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Register a new userDGCC:A New Dependency Graph based Concurrency Control Protocol for Multicore Database Systems
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Added by
Chang Yao
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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Multicore CPUs and large memories are increasingly becoming the norm in modern computer systems. However, current database management systems (DBMSs) are generally ineffective in exploiting the parallelism of such systems. In particular, contention can lead to a dramatic fall in performance. In this paper, we propose a new concurrency control protocol called DGCC (Dependency Graph based Concurrency Control) that separates concurrency control from execution. DGCC builds dependency graphs for batched transactions before executing them. Using these graphs, contentions within the same batch of transactions are resolved before execution. As a result, the execution of the transactions does not need to deal with contention while maintaining full equivalence to that of serialized execution. This better exploits multicore hardware and achieves higher level of parallelism. To facilitate DGCC, we have also proposed a system architecture that does not have certain centralized control components yielding better scalability, as well as supports a more efficient recovery mechanism. Our extensive experimental study shows that DGCC achieves up to four times higher throughput compared to that of state-of-the-art concurrency control protocols for high contention workloads.
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Multi-versioned database systems have the potential to significantly increase the amount of concurrency in transaction processing because they can avoid read-write conflicts. Unfortunately, the increase in concurrency usually comes at the cost of transaction serializability. If a database user requests full serializability, modern multi-versioned systems significantly constrain read-write concurrency among conflicting transactions and employ expensive synchronization patterns in their design. In main-memory multi-core settings, these additional constraints are so burdensome that multi-versioned systems are often significantly outperformed by single-version systems. We propose Bohm, a new concurrency control protocol for main-memory multi-versioned database systems. Bohm guarantees serializable execution while ensuring that reads never block writes. In addition, Bohm does not require reads to perform any book-keeping whatsoever, thereby avoiding the overhead of tracking reads via contended writes to shared memory. This leads to excellent scalability and performance in multi-core settings. Bohm has all the above characteristics without performing validation based concurrency control. Instead, it is pessimistic, and is therefore not prone to excessive aborts in the presence of contention. An experimental evaluation shows that Bohm performs well in both high contention and low contention settings, and is able to dramatically outperform state-of-the-art multi-versioned systems despite maintaining the full set of serializability guarantees.
Although the emergence of the programmable smart contract makes blockchain systems easily embrace a wider range of industrial areas, how to execute smart contracts efficiently becomes a big challenge nowadays. Due to the existence of Byzantine nodes, the mechanism of executing smart contracts is quite different from that in database systems, so that existing successful concurrency control protocols in database systems cannot be employed directly. Moreover, even though smart contract execution follows a two-phase style, i.e, the miner node executes a batch of smart contracts in the first phase and the validators replay them in the second phase, existing parallel solutions only focus on the optimization in the first phase, but not including the second phase. In this paper, we propose a novel efficient concurrency control scheme which is the first one to do optimization in both phases. Specifically, (i) in the first phase, we give a variant of OCC (Optimistic Concurrency Control) protocol based on {em batching} feature to improve the concurrent execution efficiency for the miner and produce a schedule log with high parallelism for validators. Also, a graph partition algorithm is devised to divide the original schedule log into small pieces and further reduce the communication cost; and (ii) in the second phase, we give a deterministic OCC protocol to replay all smart contracts efficiently on multi-core validators where all cores can replay smart contracts independently. Theoretical analysis and extensive experimental results illustrate that the proposed scheme outperforms state-of-art solutions significantly.
Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, optimistic concurrency control (OCC) is better than two-phase-locking (2PL) under low contention, while the converse is true under high contention. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Instead of choosing among a small number of known algorithms, our approach searches in a policy space of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%.
Digital multimedia watermarking technology was suggested in the last decade to embed copyright information in digital objects such images, audio and video. However, the increasing use of relational database systems in many real-life applications created an ever increasing need for watermarking database systems. As a result, watermarking relational database systems is now merging as a research area that deals with the legal issue of copyright protection of database systems. Approach: In this study, we proposed an efficient database watermarking algorithm based on inserting binary image watermarks in non-numeric mutli-word attributes of selected database tuples. Results: The algorithm is robust as it resists attempts to remove or degrade the embedded watermark and it is blind as it does not require the original database in order to extract the embedded watermark. Conclusion: Experimental results demonstrated blindness and the robustness of the algorithm against common database attacks.
At present, graph model is widely used in many applications, such as knowledge graph, financial anti-fraud. Unstructured data(such as images, videos, and audios) is under explosive growing. So, queries of unstructured data content on graph are widespread in a rich vein of real-world applications. Many graph database systems have started to support unstructured data to meet such demands. However, queries over structured and unstructured data on graph are often treated as separate tasks in most systems. These tasks are executed on different module of the tools chain. Collaborative queries (i.e., involving both data types) are not yet fully supported.This paper proposes a graph database supporting collaborative queries on property graph, named PandaDB. Its to fulfill the emerging demands about querying unstructured data on property graph model. PandaDB introduces CypherPlus, a query language which enables the users to express collaborative queries using cypher semantics by introducing sub-property and a series of logical operators. PandaDB is built based on Neo4j, manage the unstructured data in the format of BLOB. The computable pattern is proposed to introduce the content of unstructured data into computation. Moreover, to support the large-scale query, this paper proposes the semantic index, cache and index the extracted computable pattern. The collaborative query on graph is optimized by the min-cost optimization method. Experimental results on both public and in-house datasets show the performance achieved by PandaDB and its effectiveness.
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