def __init__(self, arguments):
super().__init__(arguments)
# auth configuration
connection_string = self.get_from_env(
"APPLICATIONINSIGHTS_CONNECTION_STRING")
# Create clients
self.exporter = AzureMonitorTraceExporter.from_connection_string(
connection_string)
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
self.spans_list = []
for _ in range(self.args.num_spans):
with tracer.start_as_current_span(name="name") as span:
self.spans_list.append(span)
import requests
from opentelemetry import trace
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchExportSpanProcessor
import flask
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# The preferred tracer implementation must be set, as the opentelemetry-api
# defines the interface with a no-op implementation.
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
exporter = AzureMonitorTraceExporter.from_connection_string(
os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"])
# SpanExporter receives the spans and send them to the target location.
span_processor = BatchExportSpanProcessor(exporter)
trace.get_tracer_provider().add_span_processor(span_processor)
# Integrations are the glue that binds the OpenTelemetry API and the
# frameworks and libraries that are used together, automatically creating
# Spans and propagating context as appropriate.
# Enable instrumentation in the requests library.
RequestsInstrumentor().instrument()
app = flask.Flask(__name__)
# Enable instrumentation in the flask library.
FlaskInstrumentor().instrument_app(app)
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
app = Flask(__name__)
app.config.from_object(Config)
db = SQLAlchemy(app)
# Set global TracerProvider before instrumenting
trace.set_tracer_provider(
TracerProvider(resource=Resource.create({SERVICE_NAME: "flask-sample"})))
# Enable tracing for sqlalchemy library
SQLAlchemyInstrumentor().instrument()
# Enable tracing for Flask library
FlaskInstrumentor().instrument_app(app)
# Enable tracing for requests library
RequestsInstrumentor().instrument()
trace_exporter = AzureMonitorTraceExporter(
connection_string=Config.CONNECTION_STRING)
trace.get_tracer_provider().add_span_processor(
BatchSpanProcessor(trace_exporter))
# Import here to avoid circular imports
from app import routes # noqa isort:skip
app.run(host='localhost', port=5000)
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""
An example to show an application instrumented with the Opentelemetry requests instrumentations.
Calls made with the requests library will be automatically tracked and telemetry is exported to
application insights with the AzureMonitorTraceExporter.
"""
import os
import requests
from opentelemetry import trace
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchExportSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
RequestsInstrumentor().instrument()
span_processor = BatchExportSpanProcessor(
AzureMonitorTraceExporter(
connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]))
trace.get_tracer_provider().add_span_processor(span_processor)
with tracer.start_as_current_span("parent"):
response = requests.get("https://azure.microsoft.com/", timeout=5)
import psycopg2
from opentelemetry import trace
from opentelemetry.instrumentation.psycopg2 import Psycopg2Instrumentor
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Set global TracerProvider before instrumenting
trace.set_tracer_provider(
TracerProvider(resource=Resource.create({SERVICE_NAME: "db-sample"})))
# Enable tracing for psycopg2 library
Psycopg2Instrumentor().instrument()
trace_exporter = AzureMonitorTraceExporter()
trace.get_tracer_provider().add_span_processor(
BatchSpanProcessor(trace_exporter))
cnx = psycopg2.connect(database='test', user='******', password='******')
cursor = cnx.cursor()
cursor.execute("CREATE TABLE test (testField integer);")
cursor.execute("INSERT INTO test (testField) VALUES (123)")
cursor.close()
cnx.close()
)
)
)
logging.basicConfig(
format="%(asctime)s %(levelname)s: %(message)s",
level=logging.INFO,
datefmt="%d-%b-%y %H:%M:%S",
)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logger.addHandler(AzureLogHandler(connection_string="InstrumentationKey=<your instrumentation key"))
exporter = AzureMonitorTraceExporter.from_connection_string(
"InstrumentationKey=<your instrumentation key>"
)
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
span_processor = BatchSpanProcessor(exporter)
trace.get_tracer_provider().add_span_processor(span_processor)
@ dataclass
class RunJob:
@ staticmethod
def animation():
anime = "|/-\\"
for i in range(10):
