Load Balancing in Practice: Lessons From Real Deployments

Content Delivery: Periodic jobs should be safe to run twice, because they will be. You rarely need a new component to fix a boundary problem. That applies to content delivery as well. In practice, content delivery behaves differently: The signal you want is often already logged, just not aggregated.

Release Process: Configurations should be reviewable in a diff, not only in a console. Release Process: The best time to add an index is before the table gets large. Release Process: Failures are usually correlated, so plan for the shared dependency.

Teams working on observability usually discover this the hard way. If the rollback plan needs a meeting, it is not a rollback plan. Small pages that stay small are easier to keep fast than large ones made fast. This is most visible in observability. Consider observability specifically. Write the invariant down; otherwise it lives only in someone's memory.

Backup Strategy: You can often replace a coordination problem with an idempotency key. Backup Strategy: Anything that grows without a bound will eventually hit one. Backup Strategy: Documentation that is not tested tends to describe the previous version.

Data Pipelines: If the rollback plan needs a meeting, it is not a rollback plan. Data Pipelines: Small pages that stay small are easier to keep fast than large ones made fast. Data Pipelines: Write the invariant down; otherwise it lives only in someone's memory.

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If the rollback plan needs a meeting, it is not a rollback plan. That applies to edge caching as well. In practice, edge caching behaves differently: Small pages that stay small are easier to keep fast than large ones made fast. Write the invariant down; otherwise it lives only in someone's memory. The same reasoning holds for edge caching.

A queue smooths spikes but also hides how far behind you are. This is most visible in log analysis. Consider log analysis specifically. Retries without jitter turn a small outage into a large one. Log Analysis: Separating the reads from the writes buys room to change either side.

You can often replace a coordination problem with an idempotency key. That applies to content delivery as well. In practice, content delivery behaves differently: Anything that grows without a bound will eventually hit one. Documentation that is not tested tends to describe the previous version. The same reasoning holds for content delivery.

Edge Caching: The first thing to settle is the failure mode, not the happy path. Edge Caching: Measurements taken once are anecdotes; you need a baseline that repeats. Edge Caching: Costs usually concentrate in a small number of operations, so find those first.

Consider observability specifically. The interesting number is not the average, it is the 99th percentile. Observability: Adding a cache in front of a slow query is a fix; fixing the query is a cure. Every abstraction you add is a place where behaviour can differ from intent. That applies to observability as well.

Use statements about your own needs rather than trying to guess your partner’s intentions. You might say, “I’m comfortable with this, but not with that,” or, “I need us to stop if I say pause.” Be specific about what you mean by words such as “slow down” or “check in.” Ask your partner what they are comfortable with, and leave room for an answer without interrupting or arguing.

Cost Controls: Serving static bytes is the cheapest thing you can do at the edge. Cost Controls: A schema is an interface; changing it is a migration, not an edit. Cost Controls: Track the denominator as carefully as the numerator.

Cloud Infrastructure: If a metric has no owner, it will drift until it causes an incident. Cloud Infrastructure: The cheapest optimisation is usually removing work nobody asked for. Cloud Infrastructure: Aggregating at write time trades flexibility for predictable read cost.

In practice, api design behaves differently: Periodic jobs should be safe to run twice, because they will be. You rarely need a new component to fix a boundary problem. The same reasoning holds for api design. For api design, the constraint matters more than the feature list. The signal you want is often already logged, just not aggregated.

Search Indexing: Serving static bytes is the cheapest thing you can do at the edge. Search Indexing: A schema is an interface; changing it is a migration, not an edit. Search Indexing: Track the denominator as carefully as the numerator.

You can also state your own boundaries. Say what you are comfortable with and what you do not want, and ask questions if an answer is unclear. Good communication is not a guarantee that everything will go as expected; it is a way to make choices more explicit and respond when circumstances change.

In practice, schema markup behaves differently: A queue smooths spikes but also hides how far behind you are. Retries without jitter turn a small outage into a large one. The same reasoning holds for schema markup. For schema markup, the constraint matters more than the feature list. Separating the reads from the writes buys room to change either side.

Schema Migration: If a metric has no owner, it will drift until it causes an incident. Schema Migration: The cheapest optimisation is usually removing work nobody asked for. Schema Migration: Aggregating at write time trades flexibility for predictable read cost.

Teams working on crawl budget usually discover this the hard way. You can often replace a coordination problem with an idempotency key. Anything that grows without a bound will eventually hit one. This is most visible in crawl budget. Consider crawl budget specifically. Documentation that is not tested tends to describe the previous version.

Log Analysis: If a metric has no owner, it will drift until it causes an incident. The cheapest optimisation is usually removing work nobody asked for. That applies to log analysis as well. In practice, log analysis behaves differently: Aggregating at write time trades flexibility for predictable read cost.

Schema Markup: Configurations should be reviewable in a diff, not only in a console. Schema Markup: The best time to add an index is before the table gets large. Schema Markup: Failures are usually correlated, so plan for the shared dependency.

Backup Strategy: If the rollback plan needs a meeting, it is not a rollback plan. Backup Strategy: Small pages that stay small are easier to keep fast than large ones made fast. Backup Strategy: Write the invariant down; otherwise it lives only in someone's memory.

A design that cannot be rolled back is a design that cannot be changed safely. That applies to data pipelines as well. In practice, data pipelines behaves differently: Latency budgets are easier to defend when every hop has a stated ceiling. Caching helps only until the invalidation rules become the bottleneck. The same reasoning holds for data pipelines.