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ScaleDB is a serializable in-memory transactional database that achieves excellent scalability on multi-core machines by asynchronously updating range indexes. We find that asynchronous range index updates can significantly improve database scalability by applying updates in batches, reducing contention on critical sections. To avoid stale reads, ScaleDB uses small hash indexlets to hold delayed updates. We use in- dexlets to design ACC, an asynchronous concurrency control protocol providing serializability. With ACC, it is possible to delay range index updates without adverse performance effects on transaction execution in the common case. ACC delivers scalable serializable isolation for transactions, with high throughput and low abort rate. Evaluation on a dual- socket server with 36 cores shows that ScaleDB achieves 9.5× better query throughput than Peloton on the YCSB bench- mark and 1.8× better transaction throughput than Cicada on the TPC-C benchmark.
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Database isolation by scheduling
Transaction isolation is conventionally achieved by restricting access to the physical items in a database. To maximize performance, isolation functionality is often packaged with recovery, I/O, and data access methods in a monolithic transactional storage manager. While this design has historically afforded high performance in online transaction processing systems, industry trends indicate a growing need for a new approach in which intertwined components of the transactional storage manager are disaggregated into modular services. This paper presents a new method to modularize the isolation component. Our work builds on predicate locking, an isolation mechanism that enables this modularization by locking logical rather than physical items in a database. Predicate locking is rarely used as the core isolation mechanism because of its high theoretical complexity and perceived overhead. However, we show that this overhead can be substantially reduced in practice by optimizing for common predicate structures. We present DIBS, a transaction scheduler that employs our predicate locking optimizations to guarantee isolation as a modular service. We evaluate the performance of DIBS as the sole isolation mechanism in a data processing system. In this setting, DIBS scales up to 10.5 million transactions per second on a TATP workload. We also explore how DIBS can be applied to existing database systems to increase transaction throughput. DIBS reduces per-transaction file system writes by 90% on TATP in SQLite, resulting in a 3X improvement in throughput. Finally, DIBS reduces row contention on YCSB in MySQL, providing serializable isolation with a 1.4X improvement in throughput.
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- Award ID(s):
- 1835446
- PAR ID:
- 10309078
- Date Published:
- Journal Name:
- Proceedings of the VLDB Endowment
- Volume:
- 14
- Issue:
- 9
- ISSN:
- 2150-8097
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ScaleDB is a serializable in-memory transactional database that achieves excellent scalability on multi-core machines by asynchronously updating range indexes. We find that asynchronous range index updates can significantly improve database scalability by applying updates in batches, reducing contention on critical sections. To avoid stale reads, ScaleDB uses small hash indexlets to hold delayed updates. We use indexlets to design ACC, an asynchronous concurrency control protocol providing serializability. With ACC, it is possible to delay range index updates without adverse performance effects on transaction execution in the common case. ACC delivers scalable serializable isolation for transactions, with high throughput and low abort rate. Evaluation on a dual-socket server with 36 cores shows that ScaleDB achieves 9.5× better query throughput than Peloton on the YCSB benchmark and 1.8× better transaction throughput than Cicada on the TPC-C benchmark.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.14778/3461535.3461537
