Skip to content
源代码提交（3）
隐藏空白变更内容
行内 左右并排
README.md
浏览文件 @ 99b31de8
# TraceVAE # TraceVAE
This is the source code for "Unsupervised Anomaly Detection on Microservice Traces through Graph VAE".
## Usage
1. `pip3 install -r requirements.txt`.
## Getting started 2. Convert the dataset with `python3 -m tracegnn.cli.data_process preprocess -i [input_path] -o [dataset_path]`. The sample dataset is under `sample_dataset`. (Note: This sample dataset only shows data format and usage, and cannot be used to evaluate model performance. Please replace it with your dataset.)
sample:
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
``` ```
cd existing_repo python3 -m tracegnn.cli.data_process preprocess -i sample_dataset -o sample_dataset
git remote add origin http://www.aiops.cn/gitlab/aiops-nankai/model/tracevae.git ```
git branch -M main 3. Train the model with `bash train.sh [dataset_path]`:
git push -uf origin main ```
bash train.sh sample_dataset
```
4. Evaluate the model with `bash teset.sh [model_path] [dataset_path]`. The default model path is under `results/train/models/final.pt`:
```
bash test.sh results/train/models/final.pt sample_dataset
``` ```
## Integrate with your tools
- [ ] [Set up project integrations](http://www.aiops.cn/gitlab/aiops-nankai/model/tracevae/-/settings/integrations)
## Collaborate with your team
- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
Use the built-in continuous integration in GitLab.
- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
## Name
Choose a self-explaining name for your project.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
## License
For open source projects, say how it is licensed.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
requirements.txt 0 → 100644
浏览文件 @ 99b31de8
git+https://gitee.com/haowen-xu/ml-essentials
git+https://gitee.com/haowen-xu/tensorkit
git+https://gitee.com/mirrors/ZhuSuan.git
click
jinja2
networkx
numpy
pandas
PyYAML
python-snappy
scikit-learn
seaborn
semver
natsort
imageio
fs
lazy_object_proxy
kafka-python
tqdm
loguru
tensorboard==1.15
numba
-f https://download.pytorch.org/whl/cu116/torch_stable.html
torch==1.12.0+cu116
-f https://data.dgl.ai/wheels/repo.html
dgl-cu116
-f https://data.pyg.org/whl/torch-1.12.0+cu116.html
torch-scatter
torch-sparse
torch-cluster
torch-spline-conv
torch-geometric
sample_dataset/id_manager/latency_range.yml 0 → 100644
浏览文件 @ 99b31de8
1:
mean: 1.0
p99: 3.0
std: 1.0
3:
mean: 1.0
p99: 3.0
std: 1.0
4:
mean: 1.0
p99: 3.0
std: 1.0
5:
mean: 1.0
p99: 3.0
std: 1.0
6:
mean: 1.0
p99: 3.0
std: 1.0
7:
mean: 1.0
p99: 3.0
std: 1.0
8:
mean: 1.0
p99: 3.0
std: 1.0
9:
mean: 1.0
p99: 3.0
std: 1.0
10:
mean: 1.0
p99: 3.0
std: 1.0
11:
mean: 1.0
p99: 3.0
std: 1.0
12:
mean: 1.0
p99: 3.0
std: 1.0
13:
mean: 1.0
p99: 3.0
std: 1.0
15:
mean: 1.0
p99: 3.0
std: 1.0
16:
mean: 1.0
p99: 3.0
std: 1.0
17:
mean: 1.0
p99: 3.0
std: 1.0
19:
mean: 1.0
p99: 3.0
std: 1.0
20:
mean: 1.0
p99: 3.0
std: 1.0
21:
mean: 1.0
p99: 3.0
std: 1.0
23:
mean: 1.0
p99: 3.0
std: 1.0
26:
mean: 1.0
p99: 3.0
std: 1.0
27:
mean: 1.0
p99: 3.0
std: 1.0
28:
mean: 1.0
p99: 3.0
std: 1.0
29:
mean: 1.0
p99: 3.0
std: 1.0
30:
mean: 1.0
p99: 3.0
std: 1.0
47:
mean: 1.0
p99: 3.0
std: 1.0
48:
mean: 1.0
p99: 3.0
std: 1.0
62:
mean: 1.0
p99: 3.0
std: 1.0
63:
mean: 1.0
p99: 3.0
std: 1.0
64:
mean: 1.0
p99: 3.0
std: 1.0
65:
mean: 1.0
p99: 3.0
std: 1.0
67:
mean: 1.0
p99: 3.0
std: 1.0
68:
mean: 1.0
p99: 3.0
std: 1.0
69:
mean: 1.0
p99: 3.0
std: 1.0
70:
mean: 1.0
p99: 3.0
std: 1.0
71:
mean: 1.0
p99: 3.0
std: 1.0
72:
mean: 1.0
p99: 3.0
std: 1.0
73:
mean: 1.0
p99: 3.0
std: 1.0
74:
mean: 1.0
p99: 3.0
std: 1.0
75:
mean: 1.0
p99: 3.0
std: 1.0
76:
mean: 1.0
p99: 3.0
std: 1.0
77:
mean: 1.0
p99: 3.0
std: 1.0
78:
mean: 1.0
p99: 3.0
std: 1.0
79:
mean: 1.0
p99: 3.0
std: 1.0
80:
mean: 1.0
p99: 3.0
std: 1.0
83:
mean: 1.0
p99: 3.0
std: 1.0
84:
mean: 1.0
p99: 3.0
std: 1.0
94:
mean: 1.0
p99: 3.0
std: 1.0
96:
mean: 1.0
p99: 3.0
std: 1.0
98:
mean: 1.0
p99: 3.0
std: 1.0
99:
mean: 1.0
p99: 3.0
std: 1.0
102:
mean: 1.0
p99: 3.0
std: 1.0
103:
mean: 1.0
p99: 3.0
std: 1.0
105:
mean: 1.0
p99: 3.0
std: 1.0
106:
mean: 1.0
p99: 3.0
std: 1.0
107:
mean: 1.0
p99: 3.0
std: 1.0
108:
mean: 1.0
p99: 3.0
std: 1.0
110:
mean: 1.0
p99: 3.0
std: 1.0
112:
mean: 1.0
p99: 3.0
std: 1.0
113:
mean: 1.0
p99: 3.0
std: 1.0
115:
mean: 1.0
p99: 3.0
std: 1.0
116:
mean: 1.0
p99: 3.0
std: 1.0
117:
mean: 1.0
p99: 3.0
std: 1.0
118:
mean: 1.0
p99: 3.0
std: 1.0
119:
mean: 1.0
p99: 3.0
std: 1.0
121:
mean: 1.0
p99: 3.0
std: 1.0
122:
mean: 1.0
p99: 3.0
std: 1.0
123:
mean: 1.0
p99: 3.0
std: 1.0
124:
mean: 1.0
p99: 3.0
std: 1.0
139:
mean: 1.0
p99: 3.0
std: 1.0
140:
mean: 1.0
p99: 3.0
std: 1.0
141:
mean: 1.0
p99: 3.0
std: 1.0
142:
mean: 1.0
p99: 3.0
std: 1.0
143:
mean: 1.0
p99: 3.0
std: 1.0
144:
mean: 1.0
p99: 3.0
std: 1.0
145:
mean: 1.0
p99: 3.0
std: 1.0
146:
mean: 1.0
p99: 3.0
std: 1.0
147:
mean: 1.0
p99: 3.0
std: 1.0
148:
mean: 1.0
p99: 3.0
std: 1.0
149:
mean: 1.0
p99: 3.0
std: 1.0
150:
mean: 1.0
p99: 3.0
std: 1.0
151:
mean: 1.0
p99: 3.0
std: 1.0
152:
mean: 1.0
p99: 3.0
std: 1.0
153:
mean: 1.0
p99: 3.0
std: 1.0
154:
mean: 1.0
p99: 3.0
std: 1.0
155:
mean: 1.0
p99: 3.0
std: 1.0
156:
mean: 1.0
p99: 3.0
std: 1.0
157:
mean: 1.0
p99: 3.0
std: 1.0
158:
mean: 1.0
p99: 3.0
std: 1.0
159:
mean: 1.0
p99: 3.0
std: 1.0
160:
mean: 1.0
p99: 3.0
std: 1.0
161:
mean: 1.0
p99: 3.0
std: 1.0
181:
mean: 1.0
p99: 3.0
std: 1.0
184:
mean: 1.0
p99: 3.0
std: 1.0
186:
mean: 1.0
p99: 3.0
std: 1.0
187:
mean: 1.0
p99: 3.0
std: 1.0
188:
mean: 1.0
p99: 3.0
std: 1.0
189:
mean: 1.0
p99: 3.0
std: 1.0
190:
mean: 1.0
p99: 3.0
std: 1.0
191:
mean: 1.0
p99: 3.0
std: 1.0
192:
mean: 1.0
p99: 3.0
std: 1.0
193:
mean: 1.0
p99: 3.0
std: 1.0
194:
mean: 1.0
p99: 3.0
std: 1.0
195:
mean: 1.0
p99: 3.0
std: 1.0
196:
mean: 1.0
p99: 3.0
std: 1.0
197:
mean: 1.0
p99: 3.0
std: 1.0
198:
mean: 1.0
p99: 3.0
std: 1.0
199:
mean: 1.0
p99: 3.0
std: 1.0
200:
mean: 1.0
p99: 3.0
std: 1.0
201:
mean: 1.0
p99: 3.0
std: 1.0
202:
mean: 1.0
p99: 3.0
std: 1.0
203:
mean: 1.0
p99: 3.0
std: 1.0
204:
mean: 1.0
p99: 3.0
std: 1.0
206:
mean: 1.0
p99: 3.0
std: 1.0
207:
mean: 1.0
p99: 3.0
std: 1.0
208:
mean: 1.0
p99: 3.0
std: 1.0
209:
mean: 1.0
p99: 3.0
std: 1.0
210:
mean: 1.0
p99: 3.0
std: 1.0
211:
mean: 1.0
p99: 3.0
std: 1.0
212:
mean: 1.0
p99: 3.0
std: 1.0
213:
mean: 1.0
p99: 3.0
std: 1.0
214:
mean: 1.0
p99: 3.0
std: 1.0
215:
mean: 1.0
p99: 3.0
std: 1.0
216:
mean: 1.0
p99: 3.0
std: 1.0
217:
mean: 1.0
p99: 3.0
std: 1.0
218:
mean: 1.0
p99: 3.0
std: 1.0
219:
mean: 1.0
p99: 3.0
std: 1.0
220:
mean: 1.0
p99: 3.0
std: 1.0
221:
mean: 1.0
p99: 3.0
std: 1.0
222:
mean: 1.0
p99: 3.0
std: 1.0
224:
mean: 1.0
p99: 3.0
std: 1.0
225:
mean: 1.0
p99: 3.0
std: 1.0
226:
mean: 1.0
p99: 3.0
std: 1.0
227:
mean: 1.0
p99: 3.0
std: 1.0
228:
mean: 1.0
p99: 3.0
std: 1.0
229:
mean: 1.0
p99: 3.0
std: 1.0
230:
mean: 1.0
p99: 3.0
std: 1.0
231:
mean: 1.0
p99: 3.0
std: 1.0
235:
mean: 1.0
p99: 3.0
std: 1.0
236:
mean: 1.0
p99: 3.0
std: 1.0
238:
mean: 1.0
p99: 3.0
std: 1.0
239:
mean: 1.0
p99: 3.0
std: 1.0
240:
mean: 1.0
p99: 3.0
std: 1.0
242:
mean: 1.0
p99: 3.0
std: 1.0
243:
mean: 1.0
p99: 3.0
std: 1.0
244:
mean: 1.0
p99: 3.0
std: 1.0
245:
mean: 1.0
p99: 3.0
std: 1.0
248:
mean: 1.0
p99: 3.0
std: 1.0
249:
mean: 1.0
p99: 3.0
std: 1.0
251:
mean: 1.0
p99: 3.0
std: 1.0
252:
mean: 1.0
p99: 3.0
std: 1.0
253:
mean: 1.0
p99: 3.0
std: 1.0
254:
mean: 1.0
p99: 3.0
std: 1.0
255:
mean: 1.0
p99: 3.0
std: 1.0
256:
mean: 1.0
p99: 3.0
std: 1.0
261:
mean: 1.0
p99: 3.0
std: 1.0
263:
mean: 1.0
p99: 3.0
std: 1.0
264:
mean: 1.0
p99: 3.0
std: 1.0
266:
mean: 1.0
p99: 3.0
std: 1.0
267:
mean: 1.0
p99: 3.0
std: 1.0
268:
mean: 1.0
p99: 3.0
std: 1.0
269:
mean: 1.0
p99: 3.0
std: 1.0
270:
mean: 1.0
p99: 3.0
std: 1.0
271:
mean: 1.0
p99: 3.0
std: 1.0
272:
mean: 1.0
p99: 3.0
std: 1.0
273:
mean: 1.0
p99: 3.0
std: 1.0
274:
mean: 1.0
p99: 3.0
std: 1.0
275:
mean: 1.0
p99: 3.0
std: 1.0
276:
mean: 1.0
p99: 3.0
std: 1.0
278:
mean: 1.0
p99: 3.0
std: 1.0
281:
mean: 1.0
p99: 3.0
std: 1.0
282:
mean: 1.0
p99: 3.0
std: 1.0
283:
mean: 1.0
p99: 3.0
std: 1.0
285:
mean: 1.0
p99: 3.0
std: 1.0
291:
mean: 1.0
p99: 3.0
std: 1.0
293:
mean: 1.0
p99: 3.0
std: 1.0
299:
mean: 1.0
p99: 3.0
std: 1.0
300:
mean: 1.0
p99: 3.0
std: 1.0
301:
mean: 1.0
p99: 3.0
std: 1.0
303:
mean: 1.0
p99: 3.0
std: 1.0
305:
mean: 1.0
p99: 3.0
std: 1.0
306:
mean: 1.0
p99: 3.0
std: 1.0
308:
mean: 1.0
p99: 3.0
std: 1.0
310:
mean: 1.0
p99: 3.0
std: 1.0
314:
mean: 1.0
p99: 3.0
std: 1.0
315:
mean: 1.0
p99: 3.0
std: 1.0
316:
mean: 1.0
p99: 3.0
std: 1.0
317:
mean: 1.0
p99: 3.0
std: 1.0
318:
mean: 1.0
p99: 3.0
std: 1.0
319:
mean: 1.0
p99: 3.0
std: 1.0
320:
mean: 1.0
p99: 3.0
std: 1.0
322:
mean: 1.0
p99: 3.0
std: 1.0
323:
mean: 1.0
p99: 3.0
std: 1.0
326:
mean: 1.0
p99: 3.0
std: 1.0
327:
mean: 1.0
p99: 3.0
std: 1.0
328:
mean: 1.0
p99: 3.0
std: 1.0
329:
mean: 1.0
p99: 3.0
std: 1.0
330:
mean: 1.0
p99: 3.0
std: 1.0
331:
mean: 1.0
p99: 3.0
std: 1.0
332:
mean: 1.0
p99: 3.0
std: 1.0
333:
mean: 1.0
p99: 3.0
std: 1.0
335:
mean: 1.0
p99: 3.0
std: 1.0
336:
mean: 1.0
p99: 3.0
std: 1.0
337:
mean: 1.0
p99: 3.0
std: 1.0
339:
mean: 1.0
p99: 3.0
std: 1.0
340:
mean: 1.0
p99: 3.0
std: 1.0
341:
mean: 1.0
p99: 3.0
std: 1.0
342:
mean: 1.0
p99: 3.0
std: 1.0
344:
mean: 1.0
p99: 3.0
std: 1.0
345:
mean: 1.0
p99: 3.0
std: 1.0
346:
mean: 1.0
p99: 3.0
std: 1.0
347:
mean: 1.0
p99: 3.0
std: 1.0
349:
mean: 1.0
p99: 3.0
std: 1.0
350:
mean: 1.0
p99: 3.0
std: 1.0
351:
mean: 1.0
p99: 3.0
std: 1.0
352:
mean: 1.0
p99: 3.0
std: 1.0
353:
mean: 1.0
p99: 3.0
std: 1.0
354:
mean: 1.0
p99: 3.0
std: 1.0
355:
mean: 1.0
p99: 3.0
std: 1.0
356:
mean: 1.0
p99: 3.0
std: 1.0
358:
mean: 1.0
p99: 3.0
std: 1.0
402:
mean: 1.0
p99: 3.0
std: 1.0
403:
mean: 1.0
p99: 3.0
std: 1.0
404:
mean: 1.0
p99: 3.0
std: 1.0
406:
mean: 1.0
p99: 3.0
std: 1.0
407:
mean: 1.0
p99: 3.0
std: 1.0
411:
mean: 1.0
p99: 3.0
std: 1.0
412:
mean: 1.0
p99: 3.0
std: 1.0
413:
mean: 1.0
p99: 3.0
std: 1.0
414:
mean: 1.0
p99: 3.0
std: 1.0
417:
mean: 1.0
p99: 3.0
std: 1.0
418:
mean: 1.0
p99: 3.0
std: 1.0
421:
mean: 1.0
p99: 3.0
std: 1.0
422:
mean: 1.0
p99: 3.0
std: 1.0
424:
mean: 1.0
p99: 3.0
std: 1.0
425:
mean: 1.0
p99: 3.0
std: 1.0
427:
mean: 1.0
p99: 3.0
std: 1.0
430:
mean: 1.0
p99: 3.0
std: 1.0
431:
mean: 1.0
p99: 3.0
std: 1.0
432:
mean: 1.0
p99: 3.0
std: 1.0
433:
mean: 1.0
p99: 3.0
std: 1.0
434:
mean: 1.0
p99: 3.0
std: 1.0
435:
mean: 1.0
p99: 3.0
std: 1.0
436:
mean: 1.0
p99: 3.0
std: 1.0
437:
mean: 1.0
p99: 3.0
std: 1.0
438:
mean: 1.0
p99: 3.0
std: 1.0
439:
mean: 1.0
p99: 3.0
std: 1.0
441:
mean: 1.0
p99: 3.0
std: 1.0
442:
mean: 1.0
p99: 3.0
std: 1.0
444:
mean: 1.0
p99: 3.0
std: 1.0
445:
mean: 1.0
p99: 3.0
std: 1.0
446:
mean: 1.0
p99: 3.0
std: 1.0
447:
mean: 1.0
p99: 3.0
std: 1.0
448:
mean: 1.0
p99: 3.0
std: 1.0
454:
mean: 1.0
p99: 3.0
std: 1.0
455:
mean: 1.0
p99: 3.0
std: 1.0
456:
mean: 1.0
p99: 3.0
std: 1.0
457:
mean: 1.0
p99: 3.0
std: 1.0
458:
mean: 1.0
p99: 3.0
std: 1.0
459:
mean: 1.0
p99: 3.0
std: 1.0
470:
mean: 1.0
p99: 3.0
std: 1.0
472:
mean: 1.0
p99: 3.0
std: 1.0
473:
mean: 1.0
p99: 3.0
std: 1.0
478:
mean: 1.0
p99: 3.0
std: 1.0
479:
mean: 1.0
p99: 3.0
std: 1.0
481:
mean: 1.0
p99: 3.0
std: 1.0
493:
mean: 1.0
p99: 3.0
std: 1.0
494:
mean: 1.0
p99: 3.0
std: 1.0
495:
mean: 1.0
p99: 3.0
std: 1.0
496:
mean: 1.0
p99: 3.0
std: 1.0
497:
mean: 1.0
p99: 3.0
std: 1.0
498:
mean: 1.0
p99: 3.0
std: 1.0
499:
mean: 1.0
p99: 3.0
std: 1.0
500:
mean: 1.0
p99: 3.0
std: 1.0
501:
mean: 1.0
p99: 3.0
std: 1.0
502:
mean: 1.0
p99: 3.0
std: 1.0
503:
mean: 1.0
p99: 3.0
std: 1.0
504:
mean: 1.0
p99: 3.0
std: 1.0
505:
mean: 1.0
p99: 3.0
std: 1.0
507:
mean: 1.0
p99: 3.0
std: 1.0
509:
mean: 1.0
p99: 3.0
std: 1.0
510:
mean: 1.0
p99: 3.0
std: 1.0
511:
mean: 1.0
p99: 3.0
std: 1.0
512:
mean: 1.0
p99: 3.0
std: 1.0
514:
mean: 1.0
p99: 3.0
std: 1.0
515:
mean: 1.0
p99: 3.0
std: 1.0
526:
mean: 1.0
p99: 3.0
std: 1.0
527:
mean: 1.0
p99: 3.0
std: 1.0
544:
mean: 1.0
p99: 3.0
std: 1.0
545:
mean: 1.0
p99: 3.0
std: 1.0
546:
mean: 1.0
p99: 3.0
std: 1.0
548:
mean: 1.0
p99: 3.0
std: 1.0
549:
mean: 1.0
p99: 3.0
std: 1.0
551:
mean: 1.0
p99: 3.0
std: 1.0
552:
mean: 1.0
p99: 3.0
std: 1.0
553:
mean: 1.0
p99: 3.0
std: 1.0
557:
mean: 1.0
p99: 3.0
std: 1.0
558:
mean: 1.0
p99: 3.0
std: 1.0
559:
mean: 1.0
p99: 3.0
std: 1.0
560:
mean: 1.0
p99: 3.0
std: 1.0
561:
mean: 1.0
p99: 3.0
std: 1.0
562:
mean: 1.0
p99: 3.0
std: 1.0
563:
mean: 1.0
p99: 3.0
std: 1.0
564:
mean: 1.0
p99: 3.0
std: 1.0
565:
mean: 1.0
p99: 3.0
std: 1.0
566:
mean: 1.0
p99: 3.0
std: 1.0
567:
mean: 1.0
p99: 3.0
std: 1.0
568:
mean: 1.0
p99: 3.0
std: 1.0
570:
mean: 1.0
p99: 3.0
std: 1.0
571:
mean: 1.0
p99: 3.0
std: 1.0
572:
mean: 1.0
p99: 3.0
std: 1.0
573:
mean: 1.0
p99: 3.0
std: 1.0
574:
mean: 1.0
p99: 3.0
std: 1.0
575:
mean: 1.0
p99: 3.0
std: 1.0
576:
mean: 1.0
p99: 3.0
std: 1.0
577:
mean: 1.0
p99: 3.0
std: 1.0
578:
mean: 1.0
p99: 3.0
std: 1.0
580:
mean: 1.0
p99: 3.0
std: 1.0
582:
mean: 1.0
p99: 3.0
std: 1.0
583:
mean: 1.0
p99: 3.0
std: 1.0
584:
mean: 1.0
p99: 3.0
std: 1.0
587:
mean: 1.0
p99: 3.0
std: 1.0
588:
mean: 1.0
p99: 3.0
std: 1.0
589:
mean: 1.0
p99: 3.0
std: 1.0
590:
mean: 1.0
p99: 3.0
std: 1.0
592:
mean: 1.0
p99: 3.0
std: 1.0
593:
mean: 1.0
p99: 3.0
std: 1.0
596:
mean: 1.0
p99: 3.0
std: 1.0
598:
mean: 1.0
p99: 3.0
std: 1.0
599:
mean: 1.0
p99: 3.0
std: 1.0
600:
mean: 1.0
p99: 3.0
std: 1.0
602:
mean: 1.0
p99: 3.0
std: 1.0
628:
mean: 1.0
p99: 3.0
std: 1.0
631:
mean: 1.0
p99: 3.0
std: 1.0
632:
mean: 1.0
p99: 3.0
std: 1.0
634:
mean: 1.0
p99: 3.0
std: 1.0
635:
mean: 1.0
p99: 3.0
std: 1.0
636:
mean: 1.0
p99: 3.0
std: 1.0
638:
mean: 1.0
p99: 3.0
std: 1.0
639:
mean: 1.0
p99: 3.0
std: 1.0
640:
mean: 1.0
p99: 3.0
std: 1.0
641:
mean: 1.0
p99: 3.0
std: 1.0
642:
mean: 1.0
p99: 3.0
std: 1.0
643:
mean: 1.0
p99: 3.0
std: 1.0
644:
mean: 1.0
p99: 3.0
std: 1.0
646:
mean: 1.0
p99: 3.0
std: 1.0
647:
mean: 1.0
p99: 3.0
std: 1.0
649:
mean: 1.0
p99: 3.0
std: 1.0
650:
mean: 1.0
p99: 3.0
std: 1.0
653:
mean: 1.0
p99: 3.0
std: 1.0
655:
mean: 1.0
p99: 3.0
std: 1.0
656:
mean: 1.0
p99: 3.0
std: 1.0
657:
mean: 1.0
p99: 3.0
std: 1.0
658:
mean: 1.0
p99: 3.0
std: 1.0
659:
mean: 1.0
p99: 3.0
std: 1.0
660:
mean: 1.0
p99: 3.0
std: 1.0
661:
mean: 1.0
p99: 3.0
std: 1.0
662:
mean: 1.0
p99: 3.0
std: 1.0
663:
mean: 1.0
p99: 3.0
std: 1.0
664:
mean: 1.0
p99: 3.0
std: 1.0
665:
mean: 1.0
p99: 3.0
std: 1.0
666:
mean: 1.0
p99: 3.0
std: 1.0
667:
mean: 1.0
p99: 3.0
std: 1.0
668:
mean: 1.0
p99: 3.0
std: 1.0
669:
mean: 1.0
p99: 3.0
std: 1.0
670:
mean: 1.0
p99: 3.0
std: 1.0
671:
mean: 1.0
p99: 3.0
std: 1.0
672:
mean: 1.0
p99: 3.0
std: 1.0
673:
mean: 1.0
p99: 3.0
std: 1.0
674:
mean: 1.0
p99: 3.0
std: 1.0
675:
mean: 1.0
p99: 3.0
std: 1.0
676:
mean: 1.0
p99: 3.0
std: 1.0
677:
mean: 1.0
p99: 3.0
std: 1.0
680:
mean: 1.0
p99: 3.0
std: 1.0
683:
mean: 1.0
p99: 3.0
std: 1.0
684:
mean: 1.0
p99: 3.0
std: 1.0
685:
mean: 1.0
p99: 3.0
std: 1.0
686:
mean: 1.0
p99: 3.0
std: 1.0
687:
mean: 1.0
p99: 3.0
std: 1.0
688:
mean: 1.0
p99: 3.0
std: 1.0
689:
mean: 1.0
p99: 3.0
std: 1.0
690:
mean: 1.0
p99: 3.0
std: 1.0
692:
mean: 1.0
p99: 3.0
std: 1.0
693:
mean: 1.0
p99: 3.0
std: 1.0
696:
mean: 1.0
p99: 3.0
std: 1.0
697:
mean: 1.0
p99: 3.0
std: 1.0
698:
mean: 1.0
p99: 3.0
std: 1.0
699:
mean: 1.0
p99: 3.0
std: 1.0
700:
mean: 1.0
p99: 3.0
std: 1.0
701:
mean: 1.0
p99: 3.0
std: 1.0
702:
mean: 1.0
p99: 3.0
std: 1.0
703:
mean: 1.0
p99: 3.0
std: 1.0
704:
mean: 1.0
p99: 3.0
std: 1.0
705:
mean: 1.0
p99: 3.0
std: 1.0
706:
mean: 1.0
p99: 3.0
std: 1.0
707:
mean: 1.0
p99: 3.0
std: 1.0
708:
mean: 1.0
p99: 3.0
std: 1.0
709:
mean: 1.0
p99: 3.0
std: 1.0
710:
mean: 1.0
p99: 3.0
std: 1.0
711:
mean: 1.0
p99: 3.0
std: 1.0
712:
mean: 1.0
p99: 3.0
std: 1.0
713:
mean: 1.0
p99: 3.0
std: 1.0
714:
mean: 1.0
p99: 3.0
std: 1.0
715:
mean: 1.0
p99: 3.0
std: 1.0
716:
mean: 1.0
p99: 3.0
std: 1.0
717:
mean: 1.0
p99: 3.0
std: 1.0
718:
mean: 1.0
p99: 3.0
std: 1.0
719:
mean: 1.0
p99: 3.0
std: 1.0
720:
mean: 1.0
p99: 3.0
std: 1.0
722:
mean: 1.0
p99: 3.0
std: 1.0
723:
mean: 1.0
p99: 3.0
std: 1.0
724:
mean: 1.0
p99: 3.0
std: 1.0
725:
mean: 1.0
p99: 3.0
std: 1.0
726:
mean: 1.0
p99: 3.0
std: 1.0
729:
mean: 1.0
p99: 3.0
std: 1.0
732:
mean: 1.0
p99: 3.0
std: 1.0
733:
mean: 1.0
p99: 3.0
std: 1.0
734:
mean: 1.0
p99: 3.0
std: 1.0
735:
mean: 1.0
p99: 3.0
std: 1.0
736:
mean: 1.0
p99: 3.0
std: 1.0
738:
mean: 1.0
p99: 3.0
std: 1.0
741:
mean: 1.0
p99: 3.0
std: 1.0
743:
mean: 1.0
p99: 3.0
std: 1.0
745:
mean: 1.0
p99: 3.0
std: 1.0
746:
mean: 1.0
p99: 3.0
std: 1.0
749:
mean: 1.0
p99: 3.0
std: 1.0
751:
mean: 1.0
p99: 3.0
std: 1.0
752:
mean: 1.0
p99: 3.0
std: 1.0
753:
mean: 1.0
p99: 3.0
std: 1.0
755:
mean: 1.0
p99: 3.0
std: 1.0
756:
mean: 1.0
p99: 3.0
std: 1.0
757:
mean: 1.0
p99: 3.0
std: 1.0
758:
mean: 1.0
p99: 3.0
std: 1.0
759:
mean: 1.0
p99: 3.0
std: 1.0
761:
mean: 1.0
p99: 3.0
std: 1.0
762:
mean: 1.0
p99: 3.0
std: 1.0
768:
mean: 1.0
p99: 3.0
std: 1.0
769:
mean: 1.0
p99: 3.0
std: 1.0
776:
mean: 1.0
p99: 3.0
std: 1.0
777:
mean: 1.0
p99: 3.0
std: 1.0
778:
mean: 1.0
p99: 3.0
std: 1.0
779:
mean: 1.0
p99: 3.0
std: 1.0
781:
mean: 1.0
p99: 3.0
std: 1.0
782:
mean: 1.0
p99: 3.0
std: 1.0
785:
mean: 1.0
p99: 3.0
std: 1.0
788:
mean: 1.0
p99: 3.0
std: 1.0
790:
mean: 1.0
p99: 3.0
std: 1.0
791:
mean: 1.0
p99: 3.0
std: 1.0
792:
mean: 1.0
p99: 3.0
std: 1.0
794:
mean: 1.0
p99: 3.0
std: 1.0
796:
mean: 1.0
p99: 3.0
std: 1.0
801:
mean: 1.0
p99: 3.0
std: 1.0
802:
mean: 1.0
p99: 3.0
std: 1.0
803:
mean: 1.0
p99: 3.0
std: 1.0
804:
mean: 1.0
p99: 3.0
std: 1.0
811:
mean: 1.0
p99: 3.0
std: 1.0
812:
mean: 1.0
p99: 3.0
std: 1.0
813:
mean: 1.0
p99: 3.0
std: 1.0
814:
mean: 1.0
p99: 3.0
std: 1.0
837:
mean: 1.0
p99: 3.0
std: 1.0
843:
mean: 1.0
p99: 3.0
std: 1.0
844:
mean: 1.0
p99: 3.0
std: 1.0
845:
mean: 1.0
p99: 3.0
std: 1.0
846:
mean: 1.0
p99: 3.0
std: 1.0
847:
mean: 1.0
p99: 3.0
std: 1.0
852:
mean: 1.0
p99: 3.0
std: 1.0
870:
mean: 1.0
p99: 3.0
std: 1.0
874:
mean: 1.0
p99: 3.0
std: 1.0
876:
mean: 1.0
p99: 3.0
std: 1.0
877:
mean: 1.0
p99: 3.0
std: 1.0
878:
mean: 1.0
p99: 3.0
std: 1.0
879:
mean: 1.0
p99: 3.0
std: 1.0
880:
mean: 1.0
p99: 3.0
std: 1.0
938:
mean: 1.0
p99: 3.0
std: 1.0
939:
mean: 1.0
p99: 3.0
std: 1.0
940:
mean: 1.0
p99: 3.0
std: 1.0
941:
mean: 1.0
p99: 3.0
std: 1.0
942:
mean: 1.0
p99: 3.0
std: 1.0
943:
mean: 1.0
p99: 3.0
std: 1.0
945:
mean: 1.0
p99: 3.0
std: 1.0
946:
mean: 1.0
p99: 3.0
std: 1.0
947:
mean: 1.0
p99: 3.0
std: 1.0
948:
mean: 1.0
p99: 3.0
std: 1.0
949:
mean: 1.0
p99: 3.0
std: 1.0
950:
mean: 1.0
p99: 3.0
std: 1.0
952:
mean: 1.0
p99: 3.0
std: 1.0
953:
mean: 1.0
p99: 3.0
std: 1.0
954:
mean: 1.0
p99: 3.0
std: 1.0
955:
mean: 1.0
p99: 3.0
std: 1.0
956:
mean: 1.0
p99: 3.0
std: 1.0
957:
mean: 1.0
p99: 3.0
std: 1.0
958:
mean: 1.0
p99: 3.0
std: 1.0
959:
mean: 1.0
p99: 3.0
std: 1.0
961:
mean: 1.0
p99: 3.0
std: 1.0
962:
mean: 1.0
p99: 3.0
std: 1.0
963:
mean: 1.0
p99: 3.0
std: 1.0
964:
mean: 1.0
p99: 3.0
std: 1.0
965:
mean: 1.0
p99: 3.0
std: 1.0
966:
mean: 1.0
p99: 3.0
std: 1.0
967:
mean: 1.0
p99: 3.0
std: 1.0
968:
mean: 1.0
p99: 3.0
std: 1.0
971:
mean: 1.0
p99: 3.0
std: 1.0
972:
mean: 1.0
p99: 3.0
std: 1.0
973:
mean: 1.0
p99: 3.0
std: 1.0
975:
mean: 1.0
p99: 3.0
std: 1.0
976:
mean: 1.0
p99: 3.0
std: 1.0
977:
mean: 1.0
p99: 3.0
std: 1.0
978:
mean: 1.0
p99: 3.0
std: 1.0
989:
mean: 1.0
p99: 3.0
std: 1.0
990:
mean: 1.0
p99: 3.0
std: 1.0
992:
mean: 1.0
p99: 3.0
std: 1.0
995:
mean: 1.0
p99: 3.0
std: 1.0
996:
mean: 1.0
p99: 3.0
std: 1.0
997:
mean: 1.0
p99: 3.0
std: 1.0
999:
mean: 1.0
p99: 3.0
std: 1.0
1000:
mean: 1.0
p99: 3.0
std: 1.0
1001:
mean: 1.0
p99: 3.0
std: 1.0
1002:
mean: 1.0
p99: 3.0
std: 1.0
1013:
mean: 1.0
p99: 3.0
std: 1.0
1014:
mean: 1.0
p99: 3.0
std: 1.0
1020:
mean: 1.0
p99: 3.0
std: 1.0
1021:
mean: 1.0
p99: 3.0
std: 1.0
1023:
mean: 1.0
p99: 3.0
std: 1.0
1024:
mean: 1.0
p99: 3.0
std: 1.0
1025:
mean: 1.0
p99: 3.0
std: 1.0
1027:
mean: 1.0
p99: 3.0
std: 1.0
1029:
mean: 1.0
p99: 3.0
std: 1.0
1030:
mean: 1.0
p99: 3.0
std: 1.0
1031:
mean: 1.0
p99: 3.0
std: 1.0
1032:
mean: 1.0
p99: 3.0
std: 1.0
1033:
mean: 1.0
p99: 3.0
std: 1.0
1034:
mean: 1.0
p99: 3.0
std: 1.0
1035:
mean: 1.0
p99: 3.0
std: 1.0
1036:
mean: 1.0
p99: 3.0
std: 1.0
1045:
mean: 1.0
p99: 3.0
std: 1.0
1046:
mean: 1.0
p99: 3.0
std: 1.0
1047:
mean: 1.0
p99: 3.0
std: 1.0
1048:
mean: 1.0
p99: 3.0
std: 1.0
1049:
mean: 1.0
p99: 3.0
std: 1.0
1050:
mean: 1.0
p99: 3.0
std: 1.0
1051:
mean: 1.0
p99: 3.0
std: 1.0
1059:
mean: 1.0
p99: 3.0
std: 1.0
1061:
mean: 1.0
p99: 3.0
std: 1.0
1063:
mean: 1.0
p99: 3.0
std: 1.0
1065:
mean: 1.0
p99: 3.0
std: 1.0
1067:
mean: 1.0
p99: 3.0
std: 1.0
1069:
mean: 1.0
p99: 3.0
std: 1.0
1072:
mean: 1.0
p99: 3.0
std: 1.0
1073:
mean: 1.0
p99: 3.0
std: 1.0
1075:
mean: 1.0
p99: 3.0
std: 1.0
1076:
mean: 1.0
p99: 3.0
std: 1.0
1078:
mean: 1.0
p99: 3.0
std: 1.0
1082:
mean: 1.0
p99: 3.0
std: 1.0
1083:
mean: 1.0
p99: 3.0
std: 1.0
1084:
mean: 1.0
p99: 3.0
std: 1.0
1090:
mean: 1.0
p99: 3.0
std: 1.0
1091:
mean: 1.0
p99: 3.0
std: 1.0
1092:
mean: 1.0
p99: 3.0
std: 1.0
1096:
mean: 1.0
p99: 3.0
std: 1.0
1099:
mean: 1.0
p99: 3.0
std: 1.0
1101:
mean: 1.0
p99: 3.0
std: 1.0
1102:
mean: 1.0
p99: 3.0
std: 1.0
1103:
mean: 1.0
p99: 3.0
std: 1.0
1104:
mean: 1.0
p99: 3.0
std: 1.0
1105:
mean: 1.0
p99: 3.0
std: 1.0
1106:
mean: 1.0
p99: 3.0
std: 1.0
1107:
mean: 1.0
p99: 3.0
std: 1.0
1108:
mean: 1.0
p99: 3.0
std: 1.0
1113:
mean: 1.0
p99: 3.0
std: 1.0
1114:
mean: 1.0
p99: 3.0
std: 1.0
1115:
mean: 1.0
p99: 3.0
std: 1.0
1116:
mean: 1.0
p99: 3.0
std: 1.0
1117:
mean: 1.0
p99: 3.0
std: 1.0
1120:
mean: 1.0
p99: 3.0
std: 1.0
1122:
mean: 1.0
p99: 3.0
std: 1.0
1123:
mean: 1.0
p99: 3.0
std: 1.0
1128:
mean: 1.0
p99: 3.0
std: 1.0
1129:
mean: 1.0
p99: 3.0
std: 1.0
1130:
mean: 1.0
p99: 3.0
std: 1.0
1131:
mean: 1.0
p99: 3.0
std: 1.0
1132:
mean: 1.0
p99: 3.0
std: 1.0
1133:
mean: 1.0
p99: 3.0
std: 1.0
1134:
mean: 1.0
p99: 3.0
std: 1.0
1136:
mean: 1.0
p99: 3.0
std: 1.0
1138:
mean: 1.0
p99: 3.0
std: 1.0
1141:
mean: 1.0
p99: 3.0
std: 1.0
1142:
mean: 1.0
p99: 3.0
std: 1.0
1144:
mean: 1.0
p99: 3.0
std: 1.0
1145:
mean: 1.0
p99: 3.0
std: 1.0
1146:
mean: 1.0
p99: 3.0
std: 1.0
1148:
mean: 1.0
p99: 3.0
std: 1.0
1149:
mean: 1.0
p99: 3.0
std: 1.0
1150:
mean: 1.0
p99: 3.0
std: 1.0
1151:
mean: 1.0
p99: 3.0
std: 1.0
1152:
mean: 1.0
p99: 3.0
std: 1.0
1153:
mean: 1.0
p99: 3.0
std: 1.0
1154:
mean: 1.0
p99: 3.0
std: 1.0
1155:
mean: 1.0
p99: 3.0
std: 1.0
1156:
mean: 1.0
p99: 3.0
std: 1.0
1157:
mean: 1.0
p99: 3.0
std: 1.0
1158:
mean: 1.0
p99: 3.0
std: 1.0
1159:
mean: 1.0
p99: 3.0
std: 1.0
1160:
mean: 1.0
p99: 3.0
std: 1.0
1161:
mean: 1.0
p99: 3.0
std: 1.0
1162:
mean: 1.0
p99: 3.0
std: 1.0
1163:
mean: 1.0
p99: 3.0
std: 1.0
1165:
mean: 1.0
p99: 3.0
std: 1.0
1168:
mean: 1.0
p99: 3.0
std: 1.0
1171:
mean: 1.0
p99: 3.0
std: 1.0
1172:
mean: 1.0
p99: 3.0
std: 1.0
1181:
mean: 1.0
p99: 3.0
std: 1.0
1182:
mean: 1.0
p99: 3.0
std: 1.0
1183:
mean: 1.0
p99: 3.0
std: 1.0
1184:
mean: 1.0
p99: 3.0
std: 1.0
1185:
mean: 1.0
p99: 3.0
std: 1.0
1186:
mean: 1.0
p99: 3.0
std: 1.0
1187:
mean: 1.0
p99: 3.0
std: 1.0
1189:
mean: 1.0
p99: 3.0
std: 1.0
1190:
mean: 1.0
p99: 3.0
std: 1.0
1191:
mean: 1.0
p99: 3.0
std: 1.0
1193:
mean: 1.0
p99: 3.0
std: 1.0
1194:
mean: 1.0
p99: 3.0
std: 1.0
1195:
mean: 1.0
p99: 3.0
std: 1.0
1196:
mean: 1.0
p99: 3.0
std: 1.0
1202:
mean: 1.0
p99: 3.0
std: 1.0
1205:
mean: 1.0
p99: 3.0
std: 1.0
1207:
mean: 1.0
p99: 3.0
std: 1.0
1208:
mean: 1.0
p99: 3.0
std: 1.0
1209:
mean: 1.0
p99: 3.0
std: 1.0
1211:
mean: 1.0
p99: 3.0
std: 1.0
1212:
mean: 1.0
p99: 3.0
std: 1.0
1213:
mean: 1.0
p99: 3.0
std: 1.0
1214:
mean: 1.0
p99: 3.0
std: 1.0
1215:
mean: 1.0
p99: 3.0
std: 1.0
1216:
mean: 1.0
p99: 3.0
std: 1.0
1217:
mean: 1.0
p99: 3.0
std: 1.0
1218:
mean: 1.0
p99: 3.0
std: 1.0
1219:
mean: 1.0
p99: 3.0
std: 1.0
1220:
mean: 1.0
p99: 3.0
std: 1.0
1221:
mean: 1.0
p99: 3.0
std: 1.0
1222:
mean: 1.0
p99: 3.0
std: 1.0
1224:
mean: 1.0
p99: 3.0
std: 1.0
1226:
mean: 1.0
p99: 3.0
std: 1.0
1230:
mean: 1.0
p99: 3.0
std: 1.0
1231:
mean: 1.0
p99: 3.0
std: 1.0
1232:
mean: 1.0
p99: 3.0
std: 1.0
1233:
mean: 1.0
p99: 3.0
std: 1.0
1234:
mean: 1.0
p99: 3.0
std: 1.0
1235:
mean: 1.0
p99: 3.0
std: 1.0
1236:
mean: 1.0
p99: 3.0
std: 1.0
1237:
mean: 1.0
p99: 3.0
std: 1.0
1238:
mean: 1.0
p99: 3.0
std: 1.0
1239:
mean: 1.0
p99: 3.0
std: 1.0
1242:
mean: 1.0
p99: 3.0
std: 1.0
1243:
mean: 1.0
p99: 3.0
std: 1.0
1244:
mean: 1.0
p99: 3.0
std: 1.0
1246:
mean: 1.0
p99: 3.0
std: 1.0
1247:
mean: 1.0
p99: 3.0
std: 1.0
1248:
mean: 1.0
p99: 3.0
std: 1.0
1249:
mean: 1.0
p99: 3.0
std: 1.0
1250:
mean: 1.0
p99: 3.0
std: 1.0
1251:
mean: 1.0
p99: 3.0
std: 1.0
1252:
mean: 1.0
p99: 3.0
std: 1.0
1253:
mean: 1.0
p99: 3.0
std: 1.0
1255:
mean: 1.0
p99: 3.0
std: 1.0
1256:
mean: 1.0
p99: 3.0
std: 1.0
1258:
mean: 1.0
p99: 3.0
std: 1.0
1259:
mean: 1.0
p99: 3.0
std: 1.0
1260:
mean: 1.0
p99: 3.0
std: 1.0
1261:
mean: 1.0
p99: 3.0
std: 1.0
1262:
mean: 1.0
p99: 3.0
std: 1.0
1263:
mean: 1.0
p99: 3.0
std: 1.0
1264:
mean: 1.0
p99: 3.0
std: 1.0
1265:
mean: 1.0
p99: 3.0
std: 1.0
1266:
mean: 1.0
p99: 3.0
std: 1.0
1267:
mean: 1.0
p99: 3.0
std: 1.0
1270:
mean: 1.0
p99: 3.0
std: 1.0
1271:
mean: 1.0
p99: 3.0
std: 1.0
1272:
mean: 1.0
p99: 3.0
std: 1.0
1273:
mean: 1.0
p99: 3.0
std: 1.0
1274:
mean: 1.0
p99: 3.0
std: 1.0
1276:
mean: 1.0
p99: 3.0
std: 1.0
1277:
mean: 1.0
p99: 3.0
std: 1.0
1280:
mean: 1.0
p99: 3.0
std: 1.0
1281:
mean: 1.0
p99: 3.0
std: 1.0
1282:
mean: 1.0
p99: 3.0
std: 1.0
1284:
mean: 1.0
p99: 3.0
std: 1.0
1285:
mean: 1.0
p99: 3.0
std: 1.0
1286:
mean: 1.0
p99: 3.0
std: 1.0
1287:
mean: 1.0
p99: 3.0
std: 1.0
1288:
mean: 1.0
p99: 3.0
std: 1.0
1291:
mean: 1.0
p99: 3.0
std: 1.0
1292:
mean: 1.0
p99: 3.0
std: 1.0
1294:
mean: 1.0
p99: 3.0
std: 1.0
1295:
mean: 1.0
p99: 3.0
std: 1.0
1296:
mean: 1.0
p99: 3.0
std: 1.0
1298:
mean: 1.0
p99: 3.0
std: 1.0
1300:
mean: 1.0
p99: 3.0
std: 1.0
1301:
mean: 1.0
p99: 3.0
std: 1.0
1302:
mean: 1.0
p99: 3.0
std: 1.0
1303:
mean: 1.0
p99: 3.0
std: 1.0
1304:
mean: 1.0
p99: 3.0
std: 1.0
1305:
mean: 1.0
p99: 3.0
std: 1.0
1306:
mean: 1.0
p99: 3.0
std: 1.0
1307:
mean: 1.0
p99: 3.0
std: 1.0
1308:
mean: 1.0
p99: 3.0
std: 1.0
1310:
mean: 1.0
p99: 3.0
std: 1.0
1311:
mean: 1.0
p99: 3.0
std: 1.0
1312:
mean: 1.0
p99: 3.0
std: 1.0
1313:
mean: 1.0
p99: 3.0
std: 1.0
1314:
mean: 1.0
p99: 3.0
std: 1.0
1315:
mean: 1.0
p99: 3.0
std: 1.0
1316:
mean: 1.0
p99: 3.0
std: 1.0
1317:
mean: 1.0
p99: 3.0
std: 1.0
1318:
mean: 1.0
p99: 3.0
std: 1.0
1319:
mean: 1.0
p99: 3.0
std: 1.0
1320:
mean: 1.0
p99: 3.0
std: 1.0
1321:
mean: 1.0
p99: 3.0
std: 1.0
1322:
mean: 1.0
p99: 3.0
std: 1.0
1323:
mean: 1.0
p99: 3.0
std: 1.0
1324:
mean: 1.0
p99: 3.0
std: 1.0
1325:
mean: 1.0
p99: 3.0
std: 1.0
1326:
mean: 1.0
p99: 3.0
std: 1.0
1327:
mean: 1.0
p99: 3.0
std: 1.0
1329:
mean: 1.0
p99: 3.0
std: 1.0
1330:
mean: 1.0
p99: 3.0
std: 1.0
1331:
mean: 1.0
p99: 3.0
std: 1.0
1333:
mean: 1.0
p99: 3.0
std: 1.0
1334:
mean: 1.0
p99: 3.0
std: 1.0
1336:
mean: 1.0
p99: 3.0
std: 1.0
1337:
mean: 1.0
p99: 3.0
std: 1.0
1338:
mean: 1.0
p99: 3.0
std: 1.0
1340:
mean: 1.0
p99: 3.0
std: 1.0
1342:
mean: 1.0
p99: 3.0
std: 1.0
1344:
mean: 1.0
p99: 3.0
std: 1.0
1346:
mean: 1.0
p99: 3.0
std: 1.0
1349:
mean: 1.0
p99: 3.0
std: 1.0
1352:
mean: 1.0
p99: 3.0
std: 1.0
1355:
mean: 1.0
p99: 3.0
std: 1.0
1356:
mean: 1.0
p99: 3.0
std: 1.0
1358:
mean: 1.0
p99: 3.0
std: 1.0
1360:
mean: 1.0
p99: 3.0
std: 1.0
1364:
mean: 1.0
p99: 3.0
std: 1.0
1365:
mean: 1.0
p99: 3.0
std: 1.0
1366:
mean: 1.0
p99: 3.0
std: 1.0
1367:
mean: 1.0
p99: 3.0
std: 1.0
1369:
mean: 1.0
p99: 3.0
std: 1.0
1370:
mean: 1.0
p99: 3.0
std: 1.0
1371:
mean: 1.0
p99: 3.0
std: 1.0
1372:
mean: 1.0
p99: 3.0
std: 1.0
1373:
mean: 1.0
p99: 3.0
std: 1.0
1374:
mean: 1.0
p99: 3.0
std: 1.0
1375:
mean: 1.0
p99: 3.0
std: 1.0
1376:
mean: 1.0
p99: 3.0
std: 1.0
1377:
mean: 1.0
p99: 3.0
std: 1.0
1378:
mean: 1.0
p99: 3.0
std: 1.0
1380:
mean: 1.0
p99: 3.0
std: 1.0
1381:
mean: 1.0
p99: 3.0
std: 1.0
1382:
mean: 1.0
p99: 3.0
std: 1.0
1383:
mean: 1.0
p99: 3.0
std: 1.0
1384:
mean: 1.0
p99: 3.0
std: 1.0
1385:
mean: 1.0
p99: 3.0
std: 1.0
1386:
mean: 1.0
p99: 3.0
std: 1.0
1387:
mean: 1.0
p99: 3.0
std: 1.0
1388:
mean: 1.0
p99: 3.0
std: 1.0
1389:
mean: 1.0
p99: 3.0
std: 1.0
1390:
mean: 1.0
p99: 3.0
std: 1.0
1391:
mean: 1.0
p99: 3.0
std: 1.0
1393:
mean: 1.0
p99: 3.0
std: 1.0
1394:
mean: 1.0
p99: 3.0
std: 1.0
1395:
mean: 1.0
p99: 3.0
std: 1.0
1396:
mean: 1.0
p99: 3.0
std: 1.0
1397:
mean: 1.0
p99: 3.0
std: 1.0
1398:
mean: 1.0
p99: 3.0
std: 1.0
1399:
mean: 1.0
p99: 3.0
std: 1.0
1400:
mean: 1.0
p99: 3.0
std: 1.0
1402:
mean: 1.0
p99: 3.0
std: 1.0
1403:
mean: 1.0
p99: 3.0
std: 1.0
1404:
mean: 1.0
p99: 3.0
std: 1.0
1406:
mean: 1.0
p99: 3.0
std: 1.0
1407:
mean: 1.0
p99: 3.0
std: 1.0
1408:
mean: 1.0
p99: 3.0
std: 1.0
1409:
mean: 1.0
p99: 3.0
std: 1.0
1410:
mean: 1.0
p99: 3.0
std: 1.0
1411:
mean: 1.0
p99: 3.0
std: 1.0
1412:
mean: 1.0
p99: 3.0
std: 1.0
1413:
mean: 1.0
p99: 3.0
std: 1.0
1414:
mean: 1.0
p99: 3.0
std: 1.0
1415:
mean: 1.0
p99: 3.0
std: 1.0
1417:
mean: 1.0
p99: 3.0
std: 1.0
1420:
mean: 1.0
p99: 3.0
std: 1.0
1421:
mean: 1.0
p99: 3.0
std: 1.0
1422:
mean: 1.0
p99: 3.0
std: 1.0
1423:
mean: 1.0
p99: 3.0
std: 1.0
1424:
mean: 1.0
p99: 3.0
std: 1.0
1426:
mean: 1.0
p99: 3.0
std: 1.0
1427:
mean: 1.0
p99: 3.0
std: 1.0
1428:
mean: 1.0
p99: 3.0
std: 1.0
1429:
mean: 1.0
p99: 3.0
std: 1.0
1430:
mean: 1.0
p99: 3.0
std: 1.0
1432:
mean: 1.0
p99: 3.0
std: 1.0
1433:
mean: 1.0
p99: 3.0
std: 1.0
1434:
mean: 1.0
p99: 3.0
std: 1.0
1435:
mean: 1.0
p99: 3.0
std: 1.0
1436:
mean: 1.0
p99: 3.0
std: 1.0
1438:
mean: 1.0
p99: 3.0
std: 1.0
1442:
mean: 1.0
p99: 3.0
std: 1.0
1443:
mean: 1.0
p99: 3.0
std: 1.0
1444:
mean: 1.0
p99: 3.0
std: 1.0
1445:
mean: 1.0
p99: 3.0
std: 1.0
1450:
mean: 1.0
p99: 3.0
std: 1.0
1452:
mean: 1.0
p99: 3.0
std: 1.0
1453:
mean: 1.0
p99: 3.0
std: 1.0
1454:
mean: 1.0
p99: 3.0
std: 1.0
1455:
mean: 1.0
p99: 3.0
std: 1.0
1456:
mean: 1.0
p99: 3.0
std: 1.0
1458:
mean: 1.0
p99: 3.0
std: 1.0
1459:
mean: 1.0
p99: 3.0
std: 1.0
1462:
mean: 1.0
p99: 3.0
std: 1.0
1463:
mean: 1.0
p99: 3.0
std: 1.0
1464:
mean: 1.0
p99: 3.0
std: 1.0
1466:
mean: 1.0
p99: 3.0
std: 1.0
1469:
mean: 1.0
p99: 3.0
std: 1.0
1471:
mean: 1.0
p99: 3.0
std: 1.0
1473:
mean: 1.0
p99: 3.0
std: 1.0
1476:
mean: 1.0
p99: 3.0
std: 1.0
1479:
mean: 1.0
p99: 3.0
std: 1.0
1481:
mean: 1.0
p99: 3.0
std: 1.0
1484:
mean: 1.0
p99: 3.0
std: 1.0
1487:
mean: 1.0
p99: 3.0
std: 1.0
sample_dataset/id_manager/operation_id.yml 0 → 100644
浏览文件 @ 99b31de8
? ''
: 0
1/1: 1
10/50: 50
10/51: 51
10/52: 52
10/53: 53
100/444: 444
100/445: 445
100/446: 446
100/447: 447
100/448: 448
101/449: 449
101/450: 450
101/451: 451
101/452: 452
101/453: 453
102/454: 454
102/455: 455
102/456: 456
102/457: 457
102/458: 458
102/459: 459
103/460: 460
103/461: 461
103/462: 462
103/463: 463
103/464: 464
103/465: 465
103/466: 466
103/467: 467
103/468: 468
103/469: 469
104/470: 470
105/471: 471
106/472: 472
107/473: 473
108/474: 474
109/475: 475
109/476: 476
109/477: 477
11/54: 54
11/55: 55
11/56: 56
11/57: 57
11/58: 58
11/59: 59
11/60: 60
11/61: 61
110/478: 478
111/479: 479
112/480: 480
112/481: 481
112/482: 482
113/483: 483
113/484: 484
113/485: 485
113/486: 486
113/487: 487
114/488: 488
114/489: 489
114/490: 490
114/491: 491
115/492: 492
115/493: 493
115/494: 494
115/495: 495
115/496: 496
115/497: 497
115/498: 498
115/499: 499
115/500: 500
115/501: 501
115/502: 502
115/503: 503
116/504: 504
116/505: 505
116/506: 506
116/507: 507
116/508: 508
116/509: 509
116/510: 510
117/511: 511
117/512: 512
118/513: 513
119/514: 514
12/62: 62
12/63: 63
120/515: 515
121/516: 516
121/517: 517
121/518: 518
122/519: 519
123/520: 520
124/521: 521
124/522: 522
125/523: 523
126/524: 524
127/525: 525
127/526: 526
127/527: 527
128/528: 528
128/529: 529
128/530: 530
128/531: 531
128/532: 532
128/533: 533
128/534: 534
128/535: 535
128/536: 536
128/537: 537
128/538: 538
128/539: 539
128/540: 540
128/541: 541
128/542: 542
129/543: 543
129/544: 544
129/545: 545
129/546: 546
129/547: 547
129/548: 548
129/549: 549
129/550: 550
129/551: 551
13/64: 64
13/65: 65
13/66: 66
13/67: 67
13/68: 68
13/69: 69
13/70: 70
13/71: 71
13/72: 72
13/73: 73
130/552: 552
131/553: 553
132/554: 554
132/555: 555
133/556: 556
134/557: 557
135/558: 558
135/559: 559
135/560: 560
135/561: 561
135/562: 562
136/563: 563
136/564: 564
136/565: 565
137/566: 566
138/567: 567
139/568: 568
14/74: 74
14/75: 75
14/76: 76
140/569: 569
141/570: 570
142/571: 571
142/572: 572
142/573: 573
142/574: 574
143/575: 575
144/576: 576
144/577: 577
144/578: 578
145/579: 579
146/580: 580
146/581: 581
146/582: 582
146/583: 583
146/584: 584
146/585: 585
146/586: 586
146/587: 587
146/588: 588
146/589: 589
146/590: 590
146/591: 591
146/592: 592
146/593: 593
146/594: 594
146/595: 595
147/596: 596
148/597: 597
148/598: 598
148/599: 599
148/600: 600
148/601: 601
148/602: 602
149/603: 603
149/604: 604
149/605: 605
149/606: 606
149/607: 607
149/608: 608
149/609: 609
149/610: 610
149/611: 611
149/612: 612
149/613: 613
149/614: 614
149/615: 615
149/616: 616
149/617: 617
149/618: 618
15/77: 77
15/78: 78
15/79: 79
150/619: 619
150/620: 620
150/621: 621
150/622: 622
150/623: 623
150/624: 624
150/625: 625
150/626: 626
150/627: 627
151/628: 628
151/629: 629
151/630: 630
152/631: 631
152/632: 632
152/633: 633
152/634: 634
152/635: 635
152/636: 636
152/637: 637
152/638: 638
152/639: 639
152/640: 640
152/641: 641
152/642: 642
153/643: 643
153/644: 644
153/645: 645
153/646: 646
153/647: 647
153/648: 648
153/649: 649
154/650: 650
155/651: 651
155/652: 652
155/653: 653
155/654: 654
156/655: 655
156/656: 656
156/657: 657
156/658: 658
156/659: 659
156/660: 660
156/661: 661
156/662: 662
156/663: 663
156/664: 664
156/665: 665
156/666: 666
156/667: 667
156/668: 668
156/669: 669
156/670: 670
156/671: 671
156/672: 672
156/673: 673
156/674: 674
156/675: 675
156/676: 676
156/677: 677
156/678: 678
156/679: 679
156/680: 680
156/681: 681
157/682: 682
158/683: 683
158/684: 684
158/685: 685
158/686: 686
158/687: 687
158/688: 688
158/689: 689
158/690: 690
158/691: 691
158/692: 692
159/693: 693
159/694: 694
159/695: 695
159/696: 696
159/697: 697
159/698: 698
159/699: 699
159/700: 700
159/701: 701
159/702: 702
159/703: 703
16/80: 80
160/704: 704
160/705: 705
160/706: 706
160/707: 707
160/708: 708
160/709: 709
160/710: 710
161/711: 711
161/712: 712
162/713: 713
162/714: 714
163/715: 715
163/716: 716
163/717: 717
164/718: 718
164/719: 719
165/720: 720
166/721: 721
166/722: 722
166/723: 723
166/724: 724
166/725: 725
167/726: 726
168/727: 727
168/728: 728
168/729: 729
169/730: 730
169/731: 731
17/81: 81
170/732: 732
171/733: 733
171/734: 734
171/735: 735
171/736: 736
171/737: 737
172/738: 738
173/739: 739
174/740: 740
174/741: 741
174/742: 742
174/743: 743
174/744: 744
174/745: 745
174/746: 746
174/747: 747
174/748: 748
174/749: 749
174/750: 750
174/751: 751
174/752: 752
175/753: 753
175/754: 754
175/755: 755
175/756: 756
175/757: 757
175/758: 758
175/759: 759
175/760: 760
175/761: 761
176/762: 762
176/763: 763
176/764: 764
176/765: 765
176/766: 766
176/767: 767
176/768: 768
176/769: 769
176/770: 770
176/771: 771
176/772: 772
177/773: 773
177/774: 774
178/775: 775
178/776: 776
178/777: 777
178/778: 778
179/779: 779
179/780: 780
179/781: 781
179/782: 782
179/783: 783
179/784: 784
179/785: 785
18/82: 82
180/786: 786
180/787: 787
181/788: 788
181/789: 789
181/790: 790
181/791: 791
181/792: 792
181/793: 793
181/794: 794
181/795: 795
181/796: 796
182/797: 797
182/798: 798
183/799: 799
183/800: 800
184/801: 801
184/802: 802
185/803: 803
185/804: 804
186/805: 805
186/806: 806
186/807: 807
186/808: 808
186/809: 809
186/810: 810
187/811: 811
187/812: 812
187/813: 813
187/814: 814
188/815: 815
188/816: 816
188/817: 817
188/818: 818
188/819: 819
188/820: 820
188/821: 821
188/822: 822
188/823: 823
189/824: 824
189/825: 825
189/826: 826
189/827: 827
189/828: 828
189/829: 829
189/830: 830
189/831: 831
189/832: 832
189/833: 833
189/834: 834
189/835: 835
19/83: 83
190/836: 836
191/837: 837
192/838: 838
192/839: 839
193/840: 840
194/841: 841
195/842: 842
195/843: 843
196/844: 844
197/845: 845
198/846: 846
198/847: 847
199/848: 848
2/2: 2
2/3: 3
2/4: 4
2/5: 5
2/6: 6
2/7: 7
2/8: 8
2/9: 9
20/84: 84
200/849: 849
200/850: 850
200/851: 851
200/852: 852
200/853: 853
200/854: 854
200/855: 855
200/856: 856
200/857: 857
200/858: 858
200/859: 859
200/860: 860
200/861: 861
200/862: 862
200/863: 863
200/864: 864
200/865: 865
200/866: 866
200/867: 867
200/868: 868
201/869: 869
201/870: 870
201/871: 871
202/872: 872
203/873: 873
203/874: 874
203/875: 875
203/876: 876
203/877: 877
203/878: 878
203/879: 879
203/880: 880
204/881: 881
204/882: 882
204/883: 883
204/884: 884
204/885: 885
204/886: 886
204/887: 887
204/888: 888
204/889: 889
205/890: 890
205/891: 891
205/892: 892
205/893: 893
205/894: 894
205/895: 895
205/896: 896
205/897: 897
205/898: 898
205/899: 899
205/900: 900
205/901: 901
205/902: 902
205/903: 903
206/904: 904
206/905: 905
206/906: 906
206/907: 907
206/908: 908
206/909: 909
206/910: 910
206/911: 911
206/912: 912
207/913: 913
207/914: 914
207/915: 915
207/916: 916
207/917: 917
207/918: 918
208/919: 919
208/920: 920
208/921: 921
208/922: 922
208/923: 923
208/924: 924
208/925: 925
208/926: 926
209/927: 927
209/928: 928
209/929: 929
209/930: 930
209/931: 931
209/932: 932
209/933: 933
209/934: 934
209/935: 935
209/936: 936
209/937: 937
21/85: 85
21/86: 86
21/87: 87
21/88: 88
21/89: 89
210/938: 938
210/939: 939
210/940: 940
211/941: 941
211/942: 942
211/943: 943
211/944: 944
212/945: 945
213/946: 946
214/947: 947
215/948: 948
215/949: 949
216/950: 950
216/951: 951
217/952: 952
217/953: 953
217/954: 954
217/955: 955
217/956: 956
218/957: 957
219/958: 958
219/959: 959
219/960: 960
219/961: 961
219/962: 962
219/963: 963
219/964: 964
22/90: 90
22/91: 91
220/965: 965
220/966: 966
220/967: 967
220/968: 968
220/969: 969
220/970: 970
220/971: 971
220/972: 972
220/973: 973
221/974: 974
222/975: 975
222/976: 976
222/977: 977
223/978: 978
224/979: 979
224/980: 980
224/981: 981
224/982: 982
224/983: 983
224/984: 984
224/985: 985
224/986: 986
224/987: 987
224/988: 988
225/989: 989
225/990: 990
225/991: 991
225/992: 992
225/993: 993
225/994: 994
225/995: 995
225/996: 996
225/997: 997
225/998: 998
225/999: 999
226/1000: 1000
226/1001: 1001
226/1002: 1002
227/1003: 1003
227/1004: 1004
227/1005: 1005
227/1006: 1006
227/1007: 1007
227/1008: 1008
227/1009: 1009
227/1010: 1010
227/1011: 1011
227/1012: 1012
227/1013: 1013
227/1014: 1014
227/1015: 1015
227/1016: 1016
227/1017: 1017
227/1018: 1018
228/1019: 1019
229/1020: 1020
229/1021: 1021
229/1022: 1022
229/1023: 1023
229/1024: 1024
229/1025: 1025
229/1026: 1026
229/1027: 1027
23/92: 92
23/93: 93
230/1028: 1028
230/1029: 1029
230/1030: 1030
230/1031: 1031
230/1032: 1032
230/1033: 1033
230/1034: 1034
230/1035: 1035
231/1036: 1036
231/1037: 1037
231/1038: 1038
232/1039: 1039
232/1040: 1040
233/1041: 1041
234/1042: 1042
234/1043: 1043
234/1044: 1044
235/1045: 1045
235/1046: 1046
235/1047: 1047
235/1048: 1048
236/1049: 1049
236/1050: 1050
236/1051: 1051
236/1052: 1052
237/1053: 1053
237/1054: 1054
238/1055: 1055
239/1056: 1056
239/1057: 1057
24/94: 94
240/1058: 1058
241/1059: 1059
241/1060: 1060
241/1061: 1061
241/1062: 1062
242/1063: 1063
243/1064: 1064
244/1065: 1065
244/1066: 1066
244/1067: 1067
244/1068: 1068
244/1069: 1069
244/1070: 1070
244/1071: 1071
244/1072: 1072
244/1073: 1073
244/1074: 1074
244/1075: 1075
244/1076: 1076
244/1077: 1077
244/1078: 1078
244/1079: 1079
244/1080: 1080
244/1081: 1081
244/1082: 1082
244/1083: 1083
244/1084: 1084
245/1085: 1085
245/1086: 1086
245/1087: 1087
245/1088: 1088
245/1089: 1089
246/1090: 1090
246/1091: 1091
246/1092: 1092
247/1093: 1093
248/1094: 1094
249/1095: 1095
25/95: 95
25/96: 96
25/97: 97
25/98: 98
250/1096: 1096
250/1097: 1097
251/1098: 1098
252/1099: 1099
253/1100: 1100
254/1101: 1101
254/1102: 1102
255/1103: 1103
256/1104: 1104
256/1105: 1105
256/1106: 1106
256/1107: 1107
256/1108: 1108
257/1109: 1109
257/1110: 1110
258/1111: 1111
259/1112: 1112
26/99: 99
260/1113: 1113
260/1114: 1114
261/1115: 1115
261/1116: 1116
262/1117: 1117
262/1118: 1118
262/1119: 1119
262/1120: 1120
262/1121: 1121
262/1122: 1122
262/1123: 1123
262/1124: 1124
262/1125: 1125
262/1126: 1126
262/1127: 1127
262/1128: 1128
262/1129: 1129
262/1130: 1130
262/1131: 1131
262/1132: 1132
262/1133: 1133
262/1134: 1134
262/1135: 1135
262/1136: 1136
262/1137: 1137
262/1138: 1138
262/1139: 1139
262/1140: 1140
262/1141: 1141
262/1142: 1142
262/1143: 1143
262/1144: 1144
263/1145: 1145
263/1146: 1146
263/1147: 1147
263/1148: 1148
263/1149: 1149
263/1150: 1150
263/1151: 1151
263/1152: 1152
263/1153: 1153
263/1154: 1154
263/1155: 1155
263/1156: 1156
263/1157: 1157
263/1158: 1158
263/1159: 1159
263/1160: 1160
263/1161: 1161
263/1162: 1162
263/1163: 1163
263/1164: 1164
263/1165: 1165
263/1166: 1166
264/1167: 1167
265/1168: 1168
266/1169: 1169
267/1170: 1170
268/1171: 1171
269/1172: 1172
27/100: 100
27/101: 101
27/102: 102
270/1173: 1173
270/1174: 1174
270/1175: 1175
270/1176: 1176
270/1177: 1177
270/1178: 1178
270/1179: 1179
271/1180: 1180
271/1181: 1181
271/1182: 1182
272/1183: 1183
272/1184: 1184
272/1185: 1185
273/1186: 1186
274/1187: 1187
275/1188: 1188
275/1189: 1189
275/1190: 1190
276/1191: 1191
276/1192: 1192
276/1193: 1193
276/1194: 1194
276/1195: 1195
276/1196: 1196
276/1197: 1197
276/1198: 1198
276/1199: 1199
276/1200: 1200
276/1201: 1201
276/1202: 1202
276/1203: 1203
276/1204: 1204
276/1205: 1205
277/1206: 1206
277/1207: 1207
277/1208: 1208
277/1209: 1209
278/1210: 1210
279/1211: 1211
279/1212: 1212
279/1213: 1213
28/103: 103
28/104: 104
28/105: 105
28/106: 106
28/107: 107
28/108: 108
28/109: 109
280/1214: 1214
280/1215: 1215
280/1216: 1216
280/1217: 1217
280/1218: 1218
280/1219: 1219
280/1220: 1220
280/1221: 1221
280/1222: 1222
280/1223: 1223
280/1224: 1224
280/1225: 1225
280/1226: 1226
280/1227: 1227
280/1228: 1228
280/1229: 1229
280/1230: 1230
280/1231: 1231
280/1232: 1232
280/1233: 1233
280/1234: 1234
280/1235: 1235
280/1236: 1236
280/1237: 1237
280/1238: 1238
280/1239: 1239
280/1240: 1240
280/1241: 1241
280/1242: 1242
280/1243: 1243
280/1244: 1244
280/1245: 1245
280/1246: 1246
280/1247: 1247
280/1248: 1248
280/1249: 1249
280/1250: 1250
280/1251: 1251
280/1252: 1252
280/1253: 1253
280/1254: 1254
280/1255: 1255
280/1256: 1256
280/1257: 1257
280/1258: 1258
280/1259: 1259
280/1260: 1260
280/1261: 1261
280/1262: 1262
280/1263: 1263
281/1264: 1264
281/1265: 1265
281/1266: 1266
281/1267: 1267
281/1268: 1268
281/1269: 1269
281/1270: 1270
281/1271: 1271
281/1272: 1272
281/1273: 1273
282/1274: 1274
283/1275: 1275
284/1276: 1276
284/1277: 1277
284/1278: 1278
285/1279: 1279
286/1280: 1280
286/1281: 1281
287/1282: 1282
287/1283: 1283
287/1284: 1284
287/1285: 1285
288/1286: 1286
288/1287: 1287
288/1288: 1288
289/1289: 1289
289/1290: 1290
29/110: 110
290/1291: 1291
290/1292: 1292
291/1293: 1293
291/1294: 1294
291/1295: 1295
291/1296: 1296
291/1297: 1297
291/1298: 1298
291/1299: 1299
291/1300: 1300
291/1301: 1301
291/1302: 1302
291/1303: 1303
291/1304: 1304
291/1305: 1305
292/1306: 1306
293/1307: 1307
294/1308: 1308
294/1309: 1309
295/1310: 1310
296/1311: 1311
297/1312: 1312
297/1313: 1313
297/1314: 1314
297/1315: 1315
297/1316: 1316
297/1317: 1317
297/1318: 1318
297/1319: 1319
297/1320: 1320
297/1321: 1321
297/1322: 1322
297/1323: 1323
297/1324: 1324
297/1325: 1325
298/1326: 1326
299/1327: 1327
299/1328: 1328
299/1329: 1329
299/1330: 1330
299/1331: 1331
299/1332: 1332
299/1333: 1333
299/1334: 1334
299/1335: 1335
299/1336: 1336
299/1337: 1337
299/1338: 1338
3/10: 10
3/11: 11
3/12: 12
3/13: 13
3/14: 14
3/15: 15
3/16: 16
30/111: 111
30/112: 112
30/113: 113
30/114: 114
30/115: 115
30/116: 116
30/117: 117
30/118: 118
30/119: 119
30/120: 120
30/121: 121
30/122: 122
30/123: 123
30/124: 124
300/1339: 1339
300/1340: 1340
300/1341: 1341
300/1342: 1342
300/1343: 1343
300/1344: 1344
300/1345: 1345
300/1346: 1346
300/1347: 1347
300/1348: 1348
300/1349: 1349
301/1350: 1350
301/1351: 1351
301/1352: 1352
301/1353: 1353
301/1354: 1354
301/1355: 1355
301/1356: 1356
301/1357: 1357
301/1358: 1358
302/1359: 1359
302/1360: 1360
302/1361: 1361
302/1362: 1362
302/1363: 1363
303/1364: 1364
303/1365: 1365
303/1366: 1366
303/1367: 1367
304/1368: 1368
305/1369: 1369
305/1370: 1370
305/1371: 1371
306/1372: 1372
306/1373: 1373
307/1374: 1374
308/1375: 1375
308/1376: 1376
308/1377: 1377
308/1378: 1378
308/1379: 1379
308/1380: 1380
308/1381: 1381
308/1382: 1382
308/1383: 1383
308/1384: 1384
308/1385: 1385
308/1386: 1386
308/1387: 1387
308/1388: 1388
308/1389: 1389
308/1390: 1390
308/1391: 1391
308/1392: 1392
308/1393: 1393
309/1394: 1394
309/1395: 1395
309/1396: 1396
31/125: 125
31/126: 126
31/127: 127
31/128: 128
31/129: 129
31/130: 130
31/131: 131
31/132: 132
31/133: 133
31/134: 134
31/135: 135
31/136: 136
310/1397: 1397
310/1398: 1398
310/1399: 1399
310/1400: 1400
310/1401: 1401
310/1402: 1402
310/1403: 1403
310/1404: 1404
310/1405: 1405
310/1406: 1406
310/1407: 1407
310/1408: 1408
310/1409: 1409
310/1410: 1410
310/1411: 1411
310/1412: 1412
310/1413: 1413
310/1414: 1414
310/1415: 1415
311/1416: 1416
312/1417: 1417
313/1418: 1418
313/1419: 1419
313/1420: 1420
313/1421: 1421
314/1422: 1422
314/1423: 1423
314/1424: 1424
314/1425: 1425
314/1426: 1426
314/1427: 1427
314/1428: 1428
314/1429: 1429
314/1430: 1430
314/1431: 1431
314/1432: 1432
314/1433: 1433
314/1434: 1434
314/1435: 1435
314/1436: 1436
314/1437: 1437
314/1438: 1438
314/1439: 1439
314/1440: 1440
314/1441: 1441
314/1442: 1442
314/1443: 1443
314/1444: 1444
314/1445: 1445
314/1446: 1446
314/1447: 1447
314/1448: 1448
314/1449: 1449
314/1450: 1450
314/1451: 1451
314/1452: 1452
314/1453: 1453
314/1454: 1454
314/1455: 1455
314/1456: 1456
314/1457: 1457
314/1458: 1458
314/1459: 1459
314/1460: 1460
314/1461: 1461
314/1462: 1462
314/1463: 1463
314/1464: 1464
314/1465: 1465
314/1466: 1466
314/1467: 1467
314/1468: 1468
314/1469: 1469
314/1470: 1470
314/1471: 1471
314/1472: 1472
314/1473: 1473
314/1474: 1474
314/1475: 1475
314/1476: 1476
314/1477: 1477
314/1478: 1478
314/1479: 1479
314/1480: 1480
314/1481: 1481
314/1482: 1482
314/1483: 1483
314/1484: 1484
314/1485: 1485
314/1486: 1486
314/1487: 1487
32/137: 137
32/138: 138
32/139: 139
32/140: 140
32/141: 141
32/142: 142
32/143: 143
32/144: 144
32/145: 145
32/146: 146
32/147: 147
32/148: 148
32/149: 149
32/150: 150
32/151: 151
32/152: 152
32/153: 153
32/154: 154
32/155: 155
32/156: 156
32/157: 157
32/158: 158
32/159: 159
32/160: 160
32/161: 161
33/162: 162
33/163: 163
33/164: 164
33/165: 165
33/166: 166
33/167: 167
33/168: 168
33/169: 169
33/170: 170
33/171: 171
33/172: 172
33/173: 173
33/174: 174
33/175: 175
33/176: 176
33/177: 177
33/178: 178
33/179: 179
33/180: 180
34/181: 181
35/182: 182
36/183: 183
36/184: 184
37/185: 185
38/186: 186
38/187: 187
38/188: 188
38/189: 189
38/190: 190
38/191: 191
38/192: 192
38/193: 193
38/194: 194
39/195: 195
39/196: 196
39/197: 197
39/198: 198
39/199: 199
39/200: 200
39/201: 201
39/202: 202
39/203: 203
39/204: 204
39/205: 205
39/206: 206
39/207: 207
39/208: 208
39/209: 209
39/210: 210
39/211: 211
39/212: 212
39/213: 213
39/214: 214
39/215: 215
39/216: 216
39/217: 217
39/218: 218
39/219: 219
39/220: 220
39/221: 221
39/222: 222
39/223: 223
39/224: 224
39/225: 225
39/226: 226
39/227: 227
39/228: 228
39/229: 229
39/230: 230
39/231: 231
4/17: 17
4/18: 18
4/19: 19
4/20: 20
40/232: 232
40/233: 233
41/234: 234
41/235: 235
41/236: 236
41/237: 237
41/238: 238
41/239: 239
41/240: 240
41/241: 241
41/242: 242
41/243: 243
41/244: 244
41/245: 245
41/246: 246
41/247: 247
41/248: 248
42/249: 249
43/250: 250
44/251: 251
45/252: 252
45/253: 253
45/254: 254
45/255: 255
45/256: 256
45/257: 257
45/258: 258
45/259: 259
45/260: 260
45/261: 261
45/262: 262
45/263: 263
45/264: 264
45/265: 265
45/266: 266
46/267: 267
46/268: 268
46/269: 269
46/270: 270
46/271: 271
46/272: 272
46/273: 273
46/274: 274
46/275: 275
46/276: 276
46/277: 277
46/278: 278
47/279: 279
47/280: 280
48/281: 281
48/282: 282
48/283: 283
48/284: 284
48/285: 285
49/286: 286
49/287: 287
49/288: 288
49/289: 289
49/290: 290
49/291: 291
49/292: 292
5/21: 21
5/22: 22
5/23: 23
5/24: 24
5/25: 25
5/26: 26
50/293: 293
51/294: 294
52/295: 295
52/296: 296
52/297: 297
53/298: 298
54/299: 299
55/300: 300
55/301: 301
56/302: 302
56/303: 303
56/304: 304
56/305: 305
56/306: 306
56/307: 307
56/308: 308
56/309: 309
57/310: 310
58/311: 311
58/312: 312
58/313: 313
59/314: 314
59/315: 315
6/27: 27
6/28: 28
60/316: 316
60/317: 317
60/318: 318
61/319: 319
61/320: 320
61/321: 321
62/322: 322
62/323: 323
63/324: 324
64/325: 325
65/326: 326
65/327: 327
65/328: 328
65/329: 329
66/330: 330
66/331: 331
66/332: 332
66/333: 333
67/334: 334
68/335: 335
68/336: 336
68/337: 337
68/338: 338
68/339: 339
68/340: 340
68/341: 341
68/342: 342
68/343: 343
68/344: 344
68/345: 345
68/346: 346
68/347: 347
68/348: 348
68/349: 349
68/350: 350
69/351: 351
7/29: 29
7/30: 30
70/352: 352
71/353: 353
71/354: 354
72/355: 355
72/356: 356
73/357: 357
73/358: 358
74/359: 359
75/360: 360
76/361: 361
76/362: 362
76/363: 363
76/364: 364
76/365: 365
77/366: 366
78/367: 367
78/368: 368
78/369: 369
79/370: 370
79/371: 371
79/372: 372
79/373: 373
79/374: 374
79/375: 375
79/376: 376
79/377: 377
79/378: 378
8/31: 31
8/32: 32
8/33: 33
8/34: 34
8/35: 35
8/36: 36
8/37: 37
8/38: 38
8/39: 39
8/40: 40
8/41: 41
8/42: 42
8/43: 43
8/44: 44
8/45: 45
8/46: 46
80/379: 379
80/380: 380
80/381: 381
80/382: 382
80/383: 383
80/384: 384
80/385: 385
80/386: 386
80/387: 387
80/388: 388
80/389: 389
80/390: 390
80/391: 391
81/392: 392
81/393: 393
81/394: 394
81/395: 395
81/396: 396
81/397: 397
81/398: 398
81/399: 399
81/400: 400
81/401: 401
82/402: 402
83/403: 403
84/404: 404
85/405: 405
86/406: 406
86/407: 407
87/408: 408
88/409: 409
88/410: 410
89/411: 411
89/412: 412
9/47: 47
9/48: 48
9/49: 49
90/413: 413
90/414: 414
91/415: 415
91/416: 416
92/417: 417
93/418: 418
93/419: 419
93/420: 420
93/421: 421
93/422: 422
93/423: 423
93/424: 424
93/425: 425
93/426: 426
94/427: 427
94/428: 428
94/429: 429
94/430: 430
94/431: 431
94/432: 432
94/433: 433
95/434: 434
95/435: 435
95/436: 436
95/437: 437
95/438: 438
96/439: 439
97/440: 440
97/441: 441
98/442: 442
99/443: 443
sample_dataset/id_manager/service_id.yml 0 → 100644
浏览文件 @ 99b31de8
? ''
: 0
'1': 1
'10': 10
'100': 100
'101': 101
'102': 102
'103': 103
'104': 104
'105': 105
'106': 106
'107': 107
'108': 108
'109': 109
'11': 11
'110': 110
'111': 111
'112': 112
'113': 113
'114': 114
'115': 115
'116': 116
'117': 117
'118': 118
'119': 119
'12': 12
'120': 120
'121': 121
'122': 122
'123': 123
'124': 124
'125': 125
'126': 126
'127': 127
'128': 128
'129': 129
'13': 13
'130': 130
'131': 131
'132': 132
'133': 133
'134': 134
'135': 135
'136': 136
'137': 137
'138': 138
'139': 139
'14': 14
'140': 140
'141': 141
'142': 142
'143': 143
'144': 144
'145': 145
'146': 146
'147': 147
'148': 148
'149': 149
'15': 15
'150': 150
'151': 151
'152': 152
'153': 153
'154': 154
'155': 155
'156': 156
'157': 157
'158': 158
'159': 159
'16': 16
'160': 160
'161': 161
'162': 162
'163': 163
'164': 164
'165': 165
'166': 166
'167': 167
'168': 168
'169': 169
'17': 17
'170': 170
'171': 171
'172': 172
'173': 173
'174': 174
'175': 175
'176': 176
'177': 177
'178': 178
'179': 179
'18': 18
'180': 180
'181': 181
'182': 182
'183': 183
'184': 184
'185': 185
'186': 186
'187': 187
'188': 188
'189': 189
'19': 19
'190': 190
'191': 191
'192': 192
'193': 193
'194': 194
'195': 195
'196': 196
'197': 197
'198': 198
'199': 199
'2': 2
'20': 20
'200': 200
'201': 201
'202': 202
'203': 203
'204': 204
'205': 205
'206': 206
'207': 207
'208': 208
'209': 209
'21': 21
'210': 210
'211': 211
'212': 212
'213': 213
'214': 214
'215': 215
'216': 216
'217': 217
'218': 218
'219': 219
'22': 22
'220': 220
'221': 221
'222': 222
'223': 223
'224': 224
'225': 225
'226': 226
'227': 227
'228': 228
'229': 229
'23': 23
'230': 230
'231': 231
'232': 232
'233': 233
'234': 234
'235': 235
'236': 236
'237': 237
'238': 238
'239': 239
'24': 24
'240': 240
'241': 241
'242': 242
'243': 243
'244': 244
'245': 245
'246': 246
'247': 247
'248': 248
'249': 249
'25': 25
'250': 250
'251': 251
'252': 252
'253': 253
'254': 254
'255': 255
'256': 256
'257': 257
'258': 258
'259': 259
'26': 26
'260': 260
'261': 261
'262': 262
'263': 263
'264': 264
'265': 265
'266': 266
'267': 267
'268': 268
'269': 269
'27': 27
'270': 270
'271': 271
'272': 272
'273': 273
'274': 274
'275': 275
'276': 276
'277': 277
'278': 278
'279': 279
'28': 28
'280': 280
'281': 281
'282': 282
'283': 283
'284': 284
'285': 285
'286': 286
'287': 287
'288': 288
'289': 289
'29': 29
'290': 290
'291': 291
'292': 292
'293': 293
'294': 294
'295': 295
'296': 296
'297': 297
'298': 298
'299': 299
'3': 3
'30': 30
'300': 300
'301': 301
'302': 302
'303': 303
'304': 304
'305': 305
'306': 306
'307': 307
'308': 308
'309': 309
'31': 31
'310': 310
'311': 311
'312': 312
'313': 313
'314': 314
'32': 32
'33': 33
'34': 34
'35': 35
'36': 36
'37': 37
'38': 38
'39': 39
'4': 4
'40': 40
'41': 41
'42': 42
'43': 43
'44': 44
'45': 45
'46': 46
'47': 47
'48': 48
'49': 49
'5': 5
'50': 50
'51': 51
'52': 52
'53': 53
'54': 54
'55': 55
'56': 56
'57': 57
'58': 58
'59': 59
'6': 6
'60': 60
'61': 61
'62': 62
'63': 63
'64': 64
'65': 65
'66': 66
'67': 67
'68': 68
'69': 69
'7': 7
'70': 70
'71': 71
'72': 72
'73': 73
'74': 74
'75': 75
'76': 76
'77': 77
'78': 78
'79': 79
'8': 8
'80': 80
'81': 81
'82': 82
'83': 83
'84': 84
'85': 85
'86': 86
'87': 87
'88': 88
'89': 89
'9': 9
'90': 90
'91': 91
'92': 92
'93': 93
'94': 94
'95': 95
'96': 96
'97': 97
'98': 98
'99': 99
sample_dataset/id_manager/status_id.yml 0 → 100644
浏览文件 @ 99b31de8
? ''
: 0
'0': 1
'1': 2
'400': 3
'401': 4
'403': 5
'404': 6
'423': 7
'429': 8
'500': 9
'503': 10
'504': 11
CompletionException: 12
HystrixRuntimeException: 13
ProcessingException: 14
SOAERROR_1006: 15
SOAERROR_2017: 16
SOAERROR_2026: 17
SOAERROR_2028: 18
UNFINISHED: 19
sample_dataset/test.csv 0 → 100644
浏览文件 @ 99b31de8
traceIdHigh,traceIdLow,parentSpanId,spanId,startTime,duration,nanosecond,DBhash,status,operationName,serviceName,nodeLatencyLabel,graphLatencyLabel,graphStructureLabel
1596357529536981502,4448694259929862755,0,-1990250972,2022-05-02 18:10:09,4188,102000000,0,0,1413,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,-281558527,2022-05-02 18:10:09,3913,347000000,0,0,1399,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,-335450563,2022-05-02 18:10:09,74,256000000,0,0,1400,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,159928739,2022-05-02 18:10:09,14,166000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,1875814534,2022-05-02 18:10:09,20,180000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,-1741606249,2022-05-02 18:10:09,20,250000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,-1617443693,2022-05-02 18:10:09,12,268000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,-278055518,2022-05-02 18:10:09,12,328000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,-370879640,2022-05-02 18:10:09,11,349000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,2071447605,2022-05-02 18:10:13,15,265000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,-1819031907,2022-05-02 18:10:13,12,278000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,1842451354,2022-05-02 18:10:13,13,287000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,1935560096,2022-05-02 18:10:13,15,295000000,0,0,1402,310,0,0,1
1596357529536981502,4448694259929862755,-1990250972,-2003257890,2022-05-02 18:10:14,14,306000000,0,500,1412,310,0,0,1
1596357529536981502,4448694259929862755,-281558527,-700384075,2022-05-02 18:10:09,3907,353000000,0,0,124,30,0,0,1
1596357529536981502,4448694259929862755,-335450563,-21249935,2022-05-02 18:10:09,63,267000000,0,0,249,42,0,0,1
1596357529536981502,4448694259929862755,-2003257890,1900376561,2022-05-02 18:10:14,2,318000000,0,500,1370,305,0,0,1
1596357529536981502,4448694259929862755,-700384075,-803543939,2022-05-02 18:10:09,23,367000000,0,0,113,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,-736459037,2022-05-02 18:10:09,19,371000000,0,0,113,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,-1183351125,2022-05-02 18:10:11,16,34000000,0,0,113,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,350862234,2022-05-02 18:10:12,10,580000000,0,0,113,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,1682015471,2022-05-02 18:10:13,11,169000000,0,0,113,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,2040964166,2022-05-02 18:10:13,11,259000000,0,0,113,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,1762151399,2022-05-02 18:10:10,955,85000000,0,0,117,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,-334971362,2022-05-02 18:10:12,583,577000000,0,0,117,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,-15543737,2022-05-02 18:10:09,51,379000000,0,0,119,30,0,0,1
1596357529536981502,4448694259929862755,-700384075,583885210,2022-05-02 18:10:14,11,269000000,0,0,121,30,0,0,1
1596357529536981502,4448694259929862755,1762151399,681721006,2022-05-02 18:10:10,840,160000000,0,0,1270,281,0,0,1
1596357529536981502,4448694259929862755,-334971362,1067395986,2022-05-02 18:10:12,479,611000000,0,0,1270,281,0,0,1
1596357529536981502,4448694259929862755,-15543737,408810147,2022-05-02 18:10:09,33,397000000,0,0,1277,284,0,0,1
1596357529536981502,4448694259929862755,583885210,-304841780,2022-05-02 18:10:14,3,277000000,0,0,1369,305,0,0,1
5662566985137355690,7477298637177050925,0,-491131385,2022-05-02 17:47:41,5176,914000000,0,0,1393,308,0,1,0
5662566985137355690,7477298637177050925,-491131385,-652820602,2022-05-02 17:47:41,14,976000000,0,0,1378,308,0,1,0
5662566985137355690,7477298637177050925,-491131385,-1333335415,2022-05-02 17:47:47,11,69000000,0,0,1380,308,0,1,0
5662566985137355690,7477298637177050925,-491131385,703056482,2022-05-02 17:47:41,10,950000000,0,0,1381,308,0,1,0
5662566985137355690,7477298637177050925,-491131385,687971319,2022-05-02 17:47:47,7,83000000,0,0,1386,308,1,1,0
5662566985137355690,7477298637177050925,-491131385,1792075409,2022-05-02 17:47:41,7,963000000,0,0,1389,308,0,1,0
5662566985137355690,7477298637177050925,-491131385,48140695,2022-05-02 17:47:47,26,14000000,0,0,1391,308,0,1,0
5662566985137355690,7477298637177050925,-652820602,1379876670,2022-05-02 17:47:41,11,979000000,0,0,310,57,0,1,0
5662566985137355690,7477298637177050925,703056482,-699583531,2022-05-02 17:47:41,5,945000000,0,0,356,72,0,1,0
5662566985137355690,7477298637177050925,687971319,-1212461088,2022-05-02 17:47:47,4,86000000,0,0,1212,279,0,1,0
5662566985137355690,7477298637177050925,48140695,-1178781667,2022-05-02 17:47:47,11,19000000,0,0,1301,291,0,1,0
3646047056327432861,6658453249126145996,0,1931323897,2022-05-02 15:59:59,60,450000000,0,0,403,83,0,0,0
3646047056327432861,6658453249126145996,1931323897,711219543,2022-05-02 15:59:59,57,453000000,0,0,649,153,0,0,0
3646047056327432861,6658453249126145996,711219543,-1561583093,2022-05-02 15:59:59,4,466000000,0,0,643,153,0,0,0
3646047056327432861,6658453249126145996,711219543,81837891,2022-05-02 15:59:59,24,486000000,0,0,643,153,0,0,0
3646047056327432861,6658453249126145996,711219543,1963630877,2022-05-02 15:59:59,5,505000000,0,0,643,153,0,0,0
3646047056327432861,6658453249126145996,711219543,-1370474143,2022-05-02 15:59:59,5,505000000,0,0,643,153,0,0,0
3646047056327432861,6658453249126145996,711219543,1992379152,2022-05-02 15:59:59,17,473000000,0,500,644,153,0,0,0
3646047056327432861,6658453249126145996,1992379152,360300743,2022-05-02 15:59:59,13,467000000,0,500,673,156,0,0,0
sample_dataset/train.csv 0 → 100644
浏览文件 @ 99b31de8
traceIdHigh,traceIdLow,parentSpanId,spanId,startTime,duration,nanosecond,DBhash,status,operationName,serviceName
7565692060307280094,4149727419449628353,0,-1160783149,2022-05-02 18:32:16,8,522000000,0,0,3,2
7565692060307280094,4149727419449628353,-1160783149,-663518905,2022-05-02 18:32:16,5,525000000,0,0,6,2
7565692060307280094,4149727419449628353,-663518905,292134812,2022-05-02 18:32:16,2,528000000,0,0,17,4
7469867095312772506,9054005929149387783,0,-116188428,2022-05-02 15:59:34,9,401000000,0,0,3,2
7469867095312772506,9054005929149387783,-116188428,-936201595,2022-05-02 15:59:34,6,404000000,0,0,6,2
7469867095312772506,9054005929149387783,-936201595,203651095,2022-05-02 15:59:34,4,406000000,0,0,17,4
1302365061014104190,4713847854262197922,0,147046579,2022-05-02 16:05:08,8,262000000,0,0,3,2
1302365061014104190,4713847854262197922,147046579,-815705052,2022-05-02 16:05:08,5,265000000,0,0,6,2
1302365061014104190,4713847854262197922,-815705052,-1767599182,2022-05-02 16:05:08,3,267000000,0,0,17,4
4520607604792678538,90296779370241065,0,-1192584432,2022-05-02 15:03:15,10,800000000,0,0,3,2
4520607604792678538,90296779370241065,-1192584432,2102356101,2022-05-02 15:03:15,7,803000000,0,0,6,2
4520607604792678538,90296779370241065,2102356101,-1201056520,2022-05-02 15:03:15,5,805000000,0,0,17,4
4301483373465897605,1921512395340371532,0,-116188428,2022-05-02 15:59:34,9,401000000,0,0,3,2
4301483373465897605,1921512395340371532,-116188428,-936201595,2022-05-02 15:59:34,6,404000000,0,0,6,2
4301483373465897605,1921512395340371532,-936201595,203651095,2022-05-02 15:59:34,4,406000000,0,0,17,4
4516399473592649105,3122656494628515993,0,-940181392,2022-05-02 16:32:32,8,162000000,0,0,3,2
4516399473592649105,3122656494628515993,-940181392,-1775474648,2022-05-02 16:32:32,6,164000000,0,0,6,2
4516399473592649105,3122656494628515993,-1775474648,-614412147,2022-05-02 16:32:32,4,166000000,0,0,17,4
2001615163061348505,1103669949841503114,0,-251538214,2022-05-02 17:19:09,7,523000000,0,0,3,2
2001615163061348505,1103669949841503114,-251538214,-295534211,2022-05-02 17:19:09,5,525000000,0,0,6,2
2001615163061348505,1103669949841503114,-295534211,-1868571256,2022-05-02 17:19:09,3,527000000,0,0,17,4
sample_dataset/val.csv 0 → 100644
浏览文件 @ 99b31de8
traceIdHigh,traceIdLow,parentSpanId,spanId,startTime,duration,nanosecond,DBhash,status,operationName,serviceName
3718860522599049613,8245757295869652015,0,744605799,2022-05-02 15:34:49,10,630000000,0,0,3,2
3718860522599049613,8245757295869652015,744605799,258669622,2022-05-02 15:34:49,6,634000000,0,0,6,2
3718860522599049613,8245757295869652015,258669622,-2140429749,2022-05-02 15:34:49,4,626000000,0,0,17,4
171786705856344778,8432392905725602200,0,834436815,2022-05-02 15:38:25,22,928000000,0,0,3,2
171786705856344778,8432392905725602200,834436815,1941867149,2022-05-02 15:38:25,19,931000000,0,0,6,2
171786705856344778,8432392905725602200,1941867149,-2120879518,2022-05-02 15:38:25,17,933000000,0,0,17,4
1884068908740226663,198674487451014796,0,-1192584432,2022-05-02 15:03:15,10,800000000,0,0,3,2
1884068908740226663,198674487451014796,-1192584432,2102356101,2022-05-02 15:03:15,7,803000000,0,0,6,2
1884068908740226663,198674487451014796,2102356101,-1201056520,2022-05-02 15:03:15,5,805000000,0,0,17,4
6433112438253994909,8162558342722269388,0,-940181392,2022-05-02 16:32:32,8,162000000,0,0,3,2
6433112438253994909,8162558342722269388,-940181392,-1775474648,2022-05-02 16:32:32,6,164000000,0,0,6,2
6433112438253994909,8162558342722269388,-1775474648,-614412147,2022-05-02 16:32:32,4,166000000,0,0,17,4
6877930537907535995,8837251782173988176,0,-112915630,2022-05-02 16:57:11,7,243000000,0,0,3,2
6877930537907535995,8837251782173988176,-112915630,-1653931621,2022-05-02 16:57:11,4,236000000,0,0,6,2
6877930537907535995,8837251782173988176,-1653931621,-48204267,2022-05-02 16:57:11,2,238000000,0,0,17,4
790603823422444737,8049481303438475592,0,-269253468,2022-05-02 15:00:08,14,176000000,0,0,3,2
790603823422444737,8049481303438475592,-269253468,1292285861,2022-05-02 15:00:08,11,179000000,0,0,6,2
790603823422444737,8049481303438475592,1292285861,-205310853,2022-05-02 15:00:08,4,186000000,0,0,17,4
4016062494274678058,802407659628105677,0,-1067224499,2022-05-02 15:14:32,20,520000000,0,0,3,2
4016062494274678058,802407659628105677,-1067224499,1026915991,2022-05-02 15:14:32,17,523000000,0,0,6,2
4016062494274678058,802407659628105677,1026915991,-1777842468,2022-05-02 15:14:32,15,525000000,0,0,17,4
test.sh 0 → 100644
浏览文件 @ 99b31de8
echo "Usage: bash test.sh [model_path] [dataset_path]"
echo "MODEL: $1"
echo "DATASET: $2"
python3 -m tracegnn.models.trace_vae.test evaluate-nll -M "$1" --use-train-val -D "$2" --device cpu --use-std-limit --std-limit-global
tracegnn/cli/data_process.py 0 → 100644
浏览文件 @ 99b31de8
import math
import pickle as pkl
import random
import shutil
import sys
import click
import numpy as np
from tqdm import tqdm
from tracegnn.constants import *
from tracegnn.data import *
from tracegnn.utils import *
def get_graph_key(g):
node_types = set()
stack = [g.root]
while stack:
nd = stack.pop()
node_types.add(nd.operation_id)
stack.extend(nd.children)
return g.root.operation_id, g.max_depth, tuple(sorted(node_types))
@click.group()
def main():
pass
@main.command()
@click.option('-i', '--input-dir')
@click.option('-o', '--output-dir')
@click.option('-n', '--name', type=str, required=True)
def csv_to_db(input_dir, output_dir, name):
# check the parameters
input_dir = os.path.abspath(input_dir)
output_dir = os.path.abspath(output_dir)
input_path = os.path.join(input_dir, f"{name}.csv")
output_path = os.path.join(output_dir, "processed", name)
# Load id_manager
id_manager = TraceGraphIDManager(os.path.join(input_dir, 'id_manager'))
# process the traces
# load the graphs
if 'test' not in name:
df = load_trace_csv(input_path)
trace_graphs = df_to_trace_graphs(
df,
id_manager=id_manager,
merge_spans=True,
)
# write to db
if os.path.exists(output_path):
shutil.rmtree(output_path)
db = BytesSqliteDB(output_path, write=True)
with db, db.write_batch():
for g in tqdm(trace_graphs, desc='Save graphs'):
db.add(g.to_bytes())
else:
# read test data
df = load_trace_csv(input_path, is_test=True)
for i in range(3):
trace_graphs = df_to_trace_graphs(
df,
id_manager=id_manager,
merge_spans=True,
test_label=i
)
# write to db
if i == 0:
output_path = os.path.join(output_dir, 'processed', 'test')
elif i == 1:
output_path = os.path.join(output_dir, 'processed', 'test-drop')
else:
output_path = os.path.join(output_dir, 'processed', 'test-latency')
if os.path.exists(output_path):
shutil.rmtree(output_path)
db = BytesSqliteDB(output_path, write=True)
with db, db.write_batch():
for g in tqdm(trace_graphs, desc='Save graphs'):
db.add(g.to_bytes())
@main.command()
@click.option('-i', '--input-dir')
@click.option('-o', '--output_dir')
def preprocess(input_dir, output_dir):
print("Convert datasets...")
print("------------> Train")
os.system(f"python3 -m tracegnn.cli.data_process csv-to-db -i {input_dir} -o {output_dir} -n train")
print("------------> Val")
os.system(f"python3 -m tracegnn.cli.data_process csv-to-db -i {input_dir} -o {output_dir} -n val")
print("------------> Test")
os.system(f"python3 -m tracegnn.cli.data_process csv-to-db -i {input_dir} -o {output_dir} -n test")
print("Finished!")
if __name__ == '__main__':
main()
tracegnn/constants.py 0 → 100644
浏览文件 @ 99b31de8
import os
# if MIN_NODE_COUNT <= 2 <= MAX_NODE_COUNT, then the graph will be chosen
MAX_NODE_COUNT = int(os.environ.get('MAX_NODE_COUNT', '32'))
MAX_SPAN_COUNT = int(os.environ.get('MAX_SPAN_COUNT', '32'))
# whether or not to use multi-dimensional latency codec?
# If not set, will normalize the latency w.r.t. each operation.
USE_MULTI_DIM_LATENCY_CODEC = os.environ.get('USE_MULTI_DIM_LATENCY_CODEC', '0') == '1'
# If USE_MULTI_DIM_LATENCY_CODEC, then encode the codec parameters.
MAX_LATENCY_DIM = int(os.environ.get('MAX_LATENCY_DIM', '5'))
MAX_DEPTH = int(os.environ.get('MAX_DEPTH', '4'))
tracegnn/data/__init__.py 0 → 100644
浏览文件 @ 99b31de8
from .bytes_db import *
from .trace_graph import *
from .trace_graph_db import *
tracegnn/data/bytes_db.py 0 → 100644
浏览文件 @ 99b31de8
"""Databases for large-scale datasets."""
import bisect
import os
import pickle
import sqlite3
from contextlib import contextmanager
from typing import *
import numpy as np
import snappy
__all__ = [
'BytesDB',
'BytesSqliteDB',
'BytesMultiDB',
]
class BytesDB(object):
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if not exc_type:
self.commit()
self.close()
def __len__(self) -> int:
return self.data_count()
def __getitem__(self, item: int):
return self.get(item)
def __iter__(self):
for i in range(self.data_count()):
yield self.get(i)
def __repr__(self):
desc = self.describe().strip()
if '\n' in desc:
desc = '\n'.join(f' {l}' for l in desc.split('\n'))
desc = f'\n{desc}\n'
return f'{self.__class__.__name__}({desc})'
def describe(self) -> str:
raise NotImplementedError()
def sample_n(self, n: int) -> List[bytes]:
ret = []
indices = np.random.randint(self.data_count(), size=n)
for i in indices:
ret.append(self.get(i))
return ret
def data_count(self) -> int:
raise NotImplementedError()
def get(self, item: int) -> bytes:
raise NotImplementedError()
def add(self, val: bytes) -> int:
raise NotImplementedError()
@contextmanager
def write_batch(self):
raise NotImplementedError()
def commit(self):
raise NotImplementedError()
def optimize(self):
raise NotImplementedError()
def close(self):
raise NotImplementedError()
class BytesSqliteDB(BytesDB):
class WB(object):
def __init__(self, conn, cur, table_name, buf_size=8192):
self.conn = conn
self.cur = cur
self.table_name = table_name
self.buf = []
self.buf_size = buf_size
def add(self, id, value):
self.buf.append((id, snappy.compress(value)))
if len(self.buf) >= self.buf_size:
self.commit()
def commit(self):
if self.buf:
self.cur.executemany(
f'INSERT INTO "{self.table_name}"("key", "value") VALUES (?, ?)',
self.buf
)
self.conn.commit()
self.buf.clear()
def rollback(self):
self.conn.rollback()
self.buf.clear()
conn: sqlite3.Connection
path: str
file_name: str
_data_count: int
def __init__(self, path: str, write: bool = False, table_name: str = 'data',
file_name: str = '_bytes.db'):
self.path = path
self.table_name = table_name
self.file_name = file_name
if write and not os.path.isdir(path):
os.makedirs(path, exist_ok=True)
self.conn = sqlite3.connect(os.path.join(self.path, file_name))
self.conn.text_factory = bytes
with self._scoped_cursor() as cur:
cur.execute(
f'CREATE TABLE IF NOT EXISTS "{self.table_name}" ('
' "key" INT PRIMARY KEY,'
' "value" BLOB'
');'
)
self.conn.commit()
self._data_count = cur.execute(f'SELECT COUNT(*) FROM "{self.table_name}"').fetchone()[0]
self._wb = None
@contextmanager
def _scoped_cursor(self):
cur = self.conn.cursor()
try:
yield cur
finally:
cur.close()
def describe(self) -> str:
p = self.path
if self.file_name != '_bytes.db':
p = os.path.join(p, self.file_name)
if any(c in p for c in '(),'):
return repr(p)
return p
def data_count(self) -> int:
return self._data_count
def get(self, item: int) -> bytes:
with self._scoped_cursor() as cur:
cur.execute(f'SELECT "value" FROM "{self.table_name}" WHERE "key" = {item}')
row = cur.fetchone()
if row is not None:
return snappy.decompress(row[0])
def add(self, val: bytes) -> int:
if self._wb is None:
with self.write_batch():
return self.add(val)
else:
key = self._data_count
self._wb.add(key, val)
self._data_count += 1
return key
@contextmanager
def write_batch(self):
if self._wb is not None:
raise RuntimeError(f'Another write_batch is already open!')
try:
self._wb = self.WB(self.conn, self.conn.cursor(), self.table_name)
yield self
self._wb.commit()
self._wb = None
except:
self._wb.rollback()
self._wb = None
raise
def commit(self):
if self._wb is not None:
self._wb.commit()
def optimize(self):
pass
def close(self):
self.commit()
self._wb = None
self.conn.close()
class BytesMultiDB(BytesDB):
db_list: List[BytesDB]
db_sizes: List[int]
_db_offset: List[int]
_data_count: int
def __init__(self, *db_list):
self.db_list = list(db_list)
self.db_sizes = [db.data_count() for db in self.db_list]
self._db_offset = []
i = 0
for db in self.db_list:
self._db_offset.append(i)
i += db.data_count()
self._data_count = i
def describe(self) -> str:
return '\n'.join(f'{db.describe()},' for db in self.db_list).rstrip(',')
def data_count(self) -> int:
return self._data_count
def get(self, item: int) -> bytes:
if item < 0 or item >= self._data_count:
raise IndexError(item)
i = bisect.bisect_left(self._db_offset, item + 1) - 1
return self.db_list[i].get(item - self._db_offset[i])
def add(self, val: bytes) -> int:
raise RuntimeError(f'BytesMultiDB is not writeable.')
@contextmanager
def write_batch(self):
raise RuntimeError(f'BytesMultiDB is not writeable.')
def commit(self):
pass
def optimize(self):
raise RuntimeError(f'BytesMultiDB is not writeable.')
def close(self):
for db in self.db_list:
db.close()
tracegnn/data/trace_graph.py 0 → 100644
浏览文件 @ 99b31de8
import os
import pickle as pkl
import sys
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import *
import networkx as nx
import numpy as np
import pandas as pd
from tqdm import tqdm
from ..utils import *
__all__ = [
'TraceGraphNodeFeatures',
'TraceGraphNodeReconsScores',
'TraceGraphNode',
'TraceGraphVectors',
'TraceGraph',
'TraceGraphIDManager',
'load_trace_csv',
'df_to_trace_graphs',
]
SERVICE_ID_YAML_FILE = 'service_id.yml'
OPERATION_ID_YAML_FILE = 'operation_id.yml'
@dataclass
class TraceGraphNodeFeatures(object):
__slots__ = ['span_count', 'max_latency', 'min_latency', 'avg_latency']
span_count: int # number of duplicates in the parent
avg_latency: float # for span_count == 1, avg == max == min
max_latency: float
min_latency: float
@dataclass
class TraceGraphNodeReconsScores(object):
# probability of the node
edge_logit: float
operation_logit: float
# probability of the latency
avg_latency_nstd: float # (avg_latency - avg_latency_mean) / avg_latency_std
@dataclass
class TraceGraphSpan(object):
__slots__ = [
'span_id', 'start_time', 'latency',
]
span_id: Optional[int]
start_time: Optional[datetime]
latency: float
@dataclass
class TraceGraphNode(object):
__slots__ = [
'node_id', 'service_id', 'operation_id',
'features', 'children', 'spans', 'scores',
'anomaly',
]
node_id: Optional[int] # the node id of the graph
service_id: Optional[int] # the service id
operation_id: int # the operation id
features: TraceGraphNodeFeatures # the node features
children: List['TraceGraphNode'] # children nodes
spans: Optional[List[TraceGraphSpan]] # detailed spans information (from the original data)
scores: Optional[TraceGraphNodeReconsScores]
anomaly: Optional[int] # 1: drop anomaly; 2: latency anomaly; 3: service type anomaly
def __eq__(self, other):
return other is self
def __hash__(self):
return id(self)
@staticmethod
def new_sampled(node_id: int,
operation_id: int,
features: TraceGraphNodeFeatures,
scores: Optional[TraceGraphNodeReconsScores] = None
):
return TraceGraphNode(
node_id=node_id,
service_id=None,
operation_id=operation_id,
features=features,
children=[],
spans=None,
scores=scores,
anomaly=None,
)
def iter_bfs(self,
depth: int = 0,
with_parent: bool = False
) -> Generator[
Union[
Tuple[int, 'TraceGraphNode'],
Tuple[int, 'TraceGraphNode', 'TraceGraphNode']
],
None,
None
]:
"""Iterate through the nodes in BFS order."""
if with_parent:
depth = depth
level = [(self, None, 0)]
while level:
next_level: List[Tuple[TraceGraphNode, TraceGraphNode, int]] = []
for nd, parent, idx in level:
yield depth, idx, nd, parent
for c_idx, child in enumerate(nd.children):
next_level.append((child, nd, c_idx))
depth += 1
level = next_level
else:
depth = depth
level = [self]
while level:
next_level: List[TraceGraphNode] = []
for nd in level:
yield depth, nd
next_level.extend(nd.children)
depth += 1
level = next_level
def count_nodes(self) -> int:
ret = 0
for _ in self.iter_bfs():
ret += 1
return ret
@dataclass
class TraceGraphVectors(object):
"""Cached result of `TraceGraph.graph_vectors()`."""
__slots__ = [
'u', 'v',
'node_type',
'node_depth', 'node_idx',
'span_count', 'avg_latency', 'max_latency', 'min_latency',
'node_features',
]
# note that it is guaranteed that u[i] < v[i], i.e., upper triangle matrix
u: np.ndarray
v: np.ndarray
# node type
node_type: np.ndarray
# node depth
node_depth: np.ndarray
# node idx
node_idx: np.ndarray
# node feature
span_count: np.ndarray
avg_latency: np.ndarray
max_latency: np.ndarray
min_latency: np.ndarray
@dataclass
class TraceGraph(object):
__slots__ = [
'version',
'trace_id', 'parent_id', 'root', 'node_count', 'max_depth', 'data',
]
version: int # version control
trace_id: Optional[Tuple[int, int]]
parent_id: Optional[int]
root: TraceGraphNode
node_count: Optional[int]
max_depth: Optional[int]
data: Dict[str, Any] # any data about the graph
@staticmethod
def default_version() -> int:
return 0x2
@staticmethod
def new_sampled(root: TraceGraphNode, node_count: int, max_depth: int):
return TraceGraph(
version=TraceGraph.default_version(),
trace_id=None,
parent_id=None,
root=root,
node_count=node_count,
max_depth=max_depth,
data={},
)
@property
def edge_count(self) -> Optional[int]:
if self.node_count is not None:
return self.node_count - 1
def iter_bfs(self,
with_parent: bool = False
):
"""Iterate through the nodes in BFS order."""
yield from self.root.iter_bfs(with_parent=with_parent)
def merge_spans_and_assign_id(self):
"""
Merge spans with the same (service, operation) under the same parent,
and re-assign node IDs.
"""
node_count = 0
max_depth = 0
for depth, parent in self.iter_bfs():
max_depth = max(max_depth, depth)
# assign ID to this node
parent.node_id = node_count
node_count += 1
# merge the children of this node
children = []
for child in sorted(parent.children, key=lambda o: o.operation_id):
if children and children[-1].operation_id == child.operation_id:
prev_child = children[-1]
# merge the features
f1, f2 = prev_child.features, child.features
f1.span_count += f2.span_count
f1.avg_latency += (f2.avg_latency - f1.avg_latency) * (f2.span_count / f1.span_count)
f1.max_latency = max(f1.max_latency, f2.max_latency)
f1.min_latency = min(f1.min_latency, f2.min_latency)
# merge the children
if child.children:
if prev_child.children:
prev_child.children.extend(child.children)
else:
prev_child.children = child.children
# merge the spans
if child.spans:
if prev_child.spans:
prev_child.spans.extend(child.spans)
else:
prev_child.spans = child.spans
else:
children.append(child)
# re-assign the merged children
parent.children = children
# record node count and depth
self.node_count = node_count
self.max_depth = max_depth
def assign_node_id(self):
"""Assign node IDs to the graph nodes by pre-root order."""
node_count = 0
max_depth = 0
for depth, node in self.iter_bfs():
max_depth = max(max_depth, depth)
# assign id to this node
node.node_id = node_count
node_count += 1
# record node count and depth
self.node_count = node_count
self.max_depth = max_depth
def graph_vectors(self):
# edge index
u = np.empty([self.edge_count], dtype=np.int64)
v = np.empty([self.edge_count], dtype=np.int64)
# node type
node_type = np.zeros([self.node_count], dtype=np.int64)
# node depth
node_depth = np.zeros([self.node_count], dtype=np.int64)
# node idx
node_idx = np.zeros([self.node_count], dtype=np.int64)
# node feature
span_count = np.zeros([self.node_count], dtype=np.int64)
avg_latency = np.zeros([self.node_count], dtype=np.float32)
max_latency = np.zeros([self.node_count], dtype=np.float32)
min_latency = np.zeros([self.node_count], dtype=np.float32)
# X = np.zeros([self.node_count, x_dim], dtype=np.float32)
edge_idx = 0
for depth, idx, node, parent in self.iter_bfs(with_parent=True):
j = node.node_id
feat = node.features
# node type
node_type[j] = node.operation_id
# node depth
node_depth[j] = depth
# node idx
node_idx[j] = idx
# node feature
span_count[j] = feat.span_count
avg_latency[j] = feat.avg_latency
max_latency[j] = feat.max_latency
min_latency[j] = feat.min_latency
# X[parent.node_id, parent.operation_id] = 1 # one-hot encoded node feature
# edge index
for child in node.children:
u[edge_idx] = node.node_id
v[edge_idx] = child.node_id
edge_idx += 1
if len(u) != self.edge_count:
raise ValueError(f'`len(u)` != `self.edge_count`: {len(u)} != {self.edge_count}')
return TraceGraphVectors(
# edge index
u=u, v=v,
# node type
node_type=node_type,
# node depth
node_depth=node_depth,
# node idx
node_idx=node_idx,
# node feature
span_count=span_count,
avg_latency=avg_latency,
max_latency=max_latency,
min_latency=min_latency,
)
def networkx_graph(self, id_manager: 'TraceGraphIDManager') -> nx.Graph:
gv = self.graph_vectors()
self_nodes = {nd.node_id: nd for _, nd in self.iter_bfs()}
g = nx.Graph()
# graph
for k, v in self.data.items():
g.graph[k] = v
# nodes
g.add_nodes_from(range(self.node_count))
# edges
g.add_edges_from([(i, j) for i, j in zip(gv.u, gv.v)])
# node features
for i in range(len(gv.node_type)):
nd = g.nodes[i]
nd['node_type'] = gv.node_type[i]
nd['operation'] = id_manager.operation_id.reverse_map(gv.node_type[i])
for attr in TraceGraphNodeFeatures.__slots__:
nd[attr] = getattr(gv, attr)[i]
if self_nodes[i].scores:
nd['avg_latency_nstd'] = self_nodes[i].scores.avg_latency_nstd
return g
def to_bytes(self, protocol: int = pkl.DEFAULT_PROTOCOL) -> bytes:
return pkl.dumps(self, protocol=protocol)
@staticmethod
def from_bytes(content: bytes) -> 'TraceGraph':
r = pkl.loads(content)
# for deserializing old versions of TraceGraph
if not hasattr(r, 'version'):
r.version = 0x0
if r.version < 0x1: # upgrade 0x0 => 0x2
for _, nd in r.root.iter_bfs():
nd.scores = None
nd.anomaly = None
r.version = 0x2
if r.version < 0x2: # upgrade 0x1 => 0x2
for _, nd in r.root.iter_bfs():
nd.anomaly = None
r.version = 0x2
return r
def deepcopy(self) -> 'TraceGraph':
return TraceGraph.from_bytes(self.to_bytes())
@dataclass
class TempGraphNode(object):
__slots__ = ['trace_id', 'parent_id', 'node']
trace_id: Tuple[int, int]
parent_id: int
node: 'TraceGraphNode'
class TraceGraphIDManager(object):
__slots__ = ['root_dir', 'service_id', 'operation_id']
root_dir: str
service_id: IDAssign
operation_id: IDAssign
def __init__(self, root_dir: str):
self.root_dir = os.path.abspath(root_dir)
self.service_id = IDAssign(os.path.join(self.root_dir, SERVICE_ID_YAML_FILE))
self.operation_id = IDAssign(os.path.join(self.root_dir, OPERATION_ID_YAML_FILE))
def __enter__(self):
self.service_id.__enter__()
self.operation_id.__enter__()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.service_id.__exit__(exc_type, exc_val, exc_tb)
self.operation_id.__exit__(exc_type, exc_val, exc_tb)
@property
def num_operations(self) -> int:
return len(self.operation_id)
def dump_to(self, output_dir: str):
self.service_id.dump_to(os.path.join(output_dir, SERVICE_ID_YAML_FILE))
self.operation_id.dump_to(os.path.join(output_dir, OPERATION_ID_YAML_FILE))
def load_trace_csv(input_path: str, is_test: bool=False) -> pd.DataFrame:
if is_test:
dtype = {
'traceIdHigh': int,
'traceIdLow': int,
'spanId': int,
'parentSpanId': int,
'serviceName': str,
'operationName': str,
'startTime': str,
'duration': float,
'nanosecond': int,
'DBhash': int,
'nodeLatencyLabel': int,
'graphLatencyLabel': int,
'graphStructureLabel': int
}
else:
dtype = {
'traceIdHigh': int,
'traceIdLow': int,
'spanId': int,
'parentSpanId': int,
'serviceName': str,
'operationName': str,
'startTime': str,
'duration': float,
'nanosecond': int,
'DBhash': int,
}
return pd.read_csv(
input_path,
engine='c',
usecols=list(dtype),
dtype=dtype
)
def df_to_trace_graphs(df: pd.DataFrame,
id_manager: TraceGraphIDManager,
test_label: int = None,
min_node_count: int = 2,
max_node_count: int = 32,
summary_file: Optional[str] = None,
merge_spans: bool = False,
) -> List[TraceGraph]:
summary = []
trace_spans = {}
df = df[df['DBhash'] == 0]
# read the spans
with id_manager:
for row in tqdm(df.itertuples(), desc='Read spans', total=len(df)):
graph_label = 0
if test_label is not None:
if row.graphStructureLabel != 0:
graph_label = 1
elif row.graphLatencyLabel != 0:
graph_label = 2
if graph_label != test_label:
continue
if row.serviceName not in id_manager.service_id._mapping:
print(row.serviceName, ": Service not in file!")
continue
if f'{row.serviceName}/{row.operationName}' not in id_manager.operation_id._mapping:
print(f'{row.serviceName}/{row.operationName}', ": Operation not in file!")
continue
trace_id = (row.traceIdHigh, row.traceIdLow)
span_dict = trace_spans.get(trace_id, None)
if span_dict is None:
trace_spans[trace_id] = span_dict = {}
span_latency = row.duration
span_dict[row.spanId] = TempGraphNode(
trace_id=trace_id,
parent_id=row.parentSpanId,
node=TraceGraphNode(
node_id=None,
service_id=id_manager.service_id.get_or_assign(row.serviceName),
operation_id=id_manager.operation_id.get_or_assign(f'{row.serviceName}/{row.operationName}'),
features=TraceGraphNodeFeatures(
span_count=1,
avg_latency=span_latency,
max_latency=span_latency,
min_latency=span_latency,
),
children=[],
spans=[
TraceGraphSpan(
span_id=row.spanId,
start_time=(
datetime.strptime(row.startTime, '%Y-%m-%d %H:%M:%S') +
timedelta(microseconds=row.nanosecond / 1_000)
),
latency=span_latency,
),
],
scores=None,
anomaly=None,
)
)
summary.append(f'Span count: {len(trace_spans)}')
# construct the traces
trace_graphs = []
if test_label is None or test_label == 0:
graph_data = {}
elif test_label == 1:
graph_data = {
'is_anomaly': True,
'anomaly_type': 'drop'
}
else:
graph_data = {
'is_anomaly': True,
'anomaly_type': 'latency'
}
for _, trace in tqdm(trace_spans.items(), total=len(trace_spans), desc='Build graphs'):
nodes = sorted(
trace.values(),
key=(lambda nd: (nd.node.service_id, nd.node.operation_id, nd.node.spans[0].start_time))
)
for nd in nodes:
parent_id = nd.parent_id
if (parent_id == 0) or (parent_id not in trace):
# if only a certain service is taken from the database, then just the sub-trees
# of a trace are obtained, which leads to orphan nodes (parent_id != 0 and not in trace
trace_graphs.append(TraceGraph(
version=TraceGraph.default_version(),
trace_id=nd.trace_id,
parent_id=nd.parent_id,
root=nd.node,
node_count=None,
max_depth=None,
data=graph_data,
))
else:
trace[parent_id].node.children.append(nd.node)
# merge spans and assign id
if merge_spans:
for trace in tqdm(trace_graphs, desc='Merge spans and assign node id'):
trace.merge_spans_and_assign_id()
else:
for trace in tqdm(trace_graphs, desc='Assign node id'):
trace.assign_node_id()
# gather the final results
ret = []
too_small = 0
too_large = 0
for trace in trace_graphs:
if trace.node_count < min_node_count:
too_small += 1
elif trace.node_count > max_node_count:
too_large += 1
else:
ret.append(trace)
summary.append(f'Imported graph: {len(trace_graphs)}; dropped graph: too small = {too_small}, too large = {too_large}')
if summary_file:
with open(summary_file, 'w', encoding='utf-8') as f:
f.write('\n'.join(summary) + '\n')
else:
print('\n'.join(summary), file=sys.stderr)
return ret
tracegnn/data/trace_graph_db.py 0 → 100644
浏览文件 @ 99b31de8
"""Wraps a BytesDB into TraceGraphDB."""
import os
import pickle as pkl
import re
from contextlib import contextmanager
from typing import *
import numpy as np
from .bytes_db import *
from .trace_graph import *
__all__ = ['TraceGraphDB', 'open_trace_graph_db']
class TraceGraphDB(object):
bytes_db: BytesDB
protocol: int
def __init__(self, bytes_db: BytesDB, protocol: Optional[int] = None):
if protocol is None:
protocol = pkl.DEFAULT_PROTOCOL
self.bytes_db = bytes_db
self.protocol = protocol
def __enter__(self):
self.bytes_db.__enter__()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.bytes_db.__exit__(exc_type, exc_val, exc_tb)
def __len__(self) -> int:
return self.data_count()
def __getitem__(self, item: int):
return self.get(item)
def __iter__(self):
for i in range(self.data_count()):
yield self.get(i)
def __repr__(self):
desc = repr(self.bytes_db)
desc = desc[desc.find('(') + 1: -1]
return f'TraceGraphDB({desc})'
def sample_n(self,
n: int,
with_id: bool = False
) -> List[Union[TraceGraph, Tuple[int, TraceGraph]]]:
ret = []
indices = np.random.randint(self.data_count(), size=n)
for i in indices:
g = self.get(i)
if with_id:
ret.append((int(i), g))
else:
ret.append(g)
return ret
def data_count(self) -> int:
return self.bytes_db.data_count()
def get(self, item: int) -> TraceGraph:
return TraceGraph.from_bytes(self.bytes_db.get(item))
def add(self, g: TraceGraph) -> int:
return self.bytes_db.add(g.to_bytes(protocol=self.protocol))
@contextmanager
def write_batch(self):
with self.bytes_db.write_batch():
yield self
def commit(self):
self.bytes_db.commit()
def optimize(self):
self.bytes_db.optimize()
def close(self):
self.bytes_db.close()
def open_trace_graph_db(input_dir: str,
names: Optional[Sequence[str]] = (),
protocol: Optional[int] = None,
) -> Tuple[TraceGraphDB, TraceGraphIDManager]:
file_name = f'_bytes_{protocol}.db' if protocol else '_bytes.db'
id_manager = TraceGraphIDManager(os.path.join(input_dir, 'id_manager'))
if len(names) == 1:
db = TraceGraphDB(
BytesSqliteDB(os.path.join(input_dir, 'processed', names[0]), file_name=file_name),
protocol=protocol,
)
else:
db = TraceGraphDB(
BytesMultiDB(*[
BytesSqliteDB(os.path.join(input_dir, 'processed', name), file_name=file_name)
for name in names
]),
protocol=protocol,
)
return db, id_manager