ConfigGenerator

Vector Config Generator

Generate production-ready Vector pipeline configurations in TOML. Configure Sources, Transforms, Sinks, and VRL expressions for high-performance log and metric routing.

Output:A ready-to-use configuration file for Vector Config with best practices applied.

Vector Pipeline

Global Options

Sources

Transforms

Sinks

vector.toml
1data_dir = "/var/lib/vector"
2

3[log_schema]
4timestamp_key = "timestamp"
5level_key = "level"
6message_key = "message"
7service_key = "service"
8

9[sources.app_logs]
10type = "file"
11include = ["/var/log/app/*.log"]
12read_from = "beginning"
13

14[transforms.parse_json]
15type = "remap"
16inputs = ["app_logs"]
17source = '''
18. = parse_json!(.message)
19.timestamp = parse_timestamp!(.timestamp, format: "%Y-%m-%dT%H:%M:%S%.fZ")
20.environment = "production"
21'''
22

23[sinks.console_output]
24type = "console"
25inputs = ["parse_json"]
26target = "stdout"
27[sinks.console_output.encoding]
28codec = "json"
Not generated yet

What is this tool?

The Vector Config Generator helps you build production-ready Vector pipeline configurations in TOML format. Vector is a high-performance observability data router written in Rust, capable of collecting, transforming, and routing logs, metrics, and traces with sub-millisecond latency.

Unlike legacy tools like Fluentd or Logstash, Vector operates as a unified data pipeline for all telemetry signals. Its Vector Remap Language (VRL) provides a safety-focused transformation language with compile-time error detection, deterministic error handling, and zero runtime panics. This generator produces syntactically correct vector.toml files with Sources, Transforms, and Sinks fully configured.

How to Use This Tool

1. Define Sources: Vector sources ingest data from various origins — files (file), stdin, exec commands, journald, Kafka topics, Docker logs, Kubernetes logs, S3 buckets, GCP Pub/Sub, HTTP endpoints, syslog, TCP/UDP sockets, and Vector's own internal metrics.

2. Add Transforms: Process and enrich data in-flight using transforms. Use remap for VRL-based field mapping, filter to drop unwanted events, route for conditional splitting, reduce for aggregation, dedupe to remove duplicates, sample for rate limiting, geoip for IP enrichment, and various parsers (parse_json, parse_regex).

3. Configure Sinks: Route processed data to destinations — Elasticsearch, Loki, Kafka, S3, Datadog, Splunk, Prometheus, ClickHouse, HTTP endpoints, Google Cloud Storage, AWS CloudWatch Logs, or chain to another Vector instance. Each sink supports encoding, batching, and healthchecks.

4. Tune Global Options: Set data_dir for persistent state and log_schema for default field names across the pipeline.

Best Practices

  • Always set a dedicated data_dir for Vector's internal state to survive restarts without data loss.
  • Use VRL's try() and assert!() patterns to validate data shapes at compile time rather than runtime.
  • Configure healthcheck.enabled = true on all sinks to detect unreachable backends early.
  • Use the Vector agent/aggregator split pattern: lightweight agents on each node forward to centralized aggregators.
  • Enable batch configuration (batch.max_events, batch.timeout_secs) on sinks to optimize throughput.

Common Mistakes

  • Forgetting to set data_dir, which causes Vector to fail when sinks need persistent buffer state.
  • Using .message as a raw string instead of parsing JSON with parse_json!(), leading to unstructured data in downstream sinks.
  • Not configuring acknowledgments, which can cause data loss when the downstream sink is temporarily unavailable.
  • Over-configuring transform inputs without considering the DAG execution order, creating circular dependencies.

Security Notes

  • Use environment variables for sensitive values like API keys and passwords — Vector supports ${ENV_VAR} syntax in TOML.
  • Enable TLS on all network-facing sources (TCP, HTTP, syslog) and sinks to protect data in transit.
  • Run Vector as a non-root user with minimal file system permissions on the data_dir directory.
  • Restrict HTTP source endpoints to internal networks or use authentication plugins for public-facing endpoints.

Production Tips

  • Deploy Vector as a DaemonSet in Kubernetes with the kubernetes_logs source for automatic pod log collection.
  • Use the aggregator pattern with Vector sources to fan-in logs from multiple agents before routing to backends.
  • Monitor Vector's own health via the internal_metrics source exposed through a Prometheus sink.
  • Tune buffer.max_size and buffer.type (memory vs disk) based on your throughput requirements and acceptable latency.
  • Use VRL's redact!() function to sanitize sensitive fields like passwords and tokens before shipping logs.

Frequently Asked Questions

What is Vector and how does it compare to Fluentd or Logstash?
Vector is a high-performance observability data router written in Rust. Unlike Fluentd (Ruby) or Logstash (JRuby), Vector offers sub-millisecond latency, zero-copy architecture, and a safety-focused transformation language (VRL). It handles logs, metrics, and traces in a single binary with significantly lower resource consumption.
What is VRL (Vector Remap Language)?
VRL is Vector's domain-specific language for transforming observability data. It features compile-time error detection, deterministic error handling with try() and assert!(), and operators for parsing, merging, truncating, and enriching log events. VRL prevents runtime panics by catching all error paths at configuration time.
What configuration format does Vector use?
Vector uses TOML (Tom's Obvious Minimal Language) for configuration. The config file defines a data_dir, Sources (inputs), Transforms (processors), and Sinks (outputs) as named components in a pipeline DAG.
What are Vector's main source types?
Vector supports file, stdin, exec, journald, kafka, demo_logs, docker_logs, aws_s3, gcp_pubsub, http, syslog, tcp, udp, and vector (internal metrics) sources. Each source has specific configuration options for connection details, authentication, and data format.
How do I deploy Vector in Kubernetes?
Deploy Vector as a DaemonSet using the kubernetes_logs source for automatic pod log collection, or as a Deployment using the aggregator pattern with Vector sources for centralized log processing. Helm charts are available from the Vector project for both patterns.
Can Vector handle high throughput without data loss?
Yes. Vector uses disk-based buffers, acknowledgments, and end-to-end flow control. Configure buffer.type = 'disk' with adequate buffer.max_size, enable acknowledgments on sinks, and use the retry mechanism to handle transient failures without dropping events.

How We Keep Your Configs Safe & Valid

Built-in Error Checking

Every file is checked against official rules. We catch missing fields and bad syntax. YAML indentation errors are flagged right away. Kubernetes, Terraform, and Docker specs are all covered. API versions and labels are verified too. You get valid output every time you generate.

100% Private & Local

All tools run in your browser only. Your API keys never leave your machine. We do not use any tracking scripts. No data is sent to any server. Passwords and secrets stay on your device. Crypto operations use the Web Crypto API. Your privacy is fully protected at all times.

Secure Settings by Default

Configs use safe defaults out of the box. Containers run as non-root users. Root filesystems are set to read-only. Dangerous Linux capabilities are dropped. Network policies limit pod-to-pod traffic. TLS 1.3 is enabled for web servers. Security headers are added where needed.

Ready for CI/CD & Git

Output files are ready for your Git repo. Use them with ArgoCD, Flux, or GitHub Actions. Files use clear formatting and comments. Code review is easy for your team. Indentation and key order are consistent. Test in staging before going to production. Every file is clean and well-structured.

Infrastructure as Code

Store configs in Git alongside your code. Terraform modules include typed variables. Backend configs support remote state locking. Outputs work across multiple modules. Ansible playbooks use clear task steps. Chef and Puppet configs are also supported. Every file works with version control tools.

Monitoring & Tracing

Set up Prometheus with auto-discovery rules. Create Grafana dashboards with template variables. Add alerting rules with severity labels. Use OpenTelemetry for trace collection. Forward logs to Loki or Elasticsearch. Connect to Jaeger or Tempo for tracing. Monitor metrics, logs, and traces together.

Container & Docker Safety

Dockerfiles use multi-stage builds for small images. Base images are pinned to exact versions. Dev files are excluded from final images. Health checks are added for orchestrator use. Containers switch to non-root users. Docker Compose uses named volumes and networks. Resource limits are set in deploy configs.

Multiple Output Formats

Export as YAML, JSON, HCL, or TOML. Kubernetes uses YAML with proper separators. Terraform uses HCL with correct escaping. JSON output has consistent indentation. Copy to clipboard with one click. Preview output with syntax highlighting. Line numbers help you review quickly.

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