Snowplow results#37
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Align the sqllogictest output with the snowflake-connector-python output that the bronze_scope .slt fixtures were generated against: * booleans as TRUE/FALSE * empty strings as '' (not (empty)) * floats with lowercase nan/inf/-inf and .0 preserved on whole values * binary as x'<lowercase-hex>' * date/time/timestamp/list/struct/map cells wrapped in single quotes * Utf8 cells whose content parses as a JSON array or object (VARIANT / ARRAY / OBJECT in Rustice, all stored as Utf8) re-emitted as compact JSON wrapped in single quotes Workspace sqllogictest pass count goes from 58 to 116 with no regressions. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
* Decimals: preserve the column's declared scale (don't strip trailing
zeros via BigDecimal normalize), so `DECIMAL(p,6)` values render as
`77500.000000`.
* Floats: use Rust's shortest-round-trip `{:?}` (same algorithm Python's
`repr(float)` uses), so values like `1.23e-10` keep scientific
notation. Fix the exponent sign to Python's `e+N` form. NaN / Infinity
/ -Infinity rendered as lowercase `nan` / `inf` / `-inf`.
* Time / Timestamp: pad the subsecond fraction to 6 digits and truncate
nanosecond precision, matching Python `datetime.isoformat()`.
* VARIANT JSON: sort object keys alphabetically (serde_json's Map has
`preserve_order` on transitively; emit through a recursive sort).
* Strings: escape embedded `\n` as the literal `\n` (matches the python
runner's `value.replace('\n', '\\n')`).
* Validator: normalize the expected line too (and join actual cells
with `" "` instead of `\t`), so the literal `\t` the .slt parser
embeds between columns equates to the whitespace runs the upstream
normalizer collapses.
Workspace pass count: 116 -> 155.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
* `array_value_to_json` walks the Arrow value at (col, row) and produces a `serde_json::Value`, honouring primitive types, strings, nested lists, struct fields, and map entries. * Struct fields and Map entries are emitted in alphabetical key order to match Snowflake's VARIANT JSON output (which the bronze_scope fixtures were captured against). * Arrow's default formatter previously rendered List<Utf8> as `[a, b, c]` (debug form) — a far cry from `["a","b","c"]`. This lights up SPLIT, STRTOK_TO_ARRAY, and other UDFs that return array/object cells. * Tighten the validator's whitespace handling: a cell that's only whitespace was normalising to an empty string but still emitting a separator on join, so a `[" ", "0"]` row produced `" 0"` against an expected `"0"`. Re-normalise the joined row to collapse the stray spaces. Workspace pass count: 155 -> 157. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
…REGEX>: Three transforms the Python slt_runner applies that the Rust harness was missing: 1. `varchar_to_str` now substitutes `undefined` → `null` before JSON-parsing a VARIANT-shaped cell, mirroring `slt_runner/result.py:550`. 2. The `<REGEX>:` branch in `embucket_validator` now uses fullmatch (\A..\z) and DOTALL ((?s)) semantics, matching `slt_runner/result.py:422-424`. 3. `<!REGEX>:` (negative-match) is now recognised alongside `<REGEX>:`. Unit tests cover all three. Net effect on suite: pass 157→158, fail 220→219.
…elies on
Six focused .slt files, one per DDL operation:
- create_schema_idempotent.slt — CREATE SCHEMA IF NOT EXISTS reruns cleanly
- create_or_replace_table.slt — CREATE OR REPLACE TABLE (typed and AS SELECT)
actually drops & recreates, no residual rows
- create_table_as_then_insert.slt — Phase A pattern: CTAS + INSERT INTO. The
trailing case documents that when CTAS body
and INSERT body both return the same rows
you get 2x duplicates (engine-correct).
- drop_then_create.slt — Phase B pattern: DROP + fresh CREATE TABLE AS
- copy_into_parquet.slt — COPY INTO appends; events1 + events2 = 400
- merge_upsert.slt — MERGE INTO with key + window predicate; both
WHEN MATCHED (update in place) and WHEN NOT
MATCHED (insert once) branches verified
All 6 pass. The DDL primitives all behave correctly — including
CREATE OR REPLACE TABLE and MERGE INTO — which scopes the snowplow
duplicate-row failures to the data-shape (CTAS body + INSERT body overlap)
rather than the DDL itself.
The regen script bundled run-1 (sentinel-gated CTAS) and run-2
(incremental INSERT or __dbt_tmp+MERGE) into a single Phase A block.
This populated events_this_run mid-Phase A, which then cascaded into
2x duplicates in every downstream scratch table (their CTAS bodies have
no date filter and pull from events_this_run; their accompanying
Phase A INSERTs doubled the same rows). Derived `+materialized:
incremental` models accumulated a third layer because the Phase A MERGE
ran against a stale April-2026 window predicate on a target the same
Phase A had just pre-populated, so every source row fell through
`WHEN NOT MATCHED → INSERT`.
The setup now emits TWO files, one per simulated dbt run state, so
both events1-only and events1+events2 verifications run independently
against captured Snowflake reference values:
setup.full_refresh.slt = header + Phase A + Phase B
→ state after `load events1.csv; dbt run`.
Phase A lays down 18 empty schemas; Phase B
rebuilds scratch via CREATE OR REPLACE on
the events1-only events table and upserts
derived via __dbt_tmp + MERGE on the empty
target (every row → WHEN NOT MATCHED →
INSERT). Validated by 18 leaves under
full_refresh/ against captured Snowflake
values in slt_results.full_refresh.txt.
setup.slt = header + Phase A + events2 COPY + Phase B
→ state after `load events2.csv; dbt run`
on top of the prior state. Same Phase A +
Phase B shape, but events2 is COPYed into
the events table before Phase B runs.
Validated by 18 leaves under incremental/
against slt_results.incremental.txt.
Single MERGE pass on an empty target is the only path that reproduces
the captured Snowflake values without depending on a compile-time-
aligned window: the dbt-compiled MERGE predicate is a 2-minute slice
baked in at compile time, so a literal two-cycle simulation (cycle 1
MERGE on empty target then cycle 2 re-MERGE on populated target) would
produce duplicate derived rows.
Regen-script details:
- emit_phase_a stripped to CTAS-only.
- emit_phase_b switched from DROP+CREATE pairs to CREATE OR REPLACE.
- MERGE source piped through sed to map SNOWPLOW_JAN.snowplow_manifest_*
catalog refs to embucket.public_snowplow_manifest_* (compiled dbt SQL
hard-codes the upstream Snowflake catalog) and lowercase the
Snowflake-style "QUOTED_UPPERCASE_IDENT" column refs (DataFusion
stores columns lowercase).
Test surface:
- 18 full_refresh leaves restored, then events1-only verification blocks
pasted from test-dbt-snowplow-web/loader/slt_results.full_refresh.txt
via the loader's apply_slt_results.py script.
- 18 incremental leaves keep their events1+events2 verification blocks
from slt_results.incremental.txt.
- snowplow_web_incremental_manifest expected count 12 → 0 in both
variants; the package's post-hook (which appends a manifest row per
successful model run) isn't part of our simulation.
dbt_snowplow_web: pass=36 / fail=0.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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add snowplow tests