# Debug Transformation > Debug dlthub transformation failures. Use when a transformation fails on a different destination than it was developed on, SQL dialect errors occur after deployment, pipeline recovery is needed after a failed run, or columns are silently dropped from output. - Skill: `dlt-hub/debug-transformation` (Agent Skill) - Install (CLI): `npx skillmds@latest add dlt-hub/debug-transformation` - Raw SKILL.md: https://api.skillmd.com/api/skills/dlt-hub/debug-transformation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: dlt-hub (https://skillmd.com/u/dlt-hub) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dlt-hub/debug-transformation --- # Debug transformation Diagnose and fix dlthub transformation failures. Two main failure classes: **SQL dialect incompatibility** (transformation works on dev destination, fails on production) and **pipeline state errors** (stale packages, schema drift, failed jobs). ## When to use this skill - Transformation works on DuckDB locally but fails on BigQuery, Snowflake, or Postgres after deployment - Pipeline fails with SQL syntax errors or "unsupported function" messages - You want to verify SQL portability **before** deploying (proactive check) - Pipeline is stuck in a failed/retry loop - Columns are missing from output after the first run (NULL-only or computed/derived columns) - You want to inspect what the transformation actually produced after a successful run ## 1. SQL dialect compatibility ### 1a. Static SQL compatibility checks The toolkit includes a bundled dialect checker that reads your transformation file directly — no pipeline connection needed, and no script to write. It parses each `@dlt.hub.transformation` function's SQL in the dev dialect and transpiles it to the target dialect, catching portability issues before deployment. Tell the user: *"I'm going to run a static SQL compatibility check on your transformation file to catch any SQL dialect issues before you move to prod."* You must pass the dev and prod destination types explicitly via `--read` and `--write`. If you already have this from context (e.g. from `create-transformation`), use it directly. If not, call `get_local_pipeline_state` via MCP to retrieve the destination type for the pipeline, then ask the user for the prod destination if unknown. Run the checker yourself and read the output: ```bash uv run python ${CLAUDE_PLUGIN_ROOT}/tools/check_dialect.py transformations/_to_cdm.py --read --write ``` Expected output looks like: ``` Dialects: duckdb -> bigquery Checking 5 transformation(s) from transformations/hubspot_to_cdm.py [dim_company] OK [dim_person] WARN: double-quoted identifier "email"; verify destination quoting in bigquery [fact_activity] ERROR: transpile duckdb->bigquery failed: ... ... SUMMARY warnings: 1 errors: 1 ``` Walk the user through the results: - **OK** — safe to deploy as-is - **WARN** — portability risk worth inspecting, not a guaranteed failure; common cause is double-quoted identifiers that BigQuery/Snowflake handle differently - **ERROR** — the SQL will likely fail on the target destination; rewrite to ANSI SQL (see section 1b) - **skipped** — SQL is dynamically constructed and can't be statically analysed; inspect those functions manually This check catches the most common issues, but does not replace inspecting `Relation.to_sql()` output if deployment still fails after fixes: If either dlt destination is not covered by SQLGlot, the script stops and prints the available SQLGlot dialects. In that case, inspect dlt's actual `Relation.to_sql()` output or run a target-destination test pipeline instead. SQLGlot supports 31+ dialects — see https://sqlglot.com/sqlglot.html for dialect names. ### 1b. Common dialect-specific patterns to fix Rewrite these to ANSI SQL so SQLGlot can transpile them to any destination: | Dialect-specific pattern | Portable ANSI alternative | |---|---| | `x::TEXT`, `x::INT` (cast shorthand) | `CAST(x AS VARCHAR)`, `CAST(x AS INTEGER)` | | `IFNULL(a, b)` | `COALESCE(a, b)` | | `ILIKE` | `LOWER(x) LIKE LOWER(y)` | | `INT64` / `STRING` / `FLOAT64` as type names | `BIGINT` / `VARCHAR` / `DOUBLE` | | `EPOCH_MS()`, `STRFTIME()`, `LIST_AGG()`, etc. | No ANSI equivalent — use `query_dialect` (see below) | | `QUALIFY` clause | Wrap in subquery with `WHERE` on the window result | ### 1c. When dialect-specific SQL is unavoidable If a transformation genuinely requires a dialect-specific function with no ANSI equivalent, declare the source dialect with `query_dialect` so SQLGlot knows how to transpile: ```python @dlt.hub.transformation def my_transform(dataset: dlt.Dataset): yield dataset( "SELECT STRFTIME(created_at, '%Y-%m') AS month FROM events", query_dialect="duckdb", # tells dlt this SQL is DuckDB dialect ) ``` If SQLGlot raises `UnsupportedError` or logs warnings at `unsupported_level`, the construct has no mapping to the target dialect and must be rewritten to an ANSI equivalent or handled in application code. References: - SQLGlot supported dialects: https://sqlglot.com/sqlglot.html - SQLGlot unsupported errors: https://sqlglot.com/sqlglot.html#unsupported-errors - dlt `Relation` source: https://github.com/dlt-hub/dlt/blob/master/dlt/dataset/relation.py#L276 ## 2. Pipeline failure recovery Use this escalation order. Do not skip steps. ### Step 1: Inspect failures ```bash uv run dlthub local pipeline failed-jobs uv run dlthub local pipeline trace ``` Read the error messages before taking any recovery action. Most failures are fixable without touching destination state. ### Step 2: Clear stale packages If a prior run failed mid-load and left pending packages that keep retrying old (broken) SQL: ```bash uv run dlthub local pipeline drop-pending-packages ``` Re-run after clearing. If the underlying SQL was fixed, this is often all that is needed. ### Step 3: Reconcile local state If local pipeline state has drifted from the destination (e.g. after a partial load or schema change): ```bash uv run dlthub local pipeline sync ``` If no recoverable destination state exists, `sync` may not resolve partial retries — use `drop-pending-packages` first. ### Step 4: Selective drop (last resort) > **DuckDB only:** if the failure is `"Parser Error: Adding columns with constraints not yet supported"`, `dlthub local pipeline drop` will not fix it — go to section 3's DuckDB workaround instead. Only if the steps above do not resolve the failure and incorrect schema or tables were already loaded to the destination: ```bash uv run dlthub local pipeline drop # drop a specific resource uv run dlthub local pipeline drop --drop-all # only with explicit user confirmation ``` **Safety rules before dropping:** - Prefer dropping specific resources over `--drop-all` - Confirm pipeline name, destination, dataset, and which resources will be dropped before executing - `drop` removes destination tables and resets matching local state — this forces a full reload and may remove good data alongside bad - If uncertain which resources are safe to drop, stop and ask the user before executing - After drop: re-run transformations and validate schema/tables before further loads References: - dlthub CLI reference: https://dlthub.com/docs/hub/command-line-interface ## 3. Missing columns and schema issues dlthub silently drops columns it cannot type-infer — no error, no warning. Two root causes: ### 3a. NULL-only columns When a column is NULL-only on the first run and no `columns=` hint was provided, dlthub strips the column from the schema. Subsequent runs write data but the column is absent. ### 3b. Computed / derived columns When a transformation uses derived expressions — `md5()`, `strftime()`, `TRY_CAST`, `CASE WHEN ... END`, aggregates with aliases, or function chains — dlthub cannot infer the output type from the SQL alone. Without a `columns=` hint the column is silently dropped. **Diagnose:** compare expected vs actual columns using the MCP `get_table_schema` tool, or inspect via: ```bash uv run dlthub local pipeline show ``` Identify which computed columns are absent from the output schema. Each missing column needs an explicit `columns=` hint. **Fix:** add `columns=` hints for every affected column: ```python @dlt.hub.transformation( write_disposition="replace", columns={ "company_sk": {"data_type": "text", "nullable": False}, "joined_at": {"data_type": "timestamp", "nullable": True}, "email_hash": {"data_type": "text", "nullable": True}, # md5() result "month_bucket": {"data_type": "text", "nullable": True}, # strftime() result "event_count": {"data_type": "bigint", "nullable": True}, # COUNT() alias }, ) def dim_company(dataset: dlt.Dataset): ... ``` `data_type` values must match the key type contract established during `create-transformation` (consistently `text` or `bigint` for surrogate keys). Apply `columns=` hints for: - Any column from a `LEFT JOIN` (lookup may return NULL) - Any cast from string to typed value where the source may be empty - Any column that was NULL-only in a prior run - **Any computed or derived column**: `md5()`, `strftime()`, `TRY_CAST`, `CASE WHEN`, aggregate aliases, function chains ### Known DuckDB failure: "Parser Error: Adding columns with constraints not yet supported" This error surfaces when re-running a transformation with new or modified `columns=` hints against DuckDB — DuckDB's `ALTER TABLE` cannot add constrained columns. The workaround is to drop the dataset and re-run from scratch: ```bash # DuckDB only — this drops all tables in the dataset duckdb "DROP SCHEMA CASCADE;" ``` Then re-run the transformation script. This is DuckDB-specific behavior and will not occur on cloud destinations (BigQuery, Snowflake, Postgres). Do not use `dlthub local pipeline drop` for this error — it does not clear the DuckDB schema and will not resolve the constraint conflict. Reference: https://dlthub.com/docs/hub/transformations.md ## 4. Validate transformation output After fixing an issue and re-running, validate the output using the same checks as the happy path — see **`create-transformation` Step 9**.