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Criar um problema

Envie logs do seu produto

Dica

Este procedimento faz parte do curso que ensina como criar um início rápido. Se ainda não o fez, confira a introdução do curso.

Cada procedimento neste curso é baseado no anterior, portanto, certifique-se de ter concluído o último procedimento, envie um evento do seu produto antes de prosseguir com este.

log são gerados pelo aplicativo. Eles são registros de texto baseados em tempo que ajudam seu usuário a ver o que está acontecendo em seu sistema.

New Relic oferece uma variedade de maneiras de instrumentar seu aplicativo para enviar log para nossa Logs API.

Nesta lição, você aprenderá a enviar log do seu produto usando nosso kit de desenvolvimento de software de telemetria (SDK).

import os
import random
import datetime
from sys import getsizeof
from newrelic_telemetry_sdk import MetricClient, GaugeMetric, CountMetric, SummaryMetric
from newrelic_telemetry_sdk import EventClient, Event
metric_client = MetricClient(os.environ["NEW_RELIC_LICENSE_KEY"])
event_client = EventClient(os.environ["NEW_RELIC_LICENSE_KEY"])
db = {}
stats = {
"read_response_times": [],
"read_errors": 0,
"read_count": 0,
"create_response_times": [],
"create_errors": 0,
"create_count": 0,
"update_response_times": [],
"update_errors": 0,
"update_count": 0,
"delete_response_times": [],
"delete_errors": 0,
"delete_count": 0,
"cache_hit": 0,
}
last_push = {
"read": datetime.datetime.now(),
"create": datetime.datetime.now(),
"update": datetime.datetime.now(),
"delete": datetime.datetime.now(),
}
def read(key):
print(f"Reading...")
if random.randint(0, 30) > 10:
stats["cache_hit"] += 1
stats["read_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["read_errors"] += 1
stats["read_count"] += 1
try_send("read")
def create(key, value):
print(f"Writing...")
db[key] = value
stats["create_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["create_errors"] += 1
stats["create_count"] += 1
try_send("create")
def update(key, value):
print(f"Updating...")
db[key] = value
stats["update_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["update_errors"] += 1
stats["update_count"] += 1
try_send("update")
def delete(key):
print(f"Deleting...")
db.pop(key, None)
stats["delete_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["delete_errors"] += 1
stats["delete_count"] += 1
try_send("delete")
def try_send(type_):
print("try_send")
now = datetime.datetime.now()
interval_ms = (now - last_push[type_]).total_seconds() * 1000
if interval_ms >= 2000:
send_metrics(type_, interval_ms)
send_event(type_)
def send_metrics(type_, interval_ms):
print("sending metrics...")
keys = GaugeMetric("fdb_keys", len(db))
db_size = GaugeMetric("fdb_size", getsizeof(db))
errors = CountMetric(
name=f"fdb_{type_}_errors",
value=stats[f"{type_}_errors"],
interval_ms=interval_ms
)
cache_hits = CountMetric(
name=f"fdb_cache_hits",
value=stats["cache_hit"],
interval_ms=interval_ms
)
response_times = stats[f"{type_}_response_times"]
response_time_summary = SummaryMetric(
f"fdb_{type_}_responses",
count=len(response_times),
min=min(response_times),
max=max(response_times),
sum=sum(response_times),
interval_ms=interval_ms,
)
batch = [keys, db_size, errors, cache_hits, response_time_summary]
response = metric_client.send_batch(batch)
response.raise_for_status()
print("Sent metrics successfully!")
clear(type_)
def send_event(type_):
print("sending event...")
count = Event(
"fdb_method", {"method": type_}
)
response = event_client.send_batch(count)
response.raise_for_status()
print("Event sent successfully!")
def clear(type_):
stats[f"{type_}_response_times"] = []
stats[f"{type_}_errors"] = 0
stats["cache_hit"] = 0
stats[f"{type_}_count"] = 0
last_push[type_] = datetime.datetime.now()
db.py

Use nosso SDK

Oferecemos um SDK de telemetria de código aberto em diversas das linguagens de programação mais populares. Eles enviam dados para nossa de ingestão de API dados, incluindo nossa Log API. Desses SDKs de linguagem, Python e Java funcionam com a Log API.

Nesta lição, você aprende como instalar e usar o SDK de telemetria Python para enviar logs para o New Relic.

Mude para o diretório send-logs/flashDB do repositório do curso.

bash
$
cd ../../send-events/flashDB

Se ainda não o fez, instale o pacote newrelic-telemetry-sdk .

bash
$
pip install newrelic-telemetry-sdk

Abra o arquivo db.py no IDE de sua preferência e configure o LogClient.

import os
import random
import datetime
from sys import getsizeof
from newrelic_telemetry_sdk import MetricClient, GaugeMetric, CountMetric, SummaryMetric
from newrelic_telemetry_sdk import EventClient, Event
from newrelic_telemetry_sdk import LogClient
metric_client = MetricClient(os.environ["NEW_RELIC_LICENSE_KEY"])
event_client = EventClient(os.environ["NEW_RELIC_LICENSE_KEY"])
log_client = LogClient(os.environ["NEW_RELIC_LICENSE_KEY"])
db = {}
stats = {
"read_response_times": [],
"read_errors": 0,
"read_count": 0,
"create_response_times": [],
"create_errors": 0,
"create_count": 0,
"update_response_times": [],
"update_errors": 0,
"update_count": 0,
"delete_response_times": [],
"delete_errors": 0,
"delete_count": 0,
"cache_hit": 0,
}
last_push = {
"read": datetime.datetime.now(),
"create": datetime.datetime.now(),
"update": datetime.datetime.now(),
"delete": datetime.datetime.now(),
}
def read(key):
print(f"Reading...")
if random.randint(0, 30) > 10:
stats["cache_hit"] += 1
stats["read_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["read_errors"] += 1
stats["read_count"] += 1
try_send("read")
def create(key, value):
print(f"Writing...")
db[key] = value
stats["create_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["create_errors"] += 1
stats["create_count"] += 1
try_send("create")
def update(key, value):
print(f"Updating...")
db[key] = value
stats["update_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["update_errors"] += 1
stats["update_count"] += 1
try_send("update")
def delete(key):
print(f"Deleting...")
db.pop(key, None)
stats["delete_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["delete_errors"] += 1
stats["delete_count"] += 1
try_send("delete")
def try_send(type_):
print("try_send")
now = datetime.datetime.now()
interval_ms = (now - last_push[type_]).total_seconds() * 1000
if interval_ms >= 2000:
send_metrics(type_, interval_ms)
send_event(type_)
def send_metrics(type_, interval_ms):
print("sending metrics...")
keys = GaugeMetric("fdb_keys", len(db))
db_size = GaugeMetric("fdb_size", getsizeof(db))
errors = CountMetric(
name=f"fdb_{type_}_errors",
value=stats[f"{type_}_errors"],
interval_ms=interval_ms
)
cache_hits = CountMetric(
name=f"fdb_cache_hits",
value=stats["cache_hit"],
interval_ms=interval_ms
)
response_times = stats[f"{type_}_response_times"]
response_time_summary = SummaryMetric(
f"fdb_{type_}_responses",
count=len(response_times),
min=min(response_times),
max=max(response_times),
sum=sum(response_times),
interval_ms=interval_ms,
)
batch = [keys, db_size, errors, cache_hits, response_time_summary]
response = metric_client.send_batch(batch)
response.raise_for_status()
print("Sent metrics successfully!")
clear(type_)
def send_event(type_):
print("sending event...")
count = Event(
"fdb_method", {"method": type_}
)
response = event_client.send_batch(count)
response.raise_for_status()
print("Event sent successfully!")
def clear(type_):
stats[f"{type_}_response_times"] = []
stats[f"{type_}_errors"] = 0
stats["cache_hit"] = 0
stats[f"{type_}_count"] = 0
last_push[type_] = datetime.datetime.now()
db.py

Importante

Este exemplo espera uma variável de ambiente chamada $NEW_RELIC_LICENSE_KEY.

Instrumento seu aplicativo para enviar logs para New Relic.

import os
import random
import datetime
from sys import getsizeof
import psutil
from newrelic_telemetry_sdk import MetricClient, GaugeMetric, CountMetric, SummaryMetric
from newrelic_telemetry_sdk import EventClient, Event
from newrelic_telemetry_sdk import LogClient, Log
metric_client = MetricClient(os.environ["NEW_RELIC_LICENSE_KEY"])
event_client = EventClient(os.environ["NEW_RELIC_LICENSE_KEY"])
log_client = LogClient(os.environ["NEW_RELIC_LICENSE_KEY"])
db = {}
stats = {
"read_response_times": [],
"read_errors": 0,
"read_count": 0,
"create_response_times": [],
"create_errors": 0,
"create_count": 0,
"update_response_times": [],
"update_errors": 0,
"update_count": 0,
"delete_response_times": [],
"delete_errors": 0,
"delete_count": 0,
"cache_hit": 0,
}
last_push = {
"read": datetime.datetime.now(),
"create": datetime.datetime.now(),
"update": datetime.datetime.now(),
"delete": datetime.datetime.now(),
}
def read(key):
print(f"Reading...")
if random.randint(0, 30) > 10:
stats["cache_hit"] += 1
stats["read_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["read_errors"] += 1
stats["read_count"] += 1
try_send("read")
def create(key, value):
print(f"Writing...")
db[key] = value
stats["create_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["create_errors"] += 1
stats["create_count"] += 1
try_send("create")
def update(key, value):
print(f"Updating...")
db[key] = value
stats["update_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["update_errors"] += 1
stats["update_count"] += 1
try_send("update")
def delete(key):
print(f"Deleting...")
db.pop(key, None)
stats["delete_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["delete_errors"] += 1
stats["delete_count"] += 1
try_send("delete")
def try_send(type_):
print("try_send")
now = datetime.datetime.now()
interval_ms = (now - last_push[type_]).total_seconds() * 1000
if interval_ms >= 2000:
send_metrics(type_, interval_ms)
send_event(type_)
def send_metrics(type_, interval_ms):
print("sending metrics...")
keys = GaugeMetric("fdb_keys", len(db))
db_size = GaugeMetric("fdb_size", getsizeof(db))
errors = CountMetric(
name=f"fdb_{type_}_errors",
value=stats[f"{type_}_errors"],
interval_ms=interval_ms
)
cache_hits = CountMetric(
name=f"fdb_cache_hits",
value=stats["cache_hit"],
interval_ms=interval_ms
)
response_times = stats[f"{type_}_response_times"]
response_time_summary = SummaryMetric(
f"fdb_{type_}_responses",
count=len(response_times),
min=min(response_times),
max=max(response_times),
sum=sum(response_times),
interval_ms=interval_ms,
)
batch = [keys, db_size, errors, cache_hits, response_time_summary]
response = metric_client.send_batch(batch)
response.raise_for_status()
print("Sent metrics successfully!")
clear(type_)
def send_event(type_):
print("sending event...")
count = Event(
"fdb_method", {"method": type_}
)
response = event_client.send_batch(count)
response.raise_for_status()
print("Event sent successfully!")
def send_logs():
print("sending log...")
process = psutil.Process(os.getpid())
memory_usage = process.memory_percent()
log = Log("FlashDB is using " + str(round(memory_usage * 100, 2)) + "% memory")
response = log_client.send(log)
response.raise_for_status()
print("Log sent successfully!")
def clear(type_):
stats[f"{type_}_response_times"] = []
stats[f"{type_}_errors"] = 0
stats["cache_hit"] = 0
stats[f"{type_}_count"] = 0
last_push[type_] = datetime.datetime.now()
db.py

Aqui você instrumenta sua plataforma para enviar memory_usage como log para New Relic.

Altere o módulo try_send para enviar o log a cada 2 segundos.

import os
import random
import datetime
from sys import getsizeof
import psutil
from newrelic_telemetry_sdk import MetricClient, GaugeMetric, CountMetric, SummaryMetric
from newrelic_telemetry_sdk import EventClient, Event
from newrelic_telemetry_sdk import LogClient, Log
metric_client = MetricClient(os.environ["NEW_RELIC_LICENSE_KEY"])
event_client = EventClient(os.environ["NEW_RELIC_LICENSE_KEY"])
log_client = LogClient(os.environ["NEW_RELIC_LICENSE_KEY"])
db = {}
stats = {
"read_response_times": [],
"read_errors": 0,
"read_count": 0,
"create_response_times": [],
"create_errors": 0,
"create_count": 0,
"update_response_times": [],
"update_errors": 0,
"update_count": 0,
"delete_response_times": [],
"delete_errors": 0,
"delete_count": 0,
"cache_hit": 0,
}
last_push = {
"read": datetime.datetime.now(),
"create": datetime.datetime.now(),
"update": datetime.datetime.now(),
"delete": datetime.datetime.now(),
}
def read(key):
print(f"Reading...")
if random.randint(0, 30) > 10:
stats["cache_hit"] += 1
stats["read_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["read_errors"] += 1
stats["read_count"] += 1
try_send("read")
def create(key, value):
print(f"Writing...")
db[key] = value
stats["create_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["create_errors"] += 1
stats["create_count"] += 1
try_send("create")
def update(key, value):
print(f"Updating...")
db[key] = value
stats["update_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["update_errors"] += 1
stats["update_count"] += 1
try_send("update")
def delete(key):
print(f"Deleting...")
db.pop(key, None)
stats["delete_response_times"].append(random.uniform(0.5, 1.0))
if random.choice([True, False]):
stats["delete_errors"] += 1
stats["delete_count"] += 1
try_send("delete")
def try_send(type_):
print("try_send")
now = datetime.datetime.now()
interval_ms = (now - last_push[type_]).total_seconds() * 1000
if interval_ms >= 2000:
send_metrics(type_, interval_ms)
send_event(type_)
send_logs()
def send_metrics(type_, interval_ms):
print("sending metrics...")
keys = GaugeMetric("fdb_keys", len(db))
db_size = GaugeMetric("fdb_size", getsizeof(db))
errors = CountMetric(
name=f"fdb_{type_}_errors",
value=stats[f"{type_}_errors"],
interval_ms=interval_ms
)
cache_hits = CountMetric(
name=f"fdb_cache_hits",
value=stats["cache_hit"],
interval_ms=interval_ms
)
response_times = stats[f"{type_}_response_times"]
response_time_summary = SummaryMetric(
f"fdb_{type_}_responses",
count=len(response_times),
min=min(response_times),
max=max(response_times),
sum=sum(response_times),
interval_ms=interval_ms,
)
batch = [keys, db_size, errors, cache_hits, response_time_summary]
response = metric_client.send_batch(batch)
response.raise_for_status()
print("Sent metrics successfully!")
clear(type_)
def send_event(type_):
print("sending event...")
count = Event(
"fdb_method", {"method": type_}
)
response = event_client.send_batch(count)
response.raise_for_status()
print("Event sent successfully!")
def send_logs():
print("sending log...")
process = psutil.Process(os.getpid())
memory_usage = process.memory_percent()
log = Log("FlashDB is using " + str(round(memory_usage * 100, 2)) + "% memory")
response = log_client.send(log)
response.raise_for_status()
print("Log sent successfully!")
def clear(type_):
stats[f"{type_}_response_times"] = []
stats[f"{type_}_errors"] = 0
stats["cache_hit"] = 0
stats[f"{type_}_count"] = 0
last_push[type_] = datetime.datetime.now()
db.py

Sua plataforma agora reportará o logs configurados a cada 2 segundos.

Navegue até a raiz do seu aplicativo em build-a-quickstart-lab/send-logs/flashDB.

Execute seus serviços para verificar se ele está relatando logs.

bash
$
python simulator.py
Writing...
try_send
Reading...
try_send
Reading...
try_send
Writing...
try_send
Writing...
try_send
Reading...
sending metrics...
Sent metrics successfully!
sending event...
Event sent successfully!
sending log...
Log sent successfully!

Opções alternativas

Se o SDK da linguagem não atender às suas necessidades, experimente uma de nossas outras opções:

Neste procedimento, você instrumentou seu serviço para enviar log para New Relic. Em seguida, instrumento-o para enviar o rastreamento.

Dica

Este procedimento faz parte do curso que ensina como criar um início rápido. Continue na próxima lição, envie o rastreamento do seu produto.

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