The LDBC Social Network Benchmark Interactive workload v2: A transactional graph query benchmark with deep delete operations

The LDBC Social Network Benchmark's Interactive workload captures an OLTP scenario operating on a correlated social network graph. It consists of complex graph queries executed concurrently with a stream of updates operation. Since its initial release in 2015, the Interactive workload has become the de facto industry standard for benchmarking transactional graph data management systems. As graph systems have matured and the community's understanding of graph processing features has evolved, we initiated the renewal of this benchmark. This paper describes the Interactive v2 workload with several new features: delete operations, a cheapest path-finding query, support for larger data sets, and a novel temporal parameter curation algorithm that ensures stable runtimes for path queries

The LDBC Social Network Benchmark Interactive workload v2: A transactional graph query benchmark with deep delete operations

The LDBC Social Network Benchmark’s Interactive workload captures an OLTP scenario operating on a correlated social network graph. It consists of complex graph queries executed concurrently with a stream of updates operation. Since its initial release in 2015, the Interactive workload has become the de facto industry standard for benchmarking transactional graph data management systems. As graph systems have matured and the community’s understanding of graph processing features has evolved, we initiated the renewal of this benchmark. This paper describes the draft Interactive v2 workload with several new features: delete operations, a cheapest path-finding query, support for larger data sets, and a novel temporal parameter curation algorithm that ensures stable runtimes for path queries

LSQB: A large-scale subgraph query benchmark

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

Structural characterization of the interaction of α-synuclein nascent chains with the ribosomal surface and trigger factor

The ribosome is increasingly becoming recognized as a key hub for integrating quality control processes associated with protein biosynthesis and cotranslational folding (CTF). The molecular mechanisms by which these processes take place, however, remain largely unknown, in particular in the case of intrinsically disordered proteins (IDPs). To address this question, we studied at a residue-specific level the structure and dynamics of ribosome-nascent chain complexes (RNCs) of α-synuclein (αSyn), an IDP associated with Parkinson’s disease (PD). Using solution-state nuclear magnetic resonance (NMR) spectroscopy and coarse-grained molecular dynamics (MD) simulations, we find that, although the nascent chain (NC) has a highly disordered conformation, its N-terminal region shows resonance broadening consistent with interactions involving specific regions of the ribosome surface. We also investigated the effects of the ribosome-associated molecular chaperone trigger factor (TF) on αSyn structure and dynamics using resonance broadening to define a footprint of the TF–RNC interactions. We have used these data to construct structural models that suggest specific ways by which emerging NCs can interact with the biosynthesis and quality control machinery

The LDBC social network benchmark: Business intelligence workload

The Social Network Benchmark’s Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC’s “choke point”-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of “parameter curation” in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result – only audited results can use this trademarked term

The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space

Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficiently supporting these use cases yields a number of unique requirements, including the need for a concise query language and graph-aware query optimization techniques. The goal of the Linked Data Benchmark Council (LDBC) is to design a set of standard benchmarks that capture representative categories of graph data management problems, making the performance of systems comparable and facilitating competition among vendors. LDBC also conducts research on graph schemas and graph query languages. This paper introduces the LDBC organization and its work over the last decade

The LDBC Social Network Benchmark

The Linked Data Benchmark Council's Social Network Benchmark (LDBC SNB) is an effort intended to test various functionalities of systems used for graph-like data management. For this, LDBC SNB uses the recognizable scenario of operating a social network, characterized by its graph-shaped data. LDBC SNB consists of two workloads that focus on different functionalities: the Interactive workload (interactive transactional queries) and the Business Intelligence workload (analytical queries). This document contains the definition of the Interactive Workload and the first draft of the Business Intelligence Workload. This includes a detailed explanation of the data used in the LDBC SNB benchmark, a detailed description for all queries, and instructions on how to generate the data and run the benchmark with the provided software.Comment: For the repository containing the source code of this technical report, see https://github.com/ldbc/ldbc_snb_doc

The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space

Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficiently supporting these use cases yields a number of unique requirements, including the need for a concise query language and graph-aware query optimization techniques. The goal of the Linked Data Benchmark Council (LDBC) is to design a set of standard benchmarks that capture representative categories of graph data management problems, making the performance of systems comparable and facilitating competition among vendors. LDBC also conducts research on graph schemas and graph query languages. This paper introduces the LDBC organization and its work over the last decade

The LDBC social network benchmark: Business intelligence workload

The Social Network Benchmark’s Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC’s “choke point”-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of “parameter curation” in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result – only audited results can use this trademarked term

Towards testing ACID compliance in the LDBC Social Network Benchmark

Verifying ACID compliance is an essential part of database benchmarking, because the integrity of performance results can be undermined as the performance benefits of operating with weaker safety guarantees (at the potential cost of correctness) are well known. Traditionally, benchmarks have specified a number of tests to validate ACID compliance. However, these tests have been formulated in the context of relational database systems and SQL, whereas our scope of benchmarking are systems for graph data, many of which are non-relational. This paper presents a set of data model-agnostic ACID compliance tests for the LDBC (Linked Data Benchmark Council) Social Network Benchmark suite’s Interactive (SNB-I) workload, a transaction processing benchmark for graph databases. We test all ACID properties with a particular emphasis on isolation, covering 10 transaction anomalies in total. We present results from implementing the test suite on 5 database systems