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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 17 new columns ({'state', 'submit_time', 'gpu_time', 'start_time', 'type', 'job_id', 'duration', 'user', 'queue', 'gpu_num', 'stop_time', 'shared_mem_per_pod', 'end_time', 'node_num', 'cpu_num', 'fail_time', 'mem_per_pod_GB'}) and 28 missing columns ({'cancel_gpu_time', 'complete_rate_gpu', 'med_que_time_gpu', 'gpu_job_num', 'cancel_rate_gpu_time', 'complete_rate_cpu', 'complete_rate_gpu_time', 'med_que_time_cpu', 'avg_run_time_cpu', 'avg_que_time_gpu', 'med_run_time_gpu', 'max_gpu', 'job_num', 'fail_rate_gpu', 'cpu_job_num', 'avg_que_time_cpu', 'fail_rate_cpu', 'med_gpu_num', 'max_run_time_gpu', 'id', 'avg_run_time_gpu', 'fail_gpu_time', 'med_run_time_cpu', 'avg_gpu_num', 'cancel_rate_gpu', 'complete_gpu_time', 'cancel_rate_cpu', 'fail_rate_gpu_time'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Qinghao/AcmeTrace/data/job_trace/trace_kalos.csv (at revision a484f7763a7812fc72eb49b8ba865b12fe7638e1)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
job_id: string
user: string
node_num: int64
gpu_num: int64
cpu_num: int64
mem_per_pod_GB: int64
shared_mem_per_pod: double
type: string
state: string
submit_time: string
start_time: string
end_time: string
fail_time: string
stop_time: string
duration: int64
queue: double
gpu_time: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2233
to
{'id': Value(dtype='string', id=None), 'job_num': Value(dtype='int64', id=None), 'cpu_job_num': Value(dtype='int64', id=None), 'gpu_job_num': Value(dtype='int64', id=None), 'avg_run_time_gpu': Value(dtype='float64', id=None), 'avg_que_time_gpu': Value(dtype='float64', id=None), 'avg_gpu_num': Value(dtype='float64', id=None), 'med_run_time_gpu': Value(dtype='float64', id=None), 'med_que_time_gpu': Value(dtype='float64', id=None), 'med_gpu_num': Value(dtype='float64', id=None), 'max_run_time_gpu': Value(dtype='float64', id=None), 'max_gpu': Value(dtype='float64', id=None), 'complete_rate_gpu': Value(dtype='float64', id=None), 'cancel_rate_gpu': Value(dtype='float64', id=None), 'fail_rate_gpu': Value(dtype='float64', id=None), 'complete_gpu_time': Value(dtype='float64', id=None), 'cancel_gpu_time': Value(dtype='float64', id=None), 'fail_gpu_time': Value(dtype='float64', id=None), 'complete_rate_gpu_time': Value(dtype='float64', id=None), 'cancel_rate_gpu_time': Value(dtype='float64', id=None), 'fail_rate_gpu_time': Value(dtype='float64', id=None), 'avg_run_time_cpu': Value(dtype='float64', id=None), 'avg_que_time_cpu': Value(dtype='float64', id=None), 'med_run_time_cpu': Value(dtype='float64', id=None), 'med_que_time_cpu': Value(dtype='float64', id=None), 'complete_rate_cpu': Value(dtype='float64', id=None), 'cancel_rate_cpu': Value(dtype='float64', id=None), 'fail_rate_cpu': Value(dtype='float64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1316, in compute_config_parquet_and_info_response
parquet_operations, partial = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 909, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 17 new columns ({'state', 'submit_time', 'gpu_time', 'start_time', 'type', 'job_id', 'duration', 'user', 'queue', 'gpu_num', 'stop_time', 'shared_mem_per_pod', 'end_time', 'node_num', 'cpu_num', 'fail_time', 'mem_per_pod_GB'}) and 28 missing columns ({'cancel_gpu_time', 'complete_rate_gpu', 'med_que_time_gpu', 'gpu_job_num', 'cancel_rate_gpu_time', 'complete_rate_cpu', 'complete_rate_gpu_time', 'med_que_time_cpu', 'avg_run_time_cpu', 'avg_que_time_gpu', 'med_run_time_gpu', 'max_gpu', 'job_num', 'fail_rate_gpu', 'cpu_job_num', 'avg_que_time_cpu', 'fail_rate_cpu', 'med_gpu_num', 'max_run_time_gpu', 'id', 'avg_run_time_gpu', 'fail_gpu_time', 'med_run_time_cpu', 'avg_gpu_num', 'cancel_rate_gpu', 'complete_gpu_time', 'cancel_rate_cpu', 'fail_rate_gpu_time'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Qinghao/AcmeTrace/data/job_trace/trace_kalos.csv (at revision a484f7763a7812fc72eb49b8ba865b12fe7638e1)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Open a discussion for direct support.
id
string | job_num
int64 | cpu_job_num
int64 | gpu_job_num
int64 | avg_run_time_gpu
float64 | avg_que_time_gpu
float64 | avg_gpu_num
float64 | med_run_time_gpu
float64 | med_que_time_gpu
float64 | med_gpu_num
float64 | max_run_time_gpu
float64 | max_gpu
float64 | complete_rate_gpu
float64 | cancel_rate_gpu
float64 | fail_rate_gpu
float64 | complete_gpu_time
float64 | cancel_gpu_time
float64 | fail_gpu_time
float64 | complete_rate_gpu_time
float64 | cancel_rate_gpu_time
float64 | fail_rate_gpu_time
float64 | avg_run_time_cpu
float64 | avg_que_time_cpu
float64 | med_run_time_cpu
float64 | med_que_time_cpu
float64 | complete_rate_cpu
float64 | cancel_rate_cpu
float64 | fail_rate_cpu
float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Seren | 1,031,550 | 367,737 | 663,813 | 1,414.335 | 445.984 | 5.68 | 122 | 1 | 1 | 1,209,604 | 1,024 | 0.497 | 0.075 | 0.429 | 4,560,592,618 | 14,274,584,510 | 2,675,447,459 | 0.212 | 0.664 | 0.124 | 733.894 | 4.813 | 14 | 1 | 0.858 | 0.039 | 0.102 |
Kalos | 62,413 | 42,506 | 19,907 | 1,259.689 | 214.179 | 26.77 | 124 | 17 | 1 | 625,740 | 1,024 | 0.541 | 0.062 | 0.397 | 1,667,685,146 | 3,239,202,064 | 426,292,851 | 0.313 | 0.607 | 0.08 | 118.352 | 88.401 | 22 | 9 | 0.86 | 0.001 | 0.14 |
Philly | 112,956 | 0 | 112,956 | 18,006.055 | 3,760.604 | 1.928 | 874 | 189 | 1 | 4,240,676 | 128 | 0.604 | 0.09 | 0.305 | 2,210,176,414 | 2,025,326,056 | 2,659,215,061 | 0.321 | 0.294 | 0.386 | null | null | null | null | null | null | null |
Helios | 3,362,981 | 1,782,517 | 1,580,464 | 6,651.681 | 862.047 | 3.716 | 206 | 0 | 1 | 4,320,009 | 2,048 | 0.624 | 0.221 | 0.155 | 39,994,296,320 | 30,715,109,125 | 7,288,334,702 | 0.513 | 0.394 | 0.093 | 628.759 | 73.907 | 2 | 0 | 0.909 | 0.03 | 0.061 |
PAI | 1,260,920 | 0 | 1,037,085 | 4,787.109 | 401.742 | 0.683 | 480.667 | 9 | 0.5 | 626,371 | 8 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
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YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
Acme Trace
This repository hosts the public releases of Acme traces from the Shanghai AI Lab, encompassing workloads spanning from March 2023 to August 2023. We encourage anyone to use the traces for academic purposes, and if you had any questions, feel free to send an email to us, or file an issue on Github.
Furthermore, we have conducted a thorough analysis of the Acme workloads, detailed in our NSDI '24 paper titled Characterization of Large Language Model Development in the Datacenter.
Acme Dataset
The main trace characteristics, dataset structure and schema are:
Main Characteristics:
- Full Dataset size: 80GB (on HuggingFace)
- Dataset size: 109MB
- Duration: 6 months
- Number of independent GPU clusters: 2
- Total number of jobs: 880,740
- Total number of GPU jobs: 470,497
Dataset Structure
📦AcmeTrace
┣ 📂data
┃ ┣ 📂job_trace
┃ ┃ ┣ 📂trace_previous_work (Prior job traces for comparison)
┃ ┃ ┃ ┣ 📜helios_trace.csv
┃ ┃ ┃ ┣ 📜xxx.csv
┃ ┃ ┣ 📜trace_kalos.csv (Job trace file, collected from scheduler)
┃ ┃ ┗ 📜trace_seren.csv
┃ ┣ 📂utilization
┃ ┃ ┣ 📂ipmi (Power of different server models in Seren, collected from IPMI)
┃ ┃ ┃ ┣ 📜CPU_D_Power.csv
┃ ┃ ┃ ┣ 📜GPU_AB_Power.csv
┃ ┃ ┃ ┗ 📜GPU_C_Power.csv
┃ ┃ ┣ 📂kalos (Resource utilization logs, collected from DCGM & Prometheus)
┃ ┃ ┃ ┣ 📜DRAM_ACTIVE.csv
┃ ┃ ┃ ┣ 📜xxx.csv
┃ ┃ ┣ 📂seren
┃ ┃ ┃ ┣ 📜DRAM_ACTIVE.csv
┃ ┃ ┃ ┣ 📜xxx.csv
┃ ┃ ┣ 📂util_pkl (Processed pickle files for plotting)
┃ ┃ ┃ ┣ 📜gpu_power_kalos.pkl
┃ ┃ ┃ ┣ 📜xxx.pkl
┃ ┣ 📜cluster_summary.csv
┃ ┣ 📜generate_utilization_pkl.ipynb (Parse utilization files and generate pickles)
┃ ┗ 📜utils.py
┣ 📂figure (Examples of trace visualization)
┃ ┣ 📜bar_job_state.pdf
┃ ┣ 📜xxx.pdf
┣ 📜LICENSE.txt
┣ 📜README.md
┗ 📜analysis.ipynb (Scripts for plotting)
Schema and Description
1. Job Trace
Description
Provides rich information on all jobs submitted to scheduler in each cluster.
trace_seren.csvExample
| job_id | user | node_num | gpu_num | cpu_num | type | state | submit_time | start_time | end_time | duration | queue | gpu_time |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5778432 | u5907 | 1 | 8 | 128 | Other | FAILED | 2023-03-01 00:18:22+08:00 | 2023-03-01 00:18:54+08:00 | 2023-03-01 00:20:51+08:00 | 117 | 32 | 936.0 |
| 5778469 | u5907 | 1 | 8 | 128 | Other | COMPLETED | 2023-03-01 00:23:58+08:00 | 2023-03-01 00:24:11+08:00 | 2023-03-01 01:09:04+08:00 | 2693 | 13 | 21544.0 |
trace_kalos.csvExample
| job_id | user | node_num | gpu_num | cpu_num | mem_per_pod_GB | shared_mem_per_pod | type | state | submit_time | start_time | end_time | fail_time | stop_time | duration | queue | gpu_time |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| dlctk696s0jbvitv | uf794 | 8 | 64 | 960 | 1000 | 100.0 | Other | FAILED | 2023-05-17 11:00:58+00:00 | 2023-05-17 11:01:08+00:00 | 2023-05-17 11:01:16+00:00 | 2023-05-17 11:01:16+00:00 | 18 | 10.0 | 1152.0 | |
| dlc1t2ypl09b8qtp | uf794 | 8 | 64 | 960 | 1000 | 100.0 | Other | CANCELLED | 2023-05-17 11:28:42+00:00 | 2023-05-17 11:28:54+00:00 | 2023-05-17 11:30:04+00:00 | 2023-05-17 11:30:04+00:00 | 82 | 12.0 | 5248.0 |
Schema
| Field | Description |
|---|---|
job_id |
unique id of the job |
user |
hashed id for the user, prefix is 'u' |
node_num |
number of nodes in the job |
gpu_num |
number of GPUs required for the job |
cpu_num |
number of CPUs required for the job |
type |
workload type in LLM development |
state |
the job's status upon termination 1 |
submit_time |
the job's submission time |
start_time |
the job's start execution time |
end_time |
the job's termination time |
duration |
total job execution time of the job 2 |
queue |
total job queue time of the job 3 |
gpu_time |
total GPU resource consumed by the job 4 |
Only in Kalos:
| Field | Description |
|---|---|
mem_per_pod_GB |
Pod memory resource configuration |
shared_mem_per_pod |
Pod memory resource configuration |
fail_time |
the time that failure occurs |
stop_time |
the time that job stops |
Notes
- A job can end up with one of five statuses: (1)
COMPLETED: it is finished successfully; (2)CANCELLED: it is terminated by the user; (3)FAILED: it is terminated due to internal or external errors; (4)TIMEOUT: the execution time is out of limit; (5)NODE_FAIL: it is terminated due to the node crash.TIMEOUTandNODE_FAILare very rare in our traces, and are regarded as failed in our analysis. - Calculated from the difference between
end_timeandstart_time. (Unit: seconds) - Calculated from the difference between
start_timeandsubmit_time. (Unit: seconds) - Calculated from the product between
durationandgpu_num.
2. Resource Utilization
Description
Cluster resource utilization monitoring data, collected from DCGM, IPMI and Prometheus.
NODE_CPU_UTILIZATION.csvExample
| Time | 10.140.1.10 | 10.140.1.54 | 10.140.1.90 | 10.140.1.41 | 10.140.1.98 | 10.140.0.166 | 10.140.1.4 | 10.140.1.40 | 10.140.1.134 | 10.140.0.147 | 10.140.1.119 | 10.140.0.184 | 10.140.0.151 | 10.140.0.254 | 10.140.1.83 | 10.140.0.246 | 10.140.1.78 | 10.140.1.103 | 10.140.1.155 | 10.140.1.87 | 10.140.1.106 | 10.140.1.140 | 10.140.1.150 | 10.140.1.107 | 10.140.1.172 | 10.140.1.95 | 10.140.0.146 | 10.140.1.125 | 10.140.1.50 | 10.140.1.112 | 10.140.0.159 | 10.140.0.144 | 10.140.0.215 | 10.140.1.36 | 10.140.1.143 | 10.140.1.147 | 10.140.1.14 | 10.140.1.85 | 10.140.1.56 | 10.140.0.243 | 10.140.0.242 | 10.140.1.63 | 10.140.0.132 | 10.140.0.255 | 10.140.1.59 | 10.140.1.130 | 10.140.0.218 | 10.140.0.220 | 10.140.1.27 | 10.140.1.67 | 10.140.1.136 | 10.140.1.84 | 10.140.0.190 | 10.140.1.121 | 10.140.1.146 | 10.140.1.38 | 10.140.0.232 | 10.140.1.18 | 10.140.1.66 | 10.140.0.205 | 10.140.1.154 | 10.140.1.170 | 10.140.0.179 | 10.140.0.135 | 10.140.1.102 | 10.140.1.72 | 10.140.0.249 | 10.140.1.138 | 10.140.1.24 | 10.140.1.60 | 10.140.1.82 | 10.140.0.233 | 10.140.1.23 | 10.140.0.241 | 10.140.0.248 | 10.140.1.68 | 10.140.1.1 | 10.140.0.219 | 10.140.1.116 | 10.140.0.157 | 10.140.0.178 | 10.140.1.29 | 10.140.1.57 | 10.140.0.163 | 10.140.1.52 | 10.140.1.177 | 10.140.1.11 | 10.140.1.26 | 10.140.1.34 | 10.140.1.92 | 10.140.0.211 | 10.140.0.161 | 10.140.0.131 | 10.140.1.124 | 10.140.0.238 | 10.140.1.44 | 10.140.0.237 | 10.140.1.79 | 10.140.1.17 | 10.140.0.214 | 10.140.1.153 | 10.140.1.117 | 10.140.1.109 | 10.140.0.167 | 10.140.0.207 | 10.140.0.134 | 10.140.1.99 | 10.140.1.31 | 10.140.1.127 | 10.140.0.250 | 10.140.1.139 | 10.140.1.53 | 10.140.1.123 | 10.140.1.77 | 10.140.0.133 | 10.140.0.251 | 10.140.1.55 | 10.140.1.12 | 10.140.1.19 | 10.140.1.47 | 10.140.1.118 | 10.140.1.61 | 10.140.1.110 | 10.140.1.64 | 10.140.1.129 | 10.140.0.217 | 10.140.1.104 | 10.140.0.244 | 10.140.0.213 | 10.140.1.97 | 10.140.0.136 | 10.140.1.22 | 10.140.1.32 | 10.140.1.171 | 10.140.1.151 | 10.140.1.96 | 10.140.1.46 | 10.140.0.158 | 10.140.1.51 | 10.140.1.86 | 10.140.1.30 | 10.140.0.156 | 10.140.1.43 | 10.140.1.74 | 10.140.1.89 | 10.140.1.169 | 10.140.1.80 | 10.140.1.2 | 10.140.1.108 | 10.140.1.93 | 10.140.1.73 | 10.140.0.180 | 10.140.1.71 | 10.140.1.88 | 10.140.0.209 | 10.140.1.81 | 10.140.0.152 | 10.140.1.28 | 10.140.1.58 | 10.140.0.236 | 10.140.0.138 | 10.140.0.149 | 10.140.0.206 | 10.140.1.15 | 10.140.0.240 | 10.140.0.203 | 10.140.1.5 | 10.140.1.37 | 10.140.0.143 | 10.140.0.160 | 10.140.0.252 | 10.140.1.75 | 10.140.1.115 | 10.140.0.247 | 10.140.1.6 | 10.140.1.16 | 10.140.0.216 | 10.140.0.150 | 10.140.1.25 | 10.140.0.208 | 10.140.1.62 | 10.140.1.173 | 10.140.1.137 | 10.140.1.9 | 10.140.1.65 | 10.140.1.111 | 10.140.1.135 | 10.140.1.114 | 10.140.1.132 | 10.140.0.154 | 10.140.0.204 | 10.140.1.91 | 10.140.1.120 | 10.140.1.105 | 10.140.1.131 | 10.140.0.165 | 10.140.0.210 | 10.140.0.148 | 10.140.1.133 | 10.140.0.239 | 10.140.1.13 | 10.140.1.144 | 10.140.0.137 | 10.140.0.234 | 10.140.1.142 | 10.140.1.168 | 10.140.0.235 | 10.140.0.140 | 10.140.1.39 | 10.140.0.153 | 10.140.0.139 | 10.140.1.3 | 10.140.1.7 | 10.140.1.94 | 10.140.1.145 | 10.140.1.149 | 10.140.1.152 | 10.140.1.35 | 10.140.0.141 | 10.140.1.69 | 10.140.1.100 | 10.140.1.126 | 10.140.0.142 | 10.140.0.185 | 10.140.1.42 | 10.140.0.231 | 10.140.0.253 | 10.140.0.212 | 10.140.1.21 | 10.140.1.148 | 10.140.1.49 | 10.140.1.128 | 10.140.0.164 | 10.140.1.70 | 10.140.1.45 | 10.140.0.162 | 10.140.1.101 | 10.140.0.145 | 10.140.1.20 | 10.140.1.176 | 10.140.1.33 | 10.140.1.113 | 10.140.1.122 | 10.140.1.76 | 10.140.1.141 | 10.140.1.8 | 10.140.0.155 | 10.140.1.48 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023-07-01 08:00:00+08:00 | 8.101 | 7.809 | 8.034 | 0.437 | 0.672 | 8.988 | 8.395 | 8.205 | 8.763 | 2.037 | 6.661 | 9.177 | 9.017 | 8.096 | 14.423 | 8.04 | 0.354 | 0.34 | 0.843 | 8.66 | 0.657 | 8.104 | 0.902 | 7.006 | 0.107 | 8.298 | 8.546 | 6.413 | 8.1 | 6.633 | 8.167 | 9.246 | 9.055 | 2.963 | 7.995 | 0.707 | 8.119 | 10.531 | 6.654 | 7.707 | 4.626 | 0.848 | 25.274 | 7.95 | 8.014 | 7.908 | 9.313 | 9.184 | 7.877 | 0.484 | 8.451 | 6.137 | 0.124 | 6.163 | 0.316 | 8.343 | 9.024 | 7.922 | 8.427 | 0.455 | 67.47 | 0.395 | 7.487 | 9.142 | 7.898 | 8.071 | 7.717 | 0.755 | 7.869 | 8.193 | 8.368 | 8.911 | 8.108 | 7.934 | 8.269 | 8.161 | 8.349 | 9.252 | 6.933 | 4.823 | 7.527 | 8.42 | 7.243 | 9.166 | 8.04 | 0.092 | 7.921 | 8.28 | 8.027 | 0.365 | 8.71 | 9.302 | 0.88 | 8.055 | 8.817 | 8.07 | 9.316 | 8.064 | 8.061 | 9.319 | 7.101 | 5.221 | 7.086 | 7.701 | 9.259 | 8.857 | 5.079 | 7.944 | 8.02 | 8.244 | 8.038 | 8.269 | 5.108 | 6.971 | 1.787 | 8.095 | 8.055 | 8.275 | 8.396 | 7.787 | 6.898 | 8.224 | 16.323 | 0.671 | 8.071 | 9.125 | 8.004 | 7.888 | 8.785 | 5.412 | 0.621 | 8.004 | 7.91 | 6.727 | 10.327 | 0.413 | 8.499 | 7.735 | 8.255 | 8.087 | 8.001 | 5.908 | 8.239 | 8.279 | 7.272 | 0.14 | 8.186 | 0.526 | 6.771 | 6.386 | 6.763 | 7.308 | 6.741 | 8.047 | 8.883 | 7.059 | 8.79 | 7.864 | 8.065 | 9.474 | 0.481 | 9.179 | 9.579 | 8.157 | 9.063 | 7.339 | 8.295 | 6.81 | 9.029 | 9.037 | 8.042 | 0.717 | 6.675 | 7.838 | 8.192 | 8.038 | 9.004 | 8.621 | 8.117 | 8.177 | 22.467 | 0.198 | 3.4 | 8.086 | 7.86 | 6.891 | 4.376 | 7.144 | 5.331 | 8.924 | 7.668 | 0.332 | 7.961 | 7.958 | 8.164 | 5.741 | 8.938 | 8.969 | 6.372 | 8.816 | 8.361 | 12.62 | 9.149 | 9.151 | 8.374 | 8.831 | 9.332 | 9.181 | 8.142 | 8.653 | 1.449 | 8.268 | 8.481 | 8.568 | 0.468 | 59.942 | 66.076 | 8.191 | 8.96 | 8.223 | 0.478 | 8.023 | 9.129 | 9.6 | 8.164 | 9.518 | 8.172 | 9.551 | 8.012 | 14.544 | 8.154 | 8.069 | 9.344 | 0.357 | 8.09 | 0.463 | 8.082 | 7.657 | 8.139 | 0.164 | 8.143 | 6.56 | 6.632 | 8.018 | 8.065 | 8.288 | 8.667 | 8.078 |
Schema
| Field | Description |
|---|---|
Time |
sampling timestamp, interval is 15 seconds |
10.140.xx.xx |
server ip |
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