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Register a new userCracking In-Memory Database Index A Case Study for Adaptive Radix Tree Index
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Added by
Yidong Song
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher query processing performance than disk databases and is more suitable for real-time query processing. Therefore, there is an urgent need to reduce the index creation and update cost for in-memory databases. Database cracking technology is currently recognized as an effective method to reduce the index initialization time. However, conventional cracking algorithms are focused on simple column data structure rather than those complex index structure for in-memory databases. In order to show the feasibility of in-memory database index cracking and promote to future more extensive research, this paper conducted a case study on the Adaptive Radix Tree (ART), a popular tree index structure of in-memory databases. On the basis of carefully examining the ART index construction overhead, an algorithm using auxiliary data structures to crack the ART index is proposed.
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Very large volumes of spatial data increasingly become available and demand effective management. While there has been decades of research on spatial data management, few works consider the current state of commodity hardware, having relatively large memory and the ability of parallel multi-core processing. In this work, we re-consider the design of spatial indexing under this new reality. Specifically, we propose a main-memory indexing approach for objects with spatial extent, which is based on a classic regular space partitioning into disjoint tiles. The novelty of our index is that the contents of each tile are further partitioned into four classes. This second-level partitioning not only reduces the number of comparisons required to compute the results, but also avoids the generation and elimination of duplicate results, which is an inherent problem of spatial indexes based on disjoint space partitioning. The spatial partitions defined by our indexing scheme are totally independent, facilitating effortless parallel evaluation, as no synchronization or communication between the partitions is necessary. We show how our index can be used to efficiently process spatial range queries and drastically reduce the cost of the refinement step of the queries. In addition, we study the efficient processing of numerous range queries in batch and in parallel. Extensive experiments on real datasets confirm the efficiency of our approaches.
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In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Specifically, it is necessary for IMDBs to have efficient snapshot performance to support certain special applications (e.g., consistent checkpoint, HTAP). Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in academia to trade off throughput and latency, but industrial IMDBs such as Redis adhere to the simple fork algorithm. To understand this phenomenon, we conduct comprehensive performance evaluations on mainstream snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from academia. Finally, we have opensourced the implementation of all the above snapshot algorithms so that practitioners are able to benchmark the performance of each algorithm and select proper methods for different application scenarios.
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