def RenderObject(self, aff4_object, args):
"""Renders given object as plain JSON-friendly data structure."""
object_attributes = aff4_object.synced_attributes.copy()
for key, value in aff4_object.new_attributes.items():
object_attributes[key] = value
attributes = {}
for attribute, values in object_attributes.items():
attributes[attribute.predicate] = []
for value in values:
# This value is really a LazyDecoder() instance. We need to get at the
# real data here.
if hasattr(value, "ToRDFValue"):
value = value.ToRDFValue()
if aff4_object.age_policy != aff4.NEWEST_TIME:
attributes[attribute.predicate].append(
api_value_renderers.RenderValue(
value, limit_lists=args.limit_lists))
else:
attributes[
attribute.predicate] = api_value_renderers.RenderValue(
value, limit_lists=args.limit_lists)
return dict(aff4_class=aff4_object.__class__.__name__,
urn=utils.SmartUnicode(aff4_object.urn),
attributes=attributes,
age_policy=aff4_object.age_policy)
def _FormatResultAsJson(self, result, format_mode=None):
if result is None:
return dict(status="OK")
if format_mode == JsonMode.PROTO3_JSON_MODE:
return json.loads(
json_format.MessageToJson(result.AsPrimitiveProto()))
elif format_mode == JsonMode.GRR_ROOT_TYPES_STRIPPED_JSON_MODE:
result_dict = {}
for field, value in result.ListSetFields():
if isinstance(
field,
(rdf_structs.ProtoDynamicEmbedded,
rdf_structs.ProtoEmbedded, rdf_structs.ProtoList)):
result_dict[field.name] = api_value_renderers.RenderValue(
value)
else:
result_dict[field.name] = api_value_renderers.RenderValue(
value)["value"]
return result_dict
elif format_mode == JsonMode.GRR_TYPE_STRIPPED_JSON_MODE:
rendered_data = api_value_renderers.RenderValue(result)
return api_value_renderers.StripTypeInfo(rendered_data)
elif format_mode == JsonMode.GRR_JSON_MODE:
return api_value_renderers.RenderValue(result)
else:
raise ValueError("Invalid format_mode: %s", format_mode)
def CallApiHandler(handler, args, token=None):
"""Handles API call to a given handler with given args and token."""
try:
result = handler.Handle(args, token=token)
except NotImplementedError:
# Fall back to legacy Render() method if Handle() is not implemented.
return handler.Render(args, token=token)
expected_type = handler.result_type
if expected_type is None:
expected_type = None.__class__
if result.__class__ != expected_type:
raise UnexpectedResultTypeError("Expected %s, but got %s." % (
expected_type.__name__, result.__class__.__name__))
if result is None:
return dict(status="OK")
else:
if handler.strip_json_root_fields_types:
result_dict = {}
for field, value in result.ListSetFields():
if isinstance(field, (rdf_structs.ProtoDynamicEmbedded,
rdf_structs.ProtoEmbedded,
rdf_structs.ProtoList)):
result_dict[field.name] = api_value_renderers.RenderValue(value)
else:
result_dict[field.name] = api_value_renderers.RenderValue(
value)["value"]
else:
result_dict = api_value_renderers.RenderValue(result)
return result_dict
def Render(self, args, token=None):
flow_id = flow.GRRFlow.StartFlow(client_id=args.client_id,
flow_name=args.runner_args.flow_name,
token=token,
args=args.flow_args,
runner_args=args.runner_args)
return dict(flow_id=api_value_renderers.RenderValue(flow_id),
flow_args=api_value_renderers.RenderValue(args.flow_args),
runner_args=api_value_renderers.RenderValue(
args.runner_args))
def Render(self, args, token=None):
flow_urn = args.client_id.Add("flows").Add(args.flow_id.Basename())
flow_obj = aff4.FACTORY.Open(flow_urn, aff4_type="GRRFlow", mode="r",
token=token)
output_plugins_states = flow_obj.GetRunner().context.output_plugins_states
result = {}
for plugin_descriptor, plugin_state in output_plugins_states:
result[plugin_descriptor.plugin_name] = (
api_value_renderers.RenderValue(plugin_descriptor),
api_value_renderers.RenderValue(plugin_state))
return result
def HandleApiCall(handler, args, token=None):
"""Handles API call to a given handlers with given args and token."""
if not hasattr(handler, "Render"):
handler = api_call_handler_base.ApiCallHandler.classes[handler]()
# Privileged handlers bypass the approvals model to do things like check flow
# status across multiple clients or add labels to clients. They provide
# limited functionality and are responsible for their own checking.
if handler.privileged:
token = token.SetUID()
# Raises on access denied
API_AUTH_MGR.CheckAccess(handler, token.username)
try:
result = handler.Handle(args, token=token)
except NotImplementedError:
# Fall back to legacy Render() method if Handle() is not implemented.
return handler.Render(args, token=token)
expected_type = handler.result_type
if expected_type is None:
expected_type = None.__class__
if result.__class__.__name__ != expected_type.__name__:
raise UnexpectedResultTypeError(
"Expected %s, but got %s." %
(expected_type.__name__, result.__class__.__name__))
if result is None:
return dict(status="OK")
else:
if handler.strip_json_root_fields_types:
result_dict = {}
for field, value in result.ListSetFields():
if isinstance(field,
(structs.ProtoDynamicEmbedded,
structs.ProtoEmbedded, structs.ProtoList)):
result_dict[field.name] = api_value_renderers.RenderValue(
value)
else:
result_dict[field.name] = api_value_renderers.RenderValue(
value)["value"]
else:
result_dict = api_value_renderers.RenderValue(result)
return result_dict
def RenderObject(self, hunt, args):
runner = hunt.GetRunner()
context = runner.context
untyped_summary_part = dict(state=hunt.Get(hunt.Schema.STATE),
hunt_name=context.args.hunt_name,
create_time=context.create_time,
expires=context.expires,
client_limit=context.args.client_limit,
client_rate=context.args.client_rate,
creator=context.creator,
description=context.args.description)
typed_summary_part = {}
if args.with_full_summary:
all_clients_count, completed_clients_count, _ = hunt.GetClientsCounts(
)
untyped_summary_part.update(
dict(stats=context.usage_stats,
all_clients_count=all_clients_count,
completed_clients_count=completed_clients_count,
outstanding_clients_count=(all_clients_count -
completed_clients_count)))
typed_summary_part = dict(regex_rules=runner.args.regex_rules
or [],
integer_rules=runner.args.integer_rules
or [],
args=hunt.state.args)
try:
typed_summary_part[
"client_rule_set"] = runner.args.client_rule_set
except AttributeError:
typed_summary_part["client_rule_set"] = []
for k, v in untyped_summary_part.items():
untyped_summary_part[k] = api_value_renderers.RenderValue(v)
for k, v in typed_summary_part.items():
typed_summary_part[k] = api_value_renderers.RenderValue(v)
rendered_object = {
"summary":
dict(untyped_summary_part.items() + typed_summary_part.items())
}
return rendered_object
def RenderObject(self, aff4_object, args):
"""Renders RDFValueCollection as plain JSON-friendly data structure."""
if args.filter:
index = 0
items = []
for item in aff4_object.GenerateItems():
serialized_item = item.SerializeToString()
if re.search(re.escape(args.filter), serialized_item, re.I):
if index >= args.offset:
items.append(item)
index += 1
if args.count and len(items) >= args.count:
break
else:
items = list(
itertools.islice(
aff4_object.GenerateItems(), args.offset,
args.count and (args.offset + args.count) or sys.maxint))
result = {}
result["offset"] = args.offset
result["count"] = len(items)
result["items"] = api_value_renderers.RenderValue(
items, limit_lists=args.items_limit_lists)
if args.with_total_count:
if hasattr(aff4_object, "CalculateLength"):
total_count = aff4_object.CalculateLength()
else:
total_count = len(aff4_object)
result["total_count"] = total_count
return result
def RenderObject(self, client, unused_args):
"""Renders VFSGRRClient as plain JSON-friendly data structure."""
return dict(disk_warnings=self._GetDiskWarnings(client),
summary=api_value_renderers.RenderValue(
client.GetSummary(),
with_types=True,
with_metadata=True))
def Render(self, args, token=None):
args.flow_args.hunt_runner_args.hunt_name = "GenericHunt"
# TODO(user): The following should be asserted in a more elegant way.
# Also, it's not clear whether cron job scheduling UI is used often enough
# to justify its existence. We should check with opensource users whether
# they find this feature useful and if not, deprecate it altogether.
if args.flow_name != "CreateAndRunGenericHuntFlow":
raise ValueError(
"Only CreateAndRunGenericHuntFlow flows are supported "
"here (got: %s)." % args.flow_name)
if not args.flow_runner_args.flow_name:
args.flow_runner_args.flow_name = args.flow_name
cron_args = aff4_cronjobs.CreateCronJobFlowArgs(
description=args.description,
periodicity=args.periodicity,
flow_runner_args=args.flow_runner_args,
flow_args=args.flow_args,
allow_overruns=args.allow_overruns,
lifetime=args.lifetime)
urn = aff4_cronjobs.CRON_MANAGER.ScheduleFlow(cron_args=cron_args,
disabled=True,
token=token)
fd = aff4.FACTORY.Open(urn,
aff4_type="CronJob",
token=token,
age=aff4.ALL_TIMES)
api_cron_job = CronJobToApiCronJob(fd)
return api_value_renderers.RenderValue(api_cron_job)
def RenderArtifacts(self, artifacts, custom_artifacts=None):
if custom_artifacts is None:
custom_artifacts = set()
result = []
for artifact_val in artifacts:
descriptor = artifact_registry.ArtifactDescriptor(
artifact=artifact_val,
artifact_source=artifact_val.ToPrettyJson(extended=True),
dependencies=sorted(artifact_val.GetArtifactDependencies()),
path_dependencies=sorted(
artifact_val.GetArtifactPathDependencies()),
is_custom=artifact_val.name in custom_artifacts)
for processor in parsers.Parser.GetClassesByArtifact(
artifact_val.name):
descriptor.processors.append(
artifact_registry.ArtifactProcessorDescriptor(
name=processor.__name__,
output_types=processor.output_types,
description=processor.GetDescription()))
result.append(api_value_renderers.RenderValue(descriptor))
return result
def Render(self, unused_args, token=None):
"""Fetches and renders current user's settings."""
user_settings = self.GetUserSettings(token)
return api_value_renderers.RenderValue(user_settings,
with_types=True,
with_metadata=True)
def Render(self, args, token=None):
flow_urn = args.client_id.Add("flows").Add(args.flow_id.Basename())
flow_obj = aff4.FACTORY.Open(flow_urn,
aff4_type="GRRFlow",
mode="r",
token=token)
output_plugins_states = flow_obj.GetRunner(
).context.output_plugins_states
type_indices = {}
result = []
for plugin_descriptor, plugin_state in output_plugins_states:
type_index = type_indices.setdefault(plugin_descriptor.plugin_name,
0)
type_indices[plugin_descriptor.plugin_name] += 1
# Output plugins states are stored differently for hunts and for flows:
# as a dictionary for hunts and as a simple list for flows.
#
# TODO(user): store output plugins states in the same way for flows
# and hunts. Until this is done, we can emulate the same interface in
# the HTTP API.
api_plugin = api_output_plugin.ApiOutputPlugin(
id=plugin_descriptor.plugin_name + "_%d" % type_index,
plugin_descriptor=plugin_descriptor,
state=plugin_state)
result.append(api_plugin)
return dict(offset=0,
count=len(result),
total_count=len(result),
items=api_value_renderers.RenderValue(result))
def RenderObject(self, aff4_object, args):
"""Renders given object as plain JSON-friendly data structure."""
render_value_args = dict(limit_lists=args.limit_lists)
if args.type_info == args.TypeInformation.WITH_TYPES:
render_value_args["with_types"] = True
elif args.type_info == args.TypeInformation.WITH_TYPES_AND_METADATA:
render_value_args["with_types"] = True
render_value_args["with_metadata"] = True
object_attributes = aff4_object.synced_attributes.copy()
for key, value in aff4_object.new_attributes.items():
object_attributes[key] = value
attributes = {}
for attribute, values in object_attributes.items():
attributes[attribute.predicate] = []
for value in values:
# This value is really a LazyDecoder() instance. We need to get at the
# real data here.
if hasattr(value, "ToRDFValue"):
value = value.ToRDFValue()
if aff4_object.age_policy != aff4.NEWEST_TIME:
attributes[attribute.predicate].append(
api_value_renderers.RenderValue(
value, **render_value_args))
else:
attributes[
attribute.predicate] = api_value_renderers.RenderValue(
value, **render_value_args)
result = dict(aff4_class=aff4_object.__class__.__name__,
urn=utils.SmartUnicode(aff4_object.urn),
attributes=attributes,
age_policy=aff4_object.age_policy)
if args.type_info == args.TypeInformation.WITH_TYPES_AND_METADATA:
descriptors = {}
for attribute, _ in aff4_object.synced_attributes.items():
descriptors[attribute.predicate] = {
"description": attribute.description
}
result["metadata"] = descriptors
return result
def Render(self, args, token=None):
metadata = aff4.FACTORY.Create(
HUNTS_ROOT_PATH.Add(args.hunt_id).Add("ResultsMetadata"), mode="r",
aff4_type="HuntResultsMetadata", token=token)
# We don't need rendered type information, so we return just the "value"
# part of the result.
return api_value_renderers.RenderValue(
metadata.Get(metadata.Schema.OUTPUT_PLUGINS, {}))["value"]
def RenderRDFStruct(self, cls):
fields = []
for field_desc in cls.type_infos:
repeated = isinstance(field_desc, type_info.ProtoList)
if hasattr(field_desc, "delegate"):
field_desc = field_desc.delegate
field = {
"name": field_desc.name,
"index": field_desc.field_number,
"repeated": repeated,
"dynamic": isinstance(field_desc,
type_info.ProtoDynamicEmbedded)
}
field_type = field_desc.type
if field_type is not None:
field["type"] = field_type.__name__
if field_type == rdfvalue.EnumNamedValue:
allowed_values = []
for enum_label in sorted(field_desc.enum):
enum_value = field_desc.enum[enum_label]
allowed_values.append(
dict(name=enum_label,
value=int(enum_value),
doc=enum_value.description))
field["allowed_values"] = allowed_values
if field_desc.default is not None:
if field_type:
field_default = field_type(field_desc.default)
else:
field_default = field_desc.default
field["default"] = api_value_renderers.RenderValue(
field_default, with_types=True)
if field_desc.description:
field["doc"] = field_desc.description
if field_desc.friendly_name:
field["friendly_name"] = field_desc.friendly_name
if field_desc.labels:
field["labels"] = [
rdfvalue.SemanticDescriptor.Labels.reverse_enum[x]
for x in field_desc.labels
]
fields.append(field)
return dict(name=cls.__name__,
doc=cls.__doc__ or "",
fields=fields,
kind="struct")
def Render(self, args, token=None):
labels_index = aff4.FACTORY.Create(
aff4.VFSGRRClient.labels_index_urn, "AFF4LabelsIndex",
mode="rw", token=token)
rendered_labels = []
for label in labels_index.ListUsedLabels():
rendered_labels.append(api_value_renderers.RenderValue(label))
return dict(labels=sorted(rendered_labels))
def Render(self, args, token=None):
stats_store = aff4.FACTORY.Create(
None, aff4_type=stats_store_lib.StatsStore, mode="w", token=token)
process_ids = [pid for pid in stats_store.ListUsedProcessIds()
if pid.startswith(args.component.name.lower())]
if not process_ids:
return {}
else:
metadata = stats_store.ReadMetadata(process_id=process_ids[0])
return api_value_renderers.RenderValue(metadata)
def Render(self, args, token=None):
labels_index = aff4.FACTORY.Create(standard.LabelSet.CLIENT_LABELS_URN,
"LabelSet",
mode="r",
token=token)
rendered_labels = []
for label in labels_index.ListLabels():
label_object = aff4_rdfvalues.AFF4ObjectLabel(name=label)
rendered_labels.append(
api_value_renderers.RenderValue(label_object))
return dict(labels=rendered_labels)
def Render(self, args, token=None):
client_urn = self.GetClientTarget(args, token=token)
size_condition = file_finder.FileFinderCondition(
condition_type=file_finder.FileFinderCondition.Type.SIZE,
size=file_finder.FileFinderSizeCondition(
max_file_size=args.max_file_size))
file_finder_args = file_finder.FileFinderArgs(
paths=args.paths,
action=file_finder.FileFinderAction(action_type=args.action),
conditions=[size_condition])
# Check our flow throttling limits, will raise if there are problems.
throttler = throttle.FlowThrottler()
throttler.EnforceLimits(client_urn,
token.username,
"FileFinder",
file_finder_args,
token=token)
# Limit the whole flow to 200MB so if a glob matches lots of small files we
# still don't have too much impact.
runner_args = flow_runner.FlowRunnerArgs(client_id=client_urn,
flow_name="FileFinder",
network_bytes_limit=200 *
1000 * 1000)
flow_id = flow.GRRFlow.StartFlow(runner_args=runner_args,
token=token,
args=file_finder_args)
# Provide a url where the caller can check on the flow status.
status_url = urlparse.urljoin(
config_lib.CONFIG["AdminUI.url"], "/api/flows/%s/%s/status" %
(client_urn.Basename(), flow_id.Basename()))
return dict(
flow_id=api_value_renderers.RenderValue(flow_id),
flow_args=api_value_renderers.RenderValue(file_finder_args),
runner_args=api_value_renderers.RenderValue(runner_args),
status_url=status_url)
def RenderPrimitiveRDFValue(self, cls):
result = dict(name=cls.__name__,
doc=cls.__doc__ or "",
kind="primitive")
try:
default_value = api_value_renderers.RenderValue(cls(),
with_types=True)
result["default"] = default_value
except Exception as e: # pylint: disable=broad-except
logging.exception("Can't create default for primitive %s: %s",
cls.__name__, e)
return result
def Render(self, args, token=None):
encoded_urns = []
aff4_path = rdfvalue.RDFURN(args.aff4_path)
index_prefix = "index:dir/"
for predicate, _, timestamp in data_store.DB.ResolveRegex(
aff4_path, index_prefix + ".+", token=token,
timestamp=data_store.DB.NEWEST_TIMESTAMP, limit=1000000):
urn = aff4_path.Add(predicate[len(index_prefix):])
encoded_urns.append([api_value_renderers.RenderValue(urn),
timestamp])
return encoded_urns
def Render(self, args, token=None):
"""Creates a new hunt."""
# We only create generic hunts with /hunts/create requests.
args.hunt_runner_args.hunt_name = "GenericHunt"
# Anyone can create the hunt but it will be created in the paused
# state. Permissions are required to actually start it.
with implementation.GRRHunt.StartHunt(
runner_args=args.hunt_runner_args,
args=args.hunt_args,
token=token) as hunt:
# Nothing really to do here - hunts are always created in the paused
# state.
logging.info("User %s created a new %s hunt (%s)",
token.username, hunt.state.args.flow_runner_args.flow_name,
hunt.urn)
return dict(
status="OK",
hunt_id=api_value_renderers.RenderValue(hunt.urn),
hunt_args=api_value_renderers.RenderValue(hunt.state.args),
hunt_runner_args=api_value_renderers.RenderValue(hunt.runner.args))
def Render(self, args, token=None):
metadata = aff4.FACTORY.Create(
HUNTS_ROOT_PATH.Add(args.hunt_id).Add("ResultsMetadata"), mode="r",
aff4_type="HuntResultsMetadata", token=token)
plugins = metadata.Get(metadata.Schema.OUTPUT_PLUGINS, {})
result = []
for plugin_name, (plugin_descriptor, plugin_state) in plugins.items():
api_plugin = api_output_plugin.ApiOutputPlugin(
id=plugin_name, plugin_descriptor=plugin_descriptor,
state=plugin_state)
result.append(api_plugin)
return dict(offset=0, count=len(result), total_count=len(result),
items=api_value_renderers.RenderValue(result))
def Render(self, args, token=None):
if not args.count:
stop = None
else:
stop = args.offset + args.count
cron_jobs_urns = list(itertools.islice(
aff4_cronjobs.CRON_MANAGER.ListJobs(token=token), args.offset, stop))
cron_jobs = aff4.FACTORY.MultiOpen(
cron_jobs_urns, aff4_type="CronJob", token=token, age=aff4.ALL_TIMES)
items = [CronJobToApiCronJob(cron_job) for cron_job in cron_jobs]
items.sort(key=lambda item: item.urn)
result = dict(offset=args.offset,
count=len(items),
items=api_value_renderers.RenderValue(items))
return result
def Render(self, args, token=None):
if not self.collection_name:
raise ValueError("collection_name can't be None")
metadata = aff4.FACTORY.Create(HUNTS_ROOT_PATH.Add(
args.hunt_id).Add("ResultsMetadata"),
mode="r",
aff4_type="HuntResultsMetadata",
token=token)
plugins = metadata.Get(metadata.Schema.OUTPUT_PLUGINS, {})
plugin_descriptor = plugins.get(args.plugin_id)[0]
# Currently all logs and errors are written not in per-plugin
# collections, but in per-hunt collections. This doesn't make
# much sense, because to show errors of particular plugin, we have
# to filter the collection. Still, things are not too bad, because
# typically only one output plugin is used.
#
# TODO(user): Write errors/logs per-plugin, so that we don't
# have to do the filtering.
logs_collection = aff4.FACTORY.Open(HUNTS_ROOT_PATH.Add(
args.hunt_id).Add(self.collection_name),
mode="r",
token=token)
if len(plugins) == 1:
total_count = len(logs_collection)
logs = list(
itertools.islice(
logs_collection.GenerateItems(offset=args.offset),
args.count or None))
else:
all_logs_for_plugin = [
x for x in logs_collection
if x.plugin_descriptor == plugin_descriptor
]
total_count = len(all_logs_for_plugin)
logs = all_logs_for_plugin[args.offset:]
if args.count:
logs = logs[:args.count]
return dict(offset=args.offset,
count=len(logs),
total_count=total_count,
items=api_value_renderers.RenderValue(logs))
def Render(self, args, token=None):
if not self.attribute_name:
raise ValueError("attribute_name can't be None")
flow_urn = args.client_id.Add("flows").Add(args.flow_id.Basename())
flow_obj = aff4.FACTORY.Open(flow_urn,
aff4_type="GRRFlow",
mode="r",
token=token)
output_plugins_states = flow_obj.GetRunner(
).context.output_plugins_states
# Flow output plugins don't use collections to store status/error
# information. Instead, it's stored in plugin's state. Nevertheless,
# we emulate collections API here. Having similar API interface allows
# one to reuse the code when handling hunts and flows output plugins.
# Good example is the UI code.
type_indices = {}
found_state = None
for plugin_descriptor, plugin_state in output_plugins_states:
type_index = type_indices.setdefault(plugin_descriptor.plugin_name,
0)
type_indices[plugin_descriptor.plugin_name] += 1
if args.plugin_id == plugin_descriptor.plugin_name + "_%d" % type_index:
found_state = plugin_state
break
if not found_state:
raise RuntimeError("Flow %s doesn't have output plugin %s" %
(flow_urn, args.plugin_id))
stop = None
if args.count:
stop = args.offset + args.count
logs_collection = found_state.get(self.attribute_name, [])
sliced_collection = logs_collection[args.offset:stop]
return dict(offset=args.offset,
count=len(sliced_collection),
total_count=len(logs_collection),
items=api_value_renderers.RenderValue(sliced_collection))
def Render(self, args, token=None):
if args.flow_name and not args.flow_runner_args.flow_name:
args.flow_runner_args.flow_name = args.flow_name
cron_args = aff4_cronjobs.CreateCronJobFlowArgs(
description=args.description,
periodicity=args.periodicity,
flow_runner_args=args.flow_runner_args,
flow_args=args.flow_args,
allow_overruns=args.allow_overruns,
lifetime=args.lifetime)
urn = aff4_cronjobs.CRON_MANAGER.ScheduleFlow(
cron_args=cron_args, disabled=True, token=token)
fd = aff4.FACTORY.Open(urn, aff4_type="CronJob", token=token,
age=aff4.ALL_TIMES)
api_cron_job = CronJobToApiCronJob(fd)
return api_value_renderers.RenderValue(api_cron_job)
def RenderArtifacts(self, artifacts):
result = []
for artifact_val in artifacts:
descriptor = artifact_registry.ArtifactDescriptor(
artifact=artifact_val,
artifact_source=artifact_val.ToPrettyJson(extended=True),
dependencies=sorted(artifact_val.GetArtifactDependencies()),
path_dependencies=sorted(artifact_val.GetArtifactPathDependencies()),
error_message=artifact_val.error_message,
is_custom=artifact_val.loaded_from.startswith("datastore:"))
for processor in parsers.Parser.GetClassesByArtifact(artifact_val.name):
descriptor.processors.append(
artifact_registry.ArtifactProcessorDescriptor(
name=processor.__name__,
output_types=processor.output_types,
description=processor.GetDescription()))
result.append(api_value_renderers.RenderValue(descriptor))
return result
def _PrepareV1Request(self, method, args=None):
"""Prepares API v1 request for a given method and args."""
args_proto = None
if args:
args_proto = args.AsPrimitiveProto()
request = self.connector.BuildRequest(method, args_proto)
request.url = request.url.replace("/api/v2/", "/api/")
if args and request.data:
body_proto = args.__class__().AsPrimitiveProto()
json_format.Parse(request.data, body_proto)
body_args = args.__class__()
body_args.ParseFromString(body_proto.SerializeToString())
request.data = json.dumps(
api_value_renderers.StripTypeInfo(
api_value_renderers.RenderValue(body_args)),
cls=http_api.JSONEncoderWithRDFPrimitivesSupport)
prepped_request = request.prepare()
return request, prepped_request
