The Train Benchmark framework for evaluating incremental model validation performance
Alternatives To Trainbenchmark
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
Leveldb32,2733a day ago1February 27, 2018269bsd-3-clauseC++
LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
Fastify26,8671,1311,764a day ago235September 14, 202280otherJavaScript
Fast and low overhead web framework, for Node.js
Fasthttp19,3156451,957a day ago175September 03, 202270mitGo
Fast HTTP package for Go. Tuned for high performance. Zero memory allocations in hot paths. Up to 10x faster than net/http
Benchmarkdotnet8,8381,1259419 days ago57August 26, 2022199mitC#
Powerful .NET library for benchmarking
13 hours ago108otherJava
Source for the TechEmpower Framework Benchmarks project
Web Frameworks6,623
2 days ago13April 27, 2021185mitPHP
Which is the fastest web framework?
Bigcache6,40420649512 days ago34April 04, 202278apache-2.0Go
Efficient cache for gigabytes of data written in Go.
Criterion.rs3,3262453,126a day ago23September 10, 2022116apache-2.0Rust
Statistics-driven benchmarking library for Rust
Jquery Dynatable2,788
4 years ago1March 31, 2015232otherJavaScript
A more-fun, semantic, alternative to datatables
a month ago1February 27, 2018123gpl-2.0Erlang
Tsung is a high-performance benchmark framework for various protocols including HTTP, XMPP, LDAP, etc.
Alternatives To Trainbenchmark
Select To Compare

Alternative Project Comparisons

Train Benchmark

🚂 Summary. The Train Benchmark is a framework for measuring the performance of continuous model transformations, with a particular emphasis on the performance of incremental query reevaluation. The benchmark is actively developed since 2011.

📖 Documentation. If you are interested in implementing the benchmark on your tool, please visit the documentation.

🎥 Presentation. For a short summary, check out this presentation, delivered at the Linked Data Benchmark Council's 9th Technical User Community meeting.

📚 Publications. The definitive publication on the benchmark is our journal paper The Train Benchmark: cross-technology performance evaluation of continuous model queries. For use cases, also check out the related publications.

💻 Technologies. The framework is implemented in Java 8 (for the main components) and Groovy (for scripts). The visualization is handled by R scripts. Both the build and the benchmark process in governed by Gradle.

👋 Contributions welcome. If you would like to implement the benchmark on your tool, we recommend to read the documentation and also please do not hesitate to get in touch!

⚠️ Warning. The Train Benchmark is designed to run in an isolated server environment, e.g. virtual machines in the cloud. Some implementations may shut down or delete existing databases, so only run it on your developer workstation if you understand the consequences. See also issue #75.

⚠️ Warning. The Train Benchmark has a fork for the 2015 Transformation Tool Contest, primarily targeting EMF tools. That fork is no longer maintained. You should use this repository, containing the full, cross-technology Train Benchmark (also supporting RDF, SQL and property graph databases).

📄 Citing the benchmark. For referencing the benchmark, please cite the paper in Software and Systems Modeling with the following BibTeX snippet.

📐 Models. Pre-generated models for scale factors 1 to 4096 are available as a tar.zst package.

  author    = {G{\'{a}}bor Sz{\'{a}}rnyas and
               Benedek Izs{\'{o}} and
               Istv{\'{a}}n R{\'{a}}th and
               D{\'{a}}niel Varr{\'{o}}},
  title     = {The Train Benchmark: cross-technology performance evaluation of continuous
               model queries},
  journal   = {Software and System Modeling},
  volume    = {17},
  number    = {4},
  pages     = {1365--1393},
  year      = {2018},
  url       = {},
  doi       = {10.1007/s10270-016-0571-8},
  timestamp = {Fri, 07 Sep 2018 14:25:47 +0200},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}


The project uses the Eclipse Public License 1.0 and was supported by the MONDO EU FP7 (EU ICT-611125) project. It is primarily maintained by the MTA-BME Lendület Research Group on Cyber-Physical Systems.

Popular Performance Projects
Popular Benchmark Projects
Popular Software Performance Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.