Microsoft recently introduced Azure Synapse Analytics, which offers an integrated experience across data ingestion, storage, and querying in Apache Spark and T-SQL over data in the lake, including files and warehouse tables. In this paper, we present our experiences with designing and implementing Hyperspace, the indexing subsystem underlying Synapse. Hyperspace enables users to build multiple types of secondary indexes on their data, maintain them through a multi-user concurrency model, and leverage them automatically---without any change to their application code---for query/workload acceleration. Many requirements of Hyperspace are based on feedback from several enterprise customers. We present the details of Hyperspace's underlying design, the user-facing APIs, its concurrency control protocol for index access, its index-aware query processing techniques, and its maintenance mechanisms for handling index updates. Evaluations over standard industry benchmarks and real customer workloads show that Hyperspace can accelerate query execution by up to 10x and in certain real-world workloads, even up to two orders of magnitude.

Helios is a distributed, highly-scalable system used at Microsoft for flexible ingestion, indexing, and aggregation of large streams of real-time data that is designed to plug into relational engines. The system collects close to a quadrillion events indexing approximately 16 trillion search keys per day from hundreds of thousands of machines across tens of data centers around the world. Helios use cases within Microsoft include debugging/diagnostics in both public and government clouds, workload characterization, cluster health monitoring, deriving business insights and performing impact analysis of incidents in other large-scale systems such as Azure Data Lake and Cosmos. Helios also serves as a reference blueprint for other large-scale systems within Microsoft. We present the simple data model behind Helios, which offers great flexibility and control over costs, and enables the system to asynchronously index massive streams of data. We also present our experiences in building and operating Helios over the last five years at Microsoft.