Custom Spark DataFrameReader for querying data from a given layer.

The layer type will be inferred from the layer configuration. Therefore the api to read from index layer or versioned layer is the same. To read from an index or versioned layer, your application must perform the following operations:

• Create an instance of a SparkSession.
• Use LayerDataFrameReader.SparkSessionExt.readLayer to create a LayerDataFrameReader.
• Call query to specify the query.
• Call load to create a DataFrame that contains the data. The method will infer the data format from the layer content type.

Below is an example written in Scala that demonstrates how to query data from an index layer:

import com.here.platform.data.client.spark.LayerDataFrameReader.SparkSessionExt

val spark =
  SparkSession
    .builder()
    .appName(getClass.getSimpleName)
    .master("local[*]")
    .getOrCreate()

val dataFrame: DataFrame = spark
    .readLayer(catalogHrn, indexLayer)
    .query(
        "tileId=INBOUNDINGBOX=(23.648524, 22.689013, 62.284241, 60.218811) and eventType==SignRecognition")
    .load()

Java developers should use com.here.platform.data.client.spark.javadsl.JavaLayerDataFrameReader#readLayer instead of spark.readLayer:

Dataset<Row> df =
      JavaLayerDataFrameReader.create(spark)
          .readLayer(catalogHrn, layerId)
          .query(
              "tileId=INBOUNDINGBOX=(23.648524, 22.689013, 62.284241, 60.218811) and eventType==SignRecognition")
          .load();