JTI OpenConfig Telemetry and CrateDB 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 CrateDB plugin facilitates the writing of metrics to a CrateDB database, leveraging its PostgreSQL-compatible protocol to ensure a seamless experience for users.

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.

CrateDB

This plugin writes to CrateDB via its PostgreSQL protocol, allowing for metrics to be efficiently stored in a scalable database. CrateDB is designed for high-speed analytics, supporting time-series data and complicated queries, making it ideal for applications that require fast ingestion and analysis of large datasets. By utilizing the PostgreSQL protocol, the CrateDB output plugin ensures compatibility with existing PostgreSQL client libraries and tools, enabling a smooth integration for users who are already familiar with PostgreSQL’s ecosystem. The plugin provides options such as automatic table creation, connection parameters, and query timeouts, offering flexibility in how metrics are handled and stored within the database.

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

CrateDB

[[outputs.cratedb]]
  ## Connection parameters for accessing the database see
  ##   https://pkg.go.dev/github.com/jackc/pgx/v4#ParseConfig
  ## for available options
  url = "postgres://user:password@localhost/schema?sslmode=disable"

  ## Timeout for all CrateDB queries.
  # timeout = "5s"

  ## Name of the table to store metrics in.
  # table = "metrics"

  ## If true, and the metrics table does not exist, create it automatically.
  # table_create = false

  ## The character(s) to replace any '.' in an object key with
  # key_separator = "_"

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.

CrateDB

1. Real-Time Analytics for IoT Devices: Collect and store metrics from thousands of IoT devices. By setting up a dynamic metrics table for each device, users can perform real-time analytics on the collected data, enabling quick insights into device performance, patterns, and potential failures. This setup benefits from CrateDB’s ability to handle high-throughput data ingestion while providing the necessary analytics capabilities to derive actionable insights.

2. Website Performance Monitoring: Track key performance metrics from web applications, such as request latency and user activity. By storing metrics in CrateDB, teams can leverage the power of SQL-like queries to analyze traffic patterns, user engagement, and server performance over time, leading to optimized application performance and enhanced user experiences.

3. Financial Transaction Analysis: Manage large volumes of financial transaction data for real-time fraud detection and analysis. With CrateDB’s scalable infrastructure, users can store, query, and analyze transaction metrics efficiently, allowing for the detection of anomalies and illicit activities based on transaction patterns and trends.

Feedback

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