Machine unlearning (MU) enables the removal of selected training data from models to address privacy compliance, security, and liability issues in recommender systems. Existing MU benchmarks poorly reflect real-world recommender settings: they focus primarily on collaborative filtering, assume unrealistically large deletion requests, and overlook practical constraints such as sequential unlearning and efficiency. We present ERASE, a large-scale benchmark for MU in recommender systems designed to align with real-world usage. ERASE spans three core tasks---collaborative filtering, session-based recommendation, and next-basket recommendation---and includes unlearning realistic scenarios such as sequentially removing sensitive interactions or spam. The benchmark covers seven unlearning algorithms, including general-purpose and recommender-specific methods, across nine public datasets and nine state-of-the-art models, producing over 600 GB of reusable artifacts, such as extensive logs and over a thousand model checkpoints. The artifacts that we release enable systematic analyses of where current unlearning methods succeed and where they fail. ERASE shows that approximate unlearning matches retraining in some settings, but robustness varies widely across datasets and architectures. Repeated unlearning exposes weaknesses in general-purpose methods, especially for attention-based and recurrent models, while recommender-specific approaches behave more reliably. ERASE offers an empirical basis for the community to assess, drive, and track progress toward practical MU in recommender systems.