基于上一篇（3）Flink CEP SQL宽松近邻代码演示的延展，在上一篇中咱们应用贪心词量 +（至多匹配1行或多行），本篇将演示多种贪心词量的成果：
（1）应用贪心词量 *（匹配0行或多行）
public static void main(String[] args) { EnvironmentSettings settings = null; StreamTableEnvironment tEnv = null; try { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); settings = EnvironmentSettings.newInstance() .useBlinkPlanner() .inStreamingMode() .build(); tEnv = StreamTableEnvironment.create(env, settings); System.out.println("===============CEP_SQL_10================="); final DateTimeFormatter dateTimeFormatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss"); DataStream<Ticker> dataStream = env.fromElements( new Ticker(1, "ACME", 22, 1, LocalDateTime.parse("2021-12-10 10:00:00", dateTimeFormatter)), new Ticker(3, "ACME", 19, 1, LocalDateTime.parse("2021-12-10 10:00:02", dateTimeFormatter)), new Ticker(4, "ACME", 23, 3, LocalDateTime.parse("2021-12-10 10:00:03", dateTimeFormatter)), new Ticker(5, "Apple", 25, 2, LocalDateTime.parse("2021-12-10 10:00:04", dateTimeFormatter)), new Ticker(6, "Apple", 18, 1, LocalDateTime.parse("2021-12-10 10:00:05", dateTimeFormatter)), new Ticker(7, "Apple", 16, 1, LocalDateTime.parse("2021-12-10 10:00:06", dateTimeFormatter)), new Ticker(8, "Apple", 14, 2, LocalDateTime.parse("2021-12-10 10:00:07", dateTimeFormatter)), new Ticker(9, "Apple", 19, 2, LocalDateTime.parse("2021-12-10 10:00:08", dateTimeFormatter)), new Ticker(10, "Apple", 25, 2, LocalDateTime.parse("2021-12-10 10:00:09", dateTimeFormatter)), new Ticker(11, "Apple", 11, 1, LocalDateTime.parse("2021-12-10 10:00:11", dateTimeFormatter)), new Ticker(12, "Apple", 15, 1, LocalDateTime.parse("2021-12-10 10:00:12", dateTimeFormatter)), new Ticker(13, "Apple", 19, 1, LocalDateTime.parse("2021-12-10 10:00:13", dateTimeFormatter)), new Ticker(14, "Apple", 25, 1, LocalDateTime.parse("2021-12-10 10:00:14", dateTimeFormatter)), new Ticker(15, "Apple", 19, 1, LocalDateTime.parse("2021-12-10 10:00:15", dateTimeFormatter)), new Ticker(16, "Apple", 15, 1, LocalDateTime.parse("2021-12-10 10:00:16", dateTimeFormatter)), new Ticker(17, "Apple", 19, 1, LocalDateTime.parse("2021-12-10 10:00:17", dateTimeFormatter)), new Ticker(18, "Apple", 15, 1, LocalDateTime.parse("2021-12-10 10:00:18", dateTimeFormatter))); Table table = tEnv.fromDataStream(dataStream, Schema.newBuilder() .column("id", DataTypes.BIGINT()) .column("symbol", DataTypes.STRING()) .column("price", DataTypes.BIGINT()) .column("tax", DataTypes.BIGINT()) .column("rowtime", DataTypes.TIMESTAMP(3)) .watermark("rowtime", "rowtime - INTERVAL '1' SECOND") .build()); tEnv.createTemporaryView("CEP_SQL_10", table); String sql = "SELECT * " + "FROM CEP_SQL_10 " + " MATCH_RECOGNIZE ( " + " PARTITION BY symbol " + //按symbol分区，将雷同卡号的数据分到同一个计算节点上。 " ORDER BY rowtime " + //在窗口内，对事件工夫进行排序。 " MEASURES " + //定义如何依据匹配胜利的输出事件结构输入事件 " e1.id as id,"+ " AVG(e1.price) as avgPrice,"+ " e1.rowtime AS start_tstamp, " + " e3.rowtime AS end_tstamp " + " ONE ROW PER MATCH " + //匹配胜利输入一条 " AFTER MATCH skip to next row " + //匹配后跳转到下一行 " PATTERN ( e1 e2* e3) WITHIN INTERVAL '2' MINUTE" + " DEFINE " + //定义各事件的匹配条件 " e1 AS " + " e1.price = 25 , " + " e2 AS " + " e2.price > 10 AND e2.price <19," + " e3 AS " + " e3.price = 19 " + " ) MR"; TableResult res = tEnv.executeSql(sql); res.print(); tEnv.dropTemporaryView("CEP_SQL_10");}匹配到了三组数据
贪心词量 *（匹配0行或多行）
（2）应用贪心词量 {n}（严格匹配n行）
（3）应用贪心词量 {n，}（n或者更多行(n≥O)）