ssh always runs its command through a shell (after naïvely joining
multiple arguments with spaces), so it needs an extra level of shell
quoting. This should have no effect because we already validated user
with a regex, but it’s better for escaping to be locally correct in
case the context changes.
Signed-off-by: Anders Kaseorg <anders@zulip.com>
do_send_messages has side effects outside the database and may not
work reliably if its database effects are reordered by being inside a
transaction.
This also fixes a bug where we were doing the update incorrectly on
the Message table.
Signed-off-by: Anders Kaseorg <anders@zulip.com>
Since this was using repead individual get() calls previously, it
could not be monitored for having a consumer. Add it in, by marking
it of queue type "consumer" (the default), and adding Nagios lines for
it.
Also adjust missedmessage_emails to be monitored; it stopped using
LoopQueueProcessingWorker in 5cec566cb9, but was never added back
into the set of monitored consumers.
This low-level interface allows consuming from a queue with timeouts.
This can be used to either consume in batches (with an upper timeout),
or one-at-a-time. This is notably more performant than calling
`.get()` repeatedly (what json_drain_queue does under the hood), which
is "*highly discouraged* as it is *very inefficient*"[1].
Before this change:
```
$ ./manage.py queue_rate --count 10000 --batch
Purging queue...
Enqueue rate: 11158 / sec
Dequeue rate: 3075 / sec
```
After:
```
$ ./manage.py queue_rate --count 10000 --batch
Purging queue...
Enqueue rate: 11511 / sec
Dequeue rate: 19938 / sec
```
[1] https://www.rabbitmq.com/consumers.html#fetching
`loopworker_sleep_mock` is a file-level variable used to mock out the
sleep() call in LoopQueueProcessingWorker; don't reuse the variable
name for something else.
Despite its name, the `queue_size` method does not return the number
of items in the queue; it returns the number of items that the local
consumer has delivered but unprocessed. These are often, but not
always, the same.
RabbitMQ's queues maintain the queue of unacknowledged messages; when
a consumer connects, it sends to the consumer some number of messages
to handle, known as the "prefetch." This is a performance
optimization, to ensure the consumer code does not need to wait for a
network round-trip before having new data to consume.
The default prefetch is 0, which means that RabbitMQ immediately dumps
all outstanding messages to the consumer, which slowly processes and
acknowledges them. If a second consumer were to connect to the same
queue, they would receive no messages to process, as the first
consumer has already been allocated them. If the first consumer
disconnects or crashes, all prior events sent to it are then made
available for other consumers on the queue.
The consumer does not know the total size of the queue -- merely how
many messages it has been handed.
No change is made to the prefetch here; however, future changes may
wish to limit the prefetch, either for memory-saving, or to allow
multiple consumers to work the same queue.
Rename the method to make clear that it only contains information
about the local queue in the consumer, not the full RabbitMQ queue.
Also include the waiting message count, which is used by the
`consume()` iterator for similar purpose to the pending events list.
We modify access_stream_for_delete_or_update function to return
Subscription object also along with stream. This change will be
helpful in avoiding an extra query to get subscription object in
code for updating subscription role.
For streams in which only full members are allowed to post,
we block guest users from posting there.
Guests users were blocked from posting to admin only streams
already. So now, guest users can only post to
STREAM_POST_POLICY_EVERYONE streams.
This is not a new feature but a bugfix which should have
happened when implementing full member stream policy / guest users.
Otherwise, if consume_func raised an exception for any reason *other*
than the alarm being fired, the still-pending alarm would have fired
later at some arbitrary point in the calling code.
We need two try…finally blocks in case the signal arrives just before
signal.alarm(0).
Signed-off-by: Anders Kaseorg <anders@zulip.com>
Replaced ImageOps.fit by ImageOps.pad, in zerver/lib/upload.py, which
returns a sized and padded version of the image, expanded to fill the
requested aspect ratio and size.
Fixes part of #16370.
SIGALRM is the simplest way to set a specific maximum duration that
queue workers can take to handle a specific message. This only works
in non-threaded environments, however, as signal handlers are
per-process, not per-thread.
The MAX_CONSUME_SECONDS is set quite high, at 10s -- the longest
average worker consume time is embed_links, which hovers near 1s.
Since just knowing the recent mean does not give much information[1],
it is difficult to know how much variance is expected. As such, we
set the threshold to be such that only events which are significant
outliers will be timed out. This can be tuned downwards as more
statistics are gathered on the runtime of the workers.
The exception to this is DeferredWorker, which deals with quite-long
requests, and thus has no enforceable SLO.
[1] https://www.autodesk.com/research/publications/same-stats-different-graphs
Currently, drain_queue and json_drain_queue ack every message as it is
pulled off of the queue, until the queue is empty. This means that if
the consumer crashes between pulling a batch of messages off the
queue, and actually processing them, those messages will be
permanently lost. Sending an ACK on every message also results in a
significant amount lot of traffic to rabbitmq, with notable
performance implications.
Send a singular ACK after the processing has completed, by making
`drain_queue` into a contextmanager. Additionally, use the `multiple`
flag to ACK all of the messages at once -- or explicitly NACK the
messages if processing failed. Sending a NACK will re-queue them at
the front of the queue.
Performance of a no-op dequeue before this change:
```
$ ./manage.py queue_rate --count 50000 --batch
Purging queue...
Enqueue rate: 10847 / sec
Dequeue rate: 2479 / sec
```
Performance of a no-op dequeue after this change (a 25% increase):
```
$ ./manage.py queue_rate --count 50000 --batch
Purging queue...
Enqueue rate: 10752 / sec
Dequeue rate: 3079 / sec
```
Part of #16094.
Moved the language selection preference logic from home.py to a new
function in i18n.py to avoid repetition in analytics views and home
views.
For users who are not authenticated, we don't need to 2fa them,
we only need it once they are trying to login.
Tweaked by tabbott to be much more readable; the new style might
require new test coverage.
We add a new wildcard_mention_policy setting to handle wildcard
mentions in large streams, with a wide range of policies available to
organizations.
We set the default to the safe option for preventing accidental spam:
only stream administrators being able to use wildcard mentions in
large streams.
This prevents the memcached connection from being shared across
multiple processes, and hopefully addresses unexpected behavior from
cached functions like get_user_profile_by_id invoked inside the worker
processes.
Signed-off-by: Anders Kaseorg <anders@zulip.com>
We call build_message_send_dict from check_message instead of
do_send_messages.
This is a prep commit for adding a new setting for handling
wildcard mentions in large streams.
We extract the loop for building message dict in
do_send_messages in a separate function named
build_message_send_dict.
This is a prep commit for moving the code for building
of message dict in check_message.
There is a bug where we send event for even
those messages which do not have embedded links
as we are using single set 'links_for_embed' to
check whether we have to send event for
embedded links or not.
This commit fixes the bug by adding 'links_for_embed'
in message dict itself and send the event only
if that message has embedded links.
Anders Kaseorg
Alex Vandiver
sahil839
Aman Agrawal
akshatdalton
Mateusz Mandera
Abhijeet Prasad Bodas
Tim Abbott
Steve Howell