viirya commented on code in PR #7090:
URL: https://github.com/apache/datafusion-comet/pull/7090#discussion_r4304266507
##########
docs/source/user-guide/latest/expressions.md:
##########
@@ -41,21 +41,21 @@ Most expressions can also be disabled with
`spark.comet.expression.EXPRNAME.enab
## Status legend
-| Status | Meaning |
-| --- | --- |
+| Status | Meaning
|
Review Comment:
This file is in `.prettierignore` because re-aligning its tables rewrites
every row and causes merge conflicts between expression PRs. This diff
re-aligns the whole file (+506/−506), though the only real change is the link
on line 74. About 27 other open PRs touch this file. Could you revert it to
just that one-line change?
##########
docs/source/user-guide/latest/datasources.md:
##########
@@ -154,7 +151,7 @@ the HDFS read itself, typically while an unrelated query is
running.
```
Native Iceberg scans do not support HDFS-backed tables; those scans fall back
to Spark. See the
-[Comet and Iceberg Guide](iceberg.md).
+[Iceberg](iceberg.md).
Review Comment:
nit: "See the [Iceberg](iceberg.md)." reads as if a word is missing. Maybe
"See the [Iceberg](iceberg.md) page."
##########
docs/source/user-guide/latest/compatibility/index.md:
##########
@@ -122,8 +122,8 @@ divergence:
The following native paths silently return values that differ from Spark for
edge-case inputs.
Most also have entries in the per-category expression pages linked above; they
are collected here
so users hunting an unexpected value have a single place to check. Parquet and
Iceberg reads and
-writes have their own list, in
-[Known Issues in Parquet and Iceberg Reads and
Writes](parquet-iceberg-known-issues.md#results-that-differ-from-spark):
+writes have their own lists, under Known Issues on the
Review Comment:
This tells readers that the Parquet and Iceberg issues are under Known
Issues on those two pages, but the object-store and credential entries moved to
the S3 and Azure Limitations lists in `datasources.md`. Moving them makes
sense, since they depend on storage rather than format. But someone checking
Known Issues for a read that fails where Spark succeeds won't find them now. A
sentence in each Known Issues section would close the gap, for example:
"Storage-specific issues are listed under the [S3](datasources.md#s3) and
[Azure](datasources.md#azure) limitations."
Those two lists sit under headings that are both `### Limitations`, so their
anchors come out as `#limitations` and `#limitations-1`, and the second one
changes meaning if another Limitations heading appears earlier. Renaming them
to `### S3 Limitations` and `### Azure Limitations` would give stable anchors.
Nothing links to `#limitations` today.
##########
docs/source/conf.py:
##########
@@ -205,6 +205,11 @@
"user-guide/tuning.html": "latest/tuning.html",
# The compatibility guide became a directory of pages in #4055.
"user-guide/latest/compatibility.html": "compatibility/index.html",
+ # Parquet and Iceberg documentation was consolidated into one page each.
+ "user-guide/latest/compatibility/scans.html": "../parquet.html",
+ "user-guide/latest/compatibility/parquet-iceberg-known-issues.html":
"../parquet.html",
Review Comment:
Half of this page went to `iceberg.md#known-issues`, and this redirect lands
readers at the top of the Parquet page. Does sphinx-reredirects accept a
fragment in the target? If so, `../parquet.html#known-issues` would get them
closer, together with a line in Parquet's Known Issues pointing to Iceberg's.
The same applies to `tuning/scans.html` on the next line, with
`../parquet.html#tuning`.
##########
docs/source/user-guide/latest/iceberg.md:
##########
@@ -270,3 +280,576 @@ the expression fall back to Spark.
The native Iceberg reader populates Spark's task-level
`inputMetrics.bytesRead` (visible in the Spark UI Stages tab) using the
`bytes_read` counter from iceberg-rust's `ScanMetrics`. This counter includes
bytes read from both data files and delete files. The scan's SQL metrics,
including Iceberg's planning counters, are listed in the [Metrics
Guide](metrics.md#cometicebergnativescan).
Iceberg Java does not explicitly report `bytesRead` to Spark's task input
metrics. On the iceberg Java path, any `bytesRead` value comes from Hadoop's
filesystem-level I/O counters, not from Iceberg itself. Because Comet's native
reader and the Hadoop filesystem use different counting mechanisms, the exact
byte counts will differ between the two paths.
+
+## Writes
+
+With `spark.comet.write.iceberg.enabled=true`, Comet plans Iceberg writes with
the split-operator
+plan described below, and writes the data files of each eligible write
natively with iceberg-rust.
+A write that is not eligible falls back to iceberg-java's writer. Natively
written files differ
+from iceberg-java's in the ways listed under [Accepted
divergences](#accepted-divergences).
+
+The setting is off by default, so Spark plans its own write operator and
iceberg-java writes every
+data file. Validate the native writer against your own workloads before
enabling it, including
+the costs described under [Native Parquet write
eligibility](#native-parquet-write-eligibility)
+and [Failure handling](#failure-handling).
+
+### Overview
+
+Spark writes an Iceberg table through a single physical operator that combines
data-file
+writing with metadata writing, committing, and catalog validation. Spark's
Adaptive Query
+Execution (AQE) already re-plans the sub-query feeding that operator — the
scans, projects,
+sorts, and exchanges producing the rows — but the operator itself sits outside
AQE, so the
+data-file writing cannot be re-planned in response to how its input ran. And
because data-file
+writing is bundled with the metadata and commit steps, there is no separate
step for Comet to
+replace.
+
+When `spark.comet.write.iceberg.enabled=true`, Comet rewrites eligible Iceberg
writes into two
+operators:
+
+1. **`IcebergWrite`** — writes the data files on the executors, exactly as
iceberg-java does
+ today, and returns each task's serialized commit message. This operator and
the sub-query
+ feeding it run inside AQE.
+2. **`IcebergCommit`** — collects the commit messages on the driver and
performs the normal
+ Iceberg commit (including commit-time validation), outside AQE, exactly
once.
+
+The split moves data-file writing inside AQE and separates it from the commit,
which gives Comet
+a step to replace: when the write passes the eligibility check below, the
`IcebergWrite`
+operator's per-task Parquet write is delegated to
+[iceberg-rust](https://github.com/apache/iceberg-rust) via Comet's native
execution pipeline
+([#5361](https://github.com/apache/datafusion-comet/pull/5361)). A write that
does not pass keeps
+`IcebergWrite`, and iceberg-java writes its data files; only the plan shape
changes.
+
+### How the native write works
+
+The JVM-side planner marshals everything iceberg-rust needs — the write schema
and partition
+spec as JSON, the data location, the resolved parquet writer settings, the
writer mode
+(unpartitioned / fanout / clustered, mirroring `SparkWrite`'s own choice),
object-store
+configuration (the table's `FileIO` properties, e.g. REST-vended credentials,
merged over
+`fs.s3a.*` settings translated from the effective Hadoop configuration carried
by the table's
+`FileIO`, including catalog-specific `hadoop.*` overrides, since
`HadoopFileIO` carries its S3
+configuration in the Hadoop Configuration rather than in `FileIO` properties),
and per-task IDs —
+into the serialized native plan. On each task, iceberg-rust writes the Parquet
files and
+returns its `DataFile` metadata packed as a single in-memory Iceberg V2 data
manifest; the JVM
+decodes those bytes with Iceberg's own `ManifestFiles.read`, re-derives each
file's manifest
+metrics from the written Parquet footer with Iceberg's `MetricsConfig` logic
(so metrics modes,
+truncation, and bounds decisions are iceberg-java's by construction), and
wraps the result in
+the same `TaskCommit` message the JVM writer would have produced. Everything
iceberg-java does
+post-write — snapshot assignment, manifest-list aggregation, commit validation
and retries —
+is untouched: `IcebergCommit` performs the normal `BatchWrite.commit`.
+
+For `ResolvingFileIO`, Hadoop settings are taken from the delegate opening the
data location.
+An S3 location handled by `S3FileIO` uses its initialized FileIO properties,
so Hadoop options
+on the wrapper do not change the native endpoint or encryption settings. If
that delegate cannot
+be resolved, the write falls back to iceberg-java.
+
+Before a task opens a partition's first file, it holds the partition's initial
rows in memory
+to choose which columns to dictionary-encode using parquet-mr's size
accounting (see the
+accepted divergences below). It normally waits for at least
`write.parquet.page-row-limit`
+rows (at least 100 when the configured limit is lower), or until the partition
ends. The
+buffering threshold is `write.parquet.row-group-size-bytes`, shared across all
partitions in
+a fanout write. Reaching it makes each partition still holding rows choose
from the rows it
+already has, even if they do not fill its first page.
+
+### Configuration
+
+Standard Comet + Iceberg setup (see [Native Reader](#native-reader)) plus the
write setting, which is
+off by default:
+
+```
+# Standard Comet / Iceberg wiring
+spark.plugins=org.apache.spark.CometPlugin
+spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
+spark.sql.catalog.<name>=org.apache.iceberg.spark.SparkCatalog
+spark.sql.catalog.<name>.type=hadoop # or hive / glue
/ rest / ...
+spark.sql.catalog.<name>.warehouse=...
+
+# Split-operator plan and native Parquet writer
+spark.comet.write.iceberg.enabled=true
+
+# Lets writes whose input is a local relation (INSERT ... VALUES, a local
DataFrame) use the
+# native writer; see "Native Parquet write eligibility" below
+spark.comet.exec.localTableScan.enabled=true
+```
+
+### Supported operations
+
+The split-operator plan supports the following operations on every Spark
version Comet
+supports:
+
+- `INSERT INTO` / DataFrame `append` (`AppendData`)
+- `INSERT OVERWRITE`, static and dynamic (`OverwriteByExpression`,
+ `OverwritePartitionsDynamic`)
+- Copy-on-write `DELETE` / `UPDATE` / `MERGE` (`ReplaceData`)
+- Iceberg's `rewrite_data_files` procedure, which writes each rewritten file
group as an append
+
+For an unpartitioned copy-on-write `MERGE`, the native Iceberg writer is
reachable on Spark
+3.5+ when `spark.comet.exec.mergeRows.enabled=true`. With the flag disabled,
the JVM
+`MergeRowsExec` breaks the fully-native child chain required by
`CometIcebergWriteExec`.
+On Spark 4.1+, the native MergeRows path also preserves the semantic counters
required by the
+summary-aware writer commit contract.
+
+For copy-on-write row-level DML, the mechanism differs by Spark version: on
Spark 4.0+ the
+analyzer emits operation-coded rows that Comet's writer dispatches through
`ReplaceData`'s
+projections, while on Spark 3.4/3.5 the rewritten rows are written as a plain
row stream. The
+supported set of operations is the same either way.
+
+Merge-on-read uses Spark's `WriteDelta`, and Iceberg's JVM `DeltaWriter`
writes its rows under
+either plan, so it keeps Spark's stock write plan. On Spark 3.5+ the testing
setting
+`spark.comet.write.iceberg.splitOperator.enabled` plans it as the split plan,
with the same driver
+commit and reporting path, but `CometIcebergWriteExec` is never used for
position-delta rows.
+Spark 3.4 always leaves `WriteDelta` on Spark's stock write plan.
+
+On Spark 4.1+ the split plan matches two further stock-Spark behaviours: MERGE
metrics are
+forwarded to the writer's commit (Iceberg 1.11+ records them in the snapshot
summary), and
+cached catalog tables are recached by name after a write so cache entries
survive schema
+changes.
+
+### When Comet falls back to Spark's write operator
+
+The rewrite is skipped — and the write runs through Spark's stock combined
operator — when:
+
+- `spark.comet.write.iceberg.enabled` is `false` (the default) and the testing
setting
+ `spark.comet.write.iceberg.splitOperator.enabled`, which Comet 1.1.0
documented as a user setting,
+ is not on;
+- Comet is disabled (`spark.comet.enabled=false`), or its native execution is
+ (`spark.comet.exec.enabled=false`);
+- Comet is in plan-only mode (`spark.comet.explain.planOnly.enabled=true`);
+- the write is neither an Iceberg `SparkWrite` nor a supported Iceberg
position-delta write;
+- the table uses merge-on-read (`WriteDelta`), unless the testing setting
+ `spark.comet.write.iceberg.splitOperator.enabled` is on with Spark 3.5+;
+- the statement is CTAS / RTAS on Spark 3.4, where the staged exec writes
inline; on Spark
+ 3.5+ those statements re-plan their inner append, which is intercepted
normally;
+- the write is a streaming write (`writeStream` into an Iceberg table);
+- the write requires Spark's commit coordinator, which Comet's per-task commit
protocol does
+ not use;
+- Comet cannot reflect the Iceberg internals needed to build the two-operator
plan (for
+ example an unrecognised write class, or an Iceberg 1.5 `ReplaceIcebergData`
plan whose fields
+ it cannot read).
+
+In every fallback case the write is planned as if Comet were absent; there is
no correctness
+trade-off, only no plan change.
+
+### Native Parquet write eligibility
+
+When `spark.comet.write.iceberg.enabled=true`
+([#5361](https://github.com/apache/datafusion-comet/pull/5361)), the
`IcebergWrite` operator's
+per-task Parquet write is delegated to
[iceberg-rust](https://github.com/apache/iceberg-rust).
+The native writer must produce the same outcome as iceberg-java — the same
Parquet features,
+statistics, and manifest metadata — so a write is only eligible when every
table property it
+depends on is one the native path reproduces exactly, and additionally only
when the plan
+feeding the write is fully Comet-native. For a partitioned table that plan
includes the hash
+distribution and local sort Iceberg requests on its partition transforms;
those stay native
+because the transforms themselves have native implementations (see
+[Iceberg system functions](#iceberg-system-functions)). Ineligible writes run
through
+iceberg-java unchanged, with the reason reported as a fall-back reason in
Comet's extended EXPLAIN
+output. A write that runs natively shows `CometIcebergWrite` under
`IcebergCommit` in the physical
+plan; an ineligible write keeps `IcebergWrite`.
+
+The native writer reads its input as Arrow batches from a Comet operator, so
the write's input
+must itself run in Comet. A write whose input is a local relation, such as
`INSERT ... VALUES` or
+`df.writeTo(...).append()` on a DataFrame built from local data, is fed by
Spark's
+`LocalTableScanExec`, which Comet only converts when
`spark.comet.exec.localTableScan.enabled=true`
+(off by default). Without that setting such writes keep `IcebergWrite` and run
through
+iceberg-java.
+
+**Most Iceberg write settings are not supported.** Detection is an allowlist:
a write is
+eligible only when its entire effective configuration matches the table below,
and anything
+else — any other write-affecting property, any key added by a future Iceberg
version, any
+value outside the supported set, any reflection failure while inspecting the
write — falls
+back to iceberg-java with a reason reported in extended EXPLAIN. Checks run on
the effective
+configuration: table properties overlaid with `SparkWrite.writeProperties`,
which is where
+iceberg-java resolves per-write options and `spark.sql.iceberg.*` session
overrides.
+
+A write is eligible only when ALL of the following hold:
+
+| Setting
| Supported
values
|
+|
-------------------------------------------------------------------------------------------------------------------------------------------
|
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
+| resolved write format (`write-format` option overlaid on
`write.format.default`)
| `parquet`
|
+| `format-version`
| `1`, `2` or
`3` (on `3`, writes that carry row lineage fall back; see below)
|
+| `write.parquet.compression-codec` / `compression-level` /
`row-group-size-bytes` / `page-size-bytes` / `page-row-limit` /
`dict-size-bytes` | the sizes/limit must be positive Java ints
(`Integer.parseInt` semantics — no trimming, no values past `Int.MaxValue` —
matching iceberg-java, whose writer fails on anything else);
`compression-level` must be a Java int within the native writer's per-codec
range (zstd 1–22, gzip 0–9, brotli 0–11; ignored by both writers for
snappy/lz4/none) — iceberg-java never validates the level, so an out-of-range
value falls back rather than becoming a native task failure |
+| `write.parquet.row-group-check-min-record-count`
| unset or `100`
(the default)
|
+| `write.parquet.row-group-check-max-record-count`
| unset or
`10000` (the default)
|
+| `write.parquet.page-version`
| unset or `v1`
|
+| `write.parquet.shred-variants`
| unset or
`false` (Spark 4.x / Iceberg 1.11 resolve this into every parquet write)
|
+| `write.parquet.variant-inference-buffer-size`
| any value
(only meaningful when shredding, which is gated)
|
+| `write.parquet.bloom-filter-enabled.column.<col>`
| unset or
`false`
|
+| `write.metadata.metrics.*`
| any value
(manifest metrics are re-derived on the JVM with Iceberg's own logic)
|
+| `write.spark.fanout.enabled`
| any value (the
native writer implements both clustered and fanout modes)
|
+| `write.target-file-size-bytes`
| any value (the
two writers can choose different roll points; see accepted divergences)
|
+| data location URI scheme
| `file`,
`memory`, `s3`, `s3a`, `gs`, matched case-sensitively (`S3://` falls back).
`s3`, `s3a` and `gs` need a bucket in the authority (`s3://bucket/...`), so a
hostless form such as `s3:/bucket/key` falls back. `gs` only when the `FileIO`
opening the data location is a `GCSFileIO`; see below
|
+| resolved `table.locationProvider()`
| Iceberg's
built-in `DefaultLocationProvider`
|
+| Hadoop S3A settings for an `s3` / `s3a` data location
| only
`fs.s3a.access.key`, `secret.key`, `session.token`, `endpoint`,
`endpoint.region`, and `path.style.access`, including their
`fs.s3a.bucket.<data-bucket>.*` forms; any other effective `fs.s3a.*` setting
falls back, apart from the ignored settings listed below
|
+| Iceberg `FileIO` S3 settings for an `s3` / `s3a` data location
| the S3
endpoint, region, static/session credentials, path-style, SSE (`none`, `s3`,
`kms`, or `custom`; not `dsse-kms`), assume-role, anonymous/config-chain
settings parsed by the pinned iceberg-rust version, plus Comet's
credential-provider class and built-in web-identity properties. When a custom
provider is configured, its vendor-owned `s3.*` / `client.*` properties are
also forwarded; unsupported Iceberg-defined S3 settings still fall back
|
+| partition spec
| any, except a
`void` field whose source column was dropped beside a live field (see below)
|
+| column types
| any except
`uuid` (Spark plans it as a string; no Arrow cast reaches `fixed(16)`) and the
v3 types `variant`, `unknown`, `timestamp_ns`, `geometry` and `geography`
|
+
+Within the namespaces that shape data-file bytes — `write.parquet.*` and
`parquet.*` —
+everything not listed above must be absent: unvetted `write.parquet.*` keys
(e.g.
+`bloom-filter-max-bytes`, `stats-enabled.column.*`, keys added by future
Iceberg versions),
+any `parquet.*` table property (including `parquet.enable.dictionary`), and
any `parquet.*`
+key in the session Hadoop configuration other than the reader-only
+`parquet.hadoop.vectored.io.enabled` (with `HadoopFileIO`-backed output those
reach
+iceberg-java's writer but not the native one). Also gated explicitly: any
`encryption.*` key,
+`write.object-storage.enabled=true`, `write.location-provider.impl`, and
`io-impl`.
+
+Three checks look past properties at the table's instantiated state, because
they can be
+configured at the catalog level (or by a custom `TableOperations`) without any
table or write
+property changing: `table.locationProvider()` must be Iceberg's
`DefaultLocationProvider`;
+`table.io()` must be a recognized `FileIO` (the same allowlist the native scan
uses, minus the
+`EncryptingFileIO` family — the native writer produces plaintext files, so an
encrypting `FileIO`
+is rejected on the write side); and `table.encryption()` must be Iceberg's
+`PlaintextEncryptionManager`. Anything else falls back.
+
+A `gs` data location additionally requires that the `FileIO` actually opening
it is a
+`GCSFileIO` (for a `ResolvingFileIO`, the delegate it instantiates for that
location,
+which is a `HadoopFileIO` when the GCS `FileIO` cannot be loaded or
initialized). A
+`HadoopFileIO` takes its GCS credentials, endpoint and project from `fs.gs.*`
in the Hadoop
+Configuration, and only `fs.s3a.*` is translated into the native `FileIO`, so
the native writer
+could resolve a different storage identity or endpoint than the JVM writer
would. That
+combination falls back; a `GCSFileIO` carries its `gcs.*` settings in
`FileIO.properties()`,
+which are forwarded.
+
+For an `s3` or `s3a` data location, the gate also inspects both the table
FileIO's effective Hadoop
+configuration and `table.io().properties()`. These are separate allowlists
because Hadoop S3A
+keys are translated before they reach iceberg-rust, while Iceberg `FileIO`
keys are forwarded
+directly. When the FileIO exposes a Hadoop configuration, its initialized
values govern both the
+gate and native translation, including after session or catalog options
change. FileIO
+implementations without a Hadoop configuration, such as `S3FileIO`, use only
their initialized
+properties; session and catalog Hadoop options are neither checked nor
forwarded. Hadoop's built-in
+`core-default.xml` values are not treated as explicit settings, but
+programmatic settings and values from site or custom `*-default.xml` resources
are. Spark's
+session-wide S3A vectored-read and `downgrade.syncable.exceptions`
compatibility settings are also
+ignored, as are the S3A committer (`committer.name`, `committer.magic.enabled`,
+`committer.threads`), `bulk.delete.page.size` and `experimental.input.fadvise`
settings, because
+none of them can alter an Iceberg data-file write request. Spark 4.1 sets
`committer.name` and
+`committer.magic.enabled` itself when `spark-hadoop-cloud` is on the
classpath. Ignored settings are
+matched by their plain `fs.s3a.<name>` keys, so a per-bucket
`fs.s3a.bucket.<data-bucket>.<name>`
+form still falls back. Unknown explicit
+`fs.s3a.*`, `s3.*`, or `client.*` settings therefore fall back at planning
time instead of being
+silently ignored by the native storage backend. The exception is a
vendor-owned `s3.*` /
+`client.*` property when
+`s3.comet.credential.provider.class` is configured: the provider receives the
unfiltered FileIO
+bag and can consume that property. Iceberg-defined settings that the native
storage path cannot
+honour still fall back even with a provider. A per-bucket Hadoop setting
counts only for the exact
+data-bucket name, so configuration for a longer dotted bucket does not by
itself disable the
+native write. If Iceberg's AWS property classes cannot be loaded, vendor
`s3.*` / `client.*` keys
+fall back too and planning still completes. The fall-back reason reports only
sorted property
+names, never their values, so credentials and tokens do not enter EXPLAIN or
plan logs.
+
+On a format-version 3 table with Iceberg 1.10 or newer, a write that rewrites
existing rows falls
+back. Copy-on-write `DELETE`, `UPDATE` and `MERGE` and `rewrite_data_files`
write the row lineage
+columns `_row_id` and `_last_updated_sequence_number` into the new data files,
so that rewritten
+rows keep their ids, and the native writer does not write those columns.
Appends, overwrites,
+`CREATE TABLE ... AS SELECT` and `REPLACE TABLE ... AS SELECT` write the data
columns only, and
+Iceberg assigns the new rows their ids when it commits, so those run natively.
Earlier Iceberg
+versions write no lineage columns, so the rule never applies to them.
+
+A format-version-1 table keeps a dropped partition field as a `void` field,
and its source column
+can be dropped afterwards. A spec that mixes such a field with a live one
falls back: iceberg-java
+cannot write through it either, so the write fails with iceberg-java's own
error rather than in
+the native writer
([#6141](https://github.com/apache/datafusion-comet/issues/6141)). A spec whose
+fields are all `void` writes unpartitioned and stays eligible.
+
+Other `write.*` properties are intentionally not gated because they cannot
make the native
+writer produce different data files: distribution and ordering settings shape
the Spark plan
+identically on both paths, WAP / branch / snapshot properties act on the JVM
committer,
+`write.avro.*` / `write.orc.*` apply only to formats already excluded, and
merge-on-read
+settings route the write through `WriteDelta`, whose task writer remains
iceberg-java rather
+than the native writer. Most native eligibility rules are pinned by
+`CometIcebergWriteDetectionSuite`.
+
+Manifest `DataFile` metrics are assembled on the JVM before commit: each
written file's
+metrics are re-derived from its parquet footer through the version-matched
+`ParquetUtil.footerMetrics` and `MetricsConfig.forTable`, with float/double
NaN counts and
+bounds carried over from the native writer's tracked state. iceberg-java's
metadata decisions
+— metrics modes, the inferred-column cap
+(`write.metadata.metrics.max-inferred-column-defaults`), bound truncation, and
list/map bounds
+suppression — are therefore applied by iceberg-java's own code regardless of
what the native
+writer reports.
+
+iceberg-java's writer takes these metrics from the footer it still holds in
memory, but the native
+path reads each footer back from storage: two small reads per written file,
one for the footer's
+length and one for the footer itself, made one file after another before the
task finishes. On S3
+or GCS each read is a GET request, so the task waits two request round trips
per file. That is
+small next to uploading a large file, but a write that produces many small
files, such as a fanout
+write over many partitions, pays it for every one of them.
+
+### Failure handling
+
+Eligibility is decided entirely at plan time. That includes the reflection
surface: every
+iceberg-java class, method, and constructor the executor-side commit-message
assembly uses is
+eagerly resolved by the eligibility gate on the driver, so an Iceberg release
that moves any
+of them declines the native path with a fall-back reason instead of failing
tasks mid-write.
+Once planned, the physical plan is fixed — there is no per-task re-decision or
runtime switch
+back to the JVM writer.
+
+When a native write fails partway through a task (an object-store error, a
data-dependent cast
+failure), the error propagates as an ordinary Spark task failure and Spark's
task retry
+re-executes it — through the native writer again. Retries cannot collide: each
attempt's task
+attempt id is embedded in its data file names.
+
+The native writer's buffers are charged to Comet's memory pool, the off-heap
budget Comet's other
+native operators draw on, where iceberg-java's buffers sit on the JVM heap. A
fanout write keeps a
+data file open for every partition a task writes to. Each open file holds the
row group it is
+writing in memory, up to `write.parquet.row-group-size-bytes`, and on S3 or
GCS also the last row
+group it flushed, which is uploaded once the next one is complete or the file
closes. Each
+partition also holds the rows that have not reached its file yet. So a task
writing to many
+partitions needs memory in proportion to them. When the pool cannot grant it,
the task writes out
+and closes the partitions holding the most memory until what is left fits, and
a closed
+partition's next rows open a new file. The write then finishes with more,
smaller files than
+iceberg-java's would. When every partition keeps getting rows, a task given an
eighth of the memory
+its partitions need writes four to five files per partition instead of one,
however many partitions
+it writes to. The `files closed early to free memory` metric of its
`CometIcebergWrite` operator
+counts the files it closed early. Disabling the fanout writer
(`write.spark.fanout.enabled=false`)
+avoids the extra files: Spark then sorts each task's rows by partition, and
the task keeps one file
+open at a time. A larger `spark.memory.offHeap.size` also helps, and Iceberg's
`rewrite_data_files`
+procedure compacts small files once they are written.
+
+A write that keeps one file open, unpartitioned or clustered, has no partition
to close. When that
+file outgrows the pool, the task fails with a `CometNativeException` reading
+`Additional allocation failed for IcebergWriteExec` instead of exceeding the
executor's memory, and
+Spark retries it like any other task failure. A smaller
`write.parquet.row-group-size-bytes` or a
+larger `spark.memory.offHeap.size` lets such a write fit.
+
+Partial results are never committed. The commit set is exactly the commit
messages returned by
+successful tasks — a failed task contributes none — and if the job fails, the
driver-side
+commit operator aborts without committing anything. A failed task attempt also
deletes the
+data files it created, as iceberg-java's writer abort does. The native writer
records every
+location it hands to a file writer, and cleanup has no ownership gap. Native
keeps its cleanup
+guard armed after yielding the output batch. The JVM reads those locations
into a task failure
+listener first, then polls the native output to EOF; that EOF is the
acknowledgement that lets
+native disarm. A failure in this narrow handoff window can therefore trigger
best-effort deletion
+on both sides, which is harmless. The native guard still covers a failed
write, a task torn down
+before the write completed — for example because the operator feeding it threw
— and a failure
+encoding the manifest or building the output batch. The handoff does not
depend on decoding the
+manifest: the locations are read before the manifest is decoded, so a failure
in that decode still
+cleans up. Cleanup never masks the original failure; anything it misses is
invisible to every
+reader, since readers resolve files through committed manifests only, and is
reclaimed by Iceberg's
+normal `remove_orphan_files` maintenance.
+
+When one task fails, the tasks that had already completed leave
committed-nothing data files
+too. The committer collects each task's commit message as that task finishes,
so on a job
+failure it aborts with the completed messages and deletes their data files
through the table
+`FileIO`. (From Iceberg 1.6 on, Iceberg's own `SparkWrite.abort` skips cleanup
unless a commit
+failed with a cleanable error, so on the stock path those files are left for
+`remove_orphan_files`. Iceberg 1.5, which the Spark 3.4 profile pins, deletes
them.) A
+failure during the driver-side commit itself behaves exactly as on the stock
path: the commit
+messages carry genuine `SparkWrite$TaskCommit` objects, so Iceberg's own
`SparkWrite.abort`
+cleanup (which deletes the files listed in the commit messages for cleanable
failures) applies
+unchanged.
+
+### Accepted divergences
+
+Some differences between parquet-mr and the pinned parquet-rs / iceberg-rust
are unconditional —
+they apply to every native write and cannot be configured away. Enabling
+`spark.comet.write.iceberg.enabled` accepts them. They fall into two classes
with very
+different blast radius: differences confined to the physical bytes of a data
file (cosmetic —
+no reader decision is based on them), and differences visible in manifest
metadata (these
+outlive the write and feed later readers' pruning decisions, so each one is
analyzed
+individually below).
+
+#### Physical file layout only (cosmetic)
+
+No Iceberg reader bases a planning or correctness decision on these; they
change the bytes of
+a data file but not what any reader computes from it:
+
+- Footer key-value metadata differs: native files carry an `ARROW:schema`
entry and no
+ `iceberg.schema` entry; iceberg-java files are the opposite.
+- The Parquet root schema element is named `arrow_schema` (iceberg-java:
`table`).
+- `created_by` reads `Apache Iceberg <version> (Comet)`, where iceberg-java's
files carry
+ parquet-mr's version string.
+- No page CRC checksums: iceberg-java writes one in every page header, and
parquet-rs writes none.
+ Neither writer puts statistics in page headers; page pruning uses the column
index, which both
+ writers produce.
+- Dictionary-encoded pages are labeled `RLE_DICTIONARY` (parquet-mr v1 files:
`PLAIN_DICTIONARY`).
+- The native writer uses parquet-mr's size accounting to choose dictionary
encoding: a column
+ whose sampled rows show the dictionary saving no space is written plain,
with no dictionary page,
+ instead of carrying a dictionary page that every selective read of it would
have to fetch
+ ([#6114](https://github.com/apache/datafusion-comet/issues/6114)). The
native writer decides
+ once per partition, normally from that partition's first page of rows in the
task, and keeps the
+ decision for every file and row group it writes for the partition;
parquet-mr decides again
+ for every row group. If buffered rows reach
`write.parquet.row-group-size-bytes`, the choice
+ uses the rows collected so far. In a fanout write, all partitions share this
threshold, so
+ each partition still buffering can make its choice before it has a full
first page. Later
+ rows could have changed parquet-mr's decision, so this difference is not
limited to columns
+ close to the size cut-off. Where the page size rather than
`write.parquet.page-row-limit` ends a
+ column's first page, the native page ends at the first row past parquet-mr's
size threshold,
+ while parquet-mr only ends it at its next periodic size check, so a column
close to the
+ cut-off can be decided the other way. Close to the cut-off both encodings
take about the same
+ space. A column that keeps its dictionary and later fills it falls back to
plain on both
+ writers, but parquet-rs's dictionary page then holds every entry, where
parquet-mr's holds
+ only the entries earlier pages used.
+- Row-group boundaries: both writers close a row group by size, but parquet-mr
checks its buffered
+ size only at record-count checkpoints, while parquet-rs checks its own
estimate as it writes each
+ batch, so the boundaries land on different rows.
+- File names: both writers name files `<partition>-<task
attempt>-<operation>-<count>`, counting
+ per task. The native writer zero-pads the task attempt id and counts from
`00000`; iceberg-java
+ leaves the id unpadded and counts from `00001`.
+- Partition directory names match iceberg-java 1.8+'s
`PartitionSpec.partitionToPath` for every
+ partition type the native writer accepts. On Iceberg 1.5.x,
+ which the Spark 3.4 profile pins, iceberg-java itself spelled `timestamp`
and `timestamptz`
+ directories with `LocalDateTime.toString()` / `OffsetDateTime.toString()`
+ (`ts=1969-12-31T23:59:58.500Z`) and left the partition field name unescaped;
Comet uses the
+ 1.8+ spelling on every profile. Distinct partition values still get distinct
directories in all
+ cases, and no reader parses these names: files are resolved through
committed manifests.
+- File rolling lands on the same row grid as iceberg-java but not necessarily
on the same row.
+ Both writers re-check the current file's size against
`write.target-file-size-bytes` once
+ every 1000 rows of that file (iceberg-java's
`RollingFileWriter.ROWS_DIVISOR`; Comet hands the
+ iceberg-rust writer rows in 1000-row units, per partition file, to get the
same grid), so each
+ writer rolls only on a 1000-row boundary of its own file.
+ The shared grid is all that is shared. What each writer compares against the
target differs —
+ flushed bytes plus parquet-rs's estimate of the open row group, versus
parquet-mr's file
+ position plus its buffered size — and the two use different threshold
comparisons. These are
+ independent size estimates, so nothing bounds how far apart the two writers'
roll points are:
+ they may cross the target several grid steps apart, and the resulting files
can differ in row
+ count by an arbitrary number of 1000-row blocks. Do not rely on file-layout
parity between the
+ two writers; rely only on each file rolling on its own 1000-row boundary.
+- A fanout write that the memory pool cannot hold closes partitions before the
task ends (see
+ [Failure handling](#failure-handling)), where iceberg-java's fanout writer
keeps every file open
+ until then. So a partition can have more, smaller files than iceberg-java
writes, and a file
+ closed early ends off the 1000-row grid, as the last file of a task does. A
partition closed
+ before its rows filled a first page makes its dictionary choice from the
rows it has.
+- A fanout write lists a task's data files in file-path order, where
iceberg-java lists them in
+ its own `StructLikeMap` iteration order. Both are stable across runs, and
neither is a
+ documented ordering, but the manifest entry order becomes the scan-task
order and so the row
+ order of an unordered `SELECT *`. On a format-version 3 table it also
decides which row ids the
+ commit gives each file's rows, so the same rows can get different `_row_id`
values from the two
+ writers. The ids are unique either way, and across tasks iceberg-java's own
assignment already
+ depends on the order in which the tasks finish. Only the sorted order is
reproducible on the
+ native path: the order the native writer closes its files in depends on the
order rows arrive in
+ and on which partitions it closes early to free memory. Clustered and
unpartitioned writes append
+ in creation order on both paths and are unaffected.
+- Compressed page bytes are implementation-defined: the codec and any explicit
level are
+ translated, and a `zstd` table with no level is written at parquet-mr's
default level of 3, but
+ parquet-rs and parquet-mr embed different encoder implementations (LZ4
framing, for one), and
+ their pages and row groups end at different rows, so byte-identical output
is not achievable even
+ for a default `zstd` table. The decompressed data is identical. For the same
reason,
+ codec-level side channels (`zlib.compress.level`,
`compression.brotli.quality`,
+ `io.compression.codec.zstd.level` — the last is present in every Hadoop
configuration by
+ default) are not gated: they can only shift compressed bytes, which are
already accepted as
+ divergent.
+
+#### Manifest metadata visible to later readers
+
+Manifest metrics drive partition- and file-level pruning for every future
reader of the table,
+so a divergence here would outlive the write. This class is deliberately kept
almost empty:
+`DataFile` metrics are not taken from the native writer's manifest but
re-derived on the JVM
+from each written file's parquet footer through iceberg-java's own
`ParquetUtil.footerMetrics`
+and `MetricsConfig.forTable` (see above). Metrics modes, lower/upper bound
truncation, the
+null-count conventions, and list/map bounds suppression are therefore
iceberg-java's code
+making iceberg-java's decisions. The parity tests write the same rows through
both writers and
+compare the committed `readable_metrics` (value, null and NaN counts, and
lower and upper bounds)
+for the column types they cover. Two footer-derived values can still differ
from what
+iceberg-java's _writer-tracked_ state would have recorded, and both are
analyzed safe:
+
+- Float/double bounds involving zero may differ in sign: parquet-rs normalises
footer
+ statistics to min `-0.0` / max `+0.0` (the parquet-format recommendation),
while
+ iceberg-java's writer-tracked bounds preserve the exact sign it saw. The
native path's
+ manifest bounds inherit the normalised values — a strictly conservative
widening that cannot
+ change pruning decisions.
+- On Iceberg 1.9+, manifest `value_counts` / `null_value_counts` for
float/double columns
+ nested under a nullable struct count rows whose parent struct is null (they
come from the
+ parquet footer), while iceberg-java's writer-tracked counts do not. The
native counts are the
+ correct ones. iceberg-java's counts leave out rows whose struct is null.
When every non-null
+ struct in a file holds a value, iceberg-java records a null count of 0, and
`IS NULL` prunes a
+ file that contains matching rows
+ ([apache/iceberg#17561](https://github.com/apache/iceberg/issues/17561)).
The native counts
+ never prune such a file. `IS NOT NULL` pruning is the same for both.
+
+All content not listed above — the logical data, statistics values, and
manifest metadata —
+must match iceberg-java exactly, or the write falls back. The open bugs in
which a native write
+still differs are listed in
+[Known Issues](#writes-that-differ-from-spark).
+
+## Known Issues
+
+This section lists open issues in which Comet's Iceberg reads and writes give
a different result
+from Spark or iceberg-java, or fail where they succeed, without falling back.
Each entry links to
+its tracking issue. The cases where Comet falls back to Spark are listed under
+[Current limitations](#current-limitations) and [When Comet falls back to
Spark's write
+operator](#when-comet-falls-back-to-sparks-write-operator).
+
+Which entries apply depends on which native paths are enabled:
+
+| Native path | Setting | Default |
+| -------------- | ---------------------------------------- | ------- |
+| Iceberg scan | `spark.comet.scan.icebergNative.enabled` | `true` |
+| Iceberg writer | `spark.comet.write.iceberg.enabled` | `false` |
+
+### Reads that differ from Spark
+
+- **Migrated tables that use a name mapping.** The `migrate`, `snapshot` and
`add_files`
+ procedures set `schema.name-mapping.default` for files without Iceberg field
ids. When a query
+ also reads a top-level primitive column, the native scan returns NULL for
nested fields (struct
+ fields, list elements and map values). Filters on those fields then select
the wrong rows
+ ([#6790](https://github.com/apache/datafusion-comet/issues/6790)). The fix
needs an iceberg-rust
+ upgrade ([#6934](https://github.com/apache/datafusion-comet/issues/6934)).
+- **Data files with neither field ids nor a name mapping.** Spark reads every
nested field of such
+ a file as NULL, and the native scan returns the values stored in the file
+ ([#6794](https://github.com/apache/datafusion-comet/issues/6794)).
+- **`VERSION AS OF` reads of a column renamed after the snapshot.** The
renamed column reads as
+ NULL, and two columns that swapped names return each other's values
+ ([#6822](https://github.com/apache/datafusion-comet/issues/6822)).
+- **Reflection failures while the scan is planned.** If Comet fails to read
the table's schema or
+ metadata through reflection, it continues with an empty or partial result
instead of falling
+ back, which can resolve a column to a different field id
+ ([#5257](https://github.com/apache/datafusion-comet/issues/5257)).
+
+### Writes that differ from Spark
+
+The native Iceberg writer is disabled by default. With
`spark.comet.write.iceberg.enabled=true`:
+
+- **Brotli tables.** The native writer commits brotli-compressed files. JVM
Parquet readers can
+ read them only if Hadoop's `BrotliCodec` is on the classpath. iceberg-java
fails the write
+ instead ([#6960](https://github.com/apache/datafusion-comet/issues/6960)).
+- **`write.parquet.compression-codec=none`.** The native writer writes the
table uncompressed, and
+ iceberg-java rejects the value. The native writer also trims or lowercases a
few other settings
+ that iceberg-java reads as written
+ ([#7008](https://github.com/apache/datafusion-comet/issues/7008)).
+
+The physical differences that every native data file has, and why they are
safe, are listed in
+[Accepted divergences](#accepted-divergences).
+
+### Reads that fail where Spark succeeds
+
+- **A data file that omits an optional nested field.** The native scan fails
on a data file that
+ lacks an optional struct field, which a write with
`write.spark.accept-any-schema=true` can
+ produce without any schema change
+ ([#7033](https://github.com/apache/datafusion-comet/issues/7033)).
+- **`VERSION AS OF` reads of a column whose type was promoted after the
snapshot**
+ ([#6993](https://github.com/apache/datafusion-comet/issues/6993)).
+- **An equality delete keyed on a nested field.** Every query the native scan
runs on the table
+ fails with `field not found`, even one that reads the key
+ ([#6782](https://github.com/apache/datafusion-comet/issues/6782)).
+- **Column names that only newer Unicode case data folds together**, such as a
pair of Vithkuqi
+ letters, which Unicode 14 added. Queries on such a table fail with a
duplicate-field error
+ ([#5540](https://github.com/apache/datafusion-comet/issues/5540)).
+- **Iceberg tables whose metadata location has no host.** The native Iceberg
scan skips the
+ credential provider configured with `s3.comet.credential.provider.class`
+ ([#6536](https://github.com/apache/datafusion-comet/issues/6536)).
+
+### Writes that fail where Spark succeeds
+
+With `spark.comet.write.iceberg.enabled=true`:
+
+- **A write that keeps one file open and outgrows the off-heap pool.** An
unpartitioned or
+ clustered write fails its task when its open file outgrows the pool, where
iceberg-java writes
+ it on the JVM heap. [Failure handling](#failure-handling) describes the
+ error and the settings that let such a write fit.
+
+### Differences that are not Comet bugs
+
+- **iceberg-java 1.9 and later: `IS NULL` on a float or double field under a
NULL struct.**
+ iceberg-java leaves the rows where the struct is NULL out of the field's
null count, so a filter
+ such as `WHERE s.x IS NULL` can skip data files that hold matching rows,
with or without Comet
+ ([apache/iceberg#17561](https://github.com/apache/iceberg/issues/17561)).
Files written by Comet's
+ native Iceberg writer are not affected.
+- **iceberg-java before 1.12: a NULL struct whose first field is an identity
partition source.**
+ iceberg-java reads the struct as a struct of NULLs, and Comet returns NULL,
as iceberg-java 1.12
+ does. [apache/iceberg#17320](https://github.com/apache/iceberg/pull/17320)
is titled for nested
+ initial defaults, but it fixes this case too: it keeps one file column in
the read, so the
+ struct's definition level survives when every projected field is a constant,
and an identity
+ partition value is such a constant.
+- **Field-id reads with a read schema that swaps two nested field names.**
Spark's vectorized
Review Comment:
This entry is about Spark's vectorized Parquet reader with
`spark.sql.parquet.fieldId.read.enabled`, not about Iceberg.
`contributor-guide/data-source-audits/parquet.md` records it under the native
Parquet scan. On the old combined page that didn't matter, but after the split
it should go under Parquet's Known Issues, maybe as a "Differences that are not
Comet bugs" subsection there. The two iceberg-java entries above it stay here.
##########
docs/source/user-guide/latest/parquet.md:
##########
@@ -0,0 +1,345 @@
+<!---
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+# Parquet
+
+Comet's Parquet scan offloads decoding to native code (Rust, through
DataFusion) and produces Arrow
+batches for the rest of the plan. Comet falls back to Spark when the scan
cannot be converted, for
+example because the schema holds an unsupported type or the query uses one of
the features listed
+below. When the scan falls back to Spark, enabling
`spark.comet.convert.parquet.enabled` converts
+the data into Arrow format immediately, so the Comet pipeline can take over
after the scan, although
+the conversion may not be efficient.
+
+This page covers what the scan supports, where its results can differ from
Spark, how to tune it,
+and the known issues. Parquet files managed by Apache Iceberg are read by the
native Iceberg
+reader, described on the [Iceberg](iceberg.md) page. Reading from object
stores and HDFS is
+described in [Supported Data Sources](datasources.md#supported-storages).
+
+## Reading
+
+### Unsupported features: fall back to Spark
+
+The following features are not supported and cause Comet to fall back to Spark:
+
+- Decimals encoded in binary format.
+- `ShortType` columns, by default. When reading Parquet files written by
systems other than Spark that contain
+ columns with the logical type `UINT_8` (unsigned 8-bit integers), Comet may
produce different results than Spark.
+ Spark maps `UINT_8` to `ShortType`, but Comet's Arrow-based readers respect
the unsigned type and read the data as
+ unsigned rather than signed. Since Comet cannot distinguish `ShortType`
columns that came from `UINT_8` versus
+ signed `INT16`, by default Comet falls back to Spark when scanning Parquet
files containing `ShortType` columns.
+ This behavior can be disabled by setting
`spark.comet.scan.unsignedSmallIntSafetyCheck=false`. Note that `ByteType`
+ columns are always safe because they can only come from signed `INT8`, where
truncation preserves the signed value.
+- Default values that are nested types (e.g., maps, arrays, structs). Literal
default values are supported.
+- Spark's Datasource V2 API. When `spark.sql.sources.useV1SourceList` does not
include `parquet`, Spark uses the
+ V2 API for Parquet scans. Comet's Parquet scan only supports the V1 API.
+- `_metadata.row_index`, `_metadata.file_block_start` and
`_metadata.file_block_length`. When Spark splits a file,
+ the native scan and Spark can read a row group in different splits, so the
block values would differ. Other
+ `_metadata` columns (`file_path`, `file_name`, `file_size`,
`file_modification_time`) are supported.
+- No support for `input_file_name()`, `input_file_block_start()`, or
`input_file_block_length()` SQL functions.
+ Comet's Parquet scan does not use Spark's `FileScanRDD`, so these functions
cannot populate their values.
+- No support for `ignoreMissingFiles` or `ignoreCorruptFiles` being set to
`true`
+- `spark.sql.parquet.enableVectorizedReader=false`. Disabling the vectorized
reader opts into
+ Spark's parquet-mr semantics (silent overflow, null-on-narrowing), which
Comet's native reader
+ does not replicate. By default Comet falls back to Spark in this case. Set
+ `spark.comet.scan.allowDisabledParquetVectorizedReader=true` to opt in to
running the
+ Comet Parquet scan regardless.
+- A read schema that repeats a Parquet field id, at the top level or within a
struct, when
+ `spark.sql.parquet.fieldId.read.enabled=true`.
+- A read schema with sibling struct fields whose names collide
case-insensitively, when
+ `spark.sql.caseSensitive=false`. Spark's analyzer normally rejects such a
schema before the scan
+ is planned.
+
+Some Parquet reads return results that differ from Spark without falling back
to Spark, such as
+reads of dates and timestamps written with the legacy hybrid Julian/Gregorian
calendar
+([#5010](https://github.com/apache/datafusion-comet/issues/5010)).
+[Reads that differ from Spark](#reads-that-differ-from-spark) lists them, with
the
+issues that track them.
+
+### Unsupported features: error at scan time
+
+The following limitations raise an error at scan time rather than falling back
to Spark. More
+reads that fail where Spark succeeds are listed in
+[Reads that fail where Spark succeeds](#reads-that-fail-where-spark-succeeds):
+
+- Selecting a field by name when multiple physical siblings match, including
inside structs,
+ arrays, and maps. Comet raises a duplicate-field error instead of resolving
the collision.
+ Checks cover referenced columns, including predicates; unselected roots do
not prevent
+ reading a unique field by name or field ID. Exact-name projections of unique
children in
+ structs and arrays of structs remain supported. Casts that cannot use this
pruning reject
+ byte-identical duplicate siblings anywhere in the decoded physical subtree,
including maps.
+ Field-ID resolution retains precedence, but selecting a byte-identically
duplicated physical
+ root name still raises a duplicate-field error, even when the requested
field is renamed.
+ Names in separate groups do not collide. Spark may read a duplicate-bearing
file with an
+ explicit schema in case-sensitive mode, but its choice of sibling depends on
the field shape
+ and can produce unexpected values. Spark rejects schema inference from a
single file with
+ duplicate names; inference across files can depend on merge order.
+ Resolution is tracked in
[#5884](https://github.com/apache/datafusion-comet/issues/5884),
+ with mixed-type behavior in
[#5964](https://github.com/apache/datafusion-comet/issues/5964).
+- Invalid UTF-8 bytes in `STRING` columns. Spark permits arbitrary byte
sequences in a `STRING`
+ column (for example from `CAST(X'C1' AS STRING)`), but Comet's native
execution path is built on
+ Arrow, whose string type is strictly UTF-8. Reading a Parquet file whose
`STRING` column contains
+ non-UTF-8 bytes fails with `Parquet error: encountered non UTF-8 data`.
Disable Comet for the
+ query, or cast the column to `BINARY` before persisting, if you need to
preserve non-UTF-8 bytes.
+ By contrast, Comet decodes non-UTF-8 bytes at the JVM-to-native Arrow FFI
boundaries using
+ JVM-compatible replacement semantics. This covers native query input from
the JVM-exported Arrow
+ stream (including JVM scans, shuffle reads, and `mapInArrow`),
columnar-to-row conversion, and JVM
+ UDF results. It does not change the native Parquet reader: rejecting invalid
UTF-8 there remains
+ Gap A of [#4764](https://github.com/apache/datafusion-comet/issues/4764). See
+ [Strings with non-UTF-8
bytes](compatibility/index.md#strings-with-non-utf-8-bytes),
+ [#4121](https://github.com/apache/datafusion-comet/issues/4121), and the
tracking issue above.
+- Reading `TimestampLTZ` as `TimestampNTZ` on Spark 3.x. Spark raises an error
per
+ [SPARK-36182](https://issues.apache.org/jira/browse/SPARK-36182) because LTZ
encodes UTC-adjusted
+ instants that cannot be safely reinterpreted as timezone-free values, and
Comet matches this by
+ rejecting the read. This applies to all LTZ physical encodings (INT96,
TIMESTAMP_MICROS,
+ TIMESTAMP_MILLIS). On Spark 4.0+, this read is permitted
+ ([SPARK-47447](https://issues.apache.org/jira/browse/SPARK-47447)) and Comet
matches Spark's
+ behavior.
+
+### Schema Mismatch Handling
+
+The issues in this subsection apply only when the requested read schema
differs from the schema written
+to the Parquet file. They do **not** affect a plain `spark.read.parquet(path)`
that infers the schema
+from file metadata, because in that case the requested schema and file schema
match by construction.
+Schema mismatch happens in two real-world scenarios:
+
+1. The user provides an explicit read schema:
`spark.read.schema(<schema>).parquet(path)` (or the
+ equivalent DataFrame API).
+2. **Schema evolution / partitioned reads** where files in a single dataset
were written at different
+ times with different types, or a table-format catalog (Iceberg, Delta)
records a logical schema
+ that has evolved past one or more underlying Parquet files. Spark coerces
the file types to the
+ table types at read time.
+
+Spark's vectorized Parquet reader fully validates these conversions in
`ParquetVectorUpdaterFactory.getUpdater`
+and throws `SchemaColumnConvertNotSupportedException` for unsupported pairs.
Comet's Parquet scan
+mirrors that validation in its schema adapter; the entries below are the
remaining gaps.
+
+Note that the exact set of accepted conversions has changed between Spark
versions
+(for example, Spark 3.x's `schemaEvolution.enabled` flag gates `INT32 →
INT64`, `FLOAT → DOUBLE`,
+and `INT32 → DOUBLE` widening that Spark 4.0+ accepts unconditionally;
`TimestampLTZ → TimestampNTZ`
+is rejected by Spark 3.x but accepted by Spark 4.0+). Comet aims to follow the
per-version Spark
+behavior.
+
+- **Conversion errors precede pushed row filters**. Empty files and files
whose row groups or
+ pages are all pruned read without decode-time conversion errors. When
row-filter pushdown is
+ enabled, Comet checks rejected conversions before reading surviving data
pages, so a row filter
+ cannot suppress the error by discarding every row. Legacy LIST shape
mismatches that Spark cannot
+ clip are rejected when the file is opened, including for empty and fully
pruned files.
+- **List conversion error paths assume Spark's standard encoding**. Comet
inserts `list`
+ before the element name when reporting a rejected array element conversion.
Arrow's schema
+ omits the repeated group name, so paths for legacy LIST encodings or custom
group names may
+ differ from Spark's Parquet column path.
+- **`ParquetSchemaConvert` errors do not include the file path**. The mismatch
itself is detected and
+ rejected correctly, but the resulting Spark error message reads
+ `Encountered error while reading file . Data type mismatches…` (note the
empty path). Behavior is
+ consistent across Spark versions. See
+ [#4316](https://github.com/apache/datafusion-comet/issues/4316).
+- **Spark 3.x: extra `SparkException` layer in the cause chain**. The native
error is translated to a
+ `SparkException` whose cause is `SchemaColumnConvertNotSupportedException`
(matching what Spark would
+ throw); on Spark 3.x the executor / task error handling re-wraps this once
more on the way back to
+ the driver, producing a two-level chain (`SparkException → SparkException →
+SchemaColumnConvertNotSupportedException`) instead of the one-level chain
Spark's own vectorized
+ reader produces. Code that catches `SparkException` and inspects only the
immediate cause via
+ `e.getCause.isInstanceOf[SchemaColumnConvertNotSupportedException]` will see
the inner
+ `SparkException` instead. Walk the cause chain to recover the
+ `SchemaColumnConvertNotSupportedException`. Spark 4.0+ produces a
single-level chain, matching
+ vanilla Spark. See
[#4354](https://github.com/apache/datafusion-comet/issues/4354).
+
+## Writing
+
+Comet can also write Parquet files natively. The native Parquet writer is
experimental and disabled
+by default; enable it with `spark.comet.write.parquet.enabled=true`. Parts of
Spark's Parquet
+write behavior are not reproduced, as listed under [Known
Issues](#known-issues). Iceberg writes
+use a different native writer, described on the [Iceberg](iceberg.md#writes)
page.
+
+## Tuning
+
+### Filter Pushdown / Late Materialization
+
+Setting `spark.comet.parquet.rowFilterPushdown.enabled=true` pushes filter
evaluation into the Parquet
+decode step and lazily materializes projected columns for surviving rows. This
can significantly reduce
+CPU and memory when the filter is highly selective on a small subset of
columns. It is disabled by default
+because it can hurt when the filter is not selective or when most columns must
be read anyway. Row-group,
+page-index, and bloom-filter pruning happen regardless of this flag whenever
Spark's
+`spark.sql.parquet.filterPushdown` is on, except for filters that compare
`FLOAT` or `DOUBLE` values,
+which do not prune while this flag is on (see [Floating-point Number
Comparison](compatibility/floating-point.md)).
+
+### Row Group Assignment to Splits
+
+Spark and DataFusion's native Parquet scans use different rules to decide
which row groups belong to a
+given scan range (split). Spark assigns a row group to a split if the row
group's start offset falls
+within `[split.start, split.start + split.length)`, guaranteeing that every
task Spark plans reads at
+least one row group when the file layout permits. DataFusion's
`prune_by_range` also checks whether a
+row group's start offset falls within the split's byte range, but because row
group sizes are not aligned
+with Spark's split boundaries, the two systems can disagree on which split
"owns" a given row group.
+
+When a file contains row groups whose sizes are close to
`spark.sql.files.maxPartitionBytes`, this
+mismatch can leave some Comet scan tasks with no row groups to read. Those
tasks still load Parquet
+metadata but return zero rows, while neighboring tasks end up reading more row
groups than Spark
+intended. The overall effect is that Comet uses only a fraction of the
parallelism that Spark planned
+for the scan stage, and end-to-end scan latency increases even though the
total amount of data read
+is unchanged.
+
+Symptoms to look for:
+
+- A subset of scan tasks completes almost immediately and reports 0 input
rows, while the remaining
+ tasks read noticeably more rows than the equivalent Spark tasks would.
+- The Comet scan stage has the same number of planned tasks as Spark but a
much lower count of tasks
+ that actually do work.
+
+Workaround: lower `spark.sql.files.maxPartitionBytes` so that each split is
smaller than a single row
+group. For example, if the file's row groups are around 120 MB and
`spark.sql.files.maxPartitionBytes`
+is left at the 128 MB default, most splits will contain at most one row group
boundary and the
+mismatch is amplified; setting `spark.sql.files.maxPartitionBytes` below 120
MB (for example, 64 MB)
+distributes row groups across more splits and reduces the number of idle
tasks. Smaller values produce
+more splits overall, so some idle tasks may remain — tune the value against
your file layout.
+
+See
[#3817](https://github.com/apache/datafusion-comet/issues/3817#issuecomment-4193279630)
for a
+worked example and further discussion.
+
+## Known Issues
+
+This section lists open issues in which Comet's Parquet reads and writes give
a different result
+from Spark, or fail where Spark succeeds, without falling back. Each entry
links to its tracking
+issue. The cases where Comet falls back to Spark are listed under
[Reading](#reading).
+
+Which entries apply depends on which native paths are enabled:
+
+| Native path | Setting | Default
|
+| -------------- | ----------------------------------- |
---------------------- |
+| Parquet scan | `spark.comet.scan.enabled` | `true`
|
+| Parquet writer | `spark.comet.write.parquet.enabled` | `false` (testing
only) |
+
+`spark.comet.scan.enabled` is a testing setting that Comet's own test suites
use. It is not meant
+for production, so do not turn it off to avoid an entry on this page.
+
+### Reads that differ from Spark
+
+- **Dates and timestamps written with the legacy calendar.** Spark before 3.0,
and later versions
+ with `spark.sql.parquet.datetimeRebaseModeInWrite=LEGACY`, write these
values in the hybrid
+ Julian/Gregorian calendar. The native Parquet scan reads them as proleptic
Gregorian, so values
+ before October 15, 1582 are wrong in projections and predicates. It ignores
+ `spark.sql.parquet.datetimeRebaseModeInRead` and the file's
`org.apache.spark.legacyDateTime`
+ metadata ([#5010](https://github.com/apache/datafusion-comet/issues/5010)).
The
+ [Delta scan](delta.md) rebases.
+- **INT96 timestamps inside legacy-format lists.** INT96 timestamps outside
the years 1677 to 2262
+ read back wrong inside lists written in a legacy layout, as Hive and
+ `spark.sql.parquet.writeLegacyFormat=true` write them
+ ([#7029](https://github.com/apache/datafusion-comet/issues/7029)).
+- **A struct or array with none of its requested fields in the file.** Spark
returns NULL for the
+ column. Comet returns a non-null struct or array that holds NULLs. This
applies to Spark 3.4, 3.5
+ and 4.0, and to Spark 4.1 and later with
+ `spark.sql.legacy.parquet.returnNullStructIfAllFieldsMissing=true`
+ ([#7030](https://github.com/apache/datafusion-comet/issues/7030)).
+- **Nested BINARY values that are not valid UTF-8, read as STRING.** This
applies to a BINARY
+ column without the UTF8 annotation that is read as `string`, through the
read schema or
+ `spark.sql.parquet.binaryAsString=true`. Spark returns the bytes. Comet
returns NULL for each
+ such nested value. Top-level values match Spark in a release build, because
the invalid bytes
+ reach an Arrow string array unchecked. A debug build panics on them
+ ([#7032](https://github.com/apache/datafusion-comet/issues/7032), part of
+ [#4764](https://github.com/apache/datafusion-comet/issues/4764)).
+- **Spark 3.4: a struct matched by field id under a different name.** With
+ `spark.sql.parquet.fieldId.read.enabled=true`, a struct column that the read
schema matches to
+ the file by field id but under another name reads as NULL when Spark prunes
its nested fields
+ ([#7031](https://github.com/apache/datafusion-comet/issues/7031)).
+- **Field-id reads where one file column matches a field by id and a sibling
by name.** With
+ `spark.sql.parquet.fieldId.read.enabled=true`, Comet returns NULL for the
sibling at the top
+ level. Inside a struct, it returns the values of the field matched by id.
Spark's own readers
+ disagree here
([#6994](https://github.com/apache/datafusion-comet/issues/6994)).
+- **`TIMESTAMP_MILLIS` values that overflow when converted to microseconds.**
In nested fields, and
+ in every column of a scan that has data filters, Comet returns NULL where
Spark fails with
+ `ArithmeticException: long overflow`
+ ([#5553](https://github.com/apache/datafusion-comet/issues/5553),
+ [#5739](https://github.com/apache/datafusion-comet/issues/5739)).
+- **Reads that Spark rejects.** Comet returns rows where Spark fails the read
for
+ `TIMESTAMP(NANOS)` columns read as `timestamp` or `timestamp_ntz`
+ ([#7034](https://github.com/apache/datafusion-comet/issues/7034)), `INT`
columns read as
+ `INTERVAL DAY TO SECOND`
([#5188](https://github.com/apache/datafusion-comet/issues/5188)), and
+ Variant-annotated fields read as a plain struct
+ ([#5741](https://github.com/apache/datafusion-comet/issues/5741)).
+
+### Writes that differ from Spark
+
+With `spark.comet.write.parquet.enabled=true`:
+
+- **The datetime rebase modes are ignored.** On Spark 3.4 and 3.5, whose
default rebase mode is
+ `EXCEPTION`, the native writer writes dates and timestamps before October
15, 1582 that Spark
+ refuses to write, and Spark then fails to read them back under its default
read mode. With
+ `spark.sql.parquet.datetimeRebaseModeInRead=LEGACY`, Spark reads them
shifted by several days, on
+ every version. The files lack the `org.apache.spark.version` footer key that
tells Spark they need
+ no rebasing ([#7047](https://github.com/apache/datafusion-comet/issues/7047),
+ [#3427](https://github.com/apache/datafusion-comet/issues/3427)).
+- **`spark.sql.parquet.outputTimestampType` and
`spark.sql.parquet.writeLegacyFormat` are
+ ignored.** Timestamps are always written as `INT64` microseconds, never as
Spark's default
+ `INT96`, so with `TIMESTAMP_MILLIS` they keep the sub-millisecond digits
that Spark drops. Spark's
+ schema footer key is not written either, so a path-based read loses column
metadata such as
+ comments, and with `spark.sql.parquet.inferTimestampNTZ.enabled=false` it
reads `TIMESTAMP_NTZ`
+ columns as `TIMESTAMP`
([#7048](https://github.com/apache/datafusion-comet/issues/7048),
+ [#3425](https://github.com/apache/datafusion-comet/issues/3425)).
+- **Spark 3.4 and 3.5: `INSERT INTO` leaves the target stale.** Comet skips
the steps Spark runs
+ after a write: file index refresh, cache refresh, partition refresh and
statistics update. Later
+ queries can return the table's previous contents until `REFRESH TABLE`
+ ([#6315](https://github.com/apache/datafusion-comet/issues/6315)).
+- **Spark's Parquet writer properties are ignored.** `parquet.block.size`,
page size, dictionary,
+ writer version and statistics settings never reach the native writer
+ ([#5304](https://github.com/apache/datafusion-comet/issues/5304)).
+- **An input with no partitions writes no file.** Spark writes one file that
holds the schema
+ ([#5303](https://github.com/apache/datafusion-comet/issues/5303)).
+- **Spark SQL tests that still fail with the native writer**, not yet
classified:
+ - CTAS schema
([#3418](https://github.com/apache/datafusion-comet/issues/3418))
+ - CTAS with `CHAR_AS_VARCHAR`
([#3419](https://github.com/apache/datafusion-comet/issues/3419))
+ - CTAS with a union
([#3429](https://github.com/apache/datafusion-comet/issues/3429))
+ - `INSERT INTO` with default columns
([#3421](https://github.com/apache/datafusion-comet/issues/3421))
+ - `INSERT INTO` after `ALTER TABLE ADD COLUMNS`
([#3422](https://github.com/apache/datafusion-comet/issues/3422))
+ - `INSERT INTO` from non-projection queries
([#3423](https://github.com/apache/datafusion-comet/issues/3423))
+ - the `INSERT OVERWRITE` output schema
([#3424](https://github.com/apache/datafusion-comet/issues/3424))
+ - multiple inserts
([#3430](https://github.com/apache/datafusion-comet/issues/3430))
+ - `INSERT OVERWRITE LOCAL DIRECTORY`
([#3420](https://github.com/apache/datafusion-comet/issues/3420))
+ - empty files
([#3417](https://github.com/apache/datafusion-comet/issues/3417))
+ - `onTaskCommit`
([#3428](https://github.com/apache/datafusion-comet/issues/3428))
+
+### Reads that fail where Spark succeeds
+
+- **STRING columns that hold invalid UTF-8.** The read fails with
Review Comment:
nit: With both lists on one page now, this entry and "Duplicate field names"
below repeat the invalid UTF-8 and duplicate-field items under "Unsupported
features: error at scan time" (lines 80 and 94). Either side could be shortened
to a link to the other. Fine as a follow-up if you'd rather keep this PR to
moving text.
##########
docs/source/user-guide/latest/parquet.md:
##########
@@ -0,0 +1,345 @@
+<!---
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+# Parquet
+
+Comet's Parquet scan offloads decoding to native code (Rust, through
DataFusion) and produces Arrow
+batches for the rest of the plan. Comet falls back to Spark when the scan
cannot be converted, for
+example because the schema holds an unsupported type or the query uses one of
the features listed
+below. When the scan falls back to Spark, enabling
`spark.comet.convert.parquet.enabled` converts
+the data into Arrow format immediately, so the Comet pipeline can take over
after the scan, although
+the conversion may not be efficient.
+
+This page covers what the scan supports, where its results can differ from
Spark, how to tune it,
+and the known issues. Parquet files managed by Apache Iceberg are read by the
native Iceberg
+reader, described on the [Iceberg](iceberg.md) page. Reading from object
stores and HDFS is
+described in [Supported Data Sources](datasources.md#supported-storages).
+
+## Reading
+
+### Unsupported features: fall back to Spark
+
+The following features are not supported and cause Comet to fall back to Spark:
+
+- Decimals encoded in binary format.
+- `ShortType` columns, by default. When reading Parquet files written by
systems other than Spark that contain
+ columns with the logical type `UINT_8` (unsigned 8-bit integers), Comet may
produce different results than Spark.
+ Spark maps `UINT_8` to `ShortType`, but Comet's Arrow-based readers respect
the unsigned type and read the data as
+ unsigned rather than signed. Since Comet cannot distinguish `ShortType`
columns that came from `UINT_8` versus
+ signed `INT16`, by default Comet falls back to Spark when scanning Parquet
files containing `ShortType` columns.
+ This behavior can be disabled by setting
`spark.comet.scan.unsignedSmallIntSafetyCheck=false`. Note that `ByteType`
+ columns are always safe because they can only come from signed `INT8`, where
truncation preserves the signed value.
+- Default values that are nested types (e.g., maps, arrays, structs). Literal
default values are supported.
+- Spark's Datasource V2 API. When `spark.sql.sources.useV1SourceList` does not
include `parquet`, Spark uses the
+ V2 API for Parquet scans. Comet's Parquet scan only supports the V1 API.
+- `_metadata.row_index`, `_metadata.file_block_start` and
`_metadata.file_block_length`. When Spark splits a file,
+ the native scan and Spark can read a row group in different splits, so the
block values would differ. Other
+ `_metadata` columns (`file_path`, `file_name`, `file_size`,
`file_modification_time`) are supported.
+- No support for `input_file_name()`, `input_file_block_start()`, or
`input_file_block_length()` SQL functions.
+ Comet's Parquet scan does not use Spark's `FileScanRDD`, so these functions
cannot populate their values.
+- No support for `ignoreMissingFiles` or `ignoreCorruptFiles` being set to
`true`
+- `spark.sql.parquet.enableVectorizedReader=false`. Disabling the vectorized
reader opts into
+ Spark's parquet-mr semantics (silent overflow, null-on-narrowing), which
Comet's native reader
+ does not replicate. By default Comet falls back to Spark in this case. Set
+ `spark.comet.scan.allowDisabledParquetVectorizedReader=true` to opt in to
running the
+ Comet Parquet scan regardless.
+- A read schema that repeats a Parquet field id, at the top level or within a
struct, when
+ `spark.sql.parquet.fieldId.read.enabled=true`.
+- A read schema with sibling struct fields whose names collide
case-insensitively, when
+ `spark.sql.caseSensitive=false`. Spark's analyzer normally rejects such a
schema before the scan
+ is planned.
+
+Some Parquet reads return results that differ from Spark without falling back
to Spark, such as
+reads of dates and timestamps written with the legacy hybrid Julian/Gregorian
calendar
+([#5010](https://github.com/apache/datafusion-comet/issues/5010)).
+[Reads that differ from Spark](#reads-that-differ-from-spark) lists them, with
the
+issues that track them.
+
+### Unsupported features: error at scan time
+
+The following limitations raise an error at scan time rather than falling back
to Spark. More
+reads that fail where Spark succeeds are listed in
+[Reads that fail where Spark succeeds](#reads-that-fail-where-spark-succeeds):
+
+- Selecting a field by name when multiple physical siblings match, including
inside structs,
+ arrays, and maps. Comet raises a duplicate-field error instead of resolving
the collision.
+ Checks cover referenced columns, including predicates; unselected roots do
not prevent
+ reading a unique field by name or field ID. Exact-name projections of unique
children in
+ structs and arrays of structs remain supported. Casts that cannot use this
pruning reject
+ byte-identical duplicate siblings anywhere in the decoded physical subtree,
including maps.
+ Field-ID resolution retains precedence, but selecting a byte-identically
duplicated physical
+ root name still raises a duplicate-field error, even when the requested
field is renamed.
+ Names in separate groups do not collide. Spark may read a duplicate-bearing
file with an
+ explicit schema in case-sensitive mode, but its choice of sibling depends on
the field shape
+ and can produce unexpected values. Spark rejects schema inference from a
single file with
+ duplicate names; inference across files can depend on merge order.
+ Resolution is tracked in
[#5884](https://github.com/apache/datafusion-comet/issues/5884),
+ with mixed-type behavior in
[#5964](https://github.com/apache/datafusion-comet/issues/5964).
+- Invalid UTF-8 bytes in `STRING` columns. Spark permits arbitrary byte
sequences in a `STRING`
+ column (for example from `CAST(X'C1' AS STRING)`), but Comet's native
execution path is built on
+ Arrow, whose string type is strictly UTF-8. Reading a Parquet file whose
`STRING` column contains
+ non-UTF-8 bytes fails with `Parquet error: encountered non UTF-8 data`.
Disable Comet for the
+ query, or cast the column to `BINARY` before persisting, if you need to
preserve non-UTF-8 bytes.
+ By contrast, Comet decodes non-UTF-8 bytes at the JVM-to-native Arrow FFI
boundaries using
+ JVM-compatible replacement semantics. This covers native query input from
the JVM-exported Arrow
+ stream (including JVM scans, shuffle reads, and `mapInArrow`),
columnar-to-row conversion, and JVM
+ UDF results. It does not change the native Parquet reader: rejecting invalid
UTF-8 there remains
+ Gap A of [#4764](https://github.com/apache/datafusion-comet/issues/4764). See
+ [Strings with non-UTF-8
bytes](compatibility/index.md#strings-with-non-utf-8-bytes),
+ [#4121](https://github.com/apache/datafusion-comet/issues/4121), and the
tracking issue above.
+- Reading `TimestampLTZ` as `TimestampNTZ` on Spark 3.x. Spark raises an error
per
+ [SPARK-36182](https://issues.apache.org/jira/browse/SPARK-36182) because LTZ
encodes UTC-adjusted
+ instants that cannot be safely reinterpreted as timezone-free values, and
Comet matches this by
+ rejecting the read. This applies to all LTZ physical encodings (INT96,
TIMESTAMP_MICROS,
+ TIMESTAMP_MILLIS). On Spark 4.0+, this read is permitted
+ ([SPARK-47447](https://issues.apache.org/jira/browse/SPARK-47447)) and Comet
matches Spark's
+ behavior.
+
+### Schema Mismatch Handling
+
+The issues in this subsection apply only when the requested read schema
differs from the schema written
+to the Parquet file. They do **not** affect a plain `spark.read.parquet(path)`
that infers the schema
+from file metadata, because in that case the requested schema and file schema
match by construction.
+Schema mismatch happens in two real-world scenarios:
+
+1. The user provides an explicit read schema:
`spark.read.schema(<schema>).parquet(path)` (or the
+ equivalent DataFrame API).
+2. **Schema evolution / partitioned reads** where files in a single dataset
were written at different
+ times with different types, or a table-format catalog (Iceberg, Delta)
records a logical schema
+ that has evolved past one or more underlying Parquet files. Spark coerces
the file types to the
+ table types at read time.
+
+Spark's vectorized Parquet reader fully validates these conversions in
`ParquetVectorUpdaterFactory.getUpdater`
+and throws `SchemaColumnConvertNotSupportedException` for unsupported pairs.
Comet's Parquet scan
+mirrors that validation in its schema adapter; the entries below are the
remaining gaps.
+
+Note that the exact set of accepted conversions has changed between Spark
versions
+(for example, Spark 3.x's `schemaEvolution.enabled` flag gates `INT32 →
INT64`, `FLOAT → DOUBLE`,
+and `INT32 → DOUBLE` widening that Spark 4.0+ accepts unconditionally;
`TimestampLTZ → TimestampNTZ`
+is rejected by Spark 3.x but accepted by Spark 4.0+). Comet aims to follow the
per-version Spark
+behavior.
+
+- **Conversion errors precede pushed row filters**. Empty files and files
whose row groups or
+ pages are all pruned read without decode-time conversion errors. When
row-filter pushdown is
+ enabled, Comet checks rejected conversions before reading surviving data
pages, so a row filter
+ cannot suppress the error by discarding every row. Legacy LIST shape
mismatches that Spark cannot
+ clip are rejected when the file is opened, including for empty and fully
pruned files.
+- **List conversion error paths assume Spark's standard encoding**. Comet
inserts `list`
+ before the element name when reporting a rejected array element conversion.
Arrow's schema
+ omits the repeated group name, so paths for legacy LIST encodings or custom
group names may
+ differ from Spark's Parquet column path.
+- **`ParquetSchemaConvert` errors do not include the file path**. The mismatch
itself is detected and
+ rejected correctly, but the resulting Spark error message reads
+ `Encountered error while reading file . Data type mismatches…` (note the
empty path). Behavior is
+ consistent across Spark versions. See
+ [#4316](https://github.com/apache/datafusion-comet/issues/4316).
+- **Spark 3.x: extra `SparkException` layer in the cause chain**. The native
error is translated to a
+ `SparkException` whose cause is `SchemaColumnConvertNotSupportedException`
(matching what Spark would
+ throw); on Spark 3.x the executor / task error handling re-wraps this once
more on the way back to
+ the driver, producing a two-level chain (`SparkException → SparkException →
+SchemaColumnConvertNotSupportedException`) instead of the one-level chain
Spark's own vectorized
+ reader produces. Code that catches `SparkException` and inspects only the
immediate cause via
+ `e.getCause.isInstanceOf[SchemaColumnConvertNotSupportedException]` will see
the inner
+ `SparkException` instead. Walk the cause chain to recover the
+ `SchemaColumnConvertNotSupportedException`. Spark 4.0+ produces a
single-level chain, matching
+ vanilla Spark. See
[#4354](https://github.com/apache/datafusion-comet/issues/4354).
+
+## Writing
+
+Comet can also write Parquet files natively. The native Parquet writer is
experimental and disabled
Review Comment:
"Experimental and disabled by default" understates it compared with the rest
of the docs. `CometConf` puts `spark.comet.write.parquet.enabled` in the
testing category and says it "should not be used in production", and the table
under Known Issues on this page says "testing only". Could this say the same?
It could also list the scope limits from `operators.md` (non-partitioned,
non-bucketed, no `maxRecordsPerFile`), since this section is now where readers
will look for them.
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