More Like this

1. ScaleDB: A Scalable, Asynchronous In-Memory Database

   Mehdi, Syed Akbar; Hwang, Deukyeon; Peter, Simon; Alvisi, Lorenzo (July 2023, Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation)

   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. 

   more » « less
2. Database isolation by scheduling

   https://doi.org/10.14778/3461535.3461537  

   Gaffney, Kevin P.; Claus, Robert; Patel, Jignesh M. (May 2021, Proceedings of the VLDB Endowment)

   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. 

   more » « less
3. Regular Sequential Serializability and Regular Sequential Consistency

   https://doi.org/10.1145/3477132.3483566  

   Helt, Jeffrey; Burke, Matthew; Levy, Amit; Lloyd, Wyatt (October 2021, ACM Symposium on Operating Systems Principles)

   Strictly serializable (linearizable) services appear to execute transactions (operations) sequentially, in an order consistent with real time. This restricts a transaction's (operation's) possible return values and in turn, simplifies application programming. In exchange, strictly serializable (linearizable) services perform worse than those with weaker consistency. But switching to such services can break applications. This work introduces two new consistency models to ease this trade-off: regular sequential serializability (RSS) and regular sequential consistency (RSC). They are just as strong for applications: we prove any application invariant that holds when using a strictly serializable (linearizable) service also holds when using an RSS (RSC) service. Yet they relax the constraints on services---they allow new, better-performing designs. To demonstrate this, we design, implement, and evaluate variants of two systems, Spanner and Gryff, relaxing their consistency to RSS and RSC, respectively. The new variants achieve better read-only transaction and read tail latency than their counterparts. 

   more » « less
4. Performance Evaluation of TPC-C Benchmark on Various Cloud Providers

   https://doi.org/10.1109/UEMCON51285.2020.9298047  

   Avula, Raghu Nandan; Zou, Cliff (October 2020, 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON))

   null (Ed.)

   With the hardware costs becoming cheaper day by day and with industry giants focus, Cloud computing has exploded and reached leaps and bounds in the last 15 years. With the power of creating, using and destroying virtual machines in the cloud at the tip of mouse click, industries have started moving their core applications to the cloud. This has reduced the hassle for industries to maintain the hardware by themselves. Tech giants like Amazon, Microsoft and Google are head of the game and the fierce competition between them has led to astonishing innovation. With so many players in the cloud market, it is essential for cloud users to know how each of the services provided by these cloud service providers are performing against each other. In this paper we have evaluated the performance of famous OLTP benchmark TPC-C on these cloud providers. It is observed that Amazon's AWS has performed better than Microsoft Azure and Google Cloud Platform in terms of the number of transactions/orders per second, and I/O reads/writes. We have done the extended comparison with respect to transaction throughput, database throughput and Machine throughput. 

   more » « less
5. QRLIT: Quantum Reinforcement Learning for Database Index Tuning

   https://doi.org/10.3390/fi16120439  

   Barbosa, Diogo; Gruenwald, Le; D’Orazio, Laurent; Bernardino, Jorge (December 2024, Future Internet)

   Selecting indexes capable of reducing the cost of query processing in database systems is a challenging task, especially in large-scale applications. Quantum computing has been investigated with promising results in areas related to database management, such as query optimization, transaction scheduling, and index tuning. Promising results have also been seen when reinforcement learning is applied for database tuning in classical computing. However, there is no existing research with implementation details and experiment results for index tuning that takes advantage of both quantum computing and reinforcement learning. This paper proposes a new algorithm called QRLIT that uses the power of quantum computing and reinforcement learning for database index tuning. Experiments using the database TPC-H benchmark show that QRLIT exhibits superior performance and a faster convergence compared to its classical counterpart. 

   more » « less