Nginx and Apache Hudi Integration

Input and output integration overview

The Nginx plugin for Telegraf is designed to collect status metrics from Nginx web servers, providing real-time insights into server operation metrics.

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

Integration details

Nginx

This plugin gathers status metrics from Nginx. It utilizes the ngx_http_stub_status_module to collect basic metrics related to the server’s performance. The plugin provides valuable insights into active connections, requests handled, and the current state of various metrics. This real-time data is essential for monitoring web server performance and ensuring optimal operations. The configuration allows users to specify the URL for the Nginx status endpoint, set timeouts, and configure TLS settings if necessary.

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

Nginx

[[inputs.nginx]]
  ## An array of Nginx stub_status URI to gather stats.
  urls = ["http://localhost/server_status"]

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## HTTP response timeout (default: 5s)
  response_timeout = "5s"

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

Nginx

1. Web Performance Monitoring: Use the Nginx plugin to gather performance metrics from various Nginx servers across your infrastructure. By visualizing these metrics in real-time dashboards, teams can track performance trends, identify bottlenecks, and enhance the user experience on their web applications. Implementing such monitoring allows businesses to proactively address performance issues before they impact end-users.

2. Load Balancer Monitoring: Integrate this plugin with your load balancers to track the performance of backend Nginx servers. By collecting statistics like ‘active connections’ and ‘requests handled’, your operations team can ensure that traffic is flowing optimally and that no single server is overwhelmed. This proactive approach to load balancing prevents service downtime and enhances user experience.

3. Automated Alerting Systems: Combine the Nginx plugin with alerting services to automatically notify your team when a server’s metrics exceed predefined thresholds. For instance, if the number of active connections is too high, the system can trigger alerts so that corrective actions can be taken immediately, thus maintaining service quality and reliability.

4. Historical Data Analysis: Store the metrics collected by the Nginx plugin in a time-series database to analyze historical performance trends. This analysis can uncover periods of high traffic or poor performance, allowing for data-driven decisions about infrastructure scaling and optimization. By understanding past trends, organizations can better prepare for future demands.

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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