def spark_intersect(mutation_table_name, regions_table_name, DB_CONF, output_format, regions=None, jdbc_jar='postgresql-42.2.12.jar', groupby=None, useSQL=False, minCount=-1, tumorType=None, filter=None):
# SQL VS MINE8: 388[1h] , 1507 (25min), 1018[bin=20], (1h, no bins), 1101 (5 bins), 994 [100] - 952 [200] 916(ctcf) 941[41]
# 590 ETS1
#3h13 geco 4h37 genomic
fs_db_dir =os.getenv('MUTVIZ_FS_DB_FOLDER', "disabled")
numPartitions = int(os.getenv('MUTVIZ_NUM_PARTITIONS', -1))
memory = os.getenv('MUTVIZ_DRIVER_MEMORY', "50g")
sparkDebug = os.getenv('MUTVIZ_SPARK_DEBUG', "false") == "true"
print("USING "+str(numPartitions)+" PARTITIONS (-1:AUTO).")
start_time = time.time()
os.environ["SPARK_HOME"] = os.getenv('MUTVIZ_SPARK_HOME', "/var/lib/spark-2.4.5-bin-hadoop2.7")
os.environ["PYSPARK_PYTHON"] = sys.executable
os.environ["PYSPARK_DRIVER_PYTHON"] = sys.executable
driver_class = "org.postgresql.Driver"
cores = os.getenv('MUTVIZ_CORES', "*")
print("#### SPARK CONFIGURATION ####")
print("SPARK HOME: " + os.getenv('SPARK_HOME'))
print("Using cores: "+cores)
print("Using memory: "+memory)
print("Using partitions: "+str(numPartitions))
print("Debug enabled: " + str(sparkDebug))
if tumorType:
print("Tumor Type id: "+str(tumorType))
if filter:
print("Filter count: "+str(len(filter)))
print("#############################")
spark = SparkSession.builder \
.master("local["+cores+"]") \
.appName("Word Count") \
.config("spark.jars", jdbc_jar) \
.config("spark.driver.memory", memory) \
.config("spark.driver.cores", cores) \
.getOrCreate()
sql_ctx = SQLContext(spark.sparkContext)
sc = spark.sparkContext
properties = {'user': DB_CONF["postgres_user"], 'password':DB_CONF["postgres_pw"], 'driver': driver_class}
url = 'postgresql://'+DB_CONF["postgres_host"]+':'+DB_CONF["postgres_port"]+'/'+DB_CONF["postgres_db"]
if fs_db_dir == 'disabled':
mutations = DataFrameReader(sql_ctx).jdbc(
url='jdbc:%s' % url, table=mutation_table_name, properties=properties
)
else:
customSchema = StructType([
StructField("donor_id", IntegerType(), False),
StructField("tumor_type_id", IntegerType(), False),
StructField("chrom", IntegerType(), False),
StructField("position", IntegerType(), False),
StructField("mutation_code_id", IntegerType(), False),
StructField("trinucleotide_id_r", IntegerType(), False)]
)
mutations = spark.read.format("csv").option("header", "true").schema(customSchema).load(fs_db_dir + "/"+mutation_table_name)
def filter1(x):
return int(x["tumor_type_id"]) == int(tumorType)
def filter2(x):
return int(x["donor_id"]) in filter
if tumorType:
mutations = mutations.rdd.filter(filter1)
if filter!=None:
mutations = mutations.filter(filter2)
if mutations.isEmpty():
return []
else:
mutations = mutations.toDF()
regions_df = DataFrameReader(sql_ctx).jdbc(
url='jdbc:%s' % url, table=regions_table_name, properties=properties
)
if sparkDebug:
print("############ mutations ==> ", mutations.count())
print("############ regions ==>", regions_df.count())
# new
if useSQL :
mutations.registerTempTable("mutations")
regions_df.registerTempTable("regions")
sql_res = spark.sql("SELECT m.tumor_type_id, m.trinucleotide_id_r, count(*) from mutations as m, regions as r WHERE m.chrom=r.chrom AND m.position >= r.pos_start AND m.position <= r.pos_stop GROUP BY m.tumor_type_id, m.trinucleotide_id_r")
res = sql_res.rdd.map(lambda r: [r["tumor_type_id"], r["trinucleotide_id_r"], r["count(1)"]]).collect()
print("Spark execution took %s seconds ---" % (time.time() - start_time))
#print(sql_res.collect())
else:
# regions = regions_df.collect()
# print("====> REGIONS COLLECTED AFTER (S) %s" % (time.time() - start_time))
# rb = defaultdict(list)
# for v in regions: rb[v["chrom"]].append(v)
#
# for c in rb:
# rb[c] = sorted(rb[c], key=itemgetter('pos_start', 'pos_stop'))
regions = regions_df.collect()
regions = sorted(regions, key=itemgetter('pos_start', 'pos_stop'))
print("====> REGIONS SORTED AFTER (S) %s" % (time.time() - start_time))
regions_broadcast = sc.broadcast(regions)
print("====> REGIONS BROADCAST AFTER (S) %s" % (time.time() - start_time))
def partitionWork(p):
localMutations = list(p)
matched = []
if sparkDebug:
print("====> PROCESSING PARTITION AFTER (S) %s" % (time.time() - start_time))
if localMutations:
import copy
localRegions = copy.deepcopy(regions_broadcast.value)
if localRegions:
sorted_mutations = sorted(localMutations, key=itemgetter('position'))
sorted_regions = localRegions
cur_reg_idx = 0
cur_mut_idx = 0
while( cur_mut_idx < len(sorted_mutations) and cur_reg_idx < len(sorted_regions) ):
cur_reg = sorted_regions[cur_reg_idx]
cur_mut = sorted_mutations[cur_mut_idx]
if cur_mut["position"] < cur_reg["pos_start"]:
cur_mut_idx += 1
elif cur_mut["position"] <= cur_reg["pos_stop"]:
if cur_reg["chrom"] == cur_mut["chrom"]:
matched.append(cur_mut)
else:
# look ahead
next_region_index = cur_reg_idx + 1
while next_region_index < len(sorted_regions) and sorted_regions[next_region_index][
"pos_start"] <= cur_mut["position"]:
if sorted_regions[next_region_index]["chrom"] == cur_mut["chrom"] and \
sorted_regions[next_region_index]["pos_stop"] >= cur_mut["position"]:
matched.append(cur_mut)
next_region_index = next_region_index + 1
cur_mut_idx += 1
else:
cur_reg_idx += 1
return matched
#if numPartitions > 0:
# res = mutations.rdd.groupBy(lambda e: e["chrom"],numPartitions=numPartitions).flatMap(partitionWork)
#else:
# res = mutations.rdd.groupBy(lambda e: e["chrom"]).flatMap(partitionWork)
if sparkDebug:
print("#### NUM PARTITIONS: ", mutations.rdd.getNumPartitions)
res = mutations.rdd.mapPartitions(partitionWork)
if sparkDebug:
print("############ results ==> ", res.count())
# Grouping
#todo: if empty
if groupby:
if minCount==-1:
res = res.toDF().groupBy(groupby).count().rdd.map(output_format)
else:
res_df = res.toDF().groupBy(groupby).count()
res = res_df.filter(res_df["count"]>minCount).rdd.map(output_format)
if sparkDebug:
print("############ results after grouping ==> ", res.count())
res = res.collect()
sc.stop()
print("Spark execution took %s seconds ---" % (time.time() - start_time))
return res
def spark_intersect2(mutation_table_name, regions_table_name, DB_CONF, jdbc_jar='postgresql-42.2.12.jar'):
fs_db_dir =os.getenv('MUTVIZ_FS_DB_FOLDER', "disabled")
numPartitions = int(os.getenv('MUTVIZ_NUM_PARTITIONS', -1))
memory = os.getenv('MUTVIZ_DRIVER_MEMORY', "50g")
sparkDebug = os.getenv('MUTVIZ_SPARK_DEBUG', "false") == "true"
print("USING "+str(numPartitions)+" PARTITIONS (-1:AUTO).")
start_time = time.time()
os.environ["SPARK_HOME"] = os.getenv('MUTVIZ_SPARK_HOME', "/var/lib/spark-2.4.5-bin-hadoop2.7")
os.environ["PYSPARK_PYTHON"] = sys.executable
os.environ["PYSPARK_DRIVER_PYTHON"] = sys.executable
driver_class = "org.postgresql.Driver"
cores = os.getenv('MUTVIZ_CORES', "*")
print("#### SPARK CONFIGURATION ####")
print("SPARK HOME: " + os.getenv('SPARK_HOME'))
print("Using cores: "+cores)
print("Using memory: "+memory)
print("Using partitions: "+str(numPartitions))
print("Debug enabled: " + str(sparkDebug))
print("#############################")
spark = SparkSession.builder \
.master("local["+cores+"]") \
.appName("Word Count") \
.config("spark.jars", jdbc_jar) \
.config("spark.driver.memory", memory) \
.config("spark.driver.cores", cores) \
.getOrCreate()
sql_ctx = SQLContext(spark.sparkContext)
sc = spark.sparkContext
properties = {'user': DB_CONF["postgres_user"], 'password':DB_CONF["postgres_pw"], 'driver': driver_class}
url = 'postgresql://'+DB_CONF["postgres_host"]+':'+DB_CONF["postgres_port"]+'/'+DB_CONF["postgres_db"]
if fs_db_dir == 'disabled':
mutations = DataFrameReader(sql_ctx).jdbc(
url='jdbc:%s' % url, table=mutation_table_name, properties=properties
)
else:
customSchema = StructType([
StructField("donor_id", IntegerType(), False),
StructField("tumor_type_id", IntegerType(), False),
StructField("chrom", IntegerType(), False),
StructField("position", IntegerType(), False),
StructField("mutation_code_id", IntegerType(), False),
StructField("trinucleotide_id_r", IntegerType(), False)]
)
mutations = spark.read.format("csv").option("header", "true").schema(customSchema).load(fs_db_dir + "/"+mutation_table_name)
regions_df = DataFrameReader(sql_ctx).jdbc(
url='jdbc:%s' % url, table=regions_table_name, properties=properties
)
regions = regions_df.collect()
regions = sorted(regions, key=itemgetter('pos_start', 'pos_stop'))
print("====> REGIONS SORTED AFTER (S) %s" % (time.time() - start_time))
regions_broadcast = sc.broadcast(regions)
print("====> REGIONS BROADCAST AFTER (S) %s" % (time.time() - start_time))
def partitionWork(p):
localMutations = list(p)
matched = []
if sparkDebug:
print("====> PROCESSING PARTITION AFTER (S) %s" % (time.time() - start_time))
if localMutations:
import copy
localRegions = copy.deepcopy(regions_broadcast.value)
if localRegions:
sorted_mutations = sorted(localMutations, key=itemgetter('position'))
sorted_regions = localRegions
cur_reg_idx = 0
cur_mut_idx = 0
while( cur_mut_idx < len(sorted_mutations) and cur_reg_idx < len(sorted_regions) ):
cur_reg = sorted_regions[cur_reg_idx]
cur_mut = sorted_mutations[cur_mut_idx]
if cur_mut["position"] < cur_reg["pos_start"]:
cur_mut_idx += 1
elif cur_mut["position"] <= cur_reg["pos_stop"]:
if cur_reg["chrom"] == cur_mut["chrom"]:
matched.append(cur_mut)
else:
# look ahead
next_region_index = cur_reg_idx + 1
while next_region_index < len(sorted_regions) and sorted_regions[next_region_index][
"pos_start"] <= cur_mut["position"]:
if sorted_regions[next_region_index]["chrom"] == cur_mut["chrom"] and \
sorted_regions[next_region_index]["pos_stop"] >= cur_mut["position"]:
matched.append(cur_mut)
next_region_index = next_region_index + 1
cur_mut_idx += 1
else:
cur_reg_idx += 1
return matched
res = mutations.rdd.mapPartitions(partitionWork).toDF().toPandas()
print("Spark execution took %s seconds ---" % (time.time() - start_time))
return res
},
sort_keys=False)
except BaseException as e:
return None
try:
data_file = "airports.dat"
data_file_url = "https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat"
download_file(data_file_url, data_file)
kafka_url = "172.18.0.100:9092"
kafka_topic = "airports"
sc = SparkContext("local", "example-of-processing-data")
sc.setLogLevel("ERROR")
sqlContext = SQLContext(sc)
airports = DataFrameReader(sqlContext).csv(data_file)
producer = KafkaProducer(bootstrap_servers=kafka_url)
#for index in range(1, airports.count()):
# print(to_json(airports.take(index)[0]))
for each in airports.collect():
# logging.debug(as_json(each))
producer.send(kafka_topic, as_json(each))
finally:
producer.close()
sc.stop()
shutil.os.remove(data_file)
