{"id":21236212,"url":"https://github.com/dbiir/ts-benchmark","last_synced_at":"2025-07-10T17:32:05.956Z","repository":{"id":27071827,"uuid":"217003080","full_name":"dbiir/TS-Benchmark","owner":"dbiir","description":"时序基准评测工具","archived":false,"fork":false,"pushed_at":"2024-02-21T00:15:32.000Z","size":14775,"stargazers_count":21,"open_issues_count":6,"forks_count":10,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-05T15:51:11.129Z","etag":null,"topics":["benchmark","iot-device","sql-query","time-series"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dbiir.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-10-23T08:08:55.000Z","updated_at":"2025-01-22T06:50:11.000Z","dependencies_parsed_at":"2023-01-14T09:15:23.412Z","dependency_job_id":null,"html_url":"https://github.com/dbiir/TS-Benchmark","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/dbiir/TS-Benchmark","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbiir%2FTS-Benchmark","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbiir%2FTS-Benchmark/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbiir%2FTS-Benchmark/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbiir%2FTS-Benchmark/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dbiir","download_url":"https://codeload.github.com/dbiir/TS-Benchmark/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbiir%2FTS-Benchmark/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264619210,"owners_count":23638414,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["benchmark","iot-device","sql-query","time-series"],"created_at":"2024-11-21T00:08:06.256Z","updated_at":"2025-07-10T17:32:00.948Z","avatar_url":"https://github.com/dbiir.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TS-Benchmark: A Benchmark for Time Series Databases\n\n#### Description\n\nThis project is the source code of TS-Benchmark. Related work has been published in ICDE 2021.\nbibTex:\n```\n@inproceedings{DBLP:conf/icde/HaoQCLSTZD21,\n  author    = {Yuanzhe Hao and\n               Xiongpai Qin and\n               Yueguo Chen and\n               Yaru Li and\n               Xiaoguang Sun and\n               Yu Tao and\n               Xiao Zhang and\n               Xiaoyong Du},\n  title     = {TS-Benchmark: {A} Benchmark for Time Series Databases},\n  booktitle = {37th {IEEE} International Conference on Data Engineering, {ICDE} 2021,\n               Chania, Greece, April 19-22, 2021},\n  pages     = {588--599},\n  publisher = {{IEEE}},\n  year      = {2021},\n  url       = {https://doi.org/10.1109/ICDE51399.2021.00057},\n  doi       = {10.1109/ICDE51399.2021.00057},\n  timestamp = {Mon, 28 Jun 2021 10:16:44 +0200},\n  biburl    = {https://dblp.org/rec/conf/icde/HaoQCLSTZD21.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```\n\n#### Start\n\nThe general steps to complete the test are:\n\n1. Data generation \n``cd data_generation``\n\n2. Train DCGAN model\n``python DCGAN.py``\nRun the ``encoder_dc.py`` file to train the encoder, ``python encoder_dc.py``\nFinally execute the test ``python test_dc.py``\n\n2. Data Import \nSince each database have different build-in tools for data import, we have defined some tools related to data import in the ``tsdb-test/data/load``  directory\n\n3. build project\n``cd Tsdb-benchmark/ts-benchmark/``\n``sh build.sh``\n\n4. config parametes of database and run the benchmark\n``cd Tsdb-benchmark/ts-benchmark/``\n``vim run.sh`` (choose database and test mode)\n``sh run.sh``\n\n#### Params description\n\nThe configuration of TSDBs is shown as follows:\n\n- InfluxDB. We enlarge the default values of some important parameters of the TSM engine for better performance of the system. For example, the parameter wal-fsync- delay is set as \"0s\", the parameter ``cache-max-memory-size`` is set to 1,048,576,000 bytes, and the parameter cache-snapshot-memory-size is enlarged to “100M” and so on. Maximum memory size is sufficient. The parameter ``max-values-per-tag`` is set as 0 to allow an unlimited number of tag values.\n- TimescaleDB. The parameter ``shared-buffers`` is set as 8GB, ``maintenance-work-mem`` is set as 2GB, ``checkpoint-completion-target`` is set as 0.7, ``min_wal`` size is set as 1GB, and max wal size is 2GB. Parameters of PostgreSQL is set based on PgTune.\n- Druid. The parameter ``Roll-up`` is set as true, and ``Granu-larity`` is set as hour. For local batch import, the parameter maxRowsPerSegment is set as 10M, maxRowsInMemory is set as 20M, and maxTotalRows is set as 100M. \n- OpenTSDB. The parameter ``tsd-http-request-enable-chunked`` is enabled, ``tsd-http-request-max-chunk`` is set as 32KB, ``tsd-core-auto-create-metrics`` is set as true, and the parameter ``tsd-storage-enable-compaction`` is set to be false to improve the write performance.\n\n#### More\n\nIf you have interests in the directed graph construction and the generation via random walk. you can ref to  the ```random_walk.ipynb``` [Quick Open It!](https://nbviewer.jupyter.org/github/dbiir/TS-Benchmark/blob/master/random_walk.ipynb)\n\nMore information please ref to [for detail](./documents/时序评测工具使用手册.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbiir%2Fts-benchmark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdbiir%2Fts-benchmark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbiir%2Fts-benchmark/lists"}