def init_docs(self):
if self.ws.doc_gen == 'reverse_lookup':
self.docs = ReverseLookupDocument(self.ws.size, prefix='n1ql')
elif self.ws.doc_gen == 'reverse_range_lookup':
self.docs = ReverseRangeLookupDocument(
self.ws.size,
prefix='n1ql',
range_distance=self.ws.range_distance)
elif self.ws.doc_gen == 'ext_reverse_lookup':
self.docs = ExtReverseLookupDocument(self.ws.size,
prefix='n1ql',
num_docs=self.ws.items)
elif self.ws.doc_gen == 'join':
self.docs = JoinedDocument(self.ws.size,
prefix='n1ql',
num_docs=self.ws.items,
num_categories=self.ws.num_categories,
num_replies=self.ws.num_replies)
elif self.ws.doc_gen == 'ref':
self.docs = RefDocument(self.ws.size, prefix='n1ql')
elif self.ws.doc_gen == 'profile':
self.docs = ProfileDocument(self.ws.size, prefix='n1ql')
elif self.ws.doc_gen == 'array_indexing':
self.docs = ArrayIndexingDocument(self.ws.size,
prefix='n1ql',
array_size=self.ws.array_size,
num_docs=self.ws.items)
def __init__(self, settings, workload, prefix=None):
super(Latency, self).__init__(settings)
self.clients = []
for bucket in self.get_buckets():
client = CBGen(bucket=bucket,
host=settings.master_node,
username=bucket,
password=settings.bucket_password)
self.clients.append((bucket, client))
self.existing_keys = ExistingKey(workload.working_set,
workload.working_set_access,
prefix=prefix)
self.new_keys = NewKey(prefix=prefix, expiration=workload.expiration)
self.keys_for_removal = KeyForRemoval(prefix=prefix)
if not hasattr(workload, 'doc_gen') or workload.doc_gen == 'old':
self.new_docs = NewDocument(workload.size)
elif workload.doc_gen == 'new':
self.new_docs = NewNestedDocument(workload.size)
elif workload.doc_gen == 'reverse_lookup':
self.new_docs = ReverseLookupDocument(workload.size,
workload.doc_partitions)
elif workload.doc_gen == 'reverse_lookup_array_indexing':
self.new_docs = ReverseLookupDocumentArrayIndexing(
workload.size, workload.doc_partitions, workload.items)
self.items = workload.items
self.n1ql_op = workload.n1ql_op
def init_docs(self):
if not hasattr(self.ws, 'doc_gen') or self.ws.doc_gen == 'basic':
self.docs = Document(self.ws.size)
elif self.ws.doc_gen == 'string':
self.docs = String(self.ws.size)
elif self.ws.doc_gen == 'nested':
self.docs = NestedDocument(self.ws.size)
elif self.ws.doc_gen == 'reverse_lookup':
self.docs = ReverseLookupDocument(self.ws.size,
self.ts.prefix)
elif self.ws.doc_gen == 'reverse_range_lookup':
self.docs = ReverseRangeLookupDocument(self.ws.size,
prefix='n1ql',
range_distance=self.ws.range_distance)
elif self.ws.doc_gen == 'ext_reverse_lookup':
self.docs = ExtReverseLookupDocument(self.ws.size,
self.ts.prefix,
self.ws.items)
elif self.ws.doc_gen == 'join':
self.docs = JoinedDocument(self.ws.size,
self.ts.prefix,
self.ws.items,
self.ws.num_categories,
self.ws.num_replies)
elif self.ws.doc_gen == 'ref':
self.docs = RefDocument(self.ws.size,
self.ts.prefix)
elif self.ws.doc_gen == 'array_indexing':
self.docs = ArrayIndexingDocument(self.ws.size,
self.ts.prefix,
self.ws.array_size,
self.ws.items)
elif self.ws.doc_gen == 'profile':
self.docs = ProfileDocument(self.ws.size,
self.ts.prefix)
elif self.ws.doc_gen == 'import_export_simple':
self.docs = ImportExportDocument(self.ws.size,
self.ts.prefix)
elif self.ws.doc_gen == 'import_export_array':
self.docs = ImportExportDocumentArray(self.ws.size,
self.ts.prefix)
elif self.ws.doc_gen == 'import_export_nested':
self.docs = ImportExportDocumentNested(self.ws.size,
self.ts.prefix)
elif self.ws.doc_gen == 'large_subdoc':
self.docs = LargeDocument(self.ws.size)
elif self.ws.doc_gen == 'gsi_multiindex':
self.docs = GSIMultiIndexDocument(self.ws.size)
elif self.ws.doc_gen == 'small_plasma':
self.docs = SmallPlasmaDocument(self.ws.size)
elif self.ws.doc_gen == 'sequential_plasma':
self.docs = SequentialPlasmaDocument(self.ws.size)
elif self.ws.doc_gen == 'large_item_plasma':
self.docs = LargeItemPlasmaDocument(self.ws.size,
self.ws.item_size)
elif self.ws.doc_gen == 'varying_item_plasma':
self.docs = VaryingItemSizePlasmaDocument(self.ws.size,
self.ws.size_variation_min,
self.ws.size_variation_max)
def __init__(self, workload_settings, target_settings, shutdown_event):
super(N1QLWorker, self).__init__(workload_settings, target_settings,
shutdown_event)
self.new_queries = N1QLQueryGen(workload_settings.n1ql_queries)
self.total_workers = self.ws.n1ql_workers
self.throughput = self.ws.n1ql_throughput
self.name = 'n1ql-worker'
host, port = self.ts.node.split(':')
bucket = self.ts.bucket
if workload_settings.n1ql_op == 'ryow':
bucket += '?fetch_mutation_tokens=true'
params = {'bucket': bucket, 'host': host, 'port': port,
'username': self.ts.bucket, 'password': self.ts.password}
self.existing_keys = ExistingKey(self.ws.working_set,
self.ws.working_set_access,
'n1ql')
self.new_keys = NewKey('n1ql', self.ws.expiration)
self.keys_for_removal = KeyForRemoval('n1ql')
self.keys_for_casupdate = KeyForCASUpdate(self.total_workers, self.ws.working_set,
self.ws.working_set_access,
'n1ql')
if self.ws.doc_gen == 'merge':
self.docs = MergeDocument(self.ws.size,
self.ws.doc_partitions,
False)
elif self.ws.doc_gen == 'reverse_lookup':
self.docs = ReverseLookupDocument(self.ws.size,
self.ws.doc_partitions,
False)
elif self.ws.doc_gen == 'reverse_lookup_array_indexing':
if self.ws.updates:
self.docs = ReverseLookupDocumentArrayIndexing(
self.ws.size, self.ws.doc_partitions, self.ws.items,
delta=random.randint(0, 10))
else:
self.docs = ReverseLookupDocumentArrayIndexing(
self.ws.size, self.ws.doc_partitions, self.ws.items)
self.cb = N1QLGen(**params)
def init_docs(self):
if not hasattr(self.ws, 'doc_gen') or self.ws.doc_gen == 'basic':
self.docs = Document(self.ws.size)
elif self.ws.doc_gen == 'string':
self.docs = String(self.ws.size)
elif self.ws.doc_gen == 'nested':
self.docs = NestedDocument(self.ws.size)
elif self.ws.doc_gen == 'reverse_lookup':
self.docs = ReverseLookupDocument(self.ws.size, self.ts.prefix)
elif self.ws.doc_gen == 'reverse_range_lookup':
self.docs = ReverseRangeLookupDocument(self.ws.size,
self.ts.prefix,
self.ws.range_distance)
elif self.ws.doc_gen == 'ext_reverse_lookup':
self.docs = ExtReverseLookupDocument(self.ws.size, self.ts.prefix,
self.ws.items)
elif self.ws.doc_gen == 'hash_join':
self.docs = HashJoinDocument(self.ws.size, self.ts.prefix,
self.ws.range_distance)
elif self.ws.doc_gen == 'join':
self.docs = JoinedDocument(self.ws.size, self.ts.prefix,
self.ws.items, self.ws.num_categories,
self.ws.num_replies)
elif self.ws.doc_gen == 'ref':
self.docs = RefDocument(self.ws.size, self.ts.prefix)
elif self.ws.doc_gen == 'array_indexing':
self.docs = ArrayIndexingDocument(self.ws.size, self.ts.prefix,
self.ws.array_size,
self.ws.items)
elif self.ws.doc_gen == 'array_indexing_unique':
self.docs = ArrayIndexingUniqueDocument(self.ws.size,
self.ts.prefix,
self.ws.array_size,
self.ws.items)
elif self.ws.doc_gen == 'array_indexing_range_scan':
self.docs = ArrayIndexingRangeScanDocument(self.ws.size,
self.ts.prefix,
self.ws.array_size,
self.ws.items)
elif self.ws.doc_gen == 'profile':
self.docs = ProfileDocument(self.ws.size, self.ts.prefix)
elif self.ws.doc_gen == 'import_export_simple':
self.docs = ImportExportDocument(self.ws.size, self.ts.prefix)
elif self.ws.doc_gen == 'import_export_array':
self.docs = ImportExportDocumentArray(self.ws.size, self.ts.prefix)
elif self.ws.doc_gen == 'import_export_nested':
self.docs = ImportExportDocumentNested(self.ws.size,
self.ts.prefix)
elif self.ws.doc_gen == 'large':
self.docs = LargeDocument(self.ws.size)
elif self.ws.doc_gen == 'gsi_multiindex':
self.docs = GSIMultiIndexDocument(self.ws.size)
elif self.ws.doc_gen == 'small_plasma':
self.docs = SmallPlasmaDocument(self.ws.size)
elif self.ws.doc_gen == 'sequential_plasma':
self.docs = SequentialPlasmaDocument(self.ws.size)
elif self.ws.doc_gen == 'large_item_plasma':
self.docs = LargeItemPlasmaDocument(self.ws.size,
self.ws.item_size)
elif self.ws.doc_gen == 'varying_item_plasma':
self.docs = VaryingItemSizePlasmaDocument(
self.ws.size, self.ws.size_variation_min,
self.ws.size_variation_max)
elif self.ws.doc_gen == 'eventing_small':
self.docs = EventingSmallDocument(self.ws.size)
elif self.ws.doc_gen == 'tpc_ds':
self.docs = TpcDsDocument()
elif self.ws.doc_gen == 'package':
self.docs = PackageDocument(self.ws.size)
elif self.ws.doc_gen == 'incompressible':
self.docs = IncompressibleString(self.ws.size)
elif self.ws.doc_gen == 'big_fun':
self.docs = BigFunDocument()
elif self.ws.doc_gen == 'multibucket':
self.docs = MultiBucketDocument(self.ws.size)
elif self.ws.doc_gen == 'advancedfilter':
self.docs = AdvFilterDocument(self.ws.size)
elif self.ws.doc_gen == 'advancedfilterxattr':
self.docs = AdvFilterXattrBody(self.ws.size)
def __init__(self, workload_settings, target_settings,
shutdown_event=None):
self.ws = workload_settings
self.ts = target_settings
self.shutdown_event = shutdown_event
logger.setLevel(logging.INFO)
self.existing_keys = ExistingKey(self.ws.working_set,
self.ws.working_set_access,
self.ts.prefix)
self.new_keys = NewKey(self.ts.prefix, self.ws.expiration)
self.keys_for_removal = KeyForRemoval(self.ts.prefix)
if not hasattr(self.ws, 'doc_gen') or self.ws.doc_gen == 'old':
extra_fields = False
if (hasattr(self.ws, 'extra_doc_fields') and
self.ws['extra_doc_fields'] == 'yes'):
extra_fields = True
self.docs = NewDocument(self.ws.size, extra_fields)
elif self.ws.doc_gen == 'new':
self.docs = NewNestedDocument(self.ws.size)
elif self.ws.doc_gen == 'merge':
isRandom = True
if self.ts.prefix == 'n1ql':
isRandom = False
self.docs = MergeDocument(self.ws.size,
self.ws.doc_partitions,
isRandom)
elif self.ws.doc_gen == 'reverse_lookup':
isRandom = True
if self.ts.prefix == 'n1ql':
isRandom = False
self.docs = ReverseLookupDocument(self.ws.size,
self.ws.doc_partitions,
isRandom)
elif self.ws.doc_gen == 'reverse_lookup_array_indexing':
isRandom = True
if self.ts.prefix == 'n1ql':
isRandom = False
if self.ws.updates:
# plus 10 to all values in array when updating doc
self.docs = ReverseLookupDocumentArrayIndexing(
self.ws.size, self.ws.doc_partitions, self.ws.items,
delta=random.randint(0, 10))
else:
self.docs = ReverseLookupDocumentArrayIndexing(
self.ws.size, self.ws.doc_partitions, self.ws.items)
elif self.ws.doc_gen == 'spatial':
self.docs = NewDocumentFromSpatialFile(
self.ws.spatial.data,
self.ws.spatial.dimensionality)
elif self.ws.doc_gen == 'large_subdoc':
self.docs = NewLargeDocument(self.ws.size)
self.next_report = 0.05 # report after every 5% of completion
host, port = self.ts.node.split(':')
# Only FTS uses proxyPort and authless bucket right now.
# Instead of jumping hoops to specify proxyPort in target
# iterator/settings, which only passes down very specific attributes,
# just detect fts instead. The following does not work with
# authless bucket. FTS's worker does its own Couchbase.connect
if not (hasattr(self.ws, "fts") and hasattr(
self.ws.fts, "doc_database_url")):
# default sasl bucket
self.init_db({'bucket': self.ts.bucket, 'host': host, 'port': port,
'username': self.ts.bucket,
'password': self.ts.password})
self.fallingBehindCount = 0
