19 research outputs found
The LDBC Graphalytics Benchmark
In this document, we describe LDBC Graphalytics, an industrial-grade
benchmark for graph analysis platforms. The main goal of Graphalytics is to
enable the fair and objective comparison of graph analysis platforms. Due to
the diversity of bottlenecks and performance issues such platforms need to
address, Graphalytics consists of a set of selected deterministic algorithms
for full-graph analysis, standard graph datasets, synthetic dataset generators,
and reference output for validation purposes. Its test harness produces deep
metrics that quantify multiple kinds of systems scalability, weak and strong,
and robustness, such as failures and performance variability. The benchmark
also balances comprehensiveness with runtime necessary to obtain the deep
metrics. The benchmark comes with open-source software for generating
performance data, for validating algorithm results, for monitoring and sharing
performance data, and for obtaining the final benchmark result as a standard
performance report
The LDBC Financial Benchmark
The Linked Data Benchmark Council's Financial Benchmark (LDBC FinBench) is a
new effort that defines a graph database benchmark targeting financial
scenarios such as anti-fraud and risk control. The benchmark has one workload,
the Transaction Workload, currently. It captures OLTP scenario with complex,
simple read queries and write queries that continuously insert or delete data
in the graph. Compared to the LDBC SNB, the LDBC FinBench differs in
application scenarios, data patterns, and query patterns. This document
contains a detailed explanation of the data used in the LDBC FinBench, the
definition of transaction workload, a detailed description for all queries, and
instructions on how to use the benchmark suite.Comment: For the source code of this specification, see the ldbc_finbench_docs
repository on Githu
LDBC Graphalytics: A Benchmark for Large-Scale Graph Analysis on Parallel and Distributed Platforms
ABSTRACT In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds of system scalability, such as horizontal/vertical and weak/strong, and of robustness, such as failures and performance variability. The benchmark comes with open-source software for generating data and monitoring performance. We describe and analyze six implementations of the benchmark (three from the community, three from the industry), providing insights into the strengths and weaknesses of the platforms. Key to our contribution, vendors perform the tuning and benchmarking of their platforms
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
Gromit An In-Memory Graph Database
This work presents the implementation of an in-memory graph database management system called Gromit. This graph database represents large and complex networks using labelled property graphs, and encodes semantic information in property lists of the vertices and edges. Gromit uses a vertex-edge graph model and represent both vertices and edges as entities of the graph. Edges are stored in a doubly linked list manner in main memory. We implement breadth-first traversal and depth-first traversal to retrieve data for queries. This database supports concurrency and implements locking mechanisms for transaction management. We deploy two benchmark suites from social network domain to evaluate our implementation. These are GDBench and LDBC