LeiRui commented on a change in pull request #381: use jdbc to connect iotdb in 
spark
URL: https://github.com/apache/incubator-iotdb/pull/381#discussion_r323205475
 
 

 ##########
 File path: docs/Documentation/UserGuide/9-Tools-spark-iotdb.md
 ##########
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+<!--
+
+    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
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+    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.
+
+-->
+## 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`        | `0.9.0-SNAPSHOT`|
+
+
+## install
+mvn clean scala:compile compile install
+
+
+# 1. maven dependency
+
+```
+    <dependency>
+      <groupId>org.apache.iotdb</groupId>
+      <artifactId>spark-iotdb-connector</artifactId>
+      <version>0.9.0-SNAPSHOT</version>
+    </dependency>
+```
+
+
+# 2. spark-shell user guide
+
+```
+spark-shell --jars 
spark-iotdb-connector-0.9.0-SNAPSHOT.jar,iotdb-jdbc-0.9.0-SNAPSHOT-jar-with-dependencies.jar
+
+import org.apache.iotdb.sparkdb._
+
+val df = 
spark.read.format("org.apache.iotdb.sparkdb").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-0.9.0-SNAPSHOT.jar,iotdb-jdbc-0.9.0-SNAPSHOT-jar-with-dependencies.jar
+
+import org.apache.iotdb.sparkdb._
+
+val df = 
spark.read.format("org.apache.iotdb.sparkdb").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:
+
+<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>
+
+The existing data in the TsFile is as follows:
+
+
+<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</td><th>time</th><th>value</td><th>time</th><th>value</th><th>time</th><th>value</td></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>
+
+
+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.sparkdb._
+
+val wide_df = spark.read.format("org.apache.iotdb.sparkdb").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.sparkdb._
+
+val wide_df = Transformer.toWideForm(spark, narrow_df)
+```
 
 Review comment:
   As @jixuan1989 noted, java docs should be commented in the document too.
   So, maybe you can add # 5. Java user guide like:
   ```
   import org.apache.spark.sql.Dataset;
   import org.apache.spark.sql.Row;
   import org.apache.spark.sql.SparkSession;
   
   public class Tmp {
   
     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.sparkdb")
           .option("url","jdbc:iotdb://127.0.0.1:6667/")
           .option("sql","select * from root").load();
   
       df.printSchema();
   
       df.show();
     }
   }
   
   ```

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