felderize translates SQL from various dialects into valid Feldera SQL using LLM-based translation with optional compiler validation.

Dialects: Spark SQL is currently the only supported dialect. Support for additional dialects is planned.

cd python/felderize
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Note: pip install -e . is required before running felderize. It registers the package and CLI command.

Download the Feldera SQL compiler JAR (requires Java 19–21 installed):

felderize download-compiler

This fetches the latest sql2dbsp-jar-with-dependencies-*.jar from GitHub Releases and saves it to ~/.felderize/. The command prints the exact path — copy it for the next step. Re-run it any time to pick up a newer release; it reports whether you are already on the latest one.

Requirement: felderize needs compiler v0.304.0 or newer — earlier releases lack SQL features felderize relies on (e.g. div_null, MAKE_DATE). download-compiler always fetches the latest release, and felderize warns at validation time if the configured compiler is older than v0.304.0.

Note: Java 19, 20, or 21 must be installed and on your PATH before running validation. Check with java -version. Later versions (22+) are not supported.

Create a .env file in the python/felderize/ directory:

ANTHROPIC_API_KEY=your-key-here
FELDERA_COMPILER=~/.felderize/sql2dbsp-jar-with-dependencies-vX.Y.Z.jar
FELDERIZE_MODEL=claude-sonnet-4-6

All three variables are required. FELDERA_COMPILER is used only for validation — translation still works without it, but output SQL is not verified. You can also pass --compiler PATH and --model MODEL per command.

Note: felderize currently requires an Anthropic API key — only Claude models are supported.

Tip — use a large-context-window model. Each request bundles the full rule set (felderize/skills/spark_skills.md), validated examples, and (on the retry pass) Feldera reference documentation on top of your schema and query — easily tens of thousands of tokens, and the --validate repair and docs passes add more. Prefer a model with a large context window (e.g. a recent Claude Sonnet/Opus) so nothing is truncated and the model has the full rules in view; set it via FELDERIZE_MODEL or --model. If a program still doesn't fit, felderize stops with an error asking you to shorten the input (translate fewer views at a time, drop unused tables, or split it into smaller files).

# List available examples
felderize spark example

# Translate an example (validates by default)
felderize spark example simple

# Without compiler validation
felderize spark example simple --no-validate

# Log SQL submitted to the validator at each attempt
felderize spark example json --verbose

# Use a specific compiler binary
felderize spark example simple --compiler /path/to/sql-to-dbsp

# Output as JSON
felderize spark example simple --json-output

Available examples:

The JSON output contains:

{
  "feldera_schema": "...",   // translated DDL (CREATE TABLE statements)
  "feldera_query": "...",    // translated query (CREATE VIEW statements)
  "unsupported": [...],      // Spark features with no Feldera equivalent
  "warnings": [...],         // non-fatal notes (compiler repairs, validation result)
  "explanations": [...],     // explanations for translation decisions
  "status": "success|unsupported|error"
}

status:

• success — translated, and (with --validate) compiled cleanly.
• unsupported — translated, but some constructs have no Feldera equivalent and were emitted as CAST(NULL AS <type>) placeholders (listed in unsupported).
• error — the LLM response couldn't be parsed, or (with --validate) the output still failed to compile after the repair attempts. felderize always returns the best-effort SQL in feldera_query (salvaging it from the raw response when the reply wasn't valid JSON), with the compiler errors in warnings — it may not compile, but it always attempts a translation.

Each form below writes the translated, deployable Feldera SQL to a file. The file leads with a comment header recording the translation status and any unsupported constructs / warnings, so it is self-documenting; the status is also printed to stderr.

Separate schema and query files:

felderize spark translate schema.sql query.sql --validate -o out.sql
# → out.sql   (the translated CREATE TABLE + CREATE VIEW)

Single combined file (CREATE TABLE and CREATE VIEW statements in one file):

felderize spark translate-file combined.sql --validate -o out.sql
# → out.sql

Multiple query files against a shared schema (batch — faster than a shell loop):

felderize spark translate-batch schema.sql queries/*.sql --validate --output-dir out/
# → out/<query>_feldera.sql, one per query

Structured JSON instead of a .sql file (for automation — parse with jq):

felderize spark translate schema.sql query.sql --validate --json-output > result.json
# → result.json with feldera_schema, feldera_query, status, unsupported, warnings

translate-batch processes all queries in a single process so doc and example caches stay warm across queries. Omitting -o / --output-dir / --json-output prints the result as readable sections to the terminal instead.

Note: Running without --validate prints a warning — the output SQL has not been verified against the Feldera compiler.

All commands accept:

• --validate to validate output against the Feldera compiler (opt-in; example validates by default, use --no-validate to skip)
• --compiler PATH to specify the path to the Feldera compiler binary (overrides FELDERA_COMPILER env var)
• --model MODEL to specify the LLM model (overrides FELDERIZE_MODEL env var)
• --no-docs to disable Feldera SQL reference docs in the prompt
• --verbose to log the SQL submitted to the validator at each repair attempt
• --json-output to output results as JSON (the structured machine interface)
• -o, --output PATH (translate / translate-file) to write the translated schema + views to a deployable .sql file; the status prints to stderr so stdout/the file stay clean. (translate-batch uses --output-dir instead.)

To call felderize from your own code instead of shelling out to the CLI, use the single entry point translate_spark_to_feldera:

from felderize import translate_spark_to_feldera, Config, Status

cfg = Config.from_env()          # reads ANTHROPIC_API_KEY, FELDERA_COMPILER, FELDERIZE_MODEL
result = translate_spark_to_feldera(
    schema_sql,                  # Spark CREATE TABLE ... DDL (str)
    query_sql,                   # Spark CREATE VIEW / SELECT ... (str)
    cfg,
    validate=True,               # compile against the Feldera compiler and repair (default: False)
)

if result.status is Status.SUCCESS:
    deploy(result.feldera_schema, result.feldera_query)
else:
    # UNSUPPORTED -> NULL-placeholder views (see result.unsupported);
    # ERROR       -> best-effort SQL that did not compile.
    review(result.unsupported, result.warnings)

TranslationResult exposes feldera_schema, feldera_query, status, unsupported, warnings, explanations, and to_dict(). validate=False skips the compiler (faster, but the output is not verified).

Runnable examples:

.venv/bin/python examples/api_usage.py            # translate one schema + query
.venv/bin/python examples/translate_all_examples.py   # translate all built-in examples + summary

Environment variables (set in .env):

You can teach felderize your project-specific patterns by adding rules and examples.

Rules tell the LLM how to rewrite specific Spark constructs. Each .md file should start with a YAML frontmatter block with a name: and description: field, followed by plain-markdown bullet points:

---
name: my-project-rules
description: Project-specific Spark-to-Feldera rewrites.
---

- **[PROJ-HASH] Internal UDF `my_hash(col)`:** Rewrite as `MD5(CAST(col AS VARCHAR))`.

- **[PROJ-ID] `CUSTOM_ID` columns:** Always map to `BIGINT NOT NULL` in Feldera.

Note: Frontmatter is recommended. Files without it are still loaded but produce a warning.

Place .md files in one of these locations — all are loaded automatically, no flag needed:

Or pass one or more files explicitly (repeatable):

felderize spark translate schema.sql query.sql --rules rules1.md --rules rules2.md

Examples are validated Spark → Feldera pairs shown to the LLM alongside the built-in ones. The more precise your examples, the better the translation quality for your specific SQL patterns.

Each .md file must start with a YAML frontmatter block. Use categories: to load the example only when those SQL constructs are detected in the query being translated. Omit categories: (but keep the frontmatter) to always include the example:

---
categories: [datetime]
---

### Example: Monthly revenue

**Spark SQL:**
```sql
SELECT date_trunc('MONTH', ts) AS month, SUM(amount) AS revenue
FROM sales GROUP BY date_trunc('MONTH', ts);
\```

**Feldera SQL:**
```sql
SELECT FLOOR(ts TO MONTH) AS month, SUM(amount) AS revenue
FROM sales GROUP BY FLOOR(ts TO MONTH);
\```

Note: The frontmatter block (---) is required. Files without it are skipped.

Valid categories: aggregates, string, datetime, array, json, map, types.

Place .md files in one of these locations — loaded automatically, no flag needed:

Or pass individual files or directories explicitly (repeatable, accepts both):

felderize spark translate schema.sql query.sql --examples ex1.md --examples my_examples/

felderize translates the whole program (schema + all views) in a single LLM call:

1. Loads translation rules from the skill file (felderize/skills/spark_skills.md).
2. Trims the schema to the tables the query actually references, then sends the Spark schema + query to the LLM with the rules and validated examples.
3. Parses the translated Feldera SQL from the response. Constructs with no Feldera equivalent are emitted as CAST(NULL AS <type>) placeholders and listed in unsupported.
4. With --validate, compiles the output against the Feldera compiler and repairs it using the compiler's error feedback for up to a few attempts. If that first pass still doesn't compile, it retries once more with relevant Feldera documentation added to the prompt (from docs.feldera.com/docs/sql/); use --no-docs to skip the documentation pass.

Contact us at support@feldera.com for assistance with unsupported Spark SQL features.