Supporting Dynamic Graphs and Temporal Entity Deletions in the LDBC Social Network Benchmark's Data Generator

Waudby, Jack; Steer, Benjamin A.; Prat-Pérez, Arnau; Szárnyas, Gábor

doi:10.1145/3398682.3399165

Cited by 13 publications

(8 citation statements)

References 19 publications

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“…While we designed the benchmark to be simple, we expect that users targeting more mature systems will want to extend it to include common DBMS features such as update operations. Building on the LDBC Datagen allows users to make such extensions with a reasonable development effort, e.g., updates can be supported by using the temporal attributes produced by Datagen [54].…”

Section: Discussionmentioning

confidence: 99%

LSQB

Mhedhbi

Lissandrini

Kuiper

et al. 2021

Proceedings of the 4th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &Amp; Systems (GRADES) and

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We introduce LSQB, a new large-scale subgraph query benchmark. LSQB tests the performance of database management systems on an important class of subgraph queries overlooked by existing benchmarks. Matching a labelled structural graph pattern, referred to as subgraph matching, is the focus of LSQB. In relational terms, the benchmark tests DBMSs' join performance as a choke-point since subgraph matching is equivalent to multi-way joins between base Vertex and base Edge tables on ID attributes. The benchmark focuses on read-heavy workloads by relying on global queries which have been ignored by prior benchmarks. Global queries, also referred to as unseeded queries, are a type of queries that are only constrained by labels on the query vertices and edges. LSQB contains a total of nine queries and leverages the LDBC social network data generator for scalability. The benchmark gained both academic and industrial interest and is used internally by 5+ different vendors.

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Section: Discussionmentioning

confidence: 99%

LSQB

Mhedhbi

Lissandrini

Kuiper

et al. 2021

Proceedings of the 4th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &Amp; Systems (GRADES) and

Self Cite

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“…The LDBC SNB Interactive [21,38] is another prominent transactional benchmark with insertions, it targets an online workload on property graphs [5]. At the time of writing, the LDBC consortium is pursuing an ongoing effort [60] for a hybrid workload on property graphs, with the inclusion of general updates.…”

Section: Related Workmentioning

confidence: 99%

Teseo and the analysis of structural dynamic graphs

Leo

Boncz

2021

Proc. VLDB Endow.

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We present Teseo, a new system for the storage and analysis of dynamic structural graphs in main-memory and the addition of transactional support. Teseo introduces a novel design based on sparse arrays, large arrays interleaved with gaps, and a fat tree, where the graph is ultimately stored. Our design contrasts with early systems for the analysis of dynamic graphs, which often lack transactional support and are anchored to a vertex table as a primary index. We claim that the vertex table implies several constraints, often neglected, that can actually impair the generality, the robustness and extension opportunities of these systems. We compare Teseo with other dynamic graph systems, showing a high resilience to workload and input changes, while achieving comparable, if not superior, throughputs in updates and latencies in raw scans.

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“…The benchmark can generate data of varying sizes and provides a series of realistic usage scenarios which aim at stress-testing and discovering bottlenecks in graph-based technologies. The latest version of the benchmark at the time of writing (0.4.0) generates data which contain both insert and delete operations [108] and can be conveniently transformed into a stream of timestamped creation and deletion events.…”

Section: Ldbc Social Network Benchmarkmentioning

confidence: 99%

“…The SNB data was generated using the default parameters and the metamodel used closely resembles the original-we have only added vertices specific to InTempo, i.e., the MonitorableEntity, or specific to the evaluation, i.e., links of interest were encoded as vertices. We made minor modifications to the data which fixed a small number of consistency issues, i.e., deletions occurring before creations, which probably stem from the fact that deletions in the benchmark's output are a rather new feature [108].…”

Section: Threats To Validitymentioning

confidence: 99%

Incremental execution of temporal graph queries over runtime models with history and its applications

et al. 2021

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Modern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. This setting has sparked an interest in solutions that use a runtime model which reflects the system state and operational context to monitor and adapt the system in reaction to changes during its runtime. Few solutions focus on the evolution of the model over time, i.e., its history, although history is required for monitoring temporal behaviors and may enable more informed decision-making. One reason is that handling the history of a runtime model poses an important technical challenge, as it requires tracing a part of the model over multiple model snapshots in a timely manner. Additionally, the runtime setting calls for memory-efficient measures to store and check these snapshots. Following the common practice of representing a runtime model as a typed attributed graph, we introduce a language which supports the formulation of temporal graph queries, i.e., queries on the ordering and timing in which structural changes in the history of a runtime model occurred. We present a querying scheme for the execution of temporal graph queries over history-aware runtime models. Features such as temporal logic operators in queries, the incremental execution, the option to discard history that is no longer relevant to queries, and the in-memory storage of the model, distinguish our scheme from relevant solutions. By incorporating temporal operators, temporal graph queries can be used for runtime monitoring of temporal logic formulas. Building on this capability, we present an implementation of the scheme that is evaluated for runtime querying, monitoring, and adaptation scenarios from two application domains.

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Supporting Dynamic Graphs and Temporal Entity Deletions in the LDBC Social Network Benchmark's Data Generator

Cited by 13 publications

References 19 publications

LSQB

LSQB

Teseo and the analysis of structural dynamic graphs

Incremental execution of temporal graph queries over runtime models with history and its applications

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