I read that RDDs with the same partitioner will be co-located. This is important to me because I want to join several large Hive tables that are not partitioned. My theory i
It's happening because you are not applying partitionBy on key-value pair rdd. Your rdd must be in key-value pair. Also, your key type should be integer. I don't have sample data for your hive table. So let's demonstrate the fact using below hive table:
I have created a below dataframe using hive table :
df = spark.table("udb.emp_details_table");
+------+--------+--------+----------------+
|emp_id|emp_name|emp_dept|emp_joining_date|
+------+--------+--------+----------------+
| 1| AAA| HR| 2018-12-06|
| 1| BBB| HR| 2017-10-26|
| 2| XXX| ADMIN| 2018-10-22|
| 2| YYY| ADMIN| 2015-10-19|
| 2| ZZZ| IT| 2018-05-14|
| 3| GGG| HR| 2018-06-30|
+------+--------+--------+----------------+
Now, I wish to partition my dataframe and want to keep the similar keys in one partition. So, I have converted my dataframe to rdd as you can only apply partitionBy on rdd for re-partitioning.
myrdd = df.rdd
newrdd = myrdd.partitionBy(10,lambda k: int(k[0]))
newrdd.take(10)
I got the same error:
File "/usr/hdp/current/spark2-client/python/pyspark/rdd.py", line 1767, in add_shuffle_key
for k, v in iterator:
ValueError: too many values to unpack
Hence, we need to convert our rdd into key-value pair to use paritionBy
keypair_rdd = myrdd.map(lambda x : (x[0],x[1:]))
Now,you can see that rdd has been converted to key value pair and you can therefore distribute your data in partitions according to keys available.
[(u'1', (u'AAA', u'HR', datetime.date(2018, 12, 6))),
(u'1', (u'BBB', u'HR', datetime.date(2017, 10, 26))),
(u'2', (u'XXX', u'ADMIN', datetime.date(2018, 10, 22))),
(u'2', (u'YYY', u'ADMIN', datetime.date(2015, 10, 19))),
(u'2', (u'ZZZ', u'IT', datetime.date(2018, 5, 14))),
(u'3', (u'GGG', u'HR', datetime.date(2018, 6, 30)))]
Using a paritionBy on key-value rdd now:
newrdd = keypair_rdd.partitionBy(5,lambda k: int(k[0]))
Lets take a look at the partitions. Data is grouped and similar keys are stored into similar partitions now. Two of them are empty.
>>> print("Partitions structure: {}".format(newrdd.glom().map(len).collect()))
Partitions structure: [0, 2, 3, 1, 0]
Now lets say I want to custom partitioning my data. So I have created below function to keep keys '1' and '3' in similar partition.
def partitionFunc(key):
import random
if key == 1 or key == 3:
return 0
else:
return random.randint(1,2)
newrdd = keypair_rdd.partitionBy(5,lambda k: partitionFunc(int(k[0])))
>>> print("Partitions structure: {}".format(newrdd.glom().map(len).collect()))
Partitions structure: [3, 3, 0, 0, 0]
As you can see now that keys 1 and 3 are stored in one partition and rest on other.
I hope this helps. You can try to partitionBy your dataframe. Make sure to convert it into key value pair and keeping key as type integer.