Abstract: Techniques are described for monitoring and enabling access to information about key access patterns for nonrelational database tables managed by a nonrelational database service of a service provider network. Storage nodes store data as key-value pairs (or key-document pairs), where the records or documents of a database are addressed by a unique key for each record. The storage nodes over time receive requests to perform various operations (for example, to get, put, update, or delete data in a table) associated with records stored by the storage nodes. The storage nodes collect information indicating a frequency with which such requests are received for each key for which a storage node is responsible. This frequency information for each key can be normalized relative to a maximum frequency supported by the storage nodes to obtain an access frequency score, where the maximum frequency indicates a frequency at which additional requests to the storage node may be throttled.

Abstract: Methods, systems, and techniques for annotating large amounts of data are provided. Example embodiments provide a Scalable Annotation Architecture (a “SAS”), which builds predictive models for an annotation from the ground up, without knowledge of the data. The SAS operates by performing in an iterative fashion a process that seeds training data and hypothesizes a predictive model based upon that data, then sends samples of the data to a crowdsourcing environment to provide selective verification. This process is repeated iteratively until a desired precision is reached and then the model is employed independently in a production system. In one embodiment, the SAS is used to annotate data provided by an open data platform.