Cisco Model-Driven Telemetry and Clarify Integration
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Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
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Input and output integration overview
The Cisco Model-Driven Telemetry (MDT) plugin facilitates the collection of telemetry data from Cisco networking platforms, utilizing gRPC and TCP transport mechanisms. This plugin is essential for users looking to implement advanced telemetry solutions for better insights and operational efficiency.
The Clarify plugin allows users to publish Telegraf metrics directly to Clarify, enabling enhanced analysis and monitoring capabilities.
Integration details
Cisco Model-Driven Telemetry
Cisco model-driven telemetry (MDT) is designed to provide a robust means of consuming telemetry data from various Cisco platforms, including IOS XR, IOS XE, and NX-OS. This plugin focuses on the efficient transport of telemetry data using either TCP or gRPC protocols, offering flexibility based on the network environment and requirements. The gRPC transport is particularly advantageous as it supports TLS for enhanced security through encryption and authentication. The plugin is compatible with a range of software versions on Cisco devices, enabling organizations to leverage telemetry capabilities across their network operations. It is especially useful for network monitoring and analytics, as it enables real-time data collection directly from Cisco devices, enhancing visibility into network performance, resource utilization, and operational metrics.
Clarify
This plugin facilitates the writing of Telegraf metrics to Clarify, a platform for managing and analyzing time series data. By transforming metrics into Clarify signals, this output plugin enables seamless integration of collected telemetry data into the Clarify ecosystem. Users must obtain valid credentials, either through a credentials file or basic authentication, to configure the plugin. The configuration also provides options for fine-tuning how metrics are mapped to signals in Clarify, including the ability to specify unique identifiers using tags. Given that Clarify supports only floating point values, the plugin ensures that any unsupported types are effectively filtered out during the publishing process. This comprehensive connectivity aligns with use cases in monitoring, data analysis, and operational insights.
Configuration
Cisco Model-Driven Telemetry
[[inputs.cisco_telemetry_mdt]]
## Telemetry transport can be "tcp" or "grpc". TLS is only supported when
## using the grpc transport.
transport = "grpc"
## Address and port to host telemetry listener
service_address = ":57000"
## Grpc Maximum Message Size, default is 4MB, increase the size. This is
## stored as a uint32, and limited to 4294967295.
max_msg_size = 4000000
## Enable TLS; grpc transport only.
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Enable TLS client authentication and define allowed CA certificates; grpc
## transport only.
# tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]
## Define (for certain nested telemetry measurements with embedded tags) which fields are tags
# embedded_tags = ["Cisco-IOS-XR-qos-ma-oper:qos/interface-table/interface/input/service-policy-names/service-policy-instance/statistics/class-stats/class-name"]
## Include the delete field in every telemetry message.
# include_delete_field = false
## Specify custom name for incoming MDT source field.
# source_field_name = "mdt_source"
## Define aliases to map telemetry encoding paths to simple measurement names
[inputs.cisco_telemetry_mdt.aliases]
ifstats = "ietf-interfaces:interfaces-state/interface/statistics"
## Define Property Xformation, please refer README and https://pubhub.devnetcloud.com/media/dme-docs-9-3-3/docs/appendix/ for Model details.
[inputs.cisco_telemetry_mdt.dmes]
# Global Property Xformation.
# prop1 = "uint64 to int"
# prop2 = "uint64 to string"
# prop3 = "string to uint64"
# prop4 = "string to int64"
# prop5 = "string to float64"
# auto-prop-xfrom = "auto-float-xfrom" #Xform any property which is string, and has float number to type float64
# Per Path property xformation, Name is telemetry configuration under sensor-group, path configuration "WORD Distinguished Name"
# Per Path configuration is better as it avoid property collision issue of types.
# dnpath = '{"Name": "show ip route summary","prop": [{"Key": "routes","Value": "string"}, {"Key": "best-paths","Value": "string"}]}'
# dnpath2 = '{"Name": "show processes cpu","prop": [{"Key": "kernel_percent","Value": "float"}, {"Key": "idle_percent","Value": "float"}, {"Key": "process","Value": "string"}, {"Key": "user_percent","Value": "float"}, {"Key": "onesec","Value": "float"}]}'
# dnpath3 = '{"Name": "show processes memory physical","prop": [{"Key": "processname","Value": "string"}]}'
## Additional GRPC connection settings.
[inputs.cisco_telemetry_mdt.grpc_enforcement_policy]
## GRPC permit keepalives without calls, set to true if your clients are
## sending pings without calls in-flight. This can sometimes happen on IOS-XE
## devices where the GRPC connection is left open but subscriptions have been
## removed, and adding subsequent subscriptions does not keep a stable session.
# permit_keepalive_without_calls = false
## GRPC minimum timeout between successive pings, decreasing this value may
## help if this plugin is closing connections with ENHANCE_YOUR_CALM (too_many_pings).
# keepalive_minimum_time = "5m"
Clarify
[[outputs.clarify]]
## Credentials File (Oauth 2.0 from Clarify integration)
credentials_file = "/path/to/clarify/credentials.json"
## Clarify username password (Basic Auth from Clarify integration)
username = "i-am-bob"
password = "secret-password"
## Timeout for Clarify operations
# timeout = "20s"
## Optional tags to be included when generating the unique ID for a signal in Clarify
# id_tags = []
# clarify_id_tag = 'clarify_input_id'
Input and output integration examples
Cisco Model-Driven Telemetry
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Real-Time Network Monitoring: Utilize the Cisco MDT plugin to collect network performance metrics from Cisco routers and switches. By feeding telemetry data into a visualization tool, network operators can observe traffic trends, bandwidth usage, and error rates in real-time. This proactive monitoring allows teams to swiftly address issues before they affect network performance, resulting in a more reliable service.
-
Automated Anomaly Detection: Integrate Cisco MDT with machine learning algorithms to create an automated anomaly detection system. By continuously analyzing telemetry data, the system can identify deviations from typical operational patterns, providing alerts for unusual conditions that may signify network problems or security threats, which can aid in maintaining operational integrity.
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Dynamic Configuration Management: Leveraging the telemetry data collected from Cisco devices, organizations can implement dynamic configuration management solutions that automatically adjust network settings based on current performance indicators. For instance, if the telemetry indicates high utilization on certain links, the system could dynamically route traffic to underutilized paths, optimizing resource usage.
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Enhanced Reporting and Analytics: Use the Cisco MDT plugin to feed detailed telemetry data into analytics platforms, enabling comprehensive reporting on network health and performance. Historical and real-time analysis can guide decision-making and strategic planning, helping organizations to allocate resources more effectively and understand their network’s operational landscape better.
Clarify
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Automated Data Monitoring: By integrating the Clarify plugin with sensor data collection, organizations can automate the monitoring of environmental conditions, such as temperature and humidity. The plugin processes metrics in real-time, sending updates to Clarify where they can be analyzed for trends, alerts, and historical tracking. This use case makes it easier to maintain optimal conditions in data centers or production environments, reducing the risk of equipment failures.
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Performance Metrics Analysis: Companies can leverage this plugin to send application performance metrics to Clarify. By transmitting key indicators such as response times and error rates, developers and operations teams can utilize Clarify’s capabilities to visualize and analyze application performance over time. This insight can drive improvements in user experience and help identify areas in need of optimization.
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Sensor Data Aggregation: Utilizing the plugin to push data from multiple sensors to Clarify allows for a comprehensive view of physical environments. This aggregation is particularly beneficial in sectors such as agriculture, where metrics from various sensors can be correlated to decision-making about resource allocations, pest control, and crop management. The plugin ensures the data is accurately mapped and transformed for effective analysis.
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Real-Time Alerts and Notifications: Implement the Clarify plugin to trigger real-time alerts based on predefined thresholds within the metrics being sent. For instance, if temperature readings exceed certain levels, alerts can be generated and sent to operational staff. This proactive approach allows for immediate responses to potential issues, enhancing operational reliability and safety.
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Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
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