Maciej Bryński created SPARK-9970:
-------------------------------------
Summary: SQLContext.createDataFrame failed to properly determine
column names
Key: SPARK-9970
URL: https://issues.apache.org/jira/browse/SPARK-9970
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 1.4.0
Reporter: Maciej Bryński
Priority: Minor
Hi,
I'm trying to do "nested join" of tables.
After first join everything is ok, but second join made some of the column
names lost.
My code is following:
def joinTable(tableLeft, tableRight, columnLeft, columnRight, columnNested,
joinType = "left_outer"):
print "Joining tables %s %s" % (namestr(tableLeft), namestr(tableRight))
sys.stdout.flush()
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")
user = sqlContext.read.json(path + "user.json")
user.printSchema()
root
|-- id: long (nullable = true)
|-- name: string (nullable = true)
order = sqlContext.read.json(path + "order.json")
order.printSchema();
root
|-- id: long (nullable = true)
|-- price: double (nullable = true)
|-- userid: long (nullable = true)
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")
orders.printSchema()
root
|-- id: long (nullable = true)
|-- price: double (nullable = true)
|-- userid: long (nullable = true)
|-- lines: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- id: long (nullable = true)
| | |-- orderid: long (nullable = true)
| | |-- product: string (nullable = true)
clients = joinTable(user, orders, "id", "userid", "orders")
clients.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)
I tried to check if groupBy isn't the root of the problem but it's looks right
grouped = orders.rdd.groupBy(lambda r: r.userid)
print grouped.map(lambda x: list(x[1])).collect()
[[Row(id=1, price=202.3, userid=1, lines=[Row(id=1, orderid=1, product=u'XXX'),
Row(id=2, orderid=1, product=u'YYY')]), Row(id=2, price=343.99, userid=1,
lines=[Row(id=3, orderid=2, product=u'XXX')])], [Row(id=3, price=399.99,
userid=2, lines=[Row(id=4, orderid=3, product=u'XXX')])]]
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
