Cisco Model-Driven Telemetry and MongoDB Integration

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 MongoDB Telegraf Plugin enables users to send metrics to a MongoDB database, automatically managing time series collections.

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.

MongoDB

This plugin sends metrics to MongoDB and seamlessly integrates with its time series functionality, allowing for automatic creation of collections as time series when they don’t already exist. It requires MongoDB version 5.0 or higher to utilize the time series collections feature, which is vital for efficiently storing and querying time-based data. This plugin enhances the monitoring capabilities by ensuring that all relevant metrics are stored and organized correctly within MongoDB, providing users the ability to leverage MongoDB’s powerful querying and aggregation features for time series analysis.

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"

MongoDB

[[outputs.mongodb]]
              # connection string examples for mongodb
              dsn = "mongodb://localhost:27017"
              # dsn = "mongodb://mongod1:27017,mongod2:27017,mongod3:27017/admin&replicaSet=myReplSet&w=1"

              # overrides serverSelectionTimeoutMS in dsn if set
              # timeout = "30s"

              # default authentication, optional
              # authentication = "NONE"

              # for SCRAM-SHA-256 authentication
              # authentication = "SCRAM"
              # username = "root"
              # password = "***"

              # for x509 certificate authentication
              # authentication = "X509"
              # tls_ca = "ca.pem"
              # tls_key = "client.pem"
              # # tls_key_pwd = "changeme" # required for encrypted tls_key
              # insecure_skip_verify = false

              # database to store measurements and time series collections
              # database = "telegraf"

              # granularity can be seconds, minutes, or hours.
              # configuring this value will be based on your input collection frequency.
              # see https://docs.mongodb.com/manual/core/timeseries-collections/#create-a-time-series-collection
              # granularity = "seconds"

              # optionally set a TTL to automatically expire documents from the measurement collections.
              # ttl = "360h"

Input and output integration examples

Cisco Model-Driven Telemetry

1. 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.

2. 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.

3. 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.

4. 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.

MongoDB

1. Dynamic Logging to MongoDB for IoT Devices: Utilize this plugin to collect and store metrics from a fleet of IoT devices in real-time. By sending device logs directly to MongoDB, you can create a centralized database that allows for easy access and querying of health metrics and performance data, enabling proactive maintenance and troubleshooting based on historical trends.

2. Time Series Analysis of Web Traffic: Use the MongoDB Telegraf Plugin to gather and analyze web traffic metrics over time. This application can help you understand peak usage times, user interactions, and behavior patterns, which can guide marketing strategies and infrastructure scaling decisions for improved user experience.

3. Automated Monitoring and Alerting System: Integrate the MongoDB plugin into an automated monitoring system that tracks application performance metrics. With time series collections, you can set up alerts based on specific thresholds, allowing your team to respond to potential issues before they affect users. This proactive management can enhance service reliability and overall performance.

4. Data Retention and TTL Management in Metrics Storage: Leverage the TTL feature for documents within MongoDB collections to auto-expire outdated metrics. This is particularly useful for environments where only recent performance data is relevant, preventing your MongoDB database from becoming cluttered with old metrics and ensuring efficient data management.

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