Somewhat surprisingly, the behavior of analytical query engines is crucially affected by the dynamic memory allocator used. Memory allocators highly influence performance, scalability, memory efficiency and memory fairness to other processes. In this work, we provide the first comprehensive experimental analysis on the impact of memory allocation for high-performance query engines. We test five state-of-the-art dynamic memory allocators and discuss their strengths and weaknesses within our DBMS. The right allocator can increase the performance of TPC-DS (SF 100) by 2.7x on a 4-socket Intel Xeon server.

Concurrency control is one of the most performance critical steps in modern many-core database systems. Achieving higher throughput on multi-socket servers is difficult and many concurrency control algorithms reduce the amount of accepted schedules in favor of transaction throughput or relax the isolation level which introduces unwanted anomalies. Both approaches lead to unexpected transaction behavior that is difficult to understand by the database users. We introduce a novel multi-version concurrency protocol that achieves high performance while reducing the number of aborted schedules to a minimum and providing the best isolation level. Our approach leverages the idea of a graph-based scheduler that uses the concept of conflict graphs. As conflict serializable histories can be represented by acyclic conflict graphs, our scheduler maintains the conflict graph and allows all transactions that keep the graph acyclic. All conflict serializable schedules can be accepted by such a graph-based algorithm due to the conflict graph theorem. Hence, only transaction schedules that truly violate the serializability constraints need to abort. Our developed approach is able to accept the useful intersection of commit order preserving conflict serializable (COCSR) and recoverable (RC) schedules which are the two most desirable classes in terms of correctness and user experience. We show experimentally that our graph-based scheduler has very competitive throughput in pure transactional workloads while providing fewer aborts and improved user experience. Our multi-version extension helps to efficiently perform long-running read transactions on the same up-to-date database. Moreover, our graph-based scheduler can outperform the competitors on mixed workloads.

Developers often prefer flexibility over upfront schema design, making semi-structured data formats such as JSON increasingly popular. Large amounts of JSON data are therefore stored and analyzed by relational database systems. In existing systems, however, JSON's lack of a fixed schema results in slow analytics. In this paper, we present JSON tiles, which, without losing the flexibility of JSON, enables relational systems to perform analytics on JSON data at native speed. JSON tiles automatically detects the most important keys and extracts them transparently -often achieving scan performance similar to columnar storage. At the same time, JSON tiles is capable of handling heterogeneous and changing data. Furthermore, we automatically collect statistics that enable the query optimizer to find good execution plans. Our experimental evaluation compares against state-of-the-art systems and research proposals and shows that our approach is both robust and efficient.

Cloud analytical databases employ a disaggregated storage model, where the elastic compute layer accesses data persisted on remote cloud storage in block-oriented columnar formats. Given the high latency and low bandwidth to remote storage and the limited size of fast local storage, caching data at the compute node is important and has resulted in a renewed interest in caching for analytics. Today, each DBMS builds its own caching solution, usually based on file-or block-level LRU. In this paper, we advocate a new architecture of a smart cache storage system called Crystal , that is co-located with compute. Crystal's clients are DBMS-specific "data sources" with push-down predicates. Similar in spirit to a DBMS, Crystal incorporates query processing and optimization components focusing on efficient caching and serving of single-table hyper-rectangles called regions. Results show that Crystal, with a small DBMS-specific data source connector, can significantly improve query latencies on unmodified Spark and Greenplum while also saving on bandwidth from remote storage.