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Effective query optimization remains an open problem for Big Data Management Systems. In this work, we revisit an old idea, runtime dynamic optimization, and adapt it to a big data management system, AsterixDB. The approach runs in stages (re-optimization points), starting by first executing all predicates local to a single dataset. The intermediate result created by a stage is then used to re-optimize the remaining query. This re-optimization approach avoids inaccurate intermediate result cardinality estimates, thus leading to much better execution plans. While it introduces overhead for materializing intermediate results, experiments show that this overhead is relatively small and is an acceptable price to pay given the optimization benefits. 

Secondary indexes in relational database systems are traditionally built under the assumption that one data record maps to one indexed value. Nowadays, particularly in NoSQL systems, single data records can hold collections of values that users want to access efficiently in an ad-hoc manner. Multi-valued indexes aim to give users the best of both worlds: (i) to keep a more natural data model of records with collections of values, and (ii) to reap the benefits of a secondary index. In this paper, we detail the steps taken to realize multi-valued indexes in AsterixDB, a Big Data management system with a structured query language operating over a collection of docu- ments. This includes (a) creating the specification language for such indexes, (b) illustrating data flows for bulk-loading and maintaining an index, and (c) discussing query plans to take advantage of multi-valued indexes for use in predicates with existential and universal quantification. We conclude with ex- periments that compare AsterixDB multi-valued indexes against similar indexes in MongoDB and Couchbase Query. 

Log-Structured Merge-trees (LSM-trees) have been widely used in modern NoSQL systems. Due to their out-of-place update design, LSM-trees have introduced memory walls among the memory components of multiple LSM-trees and between the write memory and the buffer cache. Optimal memory allocation among these regions is non-trivial because it is highly workload-dependent. Existing LSM-tree implementations instead adopt static memory allocation schemes due to their simplicity and robustness, sacrificing performance. In this paper, we attempt to break down these memory walls in LSM-based storage systems. We first present a memory management architecture that enables adaptive memory management. We then present a partitioned memory component structure with new flush policies to better exploit the write memory to minimize the write cost. To break down the memory wall between the write memory and the buffer cache, we further introduce a memory tuner that tunes the memory allocation between these two regions. We have conducted extensive experiments in the context of Apache AsterixDB using the YCSB and TPC-C benchmarks and we present the results here.