Apache Spark

1. Overview

IoTDB provides the Spark-IoTDB-Connector, a Spark connector for IoTDB's tree model, which supports reading and writing data from/to IoTDB's tree model in Spark environments.

2. Compatibility Requirements

Software	Version
Spark	2.4.0-latest
Scala	2.11, 2.12

• The spark-iotdb-connector is compatible with Java, Scala-based Spark, and PySpark.

3. Deployment Methods

There are two usage scenarios for the spark-iotdb-connector: IDE development and spark-shell debugging.

3.1 IDE Development

For IDE development, simply add the following dependency to your pom.xml file.

<dependency>
      <groupId>org.apache.iotdb</groupId>
      <!-- spark-iotdb-connector_2.11 or spark-iotdb-connector_2.13 -->
      <artifactId>spark-iotdb-connector_2.12.10</artifactId>
      <version>${iotdb.version}</version>
    </dependency>

3.2 spark-shell Debugging

To use the spark-iotdb-connector in spark-shell, follow these steps:

• Download the with-dependencies JAR package from the official website
• Copy the JAR package to the ${SPARK_HOME}/jars directory using the following command:

cp spark-iotdb-connector_2.12.10-${iotdb.version}.jar $SPARK_HOME/jars/

To ensure Spark can connect to IoTDB via JDBC, perform the following steps:

• Compile the IoTDB-JDBC connector by running:

mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependencies

• The compiled JAR package will be located in the following directory:

$IoTDB_HOME/iotdb-client/jdbc/target/iotdb-jdbc-{version}-SNAPSHOT-jar-with-dependencies.jar

• Copy the JAR package to the ${SPARK_HOME}/jars directory using the following command:

cp iotdb-jdbc-{version}-SNAPSHOT-jar-with-dependencies.jar $SPARK_HOME/jars/

4. Usage

4.1 Parameter Description

Parameter	Description	Default Value	Usage Scope	Nullable
url	Specifies the JDBC URL of IoTDB	null	read, write	FALSE
user	IoTDB username	root	read, write	TRUE
password	IoTDB password	root	read, write	TRUE
sql	Specifies the SQL query statement	null	read	TRUE
numPartition	Specifies the number of DataFrame partitions for read operations, and the write concurrency for write operations	1	read, write	TRUE
lowerBound	Query start timestamp (inclusive)	0	read	TRUE
upperBound	Query end timestamp (inclusive)	0	read	TRUE

4.2 Reading Data

• Read data from IoTDB into a DataFrame

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

val df = spark.read.format("org.apache.iotdb.spark.db")
  .option("user", "root")
  .option("password", "root")
  .option("url", "jdbc:iotdb://127.0.0.1:6667/")
  .option("sql", "select ** from root") // Query SQL
  .option("lowerBound", "0") // Timestamp lower bound
  .option("upperBound", "100000000") // Timestamp upper bound
  .option("numPartition", "5") // Number of partitions
  .load

df.printSchema()

df.show()

4.3 Writing Data

// Construct narrow table data
val df = spark.createDataFrame(List(
  (1L, "root.test.d0", 1, 1L, 1.0F, 1.0D, true, "hello"),
  (2L, "root.test.d0", 2, 2L, 2.0F, 2.0D, false, "world")))

val dfWithColumn = df.withColumnRenamed("_1", "Time")
  .withColumnRenamed("_2", "Device")
  .withColumnRenamed("_3", "s0")
  .withColumnRenamed("_4", "s1")
  .withColumnRenamed("_5", "s2")
  .withColumnRenamed("_6", "s3")
  .withColumnRenamed("_7", "s4")
  .withColumnRenamed("_8", "s5")

// Write narrow table data
dfWithColumn
  .write
  .format("org.apache.iotdb.spark.db")
  .option("url", "jdbc:iotdb://127.0.0.1:6667/")
  .save

// Construct wide table data
val df = spark.createDataFrame(List(
  (1L, 1, 1L, 1.0F, 1.0D, true, "hello"),
  (2L, 2, 2L, 2.0F, 2.0D, false, "world")))

val dfWithColumn = df.withColumnRenamed("_1", "Time")
  .withColumnRenamed("_2", "root.test.d0.s0")
  .withColumnRenamed("_3", "root.test.d0.s1")
  .withColumnRenamed("_4", "root.test.d0.s2")
  .withColumnRenamed("_5", "root.test.d0.s3")
  .withColumnRenamed("_6", "root.test.d0.s4")
  .withColumnRenamed("_7", "root.test.d0.s5")

// Write wide table data
dfWithColumn.write.format("org.apache.iotdb.spark.db")
  .option("url", "jdbc:iotdb://127.0.0.1:6667/")
  .option("numPartition", "10")
  .save

5. Wide Table vs Narrow Table

5.1 Data Format Example

Taking the TsFile structure as an example, assume there are three measurements in the TsFile schema: status, temperature, and hardware.

• Basic information:

Name	Type	Encoding
status	Boolean	PLAIN
temperature	Float	RLE
hardware	Text	PLAIN

• Data:
  • d1:root.ln.wf01.wt01
  • d2:root.ln.wf02.wt02

time	d1.status	time	d1.temperature	time	d2.hardware	time	d2.status
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

• Wide table (default) format:

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

• Narrow table format:

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

Note: Corrected the device path typo in the original narrow table example (from root.ln.wf02.wt01 to root.ln.wf01.wt01) to match the data definition.

5.2 Data Conversion Example

• Convert from wide table to narrow table

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)

• Convert from narrow table to wide table

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

val wide_df = Transformer.toWideForm(spark, narrow_df)