9.11. Parquet Converter

The Parquet converter handles data written by Apache Parque. To use the Parquet converter, specify type = "parquet" in your converter definition.

9.11.1. Configuration

The Parquet converter supports parsing whole Parquet files. Due to the Parquet random-access API, the file path must be specified in the EvaluationContext. Further, pure streaming conversion is not possible (i.e. using bash pipe redirection into the ingest or convert command).

As Parquet does not define any object model, standard practice is to parse a Parquet file into Avro GenericRecords.

The Avro GenericRecord being parsed is available to field transforms as $0.

9.11.2. Avro Paths

Because Parquet files are converted into Avro records, it is possible to use Avro paths to select elements. See Avro Converter for details on Avro paths. Note that the result of an Avro path expression will be typed appropriately according to the Parquet column type (e.g. String, Double, List, etc).

9.11.3. Parquet Transform Functions

GeoMesa defines several Parquet-specific transform functions, in addition to the ones defined under Avro Transform Functions.

9.11.3.1. parquetPoint

Description: Parses a nested Point structure from a Parquet record

Usage: parquetPoint($ref, $pathString)

  • $ref - a reference object (Avro root record or extracted object)
  • pathString - forward-slash delimited path string. See Avro Paths, above

The point function can parse GeoMesa-encoded Point columns, which consist of a Parquet group of two double-type columns named x and y.

9.11.3.2. parquetLineString

Description: Parses a nested LineString structure from a Parquet record

Usage: parquetLineString($ref, $pathString)

  • $ref - a reference object (Avro root record or extracted object)
  • pathString - forward-slash delimited path string. See Avro Paths, above

The linestring function can parse GeoMesa-encoded LineString columns, which consist of a Parquet group of two repeated double-type columns named x and y.

9.11.3.3. parquetPolygon

Description: Parses a nested Polygon structure from a Parquet record

Usage: parquetPolygon($ref, $pathString)

  • $ref - a reference object (Avro root record or extracted object)
  • pathString - forward-slash delimited path string. See Avro Paths, above

The polygon function can parse GeoMesa-encoded Polygon columns, which consist of a Parquet group of two list-type columns named x and y. The list elements are repeated double-type columns.

9.11.3.4. parquetMultiPoint

Description: Parses a nested MultiPoint structure from a Parquet record

Usage: parquetMultiPoint($ref, $pathString)

  • $ref - a reference object (Avro root record or extracted object)
  • pathString - forward-slash delimited path string. See Avro Paths, above

The multi-point function can parse GeoMesa-encoded MultiPoint columns, which consist of a Parquet group of two repeated double-type columns named x and y.

9.11.3.5. parquetMultiLineString

Description: Parses a nested MultiLineString structure from a Parquet record

Usage: parquetMultiLineString($ref, $pathString)

  • $ref - a reference object (Avro root record or extracted object)
  • pathString - forward-slash delimited path string. See Avro Paths, above

The multi-linestring function can parse GeoMesa-encoded MultiLineString columns, which consist of a Parquet group of two list-type columns named x and y. The list elements are repeated double-type columns.

9.11.3.6. parquetMultiPolygon

Description: Parses a nested MultiPolygon structure from a Parquet record

Usage: parquetMultiPolygon($ref, $pathString)

  • $ref - a reference object (Avro root record or extracted object)
  • pathString - forward-slash delimited path string. See Avro Paths, above

The multi-polygon function can parse GeoMesa-encoded MultiPolygon columns, which consist of a Parquet group of two list-type columns named x and y. The list elements are also lists, and the nested list elements are repeated double-type columns.

9.11.4. Example Usage

For this example we’ll consider the following JSON file:

{ "id": 1, "number": 123, "color": "red",   "physical": { "weight": 127.5,   "height": "5'11" }, "lat": 0,   "lon": 0 }
{ "id": 2, "number": 456, "color": "blue",  "physical": { "weight": 150,     "height": "5'11" }, "lat": 1,   "lon": 1 }
{ "id": 3, "number": 789, "color": "green", "physical": { "weight": 200.4,   "height": "6'2" },  "lat": 4.4, "lon": 3.3 }

This file can be converted to Parquet using Spark:

import org.apache.spark.sql.SparkSession
val session = SparkSession.builder().appName("testSpark").master("local[*]").getOrCreate()
val df = session.read.json("/tmp/example.json")
df.write.option("compression","gzip").parquet("/tmp/example.parquet")

The following SimpleFeatureType and converter would be sufficient to parse the resulting Parquet file:

{
  "geomesa" : {
    "sfts" : {
      "example" : {
         "fields" : [
          { "name" : "color",  "type" : "String" }
          { "name" : "number", "type" : "Long"   }
          { "name" : "height", "type" : "String" }
          { "name" : "weight", "type" : "Double" }
          { "name" : "geom",   "type" : "Point", "srid" : 4326 }
        ]
      }
    },
    "converters" : {
      "example" : {
        "type" : "parquet",
        "id-field" : "avroPath($0, '/id')",
        "fields" : [
          { "name" : "color",  "transform" : "avroPath($0,'/color')" },
          { "name" : "number", "transform" : "avroPath($0,'/number')" },
          { "name" : "height", "transform" : "avroPath($0,'/physical/height')" },
          { "name" : "weight", "transform" : "avroPath($0,'/physical/weight')" },
          { "name" : "geom",   "transform" : "point(avroPath($0,'/lon'),avroPath($0,'/lat'))" }
        ],
        "options" : {
          "encoding" : "UTF-8",
          "error-mode" : "skip-bad-records",
          "parse-mode" : "incremental",
          "validators" : [ "index" ]
        }
      }
    }
  }
}