3 Plugins for Safer ActiveRecord Deletions

It's a fairly common requirement for Rails applications: allow the user to delete data but provide some sort of safety valve so that it can be restored. After all, users make mistakes, and one mistaken deletion can ruin your whole day. Although ActiveRecord itself doesn't directly address this scenario, Rails developers have the choice of several plugins to handle it.

acts_as_paranoid - flags items as deleted in situ

The grandfather of them all is acts_as_paranoid. acts_as_paranoid takes the approach of adding a deleted_at attribute to your model, and overriding ActiveRecord#destroy to set that attribute instead of actually deleting the record. It then overrides ActiveRecord#find to hide the pseudo-deleted records. Unfortunately this code is a little long in the tooth, and may not play nicely with recent Rails releases.

acts_as_soft_deletable - moves deleted items to a different table

acts_as_soft_deletable takes a different approach. Instead of keeping the records in the table and flagging them (which ends up requiring quite a bit of messing around with ActiveRecord internals), it wraps the destroy method and uses it to move rows to a special table instead. On the plus side, this is less intrusive, and may result in better performance when dealing with the "real" data table, since it won't be littered with deleted records. On the minus side, it commits you to maintaining two tables for each entity, and keeping them in sync.

acts_as_trashable - serializes deleted data to a "trash" table

You can see a third approach to this problem in acts_as_trashable. This one maintains a single "trash" table into which it serializes any data you delete (at least, for models that are declared to be trashable). The nicest feature here is that the serialization includes any deleted associated child records. acts_as_trashable also includes support for taking out the trash to reduce the size of the trash table.

Depending on your version of Rails, you may find rough edges with any of these three approaches. Best bet: decide which approach feels best for your own particular data needs, and then carefully test before deploying.

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