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author | Jon Marius Venstad <jonmv@users.noreply.github.com> | 2022-02-03 10:45:59 +0100 |
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committer | GitHub <noreply@github.com> | 2022-02-03 10:45:59 +0100 |
commit | 5005e5410e284b873e9965d74fbc0f0974187a4f (patch) | |
tree | 2cae352cd86ae2f56fdbf26e537ea21c948d0b76 /messagebus | |
parent | 6aeb23317adf63eeb8631f643855f85f0657acfb (diff) | |
parent | d0d4cc82afe9aa889ceb634d20dcd53723fff7c1 (diff) |
Merge pull request #21047 from vespa-engine/jonmv/less-test-repetitions
Run dynamic throttling tests fewer times by default
Diffstat (limited to 'messagebus')
-rw-r--r-- | messagebus/src/test/java/com/yahoo/messagebus/DynamicThrottlePolicyTest.java | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/messagebus/src/test/java/com/yahoo/messagebus/DynamicThrottlePolicyTest.java b/messagebus/src/test/java/com/yahoo/messagebus/DynamicThrottlePolicyTest.java index b73dc52152a..13a4e154122 100644 --- a/messagebus/src/test/java/com/yahoo/messagebus/DynamicThrottlePolicyTest.java +++ b/messagebus/src/test/java/com/yahoo/messagebus/DynamicThrottlePolicyTest.java @@ -97,10 +97,10 @@ public class DynamicThrottlePolicyTest { /** Sort of a dummy test, as the conditions are perfect. In a more realistic scenario, below, the algorithm needs luck to climb this high. */ @Test public void singlePolicySingleWorkerWithIncreasingParallelism() { - for (int i = 0; i < 4; i++) { + for (int exponent = 0; exponent < 4; exponent++) { CustomTimer timer = new CustomTimer(); DynamicThrottlePolicy policy = new DynamicThrottlePolicy(timer); - int scaleFactor = (int) Math.pow(10, i); + int scaleFactor = (int) Math.pow(10, exponent); long operations = 3_000L * scaleFactor; int workPerSuccess = 6; int numberOfWorkers = 1; @@ -120,10 +120,10 @@ public class DynamicThrottlePolicyTest { /** A more realistic test, where throughput gradually flattens with increasing window size, and with more variance in throughput. */ @Test public void singlePolicyIncreasingWorkersWithNoParallelism() { - for (int i = 0; i < 4; i++) { + for (int exponent = 0; exponent < 4; exponent++) { CustomTimer timer = new CustomTimer(); DynamicThrottlePolicy policy = new DynamicThrottlePolicy(timer); - int scaleFactor = (int) Math.pow(10, i); + int scaleFactor = (int) Math.pow(10, exponent); long operations = 2_000L * scaleFactor; // workPerSuccess determines the latency of the simulated server, which again determines the impact of the // synthetic attractors of the algorithm, around latencies which give (close to) integer log10(1 / latency). @@ -143,14 +143,14 @@ public class DynamicThrottlePolicyTest { double maxMaxPending = numberOfWorkers * maximumTasksPerWorker; assertInRange(minMaxPending, summary.averagePending, maxMaxPending); assertInRange(minMaxPending, summary.averageWindows[0], maxMaxPending); - assertInRange(1, summary.inefficiency, 1 + 0.25 * i); // Even slower ramp-up. + assertInRange(1, summary.inefficiency, 1 + 0.25 * exponent); // Even slower ramp-up. assertInRange(0, summary.waste, 0); } } @Test public void twoWeightedPoliciesWithUnboundedTaskQueue() { - for (int i = 0; i < 10; i++) { + for (int repeat = 0; repeat < 3; repeat++) { long operations = 1_000_000; int workPerSuccess = 6 + (int) (30 * Math.random()); int numberOfWorkers = 1 + (int) (10 * Math.random()); @@ -174,7 +174,7 @@ public class DynamicThrottlePolicyTest { @Test public void tenPoliciesVeryParallelServerWithShortTaskQueue() { - for (int i = 0; i < 10; i++) { + for (int repeat = 0; repeat < 2; repeat++) { long operations = 1_000_000; int workPerSuccess = 6; int numberOfWorkers = 6; |