---
license: cc0-1.0
task_categories:
- text-classification
- text-generation
- tabular-classification
- tabular-regression
language:
- en
tags:
- synthetic
- template-generated
- database
- sql
- query-optimization
- performance
- nosql
- mongodb
- redis
- cassandra
- elasticsearch
- mysql
- postgresql
- oracle
- sql-server
pretty_name: Database Query Logs (synthetic)
size_categories:
- 1K<n<10K
---

# Database Query Logs (synthetic)

3,995 database query-log entries spanning 10 engines - MySQL, PostgreSQL, MongoDB, SQL
Server, Oracle, MariaDB, SQLite, Cassandra, Redis, and Elasticsearch - with query text,
type, complexity, execution timing, and row-count metadata.

## These queries are synthetic

The queries were **programmatically generated**, not captured from production systems.
They were produced by templating a set of query shapes across industry-flavored schema
names.

The generation shows through:

- 3,195 training rows contain **2,243 distinct queries (70.2%)**; one Elasticsearch query
  body appears 35 times.
- The same query recurs with only the schema name swapped - `DELETE FROM telecom_logs`
  and `DELETE FROM construction_logs` are otherwise byte-identical.
- Some generated SQL is **not valid**. Schema names containing hyphens appear unquoted
  (`DELETE FROM E-COMMERCE.AUDIT_LOGS`), which will not parse.

A previous version of this card described the contents as "real-world and synthetic".
There is no verified production-captured subset; treat the whole dataset as synthetic.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("robworks-software/database-query-logs-synthetic")
```

## Splits

| Split | Rows |
|-------|------|
| train | 3,195 |
| validation | 396 |
| test | 404 |
| **total** | **3,995** |

## Appropriate use

- Parser and tokenizer testing across dialects (bearing in mind some queries are invalid).
- Query-type and complexity classification.
- Teaching examples of dialect syntax differences.

## Inappropriate use

- **Performance modeling or optimizer research.** `execution_time_ms`, `rows_examined`,
  and `rows_returned` are generated values. They were not measured on any real system and
  have no relationship to what these queries would actually cost. Do not train a cost
  model on them.
- **Workload characterization.** The distribution reflects the generator's template mix,
  not any real application's query pattern.

## Limitations

- **Synthetic throughout** - see above.
- **Some queries are syntactically invalid.**
- **70.2% distinct queries**, with heavy near-duplication beyond exact repeats.
- **Timing and row-count fields are fabricated.**
- **Schema names are industry labels** (`TELECOMMUNICATIONS`, `EDUCATION`, `LOGISTICS`)
  applied cosmetically; they do not reflect real domain schemas.

## License

**CC0-1.0.** Entirely generated content, dedicated to the public domain.

## Citation

```bibtex
@dataset{database_query_logs_synthetic,
  title  = {Database Query Logs (synthetic)},
  author = {Robworks Software},
  year   = {2025},
  publisher = {Hugging Face},
  note   = {Programmatically generated queries; timing metadata is fabricated},
  url    = {https://huggingface.co/datasets/robworks-software/database-query-logs-synthetic}
}
```