如何合理地估算线程池大小
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如何合理地估算线程池大小?
这个问题虽然看起来很小,却并不那么容易回答。大家如果有更好的方法欢迎赐教,先来一个天真的估算方法:假设要求一个系统的TPS(Transaction Per Second或者Task Per Second)至少为20,然后假设每个Transaction由一个线程完成,继续假设平均每个线程处理一个Transaction的时间为4s。那么问题转化为:
如何设计线程池大小,使得可以在1s内处理完20个Transaction?
计算过程很简单,每个线程的处理能力为0.25TPS,那么要达到20TPS,显然需要20/0.25=80个线程。
很显然这个估算方法很天真,因为它没有考虑到CPU数目。一般服务器的CPU核数为16或者32,如果有80个线程,那么肯定会带来太多不必要的线程上下文切换开销。
再来第二种简单的但不知是否可行的方法(N为CPU总核数):
- 如果是CPU密集型应用,则线程池大小设置为N+1
- 如果是IO密集型应用,则线程池大小设置为2N+1
如果一台服务器上只部署这一个应用并且只有这一个线程池,那么这种估算或许合理,具体还需自行测试验证。
接下来在这个文档:服务器性能IO优化 中发现一个估算公式:
1
最佳线程数目 = ((线程等待时间+线程CPU时间)/线程CPU时间 )* CPU数目
比如平均每个线程CPU运行时间为0.5s,而线程等待时间(非CPU运行时间,比如IO)为1.5s,CPU核心数为8,那么根据上面这个公式估算得到:((0.5+1.5)/0.5)*8=32。这个公式进一步转化为:
1
最佳线程数目 = (线程等待时间与线程CPU时间之比 + 1)* CPU数目
可以得出一个结论:
线程等待时间所占比例越高,需要越多线程。线程CPU时间所占比例越高,需要越少线程。
上一种估算方法也和这个结论相合。
一个系统最快的部分是CPU,所以决定一个系统吞吐量上限的是CPU。增强CPU处理能力,可以提高系统吞吐量上限。但根据短板效应,真实的系统吞吐量并不能单纯根据CPU来计算。那要提高系统吞吐量,就需要从“系统短板”(比如网络延迟、IO)着手:
- 尽量提高短板操作的并行化比率,比如多线程下载技术
- 增强短板能力,比如用NIO替代IO
第一条可以联系到Amdahl定律,这条定律定义了串行系统并行化后的加速比计算公式:
1
加速比=优化前系统耗时 / 优化后系统耗时
加速比越大,表明系统并行化的优化效果越好。Addahl定律还给出了系统并行度、CPU数目和加速比的关系,加速比为Speedup,系统串行化比率(指串行执行代码所占比率)为F,CPU数目为N:
1
Speedup <=
1
/ (F + (
1
-F)/N)
当N足够大时,串行化比率F越小,加速比Speedup越大。
写到这里,我突然冒出一个问题。
是否使用线程池就一定比使用单线程高效呢?
答案是否定的,比如Redis就是单线程的,但它却非常高效,基本操作都能达到十万量级/s。从线程这个角度来看,部分原因在于:
- 多线程带来线程上下文切换开销,单线程就没有这种开销
- 锁
当然“Redis很快”更本质的原因在于:Redis基本都是内存操作,这种情况下单线程可以很高效地利用CPU。而多线程适用场景一般是:存在相当比例的IO和网络操作。
所以即使有上面的简单估算方法,也许看似合理,但实际上也未必合理,都需要结合系统真实情况(比如是IO密集型或者是CPU密集型或者是纯内存操作)和硬件环境(CPU、内存、硬盘读写速度、网络状况等)来不断尝试达到一个符合实际的合理估算值。
最后来一个“Dark Magic”估算方法(因为我暂时还没有搞懂它的原理),使用下面的类:
001
package
pool_size_calculate;
002
003
import
java.math.BigDecimal;
004
import
java.math.RoundingMode;
005
import
java.util.Timer;
006
import
java.util.TimerTask;
007
import
java.util.concurrent.BlockingQueue;
008
009
/**
010
* A class that calculates the optimal thread pool boundaries. It takes the
011
* desired target utilization and the desired work queue memory consumption as
012
* input and retuns thread count and work queue capacity.
013
*
014
* @author Niklas Schlimm
015
*
016
*/
017
public
abstract
class
PoolSizeCalculator {
018
019
/**
020
* The sample queue size to calculate the size of a single {@link Runnable}
021
* element.
022
*/
023
private
final
int
SAMPLE_QUEUE_SIZE =
1000
;
024
025
/**
026
* Accuracy of test run. It must finish within 20ms of the testTime
027
* otherwise we retry the test. This could be configurable.
028
*/
029
private
final
int
EPSYLON =
20
;
030
031
/**
032
* Control variable for the CPU time investigation.
033
*/
034
private
volatile
boolean
expired;
035
036
/**
037
* Time (millis) of the test run in the CPU time calculation.
038
*/
039
private
final
long
testtime =
3000
;
040
041
/**
042
* Calculates the boundaries of a thread pool for a given {@link Runnable}.
043
*
044
* @param targetUtilization
045
* the desired utilization of the CPUs (0 <= targetUtilization <= * 1) * @param targetQueueSizeBytes * the desired maximum work queue size of the thread pool (bytes) */
protected
void
calculateBoundaries(BigDecimal targetUtilization, BigDecimal targetQueueSizeBytes) { calculateOptimalCapacity(targetQueueSizeBytes); Runnable task = creatTask(); start(task); start(task);
// warm up phase long cputime = getCurrentThreadCPUTime(); start(task); // test intervall cputime = getCurrentThreadCPUTime() - cputime; long waittime = (testtime * 1000000) - cputime; calculateOptimalThreadCount(cputime, waittime, targetUtilization); } private void calculateOptimalCapacity(BigDecimal targetQueueSizeBytes) { long mem = calculateMemoryUsage(); BigDecimal queueCapacity = targetQueueSizeBytes.divide(new BigDecimal( mem), RoundingMode.HALF_UP); System.out.println("Target queue memory usage (bytes): " + targetQueueSizeBytes); System.out.println("createTask() produced " + creatTask().getClass().getName() + " which took " + mem + " bytes in a queue"); System.out.println("Formula: " + targetQueueSizeBytes + " / " + mem); System.out.println("* Recommended queue capacity (bytes): " + queueCapacity); } /** * Brian Goetz' optimal thread count formula, see 'Java Concurrency in * Practice' (chapter 8.2) * * @param cpu * cpu time consumed by considered task * @param wait * wait time of considered task * @param targetUtilization * target utilization of the system */ private void calculateOptimalThreadCount(long cpu, long wait, BigDecimal targetUtilization) { BigDecimal waitTime = new BigDecimal(wait); BigDecimal computeTime = new BigDecimal(cpu); BigDecimal numberOfCPU = new BigDecimal(Runtime.getRuntime() .availableProcessors()); BigDecimal optimalthreadcount = numberOfCPU.multiply(targetUtilization) .multiply( new BigDecimal(1).add(waitTime.divide(computeTime, RoundingMode.HALF_UP))); System.out.println("Number of CPU: " + numberOfCPU); System.out.println("Target utilization: " + targetUtilization); System.out.println("Elapsed time (nanos): " + (testtime * 1000000)); System.out.println("Compute time (nanos): " + cpu); System.out.println("Wait time (nanos): " + wait); System.out.println("Formula: " + numberOfCPU + " * " + targetUtilization + " * (1 + " + waitTime + " / " + computeTime + ")"); System.out.println("* Optimal thread count: " + optimalthreadcount); } /** * Runs the {@link Runnable} over a period defined in {@link #testtime}. * Based on Heinz Kabbutz' ideas * (http://www.javaspecialists.eu/archive/Issue124.html). * * @param task * the runnable under investigation */ public void start(Runnable task) { long start = 0; int runs = 0; do { if (++runs > 5) {
046
throw
new
IllegalStateException(
"Test not accurate"
);
047
}
048
expired =
false
;
049
start = System.currentTimeMillis();
050
Timer timer =
new
Timer();
051
timer.schedule(
new
TimerTask() {
052
public
void
run() {
053
expired =
true
;
054
}
055
}, testtime);
056
while
(!expired) {
057
task.run();
058
}
059
start = System.currentTimeMillis() - start;
060
timer.cancel();
061
}
while
(Math.abs(start - testtime) > EPSYLON);
062
collectGarbage(
3
);
063
}
064
065
private
void
collectGarbage(
int
times) {
066
for
(
int
i =
0
; i < times; i++) {
067
System.gc();
068
try
{
069
Thread.sleep(
10
);
070
}
catch
(InterruptedException e) {
071
Thread.currentThread().interrupt();
072
break
;
073
}
074
}
075
}
076
077
/**
078
* Calculates the memory usage of a single element in a work queue. Based on
079
* Heinz Kabbutz' ideas
080
* (http://www.javaspecialists.eu/archive/Issue029.html).
081
*
082
* @return memory usage of a single {@link Runnable} element in the thread
083
* pools work queue
084
*/
085
public
long
calculateMemoryUsage() {
086
BlockingQueue queue = createWorkQueue();
087
for
(
int
i =
0
; i < SAMPLE_QUEUE_SIZE; i++) {
088
queue.add(creatTask());
089
}
090
long
mem0 = Runtime.getRuntime().totalMemory()
091
- Runtime.getRuntime().freeMemory();
092
long
mem1 = Runtime.getRuntime().totalMemory()
093
- Runtime.getRuntime().freeMemory();
094
queue =
null
;
095
collectGarbage(
15
);
096
mem0 = Runtime.getRuntime().totalMemory()
097
- Runtime.getRuntime().freeMemory();
098
queue = createWorkQueue();
099
for
(
int
i =
0
; i < SAMPLE_QUEUE_SIZE; i++) {
100
queue.add(creatTask());
101
}
102
collectGarbage(
15
);
103
mem1 = Runtime.getRuntime().totalMemory()
104
- Runtime.getRuntime().freeMemory();
105
return
(mem1 - mem0) / SAMPLE_QUEUE_SIZE;
106
}
107
108
/**
109
* Create your runnable task here.
110
*
111
* @return an instance of your runnable task under investigation
112
*/
113
protected
abstract
Runnable creatTask();
114
115
/**
116
* Return an instance of the queue used in the thread pool.
117
*
118
* @return queue instance
119
*/
120
protected
abstract
BlockingQueue createWorkQueue();
121
122
/**
123
* Calculate current cpu time. Various frameworks may be used here,
124
* depending on the operating system in use. (e.g.
125
* http://www.hyperic.com/products/sigar). The more accurate the CPU time
126
* measurement, the more accurate the results for thread count boundaries.
127
*
128
* @return current cpu time of current thread
129
*/
130
protected
abstract
long
getCurrentThreadCPUTime();
131
132
}
然后自己继承这个抽象类并实现它的三个抽象方法,比如下面是我写的一个示例(任务是请求网络数据),其中我指定期望CPU利用率为1.0(即100%),任务队列总大小不超过100,000字节:
01
package
pool_size_calculate;
02
03
import
java.io.BufferedReader;
04
import
java.io.IOException;
05
import
java.io.InputStreamReader;
06
import
java.lang.management.ManagementFactory;
07
import
java.math.BigDecimal;
08
import
java.net.HttpURLConnection;
09
import
java.net.URL;
10
import
java.util.concurrent.BlockingQueue;
11
import
java.util.concurrent.LinkedBlockingQueue;
12
13
public
class
SimplePoolSizeCaculatorImpl
extends
PoolSizeCalculator {
14
15
@Override
16
protected
Runnable creatTask() {
17
return
new
AsyncIOTask();
18
}
19
20
@Override
21
protected
BlockingQueue createWorkQueue() {
22
return
new
LinkedBlockingQueue(
1000
);
23
}
24
25
@Override
26
protected
long
getCurrentThreadCPUTime() {
27
return
ManagementFactory.getThreadMXBean().getCurrentThreadCpuTime();
28
}
29
30
public
static
void
main(String[] args) {
31
PoolSizeCalculator poolSizeCalculator =
new
SimplePoolSizeCaculatorImpl();
32
poolSizeCalculator.calculateBoundaries(
new
BigDecimal(
1.0
),
new
BigDecimal(
100000
));
33
}
34
35
}
36
37
/**
38
* 自定义的异步IO任务
39
* @author Will
40
*
41
*/
42
class
AsyncIOTask
implements
Runnable {
43
44
@Override
45
public
void
run() {
46
HttpURLConnection connection =
null
;
47
BufferedReader reader =
null
;
48
try
{
49
String getURL =
"http://baidu.com"
;
50
URL getUrl =
new
URL(getURL);
51
52
connection = (HttpURLConnection) getUrl.openConnection();
53
connection.connect();
54
reader =
new
BufferedReader(
new
InputStreamReader(
55
connection.getInputStream()));
56
57
String line;
58
while
((line = reader.readLine()) !=
null
) {
59
// empty loop
60
}
61
}
62
63
catch
(IOException e) {
64
65
}
finally
{
66
if
(reader !=
null
) {
67
try
{
68
reader.close();
69
}
70
catch
(Exception e) {
71
72
}
73
}
74
connection.disconnect();
75
}
76
77
}
78
79
}
得到的输出如下:
01
Target queue memory usage (bytes): 100000
02
createTask() produced pool_size_calculate.AsyncIOTask which took 40 bytes in a queue
03
Formula: 100000 / 40
04
* Recommended queue capacity (bytes): 2500
05
Number of CPU: 4
06
Target utilization: 1
07
Elapsed time (nanos): 3000000000
08
Compute time (nanos): 47181000
09
Wait time (nanos): 2952819000
10
Formula: 4 * 1 * (1 + 2952819000 / 47181000)
11
* Optimal thread count: 256
推荐的任务队列大小为2500,线程数为256,有点出乎意料之外。我可以如下构造一个线程池:
1
ThreadPoolExecutor pool =
2
new
ThreadPoolExecutor(
256
,
256
, 0L, TimeUnit.MILLISECONDS,
new
LinkedBlockingQueue(
2500
));
转载自并发编程网 – ifeve.com原文链接地址: 如何合理地估算线程池大小?
- 如何合理地估算线程池大小?
- 如何合理地估算线程池大小
- 如何合理地估算线程池大小?
- 如何合理地估算线程池大小?
- 如何合理地估算线程池大小?
- 如何合理地估算线程池大小
- 如何合理地估算线程池大小
- 如何合理地估算线程池大小?
- 如何合理地估算线程池大小?
- 如何合理地估算线程池大小
- 如何合理地估算线程池大小
- 如何合理地估算线程池大小?
- 如何合理地估算线程池大小?
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