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# Spark IoTDB连接

## 版本

Spark和Java所需的版本如下：

| Spark Version | Scala Version | Java Version | TsFile |
| ------------- | ------------- | ------------ |------------ |
| `2.4.3`        | `2.11`        | `1.8`        | `0.9.3`|


## 安装
mvn clean scala：compile编译安装


# 1. Maven依赖

```
    <dependency>
      <groupId>org.apache.iotdb</groupId>
      <artifactId>spark-iotdb-connector</artifactId>
      <version>0.9.3</version>
    </dependency>
```


# 2. Spark-shell用户指南

```
spark-shell --jars spark-iotdb-connector-0.9.3.jar,iotdb-jdbc-0.9.3-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()
```

### 如果要对rdd进行分区，可以执行以下操作
```
spark-shell --jars spark-iotdb-connector-0.9.3.jar,iotdb-jdbc-0.9.3-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. 模式推断

以以下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：

<center>
<table style="text-align:center">
	<tr><th colspan="2">Name</th><th colspan="2">Type</th><th colspan="2">Encode</th></tr>
	<tr><td colspan="2">status</td><td colspan="2">Boolean</td><td colspan="2">PLAIN</td></tr>
	<tr><td colspan="2">temperature</td><td colspan="2">Float</td><td colspan="2">RLE</td></tr>
	<tr><td colspan="2">hardware</td><td colspan="2">Text</td><td colspan="2">PLAIN</td></tr>
</table>
</center>

TsFile中的现有数据如下：


<center>
<table style="text-align:center">
	<tr><th colspan="4">device:root.ln.wf01.wt01</th><th colspan="4">device:root.ln.wf02.wt02</th></tr>
	<tr><th colspan="2">status</th><th colspan="2">temperature</th><th colspan="2">hardware</th><th colspan="2">status</th></tr>
	<tr><th>time</th><th>value</th><th>time</th><th>value</th><th>time</th><th>value</th><th>time</th><th>value</th></tr>
	<tr><td>1</td><td>True</td><td>1</td><td>2.2</td><td>2</td><td>"aaa"</td><td>1</td><td>True</td></tr>
	<tr><td>3</td><td>True</td><td>2</td><td>2.2</td><td>4</td><td>"bbb"</td><td>2</td><td>False</td></tr>
	<tr><td>5</td><td> False </td><td>3</td><td>2.1</td><td>6</td><td>"ccc"</td><td>4</td><td>True</td></tr>
</table>
</center>
宽（默认）表形式如下：

| 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                       |

您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）

| 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. 在宽和窄之间转换

## 从宽到窄

```
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)
```

## 从窄到宽
```
import org.apache.iotdb.spark.db._

val wide_df = Transformer.toWideForm(spark, narrow_df)
```

# 5. Java用户指南
```
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()
  }
}
```