Spark - Creating Nested DataFrame

Question

I'm starting with PySpark and I'm having troubles with creating DataFrames with nested objects.

This is my example.

I have users.

$ cat user.json
{"id":1,"name":"UserA"}
{"id":2,"name":"UserB"}

Users have orders.

$ cat order.json
{"id":1,"price":202.30,"userid":1}
{"id":2,"price":343.99,"userid":1}
{"id":3,"price":399.99,"userid":2}

And I like to join it to get such a struct where orders are array nested in users.

$ 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}]}

How can I do that ? Is there any kind of nested join or something 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: I know there is possibility to do this using join and foldByKey, but is there any simpler way ?

EDIT2: I'm using solution by @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")

I add second nested structure 'lines'

>>> 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)

After this column names from lines are lost. Any ideas ?

EDIT 3: I tried to manual specify schema.

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 <class 'pyspark.sql.types.Row'>

Answer 1

This will work only in Spark 2.0 or later

First we'll need a couple of imports:

from pyspark.sql.functions import struct, collect_list

The rest is a simple aggregation and join:

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"])

For the example data the result is:

combined.show(2, False)

+---+-----+---------------------------+
|id |name |orders                     |
+---+-----+---------------------------+
|1  |UserA|[[1,202.3,1], [2,343.99,1]]|
|2  |UserB|[[3,399.99,2]]             |
+---+-----+---------------------------+

with schema:

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)

and JSON representation:

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}]}

Answer 2

First, you need to use the userid as the join key for the second DataFrame:

user.join(order, user.id == order.userid)

Then you can use a map step to transform the resulting records to your desired format.

Answer 3

For flatining your data frame from nested to normal use

dff= df.select("column with multiple columns.*")