class AnalysisJob:
def __init__(self, cfg):
self.job_bundle = cfg.job_bundle
if cfg.input_filter:
self.input_filter = TelemetrySchema(
json.load(open(cfg.input_filter)))
else:
self.input_filter = None
if cfg.input_list_file:
self.input_list = cfg.input_list_file
else:
self.input_list = None
self.job_id = str(uuid4())
self.target_queue = cfg.target_queue
self.aws_key = cfg.aws_key
self.aws_secret_key = cfg.aws_secret_key
self.input_bucket = "telemetry-published-v1"
self.job_name = cfg.name
self.job_owner = cfg.owner
self.date_limit = cfg.date_limit
# Bucket with intermediate data for this analysis job
self.analysis_bucket = "jonasfj-telemetry-analysis"
self.s3_code_path = "batch-jobs/" + self.job_id + ".tar.gz"
# S3 region of operation
self.aws_region = "us-west-2"
self.task_size_limit = 400 * 1024 * 1024
self.sqs_input_name = cfg.sqs_queue
def get_filtered_files(self):
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.input_bucket)
# date_limit is a hack that makes it easy to launch everything before
# a given date... say to back process all we have in the bucket...
if self.date_limit != None:
print "Launching limiting to before " + self.date_limit
for k, s in self.list_partitions(bucket):
if k.split('/')[-1].split('.')[1] < self.date_limit:
yield (k, s)
else:
for k, s in self.list_partitions(bucket):
yield (k, s)
def get_filtered_files_old(self):
""" Get tuples of name and size for all input files """
# Setup some auxiliary functions
allowed_values = self.input_filter.sanitize_allowed_values()
nb_dims = len(allowed_values)
def filter_includes(level, value):
return self.input_filter.is_allowed(value, allowed_values[level])
# iterate over all files in bucket, this is very slow and we should be
# be able to something much smarter using prefix listing and ordering
# to break listing.
count = 0
selected = 0
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.input_bucket)
for f in bucket.list():
count += 1
dims = self.input_filter.get_dimensions(".", f.key)
include = True
for i in xrange(nb_dims):
if not filter_includes(i, dims[i]):
include = False
break
if include:
selected += 1
yield (f.key, f.size)
if count % 5000 == 0:
print "%i files listed with %i selected" % (count, selected)
conn.close()
def list_partitions(self, bucket, prefix='', level=0):
if self.input_filter:
#print "Listing...", prefix, level
allowed_values = self.input_filter.sanitize_allowed_values()
delimiter = '/'
if level > 3:
delimiter = '.'
for k in bucket.list(prefix=prefix, delimiter=delimiter):
partitions = k.name.split("/")
if level > 3:
# split the last couple of partition components by "." instead of "/"
partitions.extend(partitions.pop().split(".", 2))
if self.input_filter.is_allowed(partitions[level],
allowed_values[level]):
if level >= 5:
for f in bucket.list(prefix=k.name):
yield (f.key, f.size)
else:
for k, s in self.list_partitions(
bucket, k.name, level + 1):
yield (k, s)
elif self.input_list:
print "Using input list..."
for line in self.input_list:
key_name = line.strip()
k = bucket.get_key(key_name)
yield (k.key, k.size)
else:
print "Don't know how to list partitions without a filter or list :("
raise ValueError("Missing both input_filter and input_list")
def generate_tasks(self):
""" Generates SQS tasks, we batch small files into a single task """
taskid = 1
taskfiles = []
tasksize = 0
total_size_of_all = 0
for key, size in self.get_filtered_files():
# If the task have reached desired size we yield it
# Note, as SQS messages are limited to 65 KiB we limit tasks to
# 100 filenames, for simplicity
# boto only uses signature version 4, hence, we're limited to 65 KiB
if 0 < len(taskfiles) and (tasksize + size > self.task_size_limit
or len(taskfiles) > 200):
# Reduce to only filenames, sort by size... smallest first they are
# faster to download when handling the job
taskfiles = [
f for f, s in sorted(taskfiles, key=lambda (f, s): s)
]
yield {
'id': self.job_id + "/" + str(taskid),
'name': self.job_name,
'owner': self.job_owner,
'code': self.s3_code_path,
'target-queue': self.target_queue,
'files': taskfiles,
'size': tasksize
}
total_size_of_all += tasksize
print "%i tasks created acc. size: %s" % (taskid,
total_size_of_all)
taskid += 1
taskfiles = []
tasksize = 0
tasksize += size
taskfiles.append((key, size))
if len(taskfiles) > 0:
taskfiles = [f for f, s in sorted(taskfiles, key=lambda (f, s): s)]
yield {
'id': self.job_id + "/" + str(taskid),
'name': self.job_name,
'owner': self.job_owner,
'code': self.s3_code_path,
'target-queue': self.target_queue,
'files': taskfiles,
'size': tasksize
}
print "Finished:"
print "%i tasks created total size: %s" % (taskid, total_size_of_all +
tasksize)
def put_sqs_tasks(self):
""" Create an SQS tasks for this analysis job """
print "Populate SQS input queue with tasks"
# Connect to SQS is desired region
conn = sqs.connect_to_region(self.aws_region,
aws_access_key_id=self.aws_key,
aws_secret_access_key=self.aws_secret_key)
# Create queue
queue = conn.get_queue(self.sqs_input_name)
queue.set_message_class(JSONMessage)
# Populate queue with tasks
for task in self.generate_tasks():
#print "enqueueing", task["id"], "size:", task["size"]
msg = queue.new_message(body=task)
queue.write(msg)
conn.close()
def setup(self):
self.upload_job_bundle()
self.put_sqs_tasks()
print "Uploaded with job_id: %s" % self.job_id
def upload_job_bundle(self):
""" Upload job bundle to S3 """
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.analysis_bucket)
k = Key(bucket)
k.key = self.s3_code_path
k.set_contents_from_filename(self.job_bundle)
conn.close()
class AnalysisJob:
def __init__(self, cfg):
self.job_bundle = cfg.job_bundle
if cfg.input_filter:
self.input_filter = TelemetrySchema(json.load(open(cfg.input_filter)))
else:
self.input_filter = None
if cfg.input_list_file:
self.input_list = cfg.input_list_file
else:
self.input_list = None
self.job_id = str(uuid4())
self.target_queue = cfg.target_queue
self.aws_key = cfg.aws_key
self.aws_secret_key = cfg.aws_secret_key
self.input_bucket = "telemetry-published-v1"
self.job_name = cfg.name
self.job_owner = cfg.owner
self.date_limit = cfg.date_limit
# Bucket with intermediate data for this analysis job
self.analysis_bucket = "jonasfj-telemetry-analysis"
self.s3_code_path = "batch-jobs/" + self.job_id + ".tar.gz"
# S3 region of operation
self.aws_region = "us-west-2"
self.task_size_limit = 400 * 1024 * 1024
self.sqs_input_name = cfg.sqs_queue
def get_filtered_files(self):
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.input_bucket)
# date_limit is a hack that makes it easy to launch everything before
# a given date... say to back process all we have in the bucket...
if self.date_limit != None:
print "Launching limiting to before " + self.date_limit
for k,s in self.list_partitions(bucket):
if k.split('/')[-1].split('.')[1] < self.date_limit:
yield (k, s)
else:
for k,s in self.list_partitions(bucket):
yield (k, s)
def get_filtered_files_old(self):
""" Get tuples of name and size for all input files """
# Setup some auxiliary functions
allowed_values = self.input_filter.sanitize_allowed_values()
nb_dims = len(allowed_values)
def filter_includes(level, value):
return self.input_filter.is_allowed(value, allowed_values[level])
# iterate over all files in bucket, this is very slow and we should be
# be able to something much smarter using prefix listing and ordering
# to break listing.
count = 0
selected = 0
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.input_bucket)
for f in bucket.list():
count += 1
dims = self.input_filter.get_dimensions(".", f.key)
include = True
for i in xrange(nb_dims):
if not filter_includes(i, dims[i]):
include = False
break
if include:
selected += 1
yield (f.key, f.size)
if count % 5000 == 0:
print "%i files listed with %i selected" % (count, selected)
conn.close()
def list_partitions(self, bucket, prefix='', level=0):
if self.input_filter:
#print "Listing...", prefix, level
allowed_values = self.input_filter.sanitize_allowed_values()
delimiter = '/'
if level > 3:
delimiter = '.'
for k in bucket.list(prefix=prefix, delimiter=delimiter):
partitions = k.name.split("/")
if level > 3:
# split the last couple of partition components by "." instead of "/"
partitions.extend(partitions.pop().split(".", 2))
if self.input_filter.is_allowed(partitions[level], allowed_values[level]):
if level >= 5:
for f in bucket.list(prefix=k.name):
yield (f.key, f.size)
else:
for k, s in self.list_partitions(bucket, k.name, level + 1):
yield (k, s)
elif self.input_list:
print "Using input list..."
for line in self.input_list:
key_name = line.strip()
k = bucket.get_key(key_name)
yield (k.key, k.size)
else:
print "Don't know how to list partitions without a filter or list :("
raise ValueError("Missing both input_filter and input_list")
def generate_tasks(self):
""" Generates SQS tasks, we batch small files into a single task """
taskid = 1
taskfiles = []
tasksize = 0
total_size_of_all = 0
for key, size in self.get_filtered_files():
# If the task have reached desired size we yield it
# Note, as SQS messages are limited to 65 KiB we limit tasks to
# 100 filenames, for simplicity
# boto only uses signature version 4, hence, we're limited to 65 KiB
if 0 < len(taskfiles) and (tasksize + size > self.task_size_limit or
len(taskfiles) > 200):
# Reduce to only filenames, sort by size... smallest first they are
# faster to download when handling the job
taskfiles = [f for f,s in sorted(taskfiles, key=lambda (f,s): s)]
yield {
'id': self.job_id + "/" + str(taskid),
'name': self.job_name,
'owner': self.job_owner,
'code': self.s3_code_path,
'target-queue': self.target_queue,
'files': taskfiles,
'size': tasksize
}
total_size_of_all += tasksize
print "%i tasks created acc. size: %s" % (taskid, total_size_of_all)
taskid += 1
taskfiles = []
tasksize = 0
tasksize += size
taskfiles.append((key, size))
if len(taskfiles) > 0:
taskfiles = [f for f,s in sorted(taskfiles, key=lambda (f,s): s)]
yield {
'id': self.job_id + "/" + str(taskid),
'name': self.job_name,
'owner': self.job_owner,
'code': self.s3_code_path,
'target-queue': self.target_queue,
'files': taskfiles,
'size': tasksize
}
print "Finished:"
print "%i tasks created total size: %s" % (taskid, total_size_of_all + tasksize)
def put_sqs_tasks(self):
""" Create an SQS tasks for this analysis job """
print "Populate SQS input queue with tasks"
# Connect to SQS is desired region
conn = sqs.connect_to_region(
self.aws_region,
aws_access_key_id = self.aws_key,
aws_secret_access_key = self.aws_secret_key
)
# Create queue
queue = conn.get_queue(self.sqs_input_name)
queue.set_message_class(JSONMessage)
# Populate queue with tasks
for task in self.generate_tasks():
#print "enqueueing", task["id"], "size:", task["size"]
msg = queue.new_message(body = task)
queue.write(msg)
conn.close()
def setup(self):
self.upload_job_bundle()
self.put_sqs_tasks()
print "Uploaded with job_id: %s" % self.job_id
def upload_job_bundle(self):
""" Upload job bundle to S3 """
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.analysis_bucket)
k = Key(bucket)
k.key = self.s3_code_path
k.set_contents_from_filename(self.job_bundle)
conn.close()
