Vector Articles

Vector Throttle Transform: Rate Limit Log Events

Vector's rate-limiting transform is surprisingly effective at preventing downstream systems from being overwhelmed by log volume, but most people misund.

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Vector TLS Encryption: Secure Sources and Sinks

Vector's TLS encryption is a powerful way to secure data in transit, but understanding how to configure it correctly for both sources and sinks can be t.

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Vector Topology Visualization: Map Your Pipeline

The most surprising thing about vector topology visualization is that it’s not about drawing pretty pictures of your data, but about making the invisibl.

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Vector Unit Tests: Validate Transforms Before Deployment

Vector unit tests are your first line of defense against broken data transformations, catching issues before they impact production systems.

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Vector Upgrade Guide: Breaking Changes and Migration

The most surprising thing about migrating to a new version of Vector is how often the core problem isn't the new features, but the subtle deprecations a.

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Vector VRL Guide: Remap Language for Log Transforms

You're trying to understand how to transform log data using Vector's VRL language, specifically when you need to remap specific values.

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Vector Backpressure: Handle Slow Sinks Gracefully

Vector's backpressure mechanism is designed to prevent downstream components from being overwhelmed by upstream data flow, ensuring stability and preven.

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Vector Batch Compression: gzip and zstd for Log Sinks

The most surprising thing about compressing vectors for log sinks is that zstd can often be faster than gzip even though it achieves higher compression .

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Vector vs Fluent Bit vs Logstash: Performance Benchmarks

The most surprising thing about comparing Vector, Fluent Bit, and Logstash is that the "best" choice often depends less on raw throughput and more on yo.

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Vector Buffer Configuration: Disk and Memory Buffers

Vector databases use memory and disk buffers to manage the storage and retrieval of vector embeddings, and misconfigurations here are a common performan.

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Vector Component Errors: Diagnose Dropped Events

The core of this issue is that your application's event producer, likely a Kafka producer or a similar message queue client, is failing to send events t.

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Vector Configuration: Sources, Transforms, and Sinks

Vector's configuration is all about defining how data flows through it, from where it starts to where it ends up, and what happens in between.

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Profile Vector CPU and Memory Usage

The most surprising thing about profiling CPU and memory usage is that the "bottleneck" you perceive is almost never the actual bottleneck.

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Vector Datadog Agent Source: Scrape Metrics

Vector Datadog Agent Source: Scrape Metrics. Datadog's Agent scrapes metrics from a source when it's configured to do so. Let's watch it happen

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Vector Deduplicate Transform: Remove Duplicate Events

The most surprising thing about vector deduplication is that it doesn't actually remove events from your logs; it just stops sending duplicates.

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Vector Disk Buffer: Persist Events Across Restarts

The most surprising thing about vector disk buffers is that they don't actually guarantee event persistence across restarts in the way most people assum.

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Vector End-to-End Acknowledgements: No Data Loss

Vector's end-to-end acknowledgements are the system's way of proving to you that your data made it where it was supposed to go, no matter how many hops .

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Vector Enrichment Tables: GeoIP and CSV Lookups

Vector Enrichment Tables let you inject contextual data into your network traffic logs, turning raw IP addresses and ports into human-readable informati.

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Vector Filter Transform: VRL Conditions for Routing

The Vector Filter Transform VFT doesn't just filter; it fundamentally redefines how data flows through your system by allowing you to dynamically change.

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Vector GraphQL API: Manage and Query Topology

GraphQL's a powerful tool for querying data, but when you're dealing with highly connected, graph-like data, it can feel like you're trying to untangle .

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Vector Helm Chart: Values Reference for Kubernetes

Vector Helm Chart: Values Reference for Kubernetes — The most surprising thing about Helm values.yaml files is that they're not just configuration; they'.

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Vector High Throughput Tuning: Millions of Events per Second

The single most surprising thing about achieving millions of vector events per second isn't about raw CPU power, but about how efficiently you can avoid.

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Vector HTTP Source: Ingest Webhooks and Custom Events

Vector's HTTP source is how you get data into Vector from external systems that can't directly push to Vector's other sources, like Kafka or files.

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Vector Kafka Source and Sink: Consume and Produce

The most surprising thing about Kafka's vector source and sink is that they don't actually do anything with the data themselves; they're just thin wrapp.

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Vector Kubernetes Filter: Enrich Logs with Pod Metadata

Vector's Kubernetes filter can inject pod metadata into your logs, turning a stream of cryptic events into rich, searchable insights.

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Vector Kubernetes Log Collection: DaemonSet Deployment

Deploying Vector as a DaemonSet for Kubernetes log collection means you're about to have a robust, efficient way to grab logs from every node in your cl.

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Vector Log to Metric: Convert Events to Prometheus Metrics

Vector, the observability data pipeline, can transform your logs into Prometheus metrics, giving you the power to monitor events that aren't explicitly .

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Vector Lua Transform: Custom Processing Logic

Vector's Lua transform lets you inject arbitrary processing logic into your data pipelines, but its real power comes from how it cleverly sidesteps trad.

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Vector Multiline Aggregation: Combine Stack Traces

Vector's multiline aggregation is how it pieces together fragmented log events, like a detective reconstructing a shattered vase, to form a coherent nar.

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Vector Internal Metrics: Monitor the Pipeline Itself

Vector's internal metrics are the system's way of telling you what's happening inside itself, rather than just what data it's processing.

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Vector OpenTelemetry Source: Ingest OTLP Traces and Metrics

OpenTelemetry data ingestion isn't just about getting data in; it's about transforming raw telemetry into a structured, queryable format that unlocks ob.

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Vector ClickHouse Output: Ship Logs for Analytics

ClickHouse's output for logging is surprisingly flexible, acting less like a rigid database and more like a powerful, queryable log archive.

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Vector Datadog Output: Send Logs and Metrics

Vector Datadog Output: Send Logs and Metrics — practical guide covering vector setup, configuration, and troubleshooting with real-world examples.

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Vector Elasticsearch Output: Index Logs at Scale

Elasticsearch is actually terrible at storing logs at scale if you're not careful. Here's how you can make it work, by treating it less like a database .

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Vector Loki Output: Forward Logs to Grafana Loki

Grafana Loki, the log aggregation system, can ingest logs from various sources, but a common challenge is efficiently forwarding logs from applications .

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Vector Prometheus Remote Write: Push Metrics from Vector

Vector's remotewrite sink lets you push Prometheus metrics from anywhere into a Prometheus Remote Write endpoint, like VictoriaMetrics, Thanos, or Corte.

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Vector S3, GCS, Azure Blob Output: Archive Logs

Vector's awss3 and azureblob sinks can archive logs to cloud object storage, but they can also be configured to write to Google Cloud Storage GCS using .

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Vector Splunk HEC Output: Send Events to Splunk

The most surprising thing about Splunk's HTTP Event Collector HEC is that it's essentially a stateless web server that can handle massive amounts of dat.

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Vector Parse Transforms: JSON, Syslog, Grok Parsing

Parsing arbitrary text logs into structured data is a fundamental problem in observability, and vector’s transform system offers a flexible way to do it.

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Vector Reduce Transform: Group and Aggregate Log Events

The most surprising thing about vector reduce transform is that it doesn't actually "reduce" data in the way you might think; it groups and aggregates.

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Vector Hot Reload: Reload Config Without Restart

Vector Hot Reload: Reload Config Without Restart — practical guide covering vector setup, configuration, and troubleshooting with real-world examples.

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Vector Route Transform: Split Events to Multiple Sinks

Vector Route Transform: Split Events to Multiple Sinks — practical guide covering vector setup, configuration, and troubleshooting with real-world examp...

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Vector Sample Transform: Reduce Log Volume by Sampling

The fundamental trick of Vector Sample Transform is that it throws away most of your logs, but makes it look like it didn't.

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Vector Schema Registry: Protobuf and Avro Decoding

The Vector Schema Registry is crucial for maintaining data consistency and enabling efficient data retrieval in vector databases.

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Vector Secrets Management: Environment Variables and Files

Vector's secrets management is surprisingly flexible, allowing you to inject sensitive values into your configuration without embedding them directly, b.

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Vector StatsD and DogStatsD Source: Collect App Metrics

Vector StatsD and DogStatsD sources let you collect application metrics, but they're often misunderstood as just simple UDP listeners.

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Vector Tap: Inspect Live Events in Pipelines

Vector Tap is how you peek inside your Vector pipelines, letting you see events as they flow through your processing stages in real-time.

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Vector Aggregator and Agent Topology: Log Routing

The most surprising thing about log routing with Vector is that it's not a fixed path; it's a dynamic, self-healing mesh.

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Vector AWS Kinesis and Firehose Source: Ingest Streams

Kinesis Data Streams acts as a highly scalable, durable, and ordered streaming data service, while Firehose is a managed service for delivering real-tim.

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