Created
June 25, 2019 16:03
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Serializing nested data in Spark
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| import org.apache.spark.sql.{Row, SaveMode} | |
| import org.apache.spark.sql.types._ | |
| val attributesType = new MapType(StringType, StringType, valueContainsNull = false) | |
| val historyEntryType = new StructType() | |
| .add("intervalStart", LongType) | |
| .add("intervalEnd", LongType) | |
| .add("type", StringType) | |
| .add("attributes", attributesType) | |
| val schema = new StructType() | |
| .add("id", LongType) | |
| .add("history", ArrayType(historyEntryType)) | |
| val data = Array( | |
| Row(1L, Array( | |
| Row(1L, 5L, "person", Map("name" -> "John")), | |
| Row(5L, 7L, "person", Map("name" -> "Jim")) | |
| )), | |
| Row(2L, Array( | |
| Row(1L, 5L, "person", Map("type" -> "person", "name" -> "Smith")), | |
| Row(5L, 7L, "person", Map("name" -> "Miller")) | |
| )) | |
| ) | |
| val rdd = session.sparkContext.parallelize(data, numSlices = 2) | |
| val df = session.sqlContext.createDataFrame(rdd, schema) | |
| println(s"Dataframe has ${df.count()} rows.") | |
| val collected = df.collect() | |
| collected.foreach { println } | |
| df.write.mode(SaveMode.Overwrite).parquet("/tmp/test.pqt") |
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