How KV works
KV is a global, low-latency, key-value data store. It stores data in a small number of centralized data centers, then caches that data in Cloudflare’s data centers after access.
KV supports exceptionally high read volumes with low latency, making it possible to build highly dynamic APIs.
While reads are periodically revalidated in the background, requests which are not in cache and need to hit the centralized back end can experience high latencies.
When you write to KV, your data is written to central data stores. Your data is not sent automatically to every location’s cache.
Initial reads from a location do not have a cached value. Data must be read from the nearest regional tier, followed by a central tier, degrading finally to the central stores for a truly cold global read. While the first access is slow globally, subsequent requests are faster, especially if requests are concentrated in a single region.
Frequent reads from the same location return the cached value without reading from anywhere else, resulting in the fastest response times. KV operates diligently to keep the latest value in the cache by refreshing from upper tiers and the central data stores in the background.
Refreshing from upper tiers and the central data stores in the background is done carefully so that assets that are being accessed continue to be kept served from the cache without any stalls.
KV is optimized for high-read applications. It stores data centrally and uses a hybrid push/pull-based replication to store data in cache. KV is suitable for use cases where you need to write relatively infrequently, but read quickly and frequently. Infrequently read values are pulled from other data centers or the central stores, while more popular values are cached in the data centers they are requested from.
To improve KV performance, increase the cacheTtl parameter up from its default 60 seconds.
KV achieves high performance by caching ↗ which makes reads eventually-consistent with writes.
Changes are usually immediately visible in the Cloudflare global network location at which they are made. Changes may take up to 60 seconds or more to be visible in other global network locations as their cached versions of the data time out.
Negative lookups indicating that the key does not exist are also cached, so the same delay exists noticing a value is created as when a value is changed.
KV does not perform like an in-memory datastore, such as Redis ↗. Accessing KV values, even when locally cached, has significantly more latency than reading a value from memory within a Worker script.
KV achieves high performance by being eventually-consistent. At the Cloudflare global network location at which changes are made, these changes are usually immediately visible. However, this is not guaranteed and therefore it is not advised to rely on this behaviour. In other global network locations changes may take up to 60 seconds or more to be visible as their cached versions of the data time-out.
Visibility of changes takes longer in locations which have recently read a previous version of a given key (including reads that indicated the key did not exist, which are also cached locally).
An approach to achieve write-after-write consistency is to send all of your writes for a given KV key through a corresponding instance of a Durable Object, and then read that value from KV in other Workers. This is useful if you need more control over writes, but are satisfied with KV’s read characteristics described above.
Refer to Data security documentation to understand how Workers KV secures data.