test: coarsen ASan sampling rate/workload to stop nightly heap OOMs

ddprof-test/src/test/java/com/datadoghq/profiler/cpu/CTimerSamplerTest.java

@@ -72,6 +72,10 @@ protected void after() throws Exception {
 
     @Override
     protected String getProfilerCommand() {
-        return "cpu=100us,event=ctimer";
+        // cpu=100us signal-based sampling is much more expensive under ASAN (the signal
+        // handler itself is ASAN-instrumented), which can inflate the sample count and,
+        // via the per-sample stack-trace materialization above, the test heap. Sample
+        // coarser under ASAN.
+        return isAsan() ? "cpu=1ms,event=ctimer" : "cpu=100us,event=ctimer";
     }
 }

ddprof-test/src/test/java/com/datadoghq/profiler/cpu/LightweightContextCpuTest.java

@@ -79,6 +79,9 @@ protected void after() throws Exception {
 
     @Override
     protected String getProfilerCommand() {
-        return "cpu=100us,lightweight=yes";
+        // cpu=100us signal-based sampling is much more expensive under ASAN (the signal
+        // handler itself is ASAN-instrumented), which can inflate the sample count and,
+        // via the per-sample accessor calls above, the test heap. Sample coarser under ASAN.
+        return isAsan() ? "cpu=1ms,lightweight=yes" : "cpu=100us,lightweight=yes";
     }
 }

ddprof-test/src/test/java/com/datadoghq/profiler/cpu/VtableReceiverFrameTest.java

@@ -18,7 +18,10 @@ public class VtableReceiverFrameTest extends AbstractProfilerTest {
 
     @Override
     protected String getProfilerCommand() {
-        return "cpu=1ms";
+        // cpu=1ms sampling over the fixed workload below still produces an ASAN-scaled
+        // number of samples (signal handling is slower under ASAN), and every sample's
+        // stack trace is materialized below, so sample coarser under ASAN.
+        return isAsan() ? "cpu=10ms" : "cpu=1ms";
     }
 
     abstract static class Shape {
@@ -41,7 +44,11 @@ static class Triangle extends Shape {
 
     private int profiledWork(Shape... shapes) {
         int result = 0;
-        for (int i = 0; i < 10_000_000; i++) {
+        // Reduce workload under ASAN: combined with the coarser sampling rate above, this
+        // bounds the number of samples (and thus the stack-trace strings materialized
+        // below) regardless of how much ASAN slows down signal handling.
+        int iterations = isAsan() ? 1_000_000 : 10_000_000;
+        for (int i = 0; i < iterations; i++) {
             for (Shape shape : shapes) {
                 result += shape.area();
             }

ddprof-test/src/test/java/com/datadoghq/profiler/metadata/MetadataNormalisationTest.java

@@ -27,14 +27,18 @@ public void test() throws Exception {
         Method arrayListAdd = ArrayList.class.getDeclaredMethod("add", Object.class);
         Constructor<?> linkedListConstructor = LinkedList.class.getConstructor();
         Method linkedListAdd = LinkedList.class.getDeclaredMethod("add", Object.class);
-        // need to invoke enough times to result in generation of accessors and to record some cpu time
+        // need to invoke enough times to result in generation of accessors and to record some cpu time.
+        // Under ASAN, cpu=100us sampling combined with this fixed iteration count produces a much
+        // larger sample set (stack-trace signal handling is slower under ASAN), and every sample's
+        // stack trace is materialized below, so reduce the iteration count to bound the sample count.
+        int iterations = isAsan() ? 10_000 : 100_000;
         int count = 0;
-        for (int i = 0; i < 100_000; i++) {
+        for (int i = 0; i < iterations; i++) {
             Object list = arrayListConstructor.newInstance();
             arrayListAdd.invoke(list, "element");
             count += ((List<?>) list).size();
         }
-        for (int i = 0; i < 100_000; i++) {
+        for (int i = 0; i < iterations; i++) {
             Object list = linkedListConstructor.newInstance();
             linkedListAdd.invoke(list, "element");
             count += ((List<?>) list).size();
@@ -63,6 +67,6 @@ public void test() throws Exception {
 
     @Override
     protected String getProfilerCommand() {
-        return "cpu=100us";
+        return isAsan() ? "cpu=1ms" : "cpu=100us";
     }
 }

ddprof-test/src/test/java/com/datadoghq/profiler/wallclock/MegamorphicCallTest.java

@@ -20,7 +20,12 @@
 public class MegamorphicCallTest extends AbstractProfilerTest {
     @Override
     protected String getProfilerCommand() {
-        return "wall=100us";
+        // wall=100us over the fixed workload below is ~10k samples/s. Under ASAN each
+        // invocation is much slower, so the same fixed-rate sampling over a much longer
+        // wall-clock duration produces hundreds of thousands of samples, and stack-trace
+        // stringification of all of them (below) OOMs the test-runner heap. Sample 10x
+        // coarser under ASAN, combined with a smaller workload, to bound the sample count.
+        return isAsan() ? "wall=1ms" : "wall=100us";
     }
 
     private static int calculation() {
@@ -60,9 +65,9 @@ public int calculate() {
         }
     }
 
-    private int profiledWork(Calculator... calculators) {
+    private int profiledWork(int iterations, Calculator... calculators) {
         int result = 0;
-        for (int i = 0; i < 1_000_000; i++) {
+        for (int i = 0; i < iterations; i++) {
             for (Calculator calculator : calculators) {
                 result += calculator.calculate();
             }
@@ -74,7 +79,11 @@ private int profiledWork(Calculator... calculators) {
     public void testITableStubs() {
         Assumptions.assumeFalse(Platform.isZing() || Platform.isJ9());
         registerCurrentThreadForWallClockProfiling();
-        int result = profiledWork(new Calculator1(), new Calculator2(), new Calculator3());
+        // Reduce workload under ASAN: combined with the coarser wall rate above, this
+        // bounds the number of samples (and thus the stack-trace strings materialized
+        // below) regardless of how much ASAN slows down each invocation.
+        int iterations = isAsan() ? 100_000 : 1_000_000;
+        int result = profiledWork(iterations, new Calculator1(), new Calculator2(), new Calculator3());
         System.err.println(result);
         stopProfiler();
         IItemCollection events = verifyEvents("datadog.MethodSample");