I've got big RDD(1gb) in yarn cluster. On local machine, which use this cluster I have only 512 mb. I'd like to iterate over values in RDD on my local machine. I can't use collect(), because it would create too big array locally which more then my heap. I need some iterative way. There is method iterator(), but it requires some additional information, I can't provide.

rdd.mapPartitions(recordsIterator => your code that processes a single chunk)
​

Or this

rdd.foreachPartition(partition => {
  partition.toArray
  // Your code
})

pyspark dataframe solution using RDD.toLocalIterator():

separator  = '|'
df_results = hiveCtx.sql(sql)
columns    = df_results.columns
print separator.join(columns)

# Use toLocalIterator() rather than collect(), as this avoids pulling all of the
# data to the driver at one time.  Rather, "the iterator will consume as much memory
# as the largest partition in this RDD."
MAX_BUFFERED_ROW_COUNT = 10000
row_count              = 0
output                 = cStringIO.StringIO()
for record in df_results.rdd.toLocalIterator():
    d = record.asDict()
    output.write(separator.join([str(d[c]) for c in columns]) + '\n')
    row_count += 1
    if row_count % MAX_BUFFERED_ROW_COUNT== 0:
        print output.getvalue().rstrip()
        # it is faster to create a new StringIO rather than clear the existing one
        # http://stackoverflow.com/questions/4330812/how-do-i-clear-a-stringio-object
        output = cStringIO.StringIO()
if row_count % MAX_BUFFERED_ROW_COUNT:
    print output.getvalue().rstrip()

Here is the same approach as suggested by @Wildlife but written in pyspark.

The nice thing about this approach - it lets user access records in RDD in order. I'm using this code to feed data from RDD into STDIN of the machine learning tool's process.

rdd = sc.parallelize(range(100), 10)
def make_part_filter(index):
    def part_filter(split_index, iterator):
        if split_index == index:
            for el in iterator:
                yield el
    return part_filter

for part_id in range(rdd.getNumPartitions()):
    part_rdd = rdd.mapPartitionsWithIndex(make_part_filter(part_id), True)
    data_from_part_rdd = part_rdd.collect()
    print "partition id: %s elements: %s" % (part_id, data_from_part_rdd)

Produces output:

partition id: 0 elements: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
partition id: 1 elements: [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
partition id: 2 elements: [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]
partition id: 3 elements: [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]
partition id: 4 elements: [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]
partition id: 5 elements: [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]
partition id: 6 elements: [60, 61, 62, 63, 64, 65, 66, 67, 68, 69]
partition id: 7 elements: [70, 71, 72, 73, 74, 75, 76, 77, 78, 79]
partition id: 8 elements: [80, 81, 82, 83, 84, 85, 86, 87, 88, 89]
partition id: 9 elements: [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]