)
def error(txt: str) -> NoReturn:
print(txt, file=sys.stderr)
sys.exit(2)
def flush_buffers() -> None:
sys.stdout.flush()
sys.stderr.flush()
def convert(src: str, dst: str, **template_vars: object) -> None:
"""Generate a file from a template
Args:
src: Path to the input file.
dst: Path to write to.
template_vars: The arguments to replace placeholder variables in the template with.
"""
# Read the template file
# We disable autoescape to prevent template variables from being escaped,
# as we're not using HTML.
env = Environment(loader=FileSystemLoader(os.path.dirname(src)), autoescape=False)
template = env.get_template(os.path.basename(src))
# Generate a string from the template.
rendered = template.render(**template_vars)
# Write the generated contents to a file
#
# We use append mode in case the files have already been written to by something else
# (for instance, as part of the instructions in a dockerfile).
with open(dst, "a") as outfile:
# In case the existing file doesn't end with a newline
outfile.write("\n")
outfile.write(rendered)
def add_worker_roles_to_shared_config(
shared_config: dict,
worker_types_set: Set[str],
worker_name: str,
worker_port: int,
) -> None:
"""Given a dictionary representing a config file shared across all workers,
append appropriate worker information to it for the current worker_type instance.
Args:
shared_config: The config dict that all worker instances share (after being
converted to YAML)
worker_types_set: The type of worker (one of those defined in WORKERS_CONFIG).
This list can be a single worker type or multiple.
worker_name: The name of the worker instance.
worker_port: The HTTP replication port that the worker instance is listening on.
"""
# The instance_map config field marks the workers that write to various replication
# streams
instance_map = shared_config.setdefault("instance_map", {})
# This is a list of the stream_writers that there can be only one of. Events can be
# sharded, and therefore doesn't belong here.
singular_stream_writers = [
"account_data",
"presence",
"receipts",
"to_device",
"typing",
]
# Worker-type specific sharding config. Now a single worker can fulfill multiple
# roles, check each.
if "pusher" in worker_types_set:
shared_config.setdefault("pusher_instances", []).append(worker_name)
if "federation_sender" in worker_types_set:
shared_config.setdefault("federation_sender_instances", []).append(worker_name)
if "event_persister" in worker_types_set:
# Event persisters write to the events stream, so we need to update
# the list of event stream writers
shared_config.setdefault("stream_writers", {}).setdefault("events", []).append(
worker_name
)
# Map of stream writer instance names to host/ports combos
instance_map[worker_name] = {
"host": "localhost",
"port": worker_port,
}
# Update the list of stream writers. It's convenient that the name of the worker
# type is the same as the stream to write. Iterate over the whole list in case there
# is more than one.
for worker in worker_types_set:
if worker in singular_stream_writers:
shared_config.setdefault("stream_writers", {}).setdefault(
worker, []
).append(worker_name)
# Map of stream writer instance names to host/ports combos
# For now, all stream writers need http replication ports
instance_map[worker_name] = {
"host": "localhost",
"port": worker_port,
}
def merge_worker_template_configs(
existing_dict: Optional[Dict[str, Any]],
to_be_merged_dict: Dict[str, Any],
) -> Dict[str, Any]:
"""When given an existing dict of worker template configuration consisting with both
dicts and lists, merge new template data from WORKERS_CONFIG(or create) and
return new dict.
Args:
existing_dict: Either an existing worker template or a fresh blank one.
to_be_merged_dict: The template from WORKERS_CONFIGS to be merged into
existing_dict.
Returns: The newly merged together dict values.
"""
new_dict: Dict[str, Any] = {}
if not existing_dict:
# It doesn't exist yet, just use the new dict(but take a copy not a reference)
new_dict = to_be_merged_dict.copy()
else:
for i in to_be_merged_dict.keys():
if (i == "endpoint_patterns") or (i == "listener_resources"):
# merge the two lists, remove duplicates
new_dict[i] = list(set(existing_dict[i] + to_be_merged_dict[i]))
elif i == "shared_extra_conf":
# merge dictionary's, the worker name will be replaced later
new_dict[i] = {**existing_dict[i], **to_be_merged_dict[i]}
elif i == "worker_extra_conf":
# There is only one worker type that has a 'worker_extra_conf' and it is
# the media_repo. Since duplicate worker types on the same worker don't
# work, this is fine.
new_dict[i] = existing_dict[i] + to_be_merged_dict[i]
else:
# Everything else should be identical, like "app", which only works
# because all apps are now generic_workers.
new_dict[i] = to_be_merged_dict[i]
return new_dict
def insert_worker_name_for_worker_config(
existing_dict: Dict[str, Any], worker_name: str
) -> Dict[str, Any]:
"""Insert a given worker name into the worker's configuration dict.
Args:
existing_dict: The worker_config dict that is imported into shared_config.
worker_name: The name of the worker to insert.
Returns: Copy of the dict with newly inserted worker name
"""
dict_to_edit = existing_dict.copy()
for k, v in dict_to_edit["shared_extra_conf"].items():
# Only proceed if it's the placeholder name string
if v == WORKER_PLACEHOLDER_NAME:
dict_to_edit["shared_extra_conf"][k] = worker_name
return dict_to_edit
def apply_requested_multiplier_for_worker(worker_types: List[str]) -> List[str]:
"""
Apply multiplier(if found) by returning a new expanded list with some basic error
checking.
Args:
worker_types: The unprocessed List of requested workers
Returns:
A new list with all requested workers expanded.
"""
# Checking performed:
# 1. if worker:2 or more is declared, it will create additional workers up to number
# 2. if worker:1, it will create a single copy of this worker as if no number was
# given
# 3. if worker:0 is declared, this worker will be ignored. This is to allow for
# scripting and automated expansion and is intended behaviour.
# 4. if worker:NaN or is a negative number, it will error and log it.
new_worker_types = []
for worker_type in worker_types:
if ":" in worker_type:
worker_type_components = split_and_strip_string(worker_type, ":", 1)
worker_count = 0
# Should only be 2 components, a type of worker(s) and an integer as a
# string. Cast the number as an int then it can be used as a counter.
try:
worker_count = int(worker_type_components[1])
except ValueError:
error(
f"Bad number in worker count for '{worker_type}': "
f"'{worker_type_components[1]}' is not an integer"
)
# As long as there are more than 0, we add one to the list to make below.
for _ in range(worker_count):
new_worker_types.append(worker_type_components[0])
else:
# If it's not a real worker_type, it will error out later.
new_worker_types.append(worker_type)
return new_worker_types
def is_sharding_allowed_for_worker_type(worker_type: str) -> bool:
"""Helper to check to make sure worker types that cannot have multiples do not.
Args:
worker_type: The type of worker to check against.
Returns: True if allowed, False if not
"""
return worker_type not in [
"background_worker",
"account_data",
"presence",
"receipts",
"typing",
"to_device",
]
def split_and_strip_string(
given_string: str, split_char: str, max_split: SupportsIndex = -1
) -> List[str]:
"""
Helper to split a string on split_char and strip whitespace from each end of each
element.
Args:
given_string: The string to split
split_char: The character to split the string on
max_split: kwarg for split() to limit how many times the split() happens
Returns:
A List of strings
"""
# Removes whitespace from ends of result strings before adding to list. Allow for
# overriding 'maxsplit' kwarg, default being -1 to signify no maximum.
return [x.strip() for x in given_string.split(split_char, maxsplit=max_split)]
def generate_base_homeserver_config() -> None:
"""Starts Synapse and generates a basic homeserver config, which will later be
modified for worker support.
Raises: CalledProcessError if calling start.py returned a non-zero exit code.
"""
# start.py already does this for us, so just call that.
# note that this script is copied in in the official, monolith dockerfile
os.environ["SYNAPSE_HTTP_PORT"] = str(MAIN_PROCESS_HTTP_LISTENER_PORT)
subprocess.run(["/usr/local/bin/python", "/start.py", "migrate_config"], check=True)
def parse_worker_types(
requested_worker_types: List[str],
) -> Dict[str, Set[str]]:
"""Read the desired list of requested workers and prepare the data for use in
generating worker config files while also checking for potential gotchas.
Args:
requested_worker_types: The list formed from the split environment variable
containing the unprocessed requests for workers.
Returns: A dict of worker names to set of worker types. Format:
{'worker_name':
{'worker_type', 'worker_type2'}
}
"""
# A counter of worker_base_name -> int. Used for determining the name for a given
# worker when generating its config file, as each worker's name is just
# worker_base_name followed by instance number
worker_base_name_counter: Dict[str, int] = defaultdict(int)
# Similar to above, but more finely grained. This is used to determine we don't have
# more than a single worker for cases where multiples would be bad(e.g. presence).
worker_type_shard_counter: Dict[str, int] = defaultdict(int)
# The final result of all this processing
dict_to_return: Dict[str, Set[str]] = {}
# Handle any multipliers requested for given workers.
multiple_processed_worker_types = apply_requested_multiplier_for_worker(
requested_worker_types
)
# Process each worker_type_string
# Examples of expected formats:
# - requested_name=type1+type2+type3
# - synchrotron
# - event_creator+event_persister
for worker_type_string in multiple_processed_worker_types:
# First, if a name is requested, use that — otherwise generate one.
worker_base_name: str = ""
if "=" in worker_type_string:
# Split on "=", remove extra whitespace from ends then make list
worker_type_split = split_and_strip_string(worker_type_string, "=")
if len(worker_type_split) > 2:
error(
"There should only be one '=' in the worker type string. "
f"Please fix: {worker_type_string}"
)
# Assign the name
worker_base_name = worker_type_split[0]
if not re.match(r"^[a-zA-Z0-9_+-]*[a-zA-Z_+-]$", worker_base_name):
# Apply a fairly narrow regex to the worker names. Some characters
# aren't safe for use in file paths or nginx configurations.
# Don't allow to end with a number because we'll add a number
# ourselves in a moment.
error(
"Invalid worker name; please choose a name consisting of "
"alphanumeric letters, _ + -, but not ending with a digit: "
f"{worker_base_name!r}"
)
# Continue processing the remainder of the worker_type string
# with the name override removed.
worker_type_string = worker_type_split[1]
# Split the worker_type_string on "+", remove whitespace from ends then make
# the list a set so it's deduplicated.
worker_types_set: Set[str] = set(
split_and_strip_string(worker_type_string, "+")
)
if not worker_base_name:
# No base name specified: generate one deterministically from set of
# types
worker_base_name = "+".join(sorted(worker_types_set))
# At this point, we have:
# worker_base_name which is the name for the worker, without counter.
# worker_types_set which is the set of worker types for this worker.
# Validate worker_type and make sure we don't allow sharding for a worker type
# that doesn't support it. Will error and stop if it is a problem,
# e.g. 'background_worker'.
for worker_type in worker_types_set:
# Verify this is a real defined worker type. If it's not, stop everything so
# it can be fixed.
if worker_type not in WORKERS_CONFIG:
error(
f"{worker_type} is an unknown worker type! Was found in "
f"'{worker_type_string}'. Please fix!"
)
if worker_type in worker_type_shard_counter:
if not is_sharding_allowed_for_worker_type(worker_type):
error(
f"There can be only a single worker with {worker_type} "
"type. Please recount and remove."
)
# Not in shard counter, must not have seen it yet, add it.
worker_type_shard_counter[worker_type] += 1
# Generate the number for the worker using incrementing counter
worker_base_name_counter[worker_base_name] += 1
worker_number = worker_base_name_counter[worker_base_name]
worker_name = f"{worker_base_name}{worker_number}"
if worker_number > 1:
# If this isn't the first worker, check that we don't have a confusing
# mixture of worker types with the same base name.
first_worker_with_base_name = dict_to_return[f"{worker_base_name}1"]
if first_worker_with_base_name != worker_types_set:
error(
f"Can not use worker_name: '{worker_name}' for worker_type(s): "
f"{worker_types_set!r}. It is already in use by "
f"worker_type(s): {first_worker_with_base_name!r}"
)
dict_to_return[worker_name] = worker_types_set
return dict_to_return
def generate_worker_files(
environ: Mapping[str, str],
config_path: str,
data_dir: str,
requested_worker_types: Dict[str, Set[str]],
) -> None:
"""Read the desired workers(if any) that is passed in and generate shared
homeserver, nginx and supervisord configs.
Args:
environ: os.environ instance.
config_path: The location of the generated Synapse main worker config file.
data_dir: The location of the synapse data directory. Where log and
user-facing config files live.
requested_worker_types: A Dict containing requested workers in the format of
{'worker_name1': {'worker_type', ...}}
"""
# Note that yaml cares about indentation, so care should be taken to insert lines
# into files at the correct indentation below.
# First read the original config file and extract the listeners block. Then we'll
# add another listener for replication. Later we'll write out the result to the
# shared config file.
listeners = [
{
"port": MAIN_PROCESS_REPLICATION_PORT,
"bind_address": MAIN_PROCESS_LOCALHOST_ADDRESS,
"type": "http",
"resources": [{"names": ["replication"]}],
}
]
with open(config_path) as file_stream:
original_config = yaml.safe_load(file_stream)
original_listeners = original_config.get("listeners")
if original_listeners:
listeners += original_listeners
# The shared homeserver config. The contents of which will be inserted into the
# base shared worker jinja2 template. This config file will be passed to all
# workers, included Synapse's main process. It is intended mainly for disabling
# functionality when certain workers are spun up, and adding a replication listener.
shared_config: Dict[str, Any] = {"listeners": listeners}
# List of dicts that describe workers.
# We pass this to the Supervisor template later to generate the appropriate
# program blocks.
worker_descriptors: List[Dict[str, Any]] = []
# Upstreams for load-balancing purposes. This dict takes the form of the worker
# type to the ports of each worker. For example:
# {
# worker_type: {1234, 1235, ...}}
# }
# and will be used to construct 'upstream' nginx directives.
nginx_upstreams: Dict[str, Set[int]] = {}
# A map of: {"endpoint": "upstream"}, where "upstream" is a str representing what
# will be placed after the proxy_pass directive. The main benefit to representing
# this data as a dict over a str is that we can easily deduplicate endpoints
# across multiple instances of the same worker. The final rendering will be combined
# with nginx_upstreams and placed in /etc/nginx/conf.d.
nginx_locations: Dict[str, str] = {}
# Create the worker configuration directory if it doesn't already exist
os.makedirs("/conf/workers", exist_ok=True)
# Start worker ports from this arbitrary port
worker_port = 18009
# A list of internal endpoints to healthcheck, starting with the main process
# which exists even if no workers do.
healthcheck_urls = ["http://localhost:8080/health"]
# Get the set of all worker types that we have configured
all_worker_types_in_use = set(chain(*requested_worker_types.values()))
# Map locations to upstreams (corresponding to worker types) in Nginx
# but only if we use the appropriate worker type
for worker_type in all_worker_types_in_use:
for endpoint_pattern in WORKERS_CONFIG[worker_type]["endpoint_patterns"]:
nginx_locations[endpoint_pattern] = f"http://{worker_type}"
# For each worker type specified by the user, create config values and write it's
# yaml config file
for worker_name, worker_types_set in requested_worker_types.items():
# The collected and processed data will live here.
worker_config: Dict[str, Any] = {}
# Merge all worker config templates for this worker into a single config
for worker_type in worker_types_set:
copy_of_template_config = WORKERS_CONFIG[worker_type].copy()
# Merge worker type template configuration data. It's a combination of lists
# and dicts, so use this helper.
worker_config = merge_worker_template_configs(
worker_config, copy_of_template_config
)
# Replace placeholder names in the config template with the actual worker name.
worker_config = insert_worker_name_for_worker_config(worker_config, worker_name)
worker_config.update(
{"name": worker_name, "port": str(worker_port), "config_path": config_path}
)
# Update the shared config with any worker_type specific options. The first of a
# given worker_type needs to stay assigned and not be replaced.
worker_config["shared_extra_conf"].update(shared_config)
shared_config = worker_config["shared_extra_conf"]
healthcheck_urls.append("http://localhost:%d/health" % (worker_port,))
# Update the shared config with sharding-related options if necessary
add_worker_roles_to_shared_config(
shared_config, worker_types_set, worker_name, worker_port
)
# Enable the worker in supervisord
worker_descriptors.append(worker_config)
# Write out the worker's logging config file
log_config_filepath = generate_worker_log_config(environ, worker_name, data_dir)
# Then a worker config file
convert(
"/conf/worker.yaml.j2",
"/conf/workers/{name}.yaml".format(name=worker_name),
**worker_config,
worker_log_config_filepath=log_config_filepath,
)
# Save this worker's port number to the correct nginx upstreams
for worker_type in worker_types_set:
nginx_upstreams.setdefault(worker_type, set()).add(worker_port)
worker_port += 1
# Build the nginx location config blocks
nginx_location_config = ""
for endpoint, upstream in nginx_locations.items():
nginx_location_config += NGINX_LOCATION_CONFIG_BLOCK.format(
endpoint=endpoint,
upstream=upstream,
)
# Determine the load-balancing upstreams to configure
nginx_upstream_config = ""
for upstream_worker_base_name, upstream_worker_ports in nginx_upstreams.items():
body = ""
for port in upstream_worker_ports:
body += f" server localhost:{port};\n"
# Add to the list of configured upstreams
nginx_upstream_config += NGINX_UPSTREAM_CONFIG_BLOCK.format(
upstream_worker_base_name=upstream_worker_base_name,
body=body,
)
# Finally, we'll write out the config files.
# log config for the master process
master_log_config = generate_worker_log_config(environ, "master", data_dir)
shared_config["log_config"] = master_log_config
# Find application service registrations
appservice_registrations = None
appservice_registration_dir = os.environ.get("SYNAPSE_AS_REGISTRATION_DIR")
if appservice_registration_dir:
# Scan for all YAML files that should be application service registrations.
appservice_registrations = [
str(reg_path.resolve())
for reg_path in Path(appservice_registration_dir).iterdir()
if reg_path.suffix.lower() in (".yaml", ".yml")
]
workers_in_use = len(requested_worker_types) > 0
# If there are workers, add the main process to the instance_map too.
if workers_in_use:
instance_map = shared_config.setdefault("instance_map", {})
instance_map[MAIN_PROCESS_INSTANCE_NAME] = {
"host": MAIN_PROCESS_LOCALHOST_ADDRESS,
"port": MAIN_PROCESS_REPLICATION_PORT,
}
# Shared homeserver config
convert(
"/conf/shared.yaml.j2",
"/conf/workers/shared.yaml",
shared_worker_config=yaml.dump(shared_config),
appservice_registrations=appservice_registrations,
enable_redis=workers_in_use,
workers_in_use=workers_in_use,
)
# Nginx config
convert(
"/conf/nginx.conf.j2",
"/etc/nginx/conf.d/matrix-synapse.conf",
worker_locations=nginx_location_config,
upstream_directives=nginx_upstream_config,
tls_cert_path=os.environ.get("SYNAPSE_TLS_CERT"),
tls_key_path=os.environ.get("SYNAPSE_TLS_KEY"),
)
# Supervisord config
os.makedirs("/etc/supervisor", exist_ok=True)
convert(
"/conf/supervisord.conf.j2",
"/etc/supervisor/supervisord.conf",
main_config_path=config_path,
enable_redis=workers_in_use,
)
convert(
"/conf/synapse.supervisord.conf.j2",
"/etc/supervisor/conf.d/synapse.conf",
workers=worker_descriptors,
main_config_path=config_path,
use_forking_launcher=environ.get("SYNAPSE_USE_EXPERIMENTAL_FORKING_LAUNCHER"),
)
# healthcheck config
convert(
"/conf/healthcheck.sh.j2",
"/healthcheck.sh",
healthcheck_urls=healthcheck_urls,
)
# Ensure the logging directory exists
log_dir = data_dir + "/logs"
if not os.path.exists(log_dir):
os.mkdir(log_dir)
def generate_worker_log_config(
environ: Mapping[str, str], worker_name: str, data_dir: str
) -> str:
"""Generate a log.config file for the given worker.
Returns: the path to the generated file
"""
# Check whether we should write worker logs to disk, in addition to the console
extra_log_template_args: Dict[str, Optional[str]] = {}
if environ.get("SYNAPSE_WORKERS_WRITE_LOGS_TO_DISK"):
extra_log_template_args["LOG_FILE_PATH"] = f"{data_dir}/logs/{worker_name}.log"
extra_log_template_args["SYNAPSE_LOG_LEVEL"] = environ.get("SYNAPSE_LOG_LEVEL")
extra_log_template_args["SYNAPSE_LOG_SENSITIVE"] = environ.get(
"SYNAPSE_LOG_SENSITIVE"
)
# Render and write the file
log_config_filepath = f"/conf/workers/{worker_name}.log.config"
convert(
"/conf/log.config",
log_config_filepath,
worker_name=worker_name,
**extra_log_template_args,
include_worker_name_in_log_line=environ.get(
"SYNAPSE_USE_EXPERIMENTAL_FORKING_LAUNCHER"
),
)
return log_config_filepath
def main(args: List[str], environ: MutableMapping[str, str]) -> None:
config_dir = environ.get("SYNAPSE_CONFIG_DIR", "/data")
config_path = environ.get("SYNAPSE_CONFIG_PATH", config_dir + "/homeserver.yaml")
data_dir = environ.get("SYNAPSE_DATA_DIR", "/data")
# override SYNAPSE_NO_TLS, we don't support TLS in worker mode,
# this needs to be handled by a frontend proxy
environ["SYNAPSE_NO_TLS"] = "yes"
# Generate the base homeserver config if one does not yet exist
if not os.path.exists(config_path):
log("Generating base homeserver config")
generate_base_homeserver_config()
else:
log("Base homeserver config exists—not regenerating")
# This script may be run multiple times (mostly by Complement, see note at top of
# file). Don't re-configure workers in this instance.
mark_filepath = "/conf/workers_have_been_configured"
if not os.path.exists(mark_filepath):
# Collect and validate worker_type requests
# Read the desired worker configuration from the environment
worker_types_env = environ.get("SYNAPSE_WORKER_TYPES", "").strip()
# Only process worker_types if they exist
if not worker_types_env:
# No workers, just the main process
worker_types = []
requested_worker_types: Dict[str, Any] = {}
else:
# Split type names by comma, ignoring whitespace.
worker_types = split_and_strip_string(worker_types_env, ",")
requested_worker_types = parse_worker_types(worker_types)
# Always regenerate all other config files
log("Generating worker config files")
generate_worker_files(environ, config_path, data_dir, requested_worker_types)
# Mark workers as being configured
with open(mark_filepath, "w") as f:
f.write("")
else:
log("Worker config exists—not regenerating")
# Lifted right out of start.py
jemallocpath = "/usr/lib/%s-linux-gnu/libjemalloc.so.2" % (platform.machine(),)
if os.path.isfile(jemallocpath):
environ["LD_PRELOAD"] = jemallocpath
else:
log("Could not find %s, will not use" % (jemallocpath,))
# Start supervisord, which will start Synapse, all of the configured worker
# processes, redis, nginx etc. according to the config we created above.
log("Starting supervisord")
flush_buffers()
os.execle(
"/usr/local/bin/supervisord",
"supervisord",
"-c",
"/etc/supervisor/supervisord.conf",
environ,
)
if __name__ == "__main__":
main(sys.argv, os.environ)