JTI OpenConfig Telemetry and Snowflake 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.

Telegraf’s SQL plugin allows seamless metric storage in SQL databases. When configured for Snowflake, it employs a specialized DSN format and dynamic table creation to map metrics to the appropriate schema.

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.

Snowflake

Telegraf’s SQL plugin is engineered to dynamically write metrics into an SQL database by creating tables and columns based on the incoming data. When configured for Snowflake, it employs the gosnowflake driver, which uses a DSN that encapsulates credentials, account details, and database configuration in a compact format. This setup allows for the automatic generation of tables where each metric is recorded with precise timestamps, thereby ensuring detailed historical tracking. Although the integration is considered experimental, it leverages Snowflake’s powerful data warehousing capabilities, making it suitable for scalable, cloud-based analytics and reporting solutions.

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

Snowflake

[[outputs.sql]]
  ## Database driver
  ## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
  ## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
  driver = "snowflake"

  ## Data source name
  ## For Snowflake, the DSN format typically includes the username, password, account identifier, and optional warehouse, database, and schema.
  ## Example DSN: "username:password@account/warehouse/db/schema"
  data_source_name = "username:password@account/warehouse/db/schema"

  ## Timestamp column name
  timestamp_column = "timestamp"

  ## Table creation template
  ## Available template variables:
  ##  {TABLE}        - table name as a quoted identifier
  ##  {TABLELITERAL} - table name as a quoted string literal
  ##  {COLUMNS}      - column definitions (list of quoted identifiers and types)
  table_template = "CREATE TABLE {TABLE} ({COLUMNS})"

  ## Table existence check template
  ## Available template variables:
  ##  {TABLE} - table name as a quoted identifier
  table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"

  ## Initialization SQL (optional)
  init_sql = ""

  ## Maximum amount of time a connection may be idle. "0s" means connections are never closed due to idle time.
  connection_max_idle_time = "0s"

  ## Maximum amount of time a connection may be reused. "0s" means connections are never closed due to age.
  connection_max_lifetime = "0s"

  ## Maximum number of connections in the idle connection pool. 0 means unlimited.
  connection_max_idle = 2

  ## Maximum number of open connections to the database. 0 means unlimited.
  connection_max_open = 0

  ## Metric type to SQL type conversion
  ## Defaults to ANSI/ISO SQL types unless overridden. Adjust if needed for Snowflake compatibility.
  #[outputs.sql.convert]
  #  integer       = "INT"
  #  real          = "DOUBLE"
  #  text          = "TEXT"
  #  timestamp     = "TIMESTAMP"
  #  defaultvalue  = "TEXT"
  #  unsigned      = "UNSIGNED"
  #  bool          = "BOOL"

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.

Snowflake

1. Cloud-Based Data Lake Integration: Utilize the plugin to stream real-time metrics from various sources into Snowflake, enabling the creation of a centralized data lake. This integration supports complex analytics and machine learning workflows on cloud data.

2. Dynamic Business Intelligence Dashboards: Leverage the plugin to automatically generate tables from incoming metrics and feed them into BI tools. This allows businesses to create dynamic dashboards that visualize performance trends and operational insights without manual schema management.

3. Scalable IoT Analytics: Deploy the plugin to capture high-frequency data from IoT devices into Snowflake. This use case facilitates the aggregation and analysis of sensor data, enabling predictive maintenance and real-time monitoring at scale.

4. Historical Trend Analysis for Compliance: Use the plugin to log and archive detailed metric data in Snowflake, which can then be queried for long-term trend analysis and compliance reporting. This setup ensures that organizations can maintain a robust audit trail and perform forensic analysis if needed.

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