Parallel execution in BIRD uses an underlying mechanism of dedicated IO loops and hierarchical locks. The original event scheduling module has been converted to do message passing in multithreaded environment. These mechanisms are crucial for understanding what happens inside BIRD and how its internal API changes.
BIRD is a fast, robust and memory-efficient routing daemon designed and implemented at the end of 20th century. We’re doing a significant amount of BIRD’s internal structure changes to make it run in multiple threads in parallel.
Locking and deadlock prevention
Most of BIRD data structures and algorithms are thread-unsafe and not even reentrant. Checking and possibly updating all of these would take an unreasonable amount of time, thus the multithreaded version uses standard mutexes to lock all the parts which have not been checked and updated yet.
The authors of original BIRD concepts wisely chose a highly modular structure which allows to create a hierarchy for locks. The main chokepoint was between protocols and tables and it has been removed by implementing asynchronous exports as described in the previous chapter.
Locks in BIRD (called domains, as they always lock some defined part of BIRD) are partially ordered. Every domain has its type and all threads are strictly required to lock the domains in the order of their respective types. The full order is defined in lib/locking.h. It’s forbidden to lock more than one domain of a type (these domains are uncomparable) and recursive locking is forbidden as well.
The locking hiearchy is (roughly; as of February 2022) like this:
- The BIRD Lock (for everything not yet checked and/or updated)
- Protocols (as of February 2022, it is BFD, RPKI, Pipe and BGP)
- Routing tables
- Global route attribute cache
- Message passing
- Internals and memory management
There are heavy checks to ensure proper locking and to help debugging any problem when any code violates the hierarchy rules. This impedes performance depending on how much that domain is contended and in some cases I have already implemented lockless (or partially lockless) data structures to overcome this.
You may ask, why are these heavy checks then employed in production builds? Risks arising from dropping some locking checks include:
- deadlocks; these are deadly in BIRD anyway so it should just fail with a meaningful message, or
- data corruption; it either kills BIRD anyway, or it results into a slow and vicious death, leaving undebuggable corefiles behind.
To be honest, I believe in principles like “every nontrivial software has at least one bug” and I also don’t trust my future self or anybody else to always write bugless code when it comes to proper locking. I also believe that if a lock becomes a bottle-neck, then we should think about what is locked inside and how to optimize that, possibly implementing a lockless or waitless data structure instead of dropping thorough consistency checks, especially in a multithreaded environment.
Choosing the right locking order
When considering the locking order of protocols and route tables, the answer was quite easy. We had to make either import or export asynchronous (or both). Major reasons for asynchronous export have been stated in the previous chapter, therefore it makes little sense to allow entering protocol context from table code.
As I write further in this text, even accessing table context from protocol code leads to contention on table locks, yet for now, it is good enough and the lock order features routing tables after protocols to make the multithreading goal easier to achieve.
The major lock level is still The BIRD Lock, containing not only the not-yet-converted protocols (like Babel, OSPF or RIP) but also processing CLI commands and reconfiguration. This involves an awful lot of direct access into other contexts which would be unnecessarily complicated to implement by message passing. Therefore, this lock is simply “the director”, sitting on the top with its own category.
The lower lock levels under routing tables are mostly for shared global data structures accessed from everywhere. We’ll address some of these later.
There has been a protocol, BFD, running in its own thread since 2013. This separation has a good reason; it needs low latency and the main BIRD loop just walks round-robin around all the available sockets and one round-trip may take a long time (even more than a minute with large configurations). BFD had its own IO loop implementation and simple message passing routines. This code could be easily updated for general use so I did it.
To understand the internal principles, we should say that in the master branch, there is a big loop centered around a poll() call, dispatching and executing everything as needed. In the sark branch, there are multiple loops of this kind. BIRD has several means how to get something dispatched from a loop.
- Requesting to read from a socket makes the main loop call your hook when there is some data received. The same happens when a socket refuses to write data. Then the data is buffered and you are called when the buffer is free to continue writing. There is also a third callback, an error hook, for obvious reasons.
- Requesting to be called back after a given amount of time. This is called timer. As is common with all timers, they aren’t precise and the callback may be delayed significantly. This was also the reason to have BFD loop separate since the very beginning, yet now the abundance of threads may lead to problems with BFD latency in large-scale configurations. We haven’t tested this yet.
- Requesting to be called back from a clean context when possible. This is useful to run anything not reentrant which might mess with the caller’s data, e.g. when a protocol decides to shutdown due to some inconsistency in received data. This is called event.
- Requesting to do some work when possible. These are also events, there is only a difference where this event is enqueued; in the main loop, there is a special work queue with an execution limit, allowing sockets and timers to be handled with a reasonable latency while still doing all the work needed. Other loops don’t have designated work queues (we may add them later).
All these, sockets, timers and events, are tightly bound to some domain. Sockets typically belong to a protocol, timers and events to a protocol or table. With the modular structure of BIRD, the easy and convenient approach to multithreading is to get more IO loops, each bound to a specific domain, running their events, timers and socket hooks in their threads.
Message passing and loop entering
To request some work in another module, the standard way is to pass a message. For this purpose, events have been modified to be sent to a given loop without locking that loop’s domain. In fact, every event queue has its own lock with a low priority, allowing to pass messages from almost any part of BIRD, and also an assigned loop which executes the events enqueued. When a message is passed to a queue executed by another loop, that target loop must be woken up so we must know what loop to wake up to avoid unnecessary delays. Then the target loop opens its mailbox and processes the task in its context.
The other way is a direct access of another domain. This approach blocks the appropriate loop from doing anything and we call it entering a birdloop to remember that the task must be fast and leave the birdloop as soon as possible. Route import is done via direct access from protocols to tables; in large setups with fast filters, this is a major point of contention (after filters have been parallelized) and will be addressed in future optimization efforts. Reconfiguration and interface updates also use direct access; more on that later. In general, this approach should be avoided unless there are good reasons to use it.
Even though direct access is bad, sending lots of messages may be even worse. Imagine one thousand post(wo)men, coming one by one every minute, ringing your doorbell and delivering one letter each to you. Horrible! Asynchronous message passing works exactly this way. After queuing the message, the source sends a byte to a pipe to wakeup the target loop to process the task. We could also periodically poll for messages instead of waking up the targets, yet it would add quite a lot of latency which we also don’t like.
Messages in BIRD don’t typically suffer from the problem of amount and the overhead is negligible compared to the overall CPU consumption. With one notable exception: route import/export.
Route export message passing
If we had to send a ping for every route we import to every exporting channel, we’d spend more time pinging than doing anything else. Been there, seen those unbelievable 80%-like figures in Perf output. Never more.
Route update is quite a complicated process. BIRD must handle large-scale configurations with lots of importers and exporters. Therefore, a triple-indirect delayed route announcement is employed:
- First, when a channel imports a route by entering a loop, it sends an event to its own loop (no ping needed in such case). This operation is idempotent, thus for several routes in a row, only one event is enqueued. This reduces several route import announcements (even hundreds in case of massive BGP withdrawals) to one single event.
- When the channel is done importing (or at least takes a coffee break and checks its mailbox), the scheduled event in its own loop is run, sending another event to the table’s loop, saying basically “Hey, table, I’ve just imported something.”. This event is also idempotent and further reduces route import announcements from multiple sources to one single event.
- The table’s announcement event is then executed from its loop, enqueuing export events for all connected channels, finally initiating route exports. As we already know, imports are done by direct access, therefore if protocols keep importing, export announcements are slowed down.
- The actual data on what has been updated is stored in a table journal. This peculiar technique is used only for informing the exporting channels that “there is something to do”.
This may seem overly complicated, yet it should work and it seems to work. In case of low load, all these notifications just come through smoothly. In case of high load, it’s common that multiple updates come for the same destination. Delaying the exports allows for the updates to settle down and export just the final result, reducing CPU load and export traffic.
Route propagation is involved in yet another problem which has to be addressed. In the old versions with synchronous route propagation, all the buffering happened after exporting routes to BGP. When a packet arrived, all the work was done in BGP receive hook – parsing, importing into a table, running all the filters and possibly sending to the peers. No more routes until the previous was done. This self-regulating mechanism doesn’t work any more.
Route table import now returns immediately after inserting the route into a table, creating a buffer there. These buffers have to be processed by other protocols’ export events. In large-scale configurations, one route import has to be processed by hundreds, even thousands of exports. Unlimited imports are a major cause of buffer bloating. This is even worse in configurations with pipes, as these multiply the exports by propagating them all the way down to other tables, eventually eating about twice the amount of memory than the single-threaded version.
There is therefore a cork to make this stop. Every table is checking how many exports it has pending, and when adding a new export to the queue, it may request a cork, saying simply “please stop the flow for a while”. When the export buffer size is reduced low enough, the table uncorks.
On the other side, there are events and sockets with a cork assigned. When trying to enqueue an event and the cork is applied, the event is instead put into the cork’s queue and released only when the cork is released. In case of sockets, when read is indicated or when poll arguments are recalculated, the corked socket is simply not checked for received packets, effectively keeping them in the TCP queue and slowing down the flow until cork is released.
The cork implementation is quite crude and rough and fragile. It may get some rework while stabilizing the multi-threaded version of BIRD or we may even completely drop it for some better mechanism. One of these candidates is this kind of API:
- (table to protocol) please do not import
- (table to protocol) you may resume imports
- (protocol to table) not processing any exports
- (protocol to table) resuming export processing
Anyway, cork works as intended in most cases at least for now.
It’s a long road to the version 2.1. This series of texts should document what is changing, why we do it and how. The previous chapter shows how the route export had to change to allow parallel execution. In the next chapter, some memory management details are to be explained together with the reasons why memory management matters. Stay tuned!