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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)）
正文完
