JTI OpenConfig Telemetry and Google BigQuery 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.

The Google BigQuery plugin allows Telegraf to write metrics to Google Cloud BigQuery, enabling robust data analytics capabilities for telemetry data.

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.

Google BigQuery

The Google BigQuery plugin for Telegraf enables seamless integration with Google Cloud’s BigQuery service, a popular data warehousing and analytics platform. This plugin facilitates the transfer of metrics collected by Telegraf into BigQuery datasets, making it easier for users to perform analyses and generate insights from their telemetry data. It requires authentication through a service account or user credentials and is designed to handle various data types, ensuring that users can maintain the integrity and accuracy of their metrics as they are stored in BigQuery tables. The configuration options allow for customization around dataset specifications and handling metrics, including the management of hyphens in metric names, which are not supported by BigQuery for streaming inserts. This plugin is particularly useful for organizations leveraging the scalability and powerful query capabilities of BigQuery to analyze large volumes of monitoring data.

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

Google BigQuery

# Configuration for Google Cloud BigQuery to send entries
[[outputs.bigquery]]
  ## Credentials File
  credentials_file = "/path/to/service/account/key.json"

  ## Google Cloud Platform Project
  # project = ""

  ## The namespace for the metric descriptor
  dataset = "telegraf"

  ## Timeout for BigQuery operations.
  # timeout = "5s"

  ## Character to replace hyphens on Metric name
  # replace_hyphen_to = "_"

  ## Write all metrics in a single compact table
  # compact_table = ""
  

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.

Google BigQuery

1. Real-Time Analytics Dashboard: Leverage the Google BigQuery plugin to feed live metrics into a custom analytics dashboard hosted on Google Cloud. This setup would allow teams to visualize performance data in real-time, providing insights into system health and usage patterns. By using BigQuery’s querying capabilities, users can easily create tailored reports and dashboards to meet their specific needs, thus enhancing decision-making processes.

2. Cost Management and Optimization Analysis: Utilize the plugin to automatically send cost-related metrics from various services into BigQuery. Analyzing this data can help businesses identify unnecessary expenses and optimize resource usage. By performing aggregation and transformation queries in BigQuery, organizations can create accurate forecasts and manage their cloud spending efficiently.

3. Cross-Team Collaboration on Monitoring Data: Enable different teams within an organization to share their monitoring data using BigQuery. With the help of this Telegraf plugin, teams can push their metrics to a central BigQuery instance, fostering collaboration. This data-sharing approach encourages best practices and cross-functional awareness, leading to collective improvements in system performance and reliability.

4. Historical Analysis for Capacity Planning: By using the BigQuery plugin, companies can collect and store historical metrics data essential for capacity planning. Analyzing trends over time can help anticipate system needs and scale infrastructure proactively. Organizations can create time-series analyses and identify patterns that inform their long-term strategic decisions.

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