def main():
parser = argparse.ArgumentParser(description=__doc__)
args = getargs(parser)
# Initialise Spark session
spark = init_sparksession(name="raw2science", shuffle_partitions=2)
# Logger to print useful debug statements
logger = get_fink_logger(spark.sparkContext.appName, args.log_level)
# debug statements
inspect_application(logger)
df = connect_to_raw_database(args.rawdatapath,
args.rawdatapath + "/*",
latestfirst=False)
# Apply quality cuts
logger.info(qualitycuts)
df = apply_user_defined_filter(df, qualitycuts)
# Apply science modules
df = apply_science_modules(df, logger)
# Add library versions
df = df.withColumn('fink_broker_version', F.lit(fbvsn))\
.withColumn('fink_science_version', F.lit(fsvsn))
# Switch publisher
df = df.withColumn('publisher', F.lit('Fink'))
# re-create partitioning columns.
# Partitioned data doesn't preserve type information (cast as int...)
df_partitionedby = df\
.withColumn("year", F.date_format("timestamp", "yyyy"))\
.withColumn("month", F.date_format("timestamp", "MM"))\
.withColumn("day", F.date_format("timestamp", "dd"))
# Append new rows in the tmp science database
countquery = df_partitionedby\
.writeStream\
.outputMode("append") \
.format("parquet") \
.option("checkpointLocation", args.checkpointpath_sci_tmp) \
.option("path", args.scitmpdatapath)\
.partitionBy("year", "month", "day") \
.start()
# Keep the Streaming running until something or someone ends it!
if args.exit_after is not None:
time.sleep(args.exit_after)
countquery.stop()
logger.info("Exiting the raw2science service normally...")
else:
# Wait for the end of queries
spark.streams.awaitAnyTermination()
def main():
parser = argparse.ArgumentParser(description=__doc__)
args = getargs(parser)
# Initialise Spark session
spark = init_sparksession(name="distribute_{}".format(args.night),
shuffle_partitions=2)
# The level here should be controlled by an argument.
logger = get_fink_logger(spark.sparkContext.appName, args.log_level)
# debug statements
inspect_application(logger)
# data path
scitmpdatapath = args.online_data_prefix + '/science'
checkpointpath_kafka = args.online_data_prefix + '/kafka_checkpoint'
# Connect to the TMP science database
df = connect_to_raw_database(
scitmpdatapath + "/year={}/month={}/day={}".format(
args.night[0:4], args.night[4:6], args.night[6:8]),
scitmpdatapath + "/year={}/month={}/day={}".format(
args.night[0:4], args.night[4:6], args.night[6:8]),
latestfirst=False)
# Drop partitioning columns
df = df.drop('year').drop('month').drop('day')
# Cast fields to ease the distribution
cnames = df.columns
cnames[cnames.index(
'timestamp')] = 'cast(timestamp as string) as timestamp'
cnames[cnames.index(
'cutoutScience')] = 'struct(cutoutScience.*) as cutoutScience'
cnames[cnames.index(
'cutoutTemplate')] = 'struct(cutoutTemplate.*) as cutoutTemplate'
cnames[cnames.index(
'cutoutDifference')] = 'struct(cutoutDifference.*) as cutoutDifference'
cnames[cnames.index(
'prv_candidates')] = 'explode(array(prv_candidates)) as prv_candidates'
cnames[cnames.index('candidate')] = 'struct(candidate.*) as candidate'
# Retrieve time-series information
to_expand = [
'jd', 'fid', 'magpsf', 'sigmapsf', 'magnr', 'sigmagnr', 'magzpsci',
'isdiffpos'
]
# Append temp columns with historical + current measurements
prefix = 'c'
for colname in to_expand:
df = concat_col(df, colname, prefix=prefix)
# quick fix for https://github.com/astrolabsoftware/fink-broker/issues/457
for colname in to_expand:
df = df.withColumnRenamed('c' + colname, 'c' + colname + 'c')
broker_list = args.distribution_servers
for userfilter in userfilters:
# The topic name is the filter name
topicname = args.substream_prefix + userfilter.split('.')[-1] + '_ztf'
# Apply user-defined filter
df_tmp = apply_user_defined_filter(df, userfilter)
# Wrap alert data
df_tmp = df_tmp.selectExpr(cnames)
# Get the DataFrame for publishing to Kafka (avro serialized)
df_kafka = get_kafka_df(df_tmp, '')
# Ensure that the topic(s) exist on the Kafka Server)
disquery = df_kafka\
.writeStream\
.format("kafka")\
.option("kafka.bootstrap.servers", broker_list)\
.option("kafka.security.protocol", "SASL_PLAINTEXT")\
.option("kafka.sasl.mechanism", "SCRAM-SHA-512")\
.option("topic", topicname)\
.option("checkpointLocation", checkpointpath_kafka + topicname)\
.trigger(processingTime='{} seconds'.format(args.tinterval)) \
.start()
# Keep the Streaming running until something or someone ends it!
if args.exit_after is not None:
time.sleep(args.exit_after)
disquery.stop()
logger.info("Exiting the distribute service normally...")
else:
# Wait for the end of queries
spark.streams.awaitAnyTermination()
def main():
parser = argparse.ArgumentParser(description=__doc__)
args = getargs(parser)
# Initialise Spark session
spark = init_sparksession(name="raw2science_{}".format(args.night),
shuffle_partitions=2)
# Logger to print useful debug statements
logger = get_fink_logger(spark.sparkContext.appName, args.log_level)
# debug statements
inspect_application(logger)
# data path
rawdatapath = args.online_data_prefix + '/raw'
scitmpdatapath = args.online_data_prefix + '/science'
checkpointpath_sci_tmp = args.online_data_prefix + '/science_checkpoint'
df = connect_to_raw_database(
rawdatapath + "/year={}/month={}/day={}".format(
args.night[0:4], args.night[4:6], args.night[6:8]),
rawdatapath + "/year={}/month={}/day={}".format(
args.night[0:4], args.night[4:6], args.night[6:8]),
latestfirst=False)
# Apply quality cuts
logger.info("Applying quality cuts")
df = df\
.filter(df['candidate.nbad'] == 0)\
.filter(df['candidate.rb'] >= 0.55)
# Apply science modules
df = apply_science_modules(df, logger)
# Add library versions
df = df.withColumn('fink_broker_version', F.lit(fbvsn))\
.withColumn('fink_science_version', F.lit(fsvsn))
# Switch publisher
df = df.withColumn('publisher', F.lit('Fink'))
# re-create partitioning columns if needed.
if 'timestamp' not in df.columns:
df = df\
.withColumn("timestamp", jd_to_datetime(df['candidate.jd']))
if "year" not in df.columns:
df = df\
.withColumn("year", F.date_format("timestamp", "yyyy"))
if "month" not in df.columns:
df = df\
.withColumn("month", F.date_format("timestamp", "MM"))
if "day" not in df.columns:
df = df\
.withColumn("day", F.date_format("timestamp", "dd"))
# Append new rows in the tmp science database
countquery = df\
.writeStream\
.outputMode("append") \
.format("parquet") \
.option("checkpointLocation", checkpointpath_sci_tmp) \
.option("path", scitmpdatapath)\
.partitionBy("year", "month", "day") \
.trigger(processingTime='{} seconds'.format(args.tinterval)) \
.start()
# Keep the Streaming running until something or someone ends it!
if args.exit_after is not None:
time.sleep(args.exit_after)
countquery.stop()
logger.info("Exiting the raw2science service normally...")
else:
# Wait for the end of queries
spark.streams.awaitAnyTermination()
def main():
parser = argparse.ArgumentParser(description=__doc__)
args = getargs(parser)
# Initialise Spark session
spark = init_sparksession(name="distribute", shuffle_partitions=2)
# The level here should be controlled by an argument.
logger = get_fink_logger(spark.sparkContext.appName, args.log_level)
# debug statements
inspect_application(logger)
# Connect to the TMP science database
df = connect_to_raw_database(args.scitmpdatapath,
args.scitmpdatapath + "/*",
latestfirst=False)
# Drop partitioning columns
df = df.drop('year').drop('month').drop('day')
# Cast fields to ease the distribution
cnames = df.columns
cnames[cnames.index(
'timestamp')] = 'cast(timestamp as string) as timestamp'
cnames[cnames.index(
'cutoutScience')] = 'struct(cutoutScience.*) as cutoutScience'
cnames[cnames.index(
'cutoutTemplate')] = 'struct(cutoutTemplate.*) as cutoutTemplate'
cnames[cnames.index(
'cutoutDifference')] = 'struct(cutoutDifference.*) as cutoutDifference'
cnames[cnames.index(
'prv_candidates')] = 'explode(array(prv_candidates)) as prv_candidates'
cnames[cnames.index('candidate')] = 'struct(candidate.*) as candidate'
broker_list = args.distribution_servers
for userfilter in userfilters:
# The topic name is the filter name
topicname = userfilter.split('.')[-1]
# Apply user-defined filter
df_tmp = apply_user_defined_filter(df, userfilter)
# Wrap alert data
df_tmp = df_tmp.selectExpr(cnames)
# Get the DataFrame for publishing to Kafka (avro serialized)
df_kafka = get_kafka_df(df_tmp, '')
# Ensure that the topic(s) exist on the Kafka Server)
disquery = df_kafka\
.writeStream\
.format("kafka")\
.option("kafka.bootstrap.servers", broker_list)\
.option("kafka.security.protocol", "SASL_PLAINTEXT")\
.option("kafka.sasl.mechanism", "SCRAM-SHA-512")\
.option("topic", topicname)\
.option("checkpointLocation", args.checkpointpath_kafka + topicname)\
.start()
# Keep the Streaming running until something or someone ends it!
if args.exit_after is not None:
time.sleep(args.exit_after)
disquery.stop()
logger.info("Exiting the distribute service normally...")
else:
# Wait for the end of queries
spark.streams.awaitAnyTermination()
def main():
parser = argparse.ArgumentParser(description=__doc__)
args = getargs(parser)
# Initialise Spark session
spark = init_sparksession(name="raw2science", shuffle_partitions=2)
# Logger to print useful debug statements
logger = get_fink_logger(spark.sparkContext.appName, args.log_level)
# debug statements
inspect_application(logger)
df = connect_to_raw_database(args.rawdatapath,
args.rawdatapath + "/*",
latestfirst=False)
# Apply level one filters
logger.info(qualitycuts)
df = apply_user_defined_filter(df, qualitycuts)
# Apply level one processor: cdsxmatch
logger.info("New processor: cdsxmatch")
colnames = [df['objectId'], df['candidate.ra'], df['candidate.dec']]
df = df.withColumn(cdsxmatch.__name__, cdsxmatch(*colnames))
# Apply level one processor: rfscore
logger.info("New processor: rfscore")
# Required alert columns
what = [
'jd', 'fid', 'magpsf', 'sigmapsf', 'magnr', 'sigmagnr', 'magzpsci',
'isdiffpos'
]
# Use for creating temp name
prefix = 'c'
what_prefix = [prefix + i for i in what]
# Append temp columns with historical + current measurements
for colname in what:
df = concat_col(df, colname, prefix=prefix)
# Perform the fit + classification.
# Note we can omit the model_path argument, and in that case the
# default model `data/models/default-model.obj` will be used.
rfscore_args = [F.col(i) for i in what_prefix]
df = df.withColumn(rfscore.__name__, rfscore(*rfscore_args))
# Apply level one processor: rfscore
logger.info("New processor: microlensing")
# Retrieve schema
schema = load_mulens_schema_twobands()
# Create standard UDF
mulens_udf = F.udf(mulens, schema)
# Required alert columns - already computed for SN
what_prefix_mulens = [
'cfid', 'cmagpsf', 'csigmapsf', 'cmagnr', 'csigmagnr', 'cmagzpsci',
'cisdiffpos'
]
mulens_args = [F.col(i) for i in what_prefix_mulens]
df = df.withColumn('mulens', mulens_udf(*mulens_args))
# Drop temp columns
df = df.drop(*what_prefix)
# Partition the data hourly
df_partitionedby = df\
.withColumn("year", F.date_format("timestamp", "yyyy"))\
.withColumn("month", F.date_format("timestamp", "MM"))\
.withColumn("day", F.date_format("timestamp", "dd"))\
.withColumn("hour", F.date_format("timestamp", "HH"))
# Append new rows in the tmp science database
countquery = df_partitionedby\
.writeStream\
.outputMode("append") \
.format("parquet") \
.option("checkpointLocation", args.checkpointpath_sci_tmp) \
.option("path", args.scitmpdatapath)\
.partitionBy("year", "month", "day", "hour") \
.start()
# Keep the Streaming running until something or someone ends it!
if args.exit_after is not None:
time.sleep(args.exit_after)
countquery.stop()
logger.info("Exiting the raw2science service normally...")
else:
# Wait for the end of queries
spark.streams.awaitAnyTermination()
def main():
parser = argparse.ArgumentParser(description=__doc__)
args = getargs(parser)
# Initialise Spark session
spark = init_sparksession(name="raw2science", shuffle_partitions=2)
# The level here should be controlled by an argument.
logger = get_fink_logger(spark.sparkContext.appName, args.log_level)
# debug statements
inspect_application(logger)
# Not very satisfactory... The problem is that latesfirst = false is
# required to create new HBase table (i.e. all the time in the CI).
# If you have a better idea, let me know!
if "travis" in args.science_db_name:
latesfirst = False
else:
latesfirst = True
df = connect_to_raw_database(args.rawdatapath, args.rawdatapath + "/*",
latesfirst)
# Apply level one filters
logger.info(filter_levelone_names)
df = apply_user_defined_filters(df, filter_levelone_names)
# Apply level one processors
logger.info(processor_levelone_names)
df = apply_user_defined_processors(df, processor_levelone_names)
# Select alert data + timestamp + added value from processors
new_colnames = ["decoded.*", "cast(timestamp as string) as timestamp"]
for i in processor_levelone_names:
new_colnames.append(i)
df = df.selectExpr(new_colnames)
df_hbase = flattenstruct(df, "candidate")
df_hbase = flattenstruct(df_hbase, "cutoutScience")
df_hbase = flattenstruct(df_hbase, "cutoutTemplate")
df_hbase = flattenstruct(df_hbase, "cutoutDifference")
df_hbase = explodearrayofstruct(df_hbase, "prv_candidates")
# Create a status column for distribution
df_hbase = df_hbase.withColumn("status", lit("dbUpdate"))
# Save the catalog on disk for later usage
catalog = construct_hbase_catalog_from_flatten_schema(
df_hbase.schema, args.science_db_name, "objectId")
science_db_catalog = args.science_db_catalog
with open(science_db_catalog, 'w') as json_file:
json.dump(catalog, json_file)
def write_to_hbase_and_monitor(df: DataFrame, epochid: int,
hbcatalog: str):
"""Write data into HBase.
The purpose of this function is to write data to HBase using
Structured Streaming tools such as foreachBatch.
Parameters
----------
df : DataFrame
Input micro-batch DataFrame.
epochid : int
ID of the micro-batch
hbcatalog : str
HBase catalog describing the data
"""
# If the table does not exist, one needs to specify
# the number of zones to use (must be greater than 3).
# TODO: remove this harcoded parameter.
df.write\
.options(catalog=hbcatalog, newtable=5)\
.format("org.apache.spark.sql.execution.datasources.hbase")\
.save()
# Query to push data into HBase
countquery = df_hbase\
.writeStream\
.outputMode("append")\
.option("checkpointLocation", args.checkpointpath_sci)\
.foreachBatch(lambda x, y: write_to_hbase_and_monitor(x, y, catalog))\
.start()
# Query to group objects by type according to SIMBAD
# Do it every 30 seconds
groupedquery_started = False
if "cross_match_alerts_per_batch" in processor_levelone_names:
df_group = df.groupBy("cross_match_alerts_per_batch").count()
groupquery = df_group\
.writeStream\
.outputMode("complete") \
.foreachBatch(write_to_csv)\
.trigger(processingTime='30 seconds'.format(args.tinterval))\
.start()
groupedquery_started = True
# Keep the Streaming running until something or someone ends it!
if args.exit_after is not None:
time.sleep(args.exit_after)
countquery.stop()
if groupedquery_started:
groupquery.stop()
logger.info("Exiting the raw2science service normally...")
else:
# Wait for the end of queries
spark.streams.awaitAnyTermination()
def main():
parser = argparse.ArgumentParser(description=__doc__)
args = getargs(parser)
# Grab the running Spark Session,
# otherwise create it.
spark = init_sparksession(
name="buildSciDB", shuffle_partitions=2, log_level="ERROR")
# FIXME!
if "travis" in args.science_db_name:
latesfirst = False
else:
latesfirst = True
df = connect_to_raw_database(
args.rawdatapath, args.rawdatapath + "/*", latesfirst)
# Apply filters and keep only good alerts
df_filt = df.withColumn(
"toKeep",
keep_alert_based_on(
col("decoded.candidate.nbad"),
col("decoded.candidate.rb"),
col("decoded.candidate.magdiff")
)
).filter("toKeep == true")
# for good alerts, perform a cross-match with SIMBAD,
# and return the types of the objects (Star, AGN, Unknown, etc.)
df_type = df_filt.withColumn(
"simbadType",
cross_match_alerts_per_batch(
col("decoded.objectId"),
col("decoded.candidate.ra"),
col("decoded.candidate.dec")
)
).selectExpr(
"decoded.*", "cast(timestamp as string) as timestamp", "simbadType")
df_hbase = flattenstruct(df_type, "candidate")
df_hbase = flattenstruct(df_hbase, "cutoutScience")
df_hbase = flattenstruct(df_hbase, "cutoutTemplate")
df_hbase = flattenstruct(df_hbase, "cutoutDifference")
df_hbase = explodearrayofstruct(df_hbase, "prv_candidates")
# Create a status column for distribution
df_hbase = df_hbase.withColumn("status", lit("dbUpdate"))
# Save the catalog on disk for later usage
catalog = construct_hbase_catalog_from_flatten_schema(
df_hbase.schema, args.science_db_name, "objectId")
science_db_catalog = args.science_db_catalog
with open(science_db_catalog, 'w') as json_file:
json.dump(catalog, json_file)
def write_to_hbase_and_monitor(
df: DataFrame, epochid: int, hbcatalog: str):
"""Write data into HBase.
The purpose of this function is to write data to HBase using
Structured Streaming tools such as foreachBatch.
Parameters
----------
df : DataFrame
Input micro-batch DataFrame.
epochid : int
ID of the micro-batch
hbcatalog : str
HBase catalog describing the data
"""
# If the table does not exist, one needs to specify
# the number of zones to use (must be greater than 3).
# TODO: remove this harcoded parameter.
df.write\
.options(catalog=hbcatalog, newtable=5)\
.format("org.apache.spark.sql.execution.datasources.hbase")\
.save()
# Query to push data into HBase
countquery = df_hbase\
.writeStream\
.outputMode("append")\
.option("checkpointLocation", args.checkpointpath_sci)\
.foreachBatch(lambda x, y: write_to_hbase_and_monitor(x, y, catalog))\
.start()
# Query to group objects by type according to SIMBAD
# Do it every 30 seconds
df_group = df_type.groupBy("simbadType").count()
groupquery = df_group\
.writeStream\
.outputMode("complete") \
.foreachBatch(write_to_csv)\
.trigger(processingTime='30 seconds'.format(args.tinterval))\
.start()
# Keep the Streaming running until something or someone ends it!
if args.exit_after is not None:
time.sleep(args.exit_after)
countquery.stop()
groupquery.stop()
print("Exiting the raw2science service normally...")
else:
# Wait for the end of queries
spark.streams.awaitAnyTermination()
