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feat: Implement OR for PromQL (#3024)
* with anit-join Signed-off-by: Ruihang Xia <[email protected]> * impl UnionDistinctOn Signed-off-by: Ruihang Xia <[email protected]> * unify schema Signed-off-by: Ruihang Xia <[email protected]> * fix clippy Signed-off-by: Ruihang Xia <[email protected]> * add sqlness case Signed-off-by: Ruihang Xia <[email protected]> * add UTs Signed-off-by: Ruihang Xia <[email protected]> * Update src/promql/src/planner.rs Co-authored-by: dennis zhuang <[email protected]> --------- Signed-off-by: Ruihang Xia <[email protected]> Co-authored-by: dennis zhuang <[email protected]>
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// Copyright 2023 Greptime Team | ||
// | ||
// Licensed under the Apache License, Version 2.0 (the "License"); | ||
// you may not use this file except in compliance with the License. | ||
// You may obtain a copy of the License at | ||
// | ||
// http://www.apache.org/licenses/LICENSE-2.0 | ||
// | ||
// Unless required by applicable law or agreed to in writing, software | ||
// distributed under the License is distributed on an "AS IS" BASIS, | ||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
// See the License for the specific language governing permissions and | ||
// limitations under the License. | ||
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//! Utils for testing extension plan | ||
use std::sync::Arc; | ||
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use common_recordbatch::DfRecordBatch as RecordBatch; | ||
use datafusion::arrow::array::Float64Array; | ||
use datafusion::arrow::datatypes::{ | ||
ArrowPrimitiveType, DataType, Field, Schema, TimestampMillisecondType, | ||
}; | ||
use datafusion::physical_plan::memory::MemoryExec; | ||
use datatypes::arrow::array::TimestampMillisecondArray; | ||
use datatypes::arrow_array::StringArray; | ||
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pub(crate) const TIME_INDEX_COLUMN: &str = "timestamp"; | ||
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pub(crate) fn prepare_test_data() -> MemoryExec { | ||
let schema = Arc::new(Schema::new(vec![ | ||
Field::new(TIME_INDEX_COLUMN, TimestampMillisecondType::DATA_TYPE, true), | ||
Field::new("value", DataType::Float64, true), | ||
Field::new("path", DataType::Utf8, true), | ||
])); | ||
let timestamp_column = Arc::new(TimestampMillisecondArray::from(vec![ | ||
0, 30_000, 60_000, 90_000, 120_000, // every 30s | ||
180_000, 240_000, // every 60s | ||
241_000, 271_000, 291_000, // others | ||
])) as _; | ||
let field_column = Arc::new(Float64Array::from(vec![1.0; 10])) as _; | ||
let path_column = Arc::new(StringArray::from(vec!["foo"; 10])) as _; | ||
let data = RecordBatch::try_new( | ||
schema.clone(), | ||
vec![timestamp_column, field_column, path_column], | ||
) | ||
.unwrap(); | ||
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MemoryExec::try_new(&[vec![data]], schema, None).unwrap() | ||
} | ||
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pub(crate) fn prepare_test_data_with_nan() -> MemoryExec { | ||
let schema = Arc::new(Schema::new(vec![ | ||
Field::new(TIME_INDEX_COLUMN, TimestampMillisecondType::DATA_TYPE, true), | ||
Field::new("value", DataType::Float64, true), | ||
])); | ||
let timestamp_column = Arc::new(TimestampMillisecondArray::from(vec![ | ||
0, 30_000, 60_000, 90_000, 120_000, // every 30s | ||
])) as _; | ||
let field_column = Arc::new(Float64Array::from(vec![0.0, f64::NAN, 6.0, f64::NAN, 12.0])) as _; | ||
let data = RecordBatch::try_new(schema.clone(), vec![timestamp_column, field_column]).unwrap(); | ||
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MemoryExec::try_new(&[vec![data]], schema, None).unwrap() | ||
} |
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