Spark: Support writing shredded variant in Iceberg-Spark #14297

aihuaxu · 2025-10-11T21:02:14Z

This change adds support for writing shredded variants in the iceberg-spark module, enabling Spark to write shredded variant data into Iceberg tables.

Ideally, this should follow the approach described in the reader/writer API proposal for Iceberg V4, where the execution engine provides the shredded writer schema before creating the Iceberg writer. This design is cleaner, as it delegates schema generation responsibility to the engine.

As an interim solution, this PR implements a writer with lazy initialization for the actual Parquet writer. It buffers a portion of the data first, derives the shredded schema from the buffered records, then initializes the Parquet writer and flushes the buffered data to the file.

The current shredding algorithm is to shred to the most common type for a field.

aihuaxu · 2025-10-15T01:57:15Z

@amogh-jahagirdar @Fokko @huaxingao Can you help take a look at this PR and if we have better approach for this?

aihuaxu · 2025-10-21T04:59:31Z

cc @RussellSpitzer, @pvary and @rdblue Seems it's better to have the implementation with new File Format proposal but want to check if this is acceptable approach as an interim solution or you see a better alternative.

parquet/src/main/java/org/apache/iceberg/parquet/ParquetWriter.java

pvary · 2025-10-21T10:01:30Z

@aihuaxu: Don't we want to do the same but instead of wrapping the ParquetWriter, we could wrap the DataWriter. The schema would be created near the SparkWrite.WriterFactory and it would be easier to move to the new API when it is ready. The added benefit would be that when other formats implement the Variant, we could reuse the code.

Would this be prohibitively complex?

huaxingao · 2025-10-21T18:32:50Z

In Spark DSv2, planning/validation happens on the driver. BatchWrite#createBatchWriterFactory runs on the driver and returns a DataWriterFactory that is serialized to executors. That factory must already carry the write schema the executors will use when they create DataWriters.

For shredded variant, we don’t know the shredded schema at planning time. We have to inspect some records to derive it. Doing a read on the driver during createBatchWriterFactory would mean starting a second job inside planning, which is not how DSv2 is intended to work.

Because of that, the current proposed Spark approach is: put the logical variant in the writer factory, on the executor, buffer the first N rows, infer the shredded schema from data, then initialize the concrete writer and flush the buffer. I believe this PR follow the same approach, which seems like a practical solution to me given DSV2's constraints.

pvary · 2025-10-22T08:47:11Z

Thanks for the explanation, @huaxingao! I see several possible workarounds for the DataWriterFactory serialization issue, but I have some more fundamental concerns about the overall approach.
I believe shredding should be driven by future reader requirements rather than by the actual data being written. Ideally, it should remain relatively stable across data files within the same table and originate from a writer job configuration—or even better, from a table-level configuration.

Even if we accept that the written data should dictate the shredding logic, Spark’s implementation—while dependent on input order—is at least somewhat stable. It drops rarely used fields, handles inconsistent types, and limits the number of columns.
I understand this is only a PoC implementation for shredding, but I’m concerned that the current simplifications make it very unstable. If I’m interpreting correctly, the logic infers the type from the first occurrence of each field and creates a column for every field. This could lead to highly inconsistent column layouts within a table, especially in IoT scenarios where multiple sensors produce vastly different data.
Did I miss anything?

aihuaxu · 2025-10-24T16:28:26Z

Thanks @huaxingao and @pvary for reviewing, and thanks to Huaxin for explaining how the writer works in Spark.

Regarding the concern about unstable schemas, Spark's approach makes sense:

If a field appears consistently with a consistent type, create both value and typed_value
If a field appears with inconsistent types, create only value
Drop fields that occur in less than 10% of sampled rows
Cap the total at 300 fields (counting value and typed_value separately)

We could implement similar heuristics. Additionally, making the shredded schema configurable would allow users to choose which fields to shred at write time based on their read patterns.

For this POC, I'd like any feedback on whether there are any significant high-level design options to consider first and if this approach is acceptable. This seems hacky. I may have missed big picture on how the writers work across Spark + Iceberg + Parquet and we may have better way.

github-actions · 2025-11-24T00:19:23Z

This pull request has been marked as stale due to 30 days of inactivity. It will be closed in 1 week if no further activity occurs. If you think that’s incorrect or this pull request requires a review, please simply write any comment. If closed, you can revive the PR at any time and @mention a reviewer or discuss it on the dev@iceberg.apache.org list. Thank you for your contributions.

Tishj · 2025-11-30T21:28:05Z

This PR caught my eye, as I've implemented the equivalent in DuckDB: duckdb/duckdb#19336

The PR description doesn't give much away, but I think the approach is similar to the proposed (interim) solution here: buffer the first rowgroup, infer the shredded schema from this, then finalize the file schema and start writing data.

We've opted to create a typed_value even though the type isn't 100% consistent within the buffered data, as long as it's the most common. I think you're losing potential compression by not doing that.

We've also added a copy option to force the shredded schema, for debugging purposes and for power users.

As for DECIMAL, it's kind of a special case in the shredding inference. We only shred on a DECIMAL type if all the decimal values we've seen for a column/field have the same width+scale, if any decimal value differs, DECIMAL won't be considered anymore when determining the shredded type of the column/field

yguy-ryft · 2025-12-24T17:43:26Z

This PR is super exciting!
Does this rely on variant shredding support in Spark? Is it supported in Spark 4.1 already, or planned for future releases?

Regarding the heuristics - I'd like to propose adding table properties as hints for variant shredding.
Similarly to properties used for bloom filters, it could be good to introduce something like write.parquet.variant-shredding-enabled.column.col1, which will hint to the writer that this column is important for shredding.
Many variants have important fields for which shredding should be enforced, and other fields which are less central and can be managed with simpler heuristics.
Would love to hear your thoughts!

aihuaxu · 2026-01-09T19:55:50Z

This PR caught my eye, as I've implemented the equivalent in DuckDB: duckdb/duckdb#19336

The PR description doesn't give much away, but I think the approach is similar to the proposed (interim) solution here: buffer the first rowgroup, infer the shredded schema from this, then finalize the file schema and start writing data.

That is correct.

We've opted to create a typed_value even though the type isn't 100% consistent within the buffered data, as long as it's the most common. I think you're losing potential compression by not doing that.

I'm still trying to improve the heuristics to use the most common one as shredding type rather than the first one and probably cap the number of shredded fields, etc. but it doesn't need 100% consistent type to be shredded.

We've also added a copy option to force the shredded schema, for debugging purposes and for power users.

Yeah. I think that makes sense for advanced user to determine the shredded schema since they may know the read pattern.

As for DECIMAL, it's kind of a special case in the shredding inference. We only shred on a DECIMAL type if all the decimal values we've seen for a column/field have the same width+scale, if any decimal value differs, DECIMAL won't be considered anymore when determining the shredded type of the column/field

Why is DECIMAL special here? If we determine DECIMAL4 to be shredded type, then we may shred as DECIMAL4 or not shred if they cannot fit in DECIMAL4, right?

aihuaxu · 2026-01-09T19:58:25Z

This PR is super exciting! Does this rely on variant shredding support in Spark? Is it supported in Spark 4.1 already, or planned for future releases?

Regarding the heuristics - I'd like to propose adding table properties as hints for variant shredding. Similarly to properties used for bloom filters, it could be good to introduce something like write.parquet.variant-shredding-enabled.column.col1, which will hint to the writer that this column is important for shredding. Many variants have important fields for which shredding should be enforced, and other fields which are less central and can be managed with simpler heuristics. Would love to hear your thoughts!

Yeah. I'm also thinking of that too. Will address that separately. Basically based on read pattern, the user can specify the shredding schema.

gkpanda4

When processing JSON objects containing null field values (e.g., {"field": null}), the variant shredding creates schema columns for these null fields instead of omitting them entirely. This would cause schema bloat.

Adding a null check in ParquetVariantUtil.java:386 in the object() method should fix it.

aihuaxu · 2026-01-15T19:39:27Z

When processing JSON objects containing null field values (e.g., {"field": null}), the variant shredding creates schema columns for these null fields instead of omitting them entirely. This would cause schema bloat.

Adding a null check in ParquetVariantUtil.java:386 in the object() method should fix it.

I addressed this null value check in VariantShreddingAnalyzer.java instead. If it's NULL, then we will not add the shredded field.

gkpanda4 · 2026-01-21T19:06:40Z

spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/source/VariantShreddingAnalyzer.java

+      }
+    }
+
+    PhysicalType getMostCommonType() {


How does this logic work when a field would have the same number of different datatypes, for eg 2 of INT8, STRING, DECIMAL8?

Will this logic choose one at random?

I have the same question. I think we should add an explicit deterministic tie-break and also add a regression test that creates a perfect tie to ensure inference is stable.

What we can do is when there is tie, we can choose the last type (order the type names) to have consistent shredding type.

gkpanda4 · 2026-01-21T19:09:48Z

spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/source/VariantShreddingAnalyzer.java

+ *   <li>shred to the most common type
+ * </ul>
+ */
+public class VariantShreddingAnalyzer {


Can you add a test scenario for a field, for example, ZIP codes like 98101, 97201, and 10001, it get parsed as different integer types (INT32 + INT16).

Should having a type family check makes more sense? Like grouping them as

Integer Family: INT8, INT16, INT32, INT64 → promote to most capable type

Decimal Family: DECIMAL4, DECIMAL8, DECIMAL16 → promote to most capable type

Boolean Family: TRUE, FALSE → treat as single boolean type

Bit on lines with Spark side implementation https://github.com/apache/spark/pull/52406/files#diff-fb3268e5296f089d5f57c168f3e9cd74a401b184db3f30982588a134d8abfa53R322-R326 where all integer types are converted to Long

I added the promotion for integer family and decimal family. True/false is already treated as single boolean type.

For Decimal type, right now when the scale doesn't match, it will not scale and will just write non-shredding value. I can address that separately - which seems we need to introduce DecimalWriter to handle such scale conversion.

Please take another look.

huaxingao · 2026-01-23T19:10:55Z

spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/SparkWriteConf.java

        if (parquetCompressionLevel != null) {
          writeProperties.put(PARQUET_COMPRESSION_LEVEL, parquetCompressionLevel);
        }
+        writeProperties.put(SparkSQLProperties.SHRED_VARIANTS, String.valueOf(shredVariants()));


Nit: shredVariants() is evaluated twice. Could we store it in a local boolean shredVariants = shredVariants()?

huaxingao · 2026-01-23T19:43:20Z

...rk/src/main/java/org/apache/iceberg/spark/source/SparkParquetWriterWithVariantShredding.java

+
+  @Override
+  public void setColumnStore(ColumnWriteStore columnStore) {
+    // Ignored for lazy initialization - will be set on actualWriter after initialization


setColumnStore is currently a no-op. That’s fine during the buffering phase, but after actualWriter is initialized, Parquet will call setColumnStore again for new row groups. Should we forward the store to actualWriter when it’s non-null (e.g.,

if (actualWriter != null) actualWriter.setColumnStore(columnStore);

) to avoid writing to a stale store?

Also, can we add a regression test that forces multiple row groups (e.g., tiny row-group size) to ensure the writer remains correct across row-group rollover?

You are right. Updated to set columnStore for new row groups and added the coverage.

huaxingao · 2026-01-23T19:44:13Z

...rk/src/main/java/org/apache/iceberg/spark/source/SparkParquetWriterWithVariantShredding.java

+            64,
+            ParquetProperties.DEFAULT_PAGE_WRITE_CHECKSUM_ENABLED,
+            null,
+            rowGroupOrdinal);


we’re constructing a new ColumnChunkPageWriteStore with a hardcoded column index truncate length (64) and fileEncryptor = null. Should we instead reuse the ParquetWriter’s configured values (truncate length / encryption) to avoid behavior differences when variant shredding is enabled? Also shall we add a small regression test that enables Parquet encryption (or sets a non-default truncate length) ?

Yeah. I forgot to update this part. Let me get the configured value.

I updated. Please help take a look.

huaxingao · 2026-01-23T19:44:35Z

spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/source/VariantShreddingAnalyzer.java

+      }
+    }
+
+    PhysicalType getMostCommonType() {


I have the same question. I think we should add an explicit deterministic tie-break and also add a regression test that creates a perfect tie to ensure inference is stable.

RussellSpitzer · 2026-01-27T19:07:28Z

parquet/src/main/java/org/apache/iceberg/parquet/ParquetVariantWriters.java

+          writeNull(valueWriter, repetitionLevel, valueDefinitionLevel);
+        } catch (IllegalArgumentException e) {


Any way we can avoid having to try/fail here? Just wondering if it's just for decimals can we do some decimal specific check or is this just much simpler.

This is simpler. Probably we can introduce a DecimalWriter to handle that by checking if there is scale mismatch and fallback to write to value field.

Do you prefer that approach?

RussellSpitzer · 2026-01-27T19:15:25Z

parquet/src/main/java/org/apache/iceberg/parquet/ParquetWriter.java


  @Override
  public void add(T value) {
+    if (model instanceof WriterLazyInitializable lazy) {


This kind of feels to me like we should be making a subclass of ParquetWriter which only takes lazy models rather than doing a special case init here based on type matching.

Another possibility, can we create the ParquetWriter after buffering? The timeline now just seems a little odd

Make Writer, Start buffering data, InitWriter

I haven't walked through this but I would have thought we would do something like

Buffer Data, Make Writer

Let me try out such refactoring.

RussellSpitzer · 2026-01-27T19:18:03Z

parquet/src/main/java/org/apache/iceberg/parquet/ParquetWriter.java

      this.closed = true;
+
+      // Force initialization if lazy writer still has buffered data
+      if (model instanceof WriterLazyInitializable lazy) {


This is a little confusing to me, this is for the case in which our model has buffered but not yet put the information in to a row group? IE we are still deciding how to shred or something like that but close has been called?

That's right. This is to handle special case that there are only a few rows which doesn't trigger schema inference logic and the file gets closing. We need to check if initialization has been done here.

I updated the comment a little bit. Hope it makes sense.

parquet/src/main/java/org/apache/iceberg/parquet/ParquetWriter.java

github-actions bot added spark parquet labels Oct 11, 2025

aihuaxu force-pushed the spark-write-iceberg-variant branch from 16b7a09 to dc4f72e Compare October 11, 2025 21:03

aihuaxu marked this pull request as ready for review October 11, 2025 21:15

aihuaxu force-pushed the spark-write-iceberg-variant branch 3 times, most recently from 97851f0 to b87e999 Compare October 13, 2025 16:47

huaxingao reviewed Oct 21, 2025

View reviewed changes

parquet/src/main/java/org/apache/iceberg/parquet/ParquetWriter.java Show resolved Hide resolved

deniskuzZ mentioned this pull request Oct 31, 2025

HIVE-29287: Iceberg: [V3] Variant Shredding support apache/hive#6152

Merged

github-actions bot added the stale label Nov 24, 2025

github-actions bot removed the stale label Dec 1, 2025

gkpanda4 reviewed Jan 14, 2026

View reviewed changes

aihuaxu force-pushed the spark-write-iceberg-variant branch from b87e999 to 2e81d79 Compare January 14, 2026 05:39

aihuaxu added 3 commits January 15, 2026 10:49

Spark shredded variant implementation

d903c5b

Add heuristics to determine the shredding schema

c570ed8

Simplify heuristics to most common type

eeeb35c

aihuaxu force-pushed the spark-write-iceberg-variant branch from 2e81d79 to 7e1b608 Compare January 15, 2026 19:35

aihuaxu force-pushed the spark-write-iceberg-variant branch from 7e1b608 to b74addb Compare January 15, 2026 19:53

aihuaxu force-pushed the spark-write-iceberg-variant branch 3 times, most recently from 7c805f6 to 67dbe97 Compare January 15, 2026 22:50

aihuaxu requested review from gkpanda4 and huaxingao January 15, 2026 22:51

Add to 4.1

5c0533e

aihuaxu force-pushed the spark-write-iceberg-variant branch from 67dbe97 to 5c0533e Compare January 16, 2026 06:25

gkpanda4 reviewed Jan 21, 2026

View reviewed changes

huaxingao reviewed Jan 23, 2026

View reviewed changes

RussellSpitzer reviewed Jan 27, 2026

View reviewed changes

parquet/src/main/java/org/apache/iceberg/parquet/ParquetWriter.java Outdated Show resolved Hide resolved

Add tie break and INT/DECIMAL promotion

d7e15a7

aihuaxu force-pushed the spark-write-iceberg-variant branch from 7758429 to d7e15a7 Compare January 28, 2026 01:32

aihuaxu requested review from RussellSpitzer, gkpanda4 and huaxingao January 28, 2026 01:32

		writeNull(valueWriter, repetitionLevel, valueDefinitionLevel);
		} catch (IllegalArgumentException e) {

Spark: Support writing shredded variant in Iceberg-Spark #14297

Are you sure you want to change the base?

Spark: Support writing shredded variant in Iceberg-Spark #14297

Uh oh!

Conversation

aihuaxu commented Oct 11, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

aihuaxu commented Oct 15, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

aihuaxu commented Oct 21, 2025

Uh oh!

Uh oh!

pvary commented Oct 21, 2025

Uh oh!

huaxingao commented Oct 21, 2025

Uh oh!

pvary commented Oct 22, 2025

Uh oh!

aihuaxu commented Oct 24, 2025

Uh oh!

github-actions bot commented Nov 24, 2025

Uh oh!

Tishj commented Nov 30, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

yguy-ryft commented Dec 24, 2025

Uh oh!

aihuaxu commented Jan 9, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

aihuaxu commented Jan 9, 2026

Uh oh!

gkpanda4 left a comment

Choose a reason for hiding this comment

Uh oh!

aihuaxu commented Jan 15, 2026

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Uh oh!

aihuaxu commented Oct 11, 2025 •

edited

Loading

aihuaxu commented Oct 15, 2025 •

edited

Loading

Tishj commented Nov 30, 2025 •

edited

Loading

aihuaxu commented Jan 9, 2026 •

edited

Loading