def run(schema_file,
data_path,
script=None,
spec_file=None,
verbose=None,
yarn=None):
"""
Main function to run pyspark job. It requires a schema file, an HDFS directory
with data and optional script with mapper/reducer functions.
"""
time0 = time.time()
# pyspark modules
from pyspark import SparkContext
# define spark context, it's main object which allow
# to communicate with spark
ctx = SparkContext(appName="AvroKeyInputFormat", pyFiles=[script])
logger = SparkLogger(ctx)
if not verbose:
logger.set_level('ERROR')
if yarn:
logger.info("YARN client mode enabled")
# load FWJR schema
rdd = ctx.textFile(schema_file, 1).collect()
# define input avro schema, the rdd is a list of lines (sc.textFile similar to readlines)
avsc = reduce(lambda x, y: x + y, rdd) # merge all entries from rdd list
schema = ''.join(avsc.split()) # remove spaces in avsc map
conf = {"avro.schema.input.key": schema}
# define newAPIHadoopFile parameters, java classes
aformat = "org.apache.avro.mapreduce.AvroKeyInputFormat"
akey = "org.apache.avro.mapred.AvroKey"
awrite = "org.apache.hadoop.io.NullWritable"
aconv = "org.apache.spark.examples.pythonconverters.AvroWrapperToJavaConverter"
# load data from HDFS
if isinstance(data_path, list):
avro_rdd = ctx.union([
ctx.newAPIHadoopFile(f, aformat, akey, awrite, aconv, conf=conf)
for f in data_path
])
else:
avro_rdd = ctx.newAPIHadoopFile(data_path,
aformat,
akey,
awrite,
aconv,
conf=conf)
# process data, here the map will read record from avro file
# if we need a whole record we'll use lambda x: x[0], e.g.
# output = avro_rdd.map(lambda x: x[0]).collect()
#
# if we need a particular key, e.g. jobid, we'll extract it
# within lambda function, e.g. lambda x: x[0]['jobid'], e.g.
# output = avro_rdd.map(lambda x: x[0]['jobid']).collect()
#
# in more general way we write mapper/reducer functions which will be
# executed by Spark via collect call
spec = None
if spec_file:
if os.path.isfile(spec_file):
spec = json.load(open(spec_file))
else:
spec = json.loads(spec_file)
if script:
obj = import_(script)
logger.info("Use user-based script %s" % obj)
if not hasattr(obj, 'MapReduce'):
logger.error('Unable to find MapReduce class in %s, %s' \
% (script, obj))
ctx.stop()
return
# we have a nested use case when one MR return WMArchive spec
# we'll loop in that case until we get non-spec output
while True:
mro = obj.MapReduce(spec)
# example of collecting records from mapper and
# passing all of them to reducer function
records = avro_rdd.map(mro.mapper).collect()
out = mro.reducer(records)
logger.info('OUTPUT %s %s' % (out, type(out)))
if is_spec(out):
spec = out
else:
break
# the map(f).reduce(f) example but it does not collect
# intermediate records
# out = avro_rdd.map(obj.mapper).reduce(obj.reducer).collect()
else:
records = avro_rdd.map(basic_mapper).collect()
out = basic_reducer(records)
ctx.stop()
if verbose:
logger.info("Elapsed time %s" % htime(time.time() - time0))
return out
def runActionsHistoryQuery(schema_file, data_path, verbose=None, yarn=None):
"""
Function to run a pyspark job to find the logs associated with the actionshistory.json file
To run use a spec file with only timerange, e.g. "spec":{ "timerange":[20180706,20180706]}}
and then execute: myspark --spec=large_query.spec --logfail
"""
if verbose:
print("### schema: %s" % schema_file)
print("### path : %s" % data_path)
print("### spec : %s" % spec_file)
time0 = time.time()
# pyspark modules
from pyspark import SparkContext
from pyspark import SQLContext, StorageLevel
# define spark context, it's main object which allow
# to communicate with spark
ctx = SparkContext(appName="AvroKeyInputFormat")
logger = SparkLogger(ctx)
if not verbose:
logger.set_level('ERROR')
if yarn:
logger.info("YARN client mode enabled")
# load FWJR schema
rdd = ctx.textFile(schema_file, 1).collect()
# define input avro schema, the rdd is a list of lines (sc.textFile similar to readlines)
avsc = reduce(lambda x, y: x + y, rdd) # merge all entries from rdd list
schema = ''.join(avsc.split()) # remove spaces in avsc map
conf = {"avro.schema.input.key": schema}
# define newAPIHadoopFile parameters, java classes
aformat = "org.apache.avro.mapreduce.AvroKeyInputFormat"
akey = "org.apache.avro.mapred.AvroKey"
awrite = "org.apache.hadoop.io.NullWritable"
aconv = "org.apache.spark.examples.pythonconverters.AvroWrapperToJavaConverter"
# load data from HDFS
if isinstance(data_path, list):
avro_rdd = ctx.union([
ctx.newAPIHadoopFile(f, aformat, akey, awrite, aconv, conf=conf)
for f in data_path
])
else:
avro_rdd = ctx.newAPIHadoopFile(data_path,
aformat,
akey,
awrite,
aconv,
conf=conf)
# load failing task
rdd_failing_tasks = ctx.textFile(
"hdfs:///cms/users/llayer/failing_tasks.csv")
#
# first step: filter the data for failing workflows and join with the actionshistory workflows
#
# filter the tasks - keep only failing that have a log file
def getFailing(row):
rec = row[0]
meta = rec.get('meta_data', {})
if meta.get('jobstate', '') != 'jobfailed':
return False
if rec.get('LFNArray', []) == []:
return False
return True
# create key-value structure for join
def avro_rdd_KV(row):
rec = row[0]
task = rec["task"]
return (task, rec)
# filter + join task names
fail_workflows = avro_rdd.filter(lambda x : getFailing(x)) \
.map(lambda x : avro_rdd_KV(x)) \
.join(rdd_failing_tasks.map(lambda x : (x,x)))
#
# second step: filter the data for logcollect tasks and join with the previous result
#
# keep only logcollect jobs
def filterLogCollect(row):
rec = row[0]
meta = rec.get('meta_data', {})
if meta.get('jobtype', '').lower() != 'logcollect':
return False
return True
# create KV structure using log archive as key and return important information
def log_KV(row):
rec = row[1][0]
task = rec["task"]
lfn_array = rec.get('LFNArray', [])
meta = rec.get('meta_data', {})
jobstate = meta.get('jobstate', '')
steps = rec.get('steps', [])
status = []
site = []
for step in steps:
status.append(step.get('status', ''))
site.append(step.get('site', ''))
out_dict = {
'task': task,
'jobstate': jobstate,
'status': status,
'site': site
}
return [(lfn, out_dict) for lfn in lfn_array if 'logArch' in lfn]
# create KV structure for log collect jobs using log archives as keys
def logcoll_KV(row):
rec = row[0]
task = rec["task"]
lfn_array = rec.get('LFNArray', [])
logCollect = ""
for lfn in lfn_array:
if 'logcollect' in lfn.lower():
logCollect = lfn
meta = rec.get('meta_data', {})
jobstate = meta.get('jobstate', '')
out_dict = {
'logcollect_task': task,
'logcollect_jobstate': jobstate,
'logcollect_lfn': logCollect
}
return [(lfn, out_dict) for lfn in lfn_array if 'logArch' in lfn]
# log collect tasks
logColl = avro_rdd.filter(lambda x: filterLogCollect(x)).flatMap(
lambda x: logcoll_KV(x))
# join the frames with log archives as keys
result = fail_workflows.flatMap(lambda x: log_KV(x)).join(logColl)
# write back the records to hdfs
result.saveAsTextFile("hdfs:///cms/users/llayer/logs10.csv")
ctx.stop()
if verbose:
logger.info("Elapsed time %s" % htime(time.time() - time0))
return 0
def run(schema_file, data_path, script=None, spec_file=None, verbose=None, yarn=None):
"""
Main function to run pyspark job. It requires a schema file, an HDFS directory
with data and optional script with mapper/reducer functions.
"""
time0 = time.time()
# pyspark modules
from pyspark import SparkContext
# define spark context, it's main object which allow
# to communicate with spark
ctx = SparkContext(appName="AvroKeyInputFormat", pyFiles=[script])
logger = SparkLogger(ctx)
if not verbose:
logger.set_level('ERROR')
if yarn:
logger.info("YARN client mode enabled")
# load FWJR schema
rdd = ctx.textFile(schema_file, 1).collect()
# define input avro schema, the rdd is a list of lines (sc.textFile similar to readlines)
avsc = reduce(lambda x, y: x + y, rdd) # merge all entries from rdd list
schema = ''.join(avsc.split()) # remove spaces in avsc map
conf = {"avro.schema.input.key": schema}
# define newAPIHadoopFile parameters, java classes
aformat="org.apache.avro.mapreduce.AvroKeyInputFormat"
akey="org.apache.avro.mapred.AvroKey"
awrite="org.apache.hadoop.io.NullWritable"
aconv="org.apache.spark.examples.pythonconverters.AvroWrapperToJavaConverter"
# load data from HDFS
if isinstance(data_path, list):
avro_rdd = ctx.union([ctx.newAPIHadoopFile(f, aformat, akey, awrite, aconv, conf=conf) for f in data_path])
else:
avro_rdd = ctx.newAPIHadoopFile(data_path, aformat, akey, awrite, aconv, conf=conf)
# process data, here the map will read record from avro file
# if we need a whole record we'll use lambda x: x[0], e.g.
# output = avro_rdd.map(lambda x: x[0]).collect()
#
# if we need a particular key, e.g. jobid, we'll extract it
# within lambda function, e.g. lambda x: x[0]['jobid'], e.g.
# output = avro_rdd.map(lambda x: x[0]['jobid']).collect()
#
# in more general way we write mapper/reducer functions which will be
# executed by Spark via collect call
spec = None
if spec_file:
if os.path.isfile(spec_file):
spec = json.load(open(spec_file))
else:
spec = json.loads(spec_file)
if script:
obj = import_(script)
logger.info("Use user-based script %s" % obj)
if not hasattr(obj, 'MapReduce'):
logger.error('Unable to find MapReduce class in %s, %s' \
% (script, obj))
ctx.stop()
return
mro = obj.MapReduce(spec)
# example of collecting records from mapper and
# passing all of them to reducer function
records = avro_rdd.map(mro.mapper).collect()
out = mro.reducer(records)
# the map(f).reduce(f) example but it does not collect
# intermediate records
# out = avro_rdd.map(obj.mapper).reduce(obj.reducer).collect()
else:
records = avro_rdd.map(basic_mapper).collect()
out = basic_reducer(records)
ctx.stop()
if verbose:
logger.info("Elapsed time %s" % htime(time.time()-time0))
return out
def run(schema_file, data_path, script=None, spec_file=None, verbose=None, rout=None, yarn=None):
"""
Main function to run pyspark job. It requires a schema file, an HDFS directory
with data and optional script with mapper/reducer functions.
"""
if script:
script = get_script(script)
if verbose:
print("### schema: %s" % schema_file)
print("### path : %s" % data_path)
print("### script: %s" % script)
print("### spec : %s" % spec_file)
time0 = time.time()
# pyspark modules
from pyspark import SparkContext
# define spark context, it's main object which allow
# to communicate with spark
ctx = SparkContext(appName="AvroKeyInputFormat", pyFiles=[script])
logger = SparkLogger(ctx)
if not verbose:
logger.set_level('ERROR')
if yarn:
logger.info("YARN client mode enabled")
# load FWJR schema
rdd = ctx.textFile(schema_file, 1).collect()
# define input avro schema, the rdd is a list of lines (sc.textFile similar to readlines)
avsc = reduce(lambda x, y: x + y, rdd) # merge all entries from rdd list
schema = ''.join(avsc.split()) # remove spaces in avsc map
conf = {"avro.schema.input.key": schema}
# define newAPIHadoopFile parameters, java classes
aformat="org.apache.avro.mapreduce.AvroKeyInputFormat"
akey="org.apache.avro.mapred.AvroKey"
awrite="org.apache.hadoop.io.NullWritable"
aconv="org.apache.spark.examples.pythonconverters.AvroWrapperToJavaConverter"
# load data from HDFS
if isinstance(data_path, list):
avro_rdd = ctx.union([ctx.newAPIHadoopFile(f, aformat, akey, awrite, aconv, conf=conf) for f in data_path])
else:
avro_rdd = ctx.newAPIHadoopFile(data_path, aformat, akey, awrite, aconv, conf=conf)
# process data, here the map will read record from avro file
# if we need a whole record we'll use lambda x: x[0], e.g.
# output = avro_rdd.map(lambda x: x[0]).collect()
#
# if we need a particular key, e.g. jobid, we'll extract it
# within lambda function, e.g. lambda x: x[0]['jobid'], e.g.
# output = avro_rdd.map(lambda x: x[0]['jobid']).collect()
#
# in more general way we write mapper/reducer functions which will be
# executed by Spark via collect call
spec = None
if spec_file:
if os.path.isfile(spec_file):
spec = json.load(open(spec_file))
else:
spec = json.loads(spec_file)
if verbose:
spec['verbose'] = 1
print("### spec %s" % json.dumps(spec))
if rout:
spec['output'] = rout
if script:
obj = import_(script)
logger.info("Use user-based script %s" % obj)
if not hasattr(obj, 'MapReduce'):
logger.error('Unable to find MapReduce class in %s, %s' \
% (script, obj))
ctx.stop()
return
# we have a nested use case when one MR return WMArchive spec
# we'll loop in that case until we get non-spec output
count = 0
while True:
mro = obj.MapReduce(spec)
mname = mro.__dict__.get('name', '').split('.')[0]
print("### Load %s" % mname)
if mname.lower().endswith('counter'):
out = avro_rdd.filter(mro.mapper).count()
if rout:
with open(rout, 'w') as ostream:
ostream.write(out)
break
# example of collecting records from mapper and
# passing all of them to reducer function
records = avro_rdd.filter(mro.mapper).collect()
out = mro.reducer(records)
if verbose:
print("### Loop count %s" % count)
if count > 3:
print("### WARNING, loop counter exceed its limit")
break
if is_spec(out):
spec = out
else:
break
count += 1
# the map(f).reduce(f) example but it does not collect
# intermediate records
# out = avro_rdd.map(obj.mapper).reduce(obj.reducer).collect()
else:
records = avro_rdd.map(basic_mapper).collect()
out = basic_reducer(records)
ctx.stop()
if verbose:
logger.info("Elapsed time %s" % htime(time.time()-time0))
return out
