Bindplane Blueprints for Elasticsearch: Production-Ready NGINX Log Pipelines for Kibana

We've just released new and easy-to-use Bindplane blueprints designed specifically for Elasticsearch as a destination. These blueprints empower teams to quickly transform raw events such as those from NGINX access and error logs into clean, structured, and ECS-compliant data optimized for high-performance visualization in Kibana.

These blueprints are pre-built, reusable processor bundles that handle the critical work of cleaning and enriching telemetry at the edge, ensuring your Elasticsearch cluster stays lean and your dashboards stay fast.

The Challenge: Managing NGINX Log Volume and Noise

NGINX is the backbone of modern web infrastructure, but at scale, it's a significant source of "data noise." High-traffic platforms often generate terabytes of logs monthly, much of which provides little analytical value. Common pain points include:

• Storage Bloat: Health checks and static asset requests (images, JS, CSS) can account for up to 70% of log volume.
• Compliance Risks: Raw logs often inadvertently capture sensitive data like API keys, PII, or session tokens in headers and URLs.
• Dashboard Complexity: Without standard mapping, you're forced to build custom visualizations for every environment instead of using Kibana's built-in web server dashboards.

What's Included in the NGINX Blueprint for Elasticsearch

The NGINX blueprint for Elasticsearch features a production-tested processor chain designed to handle these operational challenges automatically.

1. Structural Parsing and Normalization

The chain begins by first applying a regex parser to the NGINX Combined Log Format (01ES-NX-10). It extracts structured fields like Client IP, Method, and Status Code. Immediately following, the Timestamp Parser (01ES-NX-15) converts NGINX's unique time format into a standard OTLP timestamp.

• Why it matters: This ensures your histograms and time-series analysis in Kibana are accurate to the millisecond of the original event.

2. Cost-Saving Filters

The blueprint uses two dedicated exclusion processors: Filter Health Checks (01ES-NX-20) and Filter Static Assets (01ES-NX-25). These drop logs from probes like kube-probe or ELB-HealthChecker and high-volume requests for .js, .css, and image files.

• Why it matters: These logs typically lack analytical value. Filtering them at the edge can reduce your ingestion volume by over 50% before the data even leaves your network.

3. Intelligent Data Reduction

To optimize storage further, the Sample Success Responses (01ES-NX-50) processor applies a 50% sampling rate to 2xx responses. For error bursts, the Deduplicate Error Responses (01ES-NX-55) processor collapses identical 4xx/5xx errors within a 30-second window into a single record with an error_count.

• Why it matters: You preserve statistical accuracy and error visibility while preventing log storms from overwhelming your indices during an outage.

4. Compliance and Enrichment

Safety is built in with the Mask Sensitive Data (01ES-NX-35) processor, which redacts credit cards, emails, and authorization keys. Finally, the Add ECS Fields (01ES-NX-40) processor injects Elastic Common Schema attributes like event.dataset and service.name.

• Why it matters: Redaction ensures you stay compliant with privacy regulations (GDPR/PCI), while ECS mapping unlocks Kibana's built-in web server dashboards instantly.

5. Performance Optimization

The final stages include the Batch Telemetry (01ES-NX-60) processor, which groups logs into batches of 5,000, and the Delete Empty Fields (01ES-NX-99) processor, which strips null values.

• Why it matters: Batching improves Elasticsearch bulk indexing throughput by 10-20x, while removing empty fields reduces the physical footprint of every document on disk.

Real-World Results

For a typical web application, the data reduction for this out-of-the-box (OOTB) solution is dramatic, as seen below.

Get Started Today

Ready to optimize your NGINX telemetry?

1. Import the Blueprint into your local Bindplane library
2. Create a Pipeline: In the Bindplane UI, click on add processor → add processor bundle → elastic-nginx-blueprint
3. Deploy: Route the data to your Elasticsearch destination using our drag-and-drop routing if not already complete, and then kick off a rollout
4. Visualize: Open Kibana and start using the built-in NGINX dashboards immediately

From there, you can easily customize sampling rates or add custom redaction rules to fit your specific environment.

What's Next?

We're continuing to expand the Bindplane blueprint library to help teams build scalable, vendor-neutral telemetry pipelines. Have a specific use case or a blueprint request? Let us know in the Bindplane Slack Community!