[英]Spark - Creating Nested DataFrame
I'm starting with PySpark and I'm having troubles with creating DataFrames with nested objects.我从 PySpark 开始,在创建带有嵌套对象的 DataFrame 时遇到了麻烦。
This is my example.这是我的例子。
I have users.我有用户。
$ cat user.json
{"id":1,"name":"UserA"}
{"id":2,"name":"UserB"}
Users have orders.用户有订单。
$ 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.我喜欢加入它以获得这样一个结构,其中订单是嵌套在用户中的数组。
$ 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 ?我怎样才能做到这一点 ? Is there any kind of nested join or something similar ?是否有任何嵌套连接或类似的东西?
>>> 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 ?编辑:我知道有可能使用 join 和 foldByKey 来做到这一点,但有没有更简单的方法?
EDIT2: I'm using solution by @zero323 EDIT2:我正在使用@zero323 的解决方案
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'我添加了第二个嵌套结构“行”
>>> 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.在此之后,行中的列名将丢失。 Any ideas ?有任何想法吗 ?
EDIT 3: I tried to manual specify schema.编辑 3:我尝试手动指定架构。
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:错误:
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'>
This will work only in Spark 2.0 or later这仅适用于 Spark 2.0 或更高版本
First we'll need a couple of imports:首先,我们需要几个导入:
from pyspark.sql.functions import struct, collect_list
The rest is a simple aggregation and join:剩下的就是一个简单的聚合和连接:
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:对于示例数据,结果是:
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:与架构:
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:和 JSON 表示:
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}]}
First, you need to use the userid
as the join key for the second DataFrame
:首先,您需要使用userid
作为第二个DataFrame
的连接键:
user.join(order, user.id == order.userid)
Then you can use a map
step to transform the resulting records to your desired format.然后,您可以使用map
步骤将结果记录转换为您想要的格式。
用于将数据框从嵌套变为正常使用
dff= df.select("column with multiple columns.*")
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