Pub\/Sub gives a streaming architecture a durable asynchronous boundary between producers and consumers. Producers publish messages to topics without needing to know which analytics jobs, services, or data sinks will process them later. Subscribers can scale independently and can use different delivery patterns for the same stream.<\/p>\n
In Google Cloud data<\/a>, Pub\/Sub is often paired with Dataflow because the messaging service handles transport while Beam and Dataflow handle event-time processing, state, deduplication, windows, and transformations. The boundary is powerful only when delivery semantics are understood precisely.<\/p>\n The engineering work begins with message identity, acknowledgment, ordering, retention, replay, and failure behavior. These decisions determine whether a pipeline can recover safely when the network, subscriber, or downstream sink fails.<\/p>\n Pub\/Sub subscriptions are at-least-once by default. A subscriber can see a message again when acknowledgment does not complete in time or when a retry occurs. Applications should therefore be idempotent whenever possible, using stable business keys, upserts, deduplication tables, or transactional sink behavior to prevent duplicate effects.<\/p>\n Exactly-once delivery exists for supported pull subscription patterns, but it changes performance and operational characteristics. It should be selected because duplicate processing is truly expensive, not because the phrase sounds safer.<\/p>\n Data quality<\/a> includes duplicate control. A pipeline that quietly double-counts events is a quality failure even if every component reports healthy.<\/p>\n Pub\/Sub can preserve order for messages that share an ordering key and are published in the same region, but that guarantee has tradeoffs. An ordering key creates a serialization boundary for related events, and poorly chosen keys can become hot spots.<\/p>\n Many analytics pipelines do not need transport-level ordering. Dataflow has its own event-time and windowing model, and Google specifically cautions against relying on Pub\/Sub ordering for Dataflow pipelines because it can reduce performance without creating the semantic guarantee developers expect downstream.<\/p>\n If order matters, define exactly what must be ordered: all events globally, events per customer, or only state changes for one entity. Then design around that smallest useful scope.<\/p>\n Streaming systems usually have more than one clock. Publish time records when Pub\/Sub accepted a message, while event time records when the business event actually occurred. Late mobile devices, delayed integrations, and retries can create large differences between those timestamps.<\/p>\n Dataflow can use an event timestamp attribute for watermarking. That allows windows to represent business time rather than transport time, but it also forces teams to decide how long they will wait for late data and what happens after a window is considered complete.<\/p>\n Streaming design<\/a> should make those time semantics visible in code review because they affect both correctness and latency.<\/p>\n Subscribers need flow control so a fast topic does not overwhelm memory, CPU, connection pools, or downstream databases. High-level client libraries expose controls for outstanding messages and bytes, while Dataflow handles scaling according to pipeline signals and backlog.<\/p>\n Backlog is not automatically a failure. It can be an intentional buffer during bursts. The critical question is whether backlog age is growing faster than the recovery capacity of the subscriber.<\/p>\n Cloud monitoring<\/a> should track oldest unacknowledged message age, acknowledgment failures, subscriber errors, throughput, and the downstream constraints that often cause the backlog in the first place.<\/p>\n Pull and StreamingPull give subscriber clients direct control over message consumption and acknowledgment. Push can integrate naturally with HTTP services. Export subscriptions can deliver to managed destinations such as BigQuery or Cloud Storage without a custom subscriber.<\/p>\n Each pattern has different retry, authentication, throughput, and exactly-once implications. The topic does not need to be redesigned when a second consumer uses a different subscription type, which is one of the architectural benefits of separating publication from consumption.<\/p>\n Treat each subscription as an independent contract with its own retention, dead-letter, ordering, and operational requirements.<\/p>\n A malformed or semantically invalid message can be retried forever if every delivery hits the same deterministic failure. Dead-letter handling gives operators a bounded failure path and preserves evidence for investigation.<\/p>\n The subscriber should distinguish transient infrastructure errors from permanent data errors. A database timeout deserves retry; an impossible schema version may need quarantine. Mixing both into one retry loop increases cost and hides real defects.<\/p>\n Pipeline quality<\/a> is strongest when invalid events are visible and attributable rather than silently discarded.<\/p>\n Publish and subscribe permissions should be granted to service identities that need exactly those actions. Avoid broad project roles when topic- or subscription-level IAM can express the required boundary.<\/p>\n If applications span projects or organizations, document who owns the topic, schema, subscription, encryption controls, and incident response. Messaging makes technical coupling looser, but governance can become more ambiguous unless ownership is explicit.<\/p>\n Cloud IAM<\/a> is a useful model: permissions should follow workload responsibility rather than organizational convenience.<\/p>\n Dataflow uses a custom Pub\/Sub connector implementation and can deduplicate message IDs for exactly-once streaming mode. Google recommends allowing Dataflow to handle these semantics rather than enabling Pub\/Sub exactly-once delivery on the source subscription, because doing both can reduce pipeline performance.<\/p>\n Publish-side duplicates can still exist as distinct messages. If the business event has a stable transaction or event identifier, carry it explicitly and use that identity where duplicate publication must be detected.<\/p>\n Professional Data Engineer<\/a> designs should be able to explain which layer owns delivery, deduplication, transformation, and sink idempotency.<\/p>\n Retention, snapshots, and seek operations can make historical replay possible. Replay is valuable after a consumer bug, downstream outage, or transformation correction, but it can also recreate side effects if the subscriber was not designed for reprocessing.<\/p>\n Test replay with production-like data. Verify that the sink can distinguish corrected reprocessing from accidental duplicates, and that operators know the starting point and expected volume before triggering a large historical read.<\/p>\nDesign for at-least-once by default<\/h3>\n
Separate transport ordering from business ordering<\/h3>\n
Use event time deliberately<\/h3>\n
Control subscriber pressure<\/h3>\n
Choose subscription type from the consumer<\/h3>\n
Make poison messages recoverable<\/h3>\n
Secure publishers and subscribers narrowly<\/h3>\n
Design Dataflow integration around deduplication<\/h3>\n
Treat replay as a normal operation<\/h3>\n