Based on code read it looks like Spark does modulo of key for partition.
Keys of c and b end up pointing to same value. Whats the best partitioning
scheme to deal with this?
Regards
Sumit Chawla
On Thu, Jun 21, 2018 at 4:51 PM, Chawla,Sumit <sumitkcha...@gmail.com>
wrote:
> Hi
>
> I have been trying to this simple operation. I want to land all values
> with one key in same partition, and not have any different key in the same
> partition. Is this possible? I am getting b and c always getting mixed
> up in the same partition.
>
>
> rdd = sc.parallelize([('a', 5), ('d', 8), ('b', 6), ('a', 8), ('d', 9),
> ('b', 3),('c', 8)])
> from pyspark.rdd import portable_hash
>
> n = 4
>
> def partitioner(n):
> """Partition by the first item in the key tuple"""
> def partitioner_(x):
> val = x[0]
> key = portable_hash(x[0])
> print ("Val %s Assigned Key %s" % (val, key))
> return key
> return partitioner_
>
> def validate(part):
> last_key = None
> for p in part:
> k = p[0]
> if not last_key:
> last_key = k
> if k != last_key:
> print("Mixed keys in partition %s %s" % (k,last_key) )
>
> partioned = (rdd
> .keyBy(lambda kv: (kv[0], kv[1]))
> .repartitionAndSortWithinPartitions(
> numPartitions=n, partitionFunc=partitioner(n),
> ascending=False)).map(lambda x: x[1])
>
> print(partioned.getNumPartitions())
> partioned.foreachPartition(validate)
>
>
> Val a Assigned Key -7583489610679606711
> Val a Assigned Key -7583489610679606711
> Val d Assigned Key 2755936516345535118
> Val b Assigned Key -1175849324817995036
> Val c Assigned Key 1421958803217889556
> Val d Assigned Key 2755936516345535118
> Val b Assigned Key -1175849324817995036
> Mixed keys in partition b c
> Mixed keys in partition b c
>
>
> Regards
> Sumit Chawla
>
>
