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description= Strategies for rolling Vector out to production environments.;
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rollout vector documentation docs guides components download blog support observability pipelines toggle dark mode search x 𝕏 github chat rss updates open navbar dropdown menu docs guides components download blog support observability pipelines x 𝕏 github chat rss updates docs home introduction concepts setup quickstart installation package managers apt dpkg helm homebrew msi nix pacman rpm yum platforms docker kubernetes operating systems amazon linux arch linux centos debian macos nixos raspbian rhel ubuntu windows manual from archives from source vector installer deployment roles topologies going to production reference architectures aggregator architecture agent architecture unified architecture architecting hardening high availability rollout sizing and capacity planning architecture data model log events metric events pipeline model runtime model buffering model concurrency model adaptive concurrency guarantees end to end acknowledgements administration management monitoring validating optimization pgo reference vector remap language functions errors examples expressions configuration sources amqp apache metrics aws ecs metrics aws kinesis firehose aws s3 aws sqs datadog agent demo logs dnstap docker logs eventstoredb metrics exec file file descriptor fluent gcp pubsub heroku logplex host metrics http client http server internal logs internal metrics journald kafka kubernetes logs logstash mongodb metrics mqtt nats nginx metrics okta opentelemetry postgresql metrics prometheus pushgateway prometheus remote write prometheus scrape pulsar redis socket splunk hec static metrics statsd stdin syslog vector websocket windows event log transforms remap with vrl aggregate aws ec2 metadata dedupe delay exclusive route filter incremental to absolute log to metric lua metric to log reduce route sample tag cardinality limit throttle trace to log window sinks amqp appsignal aws cloudwatch logs aws cloudwatch metrics aws kinesis data firehose logs aws kinesis streams logs aws s3 aws sns aws sqs axiom azure blob storage azure logs ingestion azure monitor logs blackhole clickhouse console databend databricks zerobus datadog events datadog logs datadog metrics datadog traces doris elasticsearch file gcp chronicle unstructured gcp cloud monitoring formerly stackdriver gcp cloud storage gcp stackdriver gcp pubsub greptimedb logs greptimedb metrics honeycomb http humio logs humio metrics influxdb logs influxdb metrics kafka keep loki mezmo formerly logdna mqtt nats new relic opentelemetry papertrail postgres prometheus exporter prometheus remote write pulsar redis sematext logs sematext metrics socket splunk hec logs splunk hec metrics statsd vector webhdfs websocket websocket server global options pipeline components tls configuration api unit tests schema template syntax secrets cli environment variables api glossary meta security releases versioning vector docs home setup going to production rollout rollout strategies for rolling vector out to production environments this document assumes you ve already decided on an architecture if you have not please read the architecting document rollout strategy vector is designed to be deployed anywhere in your infrastructure making it possible to follow the best practice of deploying within your network boundaries and avoiding a single point of failure our rollout strategy takes advantage of this through incremental adoption minimizing scope allowing for safe failure and without the anxiety of switching to a new system incremental adoption if you follow our networking recommendations then you should deploy vector within each network partition i e cluster or vpc one at a time this makes it easy to adopt vector incrementally allowing for sustainable progress while building out your observability pipeline minimize scope minimizing scope is the easiest way to ensure success the scope should be minimized to one network partition and one system at a time then follow the rollout plan for each unit of scope safe failure while setting up vector it should be allowed to fail without consequence to your business this means vector should be operating on a redundant stream of data without disrupting your current production workflows this allows you to gain confidence in vector before your business depends on it avoid the big switch finally by the time you cut over to vector you should have confidence in its ability it should already be operating in a production capacity over a sustained period removing any doubt that vector will perform reliably in your production environment rollout plan follow this plan for each deployment within each network partition 1 deploy a black hole identify a single network partition i e cluster or vpc for your vector deployment deploy vector with a single blackhole sink the default within that network partition size and scale vector s instances for the conservative estimate of 10 mib s vcpu 2 stream a copy of your data stream a copy of data from your agents to vector verify that vector receives data via the vector top and vector tap commands 3 configure vector process your data according to your use cases integrate vector with your downstream systems verify data within each destination 4 size scale soak enable autoscaling so that vector can scale down appropriately soak vector for at least 24 hours and monitor performance to ensure production readiness 5 cutover safely shut down your agents to allow them to drain data without loss reconfigure your agents to send data to vector exclusively start your agents repeat for each network partition support for easy setup and maintenance of this architecture consider datadog observability pipelines which comes with support on this page close docs slideover panel docs home introduction concepts setup quickstart installation package managers apt dpkg helm homebrew msi nix pacman rpm yum platforms docker kubernetes operating systems amazon linux arch linux centos debian macos nixos raspbian rhel ubuntu windows manual from archives from source vector installer deployment roles topologies going to production reference architectures aggregator architecture agent architecture unified architecture architecting hardening high availability rollout sizing and capacity planning architecture data model log events metric events pipeline model runtime model buffering model concurrency model adaptive concurrency guarantees end to end acknowledgements administration management monitoring validating optimization pgo reference vector remap language functions errors examples expressions configuration sources amqp apache metrics aws ecs metrics aws kinesis firehose aws s3 aws sqs datadog agent demo logs dnstap docker logs eventstoredb metrics exec file file descriptor fluent gcp pubsub heroku logplex host metrics http client http server internal logs internal metrics journald kafka kubernetes logs logstash mongodb metrics mqtt nats nginx metrics okta opentelemetry postgresql metrics prometheus pushgateway prometheus remote write prometheus scrape pulsar redis socket splunk hec static metrics statsd stdin syslog vector websocket windows event log transforms remap with vrl aggregate aws ec2 metadata dedupe delay exclusive route filter incremental to absolute log to metric lua metric to log reduce route sample tag cardinality limit throttle trace to log window sinks amqp appsignal aws cloudwatch logs aws cloudwatch metrics aws kinesis data firehose logs aws kinesis streams logs aws s3 aws sns aws sqs axiom azure blob storage azure logs ingestion azure monitor logs blackhole clickhouse console databend databricks zerobus datadog events datadog logs datadog metrics datadog traces doris elasticsearch file gcp chronicle unstructured gcp cloud monitoring formerly stackdriver gcp cloud storage gcp stackdriver gcp pubsub greptimedb logs greptimedb metrics honeycomb http humio logs humio metrics influxdb logs influxdb metrics kafka keep loki mezmo formerly logdna mqtt nats new relic opentelemetry papertrail postgres prometheus exporter prometheus remote write pulsar redis sematext logs sematext metrics socket splunk hec logs splunk hec metrics statsd vector webhdfs websocket websocket server global options pipeline components tls configuration api unit tests schema template syntax secrets cli environment variables api glossary meta security releases versioning sign up to receive emails on the latest vector content and new releases thank you for joining our updates newsletter previous high availability next sizing and capacity planning vector site footer about vector contact us the team privacy cookies components sources transforms sinks setup installation deployment configuration administration going to prod community github x chat download releases x 𝕏 github chat rss 2026 datadog inc all rights reserved sidebar
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