Is that not exactly what I've done in j3/j4? The keys are identical
strings.The k is the same, the value in both instances is an associative
array.
devices = devices.map(lambda x: (dh.index('id'), {'deviceid':
x[dh.index('id')], 'foo': x[dh.index('foo')], 'bar':
x[dh.index('bar')]}))
bytes_in_out = transactions.map(lambda x: (x[th.index('deviceid')],
{'deviceid': x[th.index('deviceid')],
'foo': x[th.index('foo')],
'bar': x[th.index('bar')],
'hello': x[th.index('hello')],
'world': x[th.index('world')]}))
j3 = bytes_in_out.join(devices, 10)
j3.take(1)
j4 = devices.join(bytes_int_out, 10)
j4.take(1)
ᐧ
On Fri, Oct 17, 2014 at 5:48 PM, Davies Liu <dav...@databricks.com> wrote:
> Hey Russell,
>
> join() can only work with RDD of pairs (key, value), such as
>
> rdd1: (k, v1)
> rdd2: (k, v2)
>
> rdd1.join(rdd2) will be (k1, v1, v2)
>
> Spark SQL will be more useful for you, see
> http://spark.apache.org/docs/1.1.0/sql-programming-guide.html
>
> Davies
>
>
> On Fri, Oct 17, 2014 at 5:01 PM, Russell Jurney <russell.jur...@gmail.com>
> wrote:
>
>> https://gist.github.com/rjurney/fd5c0110fe7eb686afc9
>>
>> Any way I try to join my data fails. I can't figure out what I'm doing
>> wrong.
>>
>> --
>> Russell Jurney twitter.com/rjurney russell.jur...@gmail.com datasyndrome.
>> com
>> ᐧ
>>
>
>
--
Russell Jurney twitter.com/rjurney russell.jur...@gmail.com datasyndrome.com
