Estoy empezando con PySpark y tengo problemas para crear DataFrames con objetos nesteds.
Este es mi ejemplo.
Tengo usuarios.
$ cat user.json {"id":1,"name":"UserA"} {"id":2,"name":"UserB"} Los usuarios tienen pedidos.
$ cat order.json {"id":1,"price":202.30,"userid":1} {"id":2,"price":343.99,"userid":1} {"id":3,"price":399.99,"userid":2}
Y me gusta unirme a él para obtener una estructura en la que los pedidos se anidan en los usuarios.
$ cat join.json {"id":1, "name":"UserA", "orders":[{"id":1,"price":202.30,"userid":1},{"id":2,"price":343.99,"userid":1}]} {"id":2,"name":"UserB","orders":[{"id":3,"price":399.99,"userid":2}]}
Cómo puedo hacer eso ? ¿Hay algún tipo de unión anidada o algo similar?
>>> user = sqlContext.read.json("user.json") >>> user.printSchema(); root |-- id: long (nullable = true) |-- name: string (nullable = true) >>> order = sqlContext.read.json("order.json") >>> order.printSchema(); root |-- id: long (nullable = true) |-- price: double (nullable = true) |-- userid: long (nullable = true) >>> joined = sqlContext.read.json("join.json") >>> joined.printSchema(); root |-- id: long (nullable = true) |-- name: string (nullable = true) |-- orders: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- id: long (nullable = true) | | |-- price: double (nullable = true) | | |-- userid: long (nullable = true)
EDIT: Sé que existe la posibilidad de hacer esto usando join y foldByKey, pero ¿hay alguna forma más simple?
EDIT2: Estoy usando la solución por @ zero323
def joinTable(tableLeft, tableRight, columnLeft, columnRight, columnNested, joinType = "left_outer"): tmpTable = sqlCtx.createDataFrame(tableRight.rdd.groupBy(lambda r: r.asDict()[columnRight])) tmpTable = tmpTable.select(tmpTable._1.alias("joinColumn"), tmpTable._2.data.alias(columnNested)) return tableLeft.join(tmpTable, tableLeft[columnLeft] == tmpTable["joinColumn"], joinType).drop("joinColumn")
Añado la segunda estructura anidada ‘líneas’
>>> lines = sqlContext.read.json(path + "lines.json") >>> lines.printSchema(); root |-- id: long (nullable = true) |-- orderid: long (nullable = true) |-- product: string (nullable = true) orders = joinTable(order, lines, "id", "orderid", "lines") joined = joinTable(user, orders, "id", "userid", "orders") joined.printSchema() root |-- id: long (nullable = true) |-- name: string (nullable = true) |-- orders: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- id: long (nullable = true) | | |-- price: double (nullable = true) | | |-- userid: long (nullable = true) | | |-- lines: array (nullable = true) | | | |-- element: struct (containsNull = true) | | | | |-- _1: long (nullable = true) | | | | |-- _2: long (nullable = true) | | | | |-- _3: string (nullable = true)
Después de esta columna se pierden los nombres de las líneas. Algunas ideas ?
EDIT 3: traté de especificar manualmente el esquema.
from pyspark.sql.types import * fields = [] fields.append(StructField("_1", LongType(), True)) inner = ArrayType(lines.schema) fields.append(StructField("_2", inner)) new_schema = StructType(fields) print new_schema grouped = lines.rdd.groupBy(lambda r: r.orderid) grouped = grouped.map(lambda x: (x[0], list(x[1]))) g = sqlCtx.createDataFrame(grouped, new_schema)
Error:
TypeError: StructType(List(StructField(id,LongType,true),StructField(orderid,LongType,true),StructField(product,StringType,true))) can not accept object in type
Esto funcionará solo en Spark 2.0 o posterior
Primero necesitaremos un par de importaciones:
from pyspark.sql.functions import struct, collect_list
El rest es una simple agregación y unión.
orders = spark.read.json("/path/to/order.json") users = spark.read.json("/path/to/user.json") combined = users.join( orders .groupBy("userId") .agg(collect_list(struct(*orders.columns)).alias("orders")) .withColumnRenamed("userId", "id"), ["id"])
Para los datos de ejemplo el resultado es:
combined.show(2, False)
+---+-----+---------------------------+ |id |name |orders | +---+-----+---------------------------+ |1 |UserA|[[1,202.3,1], [2,343.99,1]]| |2 |UserB|[[3,399.99,2]] | +---+-----+---------------------------+
con esquema:
combined.printSchema()
root |-- id: long (nullable = true) |-- name: string (nullable = true) |-- orders: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- id: long (nullable = true) | | |-- price: double (nullable = true) | | |-- userid: long (nullable = true)
y representación JSON:
for x in combined.toJSON().collect(): print(x)
{"id":1,"name":"UserA","orders":[{"id":1,"price":202.3,"userid":1},{"id":2,"price":343.99,"userid":1}]} {"id":2,"name":"UserB","orders":[{"id":3,"price":399.99,"userid":2}]}
Primero, debe usar el userid como la clave de unión para el segundo DataFrame :
user.join(order, user.id == order.userid)
Luego, puede usar un paso del map para transformar los registros resultantes al formato deseado.