Arista LANZ and MongoDB Integration

Input and output integration overview

The Arista LANZ plugin is designed for reading latency and congestion metrics from Arista LANZ, helping users monitor their network performance effectively.

The MongoDB Telegraf Plugin enables users to send metrics to a MongoDB database, automatically managing time series collections.

Integration details

Arista LANZ

This plugin provides a consumer for use with Arista Networks’ Latency Analyzer (LANZ). Metrics are read from a stream of data via TCP through port 50001 on the switches management IP. The data is in Protobuffers format, allowing for efficient transportation and parsing of data. LANZ is utilized to monitor network latency and congestion in real-time, which is vital for maintaining optimal performance in networking environments. The underlying technology, Arista’s latency analysis, provides insights into various network operations and infrastructure behaviors, making it a crucial tool for network engineering and management.

MongoDB

This plugin sends metrics to MongoDB and seamlessly integrates with its time series functionality, allowing for automatic creation of collections as time series when they don’t already exist. It requires MongoDB version 5.0 or higher to utilize the time series collections feature, which is vital for efficiently storing and querying time-based data. This plugin enhances the monitoring capabilities by ensuring that all relevant metrics are stored and organized correctly within MongoDB, providing users the ability to leverage MongoDB’s powerful querying and aggregation features for time series analysis.

Configuration

Arista LANZ

[[inputs.lanz]]
  ## URL to Arista LANZ endpoint
  servers = [
    "tcp://switch1.int.example.com:50001",
    "tcp://switch2.int.example.com:50001",
  ]

MongoDB

[[outputs.mongodb]]
              # connection string examples for mongodb
              dsn = "mongodb://localhost:27017"
              # dsn = "mongodb://mongod1:27017,mongod2:27017,mongod3:27017/admin&replicaSet=myReplSet&w=1"

              # overrides serverSelectionTimeoutMS in dsn if set
              # timeout = "30s"

              # default authentication, optional
              # authentication = "NONE"

              # for SCRAM-SHA-256 authentication
              # authentication = "SCRAM"
              # username = "root"
              # password = "***"

              # for x509 certificate authentication
              # authentication = "X509"
              # tls_ca = "ca.pem"
              # tls_key = "client.pem"
              # # tls_key_pwd = "changeme" # required for encrypted tls_key
              # insecure_skip_verify = false

              # database to store measurements and time series collections
              # database = "telegraf"

              # granularity can be seconds, minutes, or hours.
              # configuring this value will be based on your input collection frequency.
              # see https://docs.mongodb.com/manual/core/timeseries-collections/#create-a-time-series-collection
              # granularity = "seconds"

              # optionally set a TTL to automatically expire documents from the measurement collections.
              # ttl = "360h"

Input and output integration examples

Arista LANZ

1. Real-Time Latency Monitoring: This plugin can be used to set up a monitoring dashboard that tracks real-time latency metrics across multiple interfaces. By gathering and visualizing this data, network admins can swiftly identify and rectify latency issues before they impact service quality. The challenge lies in efficiently handling the influx of metrics from various sources without overwhelming the infrastructure or incurring excessive processing delays.

2. Congestion Analysis for Traffic Engineering: Users can leverage the LANZ plugin to analyze congestion records, enabling the optimization of network traffic flows. By applying historical pattern recognition to the metrics collected, IT teams can make informed decisions on traffic management strategies, thus improving overall network efficiency. This requires implementing robust data storage and analysis capabilities to derive actionable insights from the raw metrics.

3. Integration with Alerting Systems: Integrate the metrics from this plugin with alerting systems to automatically notify network engineers of any significant changes in latency or congestion. By setting thresholds based on historical data trends, this use case enhances proactive incident management, allowing teams to address potential issues proactively. The technical challenge here is establishing the right balance in threshold settings to minimize false positives while ensuring genuine issues are flagged promptly.

4. Network Optimization Reports: Utilize the metrics gathered through the LANZ plugin to generate periodic reports that detail network performance, latency trends, and congestion events. These reports can help stakeholders understand network health over time and guide infrastructure investments. The challenge involves structuring and formatting the output data to make it comprehensible and actionable for various audiences.

MongoDB

1. Dynamic Logging to MongoDB for IoT Devices: Utilize this plugin to collect and store metrics from a fleet of IoT devices in real-time. By sending device logs directly to MongoDB, you can create a centralized database that allows for easy access and querying of health metrics and performance data, enabling proactive maintenance and troubleshooting based on historical trends.

2. Time Series Analysis of Web Traffic: Use the MongoDB Telegraf Plugin to gather and analyze web traffic metrics over time. This application can help you understand peak usage times, user interactions, and behavior patterns, which can guide marketing strategies and infrastructure scaling decisions for improved user experience.

3. Automated Monitoring and Alerting System: Integrate the MongoDB plugin into an automated monitoring system that tracks application performance metrics. With time series collections, you can set up alerts based on specific thresholds, allowing your team to respond to potential issues before they affect users. This proactive management can enhance service reliability and overall performance.

4. Data Retention and TTL Management in Metrics Storage: Leverage the TTL feature for documents within MongoDB collections to auto-expire outdated metrics. This is particularly useful for environments where only recent performance data is relevant, preventing your MongoDB database from becoming cluttered with old metrics and ensuring efficient data management.

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