The storage space already automatically increases up to a maximum size of 128 tebibytes (TiB), and will now automatically decrease when data is deleted. Since October 2020, the storage space allocated to your Amazon Aurora database cluster decreases dynamically when you delete data from the cluster. For a while when you deleted data from Aurora clusters, such as by dropping a table or partition, the overall allocated storage space remained the same. One of the big advantages of Amazon Aurora has been elastic storage that scales with your data eliminating the need for provisioning large storage capacity and utilize some percentage of that. For simplicity, I will be focusing on Amazon Aurora for PostgreSQL for engine-specific examples and references on architecture for storage, memory, and optimizations.Īmazon Aurora Storage Architecture and IOĪmazon Aurora is backed by a robust, scalable, and distributed storage architecture. This post attempts to shed some more light on Amazon Aurora, the Local Storage architecture as well as some options to improve performance and utilize local storage, lower IO costs as well as not run into local storage limits. What’s going on with Local Storage then? We could be still be left with low local storage or no space leading to failover if we are running databases that are generally used for OLTP but has the need to periodically run fewer but large jobs that push the local storage limits. No need to delete data or to split the database across multiple instances for storage purposes which is great. Sounds familiar? “No space left on device” is certainly not common when it comes to Amazon Aurora as storage scales automatically up to 128TB and you’re less likely to reach the limit when you scale up your application on a single Amazon Aurora database cluster. “ERROR: could not write block n of temporary file: No space left on device.”
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