BeforeDo MonitorCloser: The Ultimate Guide to Setup and OptimizationBeforeDo MonitorCloser is a monitoring utility designed to give teams tighter control over system observability, reduce noise, and streamline incident response. This guide covers everything from unboxing and installation to advanced configuration, performance tuning, and real-world optimization strategies so you can get the most reliable, actionable telemetry with minimal overhead.
What MonitorCloser does and why it matters
MonitorCloser acts as a centralized filter and enrichment layer between raw telemetry sources (metrics, logs, traces, and alerts) and your downstream observability tooling. Its core capabilities include:
- Data filtering and deduplication to reduce alert noise
- Enrichment with contextual metadata (service, region, owner)
- Dynamic routing to different backends based on policies
- Thresholding and adaptive suppression to avoid alert storms
- Lightweight local buffering for short-term network outages
Why this matters: noisy, unprioritized alerts slow responders, inflate costs, and mask real issues. MonitorCloser helps teams focus on meaningful incidents and reduces wasted time and infrastructure spend.
Key concepts and terms
- Collector: the MonitorCloser agent that runs close to telemetry sources.
- Policy: a rule that decides what to keep, drop, enrich, or route.
- Enrichment store: a local or remote repository of metadata used to annotate telemetry.
- Backends: target observability systems (e.g., Prometheus, Grafana, Elastic, Splunk, Datadog).
- Suppression window: time frame during which repeated signals can be collapsed.
- Sampling: reducing data volume by keeping a subset of events or traces.
System requirements and compatibility
Minimum recommended environment for the Collector:
- OS: Linux (Ubuntu 18.04+), macOS 10.15+, Windows Server 2019+
- CPU: 2 cores (4 cores recommended for medium workloads)
- RAM: 512 MB minimum (2 GB recommended)
- Disk: 500 MB for binaries/logs; scale with local buffering needs
- Network: outbound TLS-capable connections to backends; configurable proxy support
Compatible with standard telemetry formats: OpenTelemetry (OTLP), syslog, Prometheus exposition format, Fluent Logs, and common vendor APIs.
Installation
Option A — Package manager (recommended for servers)
- Add the official repository and GPG key.
- Install via apt/yum:
- Debian/Ubuntu:
sudo apt update && sudo apt install beforedo-monitorcloser - RHEL/CentOS:
sudo yum install beforedo-monitorcloser
- Debian/Ubuntu:
Option B — Docker
Pull and run the official image:
docker run -d --name monitorcloser -v /var/log:/var/log:ro -v /etc/monitorcloser:/etc/monitorcloser -p 4317:4317 beforedo/monitorcloser:latest Option C — Binary
Download the release archive, extract, and place the binary in /usr/local/bin/, then create a systemd service for automatic start.
Basic configuration
MonitorCloser uses a YAML configuration with sections for inputs, processors (filters/enrichers), and outputs. A minimal example:
service: name: monitorcloser telemetry: metrics: true logs: true inputs: - name: otlp protocol: grpc endpoint: 0.0.0.0:4317 processors: - name: dedupe window: 30s - name: enrich source: /etc/monitorcloser/enrichment.yml outputs: - name: datadog api_key: ${DATADOG_API_KEY} endpoint: https://api.datadoghq.com Key fields:
- inputs: where data is collected (ports, protocols).
- processors: the pipeline stages (sampling, dedupe, enrich).
- outputs: destination backends with auth and endpoint config.
Enrichment strategies
Add contextual metadata to make alerts actionable:
- Static tags: environment, team, service owner.
- Host-level metadata: instance ID, AZ/region, Kubernetes pod labels.
- Dynamic lookups: query a central CMDB or metadata service to add ownership and runbook links.
Example enrichment entry:
enrichment: - match: service:payment add: team: billing runbook: https://wiki.example.com/runbooks/payment-pager Policy design: filtering, sampling, and suppression
Design policies to reduce noise but preserve signal:
- Filter by source and severity: drop debug-level logs from prod unless traced.
- Adaptive sampling for traces: preserve 100% of errors, sample success traces at 1–5%.
- Suppression windows: group repeated alerts (e.g., same error + same host) for a 5–15 minute window, then escalate if persistent.
- Rate limits: cap events per second per source to prevent floods.
Example suppression rule:
suppression: - match: error.code:500 window: 10m collapse_by: [host, error.signature] max_alerts: 3 Routing and multi-backend strategies
Route telemetry based on type, team, or sensitivity:
- High-severity alerts -> PagerDuty + Slack + primary APM
- Low-severity logs -> Cold storage (S3/Blob) + cheaper analytics backend
- PII-containing data -> Mask/encrypt and route to secure backend only
Benefits: cost control, compliance, and focused escalation.
Security and compliance
- Enable TLS for all outbound connections and mTLS for service-to-service.
- Use secrets managers (Vault, AWS Secrets Manager) for API keys.
- Apply field-level redaction for sensitive fields (PII) before forwarding.
- Audit logs: Keep an immutable log of policy changes and critical pipeline events.
Observability and self-monitoring
Monitor the Collector itself:
- Expose health and metrics endpoints (Prometheus) for CPU, memory, processed events, dropped events, and pipeline latency.
- Track policy hit rates: which filters/suppressions drop the most data.
- Alerts for backpressure, queue saturation, or high drop rates.
Example Prometheus metrics to watch:
- monitorcloser_pipeline_latency_seconds
- monitorcloser_events_processed_total
- monitorcloser_events_dropped_total
Performance tuning
- Batch and compress outbound payloads to reduce network overhead.
- Adjust processor concurrency: more workers for high-throughput environments.
- Tune local buffer size: larger buffers for intermittent network issues, smaller for lower disk usage.
- Use sampling and deduplication early in the pipeline to avoid wasted processing.
Suggested starting knobs:
- batch_size: 1000 events
- max_concurrency: CPU_cores * 2
- buffer_size: 10000 events or 1 GB disk
Troubleshooting common issues
- No data reaching backend: check network, API keys, TLS errors, and output health metrics.
- High drop rate: inspect policy hit metrics and suppression rules; lower sampling or increase rate limits.
- Memory spikes: reduce max_concurrency or enable backpressure; inspect large enrichment lookups.
- Duplicate alerts: verify dedupe processor configuration and time windows.
Real-world examples and templates
- Small SaaS (cost-focused)
- Sample success traces at 2%, keep 100% errors, route to Datadog, store logs in S3 after 7 days.
- Simple suppression: 10m collapse by host+error.
- Large enterprise (compliance + reliability)
- Full enrichment from CMDB, strict PII redaction, route PII-free telemetry to public analytics and send restricted data to internal SIEM.
- Multi-region routing to nearest regional backend, with cross-region failover.
Maintenance and upgrades
- Run the collector as a managed service with rolling upgrades.
- Use canary deployments when changing policies — test on a subset of services first.
- Regularly review suppression and sampling rules (monthly) against incident postmortems.
Checklist for a successful rollout
- [ ] Inventory telemetry sources and owners.
- [ ] Define enrichment mapping (service → owner, runbooks).
- [ ] Create baseline filters and sampling rules.
- [ ] Configure secure backend credentials and TLS.
- [ ] Deploy to a small canary group.
- [ ] Monitor collector metrics and adjust.
- [ ] Gradually expand and review monthly.
Appendix: Example config snippets
Sampling processor:
processors: - name: sampling default_rate: 0.02 preserve: - condition: "status>=500" rate: 1.0 Deduplication processor:
processors: - name: dedupe window: 30s key_by: [error.signature, host] Suppression with escalation:
suppression: - match: error.signature: "DB_CONN_TIMEOUT" window: 15m collapse_by: [service, region] escalate_after: 3 BeforeDo MonitorCloser is most effective when policies are tailored to your environment and continuously refined. Start small, measure impact (reduced alerts, lower costs, faster MTTR), and iterate—policy changes are the most powerful lever to balance signal and noise.
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