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new b4cd2ecf0616 [SPARK-48495][SQL][DOCS] Describe shredding scheme for
Variant
b4cd2ecf0616 is described below
commit b4cd2ecf06169579bd2728649cc1b6dd407bc4c3
Author: cashmand <[email protected]>
AuthorDate: Sat Jul 13 12:31:40 2024 +0900
[SPARK-48495][SQL][DOCS] Describe shredding scheme for Variant
### What changes were proposed in this pull request?
For the Variant data type, we plan to add support for columnar storage
formats (e.g. Parquet) to write the data shredded across multiple physical
columns, and read only the data required for a given query. This PR merges a
document describing the approach we plan to take. We can continue to update it
as the implementation progresses.
### Why are the changes needed?
When implemented, can allow much better performance when reading from
columnar storage. More detail is given in the document.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
It is internal documentation, no testing should be needed.
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #46831 from cashmand/SPARK-45891.
Authored-by: cashmand <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>
---
common/variant/README.md | 4 +
common/variant/shredding.md | 244 ++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 248 insertions(+)
diff --git a/common/variant/README.md b/common/variant/README.md
index 0fc4d91f3f8a..3e1b00c49475 100644
--- a/common/variant/README.md
+++ b/common/variant/README.md
@@ -375,3 +375,7 @@ Field names are case-sensitive. Field names are required to
be unique for each o
# Versions and extensions
An implementation is not expected to parse a Variant value whose metadata
version is higher than the version supported by the implementation. However,
new types may be added to the specification without incrementing the version
ID. In such a situation, an implementation should be able to read the rest of
the Variant value if desired.
+
+# Shredding
+
+For columnar storage formats, a single Variant object may have poor read
performance when only a small number of fields are needed. A better approach is
to create separate columns for individual fields, referred to as shredding or
subcolumnarization. [shredding.md](shredding.md) describes an approach to
shredding Variant columns in Parquet and similar columnar formats.
diff --git a/common/variant/shredding.md b/common/variant/shredding.md
new file mode 100644
index 000000000000..ba5e3f8cdcc2
--- /dev/null
+++ b/common/variant/shredding.md
@@ -0,0 +1,244 @@
+# Shredding Overview
+
+The Spark Variant type is designed to store and process semi-structured data
efficiently, even with heterogeneous values. Query engines encode each variant
value in a self-describing format, and store it as a group containing **value**
and **metadata** binary fields in Parquet. Since data is often partially
homogenous, it can be beneficial to extract certain fields into separate
Parquet columns to further improve performance. We refer to this process as
"shredding". Each Parquet file rem [...]
+
+This document focuses on the shredding semantics, Parquet representation,
implications for readers and writers, as well as the Variant reconstruction.
For now, it does not discuss which fields to shred, user-facing API changes, or
any engine-specific considerations like how to use shredded columns. The
approach builds on top of the generic Spark Variant representation, and
leverages the existing Parquet specification for maximum compatibility with the
open-source ecosystem.
+
+At a high level, we replace the **value** and **metadata** of the Variant
Parquet group with one or more fields called **object**, **array**,
**typed_value** and **untyped_value**. These represent a fixed schema suitable
for constructing the full Variant value for each row.
+
+Shredding lets Spark (or any other query engine) reap the full benefits of
Parquet's columnar representation, such as more compact data encoding, min/max
statistics for data skipping, and I/O and CPU savings from pruning unnecessary
fields not accessed by a query (including the non-shredded Variant binary data).
+Without shredding, any query that accesses a Variant column must fetch all
bytes of the full binary buffer. With shredding, we can get nearly equivalent
performance as in a relational (scalar) data model.
+
+For example, `select variant_get(variant_col, ‘$.field1.inner_field2’,
‘string’) from tbl` only needs to access `inner_field2`, and the file scan
could avoid fetching the rest of the Variant value if this field was shredded
into a separate column in the Parquet schema. Similarly, for the query `select
* from tbl where variant_get(variant_col, ‘$.id’, ‘integer’) = 123`, the scan
could first decode the shredded `id` column, and only fetch/decode the full
Variant value for rows that pass th [...]
+
+# Parquet Example
+
+Consider the following Parquet schema together with how Variant values might
be mapped to it. Notice that we represent each shredded field in **object** as
a group of two fields, **typed_value** and **untyped_value**. We extract all
homogenous data items of a certain path into **typed_value**, and set aside
incompatible data items in **untyped_value**. Intuitively, incompatibilities
within the same path may occur because we store the shredding schema per
Parquet file, and each file can c [...]
+
+Typically, the expectation is that **untyped_value** exists at every level as
an option, along with one of **object**, **array** or **typed_value**. If the
actual Variant value contains a type that does not match the provided schema,
it is stored in **untyped_value**. An **untyped_value** may also be populated
if an object can be partially represented: any fields that are present in the
schema must be written to those fields, and any missing fields are written to
**untyped_valud**.
+
+```
+optional group variant_col {
+ optional binary untyped_value;
+ optional group object {
+ optional group a {
+ optional binary untyped_value;
+ optional int64 typed_value;
+ }
+ optional group b {
+ optional binary untyped_value;
+ optional group object {
+ optional group c {
+ optional binary untyped_value;
+ optional binary typed_value (STRING);
+ }
+ }
+ }
+ }
+}
+```
+
+| Variant Value | Top-level untyped_value | b.untyped_value | Non-null in a |
Non-null in b.c |
+|---------------|--------------------------|---------------|---------------|
+| {a: 123, b: {c: “hello”}} | null | null | typed_value | typed_value |
+| {a: 1.23, b: {c: “123”}} | null | null | untyped_value | typed_value |
+| {a: [1,2,3], b: {c: null}} | null | null | untyped_value | untyped_value |
+| {a: 123, c: 456} | {c: 456} | null | typed_value | null |
+| {a: 123, b: {c: "hello", d: 456}} | null | {d: 456} | typed_value |
typed_value |
+| [{a: 1, b: {c: 2}}, {a: 3, b: {c: 4}}] | [{a: 1, b: {c: 2}}, {a: 3, b: {c:
4}}] | null | null | null |
+
+# Parquet Layout
+
+The **array** and **object** fields represent Variant array and object types,
respectively. Arrays must use the three-level list structure described in
https://github.com/apache/parquet-format/blob/master/LogicalTypes.md.
+
+An **object** field must be a group. Each field name of this inner group
corresponds to the Variant value's object field name. Each inner field's type
is a recursively shredded variant value: that is, the fields of each object
field must be one or more of **object**, **array**, **typed_value** or
**untyped_value**.
+
+Similarly the elements of an **array** must be a group containing one or more
of **object**, **array**, **typed_value** or **untyped_value**.
+
+Each leaf in the schema can store an arbitrary Variant value. It contains an
**untyped_value** binary field and a **typed_value** field. If non-null,
**untyped_value** represents the value stored as a Variant binary; the metadata
and value of a normal Variant are concatenated. The **typed_value** field may
be any type that has a corresponding Variant type. For each value in the data,
at most one of the **typed_value** and **untyped_value** may be non-null. A
writer may omit either field, [...]
+
+| typed_value | untyped_value | Meaning |
+|-------------|----------------|---------|
+| null | null | Field is missing in the reconstructed Variant. |
+| null | non-null | Field may be any type in the reconstructed Variant. |
+| non-null | null | Field has this column’s type in the reconstructed Variant.
|
+| non-null | non-null | Invalid |
+
+The **typed_value** may be absent from the Parquet schema for any field, which
is equivalent to its value being always null (in which case the shredded field
is always stored as a Variant binary). By the same token, **untyped_value** may
be absent, which is equivalent to their value being always null (in which case
the field will always be missing or have the type of the **typed_value**
column).
+
+The full metadata and value can be reconstructed from **untyped_value** by
treating the leading bytes as metadata, and using the header, dictionary size
and final dictionary offset to determine the start of the Variant value
section. (See the metadata description in the common/variant/README.md for more
detail on how to interpret it.) For example, in the binary below, there is a
one-element dictionary, and the final offset (`offset[1]`) indicates that the
last dictionary entry ends at th [...]
+
+```
+ hdr sz offset[0] offset[1] bytes[0] bytes[1] value
+ --------------------------------------------------------------------
+| | | | | | |
+| 0x01 | 0x01 | 0x00 | 0x02 | ‘h’ | ‘i’ | . . . . . . . .
+|______|______|_________|_________|________|________|________________
+```
+
+# Unshredded values
+
+If all values can be represented at a given level by whichever of **object**,
**array** or **typed_value** is present, **untyped_value** is set to null.
+
+If a value cannot be represented by whichever of **object**, **array** or
**typed_value** is present in the schema, then it is stored in
**untyped_value**, and the other fields are set to null. In the Parquet example
above, if field **a** was an object or array, or a non-integer scalar, it would
be stored in **untyped_value**.
+
+If a value is an object, and the **object** field is present but does not
contain all of the fields in the value, then any remaining fields are stored in
an object in **untyped_value**. In the Parquet example above, if field **b**
was an object of the form **{"c": 1, "d": 2}"**, then the object **{"d": 2}**
would be stored in **untyped_value**, and the **c** field would be shredded
recursively under **object.c**.
+
+Note that an array is always fully shredded if there is an **array** field, so
the above consideration for **object** is not relevant for arrays: only one of
**array** or **untyped_value** may be non-null at a given level.
+
+# Using untyped_value vs. typed_value
+
+In general, it is desirable to store values in the **typed_value** field
rather than the **untyped_value** whenever possible. This will typically
improve encoding efficiency, and allow the use of Parquet statistics to filter
at the row group or page level. In the best case, the **untyped_value** fields
are all null and the engine does not need to read them (or it can omit them
from the schema on write entirely). There are two main motivations for
including the **untyped_value** column:
+
+1) In a case where there are rare type mismatches (for example, a numeric
field with rare strings like “n/a”), we allow the field to be shredded, which
could still be a significant performance benefit compared to fetching and
decoding the full value/metadata binary.
+2) Since there is a single schema per file, there would be no easy way to
recover from a type mismatch encountered late in a file write. Parquet files
can be large, and buffering all file data before starting to write could be
expensive. Including an untyped column for every field guarantees we can adhere
to the requested shredding schema.
+
+The **untyped_value** is stored in a single binary column, rather than storing
the value and metadata separately as is done in the unshredded binary format.
The motivation for storing them separately for unshredded data is that this
lets the engine encode and compress the metadata more efficiently when the
fields are consistent across rows. We chose to combine them in the shredded
fields: we expect the encoding/compression benefit to be lower, since in the
case of uniform data, the value [...]
+
+# Data Skipping
+
+Shredded columns are expected to store statistics in the same format as a
normal Parquet column. In general, the engine can only skip a row group or page
if all rows in the **untyped_value** field are null, since it is possible for a
`variant_get` expression to successfully cast a value from the
**untyped_value** to the target type. For example, if **typed_value** is of
type `int64`, then the string “123” might be contained in **untyped_value**,
which would not be reflected in statistics [...]
+
+# Shredding Semantics
+
+Variant defines a number of integer and decimal types of varying widths. When
writing, it would be quite limiting to strictly enforce the mapping between
Variant types and Parquet/Spark types. For example, if we chose to shred a
field as `int64`, and encountered the value 123 encoded as `int32`, it seems
preferable to write this to the **typed_value** column, even though it
technically loses information about the type in the original Variant object,
and would be reconstructed as an `int64`.
+
+On the other hand, storing arbitrarily casted values in the **typed_value**
column could create inconsistent behavior before and after shredding, and could
leak behavior from the writing engine to the reading engine. For example,
double-to-string casts can produce different results in different engines.
Performing such a cast while shredding (even if we somehow retained the
knowledge that the original value was a `double`) could result in confusing
behavior changes if shredding took plac [...]
+
+Our approach is a pragmatic compromise that allows the use of **typed_value**
in cases where the type can be losslessly widened without resulting in a
significant difference in the reconstructed Variant:
+
+1) All integer and decimal types in Variant are conceptually a single “number”
type. The engine may shred any number into the **typed_value** of any other
number, provided that no information about the value is lost. For example, the
integer 123 may be shredded as Decimal<9, 2>, but 1.23 may not be shredded as
any integer type.
+
+2) To ensure that behavior remains unchanged before and after shredding, we
will aim to have all Spark expressions that operate on Variant be agnostic to
the specific numeric type. For example, `cast(val as string)` should produce
the string “123” if `val` is any integer or decimal type that is exactly equal
to 123. Note that this is unlike the normal Spark behavior for `decimal` types,
which would produce “123.00” for `Decimal<9,2>`.
+
+3) One exception to the above is `schema_of_variant`, which will still report
the underlying physical type. This means that `schema_of_variant` may report
different numeric types before and after shredding.
+
+4) Other than integer and decimal, we will not allow casting between types.
For example, we will not write the string “123” to an integer **typed_value**
column, even though `variant_get(“123”, “$”, “integer”)` would produce the
integer 123. Similarly, double and float types are considered distinct from
other numeric types, and we would not write them to a numeric **typed_value**
column.
+
+# Reconstructing a Variant
+
+It is possible to recover a full Variant value using a recursive algorithm,
where the initial call is to `ConstructVariant` with the top-level fields,
which are assumed to be null if they are not present in the schema.
+
+```
+# Constructs a Variant from `untyped_value`, `object`, `array` and
`typed_value`.
+# Only one of object, array and typed_value may be non-null.
+def ConstructVariant(untyped_value, object, array, typed_value):
+ if object is null and array is null and typed_value is null: return
untyped_value
+ elif object is not null:
+ return ConstructObject(untyped_value, object)
+ elif array is not null:
+ return ConstructArray(array)
+ else:
+ # Leaf in the tree.
+ assert(untyped_value is null or untyped_value is VariantNull)
+ return coalesce(untyped_value, cast(typed_value as Variant))
+
+# Construct an object from an `object` group, and a (possibly null) Variant
untyped_value
+def ConstructObject(untyped_value, object)
+ # If untyped_value is present and is not an Object, then the result is
ambiguous.
+ assert(untyped_value is null or is_object(untyped_value))
+ all_keys = Union(untyped_value.keys, object.fields)
+ return VariantObject(all_keys.map { key ->
+ if object[field] is null: (key, untyped_value[field])
+ else: (key, ConstructVariant(null, object[field], null, null))
+ }
+
+def ConstructArray(array)
+ newVariantArray = VariantArray()
+ for i in range(array.size):
+ # Any of these may be missing from the schema, in which case they are null.
+ newVariantArray.append(ConstructVariant(array[i].untyped_value,
array[i].object, array[i].array, array[i].typed_value)
+```
+
+# Nested Parquet Example
+
+This section describes a more deeply nested example, using a top-level array
as the shredding type.
+
+Below is a sample of JSON that would be fully shredded in this example. It
contains an array of objects, containing an “a” field shredded as an array, and
a “b” field shredded as an integer.
+
+```
+[
+ {
+ "a": [1, 2, 3],
+ "b": 100
+ },
+ {
+ "a": [4, 5, 6],
+ "b": 200
+ }
+]
+```
+
+
+The corresponding Parquet schema with “a” and “b” as leaf types is:
+
+```
+optional group variant_col {
+ optional binary untyped_value;
+ optional group array (LIST) {
+ repeated group list {
+ optional group element {
+ optional binary untyped_value;
+ optional group object {
+ optional group a {
+ optional binary untyped_value;
+ optional group array (LIST) {
+ repeated group list {
+ optional group element {
+ optional int64 typed_value;
+ optional binary untyped_value;
+ }
+ }
+ }
+ }
+ optional group b {
+ optional int64 typed_value;
+ optional binary untyped_value;
+ }
+ }
+ }
+ }
+ }
+}
+```
+
+In the above example schema, if “a” is an array containing a mix of integer
and non-integer values, the engine will shred individual elements appropriately
into either **typed_value** or **untyped_value**.
+If the top-level Variant is not an array (for example, an object), the engine
cannot shred the value and it will store it in the top-level **untyped_value**.
+Similarly, if "a" is not an array, it will be stored in the **untyped_value**
under "a".
+
+Consider the following example:
+
+```
+[
+ {
+ "a": [1, 2, 3],
+ "b": 100,
+ “c”: “unexpected”
+ },
+ {
+ "a": [4, 5, 6],
+ "b": 200
+ },
+ “not an object”
+]
+```
+
+The second array element can be fully shredded, but the first and third cannot
be. The contents of `variant_col.array[*].untyped_value` would be as follows:
+
+```
+[
+ { “c”: “unexpected” },
+ NULL,
+ “not an object”
+]
+```
+
+# Backward and forward compatibility
+
+Shredding is an optional features of Variant, and readers must continue to be
able to read a group containing only a `value` and `metadata` column.
+
+We will follow the convention defined in
https://github.com/delta-io/delta/blob/master/protocol_rfcs/variant-type.md#variant-data-in-parquet,
and ignore any fields in the same group as typed_value/untyped_value that
start with `_` (underscore).
+This is intended to allow future backwards-compatible extensions. In
particular, the field names `_metadata_key_paths` and any name starting with
`_spark` are reserved, and should not be used by other implementations.
+Any extra field names that do not start with an underscore should be assumed
to be backwards incompatible, and readers should fail when reading such a
schema.
+
+Engines without shredding support are not expected to be able to read Parquet
files that use shredding. Since different files may contain conflicting schemas
(e.g. a `typed_value` column with incompatible types in two files), it may not
be possible to infer or specify a single schema that would allow all Parquet
files for a table to be read.
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