def hdfs_orc_parser():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
hadoop_namenode_is_accessible = pyunit_utils.hadoop_namenode_is_accessible()
if hadoop_namenode_is_accessible:
hdfs_name_node = pyunit_utils.hadoop_namenode()
if pyunit_utils.cannaryHDFSTest(hdfs_name_node, "/datasets/orc_parser/orc/orc_split_elim.orc"):
print("Your hive-exec version is too old. Orc parser test {0} is "
"skipped.".format("pyunit_INTERNAL_HDFS_milsongs_orc.py"))
pass
else:
hdfs_orc_file = "/datasets/orc_parser/milsongs_orc"
url_orc = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_orc_file)
hdfs_csv_file = "/datasets/orc_parser/milsongs_csv"
url_csv = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_csv_file)
multi_file_csv = h2o.import_file(url_csv)
multi_file_orc = h2o.import_file(url_orc)
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
multi_file_orc.summary()
orc_summary = h2o.frame(multi_file_orc.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
else:
raise EnvironmentError
def hdfs_orc_parser():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
hadoop_namenode_is_accessible = pyunit_utils.hadoop_namenode_is_accessible(
)
if hadoop_namenode_is_accessible:
hdfs_name_node = pyunit_utils.hadoop_namenode()
hdfs_orc_file = "/datasets/orc_parser/milsongs_orc"
url_orc = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_orc_file)
hdfs_csv_file = "/datasets/orc_parser/milsongs_csv"
url_csv = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_csv_file)
multi_file_csv = h2o.import_file(url_csv)
multi_file_orc = h2o.import_file(url_orc)
multi_file_csv.summary()
csv_summary = h2o.frame(
multi_file_csv.frame_id)["frames"][0]["columns"]
multi_file_orc.summary()
orc_summary = h2o.frame(
multi_file_orc.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
else:
raise EnvironmentError
def hdfs_orc_parser():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
hadoop_namenode_is_accessible = pyunit_utils.hadoop_namenode_is_accessible(
)
if hadoop_namenode_is_accessible:
hdfs_name_node = pyunit_utils.hadoop_namenode()
hdfs_orc_file = "/datasets/orc_parser/air05_orc"
url_orc = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_orc_file)
hdfs_csv_file = "/datasets/orc_parser/air05_csv"
url_csv = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_csv_file)
startcsv = time.time()
multi_file_csv = h2o.import_file(hdfs_csv_file, na_strings=['\\N'])
endcsv = time.time()
csv_type_dict = multi_file_csv.types
multi_file_csv.summary()
csv_summary = h2o.frame(
multi_file_csv.frame_id)["frames"][0]["columns"]
col_ind_name = dict()
# change column types from real to enum according to multi_file_csv column types
for key_name in list(csv_type_dict):
col_ind = key_name.split('C')
new_ind = int(str(col_ind[1])) - 1
col_ind_name[new_ind] = key_name
col_types = []
for ind in range(len(col_ind_name)):
col_types.append(csv_type_dict[col_ind_name[ind]])
startorc1 = time.time()
multi_file_orc1 = h2o.import_file(url_orc)
endorc1 = time.time()
h2o.remove(multi_file_orc1)
startorc = time.time()
multi_file_orc = h2o.import_file(url_orc, col_types=col_types)
endorc = time.time()
multi_file_orc.summary()
orc_summary = h2o.frame(
multi_file_orc.frame_id)["frames"][0]["columns"]
print("************** CSV parse time is {0}".format(endcsv - startcsv))
print(
"************** ORC (without column type forcing) parse time is {0}"
.format(endorc1 - startorc1))
print(
"************** ORC (with column type forcing) parse time is {0}".
format(endorc - startorc))
# compare frame read by orc by forcing column type,
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
else:
raise EnvironmentError
def import_folder():
tol_time = 200 # comparing in ms or ns for timestamp columns
tol_numeric = 1e-5 # tolerance for comparing other numeric fields
numElements2Compare = 0 # choose number of elements per column to compare. Save test time.
multi_file_csv = h2o.import_file(path=pyunit_utils.locate(
"smalldata/parser/hexdev_497/airlines_first_header"))
multi_file_gzip_comp = \
h2o.import_file(path=pyunit_utils.locate("smalldata/parser/hexdev_497/airlines_first_header.zip"))
try:
# make sure the two agrees
assert pyunit_utils.compare_frames(multi_file_csv, multi_file_gzip_comp, numElements2Compare, tol_time,
tol_numeric, True), "H2O frame parsed from multiple orc and single orc " \
"files are different!"
except: # in case the files are listed differently, we can always just check to see if the summary agrees.
multi_file_gzip_comp.summary()
zip_summary = h2o.frame(
multi_file_gzip_comp.frame_id)["frames"][0]["columns"]
multi_file_csv.summary()
csv_summary = h2o.frame(
multi_file_csv.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(zip_summary, csv_summary)
def hdfs_orc_parser():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
hadoop_namenode_is_accessible = pyunit_utils.hadoop_namenode_is_accessible(
)
if hadoop_namenode_is_accessible:
hdfs_name_node = pyunit_utils.hadoop_namenode()
if pyunit_utils.cannaryHDFSTest(
hdfs_name_node, "/datasets/orc_parser/orc/orc_split_elim.orc"):
print("Your hive-exec version is too old. Orc parser test {0} is "
"skipped.".format("pyunit_INTERNAL_HDFS_milsongs_orc.py"))
pass
else:
hdfs_orc_file = "/datasets/orc_parser/milsongs_orc"
url_orc = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_orc_file)
hdfs_csv_file = "/datasets/orc_parser/milsongs_csv"
url_csv = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_csv_file)
multi_file_csv = h2o.import_file(url_csv)
multi_file_orc = h2o.import_file(url_orc)
multi_file_csv.summary()
csv_summary = h2o.frame(
multi_file_csv.frame_id)["frames"][0]["columns"]
multi_file_orc.summary()
orc_summary = h2o.frame(
multi_file_orc.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
else:
raise EnvironmentError
def import_folder():
tol_time = 200 # comparing in ms or ns
tol_numeric = 1e-5 # tolerance for comparing other numeric fields
numElements2Compare = 100 # choose number of elements per column to compare. Save test time.
# compressed the whole directory of files.
multi_file_gzip_comp = h2o.import_file(path=pyunit_utils.locate(
"bigdata/laptop/parser/hexdev_497/milsongs_csv.zip"))
# directory containing the gzip version of csv files here.
multi_file_csv = h2o.import_file(path=pyunit_utils.locate(
"bigdata/laptop/parser/hexdev_497/milsongs_csv_gzip"))
try:
# make sure the two agrees
assert pyunit_utils.compare_frames(multi_file_csv, multi_file_gzip_comp, numElements2Compare, tol_time,
tol_numeric, True), "H2O frame parsed from multiple orc and single orc " \
"files are different!"
except: # in case the files are listed differently, we can always just check to see if the summary agrees.
multi_file_gzip_comp.summary()
zip_summary = h2o.frame(
multi_file_gzip_comp.frame_id)["frames"][0]["columns"]
multi_file_csv.summary()
csv_summary = h2o.frame(
multi_file_csv.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(zip_summary, csv_summary)
def hdfs_orc_parser():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
hadoop_namenode_is_accessible = pyunit_utils.hadoop_namenode_is_accessible()
if hadoop_namenode_is_accessible:
hdfs_name_node = pyunit_utils.hadoop_namenode()
if pyunit_utils.cannaryHDFSTest(hdfs_name_node, "/datasets/orc_parser/orc/orc_split_elim.orc"):
print("Your hive-exec version is too old. Orc parser test {0} is "
"skipped.".format("pyunit_INTERNAL_HDFS_import_folder_airline_05_orc.py"))
pass
else:
hdfs_orc_file = "/datasets/orc_parser/air05_orc"
url_orc = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_orc_file)
hdfs_csv_file = "/datasets/orc_parser/air05_csv"
url_csv = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_csv_file)
startcsv = time.time()
multi_file_csv = h2o.import_file(url_csv, na_strings=['\\N'])
endcsv = time.time()
csv_type_dict = multi_file_csv.types
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
col_ind_name = dict()
# change column types from real to enum according to multi_file_csv column types
for key_name in list(csv_type_dict):
col_ind = key_name.split('C')
new_ind = int(str(col_ind[1]))-1
col_ind_name[new_ind] = key_name
col_types = []
for ind in range(len(col_ind_name)):
col_types.append(csv_type_dict[col_ind_name[ind]])
startorc1 = time.time()
multi_file_orc1 = h2o.import_file(url_orc)
endorc1 = time.time()
h2o.remove(multi_file_orc1)
startorc = time.time()
multi_file_orc = h2o.import_file(url_orc,col_types=col_types)
endorc = time.time()
multi_file_orc.summary()
orc_summary = h2o.frame(multi_file_orc.frame_id)["frames"][0]["columns"]
print("************** CSV parse time is {0}".format(endcsv-startcsv))
print("************** ORC (without column type forcing) parse time is {0}".format(endorc1-startorc1))
print("************** ORC (with column type forcing) parse time is {0}".format(endorc-startorc))
# compare frame read by orc by forcing column type,
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
else:
raise EnvironmentError
def import_folder():
multi_file_csv = h2o.import_file(path=pyunit_utils.locate("smalldata/parser/hexdev_497/airlines_first_header"))
multi_file_gzip_comp = \
h2o.import_file(path=pyunit_utils.locate("smalldata/parser/hexdev_497/airlines_first_header.zip"))
multi_file_gzip_comp.summary()
zip_summary = h2o.frame(multi_file_gzip_comp.frame_id)["frames"][0]["columns"]
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(zip_summary, csv_summary)
def import_folder():
"""
This test will build a H2O frame from importing the bigdata/laptop/parser/orc/airlines_05p_orc_csv
from and build another H2O frame from the multi-file orc parser using multiple orc files that are
saved in the directory bigdata/laptop/parser/orc/airlines_05p_orc. It will compare the two frames
to make sure they are equal.
:return: None if passed. Otherwise, an exception will be thrown.
"""
startcsv = time.time()
multi_file_csv = h2o.import_file(path=pyunit_utils.locate(
"bigdata/laptop/parser/orc/pubdev_3200/air05_csv"),
na_strings=['\\N'])
endcsv = time.time()
csv_type_dict = multi_file_csv.types
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
col_ind_name = dict()
# change column types from real to enum according to multi_file_csv column types
for key_name in list(csv_type_dict):
col_ind = key_name.split('C')
new_ind = int(str(col_ind[1])) - 1
col_ind_name[new_ind] = key_name
col_types = []
for ind in range(len(col_ind_name)):
col_types.append(csv_type_dict[col_ind_name[ind]])
startorc1 = time.time()
multi_file_orc1 = h2o.import_file(path=pyunit_utils.locate(
"bigdata/laptop/parser/orc/pubdev_3200/air05_orc"))
endorc1 = time.time()
h2o.remove(multi_file_orc1)
startorc = time.time()
multi_file_orc = h2o.import_file(path=pyunit_utils.locate(
"bigdata/laptop/parser/orc/pubdev_3200/air05_orc"),
col_types=col_types)
endorc = time.time()
multi_file_orc.summary()
orc_summary = h2o.frame(multi_file_orc.frame_id)["frames"][0]["columns"]
print("************** CSV parse time is {0}".format(endcsv - startcsv))
print("************** ORC (without column type forcing) parse time is {0}".
format(endorc1 - startorc1))
print("************** ORC (with column type forcing) parse time is {0}".
format(endorc - startorc))
# compare frame read by orc by forcing column type,
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
def import_folder():
multi_file_csv = h2o.import_file(path=pyunit_utils.locate(
"smalldata/parser/hexdev_497/airlines_first_header"))
multi_file_gzip_comp = \
h2o.import_file(path=pyunit_utils.locate("smalldata/parser/hexdev_497/airlines_first_header.zip"))
multi_file_gzip_comp.summary()
zip_summary = h2o.frame(
multi_file_gzip_comp.frame_id)["frames"][0]["columns"]
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(zip_summary, csv_summary)
def parquet_parse_simple():
"""
Tests Parquet parser by comparing the summary of the original csv frame with the h2o parsed Parquet frame.
Basic use case of importing files with auto-detection of column types.
:return: None if passed. Otherwise, an exception will be thrown.
"""
csv = h2o.import_file(path=pyunit_utils.locate("smalldata/airlines/AirlinesTrain.csv.zip"))
parquet = h2o.import_file(path=pyunit_utils.locate("smalldata/parser/parquet/airlines-simple.snappy.parquet"))
csv.summary()
csv_summary = h2o.frame(csv.frame_id)["frames"][0]["columns"]
parquet.summary()
parquet_summary = h2o.frame(parquet.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(csv_summary, parquet_summary)
def import_folder():
"""
This test will build a H2O frame from importing the bigdata/laptop/parser/orc/airlines_05p_orc_csv
from and build another H2O frame from the multi-file orc parser using multiple orc files that are
saved in the directory bigdata/laptop/parser/orc/airlines_05p_orc. It will compare the two frames
to make sure they are equal.
:return: None if passed. Otherwise, an exception will be thrown.
"""
startcsv = time.time()
multi_file_csv = h2o.import_file(path=pyunit_utils.locate("bigdata/laptop/parser/orc/pubdev_3200/air05_csv"),
na_strings=['\\N'])
endcsv = time.time()
csv_type_dict = multi_file_csv.types
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
col_ind_name = dict()
# change column types from real to enum according to multi_file_csv column types
for key_name in list(csv_type_dict):
col_ind = key_name.split('C')
new_ind = int(str(col_ind[1]))-1
col_ind_name[new_ind] = key_name
col_types = []
for ind in range(len(col_ind_name)):
col_types.append(csv_type_dict[col_ind_name[ind]])
startorc1 = time.time()
multi_file_orc1 = h2o.import_file(path=pyunit_utils.locate("bigdata/laptop/parser/orc/pubdev_3200/air05_orc"))
endorc1 = time.time()
h2o.remove(multi_file_orc1)
startorc = time.time()
multi_file_orc = h2o.import_file(path=pyunit_utils.locate("bigdata/laptop/parser/orc/pubdev_3200/air05_orc"),
col_types=col_types)
endorc = time.time()
multi_file_orc.summary()
orc_summary = h2o.frame(multi_file_orc.frame_id)["frames"][0]["columns"]
print("************** CSV parse time is {0}".format(endcsv-startcsv))
print("************** ORC (without column type forcing) parse time is {0}".format(endorc1-startorc1))
print("************** ORC (with column type forcing) parse time is {0}".format(endorc-startorc))
# compare frame read by orc by forcing column type,
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
def import_folder():
"""
This test will build a H2O frame from importing the bigdata/laptop/parser/orc/milsongs_orc_csv
from and build another H2O frame from the multi-file orc parser using multiple orc files that are
saved in the directory bigdata/laptop/parser/orc/milsongs_orc. It will compare the two frames
to make sure they are equal.
:return: None if passed. Otherwise, an exception will be thrown.
"""
multi_file_csv = h2o.import_file(path=pyunit_utils.locate("bigdata/laptop/parser/orc/milsongs_orc_csv"))
multi_file_orc = h2o.import_file(path=pyunit_utils.locate("bigdata/laptop/parser/orc/milsongs_orc"))
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
multi_file_orc.summary()
orc_summary = h2o.frame(multi_file_orc.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
def parquet_parse_simple():
"""
Tests Parquet parser by comparing the summary of the original csv frame with the h2o parsed Parquet frame.
Basic use case of importing files with auto-detection of column types.
:return: None if passed. Otherwise, an exception will be thrown.
"""
csv = h2o.import_file(
path=pyunit_utils.locate("smalldata/airlines/AirlinesTrain.csv.zip"))
parquet = h2o.import_file(path=pyunit_utils.locate(
"smalldata/parser/parquet/airlines-simple.snappy.parquet"))
csv.summary()
csv_summary = h2o.frame(csv.frame_id)["frames"][0]["columns"]
parquet.summary()
parquet_summary = h2o.frame(parquet.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(csv_summary, parquet_summary)
def import_folder():
tol_time = 200 # comparing in ms or ns for timestamp columns
tol_numeric = 1e-5 # tolerance for comparing other numeric fields
numElements2Compare = 0 # choose number of elements per column to compare. Save test time.
multi_file_csv = h2o.import_file(path=pyunit_utils.locate("smalldata/parser/hexdev_497/airlines_first_header"))
multi_file_gzip_comp = \
h2o.import_file(path=pyunit_utils.locate("smalldata/parser/hexdev_497/airlines_first_header.zip"))
try:
# make sure the two agrees
assert pyunit_utils.compare_frames(multi_file_csv, multi_file_gzip_comp, numElements2Compare, tol_time,
tol_numeric, True), "H2O frame parsed from multiple orc and single orc " \
"files are different!"
except: # in case the files are listed differently, we can always just check to see if the summary agrees.
multi_file_gzip_comp.summary()
zip_summary = h2o.frame(multi_file_gzip_comp.frame_id)["frames"][0]["columns"]
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(zip_summary, csv_summary)
def import_folder():
tol_time = 200 # comparing in ms or ns
tol_numeric = 1e-5 # tolerance for comparing other numeric fields
numElements2Compare = 100 # choose number of elements per column to compare. Save test time.
# compressed the whole directory of files.
multi_file_gzip_comp = h2o.import_file(path=pyunit_utils.locate("bigdata/laptop/parser/hexdev_497/milsongs_csv.zip"))
# directory containing the gzip version of csv files here.
multi_file_csv = h2o.import_file(path=pyunit_utils.locate("bigdata/laptop/parser/hexdev_497/milsongs_csv_gzip"))
try:
# make sure the two agrees
assert pyunit_utils.compare_frames(multi_file_csv, multi_file_gzip_comp, numElements2Compare, tol_time,
tol_numeric, True), "H2O frame parsed from multiple orc and single orc " \
"files are different!"
except: # in case the files are listed differently, we can always just check to see if the summary agrees.
multi_file_gzip_comp.summary()
zip_summary = h2o.frame(multi_file_gzip_comp.frame_id)["frames"][0]["columns"]
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
pyunit_utils.compare_frame_summary(zip_summary, csv_summary)
def hdfs_orc_parser():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
hadoop_namenode_is_accessible = pyunit_utils.hadoop_namenode_is_accessible()
if hadoop_namenode_is_accessible:
numElements2Compare = 10
tol_time = 200
tol_numeric = 1e-5
hdfs_name_node = pyunit_utils.hadoop_namenode()
hdfs_orc_file = "/datasets/airlines_all_orc_parts"
hdfs_csv_file = "/datasets/air_csv_part"
col_types = ['real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'enum', 'real', 'enum', 'real',
'real', 'enum', 'real', 'real', 'enum', 'enum', 'real', 'enum', 'enum', 'real', 'real', 'real',
'enum', 'enum', 'enum', 'enum', 'enum', 'enum', 'enum']
# import CSV file
print("Import airlines 116M dataset in original csv format from HDFS")
url_csv = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_csv_file)
startcsv = time.time()
multi_file_csv = h2o.import_file(url_csv, na_strings=['\\N'], col_types=col_types)
endcsv = time.time()
startcsv1 = time.time()
multi_file_csv1 = h2o.import_file(url_csv)
endcsv1 = time.time()
h2o.remove(multi_file_csv1)
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
# import ORC file with same column types as CSV file
print("Import airlines 116M dataset in ORC format from HDFS")
url_orc = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_orc_file)
startorc1 = time.time()
multi_file_orc1 = h2o.import_file(url_orc)
endorc1 = time.time()
h2o.remove(multi_file_orc1)
startorc = time.time()
multi_file_orc = h2o.import_file(url_orc, col_types=col_types)
endorc = time.time()
multi_file_orc.summary()
orc_summary = h2o.frame(multi_file_orc.frame_id)["frames"][0]["columns"]
print("************** CSV (without column type forcing) parse time is {0}".format(endcsv1-startcsv1))
print("************** CSV (with column type forcing) parse time is {0}".format(endcsv-startcsv))
print("************** ORC (without column type forcing) parse time is {0}".format(endorc1-startorc1))
print("************** ORC (with column type forcing) parse time is {0}".format(endorc-startorc))
# compare frame read by orc by forcing column type,
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
else:
raise EnvironmentError
def hdfs_orc_parser():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
hadoop_namenode_is_accessible = pyunit_utils.hadoop_namenode_is_accessible()
if hadoop_namenode_is_accessible:
numElements2Compare = 10
tol_time = 200
tol_numeric = 1e-5
hdfs_name_node = pyunit_utils.hadoop_namenode()
if pyunit_utils.cannaryHDFSTest(hdfs_name_node, "/datasets/orc_parser/orc/orc_split_elim.orc"):
print("Your hive-exec version is too old. Orc parser test {0} is "
"skipped.".format("pyunit_INTERNAL_HDFS_airlines_orc.py"))
pass
else:
hdfs_orc_file = "/datasets/airlines_all_orc_parts"
hdfs_csv_file = "/datasets/air_csv_part"
col_types = ['real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'enum', 'real', 'enum', 'real',
'real', 'enum', 'real', 'real', 'enum', 'enum', 'real', 'enum', 'enum', 'real', 'real', 'real',
'enum', 'enum', 'enum', 'enum', 'enum', 'enum', 'enum']
# import CSV file
print("Import airlines 116M dataset in original csv format from HDFS")
url_csv = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_csv_file)
startcsv = time.time()
multi_file_csv = h2o.import_file(url_csv, na_strings=['\\N'], col_types=col_types)
endcsv = time.time()
startcsv1 = time.time()
multi_file_csv1 = h2o.import_file(url_csv)
endcsv1 = time.time()
h2o.remove(multi_file_csv1)
multi_file_csv.summary()
csv_summary = h2o.frame(multi_file_csv.frame_id)["frames"][0]["columns"]
# import ORC file with same column types as CSV file
print("Import airlines 116M dataset in ORC format from HDFS")
url_orc = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_orc_file)
startorc1 = time.time()
multi_file_orc1 = h2o.import_file(url_orc)
endorc1 = time.time()
h2o.remove(multi_file_orc1)
startorc = time.time()
multi_file_orc = h2o.import_file(url_orc, col_types=col_types)
endorc = time.time()
multi_file_orc.summary()
orc_summary = h2o.frame(multi_file_orc.frame_id)["frames"][0]["columns"]
print("************** CSV (without column type forcing) parse time is {0}".format(endcsv1-startcsv1))
print("************** CSV (with column type forcing) parse time is {0}".format(endcsv-startcsv))
print("************** ORC (without column type forcing) parse time is {0}".format(endorc1-startorc1))
print("************** ORC (with column type forcing) parse time is {0}".format(endorc-startorc))
# compare frame read by orc by forcing column type,
pyunit_utils.compare_frame_summary(csv_summary, orc_summary)
else:
raise EnvironmentError
