|
| 1 | +setup: |
| 2 | + - do: |
| 3 | + query.settings: |
| 4 | + body: |
| 5 | + transient: |
| 6 | + plugins.calcite.enabled : true |
| 7 | + |
| 8 | + - do: |
| 9 | + indices.create: |
| 10 | + index: test |
| 11 | + body: |
| 12 | + mappings: |
| 13 | + properties: |
| 14 | + "@timestamp": |
| 15 | + type: date |
| 16 | + timestamp: |
| 17 | + type: date |
| 18 | + size: |
| 19 | + type: long |
| 20 | + tmin: |
| 21 | + type: double |
| 22 | + metrics: |
| 23 | + type: object |
| 24 | + properties: |
| 25 | + size: |
| 26 | + type: long |
| 27 | + tmin: |
| 28 | + type: double |
| 29 | + |
| 30 | + - do: |
| 31 | + bulk: |
| 32 | + index: test |
| 33 | + refresh: true |
| 34 | + body: |
| 35 | + - '{"index": {}}' |
| 36 | + - '{ "@timestamp": "2025-01-01T00:00:00Z", "timestamp": "2025-01-01T00:00:00Z", "size": -20, "tmin": 1.0, "metrics": { "size": -20, "tmin": 1.0 } }' |
| 37 | + - '{"index": {}}' |
| 38 | + - '{ "@timestamp": "2025-01-01T01:00:00Z", "timestamp": "2025-01-01T01:00:00Z", "size": 5, "tmin": 2.5, "metrics": { "size": 5, "tmin": 2.5 } }' |
| 39 | + - '{"index": {}}' |
| 40 | + - '{ "@timestamp": "2025-01-01T02:00:00Z", "timestamp": "2025-01-01T02:00:00Z", "size": 50, "tmin": 3.2, "metrics": { "size": 50, "tmin": 3.2 } }' |
| 41 | + - '{"index": {}}' |
| 42 | + - '{ "@timestamp": "2025-01-01T03:00:00Z", "timestamp": "2025-01-01T03:00:00Z", "size": 500, "tmin": 1.8, "metrics": { "size": 500, "tmin": 1.8 } }' |
| 43 | + - '{"index": {}}' |
| 44 | + - '{ "@timestamp": "2025-01-01T04:00:00Z", "timestamp": "2025-01-01T04:00:00Z", "size": 1500, "tmin": 4.1, "metrics": { "size": 1500, "tmin": 4.1 } }' |
| 45 | + - '{"index": {}}' |
| 46 | + - '{ "@timestamp": "2025-01-01T05:00:00Z", "timestamp": "2025-01-01T05:30:00Z", "size": 3000, "tmin": 2.9, "metrics": { "size": 3000, "tmin": 2.9 } }' |
| 47 | + |
| 48 | +--- |
| 49 | +teardown: |
| 50 | + - do: |
| 51 | + query.settings: |
| 52 | + body: |
| 53 | + transient: |
| 54 | + plugins.calcite.enabled : false |
| 55 | + |
| 56 | +--- |
| 57 | +"Test aggregation by range bucket": |
| 58 | + - skip: |
| 59 | + features: |
| 60 | + - headers |
| 61 | + - allowed_warnings |
| 62 | + - do: |
| 63 | + headers: |
| 64 | + Content-Type: 'application/json' |
| 65 | + ppl: |
| 66 | + body: |
| 67 | + query: | |
| 68 | + source = test |
| 69 | + | eval range_bucket = case( |
| 70 | + `metrics.size` < -10, 'range_1', |
| 71 | + `metrics.size` >= -10 and `metrics.size` < 10, 'range_2', |
| 72 | + `metrics.size` >= 10 and `metrics.size` < 100, 'range_3', |
| 73 | + `metrics.size` >= 100 and `metrics.size` < 1000, 'range_4', |
| 74 | + `metrics.size` >= 1000 and `metrics.size` < 2000, 'range_5', |
| 75 | + `metrics.size` >= 2000, 'range_6' |
| 76 | + ) |
| 77 | + | stats min(`metrics.tmin`) as tmin, avg(`metrics.size`) as tavg, max(`metrics.size`) as tmax |
| 78 | + by range_bucket |
| 79 | +
|
| 80 | + - match: { total: 6 } |
| 81 | + - match: { schema: [{"name": "tmin", "type": "double"}, {"name": "tavg", "type": "double"}, {"name": "tmax", "type": "bigint"}, {"name": "range_bucket", "type": "string"}] } |
| 82 | + - match: { datarows: [[1.0, -20.0, -20, "range_1"], [2.5, 5.0, 5, "range_2"], [3.2, 50.0, 50, "range_3"], [1.8, 500.0, 500, "range_4"], [4.1, 1500.0, 1500, "range_5"], [2.9, 3000.0, 3000, "range_6"]] } |
| 83 | + |
| 84 | +--- |
| 85 | +"Test aggregation by range bucket and time span": |
| 86 | + - skip: |
| 87 | + features: |
| 88 | + - headers |
| 89 | + - allowed_warnings |
| 90 | + - do: |
| 91 | + headers: |
| 92 | + Content-Type: 'application/json' |
| 93 | + ppl: |
| 94 | + body: |
| 95 | + query: | |
| 96 | + source = test |
| 97 | + | eval range_bucket = case( |
| 98 | + `metrics.size` < -10, 'range_1', |
| 99 | + `metrics.size` >= -10 and `metrics.size` < 10, 'range_2', |
| 100 | + `metrics.size` >= 10 and `metrics.size` < 100, 'range_3', |
| 101 | + `metrics.size` >= 100 and `metrics.size` < 1000, 'range_4', |
| 102 | + `metrics.size` >= 1000 and `metrics.size` < 2000, 'range_5', |
| 103 | + `metrics.size` >= 2000, 'range_6' |
| 104 | + ) |
| 105 | + | stats min(`metrics.tmin`) as tmin, avg(`metrics.size`) as tavg, max(`metrics.size`) as tmax |
| 106 | + by range_bucket, span(`@timestamp`, 1h) |
| 107 | +
|
| 108 | + - match: { total: 6 } |
| 109 | + - match: { schema: [{"name": "tmin", "type": "double"}, {"name": "tavg", "type": "double"}, {"name": "tmax", "type": "bigint"}, {"name": "span(`@timestamp`,1h)", "type": "timestamp"}, {"name": "range_bucket", "type": "string"}] } |
| 110 | + - match: { datarows: [[1.0, -20.0, -20, "2025-01-01 00:00:00", "range_1"], [2.5, 5.0, 5, "2025-01-01 01:00:00", "range_2"], [3.2, 50.0, 50, "2025-01-01 02:00:00", "range_3"], [1.8, 500.0, 500, "2025-01-01 03:00:00", "range_4"], [4.1, 1500.0, 1500, "2025-01-01 04:00:00", "range_5"], [2.9, 3000.0, 3000, "2025-01-01 05:00:00", "range_6"]] } |
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