(3)Flink CEP SQL宽松近邻代码演示
上一篇我们演示了严格近邻模式的效果,接着上一篇我们来演示一下宽松近邻:
(1)pom依赖:
<dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-cep_${scala.binary.version}</artifactId> <version>${flink.version}</version> </dependency>
(2)定义一个消息对象
public static class Ticker { public long id; public String symbol; public long price; public long tax; public LocalDateTime rowtime; public Ticker() { } public Ticker(long id, String symbol, long price, long item, LocalDateTime rowtime) { this.id = id; this.symbol = symbol; this.price = price; this.tax = tax; this.rowtime = rowtime; } }
(3)构造数据,定义事件组合
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,将相同卡号的数据分到同一个计算节点上。 " ORDER BY rowtime " + //在窗口内,对事件时间进行排序。 " MEASURES " + //定义如何根据匹配成功的输入事件构造输出事件 " e1.id as id,"+ " AVG(e1.price) as avgPrice,"+ " e1.rowtime AS start_tstamp, " + " e3.rowtime AS end_tstamp " + " ONE ROW PER MATCH " + //匹配成功输出一条 " 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"); }
(4)关键代码解释:
需要借着贪婪词量来实现宽松近邻效果。
匹配到两组数据。