Vector – Data/ML Engineer Blog

Vector for Data Engineers: Building Fast, Cheap, and Observable Monitoring Pipelines

Introduction: When Your Logs Become a Tax

You’ve probably lived this:
Kubernetes cluster, microservices everywhere, logs going to three different places “temporarily,” metrics scraped by Prometheus, traces piped into some APM. Costs are creeping up, agents are multiplying, and every new team adds another shipper.

Vector exists to kill that chaos.

Vector is a high-performance observability pipeline built in Rust that collects, transforms, and routes your logs, metrics, and traces from anywhere to anywhere — with one tool and one config.(Vector)

If you’re a data or platform engineer, think of Vector as Kafka Connect + dbt, but for telemetry: sources → transforms → sinks, all in one binary.

What Is Vector, Really?

At its core, Vector is an observability data router and ETL engine:

Open source, written in Rust → low CPU, low memory, high throughput.(GitHub)
End-to-end: runs as an agent (daemon/sidecar on each node) or as an aggregator service.(GitHub)
Unified telemetry: handles logs, metrics, and (increasingly) traces.(GitHub)
Config-driven: a pipeline is just three blocks:
- sources – where data comes from (files, journald, Docker, Kubernetes, Kafka, OTLP, HTTP, etc.)
- transforms – parse, enrich, sample, aggregate using VRL (Vector Remap Language)
- sinks – where data goes (S3, Loki, Elasticsearch, Kafka, Datadog, Prometheus remote_write, ClickHouse, etc.)(Vector)

It’s not a dashboard, not a time-series DB, not an APM.
It’s the plumbing between your infrastructure and your monitoring tools.

Vector Architecture for Monitoring & Observability

Imagine a simple diagram:

Nodes/Pods → Vector agents → Vector aggregators → multiple backends (Loki, S3, Prometheus, Datadog…)

Vector fits into your monitoring stack like this:

1. Deployment Modes

Agent mode
- Runs on every node / pod (DaemonSet in K8s, sidecar with apps).
- Collects local logs/metrics and sends them to an aggregator or directly to vendors.(Vector)
Aggregator mode
- Central Vector cluster receiving telemetry from agents.
- Ideal place for heavy transforms, log → metric conversion, sampling, routing.

Many teams run: Agent Vector → Aggregator Vector → Vendors.

2. Core Pipeline Model

Each Vector instance runs a config like:

sources – file tail, syslog, Kubernetes, HTTP, OTLP, Kafka…
transforms – remap, log_to_metric, filter, sample, route, aggregate, etc.(Vector)
sinks – Loki, Elasticsearch, S3, Datadog, Splunk, Prometheus, Kafka, NATS, etc.

Conceptually:

[nginx logs] ---> [Vector agent] ---> [Vector aggregator]
                                     |--> [Loki]
                                     |--> [S3]
                                     '--> [Prometheus (metrics)]

3. Vector Remap Language (VRL)

VRL is Vector’s DSL for transforming events:

Parse log lines.
Extract fields.
Normalize schemas.
Drop PII.
Compute derived metrics.

Think of VRL as dbt for observability events — small, composable transformations.(Vector)

Hands-On Example: Logs to Metrics with Vector

Let’s do something actually useful:

Goal:
Ship application logs from Kubernetes →

full logs to Loki,

aggregated error_rate metric to Prometheus (for alerting).

Sample Vector Config (TOML)

# vector.toml

[data_source]

type = “file” include = [“/var/log/app/*.log”] ignore_older = 86400 # seconds

[transforms.parse_json]

type = “remap” inputs = [“data_source”] source = ”’ . = parse_json!(string!(.message)) ”’

[transforms.errors_only]

type = “filter” inputs = [“parse_json”] condition = ‘.level == “error”‘

[transforms.error_rate]

type = “log_to_metric” inputs = [“errors_only”] [[transforms.error_rate.metrics]] type = “counter” field = “error_count” name = “app_errors_total” namespace = “demo” tags.app = “{{ .app | default(“unknown”) }}” tags.env = “{{ .env | default(“dev”) }}” increment_by_value = true

[sinks.loki]
type   = "loki"
inputs = ["parse_json"]
endpoint = "http://loki:3100"
encoding.codec = "json"

[sinks.prom_metrics]

type = “prometheus_exporter” inputs = [“error_rate”] address = “0.0.0.0:9598”

What’s happening:

Source: tails app log files.
Transform 1: parses JSON log messages into structured fields.
Transform 2: filters only "level": "error" logs.
Transform 3: converts those logs into a Prometheus counter metric using log_to_metric.(Vector)
Sink 1: ships structured logs to Loki.
Sink 2: exposes metrics on /metrics for Prometheus to scrape.

This one Vector config gives you:

Full log context (in Loki),
Lightweight metrics for alerts (in Prometheus),
No additional agent or sidecar.

Monitoring Vector Itself (Yes, You Should)

Vector is part of your monitoring stack, so you must monitor it.

Vector exposes:

internal_logs source – Vector’s own structured logs.
internal_metrics source – metrics about buffers, queue sizes, errors, throughput, etc.(Vector)

You can wire them back into your observability stack:

[sources.vector_metrics]
type = "internal_metrics"

[sinks.vector_metrics_prom]

type = “prometheus_exporter” inputs = [“vector_metrics”] address = “0.0.0.0:9599”

Then:

Scrape :9599/metrics with Prometheus.
Use a prebuilt Vector monitoring dashboard in Grafana for health and performance.(Grafana Labs)

Key metrics to watch:

Events in / out per sink
Buffered events / queue depth
Retry counts / failed sends
CPU & memory (via node exporter, not Vector itself)

If Vector chokes, your whole observability story is blind. Treat it as a first-class citizen.

Vector vs. Other Log Shippers (Data Engineer View)

Dimension	Vector	Logstash	Fluent Bit
Language	Rust	JRuby/Java	C
Telemetry types	Logs, metrics, traces (increasingly)(GitHub)	Primarily logs	Primarily logs/metrics
Role	General observability pipeline (agent + agg)	Log pipeline, often ELK-bound	Lightweight log forwarder
Transform language	VRL (Vector Remap Language)	Grok, Ruby	Lua / built-in
Footprint	Small static binary	Heavy JVM	Very light
Vendor coupling	Multi-backend, vendor-agnostic	Often ELK-centric	Often tied to Fluentd stack

For data engineers, the big win with Vector is:

Treating telemetry as data with schemas, transforms, and routing, not just “strings to ship somewhere”.

Best Practices for Using Vector in Monitoring

1. Treat Observability Pipelines as Code

Store Vector config in Git.
Use environments: vector-dev.toml, vector-prod.toml.
CI to validate config, maybe run smoke tests.

2. Normalize Event Schemas Early

Use VRL to standardize fields: service, env, region, trace_id, span_id.
Map different log formats into a unified schema, so downstream queries & SLOs are sane.

3. Convert Logs → Metrics Strategically

Don’t alert on raw logs if you can alert on metrics:
- error rate per service
- latency buckets
- saturation metrics

Vector’s log_to_metric transform is perfect for this, and several orgs use it to massively cut storage and improve alert speed.(RapDev)

4. Sample & Filter Aggressively at the Edge

Drop useless noise close to the source:
- debug logs in prod,
- health check spam,
- trace spans with no value.
Use transforms to:
- remove sensitive fields (GDPR/PII),
- lower cardinality (truncate URLs, normalize user IDs).

5. Separate “Cold” and “Hot” Destinations

Hot: Loki / Elasticsearch / Datadog for recent data (7–30 days).
Cold: S3 / object storage as cheap archive.
Vector can dual-write to both with different encodings and sampling rates.

6. Watch Backpressure & Failure Modes

Common pitfalls:

Pitfall: Single Vector aggregator becomes a bottleneck.
Fix: Scale aggregators horizontally behind Kafka / NATS or a load balancer.
Pitfall: Unbounded buffers causing memory blowups.
Fix: Configure buffer limits & backpressure, monitor internal_metrics.
Pitfall: Blind “copy everything” configs.
Fix: Design pipelines with intent: what do you actually need for SLOs, debugging, auditing?

When Vector Is and Is Not the Right Tool

Use Vector when:

You want one agent instead of 5 different vendors’ agents.(Datadog Open Source Hub)
You need vendor-agnostic routing (migrate from ELK → Datadog → ClickHouse with minimal change).(Datadog Open Source Hub)
You want centralized observability ETL logic in a single place (scrubbing, enrichment, sampling).

Maybe don’t use Vector as:

A query engine (that’s Prometheus/Loki/ClickHouse, etc.).
A dashboard/alerting system (that’s Grafana, Datadog, etc.).
A general-purpose data lake ETL for business data (Spark, dbt, Snowflake are better fits).

Think of Vector as a specialized streaming ETL tool for telemetry only.

Conclusion & Key Takeaways

If you’re wrestling with monitoring tools, Vector gives you:

Control over what telemetry you collect and where it goes.
Performance from a Rust-based, single-binary pipeline.
Simplicity by consolidating agents and centralizing observability ETL.

For data engineers, the mental model is familiar:

Telemetry = another data domain.
You model it, route it, and transform it with the same discipline as your product data.

TL;DR Takeaways

Vector is an observability pipeline, not a dashboard or DB.
It unifies logs, metrics, and traces with one config and one agent.
VRL + log_to_metric lets you turn noisy logs into useful metrics.
Monitor Vector itself using internal_metrics and Grafana dashboards.
Use Vector to decouple your infra from any single monitoring vendor.

Image Prompt (for DALL·E / Midjourney)

“A clean, modern observability pipeline diagram showing Vector as a central Rust-powered router between microservices and multiple monitoring backends (Loki, Prometheus, S3, Datadog), minimalistic, dark background, high-contrast colors, 3D isometric style, sharp and professional.”

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