Source: https://dev-docs.nebula-graph.io/v5.3.2/clients/spark-connector/read/
Documentation version: v5.3.2

# Reading from NebulaGraph Database Developer Edition

The Spark connector allows you to read data from nodes, edges, or the result of a GQL query and then load it into a Spark DataFrame. The examples below use reader options and configuration objects.

## Reading nodes

The following samples read nodes of node type `Movie` from the graph `movie` and load the node properties into a Spark DataFrame.

### By using reader options

Replace `<current-password>` with the current password for the specified account. If the password has changed, use the new password.

````
      val df = spark
       .read
       .format("com.vesoft.nebula.spark.connector.NebulaDataSource")
       .option("type", "node")
       .option("operate_type", "read")
       .option("graph_address", "127.0.0.1:9669")
       .option("label", "User")
       .option("user", "root")
       .option("passwd", "<current-password>")
       .option("graph_name", "movie")
       .option("batch_size", "10")
       .load()

````

### By using configuration objects

````
    val connectionConfig: NebulaConnectionConfig = NebulaConnectionConfig
      .builder()
      .withGraphAddress("127.0.0.1:9669")
      .withUser("root")
      .withPasswd("<current-password>")
      .build()

    val nebulaNodeReadConfig: ReadNebulaConfig = ReadNebulaConfig
      .builder()
      .withGraphName("movie")
      .withTypeName("Movie")
      .withReturnCols(List("id"))
      .withBatchSize(10)
      .withPartitionNum(1)
      .build()
    val df = spark.read.nebula(connectionConfig, nebulaNodeReadConfig).loadNode()

````

## Reading edges

The following samples read edges of edge type `Act` from the graph `movie` and load the edge properties into a Spark DataFrame.

### By using reader options

````
      val df = spark.read
       .format("com.vesoft.nebula.spark.connector.NebulaDataSource")
       .option("type", "edge")
       .option("operate_type", "read")
       .option("graph_address", "127.0.0.1:9669")
       .option("user", "root")
       .option("passwd", "<current-password>")
       .option("graph_name", "movie")
       .option("label", "Watch")
       .option("batch_size", "10")
       .load()

````

### By using configuration objects

````
    val connectionConfig: NebulaConnectionConfig = NebulaConnectionConfig
      .builder()
      .withGraphAddress("127.0.0.1:9669")
      .withUser("root")
      .withPasswd("<current-password>")
      .build()
    val nebulaReadEdgeConfig: ReadNebulaConfig = ReadNebulaConfig
      .builder()
      .withGraphName("movie")
      .withTypeName("Act")
      .withReturnCols(null)
      .withBatchSize(1000)
      .withPartitionNum(1)
      .build()
    val df = spark.read.nebula(connectionConfig, nebulaReadEdgeConfig).loadEdge()

````

## Reading query results

The following samples read the results of a GQL query from the graph `movie` and load the results into a Spark DataFrame.

### By using reader options

````
      val df = spark
       .read
       .format("com.vesoft.nebula.spark.connector.NebulaDataSource")
       .option("type", "gql")
       .option("operate_type", "read")
       .option("graph_address", "127.0.0.1:9669")
       .option("user", "root")
       .option("passwd", "<current-password>")
       .option("gql","use movie match(v:Movie) return v.id, v.name")
       .load()

````

### By using configuration objects

````
    val connectionConfig: NebulaConnectionConfig = NebulaConnectionConfig
      .builder()
      .withGraphAddress("127.0.0.1:9669")
      .withUser("root")
      .withPasswd("<current-password>")
      .build()
     val nebulaGqlConfig:GqlNebulaConfig = GqlNebulaConfig
      .builder()
      .withGql("USE movie MATCH(v:Movie) RETURN v.id, v.name")
      .build()
    val df = spark.read.gql(connectionConfig, nebulaGqlConfig).load()

````

## More information

- [Reader options](https://dev-docs.nebula-graph.io/v5.3.2/clients/spark-connector/config/reader-options/)
- [Configuration classes](https://dev-docs.nebula-graph.io/v5.3.2/clients/spark-connector/config/)