Python Ingestion library for LogicMonitor¶

This Python Library for ingesting the metrics, logs into the LogicMonitor Platform

PushMetrics - Metrics Ingestion¶

Overview¶

LogicMonitor’s Push Metrics feature allows you to send metrics directly to the LogicMonitor platform via a dedicated API, removing the need to route the data through a LogicMonitor Collector. Once ingested, these metrics are presented alongside all other metrics gathered via LogicMonitor, providing a single pane of glass for metric monitoring and alerting.

More details are available on support site

Version¶

API version: 0.0.1
Package version: 0.0.1

Requirements.¶

Python 2.7 and 3.4+

Installation¶

pip install¶

If the python package ishosted on Github, you can install directly from Github

pip install git+ssh://git@stash.logicmonitor.com:7999/dev/lmingestpy.git

(you may need to run pip with root permission:

sudo pip install git+ssh://git@stash.logicmonitor.com:7999/dev/lmingestpy.git)

Then import the package:

import lmingest

Setuptools¶

Install via Setuptools.

python setup.py install --user

or sudo python setup.py install to install the package for all users

Then import the package:

import lmingest

Getting Started¶

Please follow the installation procedure <#Installation> and then run the following:

from __future__ import print_function
import time
import random
import lmingest

from lmingest.api.lm_metrics import MetricsApi
from lmingest.models.lm_datapoint import LMDataPoint
from lmingest.models.lm_datasource import LMDataSource
from lmingest.models.lm_datasource_instance import LMDataSourceInstance
from lmingest.models.lm_resource import LMResource

# Configure API key authorization: LMv1
configuration = lmingest.Configuration(company = 'YOUR_COMPANY', authentication={ 'id': 'YOUR_ACCESS_ID', 'key' : 'YOUR_ACCESS_KEY'})

# create an instance of the API class
metric_api = MetricsApi(lmingest.ApiClient(configuration), interval=20, batch = True)
resource = LMResource(ids={"system.hostname": "SampleDevice"}, create=True, name="SampleDevice", properties={'some.sdk': 'true'})
ds = LMDataSource(name="DSName")
instance = LMDataSourceInstance(name="instance")
dp = LMDataPoint(name="dataPoint")

while True:
  values = { time.time() : random.randint() }
  metric_api.SendMetrics(resource=resource,
                       datasource=ds,
                       instance=instance,
                       datapoint=dp,
                       values=values)
  time.sleep(10)

Documentation for API Endpoints¶

All URIs are relative to https://.logicmonitor.com/rest

MetricsAPI¶

Metrics API client: It formats and submit REST API calls to LogicMonitor.

class lmingest.api.lm_metrics.MetricsApi(api_client, batch=True, interval=30, response_callback=None)¶

This API client is for ingesting the metrics in LogicMonitor and updating the properties of the resource or instance.

Parameters

api_client (ApiClient) – The RAW HTTP REST client.
batch (bool) – Enable the batching support.
interval (int) – Batching flush interval. If batching is enabled then after that second we will flush the data to REST endpoint.
response_callback (LMResonseInterface) – Callback for response handling.

classmethod send_metrics(**kwargs)¶

This send_metrics method is used to send the metrics to rest endpoint.

Parameters

resource (lmingest.models.lm_resource.LMResource) – The Resource object.
datasource (LMDataSource) – The datasource object.
instance (LMDataSourceInstance) – The instance object.
datapoint (LMDataPoint) – The datapoint object.
values (dict) – The values dictionary.

Returns

If in MetricsApi batching is enabled then None Otherwise the REST response will be return.

update_instance_property(resource_ids, datasource, instancename, instance_properties, patch=True)¶

This update_resource_property method is used to update the property of the resource.

Parameters

resource_ids (dict) – The Resource ids.
datasource (str) – The datasource name.
instancename (str) – The instance name.
instance_properties (dict) – The properties which you want to add/update.
patch (bool) – PATCH or PUT request.

Returns

REST response will be return.

update_resource_property(resource_ids, resource_properties, patch=True)¶

This update_resource_property method is used to update the property of the resource.

Parameters

resource_ids (dict) – The Resource ids.
resource_properties (dict) – The properties which you want to add/update.
patch (bool) – PATCH or PUT request.

Returns

REST response will be return.

Documentation For Models & Configuration¶

Configuration¶

class lmingest.configuration.Configuration(**kwargs)¶

This model is used to defining the configuration.

Parameters

company (str) – The account name.
authentication (dict) – LogicMonitor supports verious type of the authentication. This variable will be used to specify the authentication key.

>>> conf = lmingest.Configuration(company="ACCOUNT_NAME",   authentication={'id': 'API_ACCESS_ID', 'key': 'API_ACCESS_KEY', 'type' : 'LMv1'})

property async_req¶

The async request.

Parameters: value – enable async request string.
Type: bool

property debug¶

Debug status

Parameters: value – The debug status, True or False.
Type: bool

property logger_file¶

The logger file.

If the logger_file is None, then add stream handler and remove file handler. Otherwise, add file handler and remove stream handler.

Parameters: value – The logger_file path.
Type: str

property logger_format¶

The logger format.

The logger_formatter will be updated when sets logger_format.

Parameters: value – The format string.
Type: str

to_debug_report()¶

Gets the essential information for debugging.

Returns: The report for debugging.

LMResource¶

class lmingest.models.lm_resource.LMResource(ids, name, description=None, properties=None, create=False)¶

This model is used to define the resource.

Parameters

ids (dict) – An array of existing resource properties that will be used to identify the resource. See Managing Resources that Ingest Push Metrics for information on the types of properties that can be used. If no resource is matched and the create parameter is set to TRUE, a new resource is created with these specified resource IDs set on it. If the system.displayname and/or system.hostname property is included as resource IDs, they will be used as host name and display name respectively in the resulting resource.
name (str) – Resource unique name. Only considered when creating a new resource.
properties (dict) – New properties for resource. Updates to existing resource properties are not considered. Depending on the property name, we will convert these properties into system, auto, or custom properties.
description (str) – Resource description. Only considered when creating a new resource.
create (bool) – Do you want to create the resource.

property create¶

Gets the create flag.

Returns: create flag.
Return type: bool

property description¶

Resource description. Only considered when creating a new resource.

Returns: The description of this LMResource.
Return type: str

property ids¶

An array of existing resource properties that will be used to identify the resource. See Managing Resources that Ingest Push Metrics for information on the types of properties that can be used. If no resource is matched and the create parameter is set to TRUE, a new resource is created with these specified resource IDs set on it. If the system.displayname and/or system.hostname property is included as resource IDs, they will be used as host name and display name respectively in the resulting resource.

Returns: The ids of this LMResource.
Return type: dict

property name¶

Resource unique name. Only considered when creating a new resource.

Returns: The name of this LMResource.
Return type: str

property properties¶

New properties for resource. Updates to existing resource properties are not considered. Depending on the property name, we will convert these properties into system, auto, or custom properties.

Returns: The properties of this LMResource.
Return type: dict

LMDataSource¶

class lmingest.models.lm_datasource.LMDataSource(name, display_name=None, group=None, id=None)¶

This model is used to defining the datasource object.

Parameters

name (str) – DataSource unique name. Used to match an existing DataSource. If no existing DataSource matches the name provided here, a new DataSource is created with this name.
display_name (str) – DataSource display name. Only considered when creating a new DataSource.
group (str) – DataSource group name. Only considered when DataSource does not already belong to a group. Used to organize the DataSource within a DataSource group. If no existing DataSource group matches, a new group is created with this name and the DataSource is organized under the new group.
id (int) – DataSource unique ID. Used only to match an existing DataSource. If no existing DataSource matches the provided ID, an error results.

property display_name¶

DataSource display name. Only considered when creating a new DataSource.

Returns: The display_name of this LMDataSource.
Return type: str

property group¶

DataSource group name. Only considered when DataSource does not already belong to a group. Used to organize the DataSource within a DataSource group. If no existing DataSource group matches, a new group is created with this name and the DataSource is organized under the new group.

Returns: The group of this LMDataSource.
Return type: str

property id¶

DataSource unique ID. Used only to match an existing DataSource. If no existing DataSource matches the provided ID, an error results.

Returns: The id of this LMDataSource. # noqa: E501
Return type: int

property name¶

DataSource unique name. Used to match an existing DataSource. If no existing DataSource matches the name provided here, a new DataSource is created with this name.

Returns: The data_source of this LMDataSource.
Return type: str

LMDataSourceInstance¶

class lmingest.models.lm_datasource_instance.LMDataSourceInstance(name, description=None, display_name=None, properties=None)¶

This model is used to defining the datasource object.

Parameters

name (str) – Instance name. If no existing instance matches, a new instance is created with this name.
display_name (str) – Instance display name. Only considered when creating a new instance.
properties (dict) – New properties for instance. Updates to existing instance properties are not considered. Depending on the property name, we will convert these properties into system, auto, or custom properties.

property display_name¶

Instance display name. Only considered when creating a new instance.

Parameters: display_name – The display_name of this LMDataSourceInstance.
Type: str

property name¶

Instance name. If no existing instance matches, a new instance is created with this name.

Returns: The name of this LMDataSourceInstance.
Return type: str

property properties¶

New properties for instance. Updates to existing instance properties are not considered. Depending on the property name, we will convert these properties into system, auto, or custom properties.

Returns: The properties of this LMDataSourceInstance.
Return type: MapStringString

LMDataPoint¶

class lmingest.models.lm_datapoint.LMDataPoint(name, aggregation_type=None, description=None, type=None)¶

This model is used to defining the datapoint object.

Parameters

name (str) – Datapoint name. If no existing datapoint matches for specified DataSource, a new datapoint is created with this name.
aggregation_type (str) – The aggregation method, if any, that should be used if data is pushed in sub-minute intervals. Only considered when creating a new datapoint. See the About the Push Metrics REST API section of this guide for more information on datapoint value aggregation intervals.
description (str) – Datapoint description. Only considered when creating a new datapoint.
type (str) – Metric type as a number in string format. Only considered when creating a new datapoint.

property aggregation_type¶

The aggregation method, if any, that should be used if data is pushed in sub-minute intervals. Only considered when creating a new datapoint.

Returns: The type of this LMDataPoint.
Return type: str

property description¶

Datapoint description. Only considered when creating a new datapoint.

Returns: The description of this LMDataPoint.
Return type: str

property name¶

Datapoint name. If no existing datapoint matches for specified DataSource, a new datapoint is created with this name.

Returns: The name of this LMDataPoint.
Return type: str

property type¶

Metric type as a number in string format. Only considered when creating a new datapoint.

Returns: The aggregation_type of this LMDataPoint.
Return type: str

LMResonseInterface¶

class lmingest.api.lm_response_interface.LMResonseInterface¶

This is the callback interface for handling the response. End user can create his own class using this one to get the response status.

classmethod error_callback(request, response, status, request_id, reason)¶

This callback gets invoked for any error or exception from the end REST endpoint.

Parameters

request (dict) – The json payload send to REST endpoint.
response (dict) – Response received from the REST endpoint.
status (int) – HTTP status code.
request_id (str) – Unique request id generated by Rest endpoint.
reason (str) – The reason for error.

classmethod success_callback(request, response, status, request_id)¶

This callback gets invoked for successful response from the end REST endpoint.

Parameters

request (dict) – The json payload send to REST endpoint.
response (dict) – Response received from the REST endpoint.
status (int) – HTTP status code.
request_id (str) – Unique request id generated by Rest endpoint.

TODO¶

[X] Exception Handling, passing any error to end user when ever he makes a Send request for that resource. e.g. SendMetrics is invoked against the resources which are not present
[X] Supporting the single request
[X] Validation all the models. e.g. no specical chars allowed in the resource name, length restriction…etc
[X] Property Updation API
[] Send* call using the unique name
[] Code commenting for code documentation
[] Any other authentication support
[] version/Compression support in send* call
[] Test cases and sample program.