Many organizations have embraced the "Lakehouse" data management paradigm, which involves constructing structured data warehouses on top of open, unstructured data lakes. This approach stands in stark contrast to traditional, closed, relational databases and introduces challenges for performance and stability of distributed query processors. Firstly, in large-scale, open Lakehouses with uncurated data, high ingestion rates, external tables, or deeply nested schemas, it is often costly or wasteful to maintain perfect and up-to-date table and column statistics. Secondly, inherently imperfect cardinality estimates with conjunctive predicates, joins and user-defined functions can lead to bad query plans. Thirdly, for the sheer magnitude of data involved, strictly relying on static query plan decisions can result in performance and stability issues such as excessive data movement, substantial disk spillage, or high memory pressure. To address these challenges, this paper presents our design, implementation, evaluation and practice of the Adaptive Query Execution (AQE) framework, which exploits natural execution pipeline breakers in query plans to collect accurate statistics and re-optimize them at runtime for both performance and robustness. In the TPC-DS benchmark, the technique demonstrates up to 25× per query speedup. At Databricks, AQE has been successfully deployed in production for multiple years. It powers billions of queries and ETL jobs to process exabytes of data per day, through key enterprise products such as Databricks Runtime, Databricks SQL, and Delta Live Tables.

This paper describes the use of Storm at Twitter. Storm is a real-time fault-tolerant and distributed stream data processing system. Storm is currently being used to run various critical computations in Twitter at scale, and in real-time. This paper describes the architecture of Storm and its methods for distributed scale-out and fault-tolerance. This paper also describes how queries (aka. topologies) are executed in Storm, and presents some operational stories based on running Storm at Twitter. We also present results from an empirical evaluation demonstrating the resilience of Storm in dealing with machine failures. Storm is under active development at Twitter and we also present some potential directions for future work.

Caching has been proposed (and implemented) by OLAP systems in order to reduce response times for multidimensional queries. Previous work on such caching has considered table level caching and query level caching. Table level caching is more suitable for static schemes. On the other hand, query level caching can be used in dynamic schemes, but is too coarse for “large” query results. Query level caching has the further drawback for small query results in that it is only effective when a new query is subsumed by a previously cached query. In this paper, we propose caching small regions of the multidimensional space called “chunks”. Chunk-based caching allows fine granularity caching, and allows queries to partially reuse the results of previous queries with which they overlap. To facilitate the computation of chunks required by a query but missing from the cache, we propose a new organization for relational tables, which we call a “chunked file.” Our experiments show that for workloads that exhibit query locality, chunked caching combined with the chunked file organization performs better than query level caching. An unexpected benefit of the chunked file organization is that, due to its multidimensional clustering properties, it can significantly improve the performance of queries that “miss” the cache entirely as compared to traditional file organizations.

Database researchers have made significant progress on several research issues related to multidimensional data analysis, including the development of fast cubing algorithms, efficient schemes for creating and maintaining precomputed group-bys, and the design of efficient storage structures for multidimensional data. However, to date there has been little or no work on multidimensional query optimization. Recently, Microsoft has proposed “OLE DB for OLAP” as a standard multidimensional interface for databases. OLE DB for OLAP defines Multi-Dimensional Expressions (MDX), which have the interesting and challenging feature of allowing clients to ask several related dimensional queries in a single MDX expression. In this paper, we present three algorithms to optimize multiple related dimensional queries. Two of the algorithms focus on how to generate a global plan from several related local plans. The third algorithm focuses on generating a good global plan without first generating local plans. We also present three new query evaluation primitives that allow related query plans to share portions of their evaluation. Our initial performance results suggest that the exploitation of common subtask evaluation and global optimization can yield substantial performance improvements when relational database systems are used as data sources for multidimensional analysis.