tasks/offline-compute/MySQL/mysql_006/init/init_db.sql

-- mysql_006 database initialization: create tables + load seed data
-- Executed via: mysql -u root < init_db.sql

-- Fix: MySQL root default auth_socket blocks pymysql TCP connections,
-- switch to mysql_native_password so pymysql (Python) can connect.
ALTER USER 'root'@'localhost' IDENTIFIED WITH mysql_native_password BY 'root123';
FLUSH PRIVILEGES;

CREATE DATABASE IF NOT EXISTS internal_platform_db
  DEFAULT CHARACTER SET utf8mb4
  DEFAULT COLLATE utf8mb4_unicode_ci;

USE internal_platform_db;

-- 1. Input table: dwm_gputj_platform_gpu_base_feature_agg_v2
-- Columns: dt, service_name, instance_uuid, agg_type, agg_time, pod_count,
--   dcgm_fi_dev_gpu_util_pod_avg, k8s_container_bs_rate_cpu_core_used_request_pod_avg,
--   k8s_container_rate_mem_working_set_request_pod_avg, k8s_dcgm_fi_dev_fb_util_pod_avg,
--   k8s_container_vgpu_gpu_util_pod_avg,
--   dcgm_fi_dev_gpu_util_pod_max, k8s_container_bs_rate_cpu_core_used_request_pod_max,
--   k8s_container_rate_mem_working_set_request_pod_max, k8s_dcgm_fi_dev_fb_util_pod_max,
--   k8s_container_vgpu_gpu_util_pod_max,
--   dcgm_fi_dev_gpu_util_model_sum,
--   nv_inference_count_model_avg, nv_inference_count_model_max,
--   nv_inference_request_duration_ms_model_avg, nv_inference_request_duration_ms_model_max,
--   nv_inference_queue_duration_ms_model_avg, nv_inference_queue_duration_ms_model_max,
--   num_queued_reqs_model_avg, num_queued_reqs_model_max,
--   nv_inference_request_success_model_avg, nv_inference_request_success_model_max,
--   nv_inference_request_failure_model_avg, nv_inference_request_failure_model_max,
--   nv_inference_request_duration_ms_perreq_avg, nv_inference_request_duration_ms_perreq_max,
--   nv_inference_queue_duration_ms_perreq_avg, nv_inference_queue_duration_ms_perreq_max,
--   nv_inference_request_duration_ms_perreq_p95, nv_inference_queue_duration_ms_perreq_p95,
--   nv_inference_count_pod_avg, nv_inference_count_pod_max,
--   is_elasticity, scene, replicas, task_priority_expect, gpu_res_expect,
--   scale_up_target_size, scale_down_target_size, scale_up_time_str, scale_down_time_str,
--   max_replicas, gpu_name, queue_name, project_name, wsid_tag,
--   scene_type, business_scene, service_scene, queue_module, compression_strategy,
--   host_gpu_num, host_num, avg_pod_count, namespace, trial_job_name,
--   stream_request_total_model_avg, stream_request_total_model_max,
--   stream_request_timeout_total_model_avg, stream_request_timeout_total_model_max,
--   stream_empty_output_total_model_avg, stream_empty_output_total_model_max,
--   stream_request_fail_total_model_avg, stream_request_fail_total_model_max,
--   stream_cost_time_ms_perreq_avg, stream_cost_time_ms_perreq_max,
--   stream_first_char_time_ms_perreq_avg, stream_first_char_time_ms_perreq_max,
--   pod_req_count_mean_abs_rate_of_change, pod_req_count_mean_abs_rate_accelerated_of_change,
--   pod_req_count_coefficient_of_variation, pod_req_count_stddev_value,
--   nv_inference_count_model_min, nv_inference_count_model_p50, nv_inference_count_model_p90,
--   nv_inference_count_model_max_minute, nv_inference_count_model_min_minute,
--   nv_inference_request_duration_ms_perreq_min, nv_inference_request_duration_ms_perreq_p50,
--   nv_inference_request_duration_ms_perreq_p90,
--   nv_inference_request_duration_ms_perreq_max_minute, nv_inference_request_duration_ms_perreq_min_minute,
--   request_count, request_failed_count, request_failed_rate_avg, request_failed_rate_max,
--   request_timeout_count, request_timeout_rate_avg, request_timeout_rate_max,
--   first_char_time_perreq_ms_avg, first_char_time_perreq_ms_max, first_char_time_perreq_ms_min,
--   output_token_rate_model_avg, output_token_rate_model_max,
--   token_interval_avg_model_avg, token_interval_avg_model_max,
--   infer_request_total_model_avg, infer_request_total_model_max,
--   infer_request_timeout_total_model_avg, infer_request_timeout_total_model_max,
--   infer_empty_output_total_model_avg, infer_empty_output_total_model_max,
--   infer_request_fail_total_model_avg, infer_request_fail_total_model_max,
--   infer_cost_time_ms_perreq_avg, infer_cost_time_ms_perreq_max,
--   infer_first_char_time_ms_perreq_avg, infer_first_char_time_ms_perreq_max

CREATE TABLE IF NOT EXISTS dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_006 (
    dt VARCHAR(256),
    service_name VARCHAR(256),
    instance_uuid VARCHAR(256),
    agg_type BIGINT,
    agg_time VARCHAR(256),
    pod_count BIGINT,
    dcgm_fi_dev_gpu_util_pod_avg DOUBLE,
    k8s_container_bs_rate_cpu_core_used_request_pod_avg DOUBLE,
    k8s_container_rate_mem_working_set_request_pod_avg DOUBLE,
    k8s_dcgm_fi_dev_fb_util_pod_avg DOUBLE,
    k8s_container_vgpu_gpu_util_pod_avg DOUBLE,
    dcgm_fi_dev_gpu_util_pod_max DOUBLE,
    k8s_container_bs_rate_cpu_core_used_request_pod_max DOUBLE,
    k8s_container_rate_mem_working_set_request_pod_max DOUBLE,
    k8s_dcgm_fi_dev_fb_util_pod_max DOUBLE,
    k8s_container_vgpu_gpu_util_pod_max DOUBLE,
    dcgm_fi_dev_gpu_util_model_sum DOUBLE,
    nv_inference_count_model_avg DOUBLE,
    nv_inference_count_model_max DOUBLE,
    nv_inference_request_duration_ms_model_avg DOUBLE,
    nv_inference_request_duration_ms_model_max DOUBLE,
    nv_inference_queue_duration_ms_model_avg DOUBLE,
    nv_inference_queue_duration_ms_model_max DOUBLE,
    num_queued_reqs_model_avg DOUBLE,
    num_queued_reqs_model_max DOUBLE,
    nv_inference_request_success_model_avg DOUBLE,
    nv_inference_request_success_model_max DOUBLE,
    nv_inference_request_failure_model_avg DOUBLE,
    nv_inference_request_failure_model_max DOUBLE,
    nv_inference_request_duration_ms_perreq_avg DOUBLE,
    nv_inference_request_duration_ms_perreq_max DOUBLE,
    nv_inference_queue_duration_ms_perreq_avg DOUBLE,
    nv_inference_queue_duration_ms_perreq_max DOUBLE,
    nv_inference_request_duration_ms_perreq_p95 DOUBLE,
    nv_inference_queue_duration_ms_perreq_p95 DOUBLE,
    nv_inference_count_pod_avg DOUBLE,
    nv_inference_count_pod_max DOUBLE,
    is_elasticity BIGINT,
    scene VARCHAR(256),
    replicas BIGINT,
    task_priority_expect BIGINT,
    gpu_res_expect DOUBLE,
    scale_up_target_size BIGINT,
    scale_down_target_size BIGINT,
    scale_up_time_str VARCHAR(256),
    scale_down_time_str VARCHAR(256),
    max_replicas BIGINT,
    gpu_name VARCHAR(256),
    queue_name VARCHAR(256),
    project_name VARCHAR(256),
    wsid_tag VARCHAR(256),
    scene_type VARCHAR(256),
    business_scene VARCHAR(256),
    service_scene VARCHAR(256),
    queue_module VARCHAR(256),
    compression_strategy VARCHAR(256),
    host_gpu_num BIGINT,
    host_num BIGINT,
    avg_pod_count DOUBLE,
    namespace VARCHAR(256),
    trial_job_name VARCHAR(256),
    stream_request_total_model_avg DOUBLE,
    stream_request_total_model_max DOUBLE,
    stream_request_timeout_total_model_avg DOUBLE,
    stream_request_timeout_total_model_max DOUBLE,
    stream_empty_output_total_model_avg DOUBLE,
    stream_empty_output_total_model_max DOUBLE,
    stream_request_fail_total_model_avg DOUBLE,
    stream_request_fail_total_model_max DOUBLE,
    stream_cost_time_ms_perreq_avg DOUBLE,
    stream_cost_time_ms_perreq_max DOUBLE,
    stream_first_char_time_ms_perreq_avg DOUBLE,
    stream_first_char_time_ms_perreq_max DOUBLE,
    pod_req_count_mean_abs_rate_of_change DOUBLE,
    pod_req_count_mean_abs_rate_accelerated_of_change DOUBLE,
    pod_req_count_coefficient_of_variation DOUBLE,
    pod_req_count_stddev_value DOUBLE,
    nv_inference_count_model_min DOUBLE,
    nv_inference_count_model_p50 DOUBLE,
    nv_inference_count_model_p90 DOUBLE,
    nv_inference_count_model_max_minute BIGINT,
    nv_inference_count_model_min_minute BIGINT,
    nv_inference_request_duration_ms_perreq_min DOUBLE,
    nv_inference_request_duration_ms_perreq_p50 DOUBLE,
    nv_inference_request_duration_ms_perreq_p90 DOUBLE,
    nv_inference_request_duration_ms_perreq_max_minute BIGINT,
    nv_inference_request_duration_ms_perreq_min_minute BIGINT,
    request_count DOUBLE,
    request_failed_count DOUBLE,
    request_failed_rate_avg DOUBLE,
    request_failed_rate_max DOUBLE,
    request_timeout_count DOUBLE,
    request_timeout_rate_avg DOUBLE,
    request_timeout_rate_max DOUBLE,
    first_char_time_perreq_ms_avg DOUBLE,
    first_char_time_perreq_ms_max DOUBLE,
    first_char_time_perreq_ms_min DOUBLE,
    output_token_rate_model_avg DOUBLE,
    output_token_rate_model_max DOUBLE,
    token_interval_avg_model_avg DOUBLE,
    token_interval_avg_model_max DOUBLE,
    infer_request_total_model_avg DOUBLE,
    infer_request_total_model_max DOUBLE,
    infer_request_timeout_total_model_avg DOUBLE,
    infer_request_timeout_total_model_max DOUBLE,
    infer_empty_output_total_model_avg DOUBLE,
    infer_empty_output_total_model_max DOUBLE,
    infer_request_fail_total_model_avg DOUBLE,
    infer_request_fail_total_model_max DOUBLE,
    infer_cost_time_ms_perreq_avg DOUBLE,
    infer_cost_time_ms_perreq_max DOUBLE,
    infer_first_char_time_ms_perreq_avg DOUBLE,
    infer_first_char_time_ms_perreq_max DOUBLE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

-- Helper: 88-column row template for feature table
-- (dt, service_name, instance_uuid, agg_type, agg_time, pod_count,
--  5 pod_avg, 5 pod_max, 1 model_sum,
--  2 inference_count, 2 req_duration_model, 2 queue_duration_model,
--  2 queued_reqs, 2 req_success, 2 req_failure,
--  2 duration_perreq, 2 queue_perreq, 2 perreq_p95,
--  2 count_pod,
--  is_elasticity, scene, replicas, task_priority_expect, gpu_res_expect,
--  scale_up_target_size, scale_down_target_size, scale_up_time_str, scale_down_time_str,
--  max_replicas, gpu_name, queue_name, project_name, wsid_tag,
--  scene_type, business_scene, service_scene, queue_module, compression_strategy,
--  host_gpu_num, host_num, avg_pod_count, namespace, trial_job_name,
--  12 stream_*, 4 pod_req_count_*, 3 nv_count_min/p50/p90, 2 max/min_minute,
--  3 perreq_min/p50/p90, 2 perreq_max/min_minute,
--  7 request_*, 3 first_char_*, 2 output_token_*, 2 token_interval_*,
--  12 infer_*)

-- svc_alpha, agg_type=1: 6 rows (5 weekday + 1 holiday)
-- Holiday dates in history: 2026-04-27
-- Weekday dates: 2026-04-22, 2026-04-23, 2026-04-24, 2026-04-25, 2026-04-28
INSERT INTO dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_006 VALUES
-- svc_alpha agg_type=1 weekday rows (nv_inference_count_model_avg ascending for P90 ranking)
('20260429', 'svc_alpha', 'uuid-001', 1, '2026-04-22 10:00:00', 2,
 40.0, 25.0, 35.0, 15.0, 45.0, 50.0, 30.0, 40.0, 20.0, 50.0, 80.0,
 80.0, 100.0, 60.0, 80.0, 8.0, 12.0, 3.0, 5.0, 100.0, 120.0, 1.0, 2.0,
 55.0, 75.0, 6.0, 10.0, 60.0, 7.0, 50.0, 70.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 70,80,90,8,3, 50,55,60,10,2, 400,3,0.005,0.02,1,0.002,0.005, 40,70,25, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260430', 'svc_alpha', 'uuid-001', 1, '2026-04-23 10:00:00', 2,
 45.0, 27.0, 37.0, 17.0, 50.0, 55.0, 32.0, 42.0, 22.0, 55.0, 85.0,
 90.0, 110.0, 65.0, 85.0, 9.0, 13.0, 4.0, 6.0, 105.0, 125.0, 1.5, 2.5,
 60.0, 80.0, 7.0, 11.0, 65.0, 8.0, 55.0, 75.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 85,95,105,9,4, 55,60,65,11,3, 420,4,0.006,0.03,1.5,0.003,0.006, 45,75,27, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260501', 'svc_alpha', 'uuid-001', 1, '2026-04-24 10:00:00', 2,
 50.0, 30.0, 40.0, 20.0, 55.0, 60.0, 35.0, 45.0, 25.0, 60.0, 100.0,
 110.0, 130.0, 75.0, 95.0, 10.0, 14.0, 5.0, 7.0, 110.0, 130.0, 2.0, 3.0,
 68.0, 88.0, 8.0, 12.0, 73.0, 9.0, 60.0, 80.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 100,110,120,10,5, 60,65,70,12,3, 450,4.5,0.008,0.04,2,0.004,0.008, 50,80,30, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260502', 'svc_alpha', 'uuid-001', 1, '2026-04-25 10:00:00', 2,
 50.0, 30.0, 40.0, 20.0, 55.0, 60.0, 35.0, 45.0, 25.0, 60.0, 100.0,
 120.5, 150.0, 80.0, 100.0, 10.0, 15.0, 5.0, 8.0, 110.0, 130.0, 2.0, 3.0,
 70.0, 90.0, 8.0, 12.0, 75.0, 9.0, 60.0, 80.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 100,110,130,10,5, 60,65,72,12,3, 500,5,0.01,0.05,2,0.004,0.01, 50,80,30, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260506', 'svc_alpha', 'uuid-001', 1, '2026-04-28 10:00:00', 2,
 55.0, 33.0, 43.0, 23.0, 60.0, 65.0, 38.0, 48.0, 28.0, 65.0, 110.0,
 140.0, 170.0, 90.0, 110.0, 14.0, 18.0, 7.0, 10.0, 120.0, 140.0, 3.5, 4.5,
 75.0, 95.0, 10.0, 14.0, 80.0, 11.0, 65.0, 85.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 130,140,160,12,7, 65,70,76,14,5, 550,6,0.012,0.06,3,0.006,0.012, 55,85,35, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
-- svc_alpha agg_type=1 holiday row (2026-04-27 is a holiday)
('20260504', 'svc_alpha', 'uuid-001', 1, '2026-04-27 10:00:00', 2,
 52.0, 32.0, 42.0, 22.0, 57.0, 62.0, 37.0, 47.0, 27.0, 62.0, 105.0,
 130.0, 160.0, 85.0, 105.0, 12.0, 17.0, 6.0, 9.0, 115.0, 135.0, 3.0, 4.0,
 72.0, 92.0, 9.0, 13.0, 77.0, 10.0, 62.0, 82.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 120,130,150,11,6, 62,67,74,13,4, 520,6,0.012,0.06,3,0.005,0.012, 52,82,32, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),

-- svc_alpha, agg_type=2: 4 rows (3 weekday + 1 holiday)
-- Weekday: 2026-04-22, 2026-04-24, 2026-04-28; Holiday: 2026-04-27
('20260429', 'svc_alpha', 'uuid-001', 2, '2026-04-22 11:00:00', 2,
 38.0, 23.0, 33.0, 13.0, 43.0, 48.0, 28.0, 38.0, 18.0, 48.0, 75.0,
 70.0, 90.0, 50.0, 70.0, 6.0, 10.0, 2.0, 4.0, 90.0, 110.0, 0.5, 1.5,
 50.0, 70.0, 5.0, 9.0, 55.0, 6.0, 45.0, 65.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 60,70,80,7,2, 45,50,55,9,1, 350,2,0.004,0.015,0.5,0.001,0.004, 35,65,20, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260501', 'svc_alpha', 'uuid-001', 2, '2026-04-24 11:00:00', 2,
 48.0, 28.0, 38.0, 18.0, 53.0, 58.0, 33.0, 43.0, 23.0, 58.0, 95.0,
 100.0, 120.0, 70.0, 90.0, 8.0, 12.0, 4.0, 6.0, 105.0, 125.0, 1.5, 2.5,
 65.0, 85.0, 7.0, 11.0, 70.0, 8.0, 55.0, 75.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 95,105,115,10,4, 58,63,68,11,2, 480,4,0.008,0.035,1.5,0.003,0.008, 48,78,28, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260506', 'svc_alpha', 'uuid-001', 2, '2026-04-28 11:00:00', 2,
 52.0, 32.0, 42.0, 22.0, 57.0, 62.0, 37.0, 47.0, 27.0, 62.0, 105.0,
 125.0, 155.0, 82.0, 102.0, 12.0, 16.0, 6.0, 9.0, 115.0, 135.0, 3.0, 4.0,
 72.0, 92.0, 9.0, 13.0, 77.0, 10.0, 60.0, 80.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 120,130,150,12,6, 65,70,76,13,4, 530,5.5,0.011,0.055,2.5,0.005,0.011, 52,82,32, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
-- svc_alpha agg_type=2 holiday row
('20260504', 'svc_alpha', 'uuid-001', 2, '2026-04-27 11:00:00', 2,
 48.0, 28.0, 38.0, 18.0, 53.0, 58.0, 33.0, 43.0, 23.0, 58.0, 98.0,
 100.0, 140.0, 75.0, 95.0, 8.0, 13.0, 4.0, 7.0, 105.0, 125.0, 1.0, 2.0,
 68.0, 88.0, 7.0, 11.0, 73.0, 8.0, 58.0, 78.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q1', 'proj1', 'wsid1', 'llm', 'bs1', 'ss1', 'qm1', 'none',
 8, 1, 3.0, 'ns_test', 'wl_alpha',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 95,105,125,9,4, 58,63,70,11,2, 480,4,0.008,0.04,1,0.003,0.008, 48,78,28, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),

-- svc_beta, agg_type=1: 3 rows (ALL weekday, NO holiday => triggers degradation for holiday predictions)
('20260430', 'svc_beta', 'uuid-002', 1, '2026-04-23 14:00:00', 1,
 35.0, 20.0, 30.0, 10.0, 40.0, 45.0, 25.0, 35.0, 15.0, 45.0, 60.0,
 60.0, 80.0, 45.0, 65.0, 5.0, 8.0, 2.0, 3.0, 85.0, 105.0, 0.5, 1.0,
 45.0, 65.0, 4.0, 7.0, 50.0, 5.0, 40.0, 60.0,
 0, 'inference', 2, 1, 1.5, 3, 1, '09:00', '21:00', 8,
 'V100', 'q2', 'proj2', 'wsid2', 'llm', 'bs2', 'ss2', 'qm2', 'none',
 4, 1, 2.0, 'ns_prod', 'wl_beta',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 50,60,70,6,1, 40,45,50,8,1, 300,1.5,0.003,0.01,0.3,0.001,0.003, 30,60,15, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260501', 'svc_beta', 'uuid-002', 1, '2026-04-24 14:00:00', 1,
 40.0, 25.0, 35.0, 15.0, 45.0, 50.0, 30.0, 40.0, 20.0, 50.0, 70.0,
 80.0, 100.0, 55.0, 75.0, 7.0, 10.0, 3.0, 4.0, 95.0, 115.0, 0.8, 1.5,
 50.0, 70.0, 5.0, 8.0, 55.0, 6.0, 45.0, 65.0,
 0, 'inference', 2, 1, 1.5, 3, 1, '09:00', '21:00', 8,
 'V100', 'q2', 'proj2', 'wsid2', 'llm', 'bs2', 'ss2', 'qm2', 'none',
 4, 1, 2.0, 'ns_prod', 'wl_beta',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 70,80,90,8,2, 45,50,55,9,2, 350,2,0.004,0.015,0.5,0.001,0.004, 35,65,20, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260506', 'svc_beta', 'uuid-002', 1, '2026-04-28 14:00:00', 1,
 45.0, 28.0, 38.0, 18.0, 50.0, 55.0, 33.0, 43.0, 23.0, 55.0, 80.0,
 100.0, 120.0, 65.0, 85.0, 9.0, 12.0, 4.0, 5.0, 105.0, 125.0, 1.2, 2.0,
 55.0, 75.0, 6.0, 9.0, 60.0, 7.0, 50.0, 70.0,
 0, 'inference', 2, 1, 1.5, 3, 1, '09:00', '21:00', 8,
 'V100', 'q2', 'proj2', 'wsid2', 'llm', 'bs2', 'ss2', 'qm2', 'none',
 4, 1, 2.0, 'ns_prod', 'wl_beta',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 90,100,110,9,3, 50,55,60,10,3, 400,3,0.005,0.02,0.8,0.002,0.005, 40,70,25, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),

-- svc_beta, agg_type=2: 3 rows (ALL weekday, NO holiday => triggers degradation for holiday predictions)
('20260429', 'svc_beta', 'uuid-002', 2, '2026-04-22 14:00:00', 1,
 32.0, 18.0, 28.0, 10.0, 37.0, 42.0, 23.0, 33.0, 15.0, 42.0, 55.0,
 55.0, 75.0, 40.0, 60.0, 4.0, 7.0, 1.5, 2.5, 80.0, 100.0, 0.3, 0.8,
 42.0, 62.0, 3.5, 6.5, 47.0, 4.5, 37.0, 57.0,
 0, 'inference', 2, 1, 1.5, 3, 1, '09:00', '21:00', 8,
 'V100', 'q2', 'proj2', 'wsid2', 'llm', 'bs2', 'ss2', 'qm2', 'none',
 4, 1, 2.0, 'ns_prod', 'wl_beta',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 45,55,65,5,1, 35,40,45,7,1, 280,1,0.002,0.008,0.2,0.001,0.002, 25,55,10, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260502', 'svc_beta', 'uuid-002', 2, '2026-04-25 14:00:00', 1,
 38.0, 23.0, 33.0, 13.0, 43.0, 48.0, 28.0, 38.0, 18.0, 48.0, 65.0,
 75.0, 95.0, 50.0, 70.0, 6.0, 9.0, 2.5, 3.5, 90.0, 110.0, 0.6, 1.2,
 48.0, 68.0, 4.5, 7.5, 52.0, 5.5, 42.0, 62.0,
 0, 'inference', 2, 1, 1.5, 3, 1, '09:00', '21:00', 8,
 'V100', 'q2', 'proj2', 'wsid2', 'llm', 'bs2', 'ss2', 'qm2', 'none',
 4, 1, 2.0, 'ns_prod', 'wl_beta',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 65,75,85,7,2, 42,47,52,9,2, 320,1.8,0.003,0.012,0.4,0.001,0.003, 32,62,17, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
('20260506', 'svc_beta', 'uuid-002', 2, '2026-04-28 14:00:00', 1,
 42.0, 26.0, 36.0, 16.0, 48.0, 52.0, 31.0, 41.0, 21.0, 52.0, 75.0,
 90.0, 110.0, 58.0, 78.0, 8.0, 11.0, 3.5, 4.5, 100.0, 120.0, 1.0, 1.8,
 52.0, 72.0, 5.5, 8.5, 57.0, 6.5, 47.0, 67.0,
 0, 'inference', 2, 1, 1.5, 3, 1, '09:00', '21:00', 8,
 'V100', 'q2', 'proj2', 'wsid2', 'llm', 'bs2', 'ss2', 'qm2', 'none',
 4, 1, 2.0, 'ns_prod', 'wl_beta',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 80,90,100,9,3, 48,53,58,10,3, 370,2.5,0.004,0.018,0.7,0.002,0.004, 38,68,22, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0);

-- ============================================================================
-- Coverage augmentation (诱饵 + 正例) for rules left UNCOVERED by 2-service seed.
-- svc_gamma: agg_type=1, time-part 16:00, history is HOLIDAY-ONLY (2026-04-27).
--   正例 (R8d): weekday predictions have feature_weekday_total=0 (<1) => degrade to
--   feature_holiday_index branch. This is the only service that exercises that branch.
--   Correct CPD breakpoint = '2026-04-26 16:00:00' (lowest metric_value on latest dt).
-- All decoys below are scoped to svc_gamma / the empty-name service, so svc_alpha and
-- svc_beta output rows are completely untouched.
INSERT INTO dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_006 VALUES
-- 正例 G1: svc_gamma holiday history row (2026-04-27, post-breakpoint, the ONLY valid row)
('20260504', 'svc_gamma', 'uuid-003', 1, '2026-04-27 16:00:00', 2,
 66.0, 44.0, 54.0, 34.0, 70.0, 70.0, 50.0, 60.0, 40.0, 75.0, 110.0,
 200.0, 210.0, 95.0, 115.0, 15.0, 20.0, 8.0, 11.0, 130.0, 150.0, 5.0, 6.0,
 85.0, 105.0, 12.0, 16.0, 90.0, 13.0, 70.0, 90.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q3', 'proj3', 'wsid3', 'llm', 'bs3', 'ss3', 'qm3', 'none',
 8, 1, 3.0, 'ns_gamma', 'wl_gamma',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 180,190,200,12,7, 80,85,90,13,4, 600,7,0.012,0.06,3,0.005,0.012, 66,90,40, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
-- 诱饵 R7 (cpd inner dedup): pre-breakpoint weekday row (2026-04-20). Filtered by post-breakpoint
-- join when correct breakpoint (04-26) is picked; LEAKS if dedup flips ASC->DESC (breakpoint 04-15).
('20260422', 'svc_gamma', 'uuid-003', 1, '2026-04-20 16:00:00', 2,
 60.0, 40.0, 50.0, 30.0, 65.0, 65.0, 45.0, 55.0, 35.0, 70.0, 100.0,
 999.0, 1000.0, 90.0, 110.0, 14.0, 19.0, 7.0, 10.0, 125.0, 145.0, 4.0, 5.0,
 80.0, 100.0, 11.0, 15.0, 85.0, 12.0, 65.0, 85.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q3', 'proj3', 'wsid3', 'llm', 'bs3', 'ss3', 'qm3', 'none',
 8, 1, 3.0, 'ns_gamma', 'wl_gamma',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 180,190,200,12,7, 80,85,90,13,4, 600,7,0.012,0.06,3,0.005,0.012, 60,85,35, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
-- 诱饵 R1 (nv_inference_count_model_avg >= 0): negative-avg row (post-breakpoint, weekday).
-- Filtered by the >=0 rule; LEAKS into svc_gamma group if that filter is dropped.
('20260506', 'svc_gamma', 'uuid-003', 1, '2026-04-28 16:00:00', 2,
 60.0, 40.0, 50.0, 30.0, 65.0, 65.0, 45.0, 55.0, 35.0, 70.0, 100.0,
 -10.0, 0.0, 90.0, 110.0, 14.0, 19.0, 7.0, 10.0, 125.0, 145.0, 4.0, 5.0,
 80.0, 100.0, 11.0, 15.0, 85.0, 12.0, 65.0, 85.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q3', 'proj3', 'wsid3', 'llm', 'bs3', 'ss3', 'qm3', 'none',
 8, 1, 3.0, 'ns_gamma', 'wl_gamma',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 180,190,200,12,7, 80,85,90,13,4, 600,7,0.012,0.06,3,0.005,0.012, 60,85,35, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
-- 诱饵 R2 (agg_type IN (1,2)): agg_type=3 row in template window (creates a 16:00/type3 future
-- slot). Filtered out of the feature CTE; LEAKS as type-3 output rows if the agg_type filter widens.
('20260506', 'svc_gamma', 'uuid-003', 3, '2026-04-29 16:00:00', 2,
 60.0, 40.0, 50.0, 30.0, 65.0, 65.0, 45.0, 55.0, 35.0, 70.0, 100.0,
 50.0, 60.0, 90.0, 110.0, 14.0, 19.0, 7.0, 10.0, 125.0, 145.0, 4.0, 5.0,
 80.0, 100.0, 11.0, 15.0, 85.0, 12.0, 65.0, 85.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q3', 'proj3', 'wsid3', 'llm', 'bs3', 'ss3', 'qm3', 'none',
 8, 1, 3.0, 'ns_gamma', 'wl_gamma',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 180,190,200,12,7, 80,85,90,13,4, 600,7,0.012,0.06,3,0.005,0.012, 60,85,35, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0),
-- 诱饵 R4 (service name <> ''): empty-name service with a full valid history row.
-- Excluded from service_list by the name<>'' rule; LEAKS ~14 output rows if that filter is dropped.
('20260504', '', 'uuid-004', 1, '2026-04-27 16:00:00', 2,
 60.0, 40.0, 50.0, 30.0, 65.0, 65.0, 45.0, 55.0, 35.0, 70.0, 100.0,
 300.0, 310.0, 90.0, 110.0, 14.0, 19.0, 7.0, 10.0, 125.0, 145.0, 4.0, 5.0,
 80.0, 100.0, 11.0, 15.0, 85.0, 12.0, 65.0, 85.0,
 0, 'inference', 3, 1, 2.0, 5, 2, '08:00', '22:00', 10,
 'A100', 'q4', 'proj4', 'wsid4', 'llm', 'bs4', 'ss4', 'qm4', 'none',
 8, 1, 3.0, 'ns_empty', 'wl_empty',
 0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0, 180,190,200,12,7, 80,85,90,13,4, 600,7,0.012,0.06,3,0.005,0.012, 60,85,35, 0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0);

-- 2. Input table: dwd_aide_inferencev2_done_service_info_h
CREATE TABLE IF NOT EXISTS dwd_aide_inferencev2_done_service_info_h_mysql_006 (
    id VARCHAR(256),
    name VARCHAR(256),
    dt VARCHAR(256)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

INSERT INTO dwd_aide_inferencev2_done_service_info_h_mysql_006 VALUES
('1', 'svc_alpha', '2026050723'),
('2', 'svc_beta', '2026050723'),
-- 正例: svc_gamma is an online service => must appear in service_list
('3', 'svc_gamma', '2026050723'),
-- 诱饵 R4 (name <> ''): empty-name service must be excluded from service_list
('4', '', '2026050723');

-- 3. Input table: dwd_gputj_platform_metric_cpd_offline_v2
-- CPD breakpoint dates must be VARCHAR matching the format 'YYYY-MM-DD HH:MM:SS'
-- to avoid implicit type conversion with feature.agg_time
CREATE TABLE IF NOT EXISTS dwd_gputj_platform_metric_cpd_offline_v2_mysql_006 (
    dt VARCHAR(256),
    service_name VARCHAR(256),
    agg_time VARCHAR(256),
    metric_name VARCHAR(256),
    metric_value DOUBLE,
    msg VARCHAR(256)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

INSERT INTO dwd_gputj_platform_metric_cpd_offline_v2_mysql_006 VALUES
-- svc_alpha: two metric_names on same dt, lowest metric_value per metric wins,
-- then pick latest dt's agg_time -> picks 2026-04-24 10:00:00
('20260423', 'svc_alpha', '2026-04-23 10:00:00', 'cpd_metric', 0.9, 'success'),
('20260423', 'svc_alpha', '2026-04-23 10:00:00', 'cpd_metric2', 0.7, 'success'),
('20260424', 'svc_alpha', '2026-04-24 10:00:00', 'cpd_metric', 0.5, 'success'),
('20260424', 'svc_alpha', '2026-04-24 10:00:00', 'cpd_metric2', 0.6, 'success'),
-- svc_beta: single breakpoint at 2026-04-22
('20260422', 'svc_beta', '2026-04-22 14:00:00', 'cpd_metric', 0.4, 'success'),
-- svc_gamma: correct breakpoint = '2026-04-26 16:00:00'.
--   诱饵 R7 (inner dedup ORDER BY metric_value ASC): same (svc_gamma, dt=20260426, cpd_metric)
--   appears twice. Lowest value (0.3 -> agg '2026-04-26') wins under ASC. If dedup flips to DESC,
--   the 0.9 row (agg '2026-04-29 16:00:00') wins => breakpoint moves past svc_gamma's only history
--   record (2026-04-27) => svc_gamma rows vanish => output diverges.
('20260426', 'svc_gamma', '2026-04-26 16:00:00', 'cpd_metric', 0.3, 'success'),
('20260426', 'svc_gamma', '2026-04-29 16:00:00', 'cpd_metric', 0.9, 'success'),
--   诱饵 R3 (msg = 'success'): a non-success row at a LATER dt with the lowest value. If the
--   msg filter is dropped, this becomes the latest breakpoint (2026-04-30) and drops svc_gamma.
('20260430', 'svc_gamma', '2026-04-30 16:00:00', 'cpd_metric', 0.1, 'failed'),
-- 诱饵 R4 (service name <> ''): breakpoint for the empty-name service so that its feature row
-- survives the post-breakpoint join. The empty service is still excluded by name<>''; if that
-- filter is dropped, it leaks ~14 output rows.
('20260426', '', '2026-04-26 16:00:00', 'cpd_metric', 0.3, 'success');

-- 4. Input table: dim_holiday_list
-- Expanded holidays to cover consecutive holidays and more boundary cases
-- Future window: 2026-05-08 (holiday), 2026-05-09 (holiday) -> consecutive holidays
-- History window: 2026-04-27 (holiday) -> only historical holiday
CREATE TABLE IF NOT EXISTS dim_holiday_list_mysql_006 (
    holiday_date VARCHAR(256)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

INSERT INTO dim_holiday_list_mysql_006 VALUES
('2026-04-27'),
('2026-05-08'),
('2026-05-09');

-- 5. Output table: dwm_gputj_platform_model_prediction_long_cpd
CREATE TABLE IF NOT EXISTS dwm_gputj_platform_model_prediction_long_cpd_mysql_006 (
    instance_uuid VARCHAR(256),
    service_name VARCHAR(256),
    workload_name VARCHAR(256),
    namespace VARCHAR(256),
    agg_time VARCHAR(256),
    agg_type BIGINT,
    is_holiday BIGINT,
    day_of_week BIGINT,
    prediction_type VARCHAR(256),
    nv_inference_count_model_avg_p90 DOUBLE,
    statistic_time_count BIGINT,
    nv_inference_request_duration_ms_model_avg DOUBLE,
    nv_inference_queue_duration_ms_model_avg DOUBLE,
    num_queued_reqs_model_avg DOUBLE,
    nv_inference_request_success_model_avg DOUBLE,
    nv_inference_request_failure_model_avg DOUBLE,
    nv_inference_request_duration_ms_perreq_avg DOUBLE,
    nv_inference_queue_duration_ms_perreq_avg DOUBLE,
    nv_inference_request_duration_ms_perreq_p95 DOUBLE,
    nv_inference_queue_duration_ms_perreq_p95 DOUBLE,
    nv_inference_request_success_model_max DOUBLE,
    nv_inference_request_failure_model_max DOUBLE,
    DCGM_FI_DEV_GPU_UTIL_pod_avg DOUBLE,
    k8s_container_bs_rate_cpu_core_used_request_pod_avg DOUBLE,
    k8s_container_rate_mem_working_set_request_pod_avg DOUBLE,
    k8s_dcgm_fi_dev_fb_util_pod_avg DOUBLE,
    k8s_container_vgpu_gpu_util_pod_avg DOUBLE,
    cpd_agg_time VARCHAR(256),
    feature_total BIGINT,
    feature_holiday_total BIGINT,
    dt VARCHAR(256)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;