Concurrency control

Summary

In information technology and computer science, especially in the fields of computer programming, operating systems, multiprocessors, and databases, concurrency control ensures that correct results for concurrent operations are generated, while getting those results as quickly as possible. Computer systems, both software and hardware, consist of modules, or components. Each component is designed to operate correctly, i.e., to obey or to meet certain consistency rules. When components that operate concurrently interact by messaging or by sharing accessed data (in memory or storage), a certain component's consistency may be violated by another component. The general area of concurrency control provides rules, methods, design methodologies, and theories to maintain the consistency of components operating concurrently while interacting, and thus the consistency and correctness of the whole system. Introducing concurrency control into a system means applying operation constraints which t

Official source

https://en.wikipedia.org/wiki/Concurrency_control

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The common "one size does not fit all" paradigm isolates transactional and analytical workloads into separate, specialized database systems. Operational data is periodically replicated to a data warehouse for analytics. Competitiveness of enterprises today, however, depends on real-time reporting on operational data, necessitating an integration of transactional and analytical processing in a single database system. The mixed workload should be able to query and modify common data in a shared schema. The database needs to provide performance guarantees for transactional workloads, and, at the same time, efficiently evaluate complex analytical queries. In this paper, we share our analysis of the performance of two main-memory databases that support mixed workloads, SAP HANA and HyPer, while evaluating the mixed workload CH-benCHmark. By examining their similarities and differences, we identify the factors that affect performance while scaling the number of concurrent transactional and analytical clients. The three main factors are (a) data freshness, i.e., how recent is the data processed by analytical queries, (b) flexibility, i.e., restricting transactional features in order to increase optimization choices and enhance performance, and (c) scheduling, i.e., how the mixed workload utilizes resources. Specifically for scheduling, we show that the absence of workload management under cases of high concurrency leads to analytical workloads overwhelming the system and severely hurting the performance of transactional workloads.

Springer International Publishing AG2014

Locking Made Easy

Jelena Antic, Georgios Chatzopoulos, Rachid Guerraoui, Vasileios Trigonakis

A priori, locking seems easy: To protect shared data from concurrent accesses, it is sufficient to lock before accessing the data and unlock after. Nevertheless, making locking efficient requires fine-tuning (a) the granularity of locks and (b) the locking strategy for each lock and possibly each workload. As a result, locking can become very complicated to design and debug. We present GLS, a middleware that makes lock-based programming simple and effective. GLS offers the classic lock-unlock interface of locks. However, in contrast to classic lock libraries, GLS does not require any effort from the programmer for allocating and initializing locks, nor for selecting the appropriate locking strategy. With GLS, all these intricacies of locking are hidden from the programmer. GLS is based on GLK, a generic lock algorithm that dynamically adapts to the contention level on the lock object. GLK is able to deliver the best performance among simple spinlocks, scalable queue-based locks, and blocking locks. Furthermore, GLS offers several debugging options for easily detecting various lock-related issues, such as deadlocks. We evaluate GLS and GLK on two modern hardware platforms, using several software systems (i.e., HamsterDB, Kyoto Cabinet, Memcached, MySQL, SQLite) and show how GLK improves their performance by 23% on average, compared to their default locking strategies. We illustrate the simplicity of using GLS and its debugging facilities by rewriting the synchronization code for Memcached and detecting two potential correctness issues.

ACM Press2016

Isolation-only Transactions by Typing and Versioning

In this paper we design a language and runtime support for isolation-only, multithreaded transactions (called tasks). Tasks allow isolation to be declared instead of having to be encoded using the low-level synchronization constructs. The key concept of our design is the use of a type system to support rollback-free and safe runtime execution of tasks. We present a first-order type system which can verify information for the concurrency controller. We use an operational semantics to formalize and prove the type soundness result and an isolation property of tasks. The semantics uses a specialized concurrency control algorithm, that is based on access versioning.

2005