thesquelched commented on PR #55679:
URL: https://github.com/apache/spark/pull/55679#issuecomment-4380857588
Unfortunately, this doesn't work. I tested by creating a table with two
partitions, then running four reads:
1. Batch read, without partition filter
2. Batch read, with partition filter
3. Stream read, without partition filter
4. Stream read, with partition filter
Here's the code and output (lightly edited to remove logspam):
```
$ spark-shell \
--jars
./spark/v4.1/spark-runtime/build/libs/iceberg-spark-runtime-4.1_2.13-1.11.0-SNAPSHOT.jar
\
--conf
spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
\
--conf
spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog \
--conf spark.sql.catalog.spark_catalog.type=hadoop \
--conf spark.sql.catalog.spark_catalog.warehouse=$PWD/testdata
WARNING: Using incubator modules: jdk.incubator.vector
26/05/05 10:36:46 WARN NativeCodeLoader: Unable to load native-hadoop
library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile:
org/apache/spark/log4j2-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use
setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 4.1.1
/_/
Using Scala version 2.13.17 (OpenJDK 64-Bit Server VM, Java 17.0.18)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context Web UI available at http://192.168.1.45:4040
Spark context available as 'sc' (master = local[*], app id =
local-1777995408212).
Spark session available as 'spark'.
scala> import spark.implicits._
import spark.implicits._
scala> import org.apache.spark.sql.streaming._
import org.apache.spark.sql.streaming._
scala> spark.range(100L).select('id, 'id % 2 as 'bucket,
concat(lit("value"), 'id) as 'value).repartition(1, 'bucket)
.writeTo("mytable").using("iceberg").partitionedBy('bucket).tableProperty("write.distribution-mode",
"none").create()
scala> spark.table("mytable").filter('id.isin(0, 1)).show
+---+------+------+
| id|bucket| value|
+---+------+------+
| 0| 0|value0|
| 1| 1|value1|
+---+------+------+
scala> spark.table("mytable").filter('bucket === 0).filter('id.isin(0,
1)).show
+---+------+------+
| id|bucket| value|
+---+------+------+
| 0| 0|value0|
+---+------+------+
scala>
spark.readStream.format("iceberg").table("mytable").writeStream.format("memory").trigger(Trigger.AvailableNow()).queryName("foo").start.awaitTermination
26/05/05 10:37:54 ERROR SparkScan: [jalpan] creating micro batch stream
26/05/05 10:37:54 ERROR SparkMicroBatchStream: [jalpan] creating micro batch
with filter []
26/05/05 10:37:55 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:37:55 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:37:55 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:37:55 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:37:55 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:37:55 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
scala> spark.readStream.format("iceberg").table("mytable").filter('bucket
=== 0).writeStream.format("memory").trigger
(Trigger.AvailableNow()).queryName("foo").start.awaitTermination
26/05/05 10:38:11 ERROR SparkScan: [jalpan] creating micro batch stream
26/05/05 10:38:11 ERROR SparkMicroBatchStream: [jalpan] creating micro batch
with filter []
26/05/05 10:38:11 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:38:11 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:38:11 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:38:11 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:38:11 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
26/05/05 10:38:11 ERROR SparkMicroBatchStream: [jalpan] planning input
partitions micro batch with filter []
```
Here's the UI metrics for...
1. Batch read, without partition filter (2 files read) ✅
<img width="472" height="1138" alt="Screenshot 2026-05-05 at 10 37 43 AM"
src="https://github.com/user-attachments/assets/763cc355-b0f8-430c-88ba-f90811ce77d9";
/>
<br/>
2. Batch read, with partition filter (1 file read) ✅
<img width="474" height="1148" alt="Screenshot 2026-05-05 at 10 37 50 AM"
src="https://github.com/user-attachments/assets/5c111777-10ad-4e6b-9f2f-cc63886e3eab";
/>
<br/>
3. Stream read, without partition filter (2 files read) ✅
<img width="479" height="993" alt="Screenshot 2026-05-05 at 10 38 31 AM"
src="https://github.com/user-attachments/assets/d56dfea4-d202-43bc-bfda-68c915d08ad0";
/>
<br/>
4. Stream read, with partition filter (2 files read) ❌
<img width="473" height="1128" alt="Screenshot 2026-05-05 at 10 38 45 AM"
src="https://github.com/user-attachments/assets/b61f23b5-12f3-4ecc-bfd8-5fa4f3284222";
/>
<br/>
You can see that there's no difference in the files read in the streaming
read, indicating that the predicate pushdown did not happen.
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