我触发HTTP云功能，然后将消息发布到我的PubSub主题。每隔几秒发布一次，这很快就会有效，通常延迟时间为1秒。但是，如果我暂时不发布任何内容（比如5分钟），那么发布会有很长的延迟（约20秒）。
如何让它一直快速？思路：
发布商功能：
"use strict";
const PubSub = require("@google-cloud/pubsub");
const pubsub = PubSub({
projectId: "pubsub-forwarders"
});
exports.testFunction = function testFunction(req, res) {
publishMessage("testtopic", "testmessage");
res.status(200).send();
}
function publishMessage(topicName, data) {
const topic = pubsub.topic(topicName);
const publisher = topic.publisher();
const dataBuffer = Buffer.from(data);
return publisher.publish(dataBuffer)
.then((results) => {
console.log("Message", JSON.stringify(results), "published.");
return JSON.stringify(results);
});
}
云功能日志：
16:57:05.938 Function execution started
16:57:05.948 Function execution took 11 ms, finished with status code: 200
16:57:22.987 Message "179492329652563" published.
非常感谢！
答案 0 :(得分：1)
原因是发送响应后Google Cloud Function正在关闭实例。
只需将异步发布更改为同步，并在成功发布后发送响应即可。它将发布时间缩短到数十毫秒。
y<-matrix(c(1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 0, 0, 1,
1, 1, 0, 0, 0, 0))
covariates:
elevation.m habitat birdsmallabund1000 birdsmallbio1000 birdmlabund1000 birdmlbio1000 totalbirdabund1000 totalbirdbio1000
1 1828.80 Shrubland 3.141687 56.23548 0.6509944 59.95943 3.792681 116.19491
2 2194.56 Shrubland 3.000875 53.71551 0.7360382 67.78902 3.736913 121.50453
3 502.92 Grassland 4.514240 80.80469 1.6084328 148.13317 6.122673 228.93787
4 944.88 Grassland 3.822037 68.41400 1.3260143 122.12721 5.148051 190.54121
5 31.09 Cropland 8.920525 159.67709 7.4512331 686.25585 16.371758 845.93294
6 27.13 Forest 1.797454 32.17478 0.4961018 45.69141 2.293556 77.86619
7 1981.20 Forest 3.088385 55.28250 0.6813842 62.75632 3.769769 118.03882
8 667.50 Grassland 3.806842 68.14256 1.0256961 94.46905 4.832538 162.61162
9 746.76 Forest 3.838743 68.71368 2.0089101 185.02212 5.847653 253.73580
10 335.28 Cropland 5.946937 106.44980 2.5720764 236.88950 8.519014 343.33930
11 1981.20 Forest 3.128799 56.00533 0.8153540 75.09276 3.944153 131.09809
12 11.00 Cropland 11.140732 199.41941 19.5284010 1798.56555 30.669133 1997.98496
13 78.02 Cropland 8.126014 145.45553 9.1868735 846.10811 17.312888 991.56364
14 359.05 Grassland 2.452983 43.90811 0.8911695 82.07812 3.344153 125.98624
15 2673.00 Shrubland 2.495147 44.66269 0.7071599 65.13039 3.202307 109.79308
16 1433.00 Forest 3.361337 60.16762 0.7642005 70.38107 4.125537 130.54869
17 986.00 Grassland 2.704057 48.40334 0.8036595 74.01893 3.507717 122.42228
18 73.00 Forest 2.330310 41.71185 3.1883850 293.65346 5.518695 335.36531
19 52.00 Cropland 10.041209 179.73819 11.2941925 1040.19205 21.335402 1219.93023
20 1343.00 Forest 3.416229 61.15091 0.8014320 73.81400 4.217661 134.96492
21 182.00 Cropland 7.466508 133.65036 3.7916468 349.20716 11.258154 482.85752
22 2569.00 Shrubland 2.873588 51.43683 0.5789976 53.32339 3.452586 104.76022
23 44.00 Cropland 10.583373 189.44232 7.8829753 726.02148 18.466348 915.46380
24 396.00 Forest 6.894909 123.41822 0.6509944 59.95943 7.545903 183.37765
25 690.00 Grassland 4.415274 79.03309 2.1759745 200.40819 6.591249 279.44129
26 341.00 Grassland 2.264121 40.52745 0.8555290 78.79133 3.119650 119.31877
rodentabund1000 rodentbio1000 mouseabund1000 ratabund1000 mousebio1000 ratbio1000 insectabund1000 insectbio1000 totalpreyabund1000
1 6.9000 97.00204 2.20 4.700 31.00000 66.00 18.176486 5.329819 28.90
2 2.5700 144.38381 0.80 1.700 46.00000 98.00 10.546850 7.401802 16.90
3 6.4800 65.38299 6.48 0.000 65.38299 0.00 28.617933 3.848231 41.20
4 2.6000 142.31028 2.60 0.000 142.31028 0.00 14.587196 7.313924 22.30
5 2.1900 51.58449 2.10 0.100 49.00000 2.58 10.673089 3.164183 29.20
6 15.2000 289.66071 11.00 4.300 208.60000 81.10 43.969812 13.152176 61.40
7 0.0792 88.23654 0.06 0.020 63.50000 24.70 5.588603 4.928905 9.44
8 4.9800 72.64881 4.98 0.000 72.64881 0.00 21.473533 4.198045 31.30
9 3.8400 277.79909 2.80 1.100 200.00000 77.80 39.422315 12.705869 49.10
10 1.0400 54.81910 0.99 0.050 52.10000 2.70 6.609045 3.327131 16.20
11 0.0304 234.19762 0.02 0.009 169.00000 65.60 40.190727 11.035245 44.20
12 1.6800 46.26389 1.60 0.080 43.90000 2.30 9.259345 2.892185 41.60
13 1.9300 46.94789 1.80 0.100 44.60000 2.30 9.743338 2.927446 29.00
14 5.4700 58.29962 5.47 0.000 58.29962 0.00 23.514118 3.500606 32.30
15 0.7930 116.01280 0.25 0.540 37.00000 79.00 5.242146 6.178571 9.24
16 4.4200 78.53620 3.20 1.200 56.50000 22.00 33.639882 4.477020 42.20
17 1.8400 71.48462 1.84 0.000 71.48462 0.00 11.868661 4.142421 17.20
18 15.1000 1421.35490 10.90 4.200 1023.00000 398.00 107.499518 48.908490 128.00
19 0.9110 51.58449 0.87 0.050 49.00000 2.60 6.632146 3.164183 28.90
20 5.1300 77.68672 3.70 1.400 55.90000 21.80 38.424278 4.436998 47.80
21 1.4800 49.80229 1.40 0.070 47.30000 2.50 8.632346 3.073644 21.40
22 0.9370 97.50520 0.30 0.640 31.20000 66.30 6.034518 5.352635 10.40
23 1.9700 48.54587 1.87 0.100 46.00000 2.40 10.450692 3.009477 30.90
24 5.8000 139.49852 4.20 1.600 100.00000 39.10 95.264864 7.194400 109.00
25 3.8500 68.44883 3.85 0.000 68.44883 0.00 19.893488 3.996622 30.30
26 8.3800 125.20267 8.38 0.000 125.20267 0.00 36.045071 6.579974 47.50
totalpreybio1000 ground canopy height meanannualrainfall.mm avgtempduringsurvey.f
1 218.5268 80 0 0.30 1008.0 65.0
2 273.2901 60 0 0.43 1384.7 58.0
3 298.1691 85 0 0.55 634.4 69.0
4 340.1654 95 0 0.20 1371.0 66.0
5 900.6816 80 0 0.35 409.9 78.0
6 380.6791 95 85 2.93 2044.1 76.0
7 211.2043 95 90 9.00 918.3 58.0
8 239.4585 95 0 0.10 734.2 65.8
9 544.2408 90 60 5.00 2004.5 68.5
10 401.4855 70 0 0.20 467.5 77.5
11 376.3310 80 75 20.00 1842.8 54.3
12 2047.1410 50 5 0.40 306.8 80.5
13 1041.4390 75 0 0.25 320.7 79.5
14 187.7865 35 10 0.11 525.8 74.3
15 231.9844 50 0 0.50 1177.5 52.9
16 213.5619 80 0 0.40 808.0 65.5
17 198.0493 75 5 0.30 718.9 67.4
18 1805.6287 95 65 6.00 3550.6 74.5
19 1274.6789 10 15 0.73 409.9 80.6
20 217.0886 73 0 0.50 797.7 65.0
21 535.7335 75 0 0.45 376.6 78.4
22 207.6181 40 0 0.55 1012.9 50.0
23 967.0191 70 0 0.45 352.4 77.0
24 330.0706 100 65 8.30 1352.1 78.0
25 351.8867 65 0 0.15 677.8 75.5
26 251.1014 70 0 0.70 1249.7 73.0
> umf <-unmarkedFrameOccu(y=y, siteCovs=covariates, obsCovs=NULL)
> summary(umf)
unmarkedFrame Object
26 sites
Maximum number of observations per site: 1
Mean number of observations per site: 1
Sites with at least one detection: 11
Tabulation of y observations:
0 1
15 11
Site-level covariates:
elevation.m habitat birdsmallabund1000 birdsmallbio1000 birdmlabund1000 birdmlbio1000 totalbirdabund1000
Min. : 11.0 Cropland :7 Min. : 1.797 Min. : 32.17 Min. : 0.4961 Min. : 45.69 Min. : 2.294
1st Qu.: 104.0 Forest :8 1st Qu.: 2.905 1st Qu.: 52.01 1st Qu.: 0.7431 1st Qu.: 68.44 1st Qu.: 3.745
Median : 585.2 Grassland:7 Median : 3.612 Median : 64.65 Median : 0.9584 Median : 88.27 Median : 4.990
Mean : 864.3 Shrubland:4 Mean : 4.830 Mean : 86.45 Mean : 3.1721 Mean : 292.15 Mean : 8.002
3rd Qu.:1410.5 3rd Qu.: 6.658 3rd Qu.:119.18 3rd Qu.: 3.0343 3rd Qu.: 279.46 3rd Qu.: 8.276
Max. :2673.0 Max. :11.141 Max. :199.42 Max. :19.5284 Max. :1798.57 Max. :30.669
totalbirdbio1000 rodentabund1000 rodentbio1000 mouseabund1000 ratabund1000 mousebio1000 ratbio1000
Min. : 77.87 Min. : 0.0304 Min. : 46.26 Min. : 0.020 Min. :0.00000 Min. : 31.00 Min. : 0.000
1st Qu.: 121.73 1st Qu.: 1.5300 1st Qu.: 55.69 1st Qu.: 1.093 1st Qu.:0.00225 1st Qu.: 46.33 1st Qu.: 0.575
Median : 172.99 Median : 2.5850 Median : 78.11 Median : 2.150 Median :0.09000 Median : 57.40 Median : 2.650
Mean : 378.60 Mean : 4.0616 Mean : 154.43 Mean : 3.218 Mean :0.84458 Mean : 113.75 Mean : 40.645
3rd Qu.: 341.35 3rd Qu.: 5.3850 3rd Qu.: 135.92 3rd Qu.: 4.112 3rd Qu.:1.17500 3rd Qu.: 93.16 3rd Qu.: 65.900
Max. :1997.98 Max. :15.2000 Max. :1421.35 Max. :11.000 Max. :4.70000 Max. :1023.00 Max. :398.000
insectabund1000 insectbio1000 totalpreyabund1000 totalpreybio1000 ground canopy height
Min. : 5.242 Min. : 2.892 Min. : 9.24 Min. : 187.8 Min. : 10.00 Min. : 0.00 Min. : 0.1000
1st Qu.: 9.380 1st Qu.: 3.370 1st Qu.: 21.62 1st Qu.: 221.9 1st Qu.: 66.25 1st Qu.: 0.00 1st Qu.: 0.3000
Median : 16.382 Median : 4.457 Median : 30.60 Median : 335.1 Median : 75.00 Median : 0.00 Median : 0.4500
Mean : 25.462 Mean : 7.163 Mean : 37.53 Mean : 540.2 Mean : 72.62 Mean :18.27 Mean : 2.2635
3rd Qu.: 35.444 3rd Qu.: 7.041 3rd Qu.: 43.70 3rd Qu.: 542.1 3rd Qu.: 88.75 3rd Qu.:13.75 3rd Qu.: 0.7225
Max. :107.500 Max. :48.908 Max. :128.00 Max. :2047.1 Max. :100.00 Max. :90.00 Max. :20.0000
meanannualrainfall.mm avgtempduringsurvey.f
Min. : 306.8 Min. :50.00
1st Qu.: 482.1 1st Qu.:65.12
Median : 802.9 Median :71.00
Mean :1017.6 Mean :69.55
3rd Qu.:1326.5 3rd Qu.:77.38
Max. :3550.6 Max. :80.60
Call:
occu(formula = ~height + elevation.m ~ height, data = umf)
Occupancy (logit-scale):
Estimate SE z P(>|z|)
(Intercept) 0.584 0.634 0.921 0.357
height -1.049 1.037 -1.011 0.312
Detection (logit-scale):
Estimate SE z P(>|z|)
(Intercept) 0.000961 NaN NaN NaN
height -0.002226 32553 -6.84e-08 1
elevation.m 2.058074 NaN NaN NaN
AIC: 38.09015
Number of sites: 26
optim convergence code: 0
optim iterations: 26
Bootstrap iterations: 0
Warning message:
In sqrt(diag(vcov(obj))) : NaNs produced
请参阅参考文献：https://cloud.google.com/functions/docs/bestpractices/tips#do_not_start_background_activities