Datasets:

ayushjoshi2012
/

sql-optimization-diagnostic-traces

license: cc-by-nc-4.0
task_categories:
  - text-generation
  - question-answering
language:
  - en
tags:
  - sql
  - database
  - query-optimization
  - explain-analyze
  - postgresql
  - mysql
  - clickhouse
  - cockroachdb
  - dba
  - fine-tuning
pretty_name: Enterprise Database Query Plan Optimization & Remediation Traces
size_categories:
  - 100<n<1K

Enterprise Database Query Plan Optimization & Remediation Traces (Community Preview)

Dataset Banner

Overview

High-concurrency database workloads frequently degrade due to optimizer plan misestimations, unindexed correlated joins, merge-sort memory spills, and non-sargable expressions.

This repository hosts the Community Preview (250 verified traces) of an architectural fine-tuning dataset spanning PostgreSQL 16, MySQL 8.0, ClickHouse 24, CockroachDB, MariaDB, and SQLite. Each record pairs an authentic production query regression and raw execution plan (EXPLAIN ANALYZE) with chain-of-thought architectural diagnosis, actionable remediation DDL, and dialect-compliant optimized queries.

Dataset Splits & Commercial Licensing

Split	Records	Format	Primary Use Case	License
Community Evaluation (This Repo)	250	Parquet	Benchmarking, zero-shot eval, research	CC BY-NC 4.0
Enterprise Commercial Corpus	2,500	Parquet + JSONL	Production fine-tuning (SFT/DPO), commercial DBA agents	Commercial Enterprise License

Complete Enterprise Corpus (2,500 Records)

For production model deployment, proprietary agent training, or private RAG pipelines without non-commercial restrictions:

👉 Acquire Enterprise Commercial License & Dataset via Dodo Payments

Data Structure & Schema

{
  "database_engine": "string",
  "slow_query": "string",
  "execution_plan": "string",
  "diagnostic_thought": "string",
  "remediation_ddl": "string",
  "optimized_query": "string"
}

Field Definitions

Field	Data Type	Description
`database_engine`	`string`	Target engine dialect & exact version (e.g., `PostgreSQL 16.2`, `ClickHouse 24.3`).
`slow_query`	`string`	Realistic unoptimized SQL query triggering runtime performance degradation.
`execution_plan`	`string`	Raw optimizer output (`EXPLAIN (ANALYZE, BUFFERS)` or JSON plan) exposing node bottlenecks.
`diagnostic_thought`	`string`	Step-by-step reasoning diagnosing node cost, row estimate deltas, and I/O buffer behavior.
`remediation_ddl`	`string`	Executable DDL statements (`CREATE INDEX CONCURRENTLY`, table parameter tuning).
`optimized_query`	`string`	Performant rewritten query strictly adhering to engine dialect grammar.

Quickstart (Hugging Face Datasets)

from datasets import load_dataset

# Load Community Preview
dataset = load_dataset("ayushjoshi2012/sql-optimization-diagnostic-traces", split="train")

# Inspect a single diagnostic trace
sample = dataset[0]
print(f"Engine: {sample['database_engine']}")
print(f"Slow Query:\n{sample['slow_query']}")
print(f"Diagnosis:\n{sample['diagnostic_thought']}")
print(f"Remediation DDL:\n{sample['remediation_ddl']}")
print(f"Rewritten Query:\n{sample['optimized_query']}")

Core Domain Taxonomy

Traces are generated across representative enterprise schema designs:

Fintech & Ledger Core: Double-entry ledger scans, implicit UUID-to-integer type coercion, balance aggregations.
IoT & Telematics: Granule skip-index mismatches, partition decompression stalls, time-bucket groupings.
B2B SaaS & Security: High-offset tenant audit logs, soft-delete booleans, unindexed RBAC joins.
Healthcare & EHR: Unindexed composite foreign keys, leading-wildcard text filters, outdated optimizer statistics.
Freight & Global Logistics: Disk-spilling merge joins on departure timestamps, Cartesian row multiplication.
AdTech & Gaming Telemetry: Unindexed streaming click joins, unbounded window function sorts, leaderboard lock contention.

Citation & Contact

For inquiries regarding custom synthetic domain generation, enterprise site licenses, or data additions, please commentinitiate a new discussion.