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Python

Logs​

note

Project's GitHub repo

Logz.io Python Handler sends logs in bulk over HTTPS to Logz.io. Logs are grouped into bulks based on their size.

If the main thread quits,the handler tries to consume the remaining logs and then exits. If the handler can't send the remaining logs, they're written to the local file system for later retrieval.

Set up Logz.io Python Handler​

Supported versions: Python 3.5 or newer.

Add the dependency to your project​

Navigate to your project's folder in the command line, and run this command to install the dependency.

pip install logzio-python-handler

If you'd like to use Trace context, you need to install the OpenTelemetry logging instrumentation dependency by running the following command:

pip install logzio-python-handler[opentelemetry-logging]

Configure Logz.io Python Handler for a standard Python project​

Use the samples in the code block below as a starting point, and replace the sample with a configuration that matches your needs.

Replace:

  • << LOG-SHIPPING-TOKEN >> - Your Logz.io account log shipping token.
  • << LISTENER-HOST >> - Logz.io listener host, as described here.
  • << LOG-TYPE >> - Log type, for searching in logz.io (defaults to "python")

For a complete list of options, see the configuration parameters below the code block.👇

Config File​
[handlers]
keys=LogzioHandler

[handler_LogzioHandler]
class=logzio.handler.LogzioHandler
formatter=logzioFormat

# Parameters must be set in order. Replace these parameters with your configuration.
args=('<<LOG-SHIPPING-TOKEN>>', '<<LOG-TYPE>>', <<TIMEOUT>>, 'https://<<LISTENER-HOST>>:8071', <<DEBUG-FLAG>>,<<NETWORKING-TIMEOUT>>,<<RETRY-LIMIT>>,<<RETRY-TIMEOUT>>)

[formatters]
keys=logzioFormat

[loggers]
keys=root

[logger_root]
handlers=LogzioHandler
level=INFO

[formatter_logzioFormat]
format={"additional_field": "value"}

args=() arguments, by order

  • Your logz.io token

  • Log type, for searching in logz.io (defaults to "python")

  • Time to sleep between draining attempts (defaults to "3")

  • Logz.io Listener address (defaults to "https://listener.logz.io:8071")

  • Debug flag. Set to True, will print debug messages to stdout. (defaults to "False")

  • Backup logs flag. Set to False, will disable the local backup of logs in case of failure. (defaults to "True")

  • Network timeout, in seconds, int or float, for sending the logs to logz.io. (defaults to 10)

  • Retries number (retry_no, defaults to 4).

  • Retry timeout (retry_timeout) in seconds (defaults to 2).

    Please note, that you have to configure those parameters by this exact order. i.e. you cannot set Debug to true, without configuring all of the previous parameters as well.

Dict Config​
LOGGING = {
    'version': 1,
    'disable_existing_loggers': False,
    'formatters': {
        'logzioFormat': {
            'format': '{"additional_field": "value"}',
            'validate': False
        }
    },
    'handlers': {
        'logzio': {
            'class': 'logzio.handler.LogzioHandler',
            'level': 'INFO',
            'formatter': 'logzioFormat',
            'token': '<<LOG-SHIPPING-TOKEN>>',
            'logzio_type': '<<LOG-TYPE>>',
            'logs_drain_timeout': 5,
            'url': 'https://<<LISTENER-HOST>>:8071'
            'retries_no': 4,
            'retry_timeout': 2,
        }
    },
    'loggers': {
        '': {
            'level': 'DEBUG',
            'handlers': ['logzio'],
            'propagate': True
        }
    }
}
Django configuration​
LOGGING = {
    'version': 1,
    'disable_existing_loggers': False,
    'formatters': {
        'verbose': {
            'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'
        },
        'logzioFormat': {
            'format': '{"additional_field": "value"}',
            'validate': False
        }
    },
    'handlers': {
        'console': {
            'class': 'logging.StreamHandler',
            'level': 'DEBUG',
            'formatter': 'verbose'
        },
        'logzio': {
            'class': 'logzio.handler.LogzioHandler',
            'level': 'INFO',
            'formatter': 'logzioFormat',
            'token': '<<LOG-SHIPPING-TOKEN>>',
            'logzio_type': "django",
            'logs_drain_timeout': 5,
            'url': 'https://<<LISTENER-HOST>>:8071'
            'debug': True,
            'network_timeout': 10,
        },
    },
    'loggers': {
        'django': {
            'handlers': ['console', ],
            'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO')
        },
        '': {
            'handlers': ['console', 'logzio'],
            'level': 'INFO'
        }
    }
}

Serverless platforms​

If you're using a serverless function, you'll need to import and add the LogzioFlusher annotation before your sender function. To do this, in the code sample below, uncomment the import statement and the @LogzioFlusher(logger) annotation line.
Note: For the LogzioFlusher to work properly, you'll need to make sure that the Logz.io. handler is added to the root logger. See the configuration above for an example.

Dynamic Extra Fields​

If you prefer, you can add extra fields to your logs dynamically, and not pre-defining them in the configuration. This way, you can allow different logs to have different extra fields. Example in the code below.

Code Example​

import logging
import logging.config
# If you're using a serverless function, uncomment.
# from logzio.flusher import LogzioFlusher

# If you'd like to leverage the dynamic extra fields feature, uncomment.
# from logzio.handler import ExtraFieldsLogFilter

# Say I have saved my configuration as a dictionary in a variable named 'LOGGING' - see 'Dict Config' sample section
logging.config.dictConfig(LOGGING)
logger = logging.getLogger('superAwesomeLogzioLogger')

# If you're using a serverless function, uncomment.
# @LogzioFlusher(logger)
def my_func():
    logger.info('Test log')
    logger.warn('Warning')

    try:
        1/0
    except:
        logger.exception("Supporting exceptions too!")

# Example additional code that demonstrates how to dynamically add/remove fields within the code, make sure class is imported.

    logger.info("Test log")  # Outputs: {"message":"Test log"}
    
    extra_fields = {"foo":"bar","counter":1}
    logger.addFilter(ExtraFieldsLogFilter(extra_fields))
    logger.warning("Warning test log")  # Outputs: {"message":"Warning test log","foo":"bar","counter":1}
    
    error_fields = {"err_msg":"Failed to run due to exception.","status_code":500}
    logger.addFilter(ExtraFieldsLogFilter(error_fields))
    logger.error("Error test log")  # Outputs: {"message":"Error test log","foo":"bar","counter":1,"err_msg":"Failed to run due to exception.","status_code":500}
    
    # If you'd like to remove filters from future logs using the logger.removeFilter option:
    logger.removeFilter(ExtraFieldsLogFilter(error_fields))
    logger.debug("Debug test log") # Outputs: {"message":"Debug test log","foo":"bar","counter":1}

Extra Fields​

In case you need to dynamic metadata to a speific log and not dynamically to the logger, other than the constant metadata from the formatter, you can use the "extra" parameter. All key values in the dictionary passed in "extra" will be presented in Logz.io as new fields in the log you are sending. Please note, that you cannot override default fields by the python logger (i.e. lineno, thread, etc..) For example:

logger.info('Warning', extra={'extra_key':'extra_value'})

Trace context​

If you're sending traces with OpenTelemetry instrumentation (auto or manual), you can correlate your logs with the trace context. That way, your logs will have traces data in it, such as service name, span id and trace id.

Make sure to install the OpenTelemetry logging instrumentation dependecy by running the following command:

pip install logzio-python-handler[opentelemetry-logging]

To enable this feature, set the add_context param in your handler configuration to True, like in this example:

LOGGING = {
    'version': 1,
    'disable_existing_loggers': False,
    'formatters': {
        'logzioFormat': {
            'format': '{"additional_field": "value"}',
            'validate': False
        }
    },
    'handlers': {
        'logzio': {
            'class': 'logzio.handler.LogzioHandler',
            'level': 'INFO',
            'formatter': 'logzioFormat',
            'token': '<<LOG-SHIPPING-TOKEN>>',
            'logzio_type': '<<LOG-TYPE>>',
            'logs_drain_timeout': 5,
            'url': 'https://<<LISTENER-HOST>>:8071'
            'retries_no': 4,
            'retry_timeout': 2,
            'add_context': True
        }
    },
    'loggers': {
        '': {
            'level': 'DEBUG',
            'handlers': ['logzio'],
            'propagate': True
        }
    }
}

Metrics​

You can send custom metrics to Logz.io from your Python application. This example uses the OpenTelemetry Python SDK and the OpenTelemetry remote write exporter, which are both in alpha/preview.

Setup in code​

Install the snappy c-library​

DEB: sudo apt-get install libsnappy-dev

RPM: sudo yum install libsnappy-devel

OSX/Brew: brew install snappy

Windows: pip install python_snappy-0.5-cp36-cp36m-win_amd64.whl

Install the exporter and opentelemtry sdk​

pip install opentelemetry-exporter-prometheus-remote-write

Add instruments to your application​

Replace the placeholders in the exporter section code (indicated by the double angle brackets << >>) to match your specifics.

ParameterDescription
LISTENER-HOSTThe Logz.io Listener URL for for your region, configured to use port 8052 for http traffic, or port 8053 for https traffic. Replace <<LISTENER-HOST>> with the host for your region. For example, listener.logz.io if your account is hosted on AWS US East, or listener-nl.logz.io if hosted on Azure West Europe. The required port depends whether HTTP or HTTPS is used: HTTP = 8070, HTTPS = 8071. and add http/https protocol (https://listener.logz.io:8053)
PROMETHEUS-METRICS-SHIPPING-TOKENYour Logz.io Prometheus Metrics account token. Replace <<PROMETHEUS-METRICS-SHIPPING-TOKEN>> with a token for the Metrics account you want to ship to.
Here's how to look up your Metrics token.
from opentelemetry import metrics
from opentelemetry.exporter.prometheus_remote_write import (
    PrometheusRemoteWriteMetricsExporter,
)
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader

# configure the Logz.io listener endpoint and Prometheus metrics account token
exporter = PrometheusRemoteWriteMetricsExporter(
    endpoint="https://<<LISTENER-HOST>>:8053",
    headers={
        "Authorization": "Bearer <<PROMETHEUS-METRICS-SHIPPING-TOKEN>>",
    }
)
# set push interval in seconds
push_interval = 15

# setup metrics export pipeline
reader = PeriodicExportingMetricReader(exporter, 1000)
provider = MeterProvider(metric_readers=[reader])
metrics.set_meter_provider(provider)
meter = metrics.get_meter(__name__)

# create a counter instrument and provide the first data point
counter = meter.create_counter(
    name="MyCounter",
    description="Description of MyCounter",
    unit="1"
)
# add labels
labels = {
    "dimension": "value",
    "from": "testlogzio"
}
counter.add(25, labels)

Types of metric instruments​

Refer to the OpenTelemetry documentation for more details.

NameBehaviorDefault aggregation
CounterMetric value can only go up or be reset to 0, calculated per counter.add(value,labels) request.Sum
UpDownCounterMetric value can arbitrarily increment or decrement, calculated per updowncounter.add(value,labels) request.Sum
ValueRecorderMetric values captured by the valuerecorder.record(value) function, calculated per request.TBD
SumObserverMetric value can only go up or be reset to 0, calculated per push interval.Sum
UpDownSumObserverMetric value can arbitrarily increment or decrement, calculated per push interval.Sum
ValueObserverMetric values captured by the valuerecorder.observe(value) function, calculated per push interval.LastValue
Counter​
# create a counter instrument
counter = meter.create_counter(
    name="MyCounter",
    description="Description of MyCounter",
    unit="1",
    value_type=int
)
# add labels
labels = {
    "dimension": "value"
}
# provide the first data point
counter.add(25, labels)
UpDownCounter​
# create a updowncounter instrument
requests_active = meter.create_updowncounter(
    name="requests_active",
    description="number of active requests",
    unit="1",
    value_type=int,
)
# add labels
labels = {
    "dimension": "value"
}
# provide the first data point
requests_active.add(-2, labels)
ValueRecorder​
# create a valuerecorder instrument
requests_size = meter.create_valuerecorder(
    name="requests_size",
    description="size of requests",
    unit="1",
    value_type=int,
)
# add labels
labels = {
    "dimension": "value"
}
# provide the first data point
requests_size.record(85, labels)
SumObserver​
import psutil
# Callback to gather RAM usage
def get_ram_usage_callback(observer):
    ram_percent = psutil.virtual_memory().percent
    # add labels
    labels = {
        "dimension": "value"
    }
    observer.observe(ram_percent, labels)
# create a sumobserver instrument
meter.register_sumobserver(
    callback=get_ram_usage_callback,
    name="ram_usage",
    description="ram usage",
    unit="1",
    value_type=float,
)
UpDownSumObserver​
# Callback to gather RAM usage
def get_ram_usage_callback(observer):
    ram_percent = psutil.virtual_memory().percent
    # add labels
    labels = {
        "dimension": "value"
    }
    observer.observe(ram_percent, labels)
# create a updownsumobserver instrument
meter.register_updownsumobserver(
    callback=get_ram_usage_callback,
    name="ram_usage",
    description="ram usage",
    unit="1",
    value_type=float,
)
ValueObserver​
import psutil
def get_cpu_usage_callback(observer):
    for (number, percent) in enumerate(psutil.cpu_percent(percpu=True)):
        labels = {"cpu_number": str(number)}
        observer.observe(percent, labels)
# create a valueobserver instrument
meter.register_valueobserver(
    callback=get_cpu_usage_callback,
    name="cpu_percent",
    description="per-cpu usage",
    unit="1",
    value_type=float,
)

Check Logz.io for your metrics​

Give your data some time to get from your system to ours, then log in to your Logz.io Metrics account, and open the Logz.io Metrics tab.

Traces​

Deploy this integration to enable automatic instrumentation of your Python application using OpenTelemetry.

Architecture overview​

This integration includes:

  • Installing the OpenTelemetry Python instrumentation packages on your application host
  • Installing the OpenTelemetry collector with Logz.io exporter
  • Running your Python application in conjunction with the OpenTelemetry instrumentation

On deployment, the Python instrumentation automatically captures spans from your application and forwards them to the collector, which exports the data to your Logz.io account.

Local host Python application auto instrumentation​

Before you begin, you'll need:

  • A Python application without instrumentation
  • An active account with Logz.io
  • Port 4317 available on your host system
  • A name defined for your tracing service. You will need it to identify the traces in Logz.io.
note

This integration uses OpenTelemetry Collector Contrib, not the OpenTelemetry Collector Core.

Install general Python OpenTelemetry instrumentation components​

Run the following commands:

pip3 install opentelemetry-distro
pip3 install opentelemetry-instrumentation
opentelemetry-bootstrap --action=install
pip3 install opentelemetry-exporter-otlp

Set environment variables​

After installation, configure the exporter by running the following command:

export OTEL_TRACES_EXPORTER=otlp
export OTEL_RESOURCE_ATTRIBUTES="service.name=<<YOUR-SERVICE-NAME>>"

Replace <<YOUR-SERVICE-NAME>> with the name of your tracing service defined earlier.

Download and configure OpenTelemetry collector​

Create a dedicated directory on the host of your Python application and download the OpenTelemetry collector that is relevant to the operating system of your host.

After downloading the collector, create a configuration file config.yaml with the parameters below.

  • Replace <<TRACING-SHIPPING-TOKEN>> with the token of the account you want to ship to.

Replace <<LOGZIO_ACCOUNT_REGION_CODE>> with the applicable region code.

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: "0.0.0.0:4317"
      http:
        endpoint: "0.0.0.0:4318"

exporters:
  logzio/traces:
    account_token: "<<TRACING-SHIPPING-TOKEN>>"
    region: "<<LOGZIO_ACCOUNT_REGION_CODE>>"

  logging:

processors:
  batch:
  tail_sampling:
    policies:
      [
        {
          name: policy-errors,
          type: status_code,
          status_code: {status_codes: [ERROR]}
        },
        {
          name: policy-slow,
          type: latency,
          latency: {threshold_ms: 1000}
        }, 
        {
          name: policy-random-ok,
          type: probabilistic,
          probabilistic: {sampling_percentage: 10}
        }        
      ]

extensions:
  pprof:
    endpoint: :1777
  zpages:
    endpoint: :55679
  health_check:

service:
  extensions: [health_check, pprof, zpages]
  pipelines:
    traces:
      receivers: [otlp]
      processors: [tail_sampling, batch]
      exporters: [logging, logzio/traces]

The tail_sampling defines the decision to sample a trace after the completion of all the spans in a request. By default, this configuration collects all traces that have a span that was completed with an error, all traces that are slower than 1000 ms, and 10% of the rest of the traces.

You can add more policy configurations to the processor. For more on this, refer to OpenTelemetry Documentation.

The configurable parameters in the Logz.io default configuration are:

ParameterDescriptionDefault
threshold_msThreshold for the spand latency - all traces slower than the threshold value will be filtered in.1000
sampling_percentageSampling percentage for the probabilistic policy.10

Start the collector​

Run the following command:

<path/to>/otelcontribcol_<VERSION-NAME> --config ./config.yaml
  • Replace <path/to> with the path to the directory where you downloaded the collector.
  • Replace <VERSION-NAME> with the version name of the collector applicable to your system, e.g. otelcontribcol_darwin_amd64.

Run the OpenTelemetry instrumentation in conjunction with your Python application​

Run the following command from the directory of your Python application script:

opentelemetry-instrument python3 <YOUR-APPLICATION-SCRIPT>.py

Replace <YOUR-APPLICATION-SCRIPT> with the name of your Python application script.

Check Logz.io for your traces​

Give your traces some time to get from your system to ours, and then open Tracing.

Docker Python application auto instrumentation​

This integration enables you to auto-instrument your Python application and run a containerized OpenTelemetry collector to send your traces to Logz.io. If your application also runs in a Docker container, make sure that both the application and collector containers are on the same network.

Before you begin, you'll need:

  • A Python application without instrumentation
  • An active account with Logz.io
  • Port 4317 available on your host system
  • A name defined for your tracing service. You will need it to identify the traces in Logz.io.

Install general Python OpenTelemetry instrumentation components​

Run the following commands:

pip3 install opentelemetry-distro
pip3 install opentelemetry-instrumentation
opentelemetry-bootstrap --action=install
pip3 install opentelemetry-exporter-otlp

Set environment variables​

After installation, configure the exporter by running the following command:

export OTEL_TRACES_EXPORTER=otlp
export OTEL_RESOURCE_ATTRIBUTES="service.name=<<YOUR-SERVICE-NAME>>"

Replace <<YOUR-SERVICE-NAME>> with the name of your tracing service defined earlier.

Pull the Docker image for the OpenTelemetry collector​

docker pull otel/opentelemetry-collector-contrib:0.78.0

Create a configuration file​

Create a file config.yaml with the following content:

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: "0.0.0.0:4317"
      http:
        endpoint: "0.0.0.0:4318"
  

exporters:
  logzio/traces:
    account_token: "<<TRACING-SHIPPING-TOKEN>>"
    region: "<<LOGZIO_ACCOUNT_REGION_CODE>>"

  logging:

processors:
  batch:
  tail_sampling:
    policies:
      [
        {
          name: policy-errors,
          type: status_code,
          status_code: {status_codes: [ERROR]}
        },
        {
          name: policy-slow,
          type: latency,
          latency: {threshold_ms: 1000}
        }, 
        {
          name: policy-random-ok,
          type: probabilistic,
          probabilistic: {sampling_percentage: 10}
        }        
      ]

extensions:
  pprof:
    endpoint: :1777
  zpages:
    endpoint: :55679
  health_check:

service:
  extensions: [health_check, pprof, zpages]
  pipelines:
    traces:
      receivers: [otlp]
      processors: [tail_sampling, batch]
      exporters: [logging, logzio/traces]

Replace <<TRACING-SHIPPING-TOKEN>> with the token of the account you want to ship to.

Replace <LOGZIO_ACCOUNT_REGION_CODE> with the applicable region code.

The tail_sampling defines the decision to sample a trace after the completion of all the spans in a request. By default, this configuration collects all traces that have a span that was completed with an error, all traces that are slower than 1000 ms, and 10% of the rest of the traces.

You can add more policy configurations to the processor. For more on this, refer to OpenTelemetry Documentation.

The configurable parameters in the Logz.io default configuration are:

ParameterDescriptionDefault
threshold_msThreshold for the spand latency - all traces slower than the threshold value will be filtered in.1000
sampling_percentageSampling percentage for the probabilistic policy.10

If you already have an OpenTelemetry installation, add the following parameters to the configuration file of your existing OpenTelemetry collector:

  • Under the exporters list
  logzio/traces:
    account_token: <<TRACING-SHIPPING-TOKEN>>
    region: <<LOGZIO_ACCOUNT_REGION_CODE>>
  • Under the service list:
  extensions: [health_check, pprof, zpages]
  pipelines:
    traces:
      receivers: [otlp]
      processors: [tail_sampling, batch]
      exporters: [logzio/traces]

Replace <<TRACING-SHIPPING-TOKEN>> with the token of the account you want to ship to.

Replace <LOGZIO_ACCOUNT_REGION_CODE> with the applicable region code.

An example configuration file looks as follows:

receivers:  
  otlp:
    protocols:
      grpc:
      http:

exporters:
  logzio/traces:
    account_token: "<<TRACING-SHIPPING-TOKEN>>"
    region: "<<LOGZIO_ACCOUNT_REGION_CODE>>"

processors:
  batch:
  tail_sampling:
    policies:
      [
        {
          name: policy-errors,
          type: status_code,
          status_code: {status_codes: [ERROR]}
        },
        {
          name: policy-slow,
          type: latency,
          latency: {threshold_ms: 1000}
        }, 
        {
          name: policy-random-ok,
          type: probabilistic,
          probabilistic: {sampling_percentage: 10}
        }        
      ]

extensions:
  pprof:
    endpoint: :1777
  zpages:
    endpoint: :55679
  health_check:

service:
  extensions: [health_check, pprof, zpages]
  pipelines:
    traces:
      receivers: [otlp]
      processors: [tail_sampling, batch]
      exporters: [logzio/traces]

Replace <<TRACING-SHIPPING-TOKEN>> with the token of the account you want to ship to.

Replace <LOGZIO_ACCOUNT_REGION_CODE> with the applicable region code.

The tail_sampling defines the decision to sample a trace after the completion of all the spans in a request. By default, this configuration collects all traces that have a span that was completed with an error, all traces that are slower than 1000 ms, and 10% of the rest of the traces.

You can add more policy configurations to the processor. For more on this, refer to OpenTelemetry Documentation.

The configurable parameters in the Logz.io default configuration are:

ParameterDescriptionDefault
threshold_msThreshold for the spand latency - all traces slower than the threshold value will be filtered in.1000
sampling_percentageSampling percentage for the probabilistic policy.10

Run the container​

Mount the config.yaml as volume to the docker run command and run it as follows.

Linux​
docker run  \
--network host \
-v <PATH-TO>/config.yaml:/etc/otelcol-contrib/config.yaml \
otel/opentelemetry-collector-contrib:0.78.0

Replace <PATH-TO> to the path to the config.yaml file on your system.

Windows​
docker run  \
-v <PATH-TO>/config.yaml:/etc/otelcol-contrib/config.yaml \
-p 55678-55680:55678-55680 \
-p 1777:1777 \
-p 9411:9411 \
-p 9943:9943 \
-p 6831:6831 \
-p 6832:6832 \
-p 14250:14250 \
-p 14268:14268 \
-p 4317:4317 \
-p 55681:55681 \
otel/opentelemetry-collector-contrib:0.78.0

Replace <<TRACING-SHIPPING-TOKEN>> with the token of the account you want to ship to.

Replace <LOGZIO_ACCOUNT_REGION_CODE> with the applicable region code.

Run the OpenTelemetry instrumentation in conjunction with your Python application​

note

Normally, when you run the OTEL collector in a Docker container, your application will run in separate containers on the same host. In this case, you need to make sure that all your application containers share the same network as the OTEL collector container. One way to achieve this, is to run all containers, including the OTEL collector, with a Docker-compose configuration. Docker-compose automatically makes sure that all containers with the same configuration are sharing the same network.

Run the following command from the directory of your Python application script:

opentelemetry-instrument python3 `<<YOUR-APPLICATION-SCRIPT>>`.py

Replace <<YOUR-APPLICATION-SCRIPT>> with the name of your Python application script.

Check Logz.io for your traces​

Give your traces some time to get from your system to ours, and then open Tracing.

Kuberenetes Python application auto insturmentation​

Overview​

You can use a Helm chart to ship Traces to Logz.io via the OpenTelemetry collector. The Helm tool is used to manage packages of pre-configured Kubernetes resources that use charts.

logzio-k8s-telemetry allows you to ship traces from your Kubernetes cluster to Logz.io with the OpenTelemetry collector.

note

This chart is a fork of the opentelemtry-collector Helm chart. The main repository for Logz.io helm charts are logzio-helm.

caution

This integration uses OpenTelemetry Collector Contrib, not the OpenTelemetry Collector Core.

Standard configuration​

Deploy the Helm chart​

Add logzio-helm repo as follows:

helm repo add logzio-helm https://logzio.github.io/logzio-helm
helm repo update
Run the Helm deployment code​
helm install  \
--set logzio-k8s-telemetry.secrets.LogzioRegion=<<LOGZIO_ACCOUNT_REGION_CODE>> \
--set logzio-k8s-telemetry.secrets.TracesToken=<<TRACING-SHIPPING-TOKEN>> \
logzio-monitoring logzio-helm/logzio-monitoring -n monitoring

Replace <<TRACING-SHIPPING-TOKEN>> with the token of the account you want to ship to.

Replace <LOGZIO_ACCOUNT_REGION_CODE> with the applicable region code.

<<LOGZIO_ACCOUNT_REGION_CODE>> - Your Logz.io account region code. Available regions.

Define the logzio-k8s-telemetry service DNS​

In most cases, the service name will be logzio-k8s-telemetry.default.svc.cluster.local, where default is the namespace where you deployed the helm chart and svc.cluster.name is your cluster domain name.

If you are not sure what your cluster domain name is, you can run the following command to look it up: 

kubectl run -it --image=k8s.gcr.io/e2e-test-images/jessie-dnsutils:1.3 --restart=Never shell -- \
sh -c 'nslookup kubernetes.default | grep Name | sed "s/Name:\skubernetes.default//"'

It will deploy a small pod that extracts your cluster domain name from your Kubernetes environment. You can remove this pod after it has returned the cluster domain name.

Install general Python OpenTelemetry instrumentation components​

Run the following commands:

pip3 install opentelemetry-distro
pip3 install opentelemetry-instrumentation
opentelemetry-bootstrap --action=install
pip3 install opentelemetry-exporter-otlp

Set environment variables​

After installation, configure the exporter by running the following command:

export OTEL_TRACES_EXPORTER=otlp
export OTEL_RESOURCE_ATTRIBUTES="service.name=<<YOUR-SERVICE-NAME>>"

Replace <<YOUR-SERVICE-NAME>> with the name of your tracing service defined earlier.

Check Logz.io for your traces​

Give your traces some time to get from your system to ours, then open Logz.io.

Customizing Helm chart parameters

Configure customization options​

You can use the following options to update the Helm chart parameters:

  • Specify parameters using the --set key=value[,key=value] argument to helm install.

  • Edit the values.yaml.

  • Overide default values with your own my_values.yaml and apply it in the helm install command.

If required, you can add the following optional parameters as environment variables:

ParameterDescription
secrets.SamplingLatencyThreshold for the spand latency - all traces slower than the threshold value will be filtered in. Default 500.
secrets.SamplingProbabilitySampling percentage for the probabilistic policy. Default 10.
Example​

You can run the logzio-k8s-telemetry chart with your custom configuration file that takes precedence over the values.yaml of the chart.

For example:

note

The collector will sample ALL traces where is some span with error with this example configuration. 

baseCollectorConfig:
  processors:
    tail_sampling:
      policies:
        [
          {
            name: error-in-policy,
            type: status_code,
            status_code: {status_codes: [ERROR]}
          },
          {
            name: slow-traces-policy,
            type: latency,
            latency: {threshold_ms: 400}
          },
          {
            name: health-traces,
            type: and,
            and: {
              and_sub_policy:
              [
                {
                  name: ping-operation,
                  type: string_attribute,
                  string_attribute: { key: http.url, values: [ /health ] }
                },
                {
                  name: main-service,
                  type: string_attribute,
                  string_attribute: { key: service.name, values: [ main-service ] }
                },
                {
                  name: probability-policy-1,
                  type: probabilistic,
                  probabilistic: {sampling_percentage: 1}
                }
              ]
            }
          },
          {
            name: probability-policy,
            type: probabilistic,
            probabilistic: {sampling_percentage: 20}
          }
        ]

Command:

helm install -f <PATH-TO>/my_values.yaml \
--set logzio-k8s-telemetry.secrets.TracesToken=<<TRACES-SHIPPING-TOKEN>> \
--set logzio-k8s-telemetry.secrets.LogzioRegion=<<LOGZIO_ACCOUNT_REGION_CODE>> \
--set metricsOrTraces=true \
logzio-monitoring logzio-helm/logzio-monitoring

Replace <PATH-TO> with the path to your custom values.yaml file.

Replace <<TRACING-SHIPPING-TOKEN>> with the token of the account you want to ship to.

Replace <LOGZIO_ACCOUNT_REGION_CODE> with the applicable region code.

Uninstalling the Chart​

The uninstall command is used to remove all the Kubernetes components associated with the chart and to delete the release.

To uninstall the logzio-monitoring deployment, use the following command:

helm uninstall logzio-monitoring

Troubleshooting​

Logz.io Python handler​

For troubleshooting the Logz.io Python handler, see our Python logging troubleshooting guide.

OpenTelemetry instrumentation​

For troubleshooting the OpenTelemetry instrumentation, see our OpenTelemetry troubleshooting guide.

Distributed Tracing account​

For troubleshooting your Distributed Tracing account, see our Distributed Tracing troubleshooting guide