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Start by checking whether crawler requests are actually causing database work. A response cache can keep repeated, cacheable requests from reaching your application and querying the database, but it does not prove who caused the original usage spike—and a cache miss still runs your code. The “22 days” account in the headline is not independently verified: the Postgres provider, plan, usage logs, crawler attribution, and cache measurements are not identified.
First, find out what “database hours” measure
“Free Postgres hours” is not a universal billing unit. Without the provider and plan, it is not possible to tell whether the reported usage counts elapsed time, active compute, or something else—or to compare it directly with another database.
Cloudflare D1 documents a different free-tier model: on Workers Free, the allowance is 5 million rows read per day, 100,000 rows written per day, and 5 GB total storage. The daily free limits reset at 00:00 UTC. These are Cloudflare’s published limits, not a description of the unnamed Postgres plan in the headline. Cloudflare D1 pricing
That distinction matters: a cache may reduce repeated database queries, but it does not turn one provider’s “hours” into another provider’s row limits. Before changing databases, check the actual billing definition and usage records for your current plan.
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Verify that crawlers are driving database work
A high request count is not enough to establish crawler-caused database consumption. Match request logs to database activity by time, endpoint, and, where available, verified crawler user agents or IP ranges. A user-agent string by itself is not proof of a crawler’s identity.
For D1, Cloudflare says row usage can be examined through the D1 meta object, GraphQL Analytics API, and dashboard, including attribution by database and time period. D1 usage tracking
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- Compare request volume with database reads and writes over the same periods.
- Group requests by route and query pattern to identify endpoints that repeatedly access the database.
- Use request-level evidence to distinguish crawler traffic from ordinary visitors and automated services.
- Record a baseline before adding caching so that a later change can be evaluated against actual database usage.
Understand what a response cache can prevent
A response cache can serve a stored HTTP response instead of running the application code that would otherwise build it. If the request is served from cache, that execution can avoid the database query associated with generating the response. But this only applies to eligible requests and responses; misses, expired entries, and uncacheable responses still need application handling.
Cloudflare’s Workers Cache documentation describes caching for GET and HEAD requests, with response cache headers determining whether a response can be stored. It also documents cache tiers and collapsing concurrent requests for a cacheable, fresh URL, so a group of simultaneous requests can be served after one Worker execution rather than each independently triggering one. Workers Cache
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Do not treat Workers Cache and the Cache API as interchangeable. The Cache API operates on the cache in the data center handling the request and does not support Tiered Cache. A cached response there is not guaranteed to be available in other geographies or to prevent code execution for requests served elsewhere. Cloudflare’s cache architecture documentation
Reduce database work that remains on cache misses
Caching repeated responses and optimizing queries address different causes of database usage. A request that misses cache still executes its query, so inspect the query itself as well as the cache hit rate.
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D1 counts rows read during a query, not just rows returned to the client. Cloudflare’s example: scanning a 5,000-row table counts as 5,000 rows read even if a filter returns only a few rows. An unindexed filter can require scanning more rows; an appropriate index on the filtered field can reduce the scan. D1 FAQ
- Review frequently used filters and joins for appropriate indexes.
- Measure row reads alongside request counts; a small response may still involve a large scan.
- Check whether cache misses are concentrated on unique URLs, changing query parameters, or expired entries.
- Keep database usage and cache behavior as separate measurements: either can change without proving the cause of the other.
Choose the cache behavior that fits the request
| Behavior | Workers Cache | Cache API |
|---|---|---|
| Does cache lookup happen before Worker code runs? | Can return a cached response without executing Worker code. Cloudflare Workers Cache documentation | Cache API operations occur within Worker execution; the documentation does not describe it as a way to bypass that execution. Cloudflare cache architecture documentation |
| Geographic scope and tiering | Documented cache tiers. Cloudflare Workers Cache documentation | Local to the data center handling the request; no Tiered Cache support. Cloudflare cache architecture documentation |
| Concurrent requests for the same cacheable, fresh URL | Cloudflare documents collapsing concurrent requests so one Worker runs for the waiting group. Cloudflare Workers Cache documentation | Cloudflare says the Cache API does not collapse concurrent requests. Cloudflare Workers Cache documentation |
| Documented eligible methods and cacheability | GET and HEAD; response cache headers determine whether a response can be stored. Cloudflare Workers Cache documentation | Not stated in the cited cache-architecture material. Cloudflare cache architecture documentation |
Make the 22-day claim reproducible
The headline’s specific cause and result cannot be confirmed from the available account details. There is no identified Postgres provider or plan, usage record, crawler attribution, cache key or TTL, hit ratio, or before-and-after database measurement. The title can describe an author’s reported experience, but it should not be read as a verified benchmark or proof that a D1 cache fixed crawler-driven Postgres usage.
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To substantiate a case study, document the following together:
- The Postgres vendor and plan, what “hours” count, and when any included allowance resets.
- Dated usage or billing records covering the 22-day period.
- Request logs with evidence sufficient to attribute traffic to crawlers.
- The cache primitive used, cache key dimensions, TTL, and invalidation or staleness policy.
- How authenticated or personalized responses were handled.
- Cache hits, misses, and concurrent-request behavior, alongside database usage before and after.
A cache hit ratio alone does not identify who caused the original database load. Likewise, a later drop in database usage does not establish crawler traffic as the original cause unless request-level evidence connects the two.
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