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 ########## @@ -0,0 +1,141 @@ +<!-- + + 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. + +--> +## 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(); } } ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
