我很高興宣布Pygmalios開發的ReactiveInflux的第一個發行版。 InfluxDB錯過了Scala和Java的非阻塞驅動程序。 不變性，可測試性和可擴展性是ReactiveInflux的關鍵功能。 加上對Apache Spark的支持，它是首選武器。

• https://github.com/pygmalios/reactiveinflux

它在內部使用Play Framework WS API ，它是基于Async Http Client構建的豐富的異步HTTP客戶端 。

特征

• Scala的異步（非阻塞）接口
• Scala和Java的同步（阻塞）接口
• 同時支持Spark和Spark流
• 不變性
• 可測性
• 可擴展性

兼容性

• InfluxDB 0.11、0.10和0.9（甚至可能更舊）
• Scala 2.11和2.10
• Java 7及以上
• Apache Spark 1.4及更高版本

Scala異步（非阻塞）示例

val result = withInfluxDb(new URI("http://localhost:8086/"), "example1") { db =>db.create().flatMap { _ =>val point = Point(time = DateTime.now(),measurement = "measurement1",tags = Map("t1" -> "A", "t2" -> "B"),fields = Map("f1" -> 10.3,"f2" -> "x","f3" -> -1,"f4" -> true))db.write(point).flatMap { _ =>db.query("SELECT * FROM measurement1").flatMap { queryResult =>println(queryResult.row.mkString)db.drop()}}} }

Scala同步（阻塞）示例

implicit val awaitAtMost = 10.seconds syncInfluxDb(new URI("http://localhost:8086/"), "example1") { db =>db.create()val point = Point(time = DateTime.now(),measurement = "measurement1",tags = Map("t1" -> "A", "t2" -> "B"),fields = Map("f1" -> 10.3,"f2" -> "x","f3" -> -1,"f4" -> true))db.write(point)val queryResult = db.query("SELECT * FROM measurement1")println(queryResult.row.mkString)db.drop() }

Java同步（阻塞）示例

// Use Influx at the provided URL ReactiveInfluxConfig config = new JavaReactiveInfluxConfig(new URI("http://localhost:8086/")); long awaitAtMostMillis = 30000; try (SyncReactiveInflux reactiveInflux = new JavaSyncReactiveInflux(config, awaitAtMostMillis)) {SyncReactiveInfluxDb db = reactiveInflux.database("example1");db.create();Map tags = new HashMap<>();tags.put("t1", "A");tags.put("t2", "B");Map fields = new HashMap<>();fields.put("f1", 10.3);fields.put("f2", "x");fields.put("f3", -1);fields.put("f4", true);Point point = new JavaPoint(DateTime.now(),"measurement1",tags,fields);db.write(point);QueryResult queryResult = db.query("SELECT * FROM measurement1");System.out.println(queryResult.getRow().mkString());db.drop(); }

Apache Spark Scala示例

val point1 = Point(time = DateTime.now(),measurement = "measurement1",tags = Map("tagKey1" -> "tagValue1","tagKey2" -> "tagValue2"),fields = Map("fieldKey1" -> "fieldValue1","fieldKey2" -> 10.7) ) sc.parallelize(Seq(point1)).saveToInflux()

Apache Spark流Scala示例

val point1 = Point(time = DateTime.now(),measurement = "measurement1",tags = Map("tagKey1" -> "tagValue1","tagKey2" -> "tagValue2"),fields = Map("fieldKey1" -> "fieldValue1","fieldKey2" -> 10.7) ) val queue = new mutable.Queue[RDD[Point]] queue.enqueue(ssc.sparkContext.parallelize(Seq(point1))) ssc.queueStream(queue).saveToInflux()

Apache Spark Java示例

... SparkInflux sparkInflux = new SparkInflux("example", 1000); sparkInflux.saveToInflux(sc.parallelize(Collections.singletonList(point)));

Apache Spark流Java示例

... SparkInflux sparkInflux = new SparkInflux("example", 1000); Queue> queue = new LinkedList<>(); queue.add(ssc.sparkContext().parallelize(Collections.singletonList(point))); sparkInflux.saveToInflux(ssc.queueStream(queue));

斯洛伐克布拉迪斯拉發的高科技初創公司投資于尖端技術，以確保實時預測零售分析領域的快速增長。

翻譯自: https://www.javacodegeeks.com/2016/04/introducing-reactiveinflux-non-blocking-influxdb-driver-scala-java-supporting-apache-spark.html

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