File size: 2,474 Bytes
c6f118a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ---
license: cc-by-nc-4.0
task_categories:
- text-classification
- tabular-classification
- time-series-forecasting
language:
- en
tags:
- synthetic-data
- logs
- observability
- devops
- sre
size_categories:
- 100M<n<1B
---
# FreeSyntheticServerLogs100M
A free dataset of 100 million fully synthetic server and application log entries, built for developers and researchers who need realistic structured log data at scale — for testing log-analytics tools, alerting and observability pipelines, log-parsing systems, or training models to classify and triage logs. No real systems, users, or production data are represented in this data.
## Schema
| Column | Type | Description |
|---|---|---|
| log_id | string | Unique log entry identifier |
| log_timestamp | string | Event time (YYYY-MM-DD HH:MM:SS) |
| service_name | string | Emitting service (auth-service, api-gateway, etc.) |
| log_level | string | INFO, DEBUG, WARN, ERROR, or FATAL |
| status_code | int | HTTP status code, correlated with log level |
| response_time_ms | int | Request duration in milliseconds |
| http_method | string | GET, POST, PUT, DELETE, PATCH |
| endpoint | string | API endpoint path |
| host | string | Host identifier (region + node) |
| message | string | Log message text |
## Format
Single Parquet file, Snappy compression, ~2.0 GB, 100,000,000 rows.
## Quick Start
**pandas**
```python
import pandas as pd
df = pd.read_parquet("logs_100M.parquet")
```
**datasets**
```python
from datasets import load_dataset
ds = load_dataset("ziadatalabs/FreeSyntheticServerLogs100M")
```
**duckdb**
```python
import duckdb
duckdb.sql("SELECT * FROM 'logs_100M.parquet' LIMIT 10").show()
```
## Notes
Status codes and response times are correlated with log level — INFO and DEBUG carry 2xx/3xx codes and fast responses, WARN skews toward 4xx, and ERROR and FATAL carry 5xx codes with slower response times — so the data reflects the real relationships a log-analytics or anomaly-detection model would learn. Log-level, service, method, and endpoint distributions reflect typical production traffic. All entirely synthetic. Note: at 100M rows, load in batches (e.g. pyarrow iter_batches or duckdb) rather than all at once on memory-limited machines.
## License & Usage
Released under CC BY-NC 4.0 — personal, research, and educational use permitted, attribution required, no commercial use.
---
Created by Zia Data Labs. Questions or feedback: zia.data.team@protonmail.com
|