scenario_id string | title string | category string | description string | notes list | tags list | row_identity list | version_count int64 | version_ids list | row_counts list | mutation_count int64 | mutation_kinds list | paths list | value_change_relations list | schema_changed bool | values_changed bool | parquet_schema_changed bool | base_file string | final_file string | base_schema_json string | final_schema_json string | transitions_json string | base_fingerprint string | final_fingerprint string | manifest_path string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
add_all_null_column | Add a typed all-null column, then backfill it | additive | v1 adds a string column in which every value is null. v2 backfills it without any schema change. The column is typed string in both v1 and v2; only its values differ. | [
"The v1 column is declared string, not the Arrow null type. A column that is inferred as the null type from all-null data is a different case: see null_type_to_string."
] | [
"add-field",
"all-null",
"backfill",
"multi-version"
] | [
"id"
] | 3 | [
"v0",
"v1",
"v2"
] | [
6,
6,
6
] | 1 | [
"add_field"
] | [
"referrer"
] | [
"nulls_filled"
] | true | true | true | fixtures/add_all_null_column/v0.parquet | fixtures/add_all_null_column/v2.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"},{"name":"referrer","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"add_field","nullable":true,"path":"referrer","type":"string"}],"to":"v1"},{"from":"v1"... | sha256:ffbd13144dd0c4b932b54b17badd51ef9e9209a5e577a165cab231d0fed9b6d2 | sha256:8765da5f8f45127f829ad9538ef86cfee69ec7e05752d7af32209e19b15f51c2 | fixtures/add_all_null_column/scenario.json |
add_field_in_list_of_struct | Add a field to the structs inside a list | structural | Each element of a list<struct> column gains a nullable float64 field discount. Rows include a null list, an empty list and a list with a null element. Existing element values are unchanged. | [] | [
"list",
"struct",
"nested",
"add-field",
"nested-nulls"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
5,
5
] | 1 | [
"add_field"
] | [
"line_items[].discount"
] | [] | true | false | true | fixtures/add_field_in_list_of_struct/v0.parquet | fixtures/add_field_in_list_of_struct/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"line_items","nullable":true,"type":"list<struct<sku: string, qty: int32>>"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"line_items","nullable":true,"type":"list<struct<sku: string, qty: int32, discount: double>>"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"add_field","nullable":true,"path":"line_items[].discount","type":"double"}],"to":"v1"}... | sha256:70cac456c966e83ece8cbfa7e878625a2f2551db19687cf90590f2c2439e9e08 | sha256:529bf08d7640137b7763e571a81bc2f48871c8d089443f3b4f8f5fe8a0f1e564 | fixtures/add_field_in_list_of_struct/scenario.json |
add_nested_field | Add a field inside a struct | structural | A nullable string field postcode is appended to the address struct. Rows include a null struct, a struct whose street is null, and a struct of empty strings; a null struct stays null. Existing nested values are unchanged. | [] | [
"struct",
"nested",
"add-field",
"nested-nulls"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
5,
5
] | 1 | [
"add_field"
] | [
"address.postcode"
] | [] | true | false | true | fixtures/add_nested_field/v0.parquet | fixtures/add_nested_field/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"address","nullable":true,"type":"struct<street: string, city: string>"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"address","nullable":true,"type":"struct<street: string, city: string, postcode: string>"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"add_field","nullable":true,"path":"address.postcode","type":"string"}],"to":"v1"}] | sha256:fe2fd9399e594ce5ae78b4ba68f7dac7d891cef4ef9ab4eafc2742d1b7eb6cfc | sha256:ffc0803e00bf07b7da8ccb4f60df1121b9f160cf58c79b6a505b5450a2aeef28 | fixtures/add_nested_field/scenario.json |
add_nullable_int64_column | Add nullable int64 column | additive | A nullable int64 column is appended. Its values include zero, a negative value, nulls and both int64 extremes. Existing columns and rows are unchanged. | [] | [
"add-field",
"integer",
"nullable",
"numeric-extremes"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"add_field"
] | [
"loyalty_points"
] | [] | true | false | true | fixtures/add_nullable_int64_column/v0.parquet | fixtures/add_nullable_int64_column/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"},{"name":"loyalty_points","nullable":true,"type":"int64"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"add_field","nullable":true,"path":"loyalty_points","type":"int64"}],"to":"v1"}] | sha256:ffbd13144dd0c4b932b54b17badd51ef9e9209a5e577a165cab231d0fed9b6d2 | sha256:0e2387711427d5d742df3b176e5b6190ad79df5438382c28e06e45ee9ff1233e | fixtures/add_nullable_int64_column/scenario.json |
add_nullable_string_column | Add nullable string column | additive | A nullable string column is appended. Every existing column, value and row is unchanged. The new column holds both nulls and empty strings, which are different values. | [] | [
"add-field",
"string",
"nullable"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"add_field"
] | [
"customer_segment"
] | [] | true | false | true | fixtures/add_nullable_string_column/v0.parquet | fixtures/add_nullable_string_column/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"},{"name":"customer_segment","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"add_field","nullable":true,"path":"customer_segment","type":"string"}],"to":"v1"}] | sha256:ffbd13144dd0c4b932b54b17badd51ef9e9209a5e577a165cab231d0fed9b6d2 | sha256:3d0adf61168f53d1f9fcfbc45c5b056f18a1acdbfd57826e5dd6d970568ca47c | fixtures/add_nullable_string_column/scenario.json |
add_required_column | Add non-nullable populated column | additive | A non-nullable string column is appended, with a value in every v1 row. v0 has no such column, so the v0 rows have no value for it. Existing columns and rows are unchanged. | [
"Read by column name together with v0, the v0 rows have no value for a column that v1 declares non-nullable. See remove_required_column for the opposite direction."
] | [
"add-field",
"string",
"non-nullable"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"add_field"
] | [
"country_code"
] | [] | true | false | true | fixtures/add_required_column/v0.parquet | fixtures/add_required_column/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"},{"name":"country_code","nullable":false,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"add_field","nullable":false,"path":"country_code","type":"string"}],"to":"v1"}] | sha256:ffbd13144dd0c4b932b54b17badd51ef9e9209a5e577a165cab231d0fed9b6d2 | sha256:e9c57949b192096eb9a46534c80fabe05061656018bccc1fc10bb57ae82616bb | fixtures/add_required_column/scenario.json |
column_becomes_all_null | Populated column becomes entirely null | nullability | Every value of a nullable float64 column is null in v1. The schema is unchanged. v0 contains NaN, -0.0 and infinity, none of which is a null. | [
"v0 has exactly one null. Systems that count NaN as missing report two; that is a policy of those systems, not a property of the file."
] | [
"nullable",
"all-null",
"no-schema-change",
"nan",
"infinity"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 0 | [] | [
"discount"
] | [
"nulled"
] | false | true | false | fixtures/column_becomes_all_null/v0.parquet | fixtures/column_becomes_all_null/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"discount","nullable":true,"type":"double"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"discount","nullable":true,"type":"double"}] | [{"from":"v0","invariants":{"common_values_preserved":false,"parquet_schema_preserved":true,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[{"path":"discount","relation":"nulled"}]},"mutations":[],"to":"v1"}] | sha256:3ef9076f485c18b6a67b93cbbba562eb3554c37b29f734567bf15bd45da5d1ba | sha256:3977f21592cfa279f263a62b343471ef4a20347ffe6a6001cfab216f43d39751 | fixtures/column_becomes_all_null/scenario.json |
decimal_precision_increase | Increase decimal precision | numeric | A decimal128(9, 2) column becomes decimal128(18, 2). Scale is unchanged. Values include both extremes of the old precision and are unchanged. | [
"The Parquet fixed_len_byte_array width changes with the precision (4 to 8 bytes)."
] | [
"change-type",
"decimal",
"widening",
"numeric-extremes"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_type"
] | [
"price"
] | [] | true | false | true | fixtures/decimal_precision_increase/v0.parquet | fixtures/decimal_precision_increase/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"price","nullable":true,"type":"decimal128(9, 2)"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"price","nullable":true,"type":"decimal128(18, 2)"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"decimal128(18, 2)","old_type":"decimal128(9, 2)","path":"pric... | sha256:82d7691f28b4dad15f955c446e3a3dde7ead4c38b122f5fdce33ccd26632f258 | sha256:c7fe98a1141c4d570b4c786a20f29140f577dcc658ff87544952742adde45e7c | fixtures/decimal_precision_increase/scenario.json |
decimal_scale_increase | Increase decimal scale at fixed precision | numeric | A decimal128(9, 2) column becomes decimal128(9, 4). Precision is unchanged, so the largest representable magnitude shrinks from 9999999.99 to 99999.9999. Every v0 value still fits and every value is numerically unchanged (1.20 and 1.2000 are the same number). | [
"The type change narrows the value range while widening the scale; a v0 value such as 100000.00 would not fit. None of the fixture values are affected."
] | [
"change-type",
"decimal",
"scale"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_type"
] | [
"price"
] | [] | true | false | true | fixtures/decimal_scale_increase/v0.parquet | fixtures/decimal_scale_increase/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"price","nullable":true,"type":"decimal128(9, 2)"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"price","nullable":true,"type":"decimal128(9, 4)"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"decimal128(9, 4)","old_type":"decimal128(9, 2)","path":"price... | sha256:6704cd50d57650f1d356aea7ab387d2b9b563c9981aaeba7796306ca527e6bd1 | sha256:a79e0ee3fdef5f5be10b5e383e6270e8878eee342b012bdefdd6d4608cb4beca | fixtures/decimal_scale_increase/scenario.json |
dictionary_reencoded | Dictionary contents change without any value change | representation | Both versions store the same dictionary<int8, string> column with the same decoded values. The stored dictionaries differ: v0 has a different order and an entry that no row uses; v1 has exactly the used values. Schema, rows and values are unchanged, so both versions have the same logical fingerprint. | [
"Tools that compare dictionary indices, category lists or category codes observe a change here. The decoded values do not change.",
"The unused dictionary entry survives a PyArrow round trip; other readers may rebuild the dictionary on read and drop it."
] | [
"dictionary",
"categorical",
"encoding",
"no-schema-change",
"no-value-change"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 0 | [] | [] | [] | false | false | false | fixtures/dictionary_reencoded/v0.parquet | fixtures/dictionary_reencoded/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"status","nullable":true,"type":"dictionary<values=string, indices=int8, ordered=0>"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"status","nullable":true,"type":"dictionary<values=string, indices=int8, ordered=0>"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":true,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[],"to":"v1"}] | sha256:6dfdbf0a9de0eac2a01b3d7a88cb06abe18009dfa4d033259c14f39d7bf16d9b | sha256:6dfdbf0a9de0eac2a01b3d7a88cb06abe18009dfa4d033259c14f39d7bf16d9b | fixtures/dictionary_reencoded/scenario.json |
empty_then_populated | Empty typed file followed by a populated file | edge | v0 has a fully typed schema and zero rows, the Parquet counterpart of a header-only CSV. v1 has the identical schema and five rows whose values include NaN, both infinities, -0.0, an empty string, whitespace, the literal text NULL and a timestamp before the epoch. | [
"The row and value invariants quantify over the rows of v0, of which there are none, so rows_retained and common_values_preserved are vacuously true.",
"v0 carries complete type information in its schema and none in its values, because it has no values."
] | [
"empty",
"zero-rows",
"no-schema-change",
"nan",
"infinity",
"unicode"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
0,
5
] | 0 | [] | [] | [] | false | false | false | fixtures/empty_then_populated/v0.parquet | fixtures/empty_then_populated/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"label","nullable":true,"type":"string"},{"name":"amount","nullable":true,"type":"double"},{"name":"observed_at","nullable":true,"type":"timestamp[us, tz=UTC]"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"label","nullable":true,"type":"string"},{"name":"amount","nullable":true,"type":"double"},{"name":"observed_at","nullable":true,"type":"timestamp[us, tz=UTC]"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":true,"paths_retained":true,"row_count_preserved":false,"row_identity_preserved":false,"rows_retained":true,"value_changes":[]},"mutations":[],"to":"v1"}] | sha256:6ec61b2ba1c3d3822c5a21ae88514cfe9517ae7c1b24b0a1d7d51444304ae6a6 | sha256:4eef0003cb203518f0e771ef70c3610043e79d5524c06755bd5fe4d7e65c8eef | fixtures/empty_then_populated/scenario.json |
float64_to_int64_fractional | float64 to int64 with fractional values | numeric | A float64 column becomes int64. v0 holds fractional values; each v1 value is the v0 value truncated toward zero. The values distinguish truncation from rounding: 1.5 -> 1, 2.999 -> 2, -1.5 -> -1. | [
"Truncation is what this producer did. It is not a statement of how a cast ought to behave; rounding, rejection and nulling are all observed in practice.",
"NaN and infinities are deliberately absent: they have no int64 value, and including them would mix a second change into this scenario."
] | [
"change-type",
"floating-point",
"integer",
"narrowing",
"precision-loss"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
7,
7
] | 1 | [
"change_type"
] | [
"amount"
] | [
"truncated_toward_zero"
] | true | true | true | fixtures/float64_to_int64_fractional/v0.parquet | fixtures/float64_to_int64_fractional/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"amount","nullable":true,"type":"double"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"amount","nullable":true,"type":"int64"}] | [{"from":"v0","invariants":{"common_values_preserved":false,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[{"path":"amount","relation":"truncated_toward_zero"}]},"mutations":[{"kind":"change_type","new_type":"int64","... | sha256:166fb5770e018ab4f698312d8933ddc3e37b5ff37ed5a9a4529ebfda3fc380ff | sha256:15ee369c451d021b8d3ef93df90df199f0ac31de38f407a92e1e54e70f13697e | fixtures/float64_to_int64_fractional/scenario.json |
float64_to_int64_integral | float64 to int64, integral values only | numeric | A float64 column becomes int64. Every non-null v0 value is integral and within range, so every value is unchanged. v0 contains -0.0, which equals 0 as a number. | [] | [
"change-type",
"floating-point",
"integer",
"narrowing"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
7,
7
] | 1 | [
"change_type"
] | [
"amount"
] | [] | true | false | true | fixtures/float64_to_int64_integral/v0.parquet | fixtures/float64_to_int64_integral/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"amount","nullable":true,"type":"double"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"amount","nullable":true,"type":"int64"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"int64","old_type":"double","path":"amount"}],"to":"v1"}] | sha256:a43ff1cbf7ce56cf5187a8115795f262f1be1e1569d3dec88e0d81bbd44afb29 | sha256:d43070493146acfed94ca61906e05e2822ce6ce3bcc585525ef383b2444a3528 | fixtures/float64_to_int64_integral/scenario.json |
int32_to_decimal | int32 to decimal with every value unchanged | numeric | An int32 column becomes decimal128(12, 2), the smallest precision at scale 2 that holds every int32. Values, including both int32 extremes, are numerically unchanged (7 and 7.00 are the same number). | [
"The type moves across numeric families, from a binary integer to a scaled decimal, so it is not a width change within one family like int32 to int64.",
"At the Parquet level the physical type changes from INT32 to FIXED_LEN_BYTE_ARRAY."
] | [
"change-type",
"integer",
"decimal",
"widening",
"numeric-extremes"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_type"
] | [
"quantity"
] | [] | true | false | true | fixtures/int32_to_decimal/v0.parquet | fixtures/int32_to_decimal/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"quantity","nullable":true,"type":"int32"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"quantity","nullable":true,"type":"decimal128(12, 2)"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"decimal128(12, 2)","old_type":"int32","path":"quantity"}],"to... | sha256:f96a0bbd72e6740792655d8e4c12117ebf1eed30a4c1d7509713de59322fb06f | sha256:d4a15d122b73125afebe1ab2d80efe7df252a9bb07450c17a255d2826c46bca8 | fixtures/int32_to_decimal/scenario.json |
int64_to_float64 | int64 to float64 with values beyond 2^53 | numeric | An int64 column becomes float64. Each v1 value is the nearest float64 to the v0 value. 2^53 and -2^63 are exactly representable and unchanged; 2^53 + 1 and the int64 maximum are not, so those two values change. | [
"The v1 value for the int64 maximum is 2^63, which is outside the int64 range: casting it back to int64 overflows."
] | [
"change-type",
"integer",
"floating-point",
"precision-loss",
"numeric-extremes"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
7,
7
] | 1 | [
"change_type"
] | [
"measurement"
] | [
"nearest_float64"
] | true | true | true | fixtures/int64_to_float64/v0.parquet | fixtures/int64_to_float64/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"measurement","nullable":true,"type":"int64"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"measurement","nullable":true,"type":"double"}] | [{"from":"v0","invariants":{"common_values_preserved":false,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[{"path":"measurement","relation":"nearest_float64"}]},"mutations":[{"kind":"change_type","new_type":"double","... | sha256:86f0e7ca2a815f9a6c7111edd0e533087dd00f2350da1d27a374c3f8e0e4aad0 | sha256:07854510d0a27be81850e37bb14a963855c9cf2c7aa4faa08313bd11bf33375d | fixtures/int64_to_float64/scenario.json |
list_element_widen | List elements widen from int32 to int64 | structural | The element type of a list column changes from int32 to int64. Rows include a null list, an empty list and a list holding a null element, which are three different values. Every element value, including both int32 extremes, is unchanged. | [
"The element field name is not part of the corpus type: this writer stores a list written as list<item: ...> under the Parquet name 'element', and reads it back that way."
] | [
"list",
"nested",
"change-type",
"integer",
"widening",
"nested-nulls"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
5,
5
] | 1 | [
"change_type"
] | [
"scores[]"
] | [] | true | false | true | fixtures/list_element_widen/v0.parquet | fixtures/list_element_widen/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"scores","nullable":true,"type":"list<int32>"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"scores","nullable":true,"type":"list<int64>"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"int64","old_type":"int32","path":"scores[]"}],"to":"v1"}] | sha256:b65cc8b84c69eed5f76eb35249b02c418df4cdfdff1d3b284cf3a0a0f0183715 | sha256:509adc4dbda4efbb933f47fdb7ad6301b9ccf0990cfc338bc8921803fbb8fb81 | fixtures/list_element_widen/scenario.json |
map_value_widen | Map values widen from int32 to int64 | structural | The value type of a map<string, int32> column becomes int64. Rows include a null map, an empty map, an empty-string key and a null value. Keys, values and entry order are unchanged. | [
"Map paths treat a map as a list of key/value entries, which is how Arrow and Parquet store it: the changed path is attributes[].value."
] | [
"map",
"nested",
"change-type",
"integer",
"widening"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
5,
5
] | 1 | [
"change_type"
] | [
"attributes[].value"
] | [] | true | false | true | fixtures/map_value_widen/v0.parquet | fixtures/map_value_widen/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"attributes","nullable":true,"type":"map<string, int32>"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"attributes","nullable":true,"type":"map<string, int64>"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"int64","old_type":"int32","path":"attributes[].value"}],"to":... | sha256:a4038108aaff3ba58f20879328c2eea4c3fc61ae1bda122ac33457e4f3c58343 | sha256:67035ce47c8b7d7b13b868f9578a6c395bec92890ded44aa1d9153c58248cdba | fixtures/map_value_widen/scenario.json |
nested_field_type_change | Nested fields widen from float32 to float64 | structural | Both non-nullable fields of a nullable position struct change from float32 to float64. Every float32 value is exactly representable as float64 and is unchanged, which means the v1 value of 0.1 is 0.10000000149011612, not the float64 nearest to 0.1. | [
"One row has a null struct whose children are declared non-nullable: the children have no value in that row because their parent is null."
] | [
"struct",
"nested",
"change-type",
"floating-point",
"widening",
"nested-nulls"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
5,
5
] | 2 | [
"change_type"
] | [
"position.lat",
"position.lon"
] | [] | true | false | true | fixtures/nested_field_type_change/v0.parquet | fixtures/nested_field_type_change/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"position","nullable":true,"type":"struct<lat: float not null, lon: float not null>"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"position","nullable":true,"type":"struct<lat: double not null, lon: double not null>"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"double","old_type":"float","path":"position.lat"},{"kind":"ch... | sha256:dcc1dfd854bf8926e56ca83cbd26ffe20d144fd6f98b468142a510c8f116b2ec | sha256:e189ef799a61251126841ba16c3433a2da8673c0f4d37b546b0bafa62c294967 | fixtures/nested_field_type_change/scenario.json |
null_type_to_string | Null-typed column becomes a string column | nullability | In v0 a column has the Arrow null type: it carries no values and no value type, which is what type inference produces from a batch where the column is always null. In v1 the same column is a string column with values. | [
"Parquet has no null type. The writer stores it as an optional INT32 column with the Null logical annotation, so the Parquet physical type changes as well (INT32 -> BYTE_ARRAY)."
] | [
"change-type",
"null-type",
"type-inference",
"all-null"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
5,
5
] | 1 | [
"change_type"
] | [
"comment"
] | [
"nulls_filled"
] | true | true | true | fixtures/null_type_to_string/v0.parquet | fixtures/null_type_to_string/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"comment","nullable":true,"type":"null"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"comment","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":false,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[{"path":"comment","relation":"nulls_filled"}]},"mutations":[{"kind":"change_type","new_type":"string","old_typ... | sha256:f4a5b5c1c1abb48a519b69e78cb14b2f17640c37877d9a724085108e7d2108e6 | sha256:a73a821a2a5be6d2a0a270b14ebd3099a31cda3ea50df2c8961171d43cbf1e40 | fixtures/null_type_to_string/scenario.json |
nullable_column_backfilled | Nullable column loses its nulls; schema stays nullable | nullability | Every null in a nullable string column is filled in v1. The schema is identical in both versions: the field is still nullable although v1 contains no nulls. The empty string in v0 is a value, not a null, and is unchanged. | [
"Observing zero nulls in a batch says nothing about the declared nullability. Both files declare the column optional at the Arrow and Parquet levels."
] | [
"nullable",
"backfill",
"no-schema-change",
"empty-string"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 0 | [] | [
"email"
] | [
"nulls_filled"
] | false | true | false | fixtures/nullable_column_backfilled/v0.parquet | fixtures/nullable_column_backfilled/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"email","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"email","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":false,"parquet_schema_preserved":true,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[{"path":"email","relation":"nulls_filled"}]},"mutations":[],"to":"v1"}] | sha256:7395491df667747e7bc840a44a8a9f7dd8341bde2e6625a32da0c6a7edd3ece8 | sha256:04e5ebdbdf9144bc4866eaf5926038a0fc423b8e21d2c6f3e31bc6c4c1cf4b82 | fixtures/nullable_column_backfilled/scenario.json |
nullable_to_required | Nullable column becomes non-nullable | nullability | A nullable string column that happens to contain no nulls is declared non-nullable in v1. The data is identical; only the declaration changes (Parquet repetition optional -> required). | [] | [
"change-nullability",
"string",
"no-value-change"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_nullability"
] | [
"sku"
] | [] | true | false | true | fixtures/nullable_to_required/v0.parquet | fixtures/nullable_to_required/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"sku","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"sku","nullable":false,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_nullability","new_nullable":false,"old_nullable":true,"path":"sku"}],"to":"v1"}... | sha256:96301221808db936bc4757779a58856de486759ecc539da7c2763b6ffb1ff9cc | sha256:70f5dd82fa712a344d629f8e1333db830ebf3b3092eb9c405c9b26ccff09ec1c | fixtures/nullable_to_required/scenario.json |
remove_nested_field | Remove a field from inside a struct | structural | The postcode field is removed from the address struct. Rows include a null struct, a struct whose street is null, and a struct of empty strings. Remaining nested values are unchanged. | [] | [
"struct",
"nested",
"remove-field",
"nested-nulls"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
5,
5
] | 1 | [
"remove_field"
] | [
"address.postcode"
] | [] | true | false | true | fixtures/remove_nested_field/v0.parquet | fixtures/remove_nested_field/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"address","nullable":true,"type":"struct<street: string, city: string, postcode: string>"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"address","nullable":true,"type":"struct<street: string, city: string>"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":false,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"remove_field","nullable":true,"path":"address.postcode","type":"string"}],"to":"v1"}] | sha256:ffc0803e00bf07b7da8ccb4f60df1121b9f160cf58c79b6a505b5450a2aeef28 | sha256:fe2fd9399e594ce5ae78b4ba68f7dac7d891cef4ef9ab4eafc2742d1b7eb6cfc | fixtures/remove_nested_field/scenario.json |
remove_nullable_column | Remove nullable column | subtractive | A nullable string column that held nulls, an empty string and values is removed. The remaining columns and all rows are unchanged. | [] | [
"remove-field",
"string",
"nullable"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"remove_field"
] | [
"middle_name"
] | [] | true | false | true | fixtures/remove_nullable_column/v0.parquet | fixtures/remove_nullable_column/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"},{"name":"middle_name","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":false,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"remove_field","nullable":true,"path":"middle_name","type":"string"}],"to":"v1"}] | sha256:f90aa90d7b9cd185f9473d9a41283a10cab10e82dc9519f22f3c9633de7d28d6 | sha256:ffbd13144dd0c4b932b54b17badd51ef9e9209a5e577a165cab231d0fed9b6d2 | fixtures/remove_nullable_column/scenario.json |
remove_required_column | Remove non-nullable populated column | subtractive | A non-nullable string column with a value in every row is removed. The remaining columns and all rows are unchanged. | [] | [
"remove-field",
"string",
"required"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"remove_field"
] | [
"country_code"
] | [] | true | false | true | fixtures/remove_required_column/v0.parquet | fixtures/remove_required_column/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"},{"name":"country_code","nullable":false,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"name","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":false,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"remove_field","nullable":false,"path":"country_code","type":"string"}],"to":"v1"}] | sha256:e9c57949b192096eb9a46534c80fabe05061656018bccc1fc10bb57ae82616bb | sha256:ffbd13144dd0c4b932b54b17badd51ef9e9209a5e577a165cab231d0fed9b6d2 | fixtures/remove_required_column/scenario.json |
rename_column_with_field_id | Rename column, identified by Parquet field id | structural | Column full_name is renamed to display_name. Both files carry Parquet field ids and the renamed column keeps field id 2, which is the evidence that this is a rename. Values are unchanged; they include 'Jose' with a composed and with a decomposed accent, which are different values. | [
"Without field ids a rename is physically indistinguishable from removing one column and adding another with the same values. The corpus only records a rename when the file carries evidence for it."
] | [
"rename-field",
"field-id",
"unicode"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"rename_field"
] | [
"display_name",
"full_name"
] | [] | true | false | true | fixtures/rename_column_with_field_id/v0.parquet | fixtures/rename_column_with_field_id/v1.parquet | [{"field_id":1,"name":"id","nullable":false,"type":"int64"},{"field_id":2,"name":"full_name","nullable":true,"type":"string"},{"field_id":3,"name":"email","nullable":true,"type":"string"}] | [{"field_id":1,"name":"id","nullable":false,"type":"int64"},{"field_id":2,"name":"display_name","nullable":true,"type":"string"},{"field_id":3,"name":"email","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":false,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"field_id":2,"kind":"rename_field","old_path":"full_name","path":"display_name"}],"to":"v1"}] | sha256:bcc8c501e951e06ca6fca8d17fbb50f419f9bccb5c65d5b3a13b6b6b9fc1e99c | sha256:7da200928b89a496a2b3147837f7860b8c400974d0a7a4af56084d64a008ef4e | fixtures/rename_column_with_field_id/scenario.json |
reorder_columns | Reorder columns | structural | The same four columns appear in a different order. Names, types, nullability and values are unchanged. Neither order is alphabetical, so a reader that sorts columns by name disagrees with both. | [] | [
"reorder-fields",
"column-order"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"reorder_fields"
] | [] | [] | true | false | true | fixtures/reorder_columns/v0.parquet | fixtures/reorder_columns/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"region","nullable":true,"type":"string"},{"name":"amount","nullable":true,"type":"int64"},{"name":"active","nullable":true,"type":"bool"}] | [{"name":"amount","nullable":true,"type":"int64"},{"name":"id","nullable":false,"type":"int64"},{"name":"active","nullable":true,"type":"bool"},{"name":"region","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"reorder_fields","new_order":["amount","id","active","region"],"old_order":["id","regio... | sha256:e7bd349f7eae6691f2bc0ab2a70ac040ca666f58e8acd691ed32436048545f03 | sha256:a514e4bbb4aee2c15f1df67a2100def2bedf8bd85282eafede8c60b4f1ca2c0c | fixtures/reorder_columns/scenario.json |
required_to_nullable | Non-nullable column becomes nullable | nullability | A non-nullable string column is declared nullable in v1. The data is identical and contains no nulls in either version; only the declaration changes (Parquet repetition required -> optional). | [] | [
"change-nullability",
"string",
"no-value-change"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_nullability"
] | [
"sku"
] | [] | true | false | true | fixtures/required_to_nullable/v0.parquet | fixtures/required_to_nullable/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"sku","nullable":false,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"sku","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_nullability","new_nullable":true,"old_nullable":false,"path":"sku"}],"to":"v1"}... | sha256:70f5dd82fa712a344d629f8e1333db830ebf3b3092eb9c405c9b26ccff09ec1c | sha256:96301221808db936bc4757779a58856de486759ecc539da7c2763b6ffb1ff9cc | fixtures/required_to_nullable/scenario.json |
rows_appended_without_key | Rows appended to a dataset without a key | edge | An append-only log with no identifying column gains two rows. The first four rows, which include two identical rows, are unchanged and in the same order. The schema is unchanged. | [
"row_identity is empty: rows are identified by position, so row i of v0 is row i of v1. The duplicate rows are why no column set can serve as a key."
] | [
"keyless",
"append",
"duplicate-rows",
"no-schema-change"
] | [] | 2 | [
"v0",
"v1"
] | [
4,
6
] | 0 | [] | [] | [] | false | false | false | fixtures/rows_appended_without_key/v0.parquet | fixtures/rows_appended_without_key/v1.parquet | [{"name":"level","nullable":true,"type":"string"},{"name":"message","nullable":true,"type":"string"}] | [{"name":"level","nullable":true,"type":"string"},{"name":"message","nullable":true,"type":"string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":true,"paths_retained":true,"row_count_preserved":false,"row_identity_preserved":false,"rows_retained":true,"value_changes":[]},"mutations":[],"to":"v1"}] | sha256:87f3ef6e927225e236d82b394450de95324e6d095adba8279801412fe80c2961 | sha256:85f671701106ace08b2863716021d6bc28df921f1fbf19b6c3d243b88034a290 | fixtures/rows_appended_without_key/scenario.json |
rows_deleted | Rows deleted from a keyed dataset | edge | Two of six rows, ids 2 and 5, are absent from v1. The four remaining rows keep their values and their relative order. The schema is unchanged. | [
"rows_retained is false. common_values_preserved quantifies over the rows present in both versions, and those rows are unchanged.",
"v1 has no rows for ids 2 and 5, rather than rows with nulls or empty values. The deleted rows held an empty status (id 2) and a null amount (id 5)."
] | [
"delete",
"keyed",
"no-schema-change"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
4
] | 0 | [] | [] | [] | false | false | false | fixtures/rows_deleted/v0.parquet | fixtures/rows_deleted/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"status","nullable":true,"type":"string"},{"name":"amount","nullable":true,"type":"int64"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"status","nullable":true,"type":"string"},{"name":"amount","nullable":true,"type":"int64"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":true,"paths_retained":true,"row_count_preserved":false,"row_identity_preserved":false,"rows_retained":false,"value_changes":[]},"mutations":[],"to":"v1"}] | sha256:190cc5f8270f318300bfce01b7d28a81df23829c481f97057fdfe866d050e976 | sha256:9d4eb19479eb6a32bf96d5800da5cf576a00a58dfdbeedb920a7ed9bf8106336 | fixtures/rows_deleted/scenario.json |
string_to_binary | string to binary holding the UTF-8 bytes | representation | A string column becomes a binary column. Each v1 value is the UTF-8 encoding of the v0 value. Text and bytes are different kinds of value, so values are not preserved even though the stored bytes are identical. The composed and decomposed 'cafe' encode to different bytes. | [
"At the Parquet level both columns are BYTE_ARRAY; only the String logical annotation is removed."
] | [
"change-type",
"string",
"binary",
"unicode"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_type"
] | [
"payload"
] | [
"utf8_encoded"
] | true | true | true | fixtures/string_to_binary/v0.parquet | fixtures/string_to_binary/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"payload","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"payload","nullable":true,"type":"binary"}] | [{"from":"v0","invariants":{"common_values_preserved":false,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[{"path":"payload","relation":"utf8_encoded"}]},"mutations":[{"kind":"change_type","new_type":"binary","old_typ... | sha256:1d2caddd132217e781f4c0af6de1abd1d098bb9c58e2716ea2b8041d758d6c56 | sha256:04a2d7a0380eb970df52303bec4f6e581077e70401e730f8926d84f2e4fa0919 | fixtures/string_to_binary/scenario.json |
string_to_dictionary | string to dictionary-encoded string | representation | A plain string column becomes a dictionary<int8, string> column, as written for a pandas categorical. v0 is written without dictionary encoding; v1 with it. Decoded values are unchanged. | [
"The Arrow type change is recorded only in the stored Arrow schema. The Parquet schema is identical; the Parquet difference is in the column chunk encodings (PLAIN vs RLE_DICTIONARY).",
"Readers that ignore the stored Arrow schema may decode v1 as plain strings, or as a dictionary with int32 indices. The int8 ind... | [
"change-type",
"dictionary",
"categorical",
"encoding",
"arrow-only"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_type"
] | [
"status"
] | [] | true | false | false | fixtures/string_to_dictionary/v0.parquet | fixtures/string_to_dictionary/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"status","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"status","nullable":true,"type":"dictionary<values=string, indices=int8, ordered=0>"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":true,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"dictionary<values=string, indices=int8, ordered=0>","old_type"... | sha256:e5b66495d62b534fd0a382904d46f85c23aaf18721089e242e90676fdb4d9444 | sha256:6dfdbf0a9de0eac2a01b3d7a88cb06abe18009dfa4d033259c14f39d7bf16d9b | fixtures/string_to_dictionary/scenario.json |
string_to_large_string | string to large_string | representation | A string column becomes large_string (64-bit offsets). Values are unchanged; they include an empty string, whitespace, the literal text NULL, CJK, an emoji, and 'cafe' with the accent both composed (U+00E9) and decomposed (e + U+0301). | [
"The difference exists only in the stored Arrow schema. Both files have the same Parquet schema (BYTE_ARRAY, String), so a reader that ignores the Arrow schema sees no change at all."
] | [
"change-type",
"string",
"offsets",
"unicode",
"arrow-only"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
8,
8
] | 1 | [
"change_type"
] | [
"label"
] | [] | true | false | false | fixtures/string_to_large_string/v0.parquet | fixtures/string_to_large_string/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"label","nullable":true,"type":"string"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"label","nullable":true,"type":"large_string"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":true,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"large_string","old_type":"string","path":"label"}],"to":"v1"}] | sha256:af037b683e64ba1bd9e63dd778b55bea85f8e898f85becfa6f4450ba01a036c7 | sha256:4ed55bd1dd61a05cd056a56e3d0554a6f764bd6a03522e3189372a421d692f9d | fixtures/string_to_large_string/scenario.json |
timestamp_naive_to_utc | Naive timestamp becomes UTC timestamp | temporal | A timestamp column without a time zone becomes timestamp with tz=UTC. The stored integers are identical, so each wall-clock value in v0 is reinterpreted as the same wall-clock value in UTC. A naive value and a zoned value are not the same value. | [
"At the Parquet level this is the isAdjustedToUTC flag of the Timestamp logical type changing from false to true; the physical INT64 values are unchanged.",
"Values include one microsecond before the epoch, the epoch, a leap day and a wall-clock time that does not exist in Central European time."
] | [
"change-type",
"timestamp",
"timezone",
"utc"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
5,
5
] | 1 | [
"change_type"
] | [
"event_time"
] | [
"interpreted_as_utc"
] | true | true | true | fixtures/timestamp_naive_to_utc/v0.parquet | fixtures/timestamp_naive_to_utc/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"event_time","nullable":true,"type":"timestamp[us]"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"event_time","nullable":true,"type":"timestamp[us, tz=UTC]"}] | [{"from":"v0","invariants":{"common_values_preserved":false,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[{"path":"event_time","relation":"interpreted_as_utc"}]},"mutations":[{"kind":"change_type","new_type":"timesta... | sha256:ad6f3633367e76d56dca96b7ca6530e97c855cf26d71ccb0264a35da1752fc58 | sha256:fa7de3993fb420dfd70186ace21f0493654625a6c25682e648df34380844011b | fixtures/timestamp_naive_to_utc/scenario.json |
timestamp_ns_to_us | Timestamp unit changes from nanoseconds to microseconds | temporal | A naive timestamp column changes unit from nanoseconds to microseconds. Every v0 value is a whole number of microseconds, so every instant is unchanged. Values include both ends of the range timestamp[ns] can hold, one microsecond before the epoch, and a leap day. | [
"At the Parquet level the Timestamp logical type's unit changes from NANOS to MICROS and every stored INT64 is divided by 1000.",
"Timestamps compare by instant, independent of unit, so no value changes. The v0 extremes sit at the edges of the int64 nanosecond range; the microsecond range is 1000 times wider."
] | [
"change-type",
"timestamp",
"unit",
"nanoseconds",
"range-extremes"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_type"
] | [
"event_time"
] | [] | true | false | true | fixtures/timestamp_ns_to_us/v0.parquet | fixtures/timestamp_ns_to_us/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"event_time","nullable":true,"type":"timestamp[ns]"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"event_time","nullable":true,"type":"timestamp[us]"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"timestamp[us]","old_type":"timestamp[ns]","path":"event_time"... | sha256:fb6249b26547f63e125784d224fe1f57cd43eb540150f0a60fa28f39d7ba3cd9 | sha256:78a16095bb6fb126b9cd3f13f993c4d189659a7b963c08d708ad3586ea9b313a | fixtures/timestamp_ns_to_us/scenario.json |
widen_int32_to_int64 | Widen int32 to int64 | numeric | An int32 column becomes int64. Values, including both int32 extremes, zero and a null, are unchanged. | [] | [
"change-type",
"integer",
"widening",
"numeric-extremes"
] | [
"id"
] | 2 | [
"v0",
"v1"
] | [
6,
6
] | 1 | [
"change_type"
] | [
"quantity"
] | [] | true | false | true | fixtures/widen_int32_to_int64/v0.parquet | fixtures/widen_int32_to_int64/v1.parquet | [{"name":"id","nullable":false,"type":"int64"},{"name":"quantity","nullable":true,"type":"int32"}] | [{"name":"id","nullable":false,"type":"int64"},{"name":"quantity","nullable":true,"type":"int64"}] | [{"from":"v0","invariants":{"common_values_preserved":true,"parquet_schema_preserved":false,"paths_retained":true,"row_count_preserved":true,"row_identity_preserved":true,"rows_retained":true,"value_changes":[]},"mutations":[{"kind":"change_type","new_type":"int64","old_type":"int32","path":"quantity"}],"to":"v1"}] | sha256:ac3c16abadb0598042a7d24873373bcd88ed27198890526a07e71caa6469ba4e | sha256:49ad7406c3512c129d76d7a58b9c29d2298363952dab9cfb529fd61777c23db7 | fixtures/widen_int32_to_int64/scenario.json |
Tabular Evolution Corpus
Source, generator and validator: github.com/AndreaBozzo/tabular-evolution-corpus. This card is the
README.mdof the Hugging Face dataset repository; the GitHub repository keeps its own README.
A small, deterministic corpus of Parquet datasets that change over time. Each scenario is one logical dataset at two or more versions, with a manifest that records what structurally changed and which row and value facts hold across each transition. Every fact is verified against the files.
The corpus records facts, not compatibility verdicts. Whether a change is acceptable is for the engine, table format or data contract under test to decide.
What the viewer shows
The viewer shows the catalog, data/scenarios.parquet, with one row per
scenario (35 rows). The catalog is an index. The versioned fixture files it
points to live in the same repository under fixtures/<scenario_id>/
(v0.parquet, v1.parquet, ..., scenario.json).
The configs block above deliberately restricts the viewer and
load_dataset to the catalog. The fixture Parquet files have a different
schema in every scenario and are not rows of one dataset. Without the
explicit config, automatic data-file detection would try to load them as one.
Catalog columns
| column | type | meaning |
|---|---|---|
scenario_id |
string | stable identifier; also the fixture directory name |
title, description |
string | what changes, stated as facts |
category |
string | additive, subtractive, numeric, nullability, representation, structural, temporal, edge |
notes |
list | caveats, e.g. differences that exist only in the stored Arrow schema |
tags |
list | free-form, kebab-case |
row_identity |
list | columns that identify a row in every version; empty means rows are identified by position |
version_count, version_ids, row_counts |
int64, list, list | versions in order |
mutation_count, mutation_kinds |
int64, list | structural changes across all transitions |
paths |
list | field paths touched by a mutation or a value change |
value_change_relations |
list | how changed values relate (nearest_float64, utf8_encoded, ...) |
schema_changed, values_changed, parquet_schema_changed |
bool | quick filters |
base_file, final_file, manifest_path |
string | repository-relative paths |
base_schema_json, final_schema_json, transitions_json |
string | JSON; full detail is in scenario.json |
base_fingerprint, final_fingerprint |
string | logical fingerprints (schema + values + row order) |
Nested manifest structures are JSON strings because their shape depends on the mutation kind. Every other column is a scalar or a list of scalars.
Usage
from datasets import load_dataset
catalog = load_dataset("AndreaBozzo/tabular-evolution-corpus", split="scenarios")
To get the fixtures, download the repository files:
from huggingface_hub import snapshot_download
root = snapshot_download("AndreaBozzo/tabular-evolution-corpus", repo_type="dataset")
# root/fixtures/<scenario_id>/{v0.parquet, v1.parquet, scenario.json}
Every corpus release is a tag, both here and on GitHub. Pass
revision="v0.2.0" to either call to get exactly that release. A released tag
is never moved, and a changed corpus gets a new release.
-- DuckDB
SELECT scenario_id, mutation_kinds, row_counts
FROM read_parquet('hf://datasets/AndreaBozzo/tabular-evolution-corpus/data/scenarios.parquet')
WHERE category = 'numeric';
Dataset structure
data/scenarios.parquet catalog (this viewer)
fixtures/<scenario_id>/ one directory per scenario
v0.parquet, v1.parquet ... versions, 0-8 rows each
scenario.json manifest (JSON Schema: schema/scenario.schema.json)
schema/scenario.schema.json
SHA256SUMS
Creation
All data is synthetic, written by the generator in the source repository with
a pinned PyArrow Parquet writer (uncompressed, format 2.6, one row group).
Values are chosen to expose boundaries: NaN, infinities, -0.0, 2^53 + 1,
int and decimal extremes, composed vs decomposed Unicode, empty strings,
nested nulls, pre-epoch timestamps, timestamps at both ends of the
nanosecond range. Regeneration is logically reproducible,
and byte-reproducible with the same PyArrow version.
Intended use and limitations
Intended as test input for ingestion frameworks, dataframe libraries, query engines, lakehouse tooling, schema registries, contract validators and profilers. It is not training data, not a performance benchmark, and not a conformance suite for Arrow or Parquet (see apache/parquet-testing and the Arrow integration tests for those). Corpus 0.2.0 covers Parquet only and 35 scenarios. Nested evolution covers structs, lists and maps; datasets are keyed or identified by row position.
One writer. Every file is written by a single pinned Parquet writer
(PyArrow / Arrow C++, recorded in each file's created_by). The scenarios
vary the data, not the writer. Differences that exist only in the Arrow schema
stored in the file (large_string, dictionary encoding) are flagged in each
manifest, because readers that ignore that schema see no change. Fixtures
from other writers are planned but not included.
License
Apache-2.0. No personal data: every name, address and e-mail in the fixtures is invented or a well-known public name used as a placeholder.
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