{"id":1537,"date":"2026-02-15T09:13:33","date_gmt":"2026-02-15T09:13:33","guid":{"rendered":"https:\/\/noopsschool.com\/blog\/publish-subscribe\/"},"modified":"2026-02-15T09:13:33","modified_gmt":"2026-02-15T09:13:33","slug":"publish-subscribe","status":"publish","type":"post","link":"https:\/\/noopsschool.com\/blog\/publish-subscribe\/","title":{"rendered":"What is Publish subscribe? Meaning, Architecture, Examples, Use Cases, and How to Measure It (2026 Guide)"},"content":{"rendered":"\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Quick Definition (30\u201360 words)<\/h2>\n\n\n\n<p>Publish subscribe is a messaging pattern where senders publish events to topics and receivers subscribe to topics to receive events asynchronously. Analogy: publisher drops letters into labeled mailboxes and subscribers pick up letters from mailboxes they subscribe to. Formal: decoupled asynchronous event distribution via topics and subscriptions with optional delivery guarantees.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">What is Publish subscribe?<\/h2>\n\n\n\n<p>Publish subscribe (pub\/sub) is a messaging pattern and architectural approach for decoupling producers and consumers of data through named channels (topics). Producers publish messages without knowledge of consumers; consumers subscribe to topics to receive messages independently.<\/p>\n\n\n\n<p>What it is NOT:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Not a simple queueing primitive where a single consumer consumes and deletes messages.<\/li>\n<li>Not a request-response RPC system.<\/li>\n<li>Not automatically a database or persistent storage system (persistence varies).<\/li>\n<\/ul>\n\n\n\n<p>Key properties and constraints:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Decoupling: temporal and spatial separation between producers and consumers.<\/li>\n<li>Asynchrony: publishers do not wait for consumers.<\/li>\n<li>Routing abstractions: topics, subscriptions, filters.<\/li>\n<li>Delivery semantics: at-most-once, at-least-once, exactly-once (varies by implementation).<\/li>\n<li>Ordering guarantees: none, per-topic, per-partition, or per-key depending on system.<\/li>\n<li>Persistence\/retention: durable storage versus ephemeral in-memory forwarding.<\/li>\n<li>Scalability: horizontal scaling of brokers, partitions, or shards.<\/li>\n<li>Security: authentication, authorization, encryption, and tenant isolation concerns.<\/li>\n<\/ul>\n\n\n\n<p>Where it fits in modern cloud\/SRE workflows:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Event-driven microservices and service mesh integrations.<\/li>\n<li>Observability pipelines: metrics, traces, logs, and events transport.<\/li>\n<li>CI\/CD event triggering and automation workflows.<\/li>\n<li>Data integration and streaming ETL.<\/li>\n<li>ML feature pipelines and model serving events.<\/li>\n<li>Incident response automation and alert enrichment.<\/li>\n<li>Edge-to-cloud telemetry aggregation.<\/li>\n<\/ul>\n\n\n\n<p>Diagram description (text-only visualization):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Publishers -&gt; TopicA, TopicB (logical channels) -&gt; Broker cluster with partitions -&gt; Subscriptions (push or pull) -&gt; Consumers grouped by consumer-id -&gt; Downstream processors, storage, or sinks. Control plane manages topics, ACLs, and retention; monitoring pipeline observes lag and delivery metrics.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Publish subscribe in one sentence<\/h3>\n\n\n\n<p>A messaging pattern that decouples producers and consumers by publishing messages to topics that multiple independent subscribers can consume asynchronously.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Publish subscribe vs related terms (TABLE REQUIRED)<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Term<\/th>\n<th>How it differs from Publish subscribe<\/th>\n<th>Common confusion<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>T1<\/td>\n<td>Message Queue<\/td>\n<td>One-to-one consumption semantics often removing messages on consume<\/td>\n<td>Often conflated with pubsub<\/td>\n<\/tr>\n<tr>\n<td>T2<\/td>\n<td>Event Stream<\/td>\n<td>Continuous ordered flow with retention semantics<\/td>\n<td>See details below: T2<\/td>\n<\/tr>\n<tr>\n<td>T3<\/td>\n<td>RPC<\/td>\n<td>Synchronous request-response between client and server<\/td>\n<td>People expect sync behavior<\/td>\n<\/tr>\n<tr>\n<td>T4<\/td>\n<td>Notification<\/td>\n<td>Lightweight alerting, not full event semantics<\/td>\n<td>Notification can be pubsub use case<\/td>\n<\/tr>\n<tr>\n<td>T5<\/td>\n<td>Broker<\/td>\n<td>Component that routes messages, not the pattern itself<\/td>\n<td>People call pattern the broker<\/td>\n<\/tr>\n<tr>\n<td>T6<\/td>\n<td>Topic<\/td>\n<td>Logical channel, not a physical queue<\/td>\n<td>Topic vs queue confusion<\/td>\n<\/tr>\n<tr>\n<td>T7<\/td>\n<td>Stream Processing<\/td>\n<td>Processing over ordered data with stateful operators<\/td>\n<td>Overlaps with pubsub but different goals<\/td>\n<\/tr>\n<tr>\n<td>T8<\/td>\n<td>CDC<\/td>\n<td>Change data capture is a source of events, not delivery system<\/td>\n<td>CDC can feed pubsub<\/td>\n<\/tr>\n<tr>\n<td>T9<\/td>\n<td>Event Bus<\/td>\n<td>Larger architectural concept often implemented with pubsub<\/td>\n<td>Terminology varies by vendor<\/td>\n<\/tr>\n<tr>\n<td>T10<\/td>\n<td>Queue Group<\/td>\n<td>Consumer group pattern for load-sharing vs pubsub fan-out<\/td>\n<td>Groups make pubsub act like queue<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if any cell says \u201cSee details below\u201d)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>T2: Event streams provide durable ordered logs with retention and partitioning intended for stateful stream processing and replay. Pub\/sub can be implemented with transient or durable semantics; event streams emphasize replayability and ordering guarantees.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Why does Publish subscribe matter?<\/h2>\n\n\n\n<p>Business impact:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Revenue: Enables event-driven commerce flows (orders, inventory updates) that reduce latency and increase conversion.<\/li>\n<li>Trust: Reliable, auditable event delivery increases system correctness and customer trust.<\/li>\n<li>Risk: Misconfigured pub\/sub can cause data loss, duplicate processing, or cascading failures affecting SLAs.<\/li>\n<\/ul>\n\n\n\n<p>Engineering impact:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Incident reduction: Decoupling reduces blast radius when services fail and allows graceful degradation.<\/li>\n<li>Velocity: Teams can evolve producers and consumers independently, accelerating feature delivery.<\/li>\n<li>Complexity: Adds operational complexity around delivery semantics, retention, and scaling.<\/li>\n<\/ul>\n\n\n\n<p>SRE framing:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>SLIs\/SLOs: Typical SLIs include delivery success rate, end-to-end latency, and consumer lag.<\/li>\n<li>Error budgets: Use delivery failure rates and processing lateness to consume error budgets.<\/li>\n<li>Toil: Manage operational toil by automating topic lifecycle, provisioning, and schema governance.<\/li>\n<li>On-call: On-call rotations must include pub\/sub health playbooks and rapid mitigation steps.<\/li>\n<\/ul>\n\n\n\n<p>What breaks in production (realistic examples):<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Consumer backlog explosion due to traffic spike causing severe lag and memory pressure.<\/li>\n<li>Message duplication after retries combined with non-idempotent consumers causing inconsistent state.<\/li>\n<li>Partition imbalance causing one broker to become a hotspot and degrade throughput.<\/li>\n<li>ACL misconfiguration that exposes sensitive event streams or blocks critical subscribers.<\/li>\n<li>Retention misconfiguration that causes critical events to be deleted before consumption.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Where is Publish subscribe used? (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Layer\/Area<\/th>\n<th>How Publish subscribe appears<\/th>\n<th>Typical telemetry<\/th>\n<th>Common tools<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>L1<\/td>\n<td>Edge<\/td>\n<td>Device telemetry fan-out to cloud<\/td>\n<td>Ingest rate, drop rate, latency<\/td>\n<td>See details below: L1<\/td>\n<\/tr>\n<tr>\n<td>L2<\/td>\n<td>Network<\/td>\n<td>Network events and flow logs distributed<\/td>\n<td>Event rate, burstiness, loss<\/td>\n<td>Broker metrics<\/td>\n<\/tr>\n<tr>\n<td>L3<\/td>\n<td>Service<\/td>\n<td>Microservice event communication<\/td>\n<td>Publish latency, delivery success<\/td>\n<td>Kafka, NATS, Pulsar<\/td>\n<\/tr>\n<tr>\n<td>L4<\/td>\n<td>Application<\/td>\n<td>UI events and notifications<\/td>\n<td>End-to-end latency, errors<\/td>\n<td>Pub\/sub platforms<\/td>\n<\/tr>\n<tr>\n<td>L5<\/td>\n<td>Data<\/td>\n<td>CDC and streaming ETL<\/td>\n<td>Consumer lag, retention size<\/td>\n<td>See details below: L5<\/td>\n<\/tr>\n<tr>\n<td>L6<\/td>\n<td>Kubernetes<\/td>\n<td>K-native eventing and internal operators<\/td>\n<td>Pod restarts, delivery retries<\/td>\n<td>Knative, Keda<\/td>\n<\/tr>\n<tr>\n<td>L7<\/td>\n<td>Serverless<\/td>\n<td>Trigger functions on topic events<\/td>\n<td>Invocation rate, cold starts<\/td>\n<td>Managed pubsub services<\/td>\n<\/tr>\n<tr>\n<td>L8<\/td>\n<td>CI\/CD<\/td>\n<td>Pipeline triggers and status events<\/td>\n<td>Event rate, success rates<\/td>\n<td>Pipeline event bus<\/td>\n<\/tr>\n<tr>\n<td>L9<\/td>\n<td>Observability<\/td>\n<td>Telemetry routing and enrichment<\/td>\n<td>Ingest latency, loss<\/td>\n<td>Collector and broker<\/td>\n<\/tr>\n<tr>\n<td>L10<\/td>\n<td>Security<\/td>\n<td>Audit events, alerts bus<\/td>\n<td>Alert volumes, delivery times<\/td>\n<td>SIEM integration<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>L1: Edge scenarios include sensors and mobile devices publishing telemetry to topic gateways with batching and compression.<\/li>\n<li>L5: Data layer uses pubsub for CDC into data lakes and streaming processors; retention and schema evolution are important.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">When should you use Publish subscribe?<\/h2>\n\n\n\n<p>When necessary:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Asynchronous decoupling is required between producers and many consumers.<\/li>\n<li>You need fan-out delivery to multiple independent systems.<\/li>\n<li>You require event-driven automation, notifications, or integration across bounded contexts.<\/li>\n<li>Replaying events is necessary for debugging, audits, or rebuilding state (if durable).<\/li>\n<\/ul>\n\n\n\n<p>When optional:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Simple task queues with single consumer semantics.<\/li>\n<li>Direct synchronous RPC remains simpler and lower latency.<\/li>\n<li>Low-volume, simple workflows that don\u2019t need fan-out.<\/li>\n<\/ul>\n\n\n\n<p>When NOT to use \/ overuse it:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>For strict transactional workflows requiring global ACID without careful compensation.<\/li>\n<li>For tiny teams when added operational complexity is unwarranted.<\/li>\n<li>For point-to-point request-response interactions needing immediate return values.<\/li>\n<\/ul>\n\n\n\n<p>Decision checklist:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If you need decoupling and fan-out -&gt; use pub\/sub.<\/li>\n<li>If you need strict synchronous response and low latency -&gt; use RPC.<\/li>\n<li>If you need message ordering and replay -&gt; choose event stream with retention.<\/li>\n<li>If you need single-consumer task distribution -&gt; use message queue or consumer group pattern.<\/li>\n<\/ul>\n\n\n\n<p>Maturity ladder:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Beginner: Managed cloud pub\/sub with default settings, simple topics, push delivery.<\/li>\n<li>Intermediate: Partitioned topics, consumer groups, idempotent processing, metrics and basic SLIs.<\/li>\n<li>Advanced: Multi-region replication, exactly-once semantics, schema registry, automated provisioning, cost-aware retention policies, and cross-tenant security.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">How does Publish subscribe work?<\/h2>\n\n\n\n<p>Components and workflow:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Publishers: produce messages and publish to a topic. They authenticate and send payloads with metadata.<\/li>\n<li>Broker\/Server cluster: accepts messages, routes them to topic partitions, stores them (durable or ephemeral), and manages subscriptions.<\/li>\n<li>Topic: logical channel, can be partitioned for scale.<\/li>\n<li>Subscription: binds a consumer to a topic; can be push or pull, filtered, or pattern-based.<\/li>\n<li>Consumers: subscribe and receive messages, acknowledge processing, and optionally commit offsets.<\/li>\n<li>Control plane: manages topic lifecycle, ACLs, and policies.<\/li>\n<li>Schema registry &amp; governance: ensures message compatibility and validation.<\/li>\n<li>Delivery guarantees: at-most-once, at-least-once, exactly-once transactions.<\/li>\n<\/ul>\n\n\n\n<p>Data flow and lifecycle:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Publisher writes message to topic with key and metadata.<\/li>\n<li>Broker assigns message to partition and persists based on retention policy.<\/li>\n<li>Broker notifies subscribers or stores for pull retrieval.<\/li>\n<li>Consumer fetches or receives message and processes payload.<\/li>\n<li>Consumer acknowledges success or requests retry.<\/li>\n<li>Broker updates offset\/ack state and may mark message as delivered.<\/li>\n<li>Message expires after retention or compaction as configured.<\/li>\n<\/ol>\n\n\n\n<p>Edge cases and failure modes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Network partition causing split-brain brokers and duplicate delivery.<\/li>\n<li>Consumer slow processing causing backlog and retention pressure.<\/li>\n<li>Broker disk failures causing data loss if replication insufficient.<\/li>\n<li>Schema drift causing deserialization failures in consumers.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Typical architecture patterns for Publish subscribe<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Simple fan-out: One topic with push subscriptions to multiple independent consumers. Use for notifications and broadcasting.<\/li>\n<li>Partitioned stream: Topic partitions for scale and ordering per key. Use for high-throughput event streams and stateful processing.<\/li>\n<li>Consumer groups: Multiple consumers share work from partitions for load balancing. Use for horizontally scalable workers.<\/li>\n<li>Event sourcing: Events stored as source of truth in ordered stream, processed by materializers. Use for auditability and reconstructing state.<\/li>\n<li>Brokerless direct: Lightweight brokerless protocols (e.g., peer-based or direct multicast) for low-latency intra-cluster events. Use for edge or constrained environments.<\/li>\n<li>Hybrid push-pull: Push for low-latency notifications and pull for backpressure control and retry. Use when varying consumer capabilities exist.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Failure modes &amp; mitigation (TABLE REQUIRED)<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Failure mode<\/th>\n<th>Symptom<\/th>\n<th>Likely cause<\/th>\n<th>Mitigation<\/th>\n<th>Observability signal<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>F1<\/td>\n<td>Consumer backlog<\/td>\n<td>Lag growth and memory pressure<\/td>\n<td>Consumer slow or crashed<\/td>\n<td>Autoscale consumers; backpressure<\/td>\n<td>Consumer lag metric rising<\/td>\n<\/tr>\n<tr>\n<td>F2<\/td>\n<td>Duplicate delivery<\/td>\n<td>Duplicate records downstream<\/td>\n<td>At-least-once semantics or retries<\/td>\n<td>Idempotent processing; dedupe keys<\/td>\n<td>Duplicate-id counter<\/td>\n<\/tr>\n<tr>\n<td>F3<\/td>\n<td>Broker hotspot<\/td>\n<td>One broker CPU or IO high<\/td>\n<td>Partition imbalance<\/td>\n<td>Rebalance partitions; reassign<\/td>\n<td>Broker CPU and IO spikes<\/td>\n<\/tr>\n<tr>\n<td>F4<\/td>\n<td>Message loss<\/td>\n<td>Missing events or gaps<\/td>\n<td>Insufficient replication or retention<\/td>\n<td>Increase replication; longer retention<\/td>\n<td>Offset gaps or missing sequence<\/td>\n<\/tr>\n<tr>\n<td>F5<\/td>\n<td>Ordering break<\/td>\n<td>Out-of-order events<\/td>\n<td>Wrong partitioning or parallelism<\/td>\n<td>Partition by key or use single-partition<\/td>\n<td>Out-of-order error rate<\/td>\n<\/tr>\n<tr>\n<td>F6<\/td>\n<td>Schema failure<\/td>\n<td>Deserialization errors<\/td>\n<td>Schema mismatch or evolution issue<\/td>\n<td>Schema registry with compatibility<\/td>\n<td>Deserialization error rate<\/td>\n<\/tr>\n<tr>\n<td>F7<\/td>\n<td>ACL misconfig<\/td>\n<td>Unauthorized access errors<\/td>\n<td>Misconfigured IAM policies<\/td>\n<td>Tighten policies and audits<\/td>\n<td>Auth failure logs<\/td>\n<\/tr>\n<tr>\n<td>F8<\/td>\n<td>Network partition<\/td>\n<td>Split-brain or stalls<\/td>\n<td>Network flaps between brokers<\/td>\n<td>Multi-zone replicas; retry policies<\/td>\n<td>Broker cluster health alerts<\/td>\n<\/tr>\n<tr>\n<td>F9<\/td>\n<td>Retention overflow<\/td>\n<td>Storage saturation<\/td>\n<td>Retention misconfigured<\/td>\n<td>Increase storage or shorten retention<\/td>\n<td>Disk utilization trend<\/td>\n<\/tr>\n<tr>\n<td>F10<\/td>\n<td>Throttling<\/td>\n<td>Publish errors or 429<\/td>\n<td>Exceeding provisioned throughput<\/td>\n<td>Rate limiting and backoff<\/td>\n<td>429 or rate-limit metrics<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None required.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Key Concepts, Keywords &amp; Terminology for Publish subscribe<\/h2>\n\n\n\n<p>Below are 40+ concise glossary entries. Each line: Term \u2014 short definition \u2014 why it matters \u2014 common pitfall.<\/p>\n\n\n\n<p>Pub\/Sub \u2014 Asynchronous message delivery model \u2014 Decouples producers and consumers \u2014 Treating it like RPC.\nTopic \u2014 Named channel for messages \u2014 Logical separation of event types \u2014 Using topic as table substitute.\nSubscription \u2014 Consumer binding to a topic \u2014 Controls delivery semantics \u2014 Forgotten cleanup leads to leaks.\nBroker \u2014 Server that routes and stores messages \u2014 Central operational component \u2014 Single point of failure if misconfigured.\nPartition \u2014 Shard of a topic for scale \u2014 Enables parallelism and ordering per key \u2014 Hot partitions create imbalance.\nOffset \u2014 Position marker in a partition \u2014 Tracks consumer progress \u2014 Incorrect commits cause replay issues.\nConsumer group \u2014 Set of consumers that share partitions \u2014 Load-balancing processing \u2014 Misunderstanding leads to duplicate work.\nPush delivery \u2014 Broker pushes messages to consumers \u2014 Low-latency but needs addressable endpoints \u2014 Overwhelms endpoints on spikes.\nPull delivery \u2014 Consumers fetch messages on demand \u2014 Backpressure friendly \u2014 Polling too frequently wastes resources.\nRetention \u2014 How long messages are kept \u2014 Enables replay and late consumers \u2014 Too long leads to high storage costs.\nCompaction \u2014 Keeps latest message per key \u2014 Useful for state streams \u2014 Misuse loses history.\nExactly-once \u2014 Strong delivery guarantee with transactions \u2014 Simplifies consumer semantics \u2014 Expensive and implementation-dependent.\nAt-least-once \u2014 Messages may be redelivered until ack \u2014 Safer but duplicates possible \u2014 Requires idempotency.\nAt-most-once \u2014 Messages delivered at most once \u2014 Low duplication risk but can lose data \u2014 Unsuitable for critical events.\nSchema registry \u2014 Stores message schemas and compatibility rules \u2014 Prevents breaking changes \u2014 Absent registry leads to runtime failures.\nSerialization \u2014 Format like JSON, Avro, Protobuf \u2014 Impacts size and performance \u2014 Choosing text formats increases costs.\nMessage key \u2014 Routing key for partitioning \u2014 Enables ordering and affinity \u2014 Poor key choice causes hotspots.\nThroughput \u2014 Messages per second capacity \u2014 A main scaling target \u2014 Ignoring peaks causes backlog.\nEnd-to-end latency \u2014 Time from publish to processed ack \u2014 User-visible performance metric \u2014 Single outliers can break SLAs.\nAcknowledgement \u2014 Consumer\u2019s confirmation of processing \u2014 Drives delivery guarantees \u2014 Missing ack leads to re-delivery.\nDead-letter queue \u2014 Sink for unprocessable messages \u2014 Prevents poison message loops \u2014 Forgotten DLQ causes retries forever.\nBackpressure \u2014 Mechanism to slow producers or brokers \u2014 Prevents overload \u2014 Lacking it causes crashes.\nRetry policy \u2014 How and when messages are retried \u2014 Balances recovery and duplicates \u2014 Aggressive retries create stampedes.\nIdempotency \u2014 Safe repeated processing of same message \u2014 Enables at-least-once semantics \u2014 Hard when side effects are external.\nRebalance \u2014 Movement of partitions among consumers \u2014 Maintains load balance \u2014 Frequent rebalances cause churn.\nCompeting consumers \u2014 Consumers competing for messages \u2014 Enables scale horizontally \u2014 Accidental competing breaks fan-out.\nMultitenancy \u2014 Multiple teams on same broker cluster \u2014 Resource efficiency but isolation risk \u2014 No quotas leads to noisy neighbor issues.\nReplication factor \u2014 Number of copies of data \u2014 Fault tolerance knob \u2014 Low replication risks data loss.\nExactly-once-in-stream-processing \u2014 End-to-end semantic for stateful processors \u2014 Simplifies correctness \u2014 Requires careful state checkpointing.\nCheckpointing \u2014 Periodic persistence of consumer offsets \u2014 Enables recovery \u2014 Missing checkpoints cause reprocessing.\nMessage envelope \u2014 Metadata wrapper around payload \u2014 Provides tracing and routing \u2014 Overpopulation increases payload.\nTracing context \u2014 Distributed trace headers in messages \u2014 Enables observability \u2014 Missing headers breaks root cause analysis.\nSchema evolution \u2014 Compatibility rules for changing schemas \u2014 Supports gradual change \u2014 Breaking changes cause outages.\nMulti-region replication \u2014 Cross-region event distribution \u2014 Improves locality and DR \u2014 Higher cost and latency.\nTopic partitioning strategy \u2014 How keys map to partitions \u2014 Affects ordering and balance \u2014 Poor strategy causes hotspots.\nSecurity token \u2014 Credential used to publish\/subscribe \u2014 Ensures access control \u2014 Long-lived creds are risk.\nAudit log \u2014 Immutable history of events and actions \u2014 Important for forensics \u2014 Not all systems persist audit by default.\nTTL \u2014 Time-to-live per message \u2014 Controls freshness \u2014 Too short loses slow consumers.\nConsumer offset commit \u2014 Persisting progress \u2014 Enables at-least-once semantics \u2014 Committing early leads to data loss.\nBroker autoscaling \u2014 Dynamically adjusting broker resources \u2014 Controls cost and capacity \u2014 Slow scaling causes outages.\nSchema compatibility mode \u2014 Rules for backward\/forward compatibility \u2014 Reduces breaking upgrades \u2014 Incorrect mode blocks changes.\nEvent sourcing \u2014 Storing events as business truth \u2014 Enables replay and audit \u2014 Overuse complicates queries.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">How to Measure Publish subscribe (Metrics, SLIs, SLOs) (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Metric\/SLI<\/th>\n<th>What it tells you<\/th>\n<th>How to measure<\/th>\n<th>Starting target<\/th>\n<th>Gotchas<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>M1<\/td>\n<td>Delivery success rate<\/td>\n<td>Fraction of messages delivered<\/td>\n<td>Delivered \/ Published per interval<\/td>\n<td>99.95%<\/td>\n<td>Count semantics vary<\/td>\n<\/tr>\n<tr>\n<td>M2<\/td>\n<td>End-to-end latency<\/td>\n<td>Time from publish to ack<\/td>\n<td>Percentiles of delivery time<\/td>\n<td>P99 &lt; 1s for low-latency apps<\/td>\n<td>Tail spikes during bursts<\/td>\n<\/tr>\n<tr>\n<td>M3<\/td>\n<td>Consumer lag<\/td>\n<td>Messages behind latest offset<\/td>\n<td>Latest offset &#8211; consumer offset<\/td>\n<td>Near zero for streaming apps<\/td>\n<td>Lag can be per-partition<\/td>\n<\/tr>\n<tr>\n<td>M4<\/td>\n<td>Publish error rate<\/td>\n<td>Failures publishing messages<\/td>\n<td>Publish failures \/ attempts<\/td>\n<td>&lt; 0.01%<\/td>\n<td>Retry masking may hide true errors<\/td>\n<\/tr>\n<tr>\n<td>M5<\/td>\n<td>Retry count<\/td>\n<td>Number of automatic retries<\/td>\n<td>Sum retries over period<\/td>\n<td>Keep low; depends on app<\/td>\n<td>Retries can cause duplicates<\/td>\n<\/tr>\n<tr>\n<td>M6<\/td>\n<td>Duplicate rate<\/td>\n<td>Duplicate deliveries seen by consumer<\/td>\n<td>Duplicate ids \/ processed<\/td>\n<td>&lt; 0.1%<\/td>\n<td>Requires id tracking<\/td>\n<\/tr>\n<tr>\n<td>M7<\/td>\n<td>Retention utilization<\/td>\n<td>Storage used by topics<\/td>\n<td>Bytes stored \/ provisioned<\/td>\n<td>&lt; 70%<\/td>\n<td>Compaction changes apparent usage<\/td>\n<\/tr>\n<tr>\n<td>M8<\/td>\n<td>Broker CPU utilization<\/td>\n<td>Broker resource health<\/td>\n<td>CPU percent average<\/td>\n<td>&lt; 70%<\/td>\n<td>Short spikes impact tail latency<\/td>\n<\/tr>\n<tr>\n<td>M9<\/td>\n<td>Consumer throughput<\/td>\n<td>Messages processed per second<\/td>\n<td>Processed per consumer<\/td>\n<td>Matches expected capacity<\/td>\n<td>Varies with message size<\/td>\n<\/tr>\n<tr>\n<td>M10<\/td>\n<td>DLQ rate<\/td>\n<td>Poison messages volume<\/td>\n<td>DLQ messages per period<\/td>\n<td>Low; specific thresholds<\/td>\n<td>DLQ growth indicates processing bugs<\/td>\n<\/tr>\n<tr>\n<td>M11<\/td>\n<td>Auth failure rate<\/td>\n<td>Unauthorized access attempts<\/td>\n<td>Auth failures \/ attempts<\/td>\n<td>Near zero<\/td>\n<td>Misconfig can spike failures<\/td>\n<\/tr>\n<tr>\n<td>M12<\/td>\n<td>Rebalance frequency<\/td>\n<td>Partition movement events<\/td>\n<td>Rebalances per hour<\/td>\n<td>Low and predictable<\/td>\n<td>Frequent rebalances cause instability<\/td>\n<\/tr>\n<tr>\n<td>M13<\/td>\n<td>Schema errors<\/td>\n<td>Deserialization failures<\/td>\n<td>Errors per thousand messages<\/td>\n<td>&lt; 0.01%<\/td>\n<td>Silent schema drift possible<\/td>\n<\/tr>\n<tr>\n<td>M14<\/td>\n<td>End-to-end success SLI<\/td>\n<td>Business-level event completion<\/td>\n<td>Successes \/ published<\/td>\n<td>99.9% for critical flows<\/td>\n<td>Downstream failures can affect it<\/td>\n<\/tr>\n<tr>\n<td>M15<\/td>\n<td>Storage write latency<\/td>\n<td>Broker write time<\/td>\n<td>Percentiles of write operation<\/td>\n<td>P99 small, e.g., &lt;10ms<\/td>\n<td>Disk saturation impacts writes<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None required.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Best tools to measure Publish subscribe<\/h3>\n\n\n\n<p>Follow this exact structure for 5\u20138 tools.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Prometheus + OpenMetrics<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Publish subscribe: Broker and consumer metrics, lag, throughput, resource usage.<\/li>\n<li>Best-fit environment: Kubernetes and self-managed clusters.<\/li>\n<li>Setup outline:<\/li>\n<li>Export broker and consumer metrics via exporters.<\/li>\n<li>Instrument client libraries for custom metrics.<\/li>\n<li>Collect and scrape with Prometheus.<\/li>\n<li>Use recording rules for SLI calculations.<\/li>\n<li>Export to long-term storage if needed.<\/li>\n<li>Strengths:<\/li>\n<li>Flexible query language for SLIs.<\/li>\n<li>Strong Kubernetes integration.<\/li>\n<li>Limitations:<\/li>\n<li>Not ideal for high-cardinality time series without remote storage.<\/li>\n<li>Long-term retention requires extra components.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Managed Cloud Pub\/Sub Metrics (Managed provider)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Publish subscribe: Native broker metrics like publish rate, ack rate, and error counts.<\/li>\n<li>Best-fit environment: Managed pubsub services in cloud.<\/li>\n<li>Setup outline:<\/li>\n<li>Enable service metrics collection in cloud monitoring.<\/li>\n<li>Configure billing and retention alerts.<\/li>\n<li>Map metrics to SLIs and SLOs.<\/li>\n<li>Strengths:<\/li>\n<li>Low operational overhead.<\/li>\n<li>Platform-level insights and defaults.<\/li>\n<li>Limitations:<\/li>\n<li>Varies by provider; some internal metrics not exposed.<\/li>\n<li>Integration to custom observability may be limited.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Distributed Tracing (OpenTelemetry)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Publish subscribe: End-to-end latency including publish and consume spans.<\/li>\n<li>Best-fit environment: Microservices and event-driven systems.<\/li>\n<li>Setup outline:<\/li>\n<li>Instrument publishers and consumers to propagate trace context.<\/li>\n<li>Collect spans and view traces for pipeline latency.<\/li>\n<li>Correlate with message IDs and offsets.<\/li>\n<li>Strengths:<\/li>\n<li>Correlates across services for root cause analysis.<\/li>\n<li>Visualizes event paths and latency contributors.<\/li>\n<li>Limitations:<\/li>\n<li>Requires consistent propagation; can miss ephemeral components.<\/li>\n<li>High-cardinality traces cost more.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Kafka \/ Pulsar native metrics + JMX<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Publish subscribe: Broker internals, partition health, replication lag, ISR.<\/li>\n<li>Best-fit environment: Kafka or Pulsar self-managed clusters.<\/li>\n<li>Setup outline:<\/li>\n<li>Enable JMX metrics and export to Prometheus or metrics backend.<\/li>\n<li>Track partition metrics, ISR, under-replicated partitions.<\/li>\n<li>Alert on ISR changes and under-replicated partitions.<\/li>\n<li>Strengths:<\/li>\n<li>Very detailed lifecycle and broker internals.<\/li>\n<li>Mature tooling ecosystem.<\/li>\n<li>Limitations:<\/li>\n<li>Operational complexity and JMX tuning required.<\/li>\n<li>Large metric volume if not aggregated.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Log-based monitoring (ELK or cloud logs)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Publish subscribe: Audit trails, errors, auth failures, dead-letter logs.<\/li>\n<li>Best-fit environment: Compliance, security, and deep troubleshooting.<\/li>\n<li>Setup outline:<\/li>\n<li>Centralize broker and consumer logs.<\/li>\n<li>Parse and index message IDs and offsets.<\/li>\n<li>Create alerts for DLQ spikes and auth errors.<\/li>\n<li>Strengths:<\/li>\n<li>Rich contextual logs for postmortem.<\/li>\n<li>Useful for forensic analysis.<\/li>\n<li>Limitations:<\/li>\n<li>Searching large logs can be slow and costly.<\/li>\n<li>Not a substitute for real-time metrics.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Recommended dashboards &amp; alerts for Publish subscribe<\/h3>\n\n\n\n<p>Executive dashboard:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Total publish rate and trend: Business traffic signal.<\/li>\n<li>End-to-end success SLI: Shows compliance with SLO.<\/li>\n<li>Top topics by volume: Business prioritization.<\/li>\n<li>Storage and retention utilization: Cost and capacity signal.<\/li>\n<\/ul>\n\n\n\n<p>On-call dashboard:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Consumer lag heatmap by partition and topic: Shows backlog hotspots.<\/li>\n<li>Broker health: CPU, disk, network, ISR counts.<\/li>\n<li>Publishing errors and 5xx rates: Upstream publisher failures.<\/li>\n<li>DLQ rate and recent entries: Poison message indicator.<\/li>\n<\/ul>\n\n\n\n<p>Debug dashboard:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Per-partition throughput and latency percentiles.<\/li>\n<li>Recent rebalance events and consumer group membership.<\/li>\n<li>Schema error logs and failing deserializations.<\/li>\n<li>Trace samples for slow end-to-end paths.<\/li>\n<\/ul>\n\n\n\n<p>Alerting guidance:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Page (P1\/P2) for: sustained consumer lag above threshold for critical topics, broker under-replicated partitions, cluster unavailable.<\/li>\n<li>Ticket-only alerts for: minor publish errors, nearing retention storage thresholds.<\/li>\n<li>Burn-rate guidance: use error budget burn rate alerts to escalate; e.g., page if error budget burn rate &gt; 2x sustained for 15m.<\/li>\n<li>Noise reduction: dedupe alerts by topic and partition cluster, group related alerts, suppress noise during planned maintenance.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Implementation Guide (Step-by-step)<\/h2>\n\n\n\n<p>1) Prerequisites\n&#8211; Define event domain and ownership.\n&#8211; Select pub\/sub platform and delivery semantics.\n&#8211; Establish schema registry and versioning policy.\n&#8211; Provision monitoring and alerting infrastructure.\n&#8211; Define security and tenant isolation model.<\/p>\n\n\n\n<p>2) Instrumentation plan\n&#8211; Instrument publishers for publish latency and errors.\n&#8211; Instrument consumers for processing time, acks, and failures.\n&#8211; Propagate trace context and message IDs.\n&#8211; Emit metrics for DLQ writes and retry counts.<\/p>\n\n\n\n<p>3) Data collection\n&#8211; Centralize metrics with Prometheus or cloud monitoring.\n&#8211; Centralize logs including message IDs and offsets.\n&#8211; Collect traces via OpenTelemetry.\n&#8211; Retain delivery audit logs for compliance windows.<\/p>\n\n\n\n<p>4) SLO design\n&#8211; Define SLIs: delivery success rate, end-to-end latency, consumer lag.\n&#8211; Set SLOs aligned to business priorities (e.g., 99.9% delivery for orders).\n&#8211; Define error budgets and what consumes them.<\/p>\n\n\n\n<p>5) Dashboards\n&#8211; Build executive, on-call, and debug dashboards as above.\n&#8211; Include annotatable maintenance windows.\n&#8211; Add historical baselines for anomaly detection.<\/p>\n\n\n\n<p>6) Alerts &amp; routing\n&#8211; Set threshold-based alerts for critical metrics.\n&#8211; Configure on-call routing and escalation policies.\n&#8211; Integrate alerts with incident management for runbooks.<\/p>\n\n\n\n<p>7) Runbooks &amp; automation\n&#8211; Create runbooks for common incidents: backlog, broker down, DLQ spikes.\n&#8211; Automate remediation: consumer autoscaling, topic rebalancing, DLQ moves.\n&#8211; Automate topic provisioning and ACL assignments.<\/p>\n\n\n\n<p>8) Validation (load\/chaos\/game days)\n&#8211; Load test publishers and consumers to target throughput.\n&#8211; Run chaos experiments: broker failover, partition loss, network partition.\n&#8211; Execute game days simulating consumer lag and DLQ floods.<\/p>\n\n\n\n<p>9) Continuous improvement\n&#8211; Review postmortems and tune retention, partitioning, and scaling.\n&#8211; Automate routine fixes and reduce manual toil.\n&#8211; Evolve SLOs based on observed behavior.<\/p>\n\n\n\n<p>Pre-production checklist:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Schema registry configured and consumers validated.<\/li>\n<li>Monitoring and alerting enabled and smoke-tested.<\/li>\n<li>Security policies and ACLs reviewed.<\/li>\n<li>Retention and compaction settings set.<\/li>\n<li>Backpressure and retry behavior defined.<\/li>\n<\/ul>\n\n\n\n<p>Production readiness checklist:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Autoscaling rules for consumers and brokers tested.<\/li>\n<li>DR plan and backups for broker metadata confirmed.<\/li>\n<li>Runbooks accessible and page tests performed.<\/li>\n<li>Cost and retention policy validated for expected ingest.<\/li>\n<li>Observability correlation across metrics, logs, traces.<\/li>\n<\/ul>\n\n\n\n<p>Incident checklist specific to Publish subscribe:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identify affected topics and consumer groups.<\/li>\n<li>Check consumer lag and broker ISR and under-replicated partitions.<\/li>\n<li>Inspect DLQ counts and recent poison messages.<\/li>\n<li>Validate schema compatibility and recent deployment changes.<\/li>\n<li>Execute mitigation: scale consumers, pause producers, or extend retention.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Use Cases of Publish subscribe<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p>Telemetry ingestion\n&#8211; Context: Devices stream telemetry to cloud.\n&#8211; Problem: High fan-in from many producers.\n&#8211; Why pub\/sub: Scales ingestion and allows multiple consumers for storage, analytics.\n&#8211; What to measure: Ingest rate, drop rate, latency.\n&#8211; Typical tools: Cloud pubsub managed services or Kafka.<\/p>\n<\/li>\n<li>\n<p>Order event broadcasting\n&#8211; Context: E-commerce order events need inventory, billing, and analytics.\n&#8211; Problem: Coupling between services slows changes.\n&#8211; Why pub\/sub: Fan-out single source of truth to multiple services.\n&#8211; What to measure: Delivery success rate, end-to-end latency.\n&#8211; Typical tools: Kafka or managed pubsub.<\/p>\n<\/li>\n<li>\n<p>Feature flagging and config distribution\n&#8211; Context: Distribute config updates across services in real time.\n&#8211; Problem: Propagating changes reliably and fast.\n&#8211; Why pub\/sub: Low-latency broadcast with subscription filtering.\n&#8211; What to measure: Time to propagation, error rates.\n&#8211; Typical tools: Lightweight pubsub with filtering.<\/p>\n<\/li>\n<li>\n<p>Change Data Capture (CDC) for analytics\n&#8211; Context: Database changes need replicated to data pipelines.\n&#8211; Problem: Avoid bulk sync and reduce latency.\n&#8211; Why pub\/sub: Stream changes to multiple sinks and enable replay.\n&#8211; What to measure: CDC lag, completeness.\n&#8211; Typical tools: Kafka or cloud event streams.<\/p>\n<\/li>\n<li>\n<p>ML inference pipelines\n&#8211; Context: Model inferences and feedback loops.\n&#8211; Problem: Decouple data collection, feature computation, serving.\n&#8211; Why pub\/sub: Streams for real-time features and model feedback.\n&#8211; What to measure: Latency, throughput, duplicate rate.\n&#8211; Typical tools: Streaming platforms and serverless consumers.<\/p>\n<\/li>\n<li>\n<p>CI\/CD event bus\n&#8211; Context: Build and deploy pipelines reacting to code events.\n&#8211; Problem: Orchestration of heterogeneous steps.\n&#8211; Why pub\/sub: Loose coupling and replay for retries.\n&#8211; What to measure: Event processing time and success.\n&#8211; Typical tools: Managed event buses.<\/p>\n<\/li>\n<li>\n<p>Security alert routing\n&#8211; Context: Alerts from sensors to SIEM and response systems.\n&#8211; Problem: Multiple consumers and enrichment steps.\n&#8211; Why pub\/sub: Fan-out and enrichment pipelines.\n&#8211; What to measure: Delivery and processing latency.\n&#8211; Typical tools: Pubsub integrated with SIEM.<\/p>\n<\/li>\n<li>\n<p>Multiplayer game events\n&#8211; Context: Real-time state updates to many clients.\n&#8211; Problem: Broadcast state changes efficiently.\n&#8211; Why pub\/sub: Scalable fan-out with low latency.\n&#8211; What to measure: Latency percentiles and drop rates.\n&#8211; Typical tools: Specialized pubsub or streaming services.<\/p>\n<\/li>\n<li>\n<p>Audit trails and compliance streams\n&#8211; Context: Immutable logs for regulatory reporting.\n&#8211; Problem: Centralized, durable event storage.\n&#8211; Why pub\/sub: Durable retention and replay for audits.\n&#8211; What to measure: Retention integrity and completeness.\n&#8211; Typical tools: Event stores with long retention.<\/p>\n<\/li>\n<li>\n<p>Edge aggregation\n&#8211; Context: Collecting metrics from edge nodes into central processors.\n&#8211; Problem: Intermittent connectivity and bursts.\n&#8211; Why pub\/sub: Buffering and replay when connectivity restores.\n&#8211; What to measure: Publish retries and backlog after reattach.\n&#8211; Typical tools: Edge gateways with buffering.<\/p>\n<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Scenario Examples (Realistic, End-to-End)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #1 \u2014 Kubernetes event-driven microservices<\/h3>\n\n\n\n<p><strong>Context:<\/strong> A set of microservices on Kubernetes coordinate via events for order processing.\n<strong>Goal:<\/strong> Decouple services and ensure resilient processing with consumer autoscaling.\n<strong>Why Publish subscribe matters here:<\/strong> Enables independent deployments and autoscaling based on message backlog.\n<strong>Architecture \/ workflow:<\/strong> Producers in pods publish to a Kafka cluster or managed pubsub; consumers in Kubernetes scale via KEDA or custom controllers; messages processed and acknowledged; results persisted to DB.\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Define topics per domain entity.<\/li>\n<li>Deploy Kafka or managed provider with storage and replication.<\/li>\n<li>Instrument publishers with retries and tracing.<\/li>\n<li>Use KEDA to autoscale consumer deployments based on lag.<\/li>\n<li>Configure schema registry and DLQ.<\/li>\n<li>Add SLOs and dashboards.\n<strong>What to measure:<\/strong> Consumer lag, delivery success rate, P95 latency, rebalance frequency.\n<strong>Tools to use and why:<\/strong> Kafka for throughput and partitioning; KEDA for consumer autoscale; Prometheus for metrics.\n<strong>Common pitfalls:<\/strong> Frequent rebalances due to pod churn; forgetting idempotency.\n<strong>Validation:<\/strong> Load test to required throughput and run pod termination chaos.\n<strong>Outcome:<\/strong> Independent throughput scaling and reduced coupling.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #2 \u2014 Serverless notification system (managed-PaaS)<\/h3>\n\n\n\n<p><strong>Context:<\/strong> SaaS app needs to notify users via email and push when events occur.\n<strong>Goal:<\/strong> Use a serverless architecture to minimize ops.\n<strong>Why Publish subscribe matters here:<\/strong> Triggers multiple serverless functions and third-party integrations.\n<strong>Architecture \/ workflow:<\/strong> App publishes to managed cloud pubsub; push subscriptions trigger serverless functions that send notifications and write audit logs.\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Create topic for notifications.<\/li>\n<li>Configure push subscriptions to serverless endpoints.<\/li>\n<li>Implement retry and DLQ for failed deliveries.<\/li>\n<li>Add monitoring for publish errors and function concurrency.<\/li>\n<li>Set SLOs for notification delivery percentiles.\n<strong>What to measure:<\/strong> Publish errors, function failures, DLQ writes, end-to-end latency.\n<strong>Tools to use and why:<\/strong> Managed pubsub and serverless functions for low ops.\n<strong>Common pitfalls:<\/strong> Cold starts causing latency; uncontrolled fan-out causing cost spikes.\n<strong>Validation:<\/strong> Simulate notification bursts and validate SLA.\n<strong>Outcome:<\/strong> Low operational overhead and scalable notifications.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #3 \u2014 Incident response automation and postmortem<\/h3>\n\n\n\n<p><strong>Context:<\/strong> Security system produces alerts that must be enriched and routed to responders.\n<strong>Goal:<\/strong> Automate triage and notify on-call with context.\n<strong>Why Publish subscribe matters here:<\/strong> Multiple enrichment services and responders subscribe to alerts stream.\n<strong>Architecture \/ workflow:<\/strong> SIEM publishes alerts to topics; enrichers subscribe and append context; responders receive enriched alerts; failed enrichments go to DLQ.\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Design alert topic with structured schema.<\/li>\n<li>Implement enrichers as idempotent consumers.<\/li>\n<li>Store metadata and trace context.<\/li>\n<li>Create runbook consumers that trigger on high-severity alerts.<\/li>\n<li>Add SLOs and postmortem logging.\n<strong>What to measure:<\/strong> Time to enrichment, DLQ rate, automation success rate.\n<strong>Tools to use and why:<\/strong> Pubsub with strong tracing; log aggregation for audits.\n<strong>Common pitfalls:<\/strong> Enrichment latency leading to stale alerts; missing trace headers.\n<strong>Validation:<\/strong> Inject synthetic alerts and measure response time.\n<strong>Outcome:<\/strong> Faster, consistent incident triage and auditable postmortems.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #4 \u2014 Cost vs performance trade-off in retention and throughput<\/h3>\n\n\n\n<p><strong>Context:<\/strong> A company stores high-volume telemetry and must balance retention cost and replay needs.\n<strong>Goal:<\/strong> Optimize retention across hot and cold tiers for cost and performance.\n<strong>Why Publish subscribe matters here:<\/strong> Retention directly impacts storage cost and replayability.\n<strong>Architecture \/ workflow:<\/strong> Telemetry publishes to partitioned topic; older data moves to cheaper cold storage; consumers read from hot tier for real-time processing and from cold tier for batch analytics.\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Measure ingest rate and consumer replay needs.<\/li>\n<li>Configure tiered storage and compaction where appropriate.<\/li>\n<li>Implement lifecycle policies to move data to cold tier after X days.<\/li>\n<li>Monitor cost and access patterns.<\/li>\n<li>Adjust partitioning and retention thresholds.\n<strong>What to measure:<\/strong> Storage cost per GB, access latency for cold reads, consumer replay times.\n<strong>Tools to use and why:<\/strong> Streaming platform with tiered storage; cost monitoring tools.\n<strong>Common pitfalls:<\/strong> Unexpected cold read latency; losing replay capability by compacting too aggressively.\n<strong>Validation:<\/strong> Simulate replays from cold tier and measure performance and cost.\n<strong>Outcome:<\/strong> Lower storage cost with acceptable replay latency.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Common Mistakes, Anti-patterns, and Troubleshooting<\/h2>\n\n\n\n<p>List includes 20 entries (symptom -&gt; root cause -&gt; fix).<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Symptom: Growing consumer lag. Root cause: Downstream consumer slow or crashed. Fix: Autoscale consumers, add backpressure, profile consumers.<\/li>\n<li>Symptom: Duplicated side-effects. Root cause: At-least-once delivery and non-idempotent handlers. Fix: Implement idempotency keys and dedupe logic.<\/li>\n<li>Symptom: Under-replicated partitions. Root cause: Broker failure or replication not configured. Fix: Increase replication factor and monitor ISR.<\/li>\n<li>Symptom: Sudden storage spike. Root cause: Retention misconfigured or burst retention. Fix: Adjust retention, monitor storage, add quotas.<\/li>\n<li>Symptom: High publish error rate. Root cause: Network or auth issues. Fix: Check ACLs, network paths, and implement exponential backoff.<\/li>\n<li>Symptom: Order switched for related events. Root cause: Wrong partitioning key. Fix: Partition by stable key to preserve ordering.<\/li>\n<li>Symptom: Frequent consumer rebalances. Root cause: Short session timeouts or unstable membership. Fix: Increase session timeouts and reduce churn.<\/li>\n<li>Symptom: Missing events in analytics. Root cause: Early offset commits or filter errors. Fix: Verify commit points and subscription filters.<\/li>\n<li>Symptom: DLQ growth. Root cause: Poison messages or deserialization errors. Fix: Inspect DLQ, fix schema or message handling.<\/li>\n<li>Symptom: Broker hotspot CPU. Root cause: Skewed partition assignment. Fix: Repartition or redistribute keys.<\/li>\n<li>Symptom: High tail latency. Root cause: Disk IO or GC pauses. Fix: Tune JVM, use faster disks, and monitor GC.<\/li>\n<li>Symptom: Unauthorized access attempts. Root cause: Misapplied ACLs or leaked tokens. Fix: Rotate keys, tighten ACLs, audit access.<\/li>\n<li>Symptom: Schema compatibility errors on deploy. Root cause: Breaking schema change. Fix: Use schema registry with compatibility, migrate clients.<\/li>\n<li>Symptom: Cost blowout on serverless consumers. Root cause: Fan-out causing many function invocations. Fix: Batch events or add aggregation layer.<\/li>\n<li>Symptom: Trace gaps across pubsub. Root cause: Trace context dropped in messages. Fix: Ensure propagation headers included in message envelope.<\/li>\n<li>Symptom: Slow consumer startup. Root cause: Heavy initialization during cold start. Fix: Warm containers or optimize startup path.<\/li>\n<li>Symptom: Lost metrics granularity. Root cause: High-cardinality metrics from many topics. Fix: Aggregate metrics and use labels sparingly.<\/li>\n<li>Symptom: Test environment mismatch. Root cause: Different retention and scaling settings. Fix: Mirror production config or document differences.<\/li>\n<li>Symptom: Consumer memory leaks. Root cause: Unbounded message buffering or wrong client library use. Fix: Fix buffering and update libraries.<\/li>\n<li>Symptom: Observability blind spot. Root cause: Not instrumenting publish\/ack paths. Fix: Add metrics and tracing for both producers and consumers.<\/li>\n<\/ol>\n\n\n\n<p>Observability pitfalls (at least 5 included above): missing trace context, lost metrics granularity, not instrumenting ack paths, inconsistent message IDs, inadequate DLQ monitoring.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Best Practices &amp; Operating Model<\/h2>\n\n\n\n<p>Ownership and on-call:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Assign topic ownership by team or business domain.<\/li>\n<li>Have on-call rotations that include pubsub incident responsibilities.<\/li>\n<li>Maintain runbooks with clear escalation paths.<\/li>\n<\/ul>\n\n\n\n<p>Runbooks vs playbooks:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Runbooks: Step-by-step operational procedures for known failures.<\/li>\n<li>Playbooks: Higher-level decision guides for complex incidents or escalations.<\/li>\n<\/ul>\n\n\n\n<p>Safe deployments:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use canary deployments for consumers and schema changes.<\/li>\n<li>Rollback quickly on increased DLQ or schema errors.<\/li>\n<li>Coordinate schema changes through registry with compatibility checks.<\/li>\n<\/ul>\n\n\n\n<p>Toil reduction and automation:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Automate topic provisioning, ACLs, and quotas.<\/li>\n<li>Auto-scale consumers based on lag.<\/li>\n<li>Automate replay and DLQ remediation where safe.<\/li>\n<\/ul>\n\n\n\n<p>Security basics:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enforce least privilege IAM for publish\/subscribe.<\/li>\n<li>Use TLS for transport and sign message envelopes where necessary.<\/li>\n<li>Rotate credentials and monitor for suspicious activity.<\/li>\n<\/ul>\n\n\n\n<p>Weekly\/monthly routines:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Weekly: Review consumer lag and DLQ trends; check broker health.<\/li>\n<li>Monthly: Review topic usage, retention costs, and schema changes.<\/li>\n<li>Quarterly: Run DR and failover exercises.<\/li>\n<\/ul>\n\n\n\n<p>Postmortem review items related to pubsub:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Timeline of publish\/consume events and offsets.<\/li>\n<li>DLQ events and root causes.<\/li>\n<li>Schema changes and deployments around incident time.<\/li>\n<li>Actionable remediation and owner assignment.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Tooling &amp; Integration Map for Publish subscribe (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Category<\/th>\n<th>What it does<\/th>\n<th>Key integrations<\/th>\n<th>Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>I1<\/td>\n<td>Broker<\/td>\n<td>Stores and routes messages<\/td>\n<td>Consumers, producers, schema registry<\/td>\n<td>Self-managed or managed<\/td>\n<\/tr>\n<tr>\n<td>I2<\/td>\n<td>Schema registry<\/td>\n<td>Validates message schemas<\/td>\n<td>Producers and consumers<\/td>\n<td>Essential for compatibility<\/td>\n<\/tr>\n<tr>\n<td>I3<\/td>\n<td>Monitoring<\/td>\n<td>Collects metrics and alerts<\/td>\n<td>Brokers, clients, dashboards<\/td>\n<td>Prometheus common<\/td>\n<\/tr>\n<tr>\n<td>I4<\/td>\n<td>Tracing<\/td>\n<td>Correlates spans across events<\/td>\n<td>Producers and consumers<\/td>\n<td>OpenTelemetry preferred<\/td>\n<\/tr>\n<tr>\n<td>I5<\/td>\n<td>Logging<\/td>\n<td>Centralized broker and DLQ logs<\/td>\n<td>SIEM and auditing tools<\/td>\n<td>Useful for forensics<\/td>\n<\/tr>\n<tr>\n<td>I6<\/td>\n<td>DLQ system<\/td>\n<td>Stores failed messages<\/td>\n<td>Alerting and remediation<\/td>\n<td>Critical for poison handling<\/td>\n<\/tr>\n<tr>\n<td>I7<\/td>\n<td>Autoscaler<\/td>\n<td>Scales consumers on lag<\/td>\n<td>Kubernetes and serverless<\/td>\n<td>KEDA or custom autoscalers<\/td>\n<\/tr>\n<tr>\n<td>I8<\/td>\n<td>Security<\/td>\n<td>IAM and encryption<\/td>\n<td>Broker and control plane<\/td>\n<td>Enforce least privilege<\/td>\n<\/tr>\n<tr>\n<td>I9<\/td>\n<td>Storage<\/td>\n<td>Tiered storage for retention<\/td>\n<td>Hot and cold tiers<\/td>\n<td>Cost and performance trade-offs<\/td>\n<\/tr>\n<tr>\n<td>I10<\/td>\n<td>Stream processor<\/td>\n<td>Stateful processing and joins<\/td>\n<td>Topics and sinks<\/td>\n<td>Flink, Kafka Streams, Pulsar IO<\/td>\n<\/tr>\n<tr>\n<td>I11<\/td>\n<td>Connector framework<\/td>\n<td>Integrates with sinks\/sources<\/td>\n<td>Databases, data lakes<\/td>\n<td>Pre-built connectors accelerate adoption<\/td>\n<\/tr>\n<tr>\n<td>I12<\/td>\n<td>Cost monitor<\/td>\n<td>Tracks topic and consumer costs<\/td>\n<td>Billing systems<\/td>\n<td>Alerts when limits exceeded<\/td>\n<\/tr>\n<tr>\n<td>I13<\/td>\n<td>Governance<\/td>\n<td>Topic lifecycle and ownership<\/td>\n<td>SCM and ticketing<\/td>\n<td>Enforces policies<\/td>\n<\/tr>\n<tr>\n<td>I14<\/td>\n<td>Replay tools<\/td>\n<td>Facilitate replay of past events<\/td>\n<td>Consumers and topics<\/td>\n<td>Essential for recovery<\/td>\n<\/tr>\n<tr>\n<td>I15<\/td>\n<td>Dev tooling<\/td>\n<td>Local emulators and testing<\/td>\n<td>CI\/CD and local dev<\/td>\n<td>Speeds developer feedback<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None required.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What is the difference between pub\/sub and message queue?<\/h3>\n\n\n\n<p>Pub\/sub is fan-out to multiple subscribers; message queues typically deliver to a single consumer for work distribution.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Do pub\/sub systems guarantee message order?<\/h3>\n\n\n\n<p>Varies by implementation; ordering often guaranteed per-partition or per-key, not globally.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How do I handle duplicate messages?<\/h3>\n\n\n\n<p>Design idempotent consumers or use deduplication keys and store processed IDs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is a dead-letter queue and when to use it?<\/h3>\n\n\n\n<p>A DLQ stores messages that repeatedly fail processing; use it to prevent poison message loops.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How long should I retain messages?<\/h3>\n\n\n\n<p>Depends on business needs; balance replay requirements with storage cost.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can pub\/sub be used for transactions?<\/h3>\n\n\n\n<p>Not usually; use event sourcing with careful patterns or platforms that support transactional writes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to monitor consumer lag?<\/h3>\n\n\n\n<p>Track latest partition offset minus consumer offset per partition and alert on sustained growth.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What delivery semantics should I choose?<\/h3>\n\n\n\n<p>Choose at-least-once for durability and implement idempotency; choose exactly-once only if platform supports and needed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to secure topics and messages?<\/h3>\n\n\n\n<p>Use TLS, IAM, per-topic ACLs, and audit logs; avoid wide-open permissions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What causes partition hotspots?<\/h3>\n\n\n\n<p>Skewed key distribution; fix by choosing a better partitioning key or increasing partitions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Should I use managed pubsub or self-hosted?<\/h3>\n\n\n\n<p>Managed reduces ops; self-hosted gives control. Decision depends on scale, compliance, and cost.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to perform schema changes safely?<\/h3>\n\n\n\n<p>Use a schema registry with compatibility rules and phased rollouts; avoid breaking changes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to replay events safely?<\/h3>\n\n\n\n<p>Ensure consumers can idempotently process replays and apply proper offset controls or replay tools.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How many partitions should I create?<\/h3>\n\n\n\n<p>Depends on throughput and consumer concurrency; start conservative and scale with reassignment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to reduce noise in alerts?<\/h3>\n\n\n\n<p>Group alerts by cluster and topic, dedupe repeated incidents, and suppress during maintenance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to debug a missing event?<\/h3>\n\n\n\n<p>Check publish logs, broker offsets, and DLQ; use tracing context and audit logs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When does pub\/sub become too costly?<\/h3>\n\n\n\n<p>When retention, fan-out, and write rates grow without lifecycle policy; implement tiered storage and quotas.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is schema registry necessary?<\/h3>\n\n\n\n<p>Strongly recommended for teams at scale to prevent runtime deserialization failures.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>Publish subscribe is a foundational pattern for decoupling, scaling, and enabling event-driven architectures. Its benefits include independent team velocity, resilient architectures, and powerful integrations across observability and automation. However, it introduces operational complexity that must be managed through observability, governance, and automation.<\/p>\n\n\n\n<p>Next 7 days plan:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Day 1: Inventory existing event paths and topic ownership.<\/li>\n<li>Day 2: Add basic metrics and tracing to a pilot topic.<\/li>\n<li>Day 3: Configure DLQ, schema registry, and alert on DLQ spikes.<\/li>\n<li>Day 4: Define SLOs for critical topics and add dashboards.<\/li>\n<li>Day 5: Run a load test to validate throughput and lag.<\/li>\n<li>Day 6: Conduct a small chaos test (consumer restart).<\/li>\n<li>Day 7: Review findings, update runbooks, and schedule follow-ups.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Appendix \u2014 Publish subscribe Keyword Cluster (SEO)<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Primary keywords<\/li>\n<li>publish subscribe<\/li>\n<li>pub sub pattern<\/li>\n<li>publish subscribe architecture<\/li>\n<li>pubsub<\/li>\n<li>event-driven architecture<\/li>\n<li>message broker<\/li>\n<li>pub\/sub messaging<\/li>\n<li>pubsub system<\/li>\n<li>publish subscribe pattern<\/li>\n<li>\n<p>pubsub architecture<\/p>\n<\/li>\n<li>\n<p>Secondary keywords<\/p>\n<\/li>\n<li>event streaming<\/li>\n<li>message queue vs pubsub<\/li>\n<li>topic and subscription<\/li>\n<li>consumer lag<\/li>\n<li>message retention<\/li>\n<li>partitioned topics<\/li>\n<li>exactly-once delivery<\/li>\n<li>at-least-once delivery<\/li>\n<li>dead-letter queue<\/li>\n<li>\n<p>schema registry<\/p>\n<\/li>\n<li>\n<p>Long-tail questions<\/p>\n<\/li>\n<li>what is publish subscribe pattern in microservices<\/li>\n<li>how does pubsub work in kubernetes<\/li>\n<li>pubsub vs message queue differences<\/li>\n<li>how to measure consumer lag in pubsub<\/li>\n<li>best practices for pubsub security<\/li>\n<li>how to handle duplicates in pubsub<\/li>\n<li>how to set SLOs for event-driven systems<\/li>\n<li>how to monitor pubsub topics and partitions<\/li>\n<li>strategies for partitioning pubsub topics<\/li>\n<li>\n<p>how to configure DLQ for pubsub systems<\/p>\n<\/li>\n<li>\n<p>Related terminology<\/p>\n<\/li>\n<li>topic partitioning<\/li>\n<li>consumer groups<\/li>\n<li>publish latency<\/li>\n<li>message key routing<\/li>\n<li>retention policy<\/li>\n<li>compaction<\/li>\n<li>replica factor<\/li>\n<li>broker cluster<\/li>\n<li>trace context propagation<\/li>\n<li>backpressure handling<\/li>\n<li>autoscaling consumers<\/li>\n<li>idempotent consumers<\/li>\n<li>schema compatibility<\/li>\n<li>tiered storage<\/li>\n<li>replayability<\/li>\n<li>audit trail<\/li>\n<li>ingestion pipeline<\/li>\n<li>stream processing<\/li>\n<li>connector framework<\/li>\n<li>message envelope<\/li>\n<li>authentication and authorization<\/li>\n<li>telemetry ingestion<\/li>\n<li>serverless triggers<\/li>\n<li>event sourcing<\/li>\n<li>CDC to pubsub<\/li>\n<li>observability for pubsub<\/li>\n<li>DLQ remediation<\/li>\n<li>partition hot-spot<\/li>\n<li>exactly-once semantics<\/li>\n<li>at-most-once semantics<\/li>\n<li>maintenance window annotation<\/li>\n<li>topic lifecycle<\/li>\n<li>governance and ownership<\/li>\n<li>cost optimization for retention<\/li>\n<li>schema evolution strategy<\/li>\n<li>message serialization formats<\/li>\n<li>consumer offset commit<\/li>\n<li>rebalance events<\/li>\n<li>storage utilization trends<\/li>\n<li>publisher backoff strategy<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>&#8212;<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[430],"tags":[],"class_list":["post-1537","post","type-post","status-publish","format-standard","hentry","category-what-is-series"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is Publish subscribe? 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