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Vicky Risk
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Scaling across (many) cores
===========================
Problem statement
-----------------
The general issue is how to insure that the resolver scales.
Currently resolvers are CPU bound, and it seems likely that both
instructions-per-cycle and CPU frequency will not increase radically,
scaling will need to be across multiple cores.
How can we best scale a recursive resolver across multiple cores?
Image of how resolution looks like
----------------------------------
Receive the query. @# <------------------------\
| |
| |
v |
Parse it, etc. $ |
| |
| |
v |
Look into the cache. $# |
Cry <---- No <---------- Is it there? -----------> Yes ---------\ |
| ^ | |
Prepare upstream query $ | | |
| | | |
v | | |
Send an upstream query (#) | | |
| | | |
| | | |
v | | |
Wait for answer @(#) | | |
| | | |
v | | |
Parse $ | | |
| | | |
v | | |
Is it enough? $ ----> No ---------/ | |
| | |
Yes | |
| | |
\-----------------------> Build answer $ <----------------------/ |
| |
| |
v |
Send answer # -----------------------------/
This is simplified version, however. There may be other tasks (validation, for
example), which are not drawn mostly for simplicity, as they don't produce more
problems. The validation would be done as part of some computational task and
they could do more lookups in the cache or upstream queries.
Also, multiple queries may generate the same upstream query, so they should be
aggregated together somehow.
Legend
~~~~~~
* $ - CPU intensive
* @ - Waiting for external event
* # - Possible interaction with other tasks
Goals
-----
* Run the CPU intensive tasks in multiple threads to allow concurrency.
* Minimize waiting for locks.
* Don't require too much memory.
* Minimize the number of upstream queries (both because they are slow and
expensive and also because we don't want to eat too much bandwidth and spam
the authoritative servers).
* Design simple enough so it can be implemented.
Naïve version
-------------
Let's look at possible approaches and list their pros and cons. Many of the
simple versions would not really work, but let's have a look at them anyway,
because thinking about them might bring some solutions for the real versions.
We take one query, handle it fully, with blocking waits for the answers. After
this is done, we take another. The cache is private for each one process.
Advantages:
* Very simple.
* No locks.
Disadvantages:
* To scale across cores, we need to run *a lot* of processes, since they'd be
waiting for something most of their time. That means a lot of memory eaten,
because each one has its own cache. Also, running so many processes may be
problematic, processes are not very cheap.
* Many things would be asked multiple times, because the caches are not
shared.
Threads
~~~~~~~
Some of the problems could be solved by using threads, but they'd not improve
it much, since threads are not really cheap either (starting several hundred
threads might not be a good idea either).
Also, threads bring other problems. When we still assume separate caches (for
caches, see below), we need to ensure safe access to logging, configuration,
network, etc. These could be a bottleneck (eg. if we lock every time we read a
packet from network, when there are many threads, they'll just fight over the
lock).
Supercache
~~~~~~~~~~
The problem with cache could be solved by placing a ``supercache'' between the
resolvers and the Internet. That one would do almost no processing, it would
just take the query, looked up in the cache and either answered from the cache
or forwarded the query to the external world. It would store the answer and
forward it back.
The cache, if single-threaded, could be a bottle-neck. To solve it, there could
be several approaches:
Layered cache::
Each process has it's own small cache, which catches many queries. Then, a
group of processes shares another level of bigger cache, which catches most
of the queries that get past the private caches. We further group them and
each level handles less queries from each process, so they can keep up.
However, with each level, we add some overhead to do another lookup.
Segmented cache::
We have several caches of the same level, in parallel. When we would ask a
cache, we hash the query and decide which cache to ask by the hash. Only that
cache would have that answer if any and each could run in a separate process.
The only problem is, could there be a pattern of queries that would skew to
use only one cache while the rest would be idle?
Shared cache access::
A cache would be accessed by multiple processes/threads. See below for
details, but there's a risk of lock contention on the cache (it depends on
the data structure).
Upstream queries
~~~~~~~~~~~~~~~~
Before doing an upstream query, we look into the cache to ensure we don't have
the information yet. When we get the answer, we want to update the cache.
This suggests the upstream queries are tightly coupled with the cache. Now,
when we have several cache processes/threads, each can have some set of opened
sockets which are not shared with other caches to do the lookups. This way we
can avoid locking the upstream network communication.
Also, we can have three conceptual states for data in cache, and act
differently when it is requested.
Present::
If it is available, in positive or negative version, we just provide the
answer right away.
Not present::
The continuation of processing is queued somehow (blocked/callback is
stored/whatever). An upstream query is sent and we get to the next state.
Waiting for answer::
If another query for the same thing arrives, we just queue it the same way
and keep waiting. When the answer comes, all the queued tasks are resumed.
If the TTL > 0, we store the answer and set it to ``present''.
We want to do aggregation of upstream queries anyway, using cache for it saves
some more processing and possibly locks.
Multiple parallel queries
-------------------------
It seems obvious we can't afford to have a thread or process for each
outstanding query. We need to handle multiple queries in each one at any given
time.
Coroutines
~~~~~~~~~~
The OS-level threads might be too expensive, but coroutines might be cheap
enough. In that way, we could still write a code that would be easy to read,
but limit the number of OS threads to reasonable number.
In this model, when a query comes, a new coroutine/user-level thread is created
for it. We use special reads and writes whenever there's an operation that
could block. These reads and writes would internally schedule the operation
and switch to another coroutine (if there's any ready to be executed).
Each thread/process maintains its own set of coroutines and they do not
migrate. This way, the queue of coroutines is kept lock-less, as well as any
private caches. Only the shared caches are protected by a lock.
[NOTE]
The `coro` unit we have in the current code is *not* considered a coroutine
library here. We would need a coroutine library where we have real stack for
each coroutine and we switch the stacks on coroutine switch. That is possible
with reasonable amount of dark magic (see `ucontext.h`, for example, but there
are surely some higher-level libraries for that).
There are some trouble with multiple coroutines waiting on the same event, like
the same upstream query (possibly even coroutines from different threads), but
it should be possible to solve.
Event-based
~~~~~~~~~~~
We use events (`asio` and stuff) for writing it. Each outstanding query is an
object with some callbacks on it. When we would do a possibly blocking
operation, we schedule a callback to happen once the operation finishes.
This is more lightweight than the coroutines (the query objects will be smaller
than the stacks for coroutines), but it is harder to write and read for.
[NOTE]
Do not consider cross-breeding the models. That leads to space-time distortions
and brain damage. Implementing one on top of other is OK, but mixing it in the
same bit of code is a way do madhouse.
Landlords and peasants
~~~~~~~~~~~~~~~~~~~~~~
In both the coroutines and event-based models, the cache and other shared
things are easier to imagine as objects the working threads fight over to hold
for a short while. In this model, it is easier to imagine each such shared
object as something owned by a landlord that doesn't let anyone else on it,
but you can send requests to him.
A query is an object once again, with some kind of state machine.
Then there are two kinds of threads. The peasants are just to do the heavy
work. There's a global work-queue for peasants. Once a peasant is idle, it
comes to the queue and picks up a handful of queries from there. It does as
much on each the query as possible without requiring any shared resource.
The other kind, the landlords, have a resource to watch over each. So we would
have a cache (or several parts of cache), the sockets for accepting queries and
answering them, possibly more. Each of these would have a separate landlord
thread and a queue of tasks to do on the resource (look up something, send an
answer...).
Similarly, the landlord would take a handful of tasks from its queue and start
handling them. It would possibly produce some more tasks for the peasants.
The point here is, all the synchronisation is done on the queues, not on the
shared resources themselves. And, we would append to a queues once the whole
batch was completed. By tweaking the size of the batch, we could balance the
lock contention, throughput and RTT. The append/remove would be a quick
operation, and the cost of locks would amortize in the larger amount of queries
handled per one lock operation.
The possible downside is, a query needs to travel across several threads during
its lifetime. It might turn out it is faster to move the query between cores
than accessing the cache from several threads, since it is smaller, but it
might be slower as well.
It would be critical to make some kind of queue that is fast to append to and
fast to take out first n items. Also, the tasks in the queues can be just
abstract `boost::function<void (Worker&)>` functors, and each worker would just
iterate through the queue, calling each functor. The parameter would be to
allow easy generation of more tasks for other queues (they would be stored
privately first, and appended to remote queues at the end of batch).
Also, if we wanted to generate multiple parallel upstream queries from a single
query, we would need to be careful. A query object would not have a lock on
itself and the upstream queries could end up in a different caches/threads. To
protect the original query, we would add another landlord that would aggregate
answers together and let the query continue processing once it got enough
answers. That way, the answers would be pushed all to the same threads and they
could not fight over the query.
[NOTE]
This model would work only with threads, not processes.
Shared caches
-------------
While it seems it is good to have some sort of L1 cache with pre-rendered
answers (according to measurements in the #2777 ticket), we probably need some
kind of larger shared cache.
If we had just a single shared cache protected by lock, there'd be a lot of
lock contention on the lock.
Partitioning the cache
~~~~~~~~~~~~~~~~~~~~~~
We split the cache into parts, either by the layers or by parallel bits we
switch between by a hash. If we take it to the extreme, a lock on each hash
bucket would be this kind, though that might be wasting resources (how
expensive is it to create a lock?).
Landlords
~~~~~~~~~
The landlords do synchronizations themselves. Still, the cache would need to be
partitioned.
RCU
~~~
The RCU is a lock-less synchronization mechanism. An item is accessed through a
pointer. An updater creates a copy of the structure (in our case, it would be
content of single hash bucket) and then atomically replaces the pointer. The
readers from before have the old version, the new ones get the new version.
When all the old readers die out, the old copy is reclaimed. Also, the
reclamation can AFAIK be postponed for later times when we are slightly more
idle or to a different thread.
We could use it for cache ‒ in the fast track, we would just read the cache. In
the slow one, we would have to wait in queue to do the update, in a single
updater thread (because we don't really want to be updating the same cell twice
at the same time).
Proposals
---------
In either case, we would have some kind of L1 cache with pre-rendered answers.
For these proposals (except the third), we wouldn't care if we split the cache
into parallel chunks or layers.
Hybrid RCU/Landlord
~~~~~~~~~~~~~~~~~~~
The landlord approach, just read only accesses to the cache are done directly
by the peasants. Only if they don't find what they want, they'd append the
queue to the task of the landlord. The landlord would be doing the RCU updates.
It could happen that by the time the landlord gets to the task the answer is
already there, but that would not matter much.
Accessing network would be from landlords.
Coroutines+RCU
~~~~~~~~~~~~~~
We would do the coroutines, and the reads from shared cache would go without
locking. When doing write, we would have to lock.
To avoid locking, each worker thread would have its own set of upstream sockets
and we would dup the sockets from users so we don't have to lock that.
Multiple processes with coroutines and RCU
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This would need the layered cache. The upper caches would be mapped to local
memory for read-only access. Each cache would be a separate process. The
process would do the updates ‒ if the answer was not there, the process would
be asked by some kind of IPC to pull it from upstream cache or network.