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Merged
sempervent merged 5 commits into from
Sep 30, 2026
Merged

Release 1.2.2: Update Probe Data Storage Types #30

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4dd04c5
Update Probe Data Storage Types (#29)
midgemacf Sep 30, 2026
bd4e24a
Update CHANGELOG for versions 1.2.1 and 1.2.2
sempervent Sep 30, 2026
07d462e
Bump version from 1.2.1 to 1.2.2
sempervent Sep 30, 2026
85cc520
Reformat columns for better readability in orm.py
sempervent Sep 30, 2026
41a22f0
lint: format opensample/db/orm.py with ruff
Sep 30, 2026
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6 changes: 6 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -36,7 +36,13 @@ This project adheres to [Semantic Versioning](https://semver.org/).

*Unreleased* versions radiate potential—-and dread. Once you merge an infernal PR, move its bullet under a new version heading with the actual release date.*



-->
## [1.2.2] = 2026-09-30
### Changed
- ⚡ `probe_data` table now has `value_float` and `value_jsonb` columns instead of one `value` (jsonb type) column.

## [1.2.1] - 2026-07-08
### Fixed
- 🩹 Addressed CVEs by updating minimum package versions for urllib3 2.7.0, pillow 12.3.0, mako 1.3.12, starlette 1.3.1, fastapi 0.139.0, idna 3.18, pytest 9.1.1, python-dotenv 1.2.2, fonttools 4.63.0, requests 2.34.2, markdown 3.10.2, pymdown-extensions 10.21.3, pygments 2.20.0, and python-multipart 0.0.32.
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5 changes: 4 additions & 1 deletion opensampl/db/orm.py
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Expand Up @@ -280,7 +280,10 @@ class ProbeData(Base):
primary_key=True,
comment="Foreign key to the metric type being measured",
)
value = Column(JSONB, comment="Measurement value stored as JSON; value's expected type defined via metric")
value_float = Column(Float, nullable=True, comment="Float type value; value's expected type defined via metric")
value_jsonb = Column(
JSONB, nullable=True, comment="Measurement value stored as JSON; value's expected type defined via metric"
)


class MetricType(Base):
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28 changes: 19 additions & 9 deletions opensampl/load_data.py
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Expand Up @@ -138,18 +138,28 @@ def load_time_data(
df["probe_uuid"] = data_definition.probe.uuid # ty: ignore[possibly-unbound-attribute]
df["reference_uuid"] = data_definition.reference.uuid # ty: ignore[possibly-unbound-attribute]
df["metric_type_uuid"] = data_definition.metric.uuid # ty: ignore[possibly-unbound-attribute]
logger.debug(df.head())
# Ensure correct dtypes

# Ensure correct time dtypes
df["time"] = pd.to_datetime(df["time"], format="mixed", utc=True, errors="raise")
df["value"] = df["value"].apply(json.dumps)

if data_definition.metric.is_numeric():
df["value_float"] = pd.to_numeric(df["value"], errors="raise")
df["value_jsonb"] = None
else:
df["value_float"] = None
df["value_jsonb"] = df["value"].apply(json.dumps)

df = df.drop(columns=["value"]) # Drop original value column, as we now have value_float and value_jsonb
logger.debug(df.head())

records = df.to_dict(orient="records")
insert_stmt = text(f"""
INSERT INTO {ProbeData.__table__.schema}.{ProbeData.__tablename__}
(time, probe_uuid, reference_uuid, metric_type_uuid, value)
VALUES (:time, :probe_uuid, :reference_uuid, :metric_type_uuid, :value)
ON CONFLICT (time, probe_uuid, reference_uuid, metric_type_uuid)
DO NOTHING
""") # noqa: S608
INSERT INTO {ProbeData.__table__.schema}.{ProbeData.__tablename__}
(time, probe_uuid, reference_uuid, metric_type_uuid, value_float, value_jsonb)
VALUES (:time, :probe_uuid, :reference_uuid, :metric_type_uuid, :value_float, :value_jsonb)
ON CONFLICT (time, probe_uuid, reference_uuid, metric_type_uuid)
DO NOTHING
""") # noqa: S608

try:
result = session.execute(insert_stmt, records)
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4 changes: 4 additions & 0 deletions opensampl/metrics.py
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Expand Up @@ -21,6 +21,10 @@ def convert_to_type(self, value: Any) -> Any:
"""Convert a given value to the expected type for the Metric"""
return self.value_type(value)

def is_numeric(self) -> bool:
"""Return True if the metric's value_type is numeric (int or float)"""
return self.value_type in [int, float]

@field_serializer("value_type")
def serialize_type(self, value: type):
"""Return the name of value_type for serializing"""
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2 changes: 1 addition & 1 deletion opensampl/server/grafana/grafana-dashboards/ntp_dash.json
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Expand Up @@ -1313,7 +1313,7 @@
"editorMode": "code",
"format": "table",
"rawQuery": true,
"rawSql": "WITH probe_ref AS (\n SELECT\n uuid,\n COALESCE(pm.name, CONCAT(pm.ip_address, ' ', pm.probe_id)) AS probe_name\n FROM castdb.probe_metadata pm\n)\nSELECT\n time_bucket('1 minute'::interval, pd.time AT TIME ZONE 'UTC') AS time,\n pd.probe_uuid,\n pr.probe_name,\n pd.reference_uuid,\n AVG(pd.value::float * 1e9) FILTER (WHERE lower(m.name) = 'phase offset') AS phase_offset,\n AVG(pd.value::float * 1e9) FILTER (WHERE lower(m.name) = 'jitter') AS jitter,\n AVG((pd.value)::float) FILTER (WHERE lower(m.name) = 'stratum') AS stratum,\n AVG((pd.value)::float) FILTER (WHERE lower(m.name) = 'sync health') AS sync_health\nFROM castdb.probe_data pd\nJOIN probe_ref pr\n ON pd.probe_uuid = pr.uuid\nJOIN castdb.metric_type m\n ON pd.metric_type_uuid = m.uuid\nWHERE pd.probe_uuid = ANY(ARRAY[${ntp_probe:sqlstring}]::text[]) AND $__timeFilter(pd.time)\nGROUP BY 1, 2, 3, 4\nORDER BY\n 1, 3;",
"rawSql": "WITH probe_ref AS (\n SELECT\n uuid,\n COALESCE(pm.name, CONCAT(pm.ip_address, ' ', pm.probe_id)) AS probe_name\n FROM castdb.probe_metadata pm\n)\nSELECT\n time_bucket('1 minute'::interval, pd.time AT TIME ZONE 'UTC') AS time,\n pd.probe_uuid,\n pr.probe_name,\n pd.reference_uuid,\n AVG(pd.value_float * 1e9) FILTER (WHERE lower(m.name) = 'phase offset') AS phase_offset,\n AVG(pd.value_float * 1e9) FILTER (WHERE lower(m.name) = 'jitter') AS jitter,\n AVG((pd.value)::float) FILTER (WHERE lower(m.name) = 'stratum') AS stratum,\n AVG((pd.value)::float) FILTER (WHERE lower(m.name) = 'sync health') AS sync_health\nFROM castdb.probe_data pd\nJOIN probe_ref pr\n ON pd.probe_uuid = pr.uuid\nJOIN castdb.metric_type m\n ON pd.metric_type_uuid = m.uuid\nWHERE pd.probe_uuid = ANY(ARRAY[${ntp_probe:sqlstring}]::text[]) AND $__timeFilter(pd.time)\nGROUP BY 1, 2, 3, 4\nORDER BY\n 1, 3;",
"refId": "A",
"sql": {
"columns": [
Expand Down
8 changes: 4 additions & 4 deletions opensampl/server/grafana/grafana-dashboards/public-timing-dashboard.json
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Expand Up @@ -650,7 +650,7 @@
"editorMode": "code",
"format": "time_series",
"rawQuery": true,
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n coalesce(pm.name, concat(pm.ip_address, ' Interface ', pm.probe_id)),\n AVG(pd.value::FLOAT) * 1e9 AS value\nFROM castdb.probe_data pd JOIN castdb.probe_metadata pm ON pd.probe_uuid = pm.uuid\nWHERE\n $__timeFilter(pd.time)\n AND pm.vendor IN ('ADVA', 'MicrochipTP4100', 'NTP')\n AND coalesce(pm.public, true)\n AND (trim('${clock_name:csv}') = '' OR pd.probe_uuid = ANY(string_to_array(trim('${clock_name:csv}'), ',')))\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid,\n pm.name,\n pm.ip_address,\n pm.probe_id\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')\n",
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n coalesce(pm.name, concat(pm.ip_address, ' Interface ', pm.probe_id)),\n AVG(pd.value_float) * 1e9 AS value\nFROM castdb.probe_data pd JOIN castdb.probe_metadata pm ON pd.probe_uuid = pm.uuid\nWHERE\n $__timeFilter(pd.time)\n AND pm.vendor IN ('ADVA', 'MicrochipTP4100', 'NTP')\n AND coalesce(pm.public, true)\n AND (trim('${clock_name:csv}') = '' OR pd.probe_uuid = ANY(string_to_array(trim('${clock_name:csv}'), ',')))\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid,\n pm.name,\n pm.ip_address,\n pm.probe_id\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')\n",
"refId": "A",
"sql": {
"columns": [
Expand Down Expand Up @@ -776,7 +776,7 @@
"editorMode": "code",
"format": "time_series",
"rawQuery": true,
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n COALESCE(pm.name, CONCAT(pm.ip_address, ' Interface ', pm.probe_id)),\n (MAX(pd.value::FLOAT) - MIN(pd.value::FLOAT)) * 1e9 AS value\nFROM castdb.probe_data pd JOIN castdb.probe_metadata pm ON pd.probe_uuid = pm.uuid\nWHERE\n $__timeFilter(pd.time)\n AND pm.vendor IN ('ADVA', 'MicrochipTP4100', 'NTP')\n AND coalesce(pm.public, true)\n AND (trim('${clock_name:csv}') = '' OR pd.probe_uuid = ANY(string_to_array(trim('${clock_name:csv}'), ',')))\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid,\n pm.name,\n pm.ip_address,\n pm.probe_id\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')\n",
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n COALESCE(pm.name, CONCAT(pm.ip_address, ' Interface ', pm.probe_id)),\n (MAX(pd.value_float) - MIN(pd.value_float)) * 1e9 AS value\nFROM castdb.probe_data pd JOIN castdb.probe_metadata pm ON pd.probe_uuid = pm.uuid\nWHERE\n $__timeFilter(pd.time)\n AND pm.vendor IN ('ADVA', 'MicrochipTP4100', 'NTP')\n AND coalesce(pm.public, true)\n AND (trim('${clock_name:csv}') = '' OR pd.probe_uuid = ANY(string_to_array(trim('${clock_name:csv}'), ',')))\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid,\n pm.name,\n pm.ip_address,\n pm.probe_id\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')\n",
"refId": "A",
"sql": {
"columns": [
Expand Down Expand Up @@ -916,7 +916,7 @@
"editorMode": "code",
"format": "time_series",
"rawQuery": true,
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n AVG(pd.value::FLOAT) * 1e9 AS value\nFROM castdb.probe_data pd\nWHERE\n $__timeFilter(pd.time)\n AND pd.probe_uuid = ${clock_name:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')\n",
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n AVG(pd.value_float) * 1e9 AS value\nFROM castdb.probe_data pd\nWHERE\n $__timeFilter(pd.time)\n AND pd.probe_uuid = ${clock_name:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')\n",
"refId": "A",
"sql": {
"columns": [
Expand Down Expand Up @@ -1027,7 +1027,7 @@
"editorMode": "code",
"format": "time_series",
"rawQuery": true,
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n (MAX(pd.value::FLOAT) - MIN(pd.value::FLOAT)) * 1e9 AS value\nFROM castdb.probe_data pd\nWHERE\n $__timeFilter(pd.time)\n AND pd.probe_uuid = ${clock_name:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')\n",
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n (MAX(pd.value_float) - MIN(pd.value_float)) * 1e9 AS value\nFROM castdb.probe_data pd\nWHERE\n $__timeFilter(pd.time)\n AND pd.probe_uuid = ${clock_name:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')\n",
"refId": "A",
"sql": {
"columns": [
Expand Down
12 changes: 6 additions & 6 deletions opensampl/server/grafana/grafana-dashboards/public_twst_dash.json
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Expand Up @@ -493,7 +493,7 @@
"format": "table",
"hide": false,
"rawQuery": true,
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n coalesce(pm.name, pm.probe_id) AS channel,\n AVG(pd.value::FLOAT) AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${ebno_uuid:sqlstring}) pd\nJOIN \n castdb.reference r ON pd.reference_uuid = r.uuid\nJOIN \n castdb.probe_metadata pm ON r.compound_reference_uuid = pm.uuid\nWHERE \n $__timeFilter(pd.time)\n and r.compound_reference_uuid in (${compound_ref:sqlstring})\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pm.name,\n pm.probe_id\nORDER BY \n time\n",
"rawSql": "SELECT \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n coalesce(pm.name, pm.probe_id) AS channel,\n AVG(pd.value_float) AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${ebno_uuid:sqlstring}) pd\nJOIN \n castdb.reference r ON pd.reference_uuid = r.uuid\nJOIN \n castdb.probe_metadata pm ON r.compound_reference_uuid = pm.uuid\nWHERE \n $__timeFilter(pd.time)\n and r.compound_reference_uuid in (${compound_ref:sqlstring})\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pm.name,\n pm.probe_id\nORDER BY \n time\n",
"refId": "B",
"sql": {
"columns": [
Expand Down Expand Up @@ -628,7 +628,7 @@
"format": "table",
"hide": false,
"rawQuery": true,
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n COALESCE(pm.name, pm.probe_id) as name,\n AVG(pd.value::FLOAT) * 1e9 AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${phase_offset_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nJOIN \n castdb.probe_metadata pm ON r.compound_reference_uuid = pm.uuid\nwhere \n $__timeFilter(pd.time)\n and r.compound_reference_uuid in (${compound_ref:sqlstring})\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pm.uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n COALESCE(pm.name, pm.probe_id) as name,\n AVG(pd.value_float) * 1e9 AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${phase_offset_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nJOIN \n castdb.probe_metadata pm ON r.compound_reference_uuid = pm.uuid\nwhere \n $__timeFilter(pd.time)\n and r.compound_reference_uuid in (${compound_ref:sqlstring})\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pm.uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"refId": "B",
"sql": {
"columns": [
Expand Down Expand Up @@ -763,7 +763,7 @@
"format": "table",
"hide": false,
"rawQuery": true,
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n COALESCE(pm.name, pm.probe_id) as name,\n (MAX(pd.value::FLOAT) - MIN(pd.value::FLOAT)) * 1e9 AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${phase_offset_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nJOIN \n castdb.probe_metadata pm ON r.compound_reference_uuid = pm.uuid\nwhere \n $__timeFilter(pd.time)\n and r.compound_reference_uuid in (${compound_ref:sqlstring})\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pm.uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n COALESCE(pm.name, pm.probe_id) as name,\n (MAX(pd.value_float) - MIN(pd.value_float)) * 1e9 AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${phase_offset_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nJOIN \n castdb.probe_metadata pm ON r.compound_reference_uuid = pm.uuid\nwhere \n $__timeFilter(pd.time)\n and r.compound_reference_uuid in (${compound_ref:sqlstring})\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pm.uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"refId": "B",
"sql": {
"columns": [
Expand Down Expand Up @@ -1246,7 +1246,7 @@
"format": "table",
"hide": false,
"rawQuery": true,
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n AVG(pd.value::FLOAT) AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${ebno_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nwhere \n $__timeFilter(pd.time)\n AND r.compound_reference_uuid = ${compound_ref:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n AVG(pd.value_float) AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${ebno_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nwhere \n $__timeFilter(pd.time)\n AND r.compound_reference_uuid = ${compound_ref:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"refId": "B",
"sql": {
"columns": [
Expand Down Expand Up @@ -1367,7 +1367,7 @@
"format": "table",
"hide": false,
"rawQuery": true,
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n AVG(pd.value::FLOAT) * 1e9 AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${phase_offset_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nwhere \n $__timeFilter(pd.time)\n AND r.compound_reference_uuid = ${compound_ref:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n AVG(pd.value_float) * 1e9 AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${phase_offset_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nwhere \n $__timeFilter(pd.time)\n AND r.compound_reference_uuid = ${compound_ref:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"refId": "B",
"sql": {
"columns": [
Expand Down Expand Up @@ -1488,7 +1488,7 @@
"format": "table",
"hide": false,
"rawQuery": true,
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n (MAX(pd.value::FLOAT) - MIN(pd.value::FLOAT)) * 1e9 AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${phase_offset_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nwhere \n $__timeFilter(pd.time)\n AND r.compound_reference_uuid = ${compound_ref:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"rawSql": "select \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC') AS time,\n (MAX(pd.value_float) - MIN(pd.value_float)) * 1e9 AS value\nFROM \n get_probe_data_by_probe_and_metric(${modem:sqlstring}, ${phase_offset_uuid:sqlstring}) pd\njoin castdb.reference r on pd.reference_uuid = r.uuid\nwhere \n $__timeFilter(pd.time)\n AND r.compound_reference_uuid = ${compound_ref:sqlstring}\nGROUP BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC'),\n pd.probe_uuid\nORDER BY \n time_bucket(${resolution:sqlstring}, pd.time AT TIME ZONE 'UTC')",
"refId": "B",
"sql": {
"columns": [
Expand Down
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