def test_saveAsTextFile_zip():
tempFile = tempfile.NamedTemporaryFile(delete=True)
tempFile.close()
Context().parallelize(range(10)).saveAsTextFile(tempFile.name + '.zip')
read_rdd = Context().textFile(tempFile.name + '.zip')
print(read_rdd.collect())
assert '5' in read_rdd.collect()
def test_local_regex_read():
# was not working before 0.3.19
tempFile = tempfile.NamedTemporaryFile(delete=True)
tempFile.close()
Context().parallelize(range(30), 30).saveAsTextFile(tempFile.name)
d = Context().textFile(tempFile.name + '/part-0000*').collect()
print(d)
assert len(d) == 10
def create_context(n_processes=0):
if not n_processes:
return Context()
p = futures.ProcessPoolExecutor(n_processes)
return Context(
pool=p,
serializer=cloudpickle.dumps,
# serializer=pickle.dumps,
deserializer=pickle.loads,
)
def test_s3_textFile_loop():
random.seed()
fn = f'{S3_TEST_PATH}/pysparkling_test_{random.random() * 999999.0:d}.txt'
rdd = Context().parallelize(f'Line {n}' for n in range(200))
rdd.saveAsTextFile(fn)
rdd_check = Context().textFile(fn)
assert (rdd.count() == rdd_check.count()
and all(e1 == e2
for e1, e2 in zip(rdd.collect(), rdd_check.collect())))
def test_gs_textFile_loop():
random.seed()
fn = '{}/pysparkling_test_{:d}.txt'.format(GS_TEST_PATH,
random.random() * 999999.0)
rdd = Context().parallelize('Line {0}'.format(n) for n in range(200))
rdd.saveAsTextFile(fn)
rdd_check = Context().textFile(fn)
assert (rdd.count() == rdd_check.count()
and all(e1 == e2
for e1, e2 in zip(rdd.collect(), rdd_check.collect())))
def test_hdfs_textFile_loop():
random.seed()
fn = f'{HDFS_TEST_PATH}/pysparkling_test_{random.random() * 999999.0:d}.txt'
print(f'HDFS test file: {fn}')
rdd = Context().parallelize(f'Hello World {x}' for x in range(10))
rdd.saveAsTextFile(fn)
read_rdd = Context().textFile(fn)
print(rdd.collect())
print(read_rdd.collect())
assert (rdd.count() == read_rdd.count()
and all(r1 == r2
for r1, r2 in zip(rdd.collect(), read_rdd.collect())))
def test_hdfs_textFile_loop():
random.seed()
fn = '{}/pysparkling_test_{:d}.txt'.format(HDFS_TEST_PATH,
random.random() * 999999.0)
print('HDFS test file: {0}'.format(fn))
rdd = Context().parallelize('Hello World {0}'.format(x) for x in range(10))
rdd.saveAsTextFile(fn)
read_rdd = Context().textFile(fn)
print(rdd.collect())
print(read_rdd.collect())
assert (rdd.count() == read_rdd.count()
and all(r1 == r2
for r1, r2 in zip(rdd.collect(), read_rdd.collect())))
def test_read_7z():
# file was created with:
# 7z a tests/data.7z tests/readme_example.py
# (brew install p7zip)
rdd = Context().textFile('tests/data.7z')
print(rdd.collect())
assert 'from pysparkling import Context' in rdd.collect()
def test_algorith_excution(self):
"""Tests the algorithm execution with basic parameters
"""
folder_path = os.path.dirname(os.path.realpath(__file__))
json_path = os.path.join(folder_path, 'data', 'objects.json')
dataset = Context(
serializer=cloudpickle.dumps,
deserializer=pickle.loads,
).textFile(json_path).map(json.loads)
dataset.persist()
number_of_cluster = 2
algorithm_settings = settings.ClusteringSetting(
number_of_cluster, 2, 0.01, AlgorithmProvider.random_distance)
labeled, centroids = kmeans.compute_cluster(
dataset, algorithm_settings, AlgorithmProvider.random_sampling,
AlgorithmProvider.random_cluster, AlgorithmProvider.dummy_update)
labeled.collect()
self.assertEqual(10, labeled.count())
first_item = labeled.first()
self.assertTrue(0 <= first_item[0] < 2)
self.assertEqual(number_of_cluster, len(centroids))
def test_session_storage_level(self):
spark = SparkSession(Context())
df = spark.range(4, numPartitions=2)
self.assertEqual(repr(df.storageLevel), repr(StorageLevel(False, False, False, False, 1)))
persisted_df = df.persist()
self.assertEqual(persisted_df.is_cached, True)
self.assertEqual(repr(persisted_df.storageLevel), repr(StorageLevel.MEMORY_ONLY))
def test_first_mp():
p = multiprocessing.Pool(4)
c = Context(pool=p, serializer=cloudpickle.dumps,
deserializer=pickle.loads)
my_rdd = c.parallelize([1, 2, 2, 4, 1, 3, 5, 9], 3)
print(my_rdd.first())
assert my_rdd.first() == 1
def test_s3_textFile_loop():
if not AWS_ACCESS_KEY_ID or not S3_TEST_PATH:
raise SkipTest
random.seed()
fn = '{}/pysparkling_test_{0}.txt'.format(S3_TEST_PATH,
int(random.random() * 999999.0))
rdd = Context().parallelize("Line {0}".format(n) for n in range(200))
rdd.saveAsTextFile(fn)
rdd_check = Context().textFile(fn)
assert (rdd.count() == rdd_check.count()
and all(e1 == e2
for e1, e2 in zip(rdd.collect(), rdd_check.collect())))
def test_gs_textFile_loop():
if not OAUTH2_CLIENT_ID or not GS_TEST_PATH:
raise SkipTest
random.seed()
fn = GS_TEST_PATH + '/pysparkling_test_{0}.txt'.format(
int(random.random() * 999999.0))
rdd = Context().parallelize("Line {0}".format(n) for n in range(200))
rdd.saveAsTextFile(fn)
rdd_check = Context().textFile(fn)
assert (rdd.count() == rdd_check.count()
and all(e1 == e2
for e1, e2 in zip(rdd.collect(), rdd_check.collect())))
def test_s3_textFile():
myrdd = Context().textFile(
's3n://aws-publicdatasets/common-crawl/crawl-data/'
'CC-MAIN-2015-11/warc.paths.*')
assert (
'common-crawl/crawl-data/CC-MAIN-2015-11/segments/1424937481488.49/'
'warc/CC-MAIN-20150226075801-00329-ip-10-28-5-156.ec2.'
'internal.warc.gz' in myrdd.collect())
def test_hdfs_textFile_loop():
if not HDFS_TEST_PATH:
raise SkipTest
random.seed()
fn = HDFS_TEST_PATH+'/pysparkling_test_{0}.txt'.format(
int(random.random()*999999.0)
)
rdd = Context().parallelize('Hello World {0}'.format(x) for x in range(10))
rdd.saveAsTextFile(fn)
read_rdd = Context().textFile(fn)
assert (
rdd.count() == read_rdd.count() and
all(r1 == r2 for r1, r2 in zip(rdd.collect(), read_rdd.collect()))
)
def test_multiprocessing():
p = multiprocessing.Pool(4)
c = Context(pool=p, serializer=cloudpickle.dumps,
deserializer=pickle.loads)
my_rdd = c.parallelize([1, 3, 4])
r = my_rdd.map(lambda x: x*x).collect()
print(r)
assert 16 in r
def test_saveAsTextFile():
tempFile = tempfile.NamedTemporaryFile(delete=True)
tempFile.close()
Context().parallelize(range(10)).saveAsTextFile(tempFile.name)
with open(tempFile.name, 'r') as f:
r = f.readlines()
print(r)
assert '5\n' in r
def test_hdfs_file_exists():
random.seed()
fn1 = f'{HDFS_TEST_PATH}/pysparkling_test_{random.random() * 999999.0:d}.txt'
fn2 = f'{HDFS_TEST_PATH}/pysparkling_test_{random.random() * 999999.0:d}.txt'
rdd = Context().parallelize(f'Hello World {x}' for x in range(10))
rdd.saveAsTextFile(fn1)
assert File(fn1).exists() and not File(fn2).exists()
def test_cache():
# this crashes in version 0.2.28
lines = Context().textFile('tests/*textFil*.py')
lines = lines.map(lambda l: '-' + l).cache()
print(len(lines.collect()))
lines = lines.map(lambda l: '+' + l)
lines = lines.map(lambda l: '-' + l).cache()
lines = lines.collect()
print(lines)
assert '-+-from pysparkling import Context' in lines
def test_hdfs_file_exists():
random.seed()
fn1 = '{}/pysparkling_test_{:d}.txt'.format(HDFS_TEST_PATH,
random.random() * 999999.0)
fn2 = '{}/pysparkling_test_{:d}.txt'.format(HDFS_TEST_PATH,
random.random() * 999999.0)
rdd = Context().parallelize('Hello World {0}'.format(x) for x in range(10))
rdd.saveAsTextFile(fn1)
assert File(fn1).exists() and not File(fn2).exists()
def test_lazy_execution_threadpool():
def indent_line(l):
return '--- ' + l
with futures.ThreadPoolExecutor(4) as p:
r = Context(pool=p).textFile('tests/test_multiprocessing.py')
r = r.map(indent_line).cache()
r.collect()
r = r.map(indent_line)
r = r.collect()
# ThreadPool is not lazy although it returns generators.
print(r)
assert '--- --- from pysparkling import Context' in r
def run_feature_extraction():
start_time = time.time()
desc = 'Feature Extraction for Images'
parser = argparse.ArgumentParser(
description=desc,
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=desc)
default_path = '/media/chris/cschulze_external_4tb/receipt_classifier_images/nonreceipts/train2014'
# default_path = '/media/chris/cschulze_external_4tb/elliot_data/train_nonpill'
# default_path = '/train_nonpill'
parser.add_argument("--input_dir",
help="input directory",
default=default_path)
parser.add_argument("--output",
help="output file",
default='image_features')
args = parser.parse_args()
# serialize and put all images in rdd:
# use json schema:
# "image_name": "",
# "bytes": ""
# "features": "array[]"
image_dir_path = args.input_dir
df, data_arr = serialize_and_make_df(image_dir_path)
print df.head()
print df.info()
# df to df_cvs:
csv_df_file = 'dataframe_csv_file.csv'
json_df_file = 'dataframe_csv_file.json'
df.to_csv(csv_df_file, header=False, index=False)
# df.to_json(json_df_file)
# rdd from df_csv
# pysparkling:
sc = Context()
# pyspark:
# conf = SparkConf().setAppName("HOG and GIST ETL")
# sc = SparkContext(conf=conf)
# rdd = sc.textFile(json_df_file)
num_parts = 4
rdd = sc.parallelize(data_arr, num_parts)
# submit image rdd to processing
rdd_features = rdd.map(get_features).coalesce(1)
# save as txt file:
rdd_features.map(dump).saveAsTextFile(args.output)
print "------------------ %f minutes elapsed ------------------------" % (
(time.time() - start_time) / 60.0)
def test_processpool_distributed_cache():
with futures.ProcessPoolExecutor(4) as p:
r = Context(
pool=p,
serializer=cloudpickle.dumps,
deserializer=pickle.loads,
).parallelize(range(3), 3)
r = r.map(lambda _: time.sleep(0.1)).cache()
r.collect()
time_start = time.time()
print(r.collect())
time_end = time.time()
assert time_end - time_start < 0.3
def test_hdfs_file_exists():
if not HDFS_TEST_PATH:
raise SkipTest
random.seed()
fn1 = HDFS_TEST_PATH + '/pysparkling_test_{0}.txt'.format(
int(random.random() * 999999.0))
fn2 = HDFS_TEST_PATH + '/pysparkling_test_{0}.txt'.format(
int(random.random() * 999999.0))
rdd = Context().parallelize('Hello World {0}'.format(x) for x in range(10))
rdd.saveAsTextFile(fn1)
assert File(fn1).exists() and not File(fn2).exists()
def test_lazy_execution_processpool():
def indent_line(l):
return '--- ' + l
with futures.ProcessPoolExecutor(4) as p:
r = Context(
pool=p,
serializer=cloudpickle.dumps,
deserializer=pickle.loads,
).textFile('tests/test_multiprocessing.py') # .take(10)
print(r.collect())
r = r.map(indent_line)
print(r.collect())
r = r.cache()
print(r.collect())
r = r.map(indent_line)
r = r.collect()
# ProcessPool is not lazy although it returns generators.
print(r)
assert '--- --- from pysparkling import Context' in r
def test_lazy_execution():
class I(object):
def __init__(self):
self.executed = False
def indent_line(self, l):
# global indent_was_executed
self.executed = True
return '--- ' + l
r = Context().textFile('tests/test_multiprocessing.py')
i = I()
r = r.map(i.indent_line)
exec_before_collect = i.executed
# at this point, no map() or foreach() should have been executed
r = r.map(i.indent_line).cache()
print(r.collect())
r = r.map(i.indent_line)
r.collect()
exec_after_collect = i.executed
print((exec_before_collect, exec_after_collect))
assert not exec_before_collect and exec_after_collect
def test_wholeTextFiles():
t = Context().wholeTextFiles('tests/*.py').lookup('tests/test_textFile.py')
print(t)
assert 'test_wholeTextFiles' in t[0]
def test_local_textFile_name():
name = Context().textFile('tests/*.py').name()
print(name)
assert name == 'tests/*.py'
def test_local_textFile_2():
line_count = Context().textFile('tests/*.py').count()
print(line_count)
assert line_count > 90
def test_local_textFile_1():
lines = Context().textFile('tests/*textFil*.py').collect()
print(lines)
assert 'from pysparkling import Context' in lines
