DNS and Apache Hudi Integration

Input and output integration overview

The DNS plugin enables users to monitor and gather statistics on DNS query times, facilitating performance analysis of DNS resolutions.

Writes metrics to Parquet files via Telegraf’s Parquet output plugin, preparing them for ingestion into Apache Hudi’s lakehouse architecture.

Integration details

DNS

This plugin gathers DNS query times in milliseconds, utilizing the capabilities of DNS queries similar to the Dig command. It provides a means to monitor and analyze DNS performance by measuring the response time from specified DNS servers, allowing network administrators and engineers to ensure optimal DNS resolution times. The plugin can be configured to target specific servers and customize the types of records queried, encompassing various DNS features such as resolving domain names to IP addresses, or retrieving details from specific records as needed, while also clearly reporting on the success or failure of each query, alongside relevant metadata.

Apache Hudi

This configuration leverages Telegraf’s Parquet plugin to serialize metrics into columnar Parquet files suitable for downstream ingestion by Apache Hudi. The plugin writes metrics grouped by metric name into files in a specified directory, buffering writes for efficiency and optionally rotating files on timers. It considers schema compatibility—metrics with incompatible schemas are dropped—ensuring consistency. Apache Hudi can then consume these Parquet files via tools like DeltaStreamer or Spark jobs, enabling transactional ingestion, time-travel queries, and upserts on your time series data.

Configuration

DNS

[[inputs.dns_query]]
  servers = ["8.8.8.8"]

  # network = "udp"

  # domains = ["."]

  # record_type = "A"

  # port = 53

  # timeout = "2s"

  # include_fields = []
  

Apache Hudi

[[outputs.parquet]]
  ## Directory to write parquet files in. If a file already exists the output
  ## will attempt to continue using the existing file.
  directory = "/var/lib/telegraf/hudi_metrics"

  ## File rotation interval (default is no rotation)
  # rotation_interval = "1h"

  ## Buffer size before writing (default is 1000 metrics)
  # buffer_size = 1000

  ## Optional: compression codec (snappy, gzip, etc.)
  # compression_codec = "snappy"

  ## When grouping metrics, each metric name goes to its own file
  ## If a metric’s schema doesn’t match the existing schema, it will be dropped

Input and output integration examples

DNS

1. Monitor DNS Performance for Multiple Servers: By deploying the DNS plugin, a user can simultaneously monitor the performance of different DNS servers, such as Google DNS and Cloudflare DNS, by specifying them in the servers array. This scenario enables comparisons of response times and reliability across different DNS providers, assisting in selecting the best option based on empirical data.

2. Analyze Query Times for High-Traffic Domains: Integrate the plugin to measure response times specifically for high-traffic domains relevant to an organization’s operations, such as internal services or customer-facing sites. By focusing on performance metrics for these domains, organizations can proactively address latency issues, ensuring service reliability and improving user experiences.

3. Alerting on DNS Timeouts: Utilize the plugin in combination with alerting systems to notify administrators whenever a DNS query exceeds a defined timeout threshold. This setup can help in proactive troubleshooting of networking issues or server misconfigurations, fostering a rapid response to potential downtime scenarios.

4. Gather Historical Data for Performance Trends: Use the plugin to collect historical data on DNS query times over extended periods. This data can be used to analyze trends and patterns in DNS performance, enabling better capacity planning, identifying periodic issues, and justifying infrastructure upgrades or changes to DNS architectures.

Apache Hudi

1. Transactional Lakehouse Metrics: Buffer and write Web service metrics as Parquet files for DeltaStreamer to ingest into Hudi, enabling upserts, ACID compliance, and time-travel on historical performance data.

2. Edge Device Batch Analytics: Telegraf running on IoT gateways writes metrics to Parquet locally, where periodic Spark jobs ingest them into Hudi for long-term analytics and traceability.

3. Schema-Enforced Abnormal Metric Handling: Use Parquet plugin’s strict schema-dropping behavior to prevent malformed or unexpected metric changes. Hudi ingestion then guarantees consistent schema and data quality in downstream datasets.

4. Data Platform Integration: Store Telegraf metrics as Parquet files in an S3/ADLS landing zone. Hudi’s Spark-based ingestion pipeline then loads them into a unified, queryable lakehouse with business events and logs.

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