mirror of
https://github.com/GreptimeTeam/greptimedb.git
synced 2026-09-13 00:42:14 +00:00
test: cover negative PromQL offsets at native timestamp bounds
Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com>
This commit is contained in:
discord9
@@ -779,7 +779,7 @@ mod test {
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use common_query::native_histogram::build_histogram_array;
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use common_query::prometheus::PROMETHEUS_STALE_NAN_BITS;
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use datafusion::arrow::array::{
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Float64Array, TimestampMicrosecondArray, TimestampNanosecondArray,
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Float64Array, TimestampMicrosecondArray, TimestampNanosecondArray, TimestampSecondArray,
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};
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use datafusion::arrow::buffer::NullBuffer;
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use datafusion::arrow::datatypes::{DataType, Field, Schema, TimeUnit};
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@@ -966,70 +966,107 @@ mod test {
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#[tokio::test]
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async fn logical_normalize_offset_survives_rebuild_and_executes() {
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let schema = Arc::new(Schema::new(vec![
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Field::new(
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TIME_INDEX_COLUMN,
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DataType::Timestamp(TimeUnit::Millisecond, None),
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false,
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for (name, time_unit, raw, offset, start, lookback_delta) in [
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(
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"millisecond offset",
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TimeUnit::Millisecond,
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0,
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1_000,
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1_000,
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0,
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),
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Field::new("value", DataType::Float64, true),
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]));
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let input = LogicalPlan::EmptyRelation(EmptyRelation {
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produce_one_row: false,
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schema: schema.clone().to_dfschema_ref().unwrap(),
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});
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let normalize = crate::extension_plan::SeriesNormalize::new(
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1_000,
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TIME_INDEX_COLUMN,
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false,
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Vec::new(),
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input.clone(),
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);
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let normalize = crate::extension_plan::SeriesNormalize::deserialize(&normalize.serialize())
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.unwrap()
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.with_exprs_and_inputs(vec![], vec![input])
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(
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"second timestamp with negative fractional offset",
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TimeUnit::Second,
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1,
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-500,
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500,
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0,
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),
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] {
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let schema = Arc::new(Schema::new(vec![
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Field::new(
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TIME_INDEX_COLUMN,
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DataType::Timestamp(time_unit, None),
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false,
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),
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Field::new("value", DataType::Float64, true),
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]));
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let input = LogicalPlan::EmptyRelation(EmptyRelation {
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produce_one_row: false,
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schema: schema.clone().to_dfschema_ref().unwrap(),
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});
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let normalize = crate::extension_plan::SeriesNormalize::new(
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offset,
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TIME_INDEX_COLUMN,
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false,
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Vec::new(),
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input.clone(),
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);
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let normalize =
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crate::extension_plan::SeriesNormalize::deserialize(&normalize.serialize())
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.unwrap()
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.with_exprs_and_inputs(vec![], vec![input])
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.unwrap();
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let normalized = LogicalPlan::Extension(Extension {
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node: Arc::new(normalize),
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});
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let plan = InstantManipulate::new(
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start,
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start,
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lookback_delta,
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1,
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TIME_INDEX_COLUMN.to_string(),
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Vec::new(),
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Some("value".to_string()),
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normalized.clone(),
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);
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let rebuilt = InstantManipulate::deserialize(&plan.serialize())
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.unwrap()
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.with_exprs_and_inputs(vec![], vec![normalized])
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.unwrap();
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let timestamp: Arc<dyn Array> = match time_unit {
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TimeUnit::Millisecond => Arc::new(TimestampMillisecondArray::from(vec![raw])),
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TimeUnit::Second => Arc::new(TimestampSecondArray::from(vec![raw])),
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_ => unreachable!(),
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};
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let batch = RecordBatch::try_new(
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schema.clone(),
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vec![timestamp, Arc::new(Float64Array::from(vec![7.0]))],
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)
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.unwrap();
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let normalized = LogicalPlan::Extension(Extension {
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node: Arc::new(normalize),
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});
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let plan = InstantManipulate::new(
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1_000,
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1_000,
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0,
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1,
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TIME_INDEX_COLUMN.to_string(),
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Vec::new(),
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Some("value".to_string()),
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normalized.clone(),
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);
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let rebuilt = InstantManipulate::deserialize(&plan.serialize())
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.unwrap()
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.with_exprs_and_inputs(vec![], vec![normalized])
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.unwrap();
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let batch = RecordBatch::try_new(
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schema.clone(),
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vec![
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Arc::new(TimestampMillisecondArray::from(vec![0])),
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Arc::new(Float64Array::from(vec![7.0])),
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],
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)
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.unwrap();
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let exec_input = Arc::new(DataSourceExec::new(Arc::new(
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MemorySourceConfig::try_new(&[vec![batch]], schema, None).unwrap(),
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)));
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let exec = rebuilt.to_execution_plan(exec_input);
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let output = datafusion::physical_plan::collect(exec, SessionContext::default().task_ctx())
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let exec_input = Arc::new(DataSourceExec::new(Arc::new(
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MemorySourceConfig::try_new(&[vec![batch]], schema, None).unwrap(),
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)));
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let output = datafusion::physical_plan::collect(
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rebuilt.to_execution_plan(exec_input),
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SessionContext::default().task_ctx(),
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)
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.await
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.unwrap();
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assert_eq!(
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output[0]
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.column(1)
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.as_any()
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.downcast_ref::<Float64Array>()
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.unwrap()
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.value(0),
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7.0
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);
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let output = &output[0];
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assert_eq!(output.num_rows(), 1, "{name}");
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assert_eq!(
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output
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.column(0)
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.as_any()
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.downcast_ref::<TimestampMillisecondArray>()
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.unwrap()
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.value(0),
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start,
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"{name}"
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);
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assert_eq!(
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output
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.column(1)
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.as_any()
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.downcast_ref::<Float64Array>()
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.unwrap()
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.value(0),
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7.0,
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"{name}"
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);
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}
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}
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#[test]
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@@ -1782,60 +1819,81 @@ mod test {
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#[test]
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fn native_nanosecond_offset_uses_wide_shifted_timeline() {
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let schema = Arc::new(Schema::new(vec![
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Field::new(
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TIME_INDEX_COLUMN,
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DataType::Timestamp(TimeUnit::Nanosecond, None),
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false,
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for (raw, offset, eval) in [
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(
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9_223_112_837_000_000_000_i64,
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259_200_000,
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9_223_372_037_000,
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),
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Field::new("value", DataType::Float64, true),
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]));
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let raw = 9_223_112_837_000_000_000_i64;
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let input = RecordBatch::try_new(
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schema.clone(),
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vec![
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Arc::new(TimestampNanosecondArray::from(vec![raw])),
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Arc::new(Float64Array::from(vec![7.0])),
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],
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)
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.unwrap();
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let stream = InstantManipulateStream {
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offset: 3 * 24 * 60 * 60 * 1_000,
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start: 9_223_372_037_000,
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end: 9_223_372_037_000,
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lookback_delta: 300_000,
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interval: 1,
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time_index: 0,
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time_unit: TimeUnit::Nanosecond,
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field_indices: [Some(1), None],
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tsid_index: None,
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reuse_tsid_column: false,
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schema: Arc::new(Schema::new(vec![
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(
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-9_223_112_837_000_000_000_i64,
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-259_200_000,
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-9_223_372_037_000,
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),
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] {
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let schema = Arc::new(Schema::new(vec![
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Field::new(
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TIME_INDEX_COLUMN,
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DataType::Timestamp(TimeUnit::Millisecond, None),
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DataType::Timestamp(TimeUnit::Nanosecond, None),
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false,
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),
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Field::new("value", DataType::Float64, true),
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])),
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input: Box::pin(
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datafusion::physical_plan::memory::MemoryStream::try_new(vec![], schema, None)
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.unwrap(),
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),
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metric: BaselineMetrics::new(&ExecutionPlanMetricsSet::new(), 0),
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num_series: Count::new(),
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};
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let output = stream.manipulate(input).unwrap();
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assert_eq!(output.num_rows(), 1);
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assert_eq!(
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output
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.column(1)
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.as_any()
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.downcast_ref::<Float64Array>()
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.unwrap()
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.value(0),
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7.0
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);
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]));
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let input = RecordBatch::try_new(
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schema.clone(),
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vec![
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Arc::new(TimestampNanosecondArray::from(vec![raw])),
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Arc::new(Float64Array::from(vec![7.0])),
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],
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)
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.unwrap();
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let stream = InstantManipulateStream {
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offset,
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start: eval,
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end: eval,
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lookback_delta: 300_000,
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interval: 1,
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time_index: 0,
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time_unit: TimeUnit::Nanosecond,
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field_indices: [Some(1), None],
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tsid_index: None,
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reuse_tsid_column: false,
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schema: Arc::new(Schema::new(vec![
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Field::new(
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TIME_INDEX_COLUMN,
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DataType::Timestamp(TimeUnit::Millisecond, None),
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false,
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),
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Field::new("value", DataType::Float64, true),
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])),
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input: Box::pin(
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datafusion::physical_plan::memory::MemoryStream::try_new(vec![], schema, None)
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.unwrap(),
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),
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metric: BaselineMetrics::new(&ExecutionPlanMetricsSet::new(), 0),
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num_series: Count::new(),
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};
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let output = stream.manipulate(input).unwrap();
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assert_eq!(output.num_rows(), 1);
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assert_eq!(
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output
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.column(0)
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.as_any()
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.downcast_ref::<TimestampMillisecondArray>()
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.unwrap()
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.value(0),
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eval
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);
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assert_eq!(
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output
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.column(1)
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.as_any()
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.downcast_ref::<Float64Array>()
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.unwrap()
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.value(0),
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7.0
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);
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}
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}
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#[tokio::test]
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@@ -806,7 +806,7 @@ impl RangeManipulateStream {
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mod test {
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use datafusion::arrow::array::{
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ArrayRef, DictionaryArray, Float64Array, StringArray, TimestampMicrosecondArray,
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TimestampNanosecondArray,
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TimestampNanosecondArray, TimestampSecondArray,
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};
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use datafusion::arrow::buffer::NullBuffer;
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use datafusion::arrow::datatypes::{
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@@ -1069,104 +1069,140 @@ mod test {
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#[tokio::test]
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async fn logical_normalize_offset_survives_rebuild_and_executes() {
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let schema = Arc::new(Schema::new(vec![
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Field::new(
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TIME_INDEX_COLUMN,
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TimestampMillisecondType::DATA_TYPE,
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false,
|
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for (name, time_unit, raw, offset, start, range, expected_payload) in [
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(
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"millisecond offset",
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TimeUnit::Millisecond,
|
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0,
|
||||
1_000,
|
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1_000,
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1_000,
|
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1_000,
|
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),
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Field::new("value", DataType::Float64, true),
|
||||
]));
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let input = LogicalPlan::EmptyRelation(EmptyRelation {
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produce_one_row: false,
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||||
schema: schema.clone().to_dfschema_ref().unwrap(),
|
||||
});
|
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let normalize = crate::extension_plan::SeriesNormalize::new(
|
||||
1_000,
|
||||
TIME_INDEX_COLUMN,
|
||||
false,
|
||||
Vec::new(),
|
||||
input.clone(),
|
||||
);
|
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let normalize = crate::extension_plan::SeriesNormalize::deserialize(&normalize.serialize())
|
||||
.unwrap()
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.with_exprs_and_inputs(vec![], vec![input])
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(
|
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"negative native lower limit with positive window",
|
||||
TimeUnit::Nanosecond,
|
||||
-9_223_112_837_000_000_000,
|
||||
-259_200_000,
|
||||
-9_223_372_037_000,
|
||||
300_000,
|
||||
-9_223_372_037_000,
|
||||
),
|
||||
(
|
||||
"second timestamp with negative fractional offset",
|
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TimeUnit::Second,
|
||||
1,
|
||||
-500,
|
||||
1_000,
|
||||
1_000,
|
||||
500,
|
||||
),
|
||||
] {
|
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let schema = Arc::new(Schema::new(vec![
|
||||
Field::new(
|
||||
TIME_INDEX_COLUMN,
|
||||
DataType::Timestamp(time_unit, None),
|
||||
false,
|
||||
),
|
||||
Field::new("value", DataType::Float64, true),
|
||||
]));
|
||||
let input = LogicalPlan::EmptyRelation(EmptyRelation {
|
||||
produce_one_row: false,
|
||||
schema: schema.clone().to_dfschema_ref().unwrap(),
|
||||
});
|
||||
let normalize = crate::extension_plan::SeriesNormalize::new(
|
||||
offset,
|
||||
TIME_INDEX_COLUMN,
|
||||
false,
|
||||
Vec::new(),
|
||||
input.clone(),
|
||||
);
|
||||
let normalize =
|
||||
crate::extension_plan::SeriesNormalize::deserialize(&normalize.serialize())
|
||||
.unwrap()
|
||||
.with_exprs_and_inputs(vec![], vec![input])
|
||||
.unwrap();
|
||||
let normalized = LogicalPlan::Extension(Extension {
|
||||
node: Arc::new(normalize),
|
||||
});
|
||||
let plan = RangeManipulate::new(
|
||||
start,
|
||||
start,
|
||||
1,
|
||||
range,
|
||||
TIME_INDEX_COLUMN.to_string(),
|
||||
vec!["value".to_string()],
|
||||
normalized.clone(),
|
||||
)
|
||||
.unwrap();
|
||||
let normalized = LogicalPlan::Extension(Extension {
|
||||
node: Arc::new(normalize),
|
||||
});
|
||||
let plan = RangeManipulate::new(
|
||||
1_000,
|
||||
1_000,
|
||||
1,
|
||||
1_000,
|
||||
TIME_INDEX_COLUMN.to_string(),
|
||||
vec!["value".to_string()],
|
||||
normalized.clone(),
|
||||
)
|
||||
.unwrap();
|
||||
let rebuilt = RangeManipulate::deserialize(&plan.serialize())
|
||||
.unwrap()
|
||||
.with_exprs_and_inputs(vec![], vec![normalized])
|
||||
let rebuilt = RangeManipulate::deserialize(&plan.serialize())
|
||||
.unwrap()
|
||||
.with_exprs_and_inputs(vec![], vec![normalized])
|
||||
.unwrap();
|
||||
let timestamp: ArrayRef = match time_unit {
|
||||
TimeUnit::Millisecond => Arc::new(TimestampMillisecondArray::from(vec![raw])),
|
||||
TimeUnit::Nanosecond => Arc::new(TimestampNanosecondArray::from(vec![raw])),
|
||||
TimeUnit::Second => Arc::new(TimestampSecondArray::from(vec![raw])),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
let batch = RecordBatch::try_new(
|
||||
schema.clone(),
|
||||
vec![timestamp, Arc::new(Float64Array::from(vec![7.0]))],
|
||||
)
|
||||
.unwrap();
|
||||
let batch = RecordBatch::try_new(
|
||||
schema.clone(),
|
||||
vec![
|
||||
Arc::new(TimestampMillisecondArray::from(vec![0])),
|
||||
Arc::new(Float64Array::from(vec![7.0])),
|
||||
],
|
||||
)
|
||||
.unwrap();
|
||||
let exec_input = Arc::new(DataSourceExec::new(Arc::new(
|
||||
MemorySourceConfig::try_new(&[vec![batch]], schema, None).unwrap(),
|
||||
)));
|
||||
let output = datafusion::physical_plan::collect(
|
||||
rebuilt.to_execution_plan(exec_input),
|
||||
SessionContext::default().task_ctx(),
|
||||
)
|
||||
.await
|
||||
.unwrap();
|
||||
assert_eq!(output.len(), 1);
|
||||
let output = &output[0];
|
||||
assert_eq!(output.num_rows(), 1);
|
||||
assert_eq!(
|
||||
output
|
||||
.column(0)
|
||||
.as_any()
|
||||
.downcast_ref::<TimestampMillisecondArray>()
|
||||
.unwrap()
|
||||
.values()
|
||||
.as_ref(),
|
||||
&[1_000]
|
||||
);
|
||||
let values = RangeArray::try_new(
|
||||
output
|
||||
.column(1)
|
||||
.as_any()
|
||||
.downcast_ref::<DictionaryArray<Int64Type>>()
|
||||
.unwrap()
|
||||
.clone(),
|
||||
)
|
||||
.unwrap();
|
||||
assert_eq!(values.get_offset_length(0), Some((0, 1)));
|
||||
assert_eq!(
|
||||
values.get(0).unwrap().to_data(),
|
||||
Float64Array::from(vec![7.0]).to_data()
|
||||
);
|
||||
let timestamps = RangeArray::try_new(
|
||||
output
|
||||
.column(2)
|
||||
.as_any()
|
||||
.downcast_ref::<DictionaryArray<Int64Type>>()
|
||||
.unwrap()
|
||||
.clone(),
|
||||
)
|
||||
.unwrap();
|
||||
assert_eq!(timestamps.get_offset_length(0), Some((0, 1)));
|
||||
assert_eq!(
|
||||
timestamps.get(0).unwrap().to_data(),
|
||||
TimestampMillisecondArray::from(vec![1_000]).to_data()
|
||||
);
|
||||
let exec_input = Arc::new(DataSourceExec::new(Arc::new(
|
||||
MemorySourceConfig::try_new(&[vec![batch]], schema, None).unwrap(),
|
||||
)));
|
||||
let output = datafusion::physical_plan::collect(
|
||||
rebuilt.to_execution_plan(exec_input),
|
||||
SessionContext::default().task_ctx(),
|
||||
)
|
||||
.await
|
||||
.unwrap();
|
||||
assert_eq!(output.len(), 1, "{name}");
|
||||
let output = &output[0];
|
||||
assert_eq!(output.num_rows(), 1, "{name}");
|
||||
assert_eq!(
|
||||
output
|
||||
.column(0)
|
||||
.as_any()
|
||||
.downcast_ref::<TimestampMillisecondArray>()
|
||||
.unwrap()
|
||||
.value(0),
|
||||
start,
|
||||
"{name}"
|
||||
);
|
||||
let values = RangeArray::try_new(
|
||||
output
|
||||
.column(1)
|
||||
.as_any()
|
||||
.downcast_ref::<DictionaryArray<Int64Type>>()
|
||||
.unwrap()
|
||||
.clone(),
|
||||
)
|
||||
.unwrap();
|
||||
assert_eq!(values.get_offset_length(0), Some((0, 1)), "{name}");
|
||||
assert_eq!(
|
||||
values.get(0).unwrap().to_data(),
|
||||
Float64Array::from(vec![7.0]).to_data(),
|
||||
"{name}"
|
||||
);
|
||||
let timestamps = RangeArray::try_new(
|
||||
output
|
||||
.column(2)
|
||||
.as_any()
|
||||
.downcast_ref::<DictionaryArray<Int64Type>>()
|
||||
.unwrap()
|
||||
.clone(),
|
||||
)
|
||||
.unwrap();
|
||||
assert_eq!(timestamps.get_offset_length(0), Some((0, 1)), "{name}");
|
||||
assert_eq!(
|
||||
timestamps.get(0).unwrap().to_data(),
|
||||
TimestampMillisecondArray::from(vec![expected_payload]).to_data(),
|
||||
"{name}"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
|
||||
Reference in New Issue
Block a user