def main():
try:
data_path = sys.argv[1]
except IndexError:
data_path = "/data1/gfz-fe/GeoMultiSens/database/sampledata/"
connection = {
"database": "usgscache",
"user": "******",
"password": "******",
"host": "localhost",
"connect_timeout": 3,
"options": "-c statement_timeout=10000"
}
inputFormat = GMSDB(data_path, connection, 26184107)
env = get_environment()
level0Set = env.read_custom(data_path, ".*?\\.bsq", True, inputFormat)
level1Set = level0Set.flat_map(L11Processor())
level1SceneSet = level1Set.group_by(0).reduce()
level12Set = level1SceneSet.flat_map(L12Processor())
#just to make program complete
result = level12Set.filter(DumbFilter())
result.output()
env.set_degree_of_parallelism(1)
env.execute(local=True)
def main():
try:
data_path = sys.argv[1]
except IndexError:
data_path = "/data1/gfz-fe/GeoMultiSens/database/sampledata/"
connection = {
"database": "usgscache",
"user": "******",
"password": "******",
"host": "localhost",
"connect_timeout": 3,
"options": "-c statement_timeout=10000"
}
inputFormat = GMSDB(data_path, connection, 26184107)
env = get_environment()
level0Set = env.read_custom(data_path, ".*?\\.bsq", True, inputFormat)
level1Set = level0Set.flat_map(L11Processor())
level1SceneSet = level1Set.group_by(0).reduce(CornerpointAdder())
level12Set = level1SceneSet.flat_map(L12Processor())
level12Set.write_custom(PrintOutput("/opt/output"))
env.set_parallelism(1)
env.execute(local=True)
def main():
try:
data_path = sys.argv[1]
except IndexError:
data_path = "/data1/gfz-fe/GeoMultiSens/database/sampledata/"
connection = {
"database": "usgscache",
"user": "******",
"password": "******",
"host": "localhost",
"connect_timeout": 3,
"options": "-c statement_timeout=10000"
}
inputFormat = GMSDB(data_path, connection, 26184107)
env = get_environment()
level0Set = env.read_custom(data_path, ".*?\\.bsq", True, inputFormat)
level1Set = level0Set.flat_map(L11Processor())
level1SceneSet = level1Set.group_by(0).reduce(CornerpointAdder())
level12Set = level1SceneSet.flat_map(L12Processor())
level12Set.write_custom(PrintOutput("/opt/output"))
env.set_parallelism(1)
env.execute(local=True)
def runner():
env = get_environment()
data = env.from_elements(tweets)
# we first map each word into a (1, word) tuple, then flat map across that, and group by the key, and sum
# aggregate on it to get (count, word) tuples
data \
.flat_map(lambda x, c: [(1, word) for word in x.lower().split()]) \
.group_by(1) \
.reduce_group(Adder(), combinable=True) \
.output()
# execute the plan locally.
env.execute(local=True)
def main():
env = get_environment()
inputFormat = GDALInputFormat(26184107)
data = env.read_custom("/opt/gms_sample/", ".*?\\.bsq", True, inputFormat)
result = data \
.flat_map(TupleToTile()) \
.flat_map(Tokenizer()) \
.filter(Filter())
result.output()
env.set_parallelism(1)
env.execute(local=True)
def reduce(self, iterator, collector):
count, word = iterator.next()
count += sum([x[0] for x in iterator])
collector.collect((count, word))
if __name__ == "__main__":
output_file = 'file:/opt/project/out.txt'
print('logging results to: %s' % (output_file, ))
env = get_environment()
data = env.from_elements("Who's there? I think I hear them. Stand, ho! Who's there?")
data \
.flat_map(lambda x, c: [(1, word) for word in x.lower().split()], (INT, STRING)) \
.group_by(1) \
.reduce_group(Adder(), (INT, STRING), combinable=True) \
.map(lambda y: 'Count: %s Word: %s' % (y[0], y[1]), STRING) \
.write_text(output_file, write_mode=WriteMode.OVERWRITE)
env.execute(local=True)
def runner():
env = get_environment()
data = env.from_elements(tweets)
# we first map each word into a (1, word) tuple, then flat map across that, and group by the key, and sum
# aggregate on it to get (count, word) tuples
data \
.flat_map(lambda tweet, c: [y for x in [[(1, word) for word in [x.lower().split(' ')[i] + ' ' + x.lower().split(' ')[1+i] for i in range(0, len(x.lower().split(' '))-1)]]
for x in tweet] for y in x]) \
.group_by(1) \
.reduce_group(Adder(), combinable=True) \
.map(lambda y: cl.insert({"_id":y[1], "value":y[0]}) if cl.find({"_id":y[1]}).count() == 0 else cl.update({"_id":y[1]}, {"value": cl.find_one({"_id":y[1]})['value'] +
y[0]})) \
.output()
# execute the plan locally.
env.execute(local=True)
def main():
env = get_environment()
inputFormat = GDALInputFormat(26184107)
data = env.read_custom("/opt/gms_sample/", ".*?\\.bsq", True,
inputFormat)
result = data \
.flat_map(TupleToTile()) \
.flat_map(Tokenizer())
result.write_custom(GMSOF("/opt/output"))
env.set_parallelism(1)
env.execute(local=True)
def classifier():
env = get_environment()
data = env.from_elements(
"Hillary is going to be a better president than Trump #usElection")
#Flat_map extrai os bigramas da string (tweet)
#Em seguida uma função de map adiciona as informações relativas a probabilidade dos bigramas acontecerem em cada classe (retirados do banco de dados)
#É necessário filtras os dados obtidos pela segunda função de map, no caso são retirados bigramas com probabilidade 0
#É aplicada uma função de reduce que faz o produtorio das probabilidades
#Em seguida esses dados são normalizados e adaptados para serem escritos no arquivo de saída
data \
.flat_map(lambda tweet, c: [(1, word) for word in [tweet.split(' ')[i] + ' ' + tweet.split(' ')[1+i] for i in range(0, len(tweet.split(' '))-1)]]) \
.map(lambda x: classify(x)) \
.filter(lambda x: x[0] != 0 and x[1] != 0) \
.reduce(Product()) \
.map(lambda y: 'Class 1 probability: %s Class 2 probability: %s' % (y[0] * (cl1.find().count() / (cl1.find().count() + cl2.find().count())),
y[1] * (cl2.find().count() / (cl1.find().count() + cl2.find().count())))) \
.write_text(output_file)
# execute the plan locally.
env.execute(local=True)
def main():
env = get_environment()
env.set_sendLargeTuples(True)
inputFormat = GDALInputFormat(26184107)
data = env.read_custom("/opt/gms_sample/", ".*?\\.bsq", True, inputFormat)
#result = data \
# .flat_map(TupleToTile()) \
# .flat_map(Tokenizer()) \
# .flat_map(TileToTuple())
result = data.filter(Filter())
result.write_custom(GMSOF("/opt/output"))
filtered = result.filter(Filter())
filtered.write_custom(GMSOF("/opt/output"))
env.set_parallelism(2)
env.execute(local=False)
x = iterator.next()
second_edge = [x[0], x[1]]
higher_vertex_id = second_edge[1]
for lowerVertexId in vertices:
collector.collect((first_edge[0], lowerVertexId, higher_vertex_id))
vertices.append(higher_vertex_id)
class TriadFilter(JoinFunction):
def join(self, value1, value2):
return value1
if __name__ == "__main__":
env = get_environment()
edges = env.from_elements(
(1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 5), (3, 4), (3, 7), (3, 8), (5, 6), (7, 8))
edges_with_degrees = edges \
.flat_map(EdgeDuplicator(), [INT, INT]) \
.group_by(0) \
.sort_group(1, Order.ASCENDING) \
.reduce_group(DegreeCounter(), [INT, INT, INT, INT]) \
.group_by(0, 2) \
.reduce(DegreeJoiner())
edges_by_degree = edges_with_degrees \
.map(EdgeByDegreeProjector(), [INT, INT])
edges_by_id = edges_by_degree \
def json_to_tuple(js, fields):
return tuple([str(js.get(f, '')) for f in fields])
if __name__ == "__main__":
# get the base path out of the runtime params
base_path = sys.argv[1]
# setup paths to input and output files on disk
dim_file = 'file://' + base_path + '/data_enrichment/dimensional_data.csv'
input_file = 'file://' + base_path + '/data_enrichment/input_data.csv'
output_file = 'file://' + base_path + '/data_enrichment/out.txt'
# remove the output file, if there is one there already
if os.path.isfile(output_file):
os.remove(output_file)
# set up the environment with a text file source
env = get_environment()
input_data = env.read_text(input_file)
dimensional_data = env.read_csv(dim_file, types=[STRING, STRING])
input_data \
.map(lambda x: json_to_tuple(json.loads(x), ['car', 'attr']), (STRING, STRING)) \
.join(dimensional_data).where(1).equal_to(0) \
.map(lambda x: 'This %s is %s' % (x[0][0], x[1][1]), STRING) \
.write_text(output_file, write_mode=WriteMode.OVERWRITE)
env.execute(local=True)
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################
from flink.plan.Environment import get_environment
from flink.functions.MapFunction import MapFunction
from flink.functions.CrossFunction import CrossFunction
from flink.functions.JoinFunction import JoinFunction
from flink.functions.CoGroupFunction import CoGroupFunction
from flink.functions.Aggregation import Max, Min, Sum
from utils import Verify, Verify2, Id
# Test multiple jobs in one Python plan file
if __name__ == "__main__":
env = get_environment()
env.set_parallelism(1)
d1 = env.from_elements(1, 6, 12)
d1 \
.first(1) \
.map_partition(Verify([1], "First with multiple jobs in one Python plan file")).output()
env.execute(local=True)
env2 = get_environment()
env2.set_parallelism(1)
d2 = env2.from_elements(1, 1, 12)
d2 \
.map(lambda x: x * 2) \