Tip
This procedure is a part of course that teaches you how to build a quickstart. If you haven't already, checkout the course introduction.
Each procedure in this course builds on top of the last one, so make sure you deploy your application before proceeding with this one.
Metrics are aggregated measurements derived from the system's performance and behaviors. If your product is a database, you might send metrics like CPU utilization, memory utilization, and query throughput. Note that metrics are generally only used if you want to limit the amount of Data sent to New Relic. Note that many metrics, such as error rates and throughput, can be computed by aggregating events.
New Relic provides you a variety of ways to instrument your application to send metrics to our metric API. In this lesson, you send metrics from your product using our telemetry software development kit (SDK).
Use our SDK
We offer an open source telemetry SDK in several of the most popular programming languages. These send data to our data ingest APIs, including our Metric API. Of these language SDKs, Python, Java, Node/TypeScript, and Go work with the Metric API.
In this lesson, you learn how to install and use the Python telemetry SDK to send metrics to New Relic.
Change to the send-metrics/flashDB
direcrory of the course repository.
$cd ../send-metrics/flashDB
Use pip
to install the newrelic-telemetry-sdk
package.
$pip install newrelic-telemetry-sdk
Store your New Relic license key in an environment variable called $NEW_RELIC_LICENSE_KEY
.
$export NEW_RELIC_LICENSE_KEY=<YOUR_USER_KEY>
Tip
You can find your New Relic license key in your account settings.
Next, you familiarize yourself with the app logic.
import osimport randomimport datetime
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")
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()
Familiarize yourself with the application
Open db.py
file in the IDE of your choice and familiarize yourself with the app logic.
This demo uses a dummy Python application that mimics the Create, Read, Update, and Delete (CRUD) operations.
import osimport randomimport datetime
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")
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()
The read
, create
, update
, and delete
are the dummy methods to mimic CRUD operations. For every CRUD operations, respective stat
is incremented to reflect that the operation has been performed. Next, you send this stats
data to New Relic.
Send metrics to New Relic
There are 3 different types of metrics:
- GaugeMetric: sends a single value at a single point in time.
- CountMetric: tracks the total number of occurrences of an event.
- SummaryMetric: tracks count, sum, min, and max values over time.
Next, instrument your application to send these metrics.
In db.py
, configure the MetricClient
.
import osimport randomimport datetime
from newrelic_telemetry_sdk import MetricClient
metric_client = MetricClient(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")
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()
Instrument your app to send the folloiwng metrics to New Relic:
keys
db_size
errors
cache_hits
response_times
import osimport randomimport datetimefrom sys import getsizeoffrom newrelic_telemetry_sdk import MetricClient, GaugeMetric, CountMetric, SummaryMetricmetric_client = MetricClient(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"] += 1stats["read_response_times"].append(random.uniform(0.5, 1.0))if random.choice([True, False]):stats["read_errors"] += 1stats["read_count"] += 1try_send("read")def create(key, value):print(f"Writing...")db[key] = valuestats["create_response_times"].append(random.uniform(0.5, 1.0))if random.choice([True, False]):stats["create_errors"] += 1stats["create_count"] += 1try_send("create")def update(key, value):print(f"Updating...")db[key] = valuestats["update_response_times"].append(random.uniform(0.5, 1.0))if random.choice([True, False]):stats["update_errors"] += 1stats["update_count"] += 1try_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"] += 1stats["delete_count"] += 1try_send("delete")def try_send(type_):print("try_send")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 clear(type_):stats[f"{type_}_response_times"] = []stats[f"{type_}_errors"] = 0stats["cache_hit"] = 0stats[f"{type_}_count"] = 0last_push[type_] = datetime.datetime.now()db.pyHere, you configure your platform to use
GaugeMetric
,CountMetric
, andSummaryMetric
to report metrics to New Relic.
Amend the try_send
module to send these metrics every 2 second.
import osimport randomimport datetimefrom sys import getsizeof
from newrelic_telemetry_sdk import MetricClient, GaugeMetric, CountMetric, SummaryMetric
metric_client = MetricClient(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)
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 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()
Your platform will now report all the configured metrics every 2 seconds.
Navigate to the root of your application at build-a-quickstart-lab/send-metrics/flashDB
.
Run your services to verify that it is reporting metrics.
$python simulator.pyWriting...try_sendWriting...try_sendReading...try_sendReading...try_sendWriting...try_sendWriting...try_sendReading...sending metrics...Sent metrics successfully!
Alternative Options
If the language SDK doesn't fit your needs or you'd like something more customized to send metrics to New Relic, try out one of our other options:
- Manual Implementation: If our SDK in your preferred language doesn't support metrics, you can always manually instrument your own library to make a POST request to the New Relic Metric API.
- Prometheus Data: Prometheus data can be sent to New Relic in two ways, remote write and OpenMetrics. At a very high level, you should use remote write if you manage your own Prometheus servers and OpenMetrics if you don't.
In this procedure, you instrumented your service to send metrics to New Relic. Next, instrument it to send events.
Tip
This procedure is a part of course that teaches you how to build a quickstart. Continue to next lesson, send events from your product.