At large companies, it has now become typical to store datasets ranging in size from a few GBs to 100s of PBs in data lakes. The scope of analytics on these datasets ranges from traditional batch-style queries (e.g., OLAP) to explorative, finding needle in a haystack type of queries (e.g., point-lookups, summarization etc.). Resorting to linear scans of these large datasets with huge clusters for every simple query is prohibitively expensive and not the top choice for many of our customers, who are constantly exploring ways to reducing their operational costs – incurring unchecked expenses are their worst nightmare. One way to alleviate this issue would be to bring in ‘indexing’ capabilities (which come de facto in the traditional database systems world) into Apache Spark™.
Among many ways to improve query performance and lowering resource consumption in database systems, indexes are particularly efficient in providing tremendous acceleration for certain workloads since they could reduce the amount of data scanned for a given query and thus also result in lowering resource costs.
Hyperspace is envisioned to be an indexing subsystem for Apache Spark that introduces the ability for users to build, maintain (through a multi-user concurrency model) and leverage indexes (automatically, without any changes to their existing code) on their data (e.g., CSV, JSON, Parquet etc.) for query/workload acceleration.
The rest of the documentation covers the necessary foundations behind Hyperspace including the API design, and how it leverages Apache Spark™ Catalyst optimizer to provide a transparent user experience.