QUERY
QUERY は、転置インデックスを持つカラムに対して Lucene スタイルのクエリ式を照合することで行をフィルタリングします。VARIANT カラム内のネストされたフィールドをたどるには、ドット記法を使用します。この関数は WHERE 句でのみ有効です。
構文
QUERY('<query_expr>'[, '<options>'])
<options> は省略可能な、セミコロン区切りの key=value ペアのリストで、検索の動作を調整します。
クエリ式の構築
ネストされた VARIANT フィールド
VARIANT カラム内の内部フィールドを指定するには、ドット記法を使用します。TiDB Cloud Lake は、オブジェクトと配列をまたいでパスを評価します。
オプション
例
スマートドライビングのデータセットを準備する
CREATE OR REPLACE TABLE frames (
id INT,
meta VARIANT,
INVERTED INDEX idx_meta (meta)
);
INSERT INTO frames VALUES
(1, '{
"frame":{"source":"dashcam_front","timestamp":"2025-10-21T08:32:05Z","location":{"city":"San Francisco","intersection":"Market & 5th","gps":[37.7825,-122.4072]}},
"vehicle":{"speed_kmh":48,"acceleration":0.8,"lane":"center"},
"signals":{"traffic_light":"green","distance_m":55,"speed_limit_kmh":50},
"detections":[
{"label":"car","confidence":0.96,"distance_m":15,"relative_speed_kmh":2},
{"label":"pedestrian","confidence":0.88,"distance_m":12,"intent":"crossing"}
],
"scene":{"weather":"clear","time_of_day":"day","visibility":"good"},
"tags":["downtown","commute","green-light"],
"model":"perception-net-v5"
}'),
(2, '{
"frame":{"source":"dashcam_front","timestamp":"2025-10-21T08:32:06Z","location":{"city":"San Francisco","intersection":"Mission & 6th","gps":[37.7829,-122.4079]}},
"vehicle":{"speed_kmh":9,"acceleration":-1.1,"lane":"center"},
"signals":{"traffic_light":"red","distance_m":18,"speed_limit_kmh":40},
"detections":[
{"label":"traffic_light","state":"red","confidence":0.99,"distance_m":18},
{"label":"bike","confidence":0.82,"distance_m":9,"relative_speed_kmh":3}
],
"scene":{"weather":"clear","time_of_day":"day","visibility":"good"},
"tags":["stop","cyclist","urban"],
"model":"perception-net-v5"
}'),
(3, '{
"frame":{"source":"dashcam_front","timestamp":"2025-10-21T08:32:07Z","location":{"city":"San Francisco","intersection":"SOMA School Zone","gps":[37.7808,-122.4016]}},
"vehicle":{"speed_kmh":28,"acceleration":0.2,"lane":"right"},
"signals":{"traffic_light":"yellow","distance_m":32,"speed_limit_kmh":25},
"detections":[
{"label":"traffic_sign","text":"SCHOOL","confidence":0.91,"distance_m":25},
{"label":"pedestrian","confidence":0.76,"distance_m":8,"intent":"waiting"}
],
"scene":{"weather":"overcast","time_of_day":"day","visibility":"moderate"},
"tags":["school-zone","caution"],
"model":"perception-net-v5"
}');
例: ブール AND
SELECT id, meta['frame']['timestamp'] AS ts
FROM frames
WHERE QUERY('meta.signals.traffic_light:red AND meta.vehicle.speed_kmh:[0 TO 10]');
-- Returns id 2
例: ブール OR
SELECT id, meta['frame']['timestamp'] AS ts
FROM frames
WHERE QUERY('meta.signals.traffic_light:red OR meta.detections.label:bike');
-- Returns id 2
例: IN リスト一致
SELECT id, meta['frame']['timestamp'] AS ts
FROM frames
WHERE QUERY('meta.tags:IN [stop urban]');
-- Returns id 2
例: 境界値を含む範囲
SELECT id, meta['frame']['timestamp'] AS ts
FROM frames
WHERE QUERY('meta.vehicle.speed_kmh:[0 TO 10]');
-- Returns id 2
例: 境界値を含まない範囲
SELECT id, meta['frame']['timestamp'] AS ts
FROM frames
WHERE QUERY('meta.vehicle.speed_kmh:{0 TO 10}');
-- Returns id 2
例: フィールドをまたいだブースト
SELECT id, meta['frame']['timestamp'] AS ts, SCORE()
FROM frames
WHERE QUERY('meta.signals.traffic_light:red^1.0 AND meta.tags:urban^2.0');
-- Returns id 2 with higher relevance
例: 高信頼度の歩行者を検出する
SELECT id, meta['frame']['timestamp'] AS ts
FROM frames
WHERE QUERY('meta.detections.label:IN [pedestrian cyclist] AND meta.detections.confidence:[0.8 TO *]');
-- Returns ids 1 and 3
例: フレーズでフィルタする
SELECT id, meta['frame']['timestamp'] AS ts
FROM frames
WHERE QUERY('meta.scene.summary:"vehicle stopped at red traffic light"');
-- Returns id 2
例: スクールゾーンフィルタ
SELECT id, meta['frame']['timestamp'] AS ts
FROM frames
WHERE QUERY('meta.detections.text:SCHOOL AND meta.scene.time_of_day:day');
-- Returns id 3