Large highly distributed data sets are poorly supported by current query technologies. Applications such as endsystem-based network management are characterized by data stored on large numbers of endsystems, with frequent local updates and relatively infrequent global one-shot queries. The challenges are scale (103 to 109 endsystems) and endsystem unavailability. In such large systems, a significant fraction of endsystems and their data will be unavailable at any given time. Existing methods to provide high data availability despite endsystem unavailability involve centralizing, redistributing or replicating the data. At large scale these methods are not scalable. We advocate a design that trades query delay for completeness, incrementally returning results as endsystems become available. We also introduce the idea of completeness prediction, which provides the user with explicit feedback about this delay/completeness tradeoff. Completeness prediction is based on replication of compact data summaries and availability models. This metadata is orders of magnitude smaller than the data. Seaweed is a scalable query infrastructure supporting incremental results, online in-network aggregation and completeness prediction. It is built on a distributed hash table (DHT) but unlike previous DHT based approaches it does not redistribute data across the network. It exploits the DHT infrastructure for failure-resilient metadata replication, query dissemination, and result aggregation. We analytically compare Seaweed’s scalability against other approaches and also evaluate the Seaweed prototype running on a large-scale network simulator driven by real-world traces.