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# Accelerating Apache Iceberg Parquet Scans using Comet

## Native Reader

Comet's native Iceberg reader relies on reflection to extract `FileScanTask`s from Iceberg, which are
then serialized to Comet's native execution engine (see
[PR #2528](https://github.com/apache/datafusion-comet/pull/2528)).

The example below uses Spark's package downloader to retrieve Comet $COMET_VERSION and Iceberg
1.8.1, but Comet has been tested with Iceberg 1.5, 1.7, 1.8, 1.9, and 1.10. The native Iceberg
reader is enabled by default. To disable it, set `spark.comet.scan.icebergNative.enabled=false`.

The example uses the Spark 3.5 / Scala 2.12 build of Comet; substitute the Comet artifact
matching your Spark and Scala versions (Comet also ships Spark 3.5 / Scala 2.13 and Spark
4.0/4.1 / Scala 2.13 jars; see the [installation guide](installation.md) for the full list).

```shell
$SPARK_HOME/bin/spark-shell \
    --packages org.apache.datafusion:comet-spark-spark3.5_2.12:$COMET_VERSION,org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.8.1,org.apache.iceberg:iceberg-core:1.8.1 \
    --repositories https://repo1.maven.org/maven2/ \
    --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
    --conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.spark_catalog.type=hadoop \
    --conf spark.sql.catalog.spark_catalog.warehouse=/tmp/warehouse \
    --conf spark.plugins=org.apache.spark.CometPlugin \
    --conf spark.shuffle.manager=org.apache.spark.sql.comet.execution.shuffle.CometShuffleManager \
    --conf spark.comet.explainFallback.enabled=true \
    --conf spark.memory.offHeap.enabled=true \
    --conf spark.memory.offHeap.size=2g
```

### Tuning

Comet’s native Iceberg reader supports fetching multiple files in parallel to hide I/O latency with the
config `spark.comet.scan.icebergNative.dataFileConcurrencyLimit`. This value defaults to 1 to
maintain test behavior on Iceberg Java tests without `ORDER BY` clauses, but we suggest increasing it to
values between 2 and 8 based on your workload.

### Supported features

The native Iceberg reader supports the following features:

**Table specifications:**

- Iceberg table spec v1 and v2 (v3 will fall back to Spark)

**Schema and data types:**

- All primitive types including UUID
- Complex types: arrays, maps, and structs
- Schema evolution (adding and dropping columns)

**Time travel and branching:**

- `VERSION AS OF` queries to read historical snapshots
- Branch reads for accessing named branches

**Delete handling (Merge-On-Read tables):**

- Positional deletes
- Equality deletes
- Mixed delete types

**Filter pushdown:**

- Equality and comparison predicates (`=`, `!=`, `>`, `>=`, `<`, `<=`)
- Logical operators (`AND`, `OR`)
- NULL checks (`IS NULL`, `IS NOT NULL`)
- `IN` and `NOT IN` list operations
- `BETWEEN` operations

**Partitioning:**

- Standard partitioning with partition pruning
- Date partitioning with `days()` transform
- Bucket partitioning
- Truncate transform
- Hour transform

**Storage:**

- Local filesystem
- Hadoop Distributed File System (HDFS)
- S3-compatible storage (AWS S3, MinIO)

### REST Catalog

Comet's native Iceberg reader also supports REST catalogs. The following example shows how to
configure Spark to use a REST catalog with Comet's native Iceberg scan:

```shell
$SPARK_HOME/bin/spark-shell \
    --packages org.apache.datafusion:comet-spark-spark3.5_2.12:$COMET_VERSION,org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.8.1,org.apache.iceberg:iceberg-core:1.8.1 \
    --repositories https://repo1.maven.org/maven2/ \
    --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
    --conf spark.sql.catalog.rest_cat=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.rest_cat.catalog-impl=org.apache.iceberg.rest.RESTCatalog \
    --conf spark.sql.catalog.rest_cat.uri=http://localhost:8181 \
    --conf spark.sql.catalog.rest_cat.warehouse=/tmp/warehouse \
    --conf spark.plugins=org.apache.spark.CometPlugin \
    --conf spark.shuffle.manager=org.apache.spark.sql.comet.execution.shuffle.CometShuffleManager \
    --conf spark.comet.explainFallback.enabled=true \
    --conf spark.memory.offHeap.enabled=true \
    --conf spark.memory.offHeap.size=2g
```

Note that REST catalogs require explicit namespace creation before creating tables:

```scala
scala> spark.sql("CREATE NAMESPACE rest_cat.db")
scala> spark.sql("CREATE TABLE rest_cat.db.test_table (id INT, name STRING) USING iceberg")
scala> spark.sql("INSERT INTO rest_cat.db.test_table VALUES (1, 'Alice'), (2, 'Bob')")
scala> spark.sql("SELECT * FROM rest_cat.db.test_table").show()
```

### Current limitations

The following scenarios will fall back to Spark's native Iceberg reader:

- Iceberg table spec v3 scans
- Iceberg writes (reads are accelerated, writes use Spark)
- Tables backed by Avro or ORC data files (only Parquet is accelerated)
- Tables partitioned on `BINARY` or `DECIMAL` (with precision >28) columns
- Scans with residual filters using `truncate`, `bucket`, `year`, `month`, `day`, or `hour`
  transform functions (partition pruning still works, but row-level filtering of these
  transforms falls back)
- Dynamic Partition Pruning under Adaptive Query Execution (non-AQE DPP is supported);
  see [#3510](https://github.com/apache/datafusion-comet/issues/3510)

### Task input metrics

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.

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.
