Cisco Model-Driven Telemetry and IoTDB Integration
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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.
This plugin saves Telegraf metrics to an Apache IoTDB backend, supporting session connection and data insertion.
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.
IoTDB
Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Its light-weight architecture, high performance, and rich feature set create a perfect fit for massive data storage, high-speed data ingestion, and complex analytics in the IoT industrial fields. IoTDB deeply integrates with Apache Hadoop, Spark, and Flink, which further enhances its capabilities in handling large scale data and sophisticated processing tasks.
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"
IoTDB
[[outputs.iotdb]]
## Configuration of IoTDB server connection
host = "127.0.0.1"
# port = "6667"
## Configuration of authentication
# user = "root"
# password = "root"
## Timeout to open a new session.
## A value of zero means no timeout.
# timeout = "5s"
## Configuration of type conversion for 64-bit unsigned int
## IoTDB currently DOES NOT support unsigned integers (version 13.x).
## 32-bit unsigned integers are safely converted into 64-bit signed integers by the plugin,
## however, this is not true for 64-bit values in general as overflows may occur.
## The following setting allows to specify the handling of 64-bit unsigned integers.
## Available values are:
## - "int64" -- convert to 64-bit signed integers and accept overflows
## - "int64_clip" -- convert to 64-bit signed integers and clip the values on overflow to 9,223,372,036,854,775,807
## - "text" -- convert to the string representation of the value
# uint64_conversion = "int64_clip"
## Configuration of TimeStamp
## TimeStamp is always saved in 64bits int. timestamp_precision specifies the unit of timestamp.
## Available value:
## "second", "millisecond", "microsecond", "nanosecond"(default)
# timestamp_precision = "nanosecond"
## Handling of tags
## Tags are not fully supported by IoTDB.
## A guide with suggestions on how to handle tags can be found here:
## https://iotdb.apache.org/UserGuide/Master/API/InfluxDB-Protocol.html
##
## Available values are:
## - "fields" -- convert tags to fields in the measurement
## - "device_id" -- attach tags to the device ID
##
## For Example, a metric named "root.sg.device" with the tags `tag1: "private"` and `tag2: "working"` and
## fields `s1: 100` and `s2: "hello"` will result in the following representations in IoTDB
## - "fields" -- root.sg.device, s1=100, s2="hello", tag1="private", tag2="working"
## - "device_id" -- root.sg.device.private.working, s1=100, s2="hello"
# convert_tags_to = "device_id"
## Handling of unsupported characters
## Some characters in different versions of IoTDB are not supported in path name
## A guide with suggetions on valid paths can be found here:
## for iotdb 0.13.x -> https://iotdb.apache.org/UserGuide/V0.13.x/Reference/Syntax-Conventions.html#identifiers
## for iotdb 1.x.x and above -> https://iotdb.apache.org/UserGuide/V1.3.x/User-Manual/Syntax-Rule.html#identifier
##
## Available values are:
## - "1.0", "1.1", "1.2", "1.3" -- enclose in `` the world having forbidden character
## such as @ $ # : [ ] { } ( ) space
## - "0.13" -- enclose in `` the world having forbidden character
## such as space
##
## Keep this section commented if you don't want to sanitize the path
# sanitize_tag = "1.3"
Input and output integration examples
Cisco Model-Driven Telemetry
-
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.
-
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.
-
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.
IoTDB
-
Real-Time IoT Monitoring: Utilize the IoTDB plugin to gather sensor data from various IoT devices and save it in an Apache IoTDB backend, facilitating real-time monitoring of environmental conditions such as temperature and humidity. This use case enables organizations to analyze trends over time and make informed decisions based on historical data, while also utilizing IoTDB’s efficient storage and querying capabilities.
-
Smart Agriculture Data Collection: Use the IoTDB plugin to collect metrics from smart agriculture sensors deployed in fields. By transmitting moisture levels, nutrient content, and atmospheric conditions to IoTDB, farmers can access detailed insights into optimal planting and watering schedules, thus improving crop yields and resource management.
-
Energy Consumption Analytics: Leverage the IoTDB plugin to track energy consumption metrics from smart meters across a utility network. This integration enables analytics to identify peaks in usage and predict future consumption patterns, ultimately supporting energy conservation initiatives and improved utility management.
-
Automated Industrial Equipment Monitoring: Use this plugin to gather operational metrics from machinery in a manufacturing plant and store them in IoTDB for analysis. This setup can help identify inefficiencies, predictive maintenance needs, and operational anomalies, ensuring optimal performance and minimizing unexpected downtimes.
Feedback
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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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