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Optimizing _unrefActive
TL;DR jump to the conclusion.
Some internal modules use timers to implement their timeout logic. For instance, lib/net.js registers a timer every time some activity happens on a socket. If a socket doesn't have any activity after some time, its timeout callback is triggered.
Because there can be a very large number of sockets created by a Node.js process, a large number of timers can be registered in a very short amount of time. For this reason, internal modules do not use the same timer facilities as user-land modules, which would be too costly in terms of resources.
Instead, they use an internal function named _unrefActive.
As described in a recent GitHub issue _unrefActive seems to take a significant part of CPU time when Node.js is under some heavy HTTP load. For instance, when running the following Node.js server:
var http = require('http');
var server = http.createServer(function (req, res) {
res.end();
})
server.listen(4242, function() {
console.log('Server listening on port 4242...');
});
and if we put it under some heavy HTTP load with wrk:
wrk -t12 -c400 -d30s http://127.0.0.1:4242/
the profiling results show that _unrefActive spends a lot of time on CPU:
ticks parent name
10456 34.4% LazyCompile: *exports._unrefActive timers.js:517:32
10275 98.3% LazyCompile: *onread net.js:492:16
4283 14.1% Stub: CompareICStub {1}
4283 100.0% LazyCompile: *exports._unrefActive timers.js:517:32
4215 98.4% LazyCompile: *onread net.js:492:16
2020 6.7% node::Parser::Execute(v8::FunctionCallbackInfo<v8::Value> const&)
1326 65.6% LazyCompile: ~socketOnData _http_server.js:339:24
1323 99.8% LazyCompile: *emit events.js:68:44
1322 99.9% LazyCompile: *readableAddChunk _stream_readable.js:134:26
1320 99.8% LazyCompile: *onread net.js:492:16
688 34.1% LazyCompile: socketOnData _http_server.js:339:24
688 100.0% LazyCompile: *emit events.js:68:44
688 100.0% LazyCompile: *readableAddChunk _stream_readable.js:134:26
688 100.0% LazyCompile: *onread net.js:492:16
1563 5.1% _getpid
Because _unrefActive's purpose is to allow internal modules to create a large number of timers efficiently, it is an issue.
Whenever a timer is added to handle timeout for a socket (or any other object that needs to implement some timeout logic), _unrefActive adds a timer to a priority queue. When an internal timer expires, unrefTimeout (also implemented in lib/timers.js) is called. It processes the priority queue and calls the timeout callback of each timer that has expired.
Using a priority queue allows quick and easy retrieval of expired timers when they expire, because they all are at the beginning of the list. However, the priority queue is implemented as a linked list. This means every time a timer is added, the list has to be traversed to determine its position in the queue. With a very large number of timer, this process can take a lot of CPU time.
In other words, the current implementation has the following characteristics:
- Cost of adding a timer: O(n).
- Cost of a timer timing out: O(1).
It is clear now why, when running a benchmark such as the one mentioned above, this solution shows poor performance. A lot of connections are created (thus a lot of timers are added to the queue) and only a few timeouts happen.
The current implementation is optimized for use cases when there's a lot of timeouts, and not a lot of timers added. In practice, this is contradictory because you can't have a lot of timeouts without a lot of timers added. The solution to this problem needs to at least have significant better performance when adding timers.
There are at least two other ways to implement a priority queue that have better performance characteristics than an ordered linked list with regards to adding timers:
- Using an unsorted array.
- Using a heap.
Finally, another popular implementation for timers management is called a timer wheel. It uses a more complex data structure and has not been implemented yet. Depending on the outcome of the comparison described in this document, it will be implemented and its performance will be tested.
An unsorted array has the advantage of providing constant time addition to the priority queue. When running the same HTTP heavy benchmark as above, this shows a significant improvement. Here's the results from v8 profiler when running the same wrk benchmark with the same server, but this time with an unordered list as the underlying implementation:
ticks parent name
8365 27.6% node::Parser::Execute(v8::FunctionCallbackInfo<v8::Value> const&)
4225 50.5% LazyCompile: ~socketOnData _http_server.js:339:24
4220 99.9% LazyCompile: *emit events.js:68:44
4220 100.0% LazyCompile: *readableAddChunk _stream_readable.js:134:26
4220 100.0% LazyCompile: *onread net.js:492:16
4095 49.0% LazyCompile: socketOnData _http_server.js:339:24
4095 100.0% LazyCompile: *emit events.js:68:44
4095 100.0% LazyCompile: *readableAddChunk _stream_readable.js:134:26
4095 100.0% LazyCompile: *onread net.js:492:16
1798 5.9% _getpid
1203 4.0% LazyCompile: *emit events.js:68:44
867 72.1% LazyCompile: *readableAddChunk _stream_readable.js:134:26
867 100.0% LazyCompile: *onread net.js:492:16
214 17.8% LazyCompile: *resume_ _stream_readable.js:717:17
214 100.0% LazyCompile: ~<anonymous> _stream_readable.js:711:30
214 100.0% LazyCompile: _tickCallback node.js:332:27
43 3.6% LazyCompile: *emitReadable_ _stream_readable.js:417:23
43 100.0% LazyCompile: ~<anonymous> _stream_readable.js:409:32
43 100.0% LazyCompile: _tickCallback node.js:332:27
30 2.5% LazyCompile: ~finish _http_outgoing.js:504:18
30 100.0% LazyCompile: *afterWrite _stream_writable.js:321:20
30 100.0% LazyCompile: ~<anonymous> _stream_writable.js:312:32
30 100.0% LazyCompile: _tickCallback node.js:332:27
801 2.6% LazyCompile: *exports._unrefActive timers.js:555:32
778 97.1% LazyCompile: *onread net.js:492:16
692 2.3% LazyCompile: socketOnData _http_server.js:339:24
692 100.0% LazyCompile: *emit events.js:68:44
692 100.0% LazyCompile: *readableAddChunk _stream_readable.js:134:26
692 100.0% LazyCompile: *onread net.js:492:16
We can see that _unrefActive is not a significant contributor. The number of requests/s also rises significantly, which indicates that overall performance is affected by this change.
However, using an unsorted array also means that when a timer fires, unrefTimeout has to traverse the whole priority queue to determine which timer fired. This characteristic is not exposed by the benchmark above, since only a few timeouts happen. However, it is easy to build a very simple micro benchmark that exposes this issue.
We can run the following code and profile it:
var timers = require('timers');
var N = 100000;
var i = 0;
while (i < N) {
var someObject = { _onTimeout: function () { } };
timers.enroll(someObject, i);
timers._unrefActive(someObject);
++i;
}
setTimeout(function() {}, N);
to see that unrefTimeout is very costly:
Note: percentage shows a share of a particular caller in the total
amount of its parent calls.
Callers occupying less than 2.0% are not shown.
ticks parent name
41831 52.2% syscall
19713 24.6% LazyCompile: unrefTimeout timers.js:470:22
9679 12.1% Stub: LoadFieldStub
9679 100.0% LazyCompile: unrefTimeout timers.js:470:22
4590 5.7% Stub: CompareICStub
4590 100.0% LazyCompile: unrefTimeout timers.js:470:22
Basically, what this micro benchmark does is to add a very large number of timers that all fire 1ms after the previous one. This means that every time unrefTimeout will be called, a lot of timers will be present in the priority queue.
We can build a macro-benchmark that is very similar to the previous micro-benchmark by writing the following server-code:
var http = require('http');
var reqIndex = 0;
var server = http.createServer(function (req, res) {
reqIndex++;
req.setTimeout(reqIndex, function() { });
});
server.listen(4242);
When stress-testing this server with the following wrk benchmark (note the use of 5000 connections instead of 1000 so that wrk can put a lot of stress on the server despite all requests timing out):
wrk -t12 -c5000 -d30s http://127.0.0.1:4242/
we can see that unrefTimeout is one of the top contributors of v8's profile:
[Bottom up (heavy) profile]:
Note: percentage shows a share of a particular caller in the total
amount of its parent calls.
Callers occupying less than 2.0% are not shown.
ticks parent name
18597 76.9% syscall
991 4.1% LazyCompile: unrefTimeout timers.js:399
968 4.0% node::HandleWrap::Close(v8::Arguments const&)
956 98.8% LazyCompile: *Socket._destroy net.js:431
869 90.9% LazyCompile: *Socket.destroy net.js:488
858 98.7% LazyCompile: ~<anonymous> http.js:1935
858 100.0% LazyCompile: *emit events.js:53
858 100.0% LazyCompile: *Socket._onTimeout net.js:324
87 9.1% LazyCompile: *onSocketFinish net.js:194
69 79.3% LazyCompile: *Writable.end _stream_writable.js:327
69 100.0% LazyCompile: *Socket.end net.js:395
69 100.0% LazyCompile: ~socket.onend http.js:2000
18 20.7% LazyCompile: ~emit events.js:53
18 100.0% LazyCompile: *Writable.end _stream_writable.js:327
18 100.0% LazyCompile: *Socket.end net.js:395
746 3.1% Stub: CompareICStub
746 100.0% LazyCompile: unrefTimeout timers.js:399
Reducing the frequency of timeouts mitigates the impact of unrefTimeout on the overall performance, but even with 1 timeout out of 10 requests, it's still a significant contributor.
A heap theoretically gives us a good balance between the cost of adding and finding a timer:
- Addition is O(log(n)).
- Retrieval of the next timer is also O(log(n)).
Unsurprisingly, because adding a timer does not happen in constant time, the heavy HTTP benchmark shows that its performance is slightly worse than when using an unordered list:
228 2.2% LazyCompile: *exports._unrefActive timers.js:534:32
However, the micro-benchmark that highlighted the very bad performance of the unordered list implementation when a lot of timeouts happen shows that a heap implementation performs well in this case:
ticks parent name
71657 93.2% syscall
1610 2.1% LazyCompile: *Heap._swap _heap.js:181:32
1475 91.6% LazyCompile: *Heap._down _heap.js:278:32
1306 88.5% LazyCompile: *Heap._down _heap.js:278:32
1124 86.1% LazyCompile: *Heap._down _heap.js:278:32
946 84.2% LazyCompile: *Heap._down _heap.js:278:32
793 83.8% LazyCompile: *Heap._down _heap.js:278:32
153 16.2% LazyCompile: *Heap.remove _heap.js:88:33
178 15.8% LazyCompile: *Heap.remove _heap.js:88:33
178 100.0% LazyCompile: *Heap.pop _heap.js:79:30
182 13.9% LazyCompile: *Heap.remove _heap.js:88:33
182 100.0% LazyCompile: *Heap.pop _heap.js:79:30
182 100.0% LazyCompile: ~<anonymous> native v8natives.js:1:1
169 11.5% LazyCompile: *Heap.remove _heap.js:88:33
169 100.0% LazyCompile: *Heap.pop _heap.js:79:30
169 100.0% LazyCompile: ~<anonymous> native v8natives.js:1:1
134 8.3% LazyCompile: *Heap.remove _heap.js:88:33
134 100.0% LazyCompile: *Heap.pop _heap.js:79:30
134 100.0% LazyCompile: ~<anonymous> native v8natives.js:1:1
Results are similar with the macro-benchmark that simulates a large number of frequent timeouts.
The unordered list implementation is the top performer when tested with the HTTP heavy benchmark mentioned at the top of the GitHub issue. However, it is clear that this implementation suffers from the same issues than the current one when most timers timeout.
The heap implementation performs much better in the case when a lot of timeouts are triggered, but it's slightly slower than the unordered list implementation when tested under the HTTP heavy benchmark without timeouts. It is also theoretically better in the general case, given that insertion and retrieval of timers both happen in O(log n) time.