[2026-02-26T01:20:34.651Z] Running test renaissance-movie-lens_0 ... [2026-02-26T01:20:34.651Z] =============================================== [2026-02-26T01:20:34.651Z] renaissance-movie-lens_0 Start Time: Thu Feb 26 01:20:34 2026 Epoch Time (ms): 1772068834450 [2026-02-26T01:20:34.651Z] variation: NoOptions [2026-02-26T01:20:34.651Z] JVM_OPTIONS: [2026-02-26T01:20:34.651Z] { \ [2026-02-26T01:20:34.651Z] echo ""; echo "TEST SETUP:"; \ [2026-02-26T01:20:34.651Z] echo "Nothing to be done for setup."; \ [2026-02-26T01:20:34.651Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_17720688337236/renaissance-movie-lens_0"; \ [2026-02-26T01:20:34.651Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_17720688337236/renaissance-movie-lens_0"; \ [2026-02-26T01:20:34.651Z] echo ""; echo "TESTING:"; \ [2026-02-26T01:20:34.651Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_2/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_17720688337236/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2026-02-26T01:20:34.652Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_17720688337236/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2026-02-26T01:20:34.652Z] echo ""; echo "TEST TEARDOWN:"; \ [2026-02-26T01:20:34.652Z] echo "Nothing to be done for teardown."; \ [2026-02-26T01:20:34.652Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_riscv64_linux_testList_2/aqa-tests/TKG/../TKG/output_17720688337236/TestTargetResult"; [2026-02-26T01:20:34.652Z] [2026-02-26T01:20:34.652Z] TEST SETUP: [2026-02-26T01:20:34.652Z] Nothing to be done for setup. [2026-02-26T01:20:34.652Z] [2026-02-26T01:20:34.652Z] TESTING: [2026-02-26T01:20:58.060Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2026-02-26T01:21:38.390Z] 01:21:32.018 WARN [dispatcher-event-loop-0] org.apache.spark.scheduler.TaskSetManager - Stage 8 contains a task of very large size (1401 KiB). The maximum recommended task size is 1000 KiB. [2026-02-26T01:21:45.831Z] Got 100004 ratings from 671 users on 9066 movies. [2026-02-26T01:21:48.908Z] Training: 60056, validation: 20285, test: 19854 [2026-02-26T01:21:48.908Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2026-02-26T01:21:49.256Z] GC before operation: completed in 577.488 ms, heap usage 316.474 MB -> 77.132 MB. [2026-02-26T01:22:22.979Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:22:36.333Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:22:52.603Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:23:03.645Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:23:11.089Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:23:18.544Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:23:25.998Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:23:32.062Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:23:33.260Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:23:33.260Z] The best model improves the baseline by 14.52%. [2026-02-26T01:23:34.457Z] Top recommended movies for user id 72: [2026-02-26T01:23:34.457Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:23:34.457Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:23:34.457Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:23:34.457Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:23:34.457Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:23:34.457Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (105097.745 ms) ====== [2026-02-26T01:23:34.457Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2026-02-26T01:23:35.223Z] GC before operation: completed in 865.073 ms, heap usage 475.493 MB -> 91.050 MB. [2026-02-26T01:23:48.578Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:23:59.610Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:24:10.648Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:24:19.722Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:24:27.208Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:24:33.259Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:24:39.319Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:24:45.366Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:24:45.715Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:24:46.062Z] The best model improves the baseline by 14.52%. [2026-02-26T01:24:46.803Z] Top recommended movies for user id 72: [2026-02-26T01:24:46.803Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:24:46.803Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:24:46.803Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:24:46.803Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:24:46.803Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:24:46.803Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (71441.357 ms) ====== [2026-02-26T01:24:46.803Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2026-02-26T01:24:47.566Z] GC before operation: completed in 939.540 ms, heap usage 910.594 MB -> 94.282 MB. [2026-02-26T01:24:58.576Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:25:09.606Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:25:18.704Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:25:27.812Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:25:33.981Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:25:40.069Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:25:47.503Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:25:52.380Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:25:53.122Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:25:53.617Z] The best model improves the baseline by 14.52%. [2026-02-26T01:25:54.409Z] Top recommended movies for user id 72: [2026-02-26T01:25:54.409Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:25:54.409Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:25:54.409Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:25:54.409Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:25:54.409Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:25:54.409Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (66662.468 ms) ====== [2026-02-26T01:25:54.409Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2026-02-26T01:25:55.207Z] GC before operation: completed in 970.438 ms, heap usage 281.788 MB -> 90.652 MB. [2026-02-26T01:26:06.363Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:26:15.413Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:26:24.768Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:26:33.823Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:26:38.701Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:26:44.739Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:26:50.767Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:26:55.648Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:26:56.837Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:26:57.180Z] The best model improves the baseline by 14.52%. [2026-02-26T01:26:57.920Z] Top recommended movies for user id 72: [2026-02-26T01:26:57.920Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:26:57.920Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:26:57.920Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:26:57.920Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:26:57.920Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:26:57.920Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (62643.982 ms) ====== [2026-02-26T01:26:57.920Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2026-02-26T01:26:59.147Z] GC before operation: completed in 985.721 ms, heap usage 275.887 MB -> 90.950 MB. [2026-02-26T01:27:08.196Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:27:19.180Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:27:28.224Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:27:37.295Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:27:42.166Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:27:48.263Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:27:53.132Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:27:59.175Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:27:59.520Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:27:59.863Z] The best model improves the baseline by 14.52%. [2026-02-26T01:28:00.606Z] Top recommended movies for user id 72: [2026-02-26T01:28:00.606Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:28:00.606Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:28:00.606Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:28:00.606Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:28:00.606Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:28:00.606Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (61571.854 ms) ====== [2026-02-26T01:28:00.606Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2026-02-26T01:28:01.414Z] GC before operation: completed in 1035.004 ms, heap usage 771.323 MB -> 94.795 MB. [2026-02-26T01:28:12.542Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:28:21.614Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:28:30.969Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:28:40.026Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:28:44.887Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:28:49.754Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:28:55.809Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:29:00.671Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:29:01.408Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:29:01.408Z] The best model improves the baseline by 14.52%. [2026-02-26T01:29:02.613Z] Top recommended movies for user id 72: [2026-02-26T01:29:02.613Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:29:02.613Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:29:02.613Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:29:02.613Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:29:02.613Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:29:02.613Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (60817.218 ms) ====== [2026-02-26T01:29:02.613Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2026-02-26T01:29:03.370Z] GC before operation: completed in 929.555 ms, heap usage 232.845 MB -> 91.127 MB. [2026-02-26T01:29:12.432Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:29:23.417Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:29:30.858Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:29:39.937Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:29:43.804Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:29:49.840Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:29:54.689Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:29:59.568Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:30:00.751Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:30:00.751Z] The best model improves the baseline by 14.52%. [2026-02-26T01:30:01.484Z] Top recommended movies for user id 72: [2026-02-26T01:30:01.484Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:30:01.484Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:30:01.484Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:30:01.484Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:30:01.484Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:30:01.484Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (58179.436 ms) ====== [2026-02-26T01:30:01.484Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2026-02-26T01:30:02.731Z] GC before operation: completed in 992.654 ms, heap usage 649.958 MB -> 94.925 MB. [2026-02-26T01:30:11.761Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:30:20.815Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:30:29.855Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:30:37.266Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:30:42.122Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:30:48.143Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:30:53.000Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:30:57.870Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:30:58.607Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:30:58.950Z] The best model improves the baseline by 14.52%. [2026-02-26T01:30:59.683Z] Top recommended movies for user id 72: [2026-02-26T01:30:59.683Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:30:59.683Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:30:59.683Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:30:59.683Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:30:59.683Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:30:59.683Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (57139.744 ms) ====== [2026-02-26T01:30:59.683Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2026-02-26T01:31:00.904Z] GC before operation: completed in 1018.357 ms, heap usage 823.842 MB -> 95.357 MB. [2026-02-26T01:31:09.956Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:31:17.645Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:31:26.745Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:31:34.166Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:31:40.229Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:31:45.098Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:31:49.996Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:31:54.861Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:31:56.051Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:31:56.051Z] The best model improves the baseline by 14.52%. [2026-02-26T01:31:56.789Z] Top recommended movies for user id 72: [2026-02-26T01:31:56.789Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:31:56.789Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:31:56.789Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:31:56.789Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:31:56.789Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:31:56.789Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (56184.142 ms) ====== [2026-02-26T01:31:56.789Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2026-02-26T01:31:58.014Z] GC before operation: completed in 988.877 ms, heap usage 277.298 MB -> 91.248 MB. [2026-02-26T01:32:07.273Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:32:16.355Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:32:23.776Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:32:32.825Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:32:37.686Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:32:42.550Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:32:48.573Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:32:53.624Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:32:53.989Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:32:54.332Z] The best model improves the baseline by 14.52%. [2026-02-26T01:32:55.081Z] Top recommended movies for user id 72: [2026-02-26T01:32:55.081Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:32:55.081Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:32:55.081Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:32:55.081Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:32:55.081Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:32:55.081Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (57068.504 ms) ====== [2026-02-26T01:32:55.081Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2026-02-26T01:32:55.839Z] GC before operation: completed in 950.696 ms, heap usage 356.260 MB -> 91.521 MB. [2026-02-26T01:33:04.884Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:33:13.926Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:33:21.336Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:33:28.750Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:33:34.783Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:33:39.653Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:33:44.587Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:33:50.689Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:33:51.034Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:33:51.034Z] The best model improves the baseline by 14.52%. [2026-02-26T01:33:51.778Z] Top recommended movies for user id 72: [2026-02-26T01:33:51.778Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:33:51.778Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:33:51.778Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:33:51.778Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:33:51.778Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:33:51.778Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (55870.694 ms) ====== [2026-02-26T01:33:51.778Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2026-02-26T01:33:52.536Z] GC before operation: completed in 946.687 ms, heap usage 861.795 MB -> 95.447 MB. [2026-02-26T01:34:01.623Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:34:10.667Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:34:18.071Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:34:25.538Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:34:31.562Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:34:36.535Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:34:41.397Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:34:47.416Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:34:47.416Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:34:47.758Z] The best model improves the baseline by 14.52%. [2026-02-26T01:34:48.493Z] Top recommended movies for user id 72: [2026-02-26T01:34:48.493Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:34:48.493Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:34:48.493Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:34:48.493Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:34:48.493Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:34:48.493Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (55655.284 ms) ====== [2026-02-26T01:34:48.493Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2026-02-26T01:34:49.234Z] GC before operation: completed in 950.429 ms, heap usage 569.514 MB -> 95.020 MB. [2026-02-26T01:34:58.291Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:35:07.328Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:35:14.743Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:35:23.797Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:35:27.802Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:35:33.934Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:35:38.807Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:35:44.851Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:35:44.851Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:35:45.206Z] The best model improves the baseline by 14.52%. [2026-02-26T01:35:45.953Z] Top recommended movies for user id 72: [2026-02-26T01:35:45.953Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:35:45.953Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:35:45.953Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:35:45.953Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:35:45.953Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:35:45.953Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (56603.134 ms) ====== [2026-02-26T01:35:45.954Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2026-02-26T01:35:46.728Z] GC before operation: completed in 951.072 ms, heap usage 317.065 MB -> 92.222 MB. [2026-02-26T01:35:55.798Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:36:04.860Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:36:13.927Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:36:21.386Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:36:26.260Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2026-02-26T01:36:32.309Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2026-02-26T01:36:37.184Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2026-02-26T01:36:42.078Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2026-02-26T01:36:42.820Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2026-02-26T01:36:42.820Z] The best model improves the baseline by 14.52%. [2026-02-26T01:36:43.557Z] Top recommended movies for user id 72: [2026-02-26T01:36:43.557Z] 1: Land of Silence and Darkness (Land des Schweigens und der Dunkelheit) (1971) (rating: 4.659, id: 67504) [2026-02-26T01:36:43.557Z] 2: Goat, The (1921) (rating: 4.659, id: 83318) [2026-02-26T01:36:43.557Z] 3: Play House, The (1921) (rating: 4.659, id: 83359) [2026-02-26T01:36:43.557Z] 4: Cops (1922) (rating: 4.659, id: 83411) [2026-02-26T01:36:43.557Z] 5: Dear Frankie (2004) (rating: 4.267, id: 8530) [2026-02-26T01:36:43.557Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (56798.660 ms) ====== [2026-02-26T01:36:43.557Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2026-02-26T01:36:44.792Z] GC before operation: completed in 953.666 ms, heap usage 235.004 MB -> 91.374 MB. [2026-02-26T01:36:53.853Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2026-02-26T01:37:01.270Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2026-02-26T01:37:10.324Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2026-02-26T01:37:17.939Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2026-02-26T01:37:17.939Z] 01:37:17.378 ERROR [Executor task launch worker for task 0.0 in stage 22486.0 (TID 21911)] org.apache.spark.executor.Executor - Exception in task 0.0 in stage 22486.0 (TID 21911) [2026-02-26T01:37:17.939Z] java.lang.ArrayStoreException: [2026-02-26T01:37:17.939Z] at scala.collection.mutable.ArrayBuilder$ofRef.addOne(ArrayBuilder.scala:129) ~[scala-library-2.13.15.jar:?] [2026-02-26T01:37:17.939Z] at scala.collection.mutable.ArrayBuilder$ofRef.addOne(ArrayBuilder.scala:112) ~[scala-library-2.13.15.jar:?] [2026-02-26T01:37:17.939Z] at scala.collection.mutable.Growable.addAll(Growable.scala:62) ~[scala-library-2.13.15.jar:?] [2026-02-26T01:37:17.939Z] at scala.collection.mutable.Growable.addAll$(Growable.scala:57) ~[scala-library-2.13.15.jar:?] [2026-02-26T01:37:17.939Z] at scala.collection.mutable.ArrayBuilder.addAll(ArrayBuilder.scala:75) ~[scala-library-2.13.15.jar:?] [2026-02-26T01:37:17.939Z] at scala.collection.IterableOnceOps.toArray(IterableOnce.scala:1505) ~[scala-library-2.13.15.jar:?] [2026-02-26T01:37:17.939Z] at scala.collection.IterableOnceOps.toArray$(IterableOnce.scala:1498) ~[scala-library-2.13.15.jar:?] [2026-02-26T01:37:17.939Z] at scala.collection.AbstractIterator.toArray(Iterator.scala:1303) ~[scala-library-2.13.15.jar:?] [2026-02-26T01:37:17.939Z] at org.apache.spark.rdd.RDD.$anonfun$take$2(RDD.scala:1492) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.939Z] at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2433) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.939Z] at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.939Z] at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.939Z] at org.apache.spark.scheduler.Task.run(Task.scala:141) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.939Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.940Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64) ~[spark-common-utils_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.940Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61) ~[spark-common-utils_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.940Z] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94) ~[spark-core_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.940Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623) [spark-core_2.13-3.5.3.jar:3.5.3] [2026-02-26T01:37:17.940Z] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) [?:?] [2026-02-26T01:37:17.940Z] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) [?:?] [2026-02-26T01:37:17.940Z] at java.lang.Thread.run(Thread.java:840) [?:?] [2026-02-26T01:37:17.940Z] 01:37:17.477 WARN [task-result-getter-3] org.apache.spark.scheduler.TaskSetManager - Lost task 0.0 in stage 22486.0 (TID 21911) (test-rise-ubuntu2404-riscv64-7.adoptopenjdk.net executor driver): java.lang.ArrayStoreException: [2026-02-26T01:37:17.940Z] at scala.collection.mutable.ArrayBuilder$ofRef.addOne(ArrayBuilder.scala:129) [2026-02-26T01:37:17.940Z] at scala.collection.mutable.ArrayBuilder$ofRef.addOne(ArrayBuilder.scala:112) [2026-02-26T01:37:17.940Z] at scala.collection.mutable.Growable.addAll(Growable.scala:62) [2026-02-26T01:37:17.940Z] at scala.collection.mutable.Growable.addAll$(Growable.scala:57) [2026-02-26T01:37:17.940Z] at scala.collection.mutable.ArrayBuilder.addAll(ArrayBuilder.scala:75) [2026-02-26T01:37:17.940Z] at scala.collection.IterableOnceOps.toArray(IterableOnce.scala:1505) [2026-02-26T01:37:17.940Z] at scala.collection.IterableOnceOps.toArray$(IterableOnce.scala:1498) [2026-02-26T01:37:17.940Z] at scala.collection.AbstractIterator.toArray(Iterator.scala:1303) [2026-02-26T01:37:17.940Z] at org.apache.spark.rdd.RDD.$anonfun$take$2(RDD.scala:1492) [2026-02-26T01:37:17.940Z] at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2433) [2026-02-26T01:37:17.940Z] at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93) [2026-02-26T01:37:17.940Z] at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166) [2026-02-26T01:37:17.940Z] at org.apache.spark.scheduler.Task.run(Task.scala:141) [2026-02-26T01:37:17.940Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620) [2026-02-26T01:37:17.940Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64) [2026-02-26T01:37:17.940Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61) [2026-02-26T01:37:17.940Z] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94) [2026-02-26T01:37:17.940Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623) [2026-02-26T01:37:17.940Z] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) [2026-02-26T01:37:17.940Z] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) [2026-02-26T01:37:17.940Z] at java.base/java.lang.Thread.run(Thread.java:840) [2026-02-26T01:37:17.940Z] [2026-02-26T01:37:17.940Z] 01:37:17.484 ERROR [task-result-getter-3] org.apache.spark.scheduler.TaskSetManager - Task 0 in stage 22486.0 failed 1 times; aborting job [2026-02-26T01:37:17.940Z] ====== movie-lens (apache-spark) [default], iteration 14 failed (SparkException) ====== [2026-02-26T01:37:18.679Z] Benchmark 'movie-lens' failed with exception: [2026-02-26T01:37:18.679Z] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 22486.0 failed 1 times, most recent failure: Lost task 0.0 in stage 22486.0 (TID 21911) (test-rise-ubuntu2404-riscv64-7.adoptopenjdk.net executor driver): java.lang.ArrayStoreException: [2026-02-26T01:37:18.679Z] at scala.collection.mutable.ArrayBuilder$ofRef.addOne(ArrayBuilder.scala:129) [2026-02-26T01:37:18.679Z] at scala.collection.mutable.ArrayBuilder$ofRef.addOne(ArrayBuilder.scala:112) [2026-02-26T01:37:18.679Z] at scala.collection.mutable.Growable.addAll(Growable.scala:62) [2026-02-26T01:37:18.679Z] at scala.collection.mutable.Growable.addAll$(Growable.scala:57) [2026-02-26T01:37:18.679Z] at scala.collection.mutable.ArrayBuilder.addAll(ArrayBuilder.scala:75) [2026-02-26T01:37:18.679Z] at scala.collection.IterableOnceOps.toArray(IterableOnce.scala:1505) [2026-02-26T01:37:18.679Z] at scala.collection.IterableOnceOps.toArray$(IterableOnce.scala:1498) [2026-02-26T01:37:18.679Z] at scala.collection.AbstractIterator.toArray(Iterator.scala:1303) [2026-02-26T01:37:18.679Z] at org.apache.spark.rdd.RDD.$anonfun$take$2(RDD.scala:1492) [2026-02-26T01:37:18.679Z] at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2433) [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93) [2026-02-26T01:37:18.679Z] at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166) [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.Task.run(Task.scala:141) [2026-02-26T01:37:18.679Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620) [2026-02-26T01:37:18.679Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64) [2026-02-26T01:37:18.679Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61) [2026-02-26T01:37:18.679Z] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94) [2026-02-26T01:37:18.679Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623) [2026-02-26T01:37:18.679Z] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) [2026-02-26T01:37:18.679Z] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) [2026-02-26T01:37:18.679Z] at java.base/java.lang.Thread.run(Thread.java:840) [2026-02-26T01:37:18.679Z] [2026-02-26T01:37:18.679Z] Driver stacktrace: [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2856) [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2792) [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2791) [2026-02-26T01:37:18.679Z] at scala.collection.immutable.List.foreach(List.scala:334) [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2791) [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1247) [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1247) [2026-02-26T01:37:18.679Z] at scala.Option.foreach(Option.scala:437) [2026-02-26T01:37:18.679Z] at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1247) [2026-02-26T01:37:18.680Z] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:3060) [2026-02-26T01:37:18.680Z] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2994) [2026-02-26T01:37:18.680Z] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2983) [2026-02-26T01:37:18.680Z] at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) [2026-02-26T01:37:18.680Z] at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:989) [2026-02-26T01:37:18.680Z] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2393) [2026-02-26T01:37:18.680Z] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2414) [2026-02-26T01:37:18.680Z] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2433) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDD.$anonfun$take$1(RDD.scala:1492) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDD.withScope(RDD.scala:410) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDD.take(RDD.scala:1465) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDD.$anonfun$isEmpty$1(RDD.scala:1602) [2026-02-26T01:37:18.680Z] at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.scala:17) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDD.withScope(RDD.scala:410) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDD.isEmpty(RDD.scala:1602) [2026-02-26T01:37:18.680Z] at org.apache.spark.mllib.recommendation.ALS.run(ALS.scala:241) [2026-02-26T01:37:18.680Z] at org.renaissance.apache.spark.MovieLens$MovieLensHelper.trainModel(MovieLens.scala:264) [2026-02-26T01:37:18.680Z] at org.renaissance.apache.spark.MovieLens$MovieLensHelper.$anonfun$trainModels$1(MovieLens.scala:240) [2026-02-26T01:37:18.680Z] at org.renaissance.apache.spark.MovieLens$MovieLensHelper.$anonfun$trainModels$1$adapted(MovieLens.scala:239) [2026-02-26T01:37:18.680Z] at scala.collection.IterableOnceOps.foreach(IterableOnce.scala:619) [2026-02-26T01:37:18.680Z] at scala.collection.IterableOnceOps.foreach$(IterableOnce.scala:617) [2026-02-26T01:37:18.680Z] at scala.collection.AbstractIterable.foreach(Iterable.scala:935) [2026-02-26T01:37:18.680Z] at org.renaissance.apache.spark.MovieLens$MovieLensHelper.trainModels(MovieLens.scala:239) [2026-02-26T01:37:18.680Z] at org.renaissance.apache.spark.MovieLens.run(MovieLens.scala:348) [2026-02-26T01:37:18.680Z] at org.renaissance.harness.ExecutionDriver.executeOperation(ExecutionDriver.java:137) [2026-02-26T01:37:18.680Z] at org.renaissance.harness.ExecutionDriver.executeBenchmark(ExecutionDriver.java:93) [2026-02-26T01:37:18.680Z] at org.renaissance.harness.RenaissanceSuite$.runBenchmarks$$anonfun$1(RenaissanceSuite.scala:172) [2026-02-26T01:37:18.680Z] at scala.runtime.function.JProcedure1.apply(JProcedure1.java:15) [2026-02-26T01:37:18.680Z] at scala.runtime.function.JProcedure1.apply(JProcedure1.java:10) [2026-02-26T01:37:18.680Z] at scala.collection.immutable.List.foreach(List.scala:334) [2026-02-26T01:37:18.680Z] at org.renaissance.harness.RenaissanceSuite$.runBenchmarks(RenaissanceSuite.scala:161) [2026-02-26T01:37:18.680Z] at org.renaissance.harness.RenaissanceSuite$.main(RenaissanceSuite.scala:130) [2026-02-26T01:37:18.680Z] at org.renaissance.harness.RenaissanceSuite.main(RenaissanceSuite.scala) [2026-02-26T01:37:18.680Z] at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) [2026-02-26T01:37:18.680Z] at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77) [2026-02-26T01:37:18.680Z] at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) [2026-02-26T01:37:18.680Z] at java.base/java.lang.reflect.Method.invoke(Method.java:569) [2026-02-26T01:37:18.680Z] at org.renaissance.core.Launcher.loadAndInvokeHarnessClass(Launcher.java:129) [2026-02-26T01:37:18.680Z] at org.renaissance.core.Launcher.launchHarnessClass(Launcher.java:78) [2026-02-26T01:37:18.680Z] at org.renaissance.core.Launcher.main(Launcher.java:43) [2026-02-26T01:37:18.680Z] Caused by: java.lang.ArrayStoreException: [2026-02-26T01:37:18.680Z] at scala.collection.mutable.ArrayBuilder$ofRef.addOne(ArrayBuilder.scala:129) [2026-02-26T01:37:18.680Z] at scala.collection.mutable.ArrayBuilder$ofRef.addOne(ArrayBuilder.scala:112) [2026-02-26T01:37:18.680Z] at scala.collection.mutable.Growable.addAll(Growable.scala:62) [2026-02-26T01:37:18.680Z] at scala.collection.mutable.Growable.addAll$(Growable.scala:57) [2026-02-26T01:37:18.680Z] at scala.collection.mutable.ArrayBuilder.addAll(ArrayBuilder.scala:75) [2026-02-26T01:37:18.680Z] at scala.collection.IterableOnceOps.toArray(IterableOnce.scala:1505) [2026-02-26T01:37:18.680Z] at scala.collection.IterableOnceOps.toArray$(IterableOnce.scala:1498) [2026-02-26T01:37:18.680Z] at scala.collection.AbstractIterator.toArray(Iterator.scala:1303) [2026-02-26T01:37:18.680Z] at org.apache.spark.rdd.RDD.$anonfun$take$2(RDD.scala:1492) [2026-02-26T01:37:18.680Z] at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2433) [2026-02-26T01:37:18.680Z] at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93) [2026-02-26T01:37:18.680Z] at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166) [2026-02-26T01:37:18.680Z] at org.apache.spark.scheduler.Task.run(Task.scala:141) [2026-02-26T01:37:18.680Z] at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620) [2026-02-26T01:37:18.680Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64) [2026-02-26T01:37:18.680Z] at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61) [2026-02-26T01:37:18.680Z] at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94) [2026-02-26T01:37:18.680Z] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623) [2026-02-26T01:37:18.680Z] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) [2026-02-26T01:37:18.680Z] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) [2026-02-26T01:37:18.680Z] at java.base/java.lang.Thread.run(Thread.java:840) [2026-02-26T01:37:19.026Z] The following benchmarks failed: movie-lens [2026-02-26T01:37:20.731Z] ----------------------------------- [2026-02-26T01:37:20.731Z] renaissance-movie-lens_0_FAILED [2026-02-26T01:37:20.731Z] ----------------------------------- [2026-02-26T01:37:20.731Z] [2026-02-26T01:37:20.731Z] TEST TEARDOWN: [2026-02-26T01:37:20.731Z] Nothing to be done for teardown. [2026-02-26T01:37:20.731Z] renaissance-movie-lens_0 Finish Time: Thu Feb 26 01:37:20 2026 Epoch Time (ms): 1772069840605