vectordotdev/vector

Features

• Telemetry Processing Engines - Provides a high-performance observability data pipeline that collects, transforms, and routes logs, metrics, and traces across distributed infrastructure.
• Observability Pipelines - Provides a high-performance engine for collecting, transforming, and routing logs, metrics, and traces across distributed infrastructure.
• Data Buffering - Provides disk-backed event buffering to ensure data durability and prevent loss during network outages or service restarts.
• Data Stream Aggregators - Centralizes data from multiple agents to scrub sensitive information, reformat logs, and sample streams before forwarding.
• Schema-Agnostic Ingestion - Provides a schema-agnostic data pipeline that processes and enriches telemetry without requiring rigid, predefined schemas.
• Flow Control - Manages data velocity and volume through sampling, rate-limiting, and backpressure-aware flow control.
• Modular Pipeline Architectures - Connects data sources, transformations, and sinks as independent, swappable components within a directed graph.
• Declarative Configuration Engines - Parses and validates pipeline definitions from structured files to orchestrate component wiring and runtime behavior.
• Reliable Data Delivery - Maintains high availability by distributing processing across nodes while using disk-backed buffers to prevent data loss.
• Telemetry and Log Collectors - Provides a lightweight agent for capturing system metrics and application logs at the source for reliable delivery.
• Log Aggregation - Centralizes the ingestion, parsing, and normalization of unstructured log data from diverse sources into structured formats.
• Log Ingestion - Provides vendor-agnostic log ingestion pipelines for collecting telemetry from diverse infrastructure sources.
• Metric Data Ingestion - Collects logs, metrics, and traces from diverse infrastructure and cloud services to centralize data.
• Stream Routing - Ingests logs and metrics from various sources and forwards them to multiple destinations to build reliable data pipelines.
• Monitoring and Observability - Provides a high-performance engine for collecting, transforming, and routing logs, metrics, and traces across distributed infrastructure.
• Observability Data Aggregators - Runs as a centralized service to receive, process, and route high-volume observability data from multiple agents to various downstream storage or analysis systems.
• Observability Data Aggregators - Operates as a lightweight edge agent or a centralized high-throughput aggregator to handle diverse observability data collection topologies.
• Telemetry Agents - Runs as a lightweight process on individual hosts to collect and forward local logs and metrics to a centralized processing layer.
• Data Filtering - Drops or retains logs, metrics, and traces based on user-defined conditions to reduce noise.
• Data Pipeline Configurations - Defines observability data collection, transformation, and routing rules using modular YAML, TOML, or JSON configuration files.
• Persistent Log Buffers - Stores data on local disk to ensure durability and prevent loss during network outages or service restarts.
• Data Transformation - Enables dynamic field manipulation, arithmetic, and data transformation during event processing.
• Backpressure Controllers - Propagates flow control signals upstream to throttle ingestion when downstream buffers reach capacity.
• Reliable Event Delivery Systems - Maintains data integrity through end-to-end acknowledgements, automatic health checks, and configurable retry policies.
• Trace Data Ingestion - Collects logs, metrics, and traces using standard transport layers like gRPC or HTTP.
• Observability Pipelines - Aggregates observability data into a central layer to separate concerns and improve control without requiring external message queuing systems.
• Logging and Telemetry - Collects logs and metrics from diverse sources including files, network protocols, and cloud services to centralize system telemetry.
• Observability Data Filters - Collects and processes telemetry from multiple upstream sources on dedicated nodes to centralize management.
• Telemetry Aggregators - Receives observability streams from multiple upstream sources to perform cross-host analysis, enrichment, and routing.
• Observability Daemons - Runs as a background process on a host to gather and route observability data from all local services to downstream destinations.
• Prometheus-Based Metric Exporters - Serves collected metrics over HTTP for retrieval, supporting custom authentication and TLS encryption.
• Cardinality Limiters - Protects downstream storage by limiting unique tag combinations on incoming metric events.
• Telemetry Aggregation Pipelines - Deploys dedicated nodes to receive, process, and route data from multiple upstream sources for optimized performance.
• Delivery Acknowledgements - Confirms that data has been successfully received by destination sinks by tracking acknowledgements through the pipeline.
• Data Enrichment - Appends contextual information to events by querying external metadata sources during transit.
• Data Format Converters - Transforms events between different formats to ensure compatibility across downstream systems.
• Kafka Connectors - Consumes log, metric, and trace data from Kafka topics using configurable consumer groups and secure authentication.
• Exactly-Once Processing Semantics - Provides exactly-once processing semantics to ensure data integrity during retries and system failures.
• Stream Processing - Manages high-volume observability data streams with buffering, backpressure, and reliable delivery guarantees.
• Data Reducers - Collapses multiple events into single records or summarizes metrics to reduce data volume.
• ClickHouse Sinks - Streams log data into ClickHouse tables with support for batching, compression, and dynamic table selection.
• Log Aggregators - Combines fragmented log lines into single events based on patterns to ensure data integrity.
• Log Aggregator Query Metrics - Extracts data from log events to generate various metric types including counters, gauges, histograms, and summaries.
• Kafka Stream Exporters - Routes processed logs and metrics into message topics for real-time streaming.
• Backpressure Management - Regulates data flow between pipeline nodes to prevent processing bottlenecks.
• S3 Data Sinks - Writes logs, metrics, and traces to S3 buckets with support for batching, storage classes, and encryption.
• Containerized Observability - Deploys and manages observability agents within containerized environments to track service performance.
• DaemonSet Deployment Controllers - Automates the deployment of observability agents across Kubernetes clusters using the DaemonSet pattern for consistent log and metric collection.
• Telemetry Routing Patterns - Routes observability data directly from client nodes to downstream services to minimize infrastructure complexity and overhead.
• Sidecar Containers - Deploys alongside an individual service to ingest and forward its specific logs and metrics.
• Asynchronous Message Passing - Uses internal channels to decouple data ingestion from processing for high-throughput performance.
• HTTP Servers - Exposes HTTP server endpoints to receive logs, metrics, and traces from external clients.
• Message Delivery Guarantees - Ensures reliable event delivery by utilizing disk-based buffers and retry logic.
• Logical Operations - Provides conditional logic and branching statements to control data processing flow within the pipeline.
• Delivery Guarantees - Ensures reliable data delivery through configurable retry policies, backoff strategies, and acknowledgements.
• Ingestion Checkpointers - Save the current file read position to disk to ensure data ingestion resumes accurately after restarts without creating duplicates.
• Container - Captures and structures standard output and error streams from running containers.
• Ingestion Filters - Provides granular control over log collection by applying label, field, and path-based selectors to ignore specific pods, containers, or files.
• Batch Metric Ingestion - Exposes HTTP endpoints to receive metrics pushed from clients with optional aggregation.
• System Metrics Collection - Gathers utilization data for CPU, memory, disk, and network resources from local hosts or containers.
• Metric and Performance Monitors - Scrapes and normalizes performance metrics from databases, servers, and cloud services to maintain visibility into system health.
• Telemetry Normalizers - Normalizes incoming logs and metrics into structured formats to ensure compatibility across observability systems.
• Networking and Internet - High-performance observability data pipeline.
• Observability and Monitoring - Observability data pipeline for logs and metrics.
• Data Ingestion - Receives log, metric, and trace data from cloud delivery streams via HTTP endpoints.
• Data Parsing - Extracts fields from various log formats including Syslog, key-value pairs, and custom patterns to normalize incoming data streams.
• OTLP Exporters - Transmits logs, metrics, and traces to external systems using the standard OpenTelemetry Protocol.
• Syslog Ingestion - Collects and parses Syslog streams using standard protocols like RFC 5424 and RFC 3164.
• Log Ingestion APIs - Processes log streams from application routers by listening for incoming connections.
• Pulsar Ingestion - Consumes log, metric, and trace events from Pulsar messaging topics.
• Processing Pipelines - Adjusts data processing configurations in real time without requiring a service restart to apply changes to the active pipeline.
• Observability Transformation Languages - Provides a dedicated language for real-time modification, filtering, and enrichment of logs and metrics.
• WebHDFS Data Sinks - Transmits observability logs to WebHDFS clusters with support for batching, compression, and pathing.
• PostgreSQL Data Sinks - Writes logs, metrics, and traces into PostgreSQL databases using configurable batching and delivery guarantees.
• Observability Data Routers - Directs incoming logs, metrics, or traces to specific destinations based on prioritized user-defined rules.
• Event Buffering Configurations - Stores events in memory buffers and emits them as batches based on defined conditions.
• Concurrent Data Pipelines - Distributes incoming data across parallel workers to automatically adapt throughput to varying volumes.
• S3 Log Ingestion - Retrieves logs, metrics, and traces from cloud storage buckets with support for various formats.
• Container Metric Collectors - Gathers performance statistics from containerized environments to provide visibility into task resource utilization.
• Data Throughput Optimizers - Optimizes delivery performance through event batching, buffering, and dynamic concurrency adjustments.
• Deployment Agents - Runs data collection and processing directly on individual nodes to capture local metrics and logs at the source.
• Edge Deployment Tools - Installs lightweight collectors on individual hosts to capture local logs and metrics at the source.
• MQTT Messaging Integrations - Subscribes to MQTT topics to stream incoming messages into the observability pipeline.
• NATS Message Ingestion - Consumes logs, metrics, and traces from NATS messaging subjects with support for persistence and security.
• Websocket Connection Managers - Connects to remote servers via WebSockets to receive log events with custom framing.
• Load Balancers - Distributes data across multiple destination nodes with health monitoring and circuit breaking to maintain stability.
• PubSub Messaging Systems - Streams logs from messaging subscriptions with configurable authentication and acknowledgement.
• Automatic Redaction - Automatically detects and masks sensitive information like PII from data streams during processing.
• Event Aggregation Services - Consolidates multiple metric events over time windows to maintain accuracy while reducing volume.
• Event Logging - Groups individual log events into summary events using shared fields and custom merge strategies.
• Retry Policies - Implements automatic retries with exponential backoff to maintain availability during network interruptions.
• Dead Letter Queues - Redirects failed or malformed data to a secondary pipeline or backup destination for inspection and later recovery.
• Health Checks - Performs connectivity checks during initialization to ensure the destination is reachable and ready to accept incoming data streams.
• Pipeline Health Monitors - Provides real-time visibility into data flow and throughput using command-line tools to verify ingestion and processing health.
• Log Transformation Pipelines - Applies custom parsing and decoding logic to incoming event envelopes before pipeline processing.
• Collection Checkpoints - Tracks read positions in log journals to ensure data continuity and prevent duplication after service restarts.
• Message Queue Integration - Polls messages from cloud queues and processes them as logs, metrics, or traces.
• Database Performance Metrics - Queries database instances at regular intervals to gather performance and operational statistics.
• Prometheus Exporters - Sends collected metric data to remote write endpoints using the Prometheus standard.
• Metadata Event Processors - Supports path-based addressing to target and manipulate specific fields within nested event objects.
• Trace Sampling - Reduces data volume by dropping a configurable percentage of incoming logs and traces.
• Data Aggregators - Centralizes data collection from multiple edge sources to perform heavy processing, filtering, and routing.
• Cloud Metadata Enrichment - Automatically attaches container-specific context to collected data to improve searchability and correlation.
• GCS Exporters - Streams observability events into Google Cloud Storage buckets with support for custom object naming and access control.
• Unified Pipeline Architectures - Combines edge collection and centralized aggregation into a single cohesive infrastructure.
• Data Forwarders - Relays logs, metrics, and traces between instances to enable distributed data pipeline architectures.
• Data Validation - Checks event values against expected criteria during processing and triggers errors or alerts when data fails to meet defined requirements.
• Data Import and Export - Transmits logs, metrics, and traces to remote servers via HTTP with support for authentication and encryption.
• MQTT Integrations - Synchronizes observability logs with message brokers using MQTT protocols.
• Data Processing - Performs local data aggregation to reduce network traffic and compute load before forwarding to global nodes.
• Response Decoders - Parses raw byte streams into structured events using configurable framing and decoding logic.
• Streaming Data Uploaders - Publishes logs to streaming services with support for custom partitioning and batching.
• Elasticsearch Exporters - Streams logs and metrics to search clusters using bulk indexing and secure authentication.
• Database Log Exporters - Streams log and metric data into database tables with support for custom authentication and request batching.
• Azure Blob Storage Exporters - Exports processed observability data to Azure Blob Storage with support for batching and compression.
• In-Memory Data Stores - Buffers events in memory or on disk to handle traffic spikes and prevent data loss during downstream outages.
• Metric Stream Exporters - Streams processed metric data into database instances using gRPC with support for authentication and compression.
• Database Log Exporters - Streams log data into databases by staging batches in object storage.
• Parallel Data Transformation - Executes stateless data transformations in parallel to maximize throughput.
• Redis Clients - Publishes logs, metrics, and traces to Redis using channels and lists.
• Configuration Hot-Reloading - Supports dynamic configuration reloads without service interruptions by watching files for changes.
• Request Retries - Retries failed network requests using configurable backoff strategies to ensure reliable data delivery.
• Request Throughput Management - Limits outgoing request rates to downstream services to prevent overloading and ensure stable data delivery.
• Automated Rollout Managers - Coordinates configuration updates and software version rollouts across distributed infrastructure to ensure consistent observability operations.
• Distributed Deployment Patterns - Supports flexible deployment patterns ranging from edge-based agents to centralized high-volume aggregation clusters.
• High Availability Systems - Deploys multiple instances in a redundant configuration to maintain data pipeline uptime and prevent service interruptions.
• Load Balancing Strategies - Deploys multiple instances behind a load balancer to ensure continuous operation and automatic failover if individual nodes become unreachable.
• Process Scaling - Distributes data processing loads across multiple nodes to handle varying volumes of logs and metrics.
• Traffic Management - Manages outbound traffic by controlling concurrency, rate limits, and retry policies for downstream services.
• Delivery Confirmations - Monitors data lifecycle and confirms successful delivery or persistence before acknowledging receipt.
• Message Broker Consumers - Integrates with existing pub-sub infrastructure to ingest data streams from distributed message brokers.
• Pulsar Topic Exporters - Streams logs and metrics to messaging topics with support for batching and dynamic routing.
• Processing Pipelines - Allows aborting transformation logic to halt event processing when specific conditions or errors occur.
• Cloud PubSub Integrations - Streams observability events to cloud messaging topics with support for authentication and reliable delivery.
• Cloud Credential Management - Supports multiple identity verification methods including service accounts and API keys for cloud services.
• API Request Authentication - Validates authorization tokens on incoming requests to restrict data ingestion to authorized clients.
• Metadata Attachments - Associates user-defined key-value pairs with events to facilitate better organization and retrieval.
• Compile-Time Feature Flags - Excludes unused components during the build process to minimize binary size and attack surface.
• CloudWatch Log Exporting - Publishes log events to cloud logging services with automatic log group and stream management.
• Cloud Monitor Log Exporters - Transmits log events to monitoring workspaces with support for credential-based authentication.
• Cloud Operations Log Exporters - Transmits log data to cloud monitoring services with automatic severity mapping.
• Monitoring Service Log Exporters - Transmits log events to monitoring endpoints with support for batching and compression.
• Contextual Logging - Automatically augments incoming log events with contextual information and custom query parameters.
• Observability Platform Exporters - Transmits log events to observability platforms using authenticated API requests.
• Initialization Health Checks - Performs a health check upon initialization to ensure the destination service is reachable and ready to accept incoming data streams.
• Journal - Collects log data from the systemd journal and augments events with contextual metadata.
• Monitoring Instance Log Exporters - Transmits log event data to monitoring instances with support for custom indexing.
• Windows Event - Captures log data from native Windows Event Log channels for processing.
• Log Stream Processing - Parses incoming byte streams into structured events by applying framing and decoding rules to identify individual log entries.
• Log Mergers - Reconstructs log entries split by container runtime size limits or custom patterns to ensure complete and readable output.
• InfluxDB Metric Ingestors - Routes metric data to database instances with support for custom tagging and authentication.
• Database Log Ingestors - Transmits log event data to databases by mapping fields into line protocol formats.
• Metric Relabeling - Modifies or updates metadata associated with metric events to ensure consistent labeling across observability pipelines.
• Metric Tagging Utilities - Associates metrics with key-value pairs to enable filtering, grouping, and dimensional analysis of time-series data.
• Pull-Based Metric Scraping - Polls HTTP endpoints to collect metrics with support for custom authentication and secure transport.
• Metrics Collection - Scrapes performance data from server status modules and normalizes the output into structured metrics.
• Metrics Exporters - Sends metric data to cloud monitoring services with support for custom namespaces and batching.
• Authenticated Metric Exporters - Transmits observability metric data to external monitoring services using configurable authentication.
• Observability Platform Log Exporting - Streams unstructured log events to security analytics platforms like Chronicle.
• Telemetry Collection and Aggregation - Exposes internal performance and health metrics as a data stream for processing alongside standard pipeline traffic.
• Trace Exporters - Sends collected trace data to monitoring endpoints using batching and delivery acknowledgements.
• Deployment Topologies - Operates as a standalone agent, aggregator, or sidecar to match diverse infrastructure requirements.
• Pipeline Batching - Groups events into batches based on size or time thresholds to optimize network throughput.
• Standard Input Ingestion - Supports reading raw byte streams from standard input for parsing into structured observability events.
• Data Encoding and Serialization - Controls how events are grouped, compressed, and serialized into specific formats before transmission to downstream services.
• Metric Timestamps - Records the precise time of occurrence for each metric event to maintain accurate temporal ordering and historical analysis.
• Quantile Digest Aggregators - Converts distribution data into histograms or summaries using custom bucket boundaries and quantile definitions.
• Event Buffering - Aggregates events into batches and buffers them locally to improve throughput and isolate downstream performance.
• Message Reconstructors - Reconstructs log entries split by container runtime size limits into complete messages.
• Multi-Layered Buffer Topologies - Routes overflow events between memory and disk storage layers to balance speed and durability.
• Stream-Based Data Pipelines - Buffers observability data through a message queue to provide high durability and elasticity for large-scale data streams.
• External Service Integrations - Provides connectors for transmitting logs and metrics to remote APIs and external services.
• Base64 Decoders - Converts various encoded formats back into raw data for pipeline processing.
• Cloud Storage - Routes logs and metrics to cloud storage using configurable batching and compression.
• System Package Manager Installations - Supports installation and updates via native operating system package managers to simplify maintenance across diverse environments.
• Load Shedding Systems - Discards incoming events when buffers reach capacity to maintain system stability during high-load scenarios.
• Multi-Zone Deployment Tools - Distributes instances across multiple data centers to maintain service availability and handle traffic if an entire zone fails.
• Message Routing - Routes logs to message brokers by configuring connection URIs and exchange settings.
• Concurrency Controllers - Automatically scales connection counts and applies backpressure to prevent service overload during downstream outages.
• Buffer and Cache Management - Buffers and batches outgoing events to improve network efficiency and system performance.
• Telemetry Transformation Scripts - Executes custom Lua logic to modify, filter, or enrich log and metric data streams during transit.
• Telemetry Redaction - Normalizes, redacts, and securely transmits sensitive telemetry data to security analytics platforms.
• End-to-End Encryption Protocols - Enforces end-to-end TLS encryption for all incoming and outgoing data connections.
• Connection Management - Manages credentials and identity verification for secure connections to external systems.
• Network Connection Security - Secures incoming data streams with TLS encryption and IP-based network access restrictions.
• Secrets Management - Integrates with external secret management systems to securely handle sensitive configuration values.
• Secure Connection Handlers - Manages TLS encryption, certificate validation, and secure connection establishment for network traffic.
• Secure Network Communication - Ensures secure data transmission using TLS and custom protocol configurations for remote peers.
• Log Transmission Security - Protects incoming log streams by enforcing authentication and TLS encryption.
• TLS Transfer Security - Protects data in transit by encrypting outbound traffic with TLS and configurable security settings.
• Transport Layer Security - Secures outbound communications using transport layer security protocols.
• YAML Configuration Files - Checks configuration files for syntax errors and logical inconsistencies before deployment to ensure pipeline correctness.
• Concurrency Optimizers - Optimizes throughput by automatically adjusting request concurrency based on downstream service feedback.
• Data Normalization Layers - Transforms event structures to match vendor standards and protocols for compatibility.
• Data Transformation Pipelines - Manages runtime failures during data processing by assigning default values, coalescing multiple expressions, or aborting execution to ensure pipeline stability.
• Concurrency Adjusters - Adjusts concurrent HTTP requests based on real-time feedback to maximize throughput and prevent overload.
• Diagnostic Logging - Streams internal diagnostic logs to configured destinations to provide visibility into pipeline health.
• Log Routing - Provides configurable log routing to external endpoints with support for authentication and batching.
• Dead Letter Routers - Forwards events that fail processing to a secondary output for analysis or debugging instead of discarding them silently.
• Authenticated Metric Ingestion - Enforces secure metric ingestion using IP allowlisting and TLS encryption to protect data during transit.
• Process Lifecycle Managers - Responds to system signals to perform graceful shutdowns and manage the lifecycle of the observability process.
• Route Performance Metrics - Transmits performance metrics to cloud monitoring services using authenticated service accounts.
• Read State Trackers - Tracks read positions in log channels to ensure data continuity after restarts.
• Metric Deduplicators - Eliminates redundant logs or metrics by comparing incoming data against a configurable cache of recent events.

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