Spark IoTDB连接器

版本

Spark和Java所需的版本如下:

Spark VersionScala VersionJava VersionTsFile
2.4.32.111.80.11.1

安装

mvn clean scala:compile compile install

1. Maven依赖

  1. <dependency>
  2. <groupId>org.apache.iotdb</groupId>
  3. <artifactId>spark-iotdb-connector</artifactId>
  4. <version>0.11.1</version>
  5. </dependency>

2. Spark-shell用户指南

  1. spark-shell --jars spark-iotdb-connector-0.11.1.jar,iotdb-jdbc-0.11.1-jar-with-dependencies.jar
  2. import org.apache.iotdb.spark.db._
  3. val df = spark.read.format("org.apache.iotdb.spark.db").option("url","jdbc:iotdb://127.0.0.1:6667/").option("sql","select * from root").load
  4. df.printSchema()
  5. df.show()

如果要对rdd进行分区,可以执行以下操作

  1. spark-shell --jars spark-iotdb-connector-0.11.1.jar,iotdb-jdbc-0.11.1-jar-with-dependencies.jar
  2. import org.apache.iotdb.spark.db._
  3. val df = spark.read.format("org.apache.iotdb.spark.db").option("url","jdbc:iotdb://127.0.0.1:6667/").option("sql","select * from root").
  4. option("lowerBound", [lower bound of time that you want query(include)]).option("upperBound", [upper bound of time that you want query(include)]).
  5. option("numPartition", [the partition number you want]).load
  6. df.printSchema()
  7. df.show()

3. 模式推断

以下TsFile结构为例:TsFile模式中有三个度量:状态,温度和硬件。 这三种测量的基本信息如下:

名称类型编码
状态BooleanPLAIN
温度FloatRLE
硬件TextPLAIN

TsFile中的现有数据如下:

device:root.ln.wf01.wt01device:root.ln.wf02.wt02
状态温度硬件状态
时间时间时间时间
1True12.22“aaa”1True
3True22.24“bbb”2False
5False32.16“ccc”4True

宽(默认)表形式如下:

timeroot.ln.wf02.wt02.temperatureroot.ln.wf02.wt02.statusroot.ln.wf02.wt02.hardwareroot.ln.wf01.wt01.temperatureroot.ln.wf01.wt01.statusroot.ln.wf01.wt01.hardware
1nulltruenull2.2truenull
2nullfalseaaa2.2nullnull
3nullnullnull2.1truenull
4nulltruebbbnullnullnull
5nullnullnullnullfalsenull
6nullnullcccnullnullnull

你还可以使用窄表形式,如下所示:(您可以参阅第4部分,了解如何使用窄表形式)

时间设备名状态硬件温度
1root.ln.wf02.wt01truenull2.2
1root.ln.wf02.wt02truenullnull
2root.ln.wf02.wt01nullnull2.2
2root.ln.wf02.wt02falseaaanull
3root.ln.wf02.wt01truenull2.1
4root.ln.wf02.wt02truebbbnull
5root.ln.wf02.wt01falsenullnull
6root.ln.wf02.wt02nullcccnull

4. 在宽和窄表之间转换

从宽到窄

  1. import org.apache.iotdb.spark.db._
  2. val wide_df = spark.read.format("org.apache.iotdb.spark.db").option("url", "jdbc:iotdb://127.0.0.1:6667/").option("sql", "select * from root where time < 1100 and time > 1000").load
  3. val narrow_df = Transformer.toNarrowForm(spark, wide_df)

从窄到宽

  1. import org.apache.iotdb.spark.db._
  2. val wide_df = Transformer.toWideForm(spark, narrow_df)

5. Java用户指南

  1. import org.apache.spark.sql.Dataset;
  2. import org.apache.spark.sql.Row;
  3. import org.apache.spark.sql.SparkSession;
  4. import org.apache.iotdb.spark.db.*
  5. public class Example {
  6. public static void main(String[] args) {
  7. SparkSession spark = SparkSession
  8. .builder()
  9. .appName("Build a DataFrame from Scratch")
  10. .master("local[*]")
  11. .getOrCreate();
  12. Dataset<Row> df = spark.read().format("org.apache.iotdb.spark.db")
  13. .option("url","jdbc:iotdb://127.0.0.1:6667/")
  14. .option("sql","select * from root").load();
  15. df.printSchema();
  16. df.show();
  17. Dataset<Row> narrowTable = Transformer.toNarrowForm(spark, df)
  18. narrowTable.show()
  19. }
  20. }