zulip/zerver/lib/utils.py

210 lines
6.0 KiB
Python

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