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types: array, map and struct types (P2-T001b, part 1) - #209
P2-T001b, part 1 of 2: Spark's ArrayType, MapType and StructType in krishiv-types::DataType, carried through every layer a type crosses.
Renderings.simpleString, catalogString, sql, typeName and json as Spark 4.2.0 prints them: struct field names quoted only in sql, NOT NULL and COMMENT '...' in sql, at most 25 fields in simpleString. DataType::from_json reads Spark's JSON form.
Type strings.ARRAY<t>, MAP<k, v> and STRUCT<name [:] t [NOT NULL] [COMMENT '...'], ...> (including STRUCT<>), plus comment string literals with Spark's unescaping. Errors follow Spark's parser:
a syntax error inside a complex type is reported at the outermost <;
type and name checks run only once the whole string parses;
a single token after a complex type is reported as "extra input" (>> is one token outside a complex type);
a bare ARRAY, MAP or STRUCT is INCOMPLETE_TYPE_DEFINITION.*.
The SQL frontend's type display now keeps field comments.
Arrow. Arrays map to List (and read back from LargeList too), maps to Map with Spark's ArrowUtils field names and non-null keys, structs to Struct. Each child field carries its own type tag, and struct field metadata is stored in krishiv.spark_metadata.
Nesting limit. Types that come from outside (type strings, Arrow, JSON, Connect, plans) may nest at most DataType::MAX_NESTING_DEPTH (32) levels. Deeper input fails with FAILED_TO_PARSE_TOO_COMPLEX, never with a stack overflow. Spark accepts deeper types; this limit is the one documented difference.
Analyzer.
Nested types meet element by element (findTypeForComplex) in conditionals and set operations, and by the tightest common type in comparisons.
Elements, values and fields become nullable when either side is nullable or when their cast may produce NULL.
A union of inputs that differ only in field names or nullability keeps the first input's names and metadata.
Casts between nested types follow Cast.canCast / canAnsiCast: a string to a nested type is refused, and ANSI suggests try_cast.
Plan codec. A new DataType.KIND_NESTED carries the type's Spark JSON. It sits behind a new semantic feature nested_types (ADR 0040, legacy level 6), so a reader without the feature refuses the plan and names the feature. Adds the golden tests/golden/nested_types/. Existing goldens are unchanged.
Spark Connect. Array, map and struct data types translate, including field metadata JSON, in the shape pyspark-client 4.2 sends them.
Every expectation comes from Spark 4.2.0:
The type-string fixture gains 121 nested cases, generated by tests/scripts/gen_type_strings.py from a Spark 4.2.0 container.
Coercion, cast and Connect shapes were checked against a Spark 4.2.0 Connect server; the cases are listed in the tests.
Tests
krishiv-types:
the fixture test now also checks json, from_json and whether the simpleString reparses;
round trips through Arrow, JSON and DDL at nesting depth 4;
the Arrow layout;
the nesting limit for every input;
malformed JSON;
the equality helpers.
krishiv-plan: nested coercion (ANSI on and off) and nested casts.
krishiv-plan-codec:
the new inventory case and its golden;
an older reader refusing the plan and naming the feature;
header honesty;
malformed nested JSON is Corrupt;
a type too deep does not encode.
krishiv-connect: the pyspark-client 4.2 shape, unset child types, bad metadata and the depth limit.
krishiv-sql: comments survive the type display. The SQL parser places three struct-field syntax errors elsewhere than Spark; they are listed in the probe test.
Analysis manifests: type-coercion drops 3 entries (2,638 of 2,858 statements now resolve as Spark resolves them), and registry-matrix drops 8.
Follow-up commits (review)
UNION inputs that differ only in field names or nullability are cast to the union's type (a relabel), so exec sees one physical type.
Nested casts run in exec: element, key/value and field casts by position (values under NULL rows are never cast), and to STRING as Spark renders it.
Only atomic types become strings implicitly; concat over arrays has the array type.
Maps do not order (INVALID_ORDERING_TYPE, SET_OPERATION_ON_MAP_TYPE); =/IN take same-shape nested types without a cast.
TRY_CAST nullability follows canUpCast; struct field names follow spark.sql.caseSensitive and Java's equalsIgnoreCase.
Nested literals and functions (array, map, named_struct, ...); comparing nested values at run time; Python value conversion. These belong to the array, map and struct function tasks.
greatest/least/min/max over maps still resolve (Spark: INVALID_ORDERING_TYPE).
Adds Spark's ArrayType, MapType and StructType (fields with nullability
and metadata) to krishiv-types::DataType, through every layer a type
crosses:
- simpleString, catalogString, sql, typeName and json as Spark 4.2 prints
them (struct field quoting, NOT NULL, COMMENT, the 25-field simpleString
truncation), and DataType::from_json;
- DDL type strings: ARRAY<..>, MAP<..>, STRUCT<..> with field options,
string literals for comments, Spark's error positions (syntax errors at
the outermost '<', checks deferred until the string parses, extra input
after a complex type, INCOMPLETE_TYPE_DEFINITION for a bare keyword);
- Arrow: List/LargeList, Map (Spark's ArrowUtils names, non-null keys),
Struct, with child tags and field metadata;
- nesting bounded at DataType::MAX_NESTING_DEPTH (32) wherever a type
comes from outside;
- analyzer: findTypeForComplex for conditionals, set operations and
comparisons, Union's structural compatibility, and Spark's casts between
nested types (string to nested refused);
- plan codec: KIND_NESTED with the type's Spark JSON, behind the new
semantic feature nested_types (older readers refuse such plans);
- Spark Connect: Array, Map and Struct data types, with field metadata.
The type-string fixture gains nested cases generated from Spark 4.2.0;
the analysis manifests lose the entries this fixes.
Signed-off-by: techysneha14 <343708768+shehatathe@users.noreply.github.com>
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Walkthrough
The PR adds nested array, map, and struct types to the shared type model. It extends parsing, serialization, Arrow and Spark Connect conversion, plan encoding and feature negotiation, and analyzer coercion.
Changes: Nested Spark type model
Layer / File(s)
Summary
Type model, comparison, and rendering crates/krishiv-types/src/data_type.rs, crates/krishiv-types/src/nested.rs, crates/krishiv-types/src/lib.rs, crates/krishiv-types/src/tests.rs, crates/krishiv-types/CLAUDE.md
DataType now represents arrays, maps, and structs. The crate adds nested-type structures, metadata, depth and comparison helpers, union_like_merge, and nested type rendering.
DDL and JSON parsing and serialization crates/krishiv-types/src/ddl.rs, crates/krishiv-types/src/json.rs, crates/krishiv-types/src/error.rs, crates/krishiv-types/Cargo.toml, crates/krishiv-types/tests/*, tests/scripts/gen_type_strings.py, tests/scripts/typestr/dump_types.scala, crates/krishiv-sql/src/*
DDL and JSON handling now parse and serialize nested types, nullability, and metadata, with errors for invalid input and excessive depth. SQL formatting and Spark type-string fixtures cover nested types and comments.
Arrow and Spark Connect conversion crates/krishiv-types/src/arrow.rs, crates/krishiv-connect/src/translate/types.rs, crates/krishiv-connect/src/translate/types/tests.rs, crates/krishiv-connect/src/translate/expression/tests.rs, crates/krishiv-connect/tests/goldens.rs
Arrow and Spark Connect conversion now handle nested arrays, maps, and structs. Arrow conversion preserves field nullability and Spark metadata; Spark Connect tests cover recursive conversion and invalid inputs.
Nested coercion and set operations crates/krishiv-plan/src/analyzer/coerce.rs, crates/krishiv-plan/src/analyzer/setops.rs, crates/krishiv-testkit/tests/type_coercion.rs
Common-type resolution and cast validation now recurse through nested types. UNION merges structurally matching nested types while combining nullability.
Nested types in plan encoding and negotiation proto/krishiv/plan/v1/plan.proto, crates/krishiv-plan-codec/src/proto/krishiv.plan.v1.pb.rs, crates/krishiv-plan-codec/src/{encode,decode,features}.rs, crates/krishiv-plan-codec/tests/*
The plan schema adds a nested kind and JSON field. The codec encodes and validates nested types, and feature discovery and tests cover nested_types negotiation and reader compatibility.
KrishivAI/krishiv-engine#195: Adds corresponding Python support for Spark-compatible nested types, JSON, and DDL.
KrishivAI/krishiv-engine#193: Defines the named plan-feature and reader-compatibility mechanism used by nested-type negotiation.
KrishivAI/krishiv-engine#178: Adds Python array and map models and recursive comparisons related to the nested type model.
Merge Risk: Low · up to 6e8c2
Nested array, map and struct support is broadly sound. A few edge cases remain: a tagged Arrow field can exceed the nesting limit, casts from void elements inside nested types are rejected, UNION may add unneeded casts, and non-ASCII field names compare case-sensitively. These are narrow and can be fixed with small follow-ups.
Security architecture risk: Moderate · up to 6e8c2
Nested types introduce a meaningful compatibility and input-validation boundary. Tagged Arrow fields can restart the nesting limit during validation, allowing recursive descent beyond the intended bound before malformed input is rejected. This creates a potential process-availability risk, although remote exploitability has not been established. Plan readers retain explicit compatibility checks.
Retained concerns
Medium · security · inferred: Tagged Arrow storage validation does not preserve the accumulated nesting budget. A chain of List children tagged array repeatedly enters is_valid_storage and restarts from_arrow at depth zero. Even incompatible input is traversed recursively before the mismatch returns, bypassing the intended rejection-depth bound and creating potential stack exhaustion wherever adversarial schemas reach conversion. Compatible tagged subtrees can also be accepted beyond the documented logical depth. The recursive validation path is introduced by this PR; remote exploitability and upstream schema limits remain unverified.
Security review details
Security Blast Radius
inferred — The identified availability risk requires control over nested Arrow layouts and their type-tag metadata. The potentially affected boundary is the process performing schema conversion, including file inference or supplied-schema registration. Evidence does not establish unauthenticated remote access, cross-tenant exposure, or the number of services sharing that process.
Security Findings and Attack Paths
inferred — A crafted tagged List chain follows from_arrow_at to from_arrow_field_at, then is_valid_storage, then the zero-depth from_arrow entrypoint. Repeating that chain delays incompatibility rejection until after recursive descent through the supplied depth. This establishes unbounded validation recursion statically, but not a reproduced remote denial of service.
Trust Boundaries and Controls
observed — Arrow type tags are parsed and checked for storage compatibility rather than accepted without validation. However, that compatibility check restarts recursive conversion without the caller's consumed depth, so eventual rejection does not itself contain stack use.
Resilience and Maintainability Implications
observed — Existing tests cover canonical Arrow acceptance at depth 32, rejection at depth 33, and very deep textual or malformed JSON input. That evidence does not exercise the repeated tagged-field rejection path. Separately, plan encoding rejects logical types exceeding its measured depth limit, providing downstream containment rather than protection for earlier Arrow validation.
Hardening Proposals
proposed — Preserve one accumulated nesting budget through tagged storage validation and enforce it before recursive descent, including for inputs that ultimately fail compatibility. Check returned tagged types against the remaining budget and cover repeated tagged fields with adversarial regression cases.
Pre-merge checks: 3 · 2
Failed checks: 2 warnings
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 69.34% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 137 functions across 26 files. (4 skipped…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Description check
The description provides a detailed summary, scope, Spark compatibility notes, and test coverage. It does not follow the required template fully: it uses ## Tests instead of ## Tests run, provides…
Rename the tests section to ## Tests run and include the exact commands executed with their result lines. Add the required ## Checklist and complete every applicable item, including DCO and Conventional Commits, SPDX, test and documenta…
Passed checks: 3 passed
Check name
Status
Explanation
Title check
The title clearly and concisely identifies the main change: adding array, map, and struct types for the specified task.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Docstring Coverage
Explanation
Docstring coverage is 69.34% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 137 functions across 26 files. (4 skipped: 4 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Full details: Description check
Explanation
The description provides a detailed summary, scope, Spark compatibility notes, and test coverage. It does not follow the required template fully: it uses ## Tests instead of ## Tests run, provides no test commands or result lines, and omits the complete checklist, including dependency and performance review items.
Resolution
Rename the tests section to ## Tests run and include the exact commands executed with their result lines. Add the required ## Checklist and complete every applicable item, including DCO and Conventional Commits, SPDX, test and documentation status, safety and error rules, Spark-observable behavior, distributed execution readiness, dependency review for the changed Cargo.toml, and performance review items. Mark non-applicable items explicitly and link the task, spec chapter, and ADRs in the Summary section, including ADR 0040 and any applicable dependency or performance ADRs.
Finishing Touches: 💡 1
⚔️ Resolve merge conflicts: 💡
Resolve merge conflict in branch p2b/t001b-nested-types
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Actionable comments posted: 4
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Inline comments:
Review comments at @crates/krishiv-plan/src/analyzer/coerce.rs:
- Around line 942-958: Update `nested_cast` to accept `DataType::Null` as a
source before applying its normal cast checks, so void elements can cast to any
target type; add `array<void>` coverage to `nested_casts_follow_spark` without
relying on the unregistered `array()` SQL function.
Review comments at @crates/krishiv-plan/src/analyzer/setops.rs:
- Around line 124-137: Update both DataType comparisons in cast_columns to use
structural equality with metadata ignored, so inputs differing only in nested
field names or nullability skip unnecessary casts and projections; leave the
existing cast behavior for structurally different types unchanged.
Review comments at @crates/krishiv-types/src/arrow.rs:
- Line 240: Update from_arrow_field_at to validate a parsed type tag from
SPARK_TYPE_METADATA_KEY against the current nesting depth before returning it,
rather than validating from depth zero, so tagged fields cannot exceed
DataType::MAX_NESTING_DEPTH.
Review comments at @crates/krishiv-types/src/nested.rs:
- Around line 215-223: Update the field-name comparisons used by Loose in
nested.rs and by the corresponding logic in coerce.rs to match Java
String.equalsIgnoreCase semantics over UTF-16 code units; do not rely on
eq_ignore_ascii_case or a chars()-based helper, and preserve the existing
case-sensitive and ignored-name behavior.
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…#209)
- UNION: inputs whose nested types differ only in field names or
nullability are cast to the union's type, so every branch reaches the
operator with the same physical type.
- Casts with arrays, maps and structs now run: element, key/value and
field casts by position (values under NULL rows are never cast), and
to STRING as [1, null], {k -> v}, {1, null}. A cast that only renames
or relaxes nullability relabels the data.
- Only atomic types become strings implicitly: upper/length/LIKE over a
nested argument raise UNEXPECTED_INPUT_TYPE; concat over arrays is the
array type, and refuses maps, structs and arrays mixed with strings.
- Comparisons and IN take nested operands that differ only in field
names or nullability as they are, with no cast.
- Maps do not order: =, <, <=>, IN and ORDER BY raise
INVALID_ORDERING_TYPE; DISTINCT, UNION, INTERSECT and EXCEPT over a map
raise SET_OPERATION_ON_MAP_TYPE; GROUP BY a map still resolves.
- TRY_CAST between nested types is nullable only unless Spark's
canUpCast holds element by element.
- Struct fields meet by spark.sql.caseSensitive, with Java's
case-insensitive comparison beyond ASCII.
- Plan codec: the decoder accepts only the canonical JSON of a nested
type, and the encoder checks the nested types it writes against the
header. A metadata key-order test pins the JSON form.
Signed-off-by: techysneha14 <343708768+shehatathe@users.noreply.github.com>
System calls cannot be simulated consistently and are left out of the measure. The reported value understates the real cost. Switch to the Walltime instrument to measure them.
Adapt the non-ANSI inline-table type widening to the nested-type rules, regenerate the registry-matrix manifest, expect the analysis error for a string-to-array try_cast, and give spill session ids more attempts under concurrent starts.
Signed-off-by: techysneha14 <343708768+shehatathe@users.noreply.github.com>
…not reclaim it
A start created its lock file under the final name and locked it afterwards;
a concurrent start's stale-session scan could lock and remove it in between,
and under load a start lost that race on every attempt. The lock is now
created under a name the scan ignores, locked, then published by a hard link.
Dead unpublished locks are cleaned up by the next start.
Also expect the NAMED_STRUCT error in the session-variable replay now that
the STRUCT type parses.
Signed-off-by: techysneha14 <343708768+shehatathe@users.noreply.github.com>
… plan: one equalsIgnoreCase
A nested type that only appears in an Arrow field the plan carries as IPC
bytes (an aggregate argument, a subquery value, a runtime-filter key, a
window offset value, the output schema) now lists the feature, so an older
reader refuses the plan by name instead of failing later. Analyzer name
matching uses the same Java-compatible equalsIgnoreCase as struct field
names. A session test pins that struct comparison ignores field names under
both spark.sql.caseSensitive settings.
Signed-off-by: techysneha14 <343708768+shehatathe@users.noreply.github.com>
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Summary
P2-T001b, part 1 of 2: Spark's
ArrayType,MapTypeandStructTypeinkrishiv-types::DataType, carried through every layer a type crosses.Renderings.
simpleString,catalogString,sql,typeNameandjsonas Spark 4.2.0 prints them: struct field names quoted only insql,NOT NULLandCOMMENT '...'insql, at most 25 fields insimpleString.DataType::from_jsonreads Spark's JSON form.Type strings.
ARRAY<t>,MAP<k, v>andSTRUCT<name [:] t [NOT NULL] [COMMENT '...'], ...>(includingSTRUCT<>), plus comment string literals with Spark's unescaping. Errors follow Spark's parser:<;>>is one token outside a complex type);ARRAY,MAPorSTRUCTisINCOMPLETE_TYPE_DEFINITION.*.The SQL frontend's type display now keeps field comments.
Arrow. Arrays map to
List(and read back fromLargeListtoo), maps toMapwith Spark'sArrowUtilsfield names and non-null keys, structs toStruct. Each child field carries its own type tag, and struct field metadata is stored inkrishiv.spark_metadata.Nesting limit. Types that come from outside (type strings, Arrow, JSON, Connect, plans) may nest at most
DataType::MAX_NESTING_DEPTH(32) levels. Deeper input fails withFAILED_TO_PARSE_TOO_COMPLEX, never with a stack overflow. Spark accepts deeper types; this limit is the one documented difference.Analyzer.
findTypeForComplex) in conditionals and set operations, and by the tightest common type in comparisons.Cast.canCast/canAnsiCast: a string to a nested type is refused, and ANSI suggeststry_cast.Plan codec. A new
DataType.KIND_NESTEDcarries the type's Spark JSON. It sits behind a new semantic featurenested_types(ADR 0040, legacy level 6), so a reader without the feature refuses the plan and names the feature. Adds the goldentests/golden/nested_types/. Existing goldens are unchanged.Spark Connect. Array, map and struct data types translate, including field metadata JSON, in the shape pyspark-client 4.2 sends them.
Every expectation comes from Spark 4.2.0:
tests/scripts/gen_type_strings.pyfrom a Spark 4.2.0 container.Tests
krishiv-types:json,from_jsonand whether thesimpleStringreparses;krishiv-plan: nested coercion (ANSI on and off) and nested casts.krishiv-plan-codec:Corrupt;krishiv-connect: the pyspark-client 4.2 shape, unset child types, bad metadata and the depth limit.krishiv-sql: comments survive the type display. The SQL parser places three struct-field syntax errors elsewhere than Spark; they are listed in the probe test.type-coerciondrops 3 entries (2,638 of 2,858 statements now resolve as Spark resolves them), andregistry-matrixdrops 8.Follow-up commits (review)
concatover arrays has the array type.INVALID_ORDERING_TYPE,SET_OPERATION_ON_MAP_TYPE);=/INtake same-shape nested types without a cast.canUpCast; struct field names followspark.sql.caseSensitiveand Java'sequalsIgnoreCase.crates/krishiv-session/tests/nested_types.rs.Not in this PR
array,map,named_struct, ...); comparing nested values at run time; Python value conversion. These belong to the array, map and struct function tasks.greatest/least/min/maxover maps still resolve (Spark:INVALID_ORDERING_TYPE).CHAR/VARCHARare part 2.