HANGZHOU, China, May 31, 2022 /PRNewswire/ -- On May 16th, 2022, LDBC-SNB released the newest audit test performance record set by Galaxybase, a high performance distributed graph database developed by CreateLink Technology. Galaxybase scored a soaring 70% improvement in throughput, and over 600% increase in average query performance over the previous record, along with rigid tests on system stability, availability, result correctness, transactional support, and recoverability.
The test was performed by an independent and impartial third-party auditor appointed by LDBC on standard cloud environment. The preparation of the test environment, data generation, data loading, test execution, and the correctness verification of test results all strictly complied with LDBC-SNB specifications. To further ensure the credibility and validity of the test results, LDBC conducted a detailed audit of the test code, the entire test environment, and test process. A full disclosure report is publicly available for download on the LDBC homepage.
Dr. Chen ZHANG, Founder and CEO of CreateLink Technology, will give a keynote speech titled "New LDBC SNB benchmark record by Galaxybase: More than 6 times faster and 70% higher throughput" to share the audit details at 2022 ACM SIGMOD/PODS International Conference on Management of Data on June 17 2022, 11 am EDT, 23 pm BST. (click to view online)
About LDBC and SNB test
Linked Data Benchmark Council (LDBC) is a globally recognized organization for graph database benchmark standards and audit test. It brings together the world's leading industrial hardware and software giants such as Intel, Oracle, as well as experts and scholars from higher education institutions. LDBC aims to develop fair, honest, and comparable methods and mechanisms to measure graph database management systems and promote the development of this cutting-edge technology across the globe, together with its member organizations.
Social Network Benchmark (SNB) is one of the benchmark suites developed by LDBC. The benchmark suite consists of two distinct workloads on common data set– Interactive workload and Business Intelligence (BI) workload, corresponding to test sets that measure the performance of graph data in interactive queries and business intelligence queries respectively. Compared with standalone test cases with simple metrics, LDBC-SNB is not only more similar with complex real-world business query scenarios, but also imposes higher requirements on the concurrent execution and transaction processing capability of graph database systems.
About the test details
The competed audit test was LDBC_SNB Interactive workload. Galaxybase successfully completed the audit test with verified system configuration conformance to the description of the benchmark and its strict requirements involving result correctness, transaction support, system recoverability, zero-timeouts, high throughput, and low response time, etc. In particular, Galaxybase ACID test has researched serializable isolation level, which is stricter than the read committed isolation level required by the SNB Interactive specification. Additionally, Galaxybase passed the recoverability and durability validation test, in which the system was shut down and rebooted when the continuous benchmark test execution has reached 2 hours, and the data of the last successfully inserted record in the LDBC log remained intact and durable in the graph database.
In the performance test, Galaxybase used 48 clients to send out concurrent queries to stress test the system. The result showed zero timeout which is far below LDBC-SNB's no-more-than-5% timeout requirement. With all official audit requirements fulfilled, Galaxybase outperformed LDBC's previous record (held by TuGraph) for all three datasets, namely 30G (80 million vertices, 500 million edges), 100G (270 million vertices, 1.7 billion edges), and 300G (800 million vertices, 5.3 billion edges), with 70% higher throughput and on average more than 6 times faster query performance. The mean response time, the P50, P90, P95, and P99 response time of Galaxybase all shows better results than the previous record-holder. In particular, the best mean response time is more than 41 times faster, and the best P90 response time is more than 72 times faster.
No matter on queries of different data scale factors under the same test tasks or on queries of different complexity for different test tasks, Galaxybase all performed better. The larger the size of the datasets, the higher the differentiation, which fully demonstrates the excellent capability of Galaxybase to support large-scale data processing tasks.
Table 1: Summary of Galaxybase test results for different scale factors |
||||
Scale factor |
Benchmark duration |
Benchmark operations |
Throughput |
Query |
SF-30 |
2h 14m 35.740s |
74900196 |
9285.86 |
100.00% |
SF-100 |
2h 05m 26.944s |
63988107 |
8501.21 operations/second |
100.00% |
SF-300 |
2h 07m 24.645s |
63989669 |
8370.52 |
100.00% |
Remarks: SF-30, SF-100 and SF-300 correspond to the original dataset size of 30 G, 100 G and 300 G respectively.
Galaxybase can support both online transaction processing (OLTP) and online analytical processing (OLAP), according to Yan ZHOU, CTO of CreateLink. Compared with other graph databases, Galaxybase demonstrates lower response time, higher throughput, and higher horizontal scalability, making it the ideal choice for high performance graph database on large dataset, under the increasing demand for real-time analytics over massively connected enterprise data,.
Galaxybase is written with Java and C++. It takes full advantage of the runtime performance and memory control with C++ and the coding efficiency and easiness of troubleshooting with Java in the development of complex and reliable systems. In terms of storage system design, Galaxybase uses an innovative proprietary native graph datastore with customized optimization for index-free adjacency of graph data, enabling vertex-edge queries to be completed in an extremely efficient manner. The core datastore engine does not rely on any third-party open-source components, allowing the system to better optimizes graph queries and graph computation in concert with the underlying storage layer. In the query execution layer, Galaxybase is able to efficiently organize memory data through its proprietary memory allocation and management mechanism, while significantly reducing the JVM's GC time by using off-heap memory. Galaxybase provides a parallel iterative graph traversal approach, that uses multi-version control to reduce lock contention. This approach adaptively allocates the number of threads for parallel iterations based on the number of neighbours during neighbour iteration to achieve best utilization of system resources.
Galaxybase provides a rich set of query interfaces and programming APIs, such as Java, Python, Golang, etc.. It also has full support for the descriptive OpenCypher query language. For the audit test, Galaxybase completed the durability test using OpenCypher. For scenarios that require high system resource consumption and execution performance, Galaxybase also provides PAR (Parameterized Algorithm Routine) API that allows users to implement customized procedures and functions running on the server-side through Java code to gain better control over the query execution process and pursue extreme performance.
About CreateLink
Founded in 2016, CreateLink Technology has become the leading graph database software vendor in China. Its product Galaxybase is a native distributed parallel graph platform that has superior performance on complex graph queries and algorithms. Earlier this year, together with a team of experts from Sun Yat-sen University, Galaxybase nailed the intelligent graph mining challenge of 5 trillion relationship graph while maintaining security, performance, availability, and data integrity, easily breaking the once-challenging scale barrier with trillion+ relationship graph.
CreateLink is actively promoting graph technology across different industries with a diversity of application scenarios. Up to now, Galaxybase has served many leading customers in finance, energy, and Internet sectors, with successful applications in AML, fraud detection, power grid optimization, IT operation monitoring and maintenance as well as other complex real-time decision-making scenarios, empowering its customers to release the value of big data by connecting the dots.
To check LDBC-SNB test report and get more details of the report, please visit the Galaxybase website.
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