JTI OpenConfig Telemetry and Cortex Integration

Input and output integration overview

The JTI OpenConfig Telemetry plugin allows users to collect real-time telemetry data from devices running Juniper’s implementation of the OpenConfig model, leveraging the Junos Telemetry Interface for efficient data retrieval.

This plugin enables Telegraf to send metrics to Cortex using the Prometheus remote write protocol, allowing seamless ingestion into Cortex’s scalable, multi-tenant time series storage.

Integration details

JTI OpenConfig Telemetry

This plugin reads data from Juniper Networks’ OpenConfig telemetry implementation using the Junos Telemetry Interface (JTI). OpenConfig is an initiative aimed at enabling standardized and open network device telemetry through a common model for various devices and protocols. The JTI allows for the collection of this telemetry data in a real-time manner from various sensors defined within the configuration. Configurable parameters for this plugin include the ability to specify device addresses, authentication credentials, sampling frequency, and multiple sensors with potentially different reporting rates. The plugin uniquely handles time-stamping either through the collection time or the timestamp provided in the data, allowing for flexibility in how data is processed. Given its support for TLS for secure communication, the plugin is well-suited for integration into both traditional and modern network management systems, enhancing visibility into network performance and reliability.

Cortex

With Telegraf’s HTTP output plugin and the prometheusremotewrite data format you can send metrics directly to Cortex, a horizontally scalable, long-term storage backend for Prometheus. Cortex supports multi-tenancy and accepts remote write requests using the Prometheus protobuf format. By using Telegraf as the collection agent and Remote Write as the transport mechanism, organizations can extend observability into sources not natively supported by Prometheus—such as Windows hosts, SNMP-enabled devices, or custom application metrics—while leveraging Cortex’s high-availability and long-retention capabilities.

Configuration

JTI OpenConfig Telemetry

[[inputs.jti_openconfig_telemetry]]
  ## List of device addresses to collect telemetry from
  servers = ["localhost:1883"]

  ## Authentication details. Username and password are must if device expects
  ## authentication. Client ID must be unique when connecting from multiple instances
  ## of telegraf to the same device
  username = "user"
  password = "pass"
  client_id = "telegraf"

  ## Frequency to get data
  sample_frequency = "1000ms"

  ## Sensors to subscribe for
  ## A identifier for each sensor can be provided in path by separating with space
  ## Else sensor path will be used as identifier
  ## When identifier is used, we can provide a list of space separated sensors.
  ## A single subscription will be created with all these sensors and data will
  ## be saved to measurement with this identifier name
  sensors = [
   "/interfaces/",
   "collection /components/ /lldp",
  ]

  ## We allow specifying sensor group level reporting rate. To do this, specify the
  ## reporting rate in Duration at the beginning of sensor paths / collection
  ## name. For entries without reporting rate, we use configured sample frequency
  sensors = [
   "1000ms customReporting /interfaces /lldp",
   "2000ms collection /components",
   "/interfaces",
  ]

  ## Timestamp Source
  ## Set to 'collection' for time of collection, and 'data' for using the time
  ## provided by the _timestamp field.
  # timestamp_source = "collection"

  ## Optional TLS Config
  # enable_tls = false
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Minimal TLS version to accept by the client
  # tls_min_version = "TLS12"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## Delay between retry attempts of failed RPC calls or streams. Defaults to 1000ms.
  ## Failed streams/calls will not be retried if 0 is provided
  retry_delay = "1000ms"

  ## Period for sending keep-alive packets on idle connections
  ## This is helpful to identify broken connections to the server
  # keep_alive_period = "10s"

  ## To treat all string values as tags, set this to true
  str_as_tags = false

Cortex

[[outputs.http]]
  ## Cortex Remote Write endpoint
  url = "http://cortex.example.com/api/v1/push"

  ## Use POST to send data
  method = "POST"

  ## Send metrics using Prometheus remote write format
  data_format = "prometheusremotewrite"

  ## Optional HTTP headers for authentication
  # [outputs.http.headers]
  #   X-Scope-OrgID = "your-tenant-id"
  #   Authorization = "Bearer YOUR_API_TOKEN"

  ## Optional TLS configuration
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

  ## Request timeout
  timeout = "10s"

Input and output integration examples

JTI OpenConfig Telemetry

1. Network Performance Monitoring: Use the JTI OpenConfig Telemetry plugin to monitor network performance metrics from multiple Juniper devices in real-time. By configuring various sensors, operators can gain insights into interface performance, traffic patterns, and error rates, allowing for proactive troubleshooting and optimization of the network.

2. Automated Fault Detection: Integrate the telemetry data collected via this plugin with a fault detection system that triggers alerts based on predefined thresholds. For example, when a specific sensor indicates a fault or threshold breach, automated scripts can be initiated to remediate the situation, dramatically improving response times.

3. Historical Performance Analysis: By forwarding the collected telemetry data into a time-series database, organizations can perform historical analysis on network performance. This enables teams to identify trends over time, spot anomalies, and make more informed decisions regarding network capacity planning and resource allocation.

4. Real-Time Dashboards for Network Operations: Leverage the real-time data gathered through this plugin to power visualization dashboards that provide network operators with live insights into performance metrics. This facilitates better operational awareness and quicker decision-making during critical events.

Cortex

1. Unified Multi-Tenant Monitoring: Use Telegraf to collect metrics from different teams or environments and push them to Cortex with separate X-Scope-OrgID headers. This enables isolated data ingestion and querying per tenant, ideal for managed services and platform teams.

2. Extending Prometheus Coverage to Edge Devices: Deploy Telegraf on edge or IoT devices to collect system metrics and send them to a centralized Cortex cluster. This approach ensures consistent observability even for environments without local Prometheus scrapers.

3. Global Service Observability with Federated Tenants: Aggregate metrics from global infrastructure by configuring Telegraf agents to push data into regional Cortex clusters, each tagged with tenant identifiers. Cortex handles deduplication and centralized access across regions.

4. Custom App Telemetry Pipeline: Collect app-specific telemetry via Telegraf’s exec or http input plugins and forward it to Cortex. This allows DevOps teams to monitor app-specific KPIs in a scalable, query-efficient format while keeping metrics logically grouped by tenant or service.

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