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
import pandas as pd
df = pd.read_parquet("logs_100M.parquet")
datasets
from datasets import load_dataset
ds = load_dataset("ziadatalabs/FreeSyntheticServerLogs100M")
duckdb
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