kqlbridge 0.6.6

KQL → Spark SQL / T-SQL transpiler for Microsoft Fabric and Databricks

KQLBridge

KQL → Spark SQL / T-SQL transpiler for Microsoft Fabric and Databricks

KQLBridge lets data engineers write queries in Kusto Query Language (KQL) and compile them to Spark SQL (Databricks, Microsoft Fabric Spark notebooks) or T-SQL (Fabric SQL Warehouse, Synapse Analytics).

from kqlbridge import translate

kql = "AppLogs | where Level == 'Error' | summarize count() by ServiceName"
sql = translate(kql, target="spark")
# → SELECT ServiceName, COUNT(*) FROM AppLogs WHERE Level = 'Error' GROUP BY ServiceName

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Why KQLBridge?

Microsoft Fabric runs two query engines side by side: Eventhouse (KQL) and Spark notebooks / SQL Warehouse (Spark SQL / T-SQL). Teams that wrote years of KQL analytics can't simply copy-paste those queries into a Spark cell. KQLBridge automates the translation.

Use cases:

• Migrate ADX / Eventhouse KQL workloads to Databricks or Fabric Spark
• Build routing agents that pick the right engine per query at runtime
• Teach polyglot data engineering — learn one language, compile to all targets

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Installation

pip install kqlbridge

Requires: Python 3.10+, no external services, no API keys.

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Quick Start

Python API

from kqlbridge import translate, detect_operators, is_supported

# Translate KQL → Spark SQL (Microsoft Fabric Lakehouse / Databricks)
sql = translate(
    "AppLogs | where TimeGenerated > ago(24h) | project Message, Level",
    target="spark"
)

# Translate KQL → T-SQL (Microsoft Fabric SQL Warehouse / Synapse)
sql = translate(
    "AppLogs | where TimeGenerated > ago(24h) | project Message, Level",
    target="tsql"
)

# Check which operators are used
ops = detect_operators("AppLogs | where Level == 'Error' | summarize count() by Host")
# → ['where', 'summarize']

# Gate unsupported queries before translating
if is_supported(kql):
    sql = translate(kql)
else:
    print("Unsupported operators — keep in KQL engine")

CLI

# Translate to Spark SQL (default)
kqlbridge translate "AppLogs | where Level == 'Error' | summarize count() by Host"

# Translate to T-SQL
kqlbridge translate "Events | where ts > ago(7d) | take 100" --tsql

# Check if a query is supported (exit 0 = yes, exit 1 = no — useful in CI)
kqlbridge check "AppLogs | where Level == 'Error' | join (Users) on UserId"

# List all supported operators
kqlbridge operators

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Supported Operators (v0.2)

KQL Operator	Spark SQL Output	Status
where	WHERE clause	✅ v0.1
project	SELECT columns	✅ v0.1
summarize count()	SELECT COUNT(*) GROUP BY	✅ v0.1
summarize sum/avg/min/max	Aggregation functions	✅ v0.1
bin(col, 1h)	DATE_TRUNC('hour', col)	✅ v0.1
ago(1h)	CURRENT_TIMESTAMP - INTERVAL '1 hours'	✅ v0.1
extend	SELECT *, expr AS alias	✅ v0.1
order by / sort by	ORDER BY col ASC/DESC	✅ v0.1
take / limit	LIMIT n	✅ v0.1
distinct	SELECT DISTINCT	✅ v0.1
join (inner)	INNER JOIN ON key	✅ v0.1
union	UNION ALL	✅ v0.1
let variables	CTEs (WITH … AS)	✅ v0.1
count()	COUNT(*) scalar	✅ v0.1

See supported_operators.md for full reference. See unsupported_operators.md for operators with no SQL equivalent.

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Eval Score

KQLBridge measures accuracy against a locked 100-query benchmark (70 standard, 20 edge-case, 10 adversarial). The eval script is the single source of truth — it is never modified by the agent loop.

python tests/eval/prepare.py
# SCORE: 85.0% (85/100)

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Architecture

KQL input
  → Lark lexer (LOCKED grammar: kql.lark)
  → Parser (MODIFIABLE: parser.py)
  → AST nodes (LOCKED: ast_nodes.py)
  → Semantic check (LOCKED: semantic.py)
  → Generator (MODIFIABLE: generators/spark_sql.py)
  → Spark SQL / T-SQL output

Locked files (oracle, grammar, types) are never touched by the agentic build loop. Modifiable files (parser, generators) are where improvements happen.

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Contributing

All contributions must include a corresponding test case in tests/eval/benchmark.json. PRs that do not include a new test case will not be merged.

git clone https://github.com/navakanth/kqlbridge
cd kqlbridge
pip install -e ".[dev]"
python tests/eval/prepare.py  # baseline score
pytest tests/                 # unit tests

See CONTRIBUTING.md for full guidelines.

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Companion Projects

• PipeQL — Pipe-first SQL syntax that compiles to the same Spark SQL target (v0.2 roadmap)
• DE-Context Kit — Routing agent + CDLC skill packages using KQLBridge under the hood

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License

Apache 2.0. See LICENSE.