mirror of https://github.com/zulip/zulip.git
210 lines
6.0 KiB
Python
210 lines
6.0 KiB
Python
import hashlib
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import heapq
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import itertools
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import re
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import secrets
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from itertools import zip_longest
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from time import sleep
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from typing import Any, Callable, Iterator, List, Optional, Sequence, Set, Tuple, TypeVar
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from django.conf import settings
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T = TypeVar("T")
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def statsd_key(val: str, clean_periods: bool = False) -> str:
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if ":" in val:
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val = val.split(":")[0]
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val = val.replace("-", "_")
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if clean_periods:
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val = val.replace(".", "_")
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return val
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class StatsDWrapper:
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"""Transparently either submit metrics to statsd
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or do nothing without erroring out"""
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# Backported support for gauge deltas
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# as our statsd server supports them but supporting
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# pystatsd is not released yet
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def _our_gauge(self, stat: str, value: float, rate: float = 1, delta: bool = False) -> None:
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"""Set a gauge value."""
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from django_statsd.clients import statsd
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if delta:
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value_str = f"{value:+g}|g"
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else:
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value_str = f"{value:g}|g"
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statsd._send(stat, value_str, rate)
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def __getattr__(self, name: str) -> Any:
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# Hand off to statsd if we have it enabled
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# otherwise do nothing
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if name in ["timer", "timing", "incr", "decr", "gauge"]:
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if settings.STATSD_HOST != "":
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from django_statsd.clients import statsd
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if name == "gauge":
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return self._our_gauge
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else:
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return getattr(statsd, name)
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else:
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return lambda *args, **kwargs: None
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raise AttributeError
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statsd = StatsDWrapper()
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# Runs the callback with slices of all_list of a given batch_size
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def run_in_batches(
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all_list: Sequence[T],
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batch_size: int,
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callback: Callable[[Sequence[T]], None],
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sleep_time: int = 0,
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logger: Optional[Callable[[str], None]] = None,
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) -> None:
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if len(all_list) == 0:
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return
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limit = (len(all_list) // batch_size) + 1
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for i in range(limit):
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start = i * batch_size
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end = (i + 1) * batch_size
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if end >= len(all_list):
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end = len(all_list)
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batch = all_list[start:end]
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if logger:
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logger(f"Executing {end-start} in batch {i+1} of {limit}")
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callback(batch)
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if i != limit - 1:
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sleep(sleep_time)
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def make_safe_digest(string: str, hash_func: Callable[[bytes], Any] = hashlib.sha1) -> str:
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"""
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return a hex digest of `string`.
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"""
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# hashlib.sha1, md5, etc. expect bytes, so non-ASCII strings must
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# be encoded.
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return hash_func(string.encode("utf-8")).hexdigest()
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def log_statsd_event(name: str) -> None:
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"""
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Sends a single event to statsd with the desired name and the current timestamp
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This can be used to provide vertical lines in generated graphs,
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for example when doing a prod deploy, bankruptcy request, or
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other one-off events
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Note that to draw this event as a vertical line in graphite
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you can use the drawAsInfinite() command
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"""
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event_name = f"events.{name}"
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statsd.incr(event_name)
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def generate_api_key() -> str:
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api_key = ""
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while len(api_key) < 32:
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# One iteration suffices 99.4992% of the time.
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api_key += secrets.token_urlsafe(3 * 9).replace("_", "").replace("-", "")
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return api_key[:32]
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def has_api_key_format(key: str) -> bool:
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return bool(re.fullmatch(r"([A-Za-z0-9]){32}", key))
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def assert_is_not_none(value: Optional[T]) -> T:
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assert value is not None
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return value
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def query_chunker(
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queries: List[Any],
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id_collector: Optional[Set[int]] = None,
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chunk_size: int = 1000,
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db_chunk_size: Optional[int] = None,
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) -> Iterator[Any]:
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"""
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This merges one or more Django ascending-id queries into
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a generator that returns chunks of chunk_size row objects
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during each yield, preserving id order across all results..
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Queries should satisfy these conditions:
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- They should be Django filters.
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- They should return Django objects with "id" attributes.
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- They should be disjoint.
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The generator also populates id_collector, which we use
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internally to enforce unique ids, but which the caller
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can pass in to us if they want the side effect of collecting
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all ids.
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"""
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if db_chunk_size is None:
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db_chunk_size = chunk_size // len(queries)
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assert db_chunk_size >= 2
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assert chunk_size >= 2
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if id_collector is not None:
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assert len(id_collector) == 0
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else:
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id_collector = set()
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def chunkify(q: Any, i: int) -> Iterator[Tuple[int, int, Any]]:
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q = q.order_by("id")
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min_id = -1
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while True:
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rows = list(q.filter(id__gt=min_id)[0:db_chunk_size])
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if len(rows) == 0:
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break
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for row in rows:
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yield (row.id, i, row)
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min_id = rows[-1].id
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iterators = [chunkify(q, i) for i, q in enumerate(queries)]
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merged_query = heapq.merge(*iterators)
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while True:
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tup_chunk = list(itertools.islice(merged_query, 0, chunk_size))
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if len(tup_chunk) == 0:
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break
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# Do duplicate-id management here.
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tup_ids = {tup[0] for tup in tup_chunk}
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assert len(tup_ids) == len(tup_chunk)
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assert len(tup_ids.intersection(id_collector)) == 0
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id_collector.update(tup_ids)
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yield [row for row_id, i, row in tup_chunk]
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def process_list_in_batches(
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lst: List[Any], chunk_size: int, process_batch: Callable[[List[Any]], None]
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) -> None:
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offset = 0
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while True:
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items = lst[offset : offset + chunk_size]
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if not items:
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break
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process_batch(items)
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offset += chunk_size
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def split_by(array: List[Any], group_size: int, filler: Any) -> List[List[Any]]:
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"""
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Group elements into list of size `group_size` and fill empty cells with
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`filler`. Recipe from https://docs.python.org/3/library/itertools.html
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"""
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args = [iter(array)] * group_size
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return list(map(list, zip_longest(*args, fillvalue=filler)))
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