Spark IoTDB Connecter

version

The versions required for Spark and Java are as follow:

Spark Version	Scala Version	Java Version	TsFile
`>= 2.2`	`2.11`	`1.8`	`0.10.0`

install

mvn clean scala:compile compile install

1. maven dependency

    <dependency>
      <groupId>org.apache.iotdb</groupId>
      <artifactId>spark-iotdb-connector</artifactId>
      <version>0.10.0</version>
    </dependency>

2. spark-shell user guide

spark-shell --jars spark-iotdb-connector-0.10.0.jar,iotdb-jdbc-0.10.0-jar-with-dependencies.jar

import org.apache.iotdb.spark.db._

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

df.printSchema()

df.show()

if you want to partition your rdd, you can do as following

spark-shell --jars spark-iotdb-connector-0.10.0.jar,iotdb-jdbc-0.10.0-jar-with-dependencies.jar

import org.apache.iotdb.spark.db._

val df = spark.read.format("org.apache.iotdb.spark.db").option("url","jdbc:iotdb://127.0.0.1:6667/").option("sql","select * from root").
                        option("lowerBound", [lower bound of time that you want query(include)]).option("upperBound", [upper bound of time that you want query(include)]).
                        option("numPartition", [the partition number you want]).load

df.printSchema()

df.show()

3. Schema Inference

Take the following TsFile structure as an example: There are three Measurements in the TsFile schema: status, temperature, and hardware. The basic information of these three measurements is as follows:

name	type	encode
status	Boolean	PLAIN
temperature	Float	RLE
hardware	Text	PLAIN

The existing data in the TsFile is as follows:

root.ln.wf01.wt01		root.ln.wf02.wt02
status		temperature		hardware		status
time	value	time	value	time	value
1	True	1	2.2	2	“aaa”	1	True
3	True	2	2.2	4	“bbb”	2	False
5	False	3	2.1	6	“ccc”	4	True

The wide(default) table form is as follows:

time	root.ln.wf02.wt02.temperature	root.ln.wf02.wt02.status	root.ln.wf02.wt02.hardware	root.ln.wf01.wt01.temperature	root.ln.wf01.wt01.status	root.ln.wf01.wt01.hardware
1	null	true	null	2.2	true	null
2	null	false	aaa	2.2	null	null
3	null	null	null	2.1	true	null
4	null	true	bbb	null	null	null
5	null	null	null	null	false	null
6	null	null	ccc	null	null	null

You can also use narrow table form which as follows: (You can see part 4 about how to use narrow form)

time	device_name	status	hardware	temperature
1	root.ln.wf02.wt01	true	null	2.2
1	root.ln.wf02.wt02	true	null	null
2	root.ln.wf02.wt01	null	null	2.2
2	root.ln.wf02.wt02	false	aaa	null
3	root.ln.wf02.wt01	true	null	2.1
4	root.ln.wf02.wt02	true	bbb	null
5	root.ln.wf02.wt01	false	null	null
6	root.ln.wf02.wt02	null	ccc	null

4. Transform between wide and narrow table

from wide to narrow

import org.apache.iotdb.spark.db._

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
val narrow_df = Transformer.toNarrowForm(spark, wide_df)

from narrow to wide

import org.apache.iotdb.spark.db._

val wide_df = Transformer.toWideForm(spark, narrow_df)

5. Java user guide

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.iotdb.spark.db.*

public class Example {

  public static void main(String[] args) {
    SparkSession spark = SparkSession
        .builder()
        .appName("Build a DataFrame from Scratch")
        .master("local[*]")
        .getOrCreate();

    Dataset<Row> df = spark.read().format("org.apache.iotdb.spark.db")
        .option("url","jdbc:iotdb://127.0.0.1:6667/")
        .option("sql","select * from root").load();

    df.printSchema();

    df.show();
    
    Dataset<Row> narrowTable = Transformer.toNarrowForm(spark, df)
    narrowTable.show()
  }
}