Datasets:
File size: 8,708 Bytes
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WITH base_trace AS (
-- Step 1: Find trace_ids with check.aborted but NOT check.completed (HAVING inverted condition)
SELECT trace_id
FROM internal_platform_db.t_quality_check_span_prestosql_012
WHERE databus_imp_date >= '2026060600'
AND databus_imp_date <= '2026060800'
AND span_name IN ('check.aborted', 'check.completed')
GROUP BY trace_id
HAVING COUNT(CASE WHEN span_name = 'check.aborted' THEN 1 END) > 0
AND COUNT(CASE WHEN span_name = 'check.completed' THEN 1 END) = 0
),
combined_spans AS (
-- Step 2: All original spans for ALL traces + synthetic check.completed for aborted-only traces
SELECT
trace_id,
span_name,
start_time,
end_time,
status_code,
project_id,
task_id,
check_type
FROM internal_platform_db.t_quality_check_span_prestosql_012
WHERE databus_imp_date >= '2026060600'
AND databus_imp_date <= '2026060800'
AND span_name IN ('check.aborted', 'check.start', 'check.completed', 'data.scan', 'rule.evaluate', 'resource.allocate')
UNION ALL
SELECT
trace_id,
'check.completed' AS span_name,
MAX(CASE WHEN span_name = 'check.start' THEN start_time END) AS start_time,
MAX(CASE WHEN span_name = 'check.aborted' THEN end_time END) AS end_time,
2 AS status_code,
MAX(CASE WHEN span_name = 'check.aborted' THEN project_id END) AS project_id,
MAX(CASE WHEN span_name = 'check.aborted' THEN task_id END) AS task_id,
MAX(CASE WHEN span_name = 'check.aborted' THEN check_type END) AS check_type
FROM internal_platform_db.t_quality_check_span_prestosql_012
WHERE databus_imp_date >= '2026060600'
AND databus_imp_date <= '2026060800'
AND trace_id IN (SELECT trace_id FROM base_trace)
AND span_name IN ('check.start', 'check.aborted')
GROUP BY trace_id
),
base_data_time_fixed AS (
-- Step 3: Fix check.aborted start_time -> earliest of code-related spans
SELECT
trace_id,
span_name,
CASE
WHEN span_name = 'check.aborted'
THEN MIN(CASE WHEN span_name IN ('data.scan', 'rule.evaluate', 'check.aborted') THEN start_time END)
OVER(PARTITION BY trace_id)
ELSE start_time
END AS start_time,
end_time,
status_code,
project_id,
task_id,
check_type
FROM combined_spans
),
base_data AS (
-- Step 4: Convert timestamps, compute diff_days for cross-day splitting
SELECT
trace_id,
span_name,
start_time,
end_time,
status_code,
project_id,
task_id,
check_type,
from_unixtime(CAST(start_time AS BIGINT) / 1000) AS start_date,
from_unixtime(CAST(end_time AS BIGINT) / 1000) AS end_date,
date_diff('day', from_unixtime(CAST(start_time AS BIGINT) / 1000),
from_unixtime(CAST(end_time AS BIGINT) / 1000)) AS diff_days,
CAST(CAST(start_time AS BIGINT) / 86400000 AS BIGINT) * 86400000 AS start_day_midnight_ms
FROM base_data_time_fixed
),
pos_series AS (
-- Step 5: Position series for cross-day expansion
SELECT 0 AS pos UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3
),
daily_split_spans AS (
-- Step 6: INNER JOIN with pos_series, compute boundary timestamps per day
SELECT
trace_id,
span_name,
project_id,
task_id,
check_type,
status_code,
start_date,
end_date,
diff_days,
date_add('day', s.pos, start_date) AS calc_date,
CASE WHEN s.pos = 0 THEN start_time
ELSE CAST(start_day_midnight_ms + s.pos * 86400000 AS VARCHAR)
END AS split_start_time,
CASE WHEN s.pos = diff_days THEN end_time
ELSE CAST(start_day_midnight_ms + (s.pos + 1) * 86400000 - 1 AS VARCHAR)
END AS split_end_time,
start_time AS span_start_time,
end_time AS span_end_time
FROM base_data b
INNER JOIN pos_series s ON s.pos <= b.diff_days
),
span_metrics AS (
-- Step 7: GROUP BY trace_id, calc_date -> compute instance_run_time, code_run_time, status_code
SELECT
trace_id,
calc_date,
MAX(project_id) AS project_id,
MAX(task_id) AS task_id,
MAX(check_type) AS check_type,
MAX(MAX(CASE WHEN span_name = 'check.completed' THEN status_code END))
OVER(PARTITION BY trace_id) AS status_code,
CAST(ROUND(
(MAX(CASE WHEN span_name = 'check.completed' THEN CAST(split_end_time AS BIGINT) END) -
MIN(CASE WHEN span_name = 'check.completed' THEN CAST(split_start_time AS BIGINT) END)) / 1000.0
) AS INT) AS instance_run_time,
CAST(ROUND(
(MAX(CASE WHEN span_name IN ('data.scan', 'rule.evaluate', 'check.aborted') THEN CAST(split_end_time AS BIGINT) END) -
MIN(CASE WHEN span_name IN ('data.scan', 'rule.evaluate', 'check.aborted') THEN CAST(split_start_time AS BIGINT) END)) / 1000.0
) AS INT) AS code_run_time
FROM daily_split_spans
GROUP BY trace_id, calc_date
),
gpu_stats AS (
-- Step 8: FLOOR 5-min window bucketing + pod mapping + ROW_NUMBER config dedup + gpu_hour
SELECT
ig.instance_uuid,
lc.host_gpu_num * ig.total_run_time_m / 60.0 AS gpu_hour,
ig.gpu_name,
lc.resource_id,
CASE WHEN lc.is_dedicated = 'true' THEN true ELSE false END AS is_dedicated
FROM (
-- Aggregate GPU run time per instance
SELECT
m.instance_uuid,
MAX(p.gpu_name) AS gpu_name,
SUM(p.run_time_m) AS total_run_time_m
FROM (
-- pod_run_time: count distinct minutes per (pod_name, time_5min)
SELECT
pod_name,
MAX(gpu_name) AS gpu_name,
time_5min,
CAST(COUNT(DISTINCT minute_timestamp) AS DOUBLE) AS run_time_m
FROM (
-- pod_run_minutes: FLOOR bucketing dedup
SELECT
pod_name,
gpu_name,
FLOOR(CAST(pkg_time AS BIGINT) / 60) * 60 AS minute_timestamp,
FLOOR(CAST(pkg_time AS BIGINT) / 300) * 300 AS time_5min
FROM internal_platform_db.t_quality_gpu_monitor_prestosql_012
WHERE metric IN ('k8s_container_vgpu_gpu_mem_usage', 'k8s_dcgm_fi_dev_fb_util')
GROUP BY pod_name, gpu_name,
FLOOR(CAST(pkg_time AS BIGINT) / 60) * 60,
FLOOR(CAST(pkg_time AS BIGINT) / 300) * 300
) pod_run_minutes
GROUP BY pod_name, time_5min
) p
INNER JOIN internal_platform_db.dwd_quality_podname_prestosql_012 m
ON p.pod_name = m.pod_name
GROUP BY m.instance_uuid
) ig
LEFT JOIN (
-- latest_task_config: ROW_NUMBER dedup from UNION ALL of two config tables
SELECT
instance_uuid,
host_gpu_num,
resource_id,
is_dedicated
FROM (
SELECT
instance_uuid,
host_gpu_num,
resource_id,
is_dedicated,
ROW_NUMBER() OVER (PARTITION BY instance_uuid ORDER BY last_modify DESC) AS rn
FROM (
SELECT instance_uuid, host_gpu_num, last_modify, resource_id, is_dedicated
FROM internal_platform_db.dwd_quality_task_config_prestosql_012
WHERE databus_imp_date = (
SELECT MAX(databus_imp_date)
FROM internal_platform_db.dwd_quality_task_config_prestosql_012
)
UNION ALL
SELECT instance_uuid, host_gpu_num, last_modify, resource_id, is_dedicated
FROM internal_platform_db.quality_task_config_prestosql_012
WHERE databus_imp_date = (
SELECT MAX(databus_imp_date)
FROM internal_platform_db.quality_task_config_prestosql_012
)
) combined
) ranked
WHERE rn = 1
) lc ON ig.instance_uuid = lc.instance_uuid
)
-- Final: JOIN span_metrics with gpu_stats
SELECT
date_format(sm.calc_date, '%Y-%m-%d') AS p_date,
sm.trace_id,
sm.project_id,
sm.task_id,
sm.check_type,
sm.status_code,
sm.instance_run_time,
sm.code_run_time,
gs.gpu_hour,
gs.resource_id,
gs.is_dedicated
FROM span_metrics sm
LEFT JOIN gpu_stats gs ON sm.trace_id = gs.instance_uuid
;
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