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Copy file name to clipboardExpand all lines: src/content/articles/blue-green-canary-deployment-strategy-comparison/index.mdx
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Before we dive into the database problem, let's establish baseline understanding of what these strategies actually do.
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__Blue/green deployment__ maintains two identical production environments. Blue runs your current version. When you're ready to release, you deploy to green, test it thoroughly, then switch all traffic at once. If something breaks, you switch back to blue.
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__Canary deployment__ takes the opposite approach. Instead of switching everyone simultaneously, you route a small percentage of traffic — say, 5%—to the new version. You watch metrics, gradually increase the percentage, and only complete the rollout once you're confident the new version is healthy.
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<List
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variant="zebra-list"
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items={[
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{
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lead: "Blue/green deployment",
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text: "Maintains two identical production environments. Blue runs your current version. When you're ready to release, you deploy to green, test it thoroughly, then switch all traffic at once. If something breaks, you switch back to blue.",
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},
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{
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lead: "Canary deployment",
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text: "Takes the opposite approach. Instead of switching everyone simultaneously, you route a small percentage of traffic — say, 5% — to the new version. You watch metrics, gradually increase the percentage, and only complete the rollout once you're confident the new version is healthy.",
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},
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]}
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/>
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The fundamental difference comes down to exposure:
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The solution is to __never__ deploy schema changes and code changes together. Instead, use a multi-phase approach:
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<List
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variant="check-icons-list"
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variant="side-by-side-list"
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classes={{
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wrapper: "sm:ml-6 sm:mr-18 mb-6",
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}}
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items={[
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{
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lead: "Phase 1 (Expand):",
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]}
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/>
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<Newsletter />
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This pattern works with both strategies, but it's __mandatory__ for canary deployments. Blue/green can sometimes shortcut this with careful timing and instant cutover, but you're accepting more risk.
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```sql
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Now that we've established database compatibility as the primary constraint, the decision framework becomes clearer. Here's how to choose.
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### Choose Blue/Green When
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### Choose Blue / Green When
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Your deployments frequently include database migrations that can't easily follow expand-contract patterns. Legacy systems with tightly coupled schemas often fall into this category.
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You need instant, complete rollback. Blue/green's traffic switch is as close to "undo" as deployments get. If your downtime tolerance is measured in seconds rather than minutes, this matters.
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<Newsletter />
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Your team is smaller or less experienced with progressive delivery. Blue/green is conceptually simpler: deploy, test, switch. There's less tooling to maintain and fewer operational scenarios to train for.
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You can afford the infrastructure cost. Running two complete environments doubles your compute costs during deployments. For many organizations, this is acceptable for the operational simplicity it provides.
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The key insight is that the database constraint drives the decision __per deployment__, not as a blanket policy. A team might deploy three canary releases in a row, then switch to blue/green for a migration, then back to canary. The tooling supports both; the deployment contents determine which to use.
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## Conclusion
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The deployment strategy debate often focuses on the wrong things — rollback speed, blast radius, tooling sophistication. These matter, but they're secondary to the fundamental question: what are you actually deploying?
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]}
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/>
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## Conclusion
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The deployment strategy debate often focuses on the wrong things — rollback speed, blast radius, tooling sophistication. These matter, but they're secondary to the fundamental question: what are you actually deploying?
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For database schema changes, blue/green's instant cutover is often the only safe option. For application logic changes, canary's gradual rollout provides better production validation with limited risk. Most real-world systems need both approaches, selected based on what each specific deployment contains.
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The best deployment strategy isn't the most technically impressive one. It's the one your team can operate reliably, that matches your database change patterns, and that you'll actually use consistently. Start with the simpler option that works for your constraints, and add sophistication only when you have specific problems that require it.
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