<!--

    Licensed to the Apache Software Foundation (ASF) under one
    or more contributor license agreements.  See the NOTICE file
    distributed with this work for additional information
    regarding copyright ownership.  The ASF licenses this file
    to you under the Apache License, Version 2.0 (the
    "License"); you may not use this file except in compliance
    with the License.  You may obtain a copy of the License at
    
        http://www.apache.org/licenses/LICENSE-2.0
    
    Unless required by applicable law or agreed to in writing,
    software distributed under the License is distributed on an
    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    KIND, either express or implied.  See the License for the
    specific language governing permissions and limitations
    under the License.

-->
## Spark-IoTDB
### version

The versions required for Spark and Java are as follow:

| Spark Version | Scala Version | Java Version | TsFile |
| :-------------: | :-------------: | :------------: |:------------: |
| `2.4.3`        | `2.11`        | `1.8`        | `0.12.0`|


> Currently we only support spark version 2.4.3 and there are some known issue on 2.4.7, do no use it


### Install
mvn clean scala:compile compile install


#### Maven Dependency

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


#### spark-shell user guide

```
spark-shell --jars spark-iotdb-connector-0.12.0.jar,iotdb-jdbc-0.12.0-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()
```

To partition rdd:

```
spark-shell --jars spark-iotdb-connector-0.12.0.jar,iotdb-jdbc-0.12.0-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()
```

#### 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:

<img width="517" alt="SI " src="https://user-images.githubusercontent.com/69114052/98197835-99a64980-1f62-11eb-84af-8301b8a6aad5.png">

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                          |

#### 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)
```

#### 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()
  }
}
```

