---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- nginx
- access-logs
- synthetic-data
- observability
- http
- apache
- log-analysis
- mindweave
- web-server
- server-logs
- monitoring
- test-data
- api-logs
- siem
- anomaly-detection
- devops
pretty_name: Web Server Access Logs (Synthetic) (Free Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: access_logs
  data_files: data/access_logs.csv
  default: true
- config_name: servers
  data_files: data/servers.csv
---

# Web Server Access Logs (Synthetic) (Free Sample)

> **This is a free sample** with 5,003 rows. The full dataset has **50,048 rows** across 2 tables.

Realistic HTTP access logs from a simulated SaaS company running an
e-commerce API and marketing website. 50,000 requests across 3 servers
over 12 months.

Includes realistic patterns: weekday/weekend traffic variation, peak hours,
seasonal trends, bot traffic, and two injected anomalies (DDoS attempt and
database outage) for anomaly detection training.

Each log entry includes: timestamp, server, HTTP method, path, status code,
response time, bytes sent, user agent, IP address, and referrer.

Ideal for: DevOps monitoring dashboards, log analysis pipelines, anomaly
detection ML models, SIEM testing, and observability tool development.


## Sample tables

| Table | Sample Rows |
|-------|------------|
| access_logs | 5,000 |
| servers | 3 |
| **Total** | **5,003** |

## Full dataset

The complete dataset includes all tables with full row counts:

| Table | Full Rows |
|-------|----------|
| access_logs | 50,045 |
| servers | 3 |
| **Total** | **50,048** |

**Formats included:** CSV, Parquet, SQLite

**[Get the full dataset on Gumroad](https://mindweavetech.gumroad.com)**

## About

Generated by [Mindweave Technologies](https://mindweave.tech) -- realistic synthetic datasets for developers, QA teams, and data engineers.

Every dataset features:
- Enforced foreign key relationships across all tables
- Realistic statistical distributions (not uniform random)
- Temporal patterns (seasonal, time-of-day, day-of-week)
- Injected anomalies for ML training and anomaly detection
- Deterministic generation (same seed = same output)

Browse all datasets: [https://mindweavetech.gumroad.com](https://mindweavetech.gumroad.com)