maomaodev opened a new pull request, #7476:
URL: https://github.com/apache/kyuubi/pull/7476
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### Why are the changes needed?
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Part 2 of 2 to add KSHC support for dynamic partition pruning (DPP). See
https://github.com/apache/kyuubi/issues/6943.
- [x] Add DPP support in `HiveScan` for non-Parquet/ORC tables.
- [x] Add DPP support in `ParquetScan` / `ORCScan` for Parquet/ORC tables.
### How was this patch tested?
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#### 1. UT & TPC-DS benchmark
- Unit tests
- Manual test: TPC-DS benchmark (10 GB dataset, ORC and Parquet separately).
Spark configuration used for the benchmark(Spark 3.5.7, Kyuubi 1.12.0-SNAPSHOT):
```
spark.driver.cores 1
spark.driver.memory 4g
spark.executor.cores 1
spark.executor.instances 10
spark.executor.memory 4g
spark.master yarn
spark.shuffle.service.enabled true
spark.yarn.appMasterEnv.JAVA_HOME /usr/local/jdk-17
spark.executorEnv.JAVA_HOME /usr/local/jdk-17
```
#### 2. ORC benchmark
- **Overall performance (sum of 99)**
| Dimension | Vanilla Spark | KSHC Before | KSHC Now |
| ----------------- | ------------: | ----------: | ----------: |
| Total time | 2481.85 s | 3353.43 s | 2197.26 s |
| vs. Vanilla Spark | — | +35.12% | −11.47% |
| vs. KSHC Before | — | — | **−34.48%** |
- **DPP hit subset (73/99)**
DPP trigger was detected by matching `runtime partition filter` in the
driver logs.
```
3,4,5,6,7,8,10,11,12,13,14,15,17,18,19,20,23,25,26,27,29,30,31,32,33,
35,36,38,40,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,60,61,63,
64,65,66,67,68,69,70,71,72,74,75,77,78,79,80,81,83,85,86,87,89,91,92,
97,98
```
| Dimension | Vanilla Spark | KSHC Before | KSHC Now |
| ----------------- | ------------: | ----------: | ----------: |
| Subset total time | 1823.48 s | 2642.11 s | 1484.39 s |
| vs. Vanilla Spark | — | +44.89% | −18.60% |
| vs. KSHC Before | — | — | **−43.82%** |
On the DPP-hit subset, KSHC Now provides a 43.82% speedup over KSHC Before,
noticeably larger than the overall 34.48%, indicating the performance benefit
mainly comes from queries where DPP is triggered.
#### 3. Parquet benchmark
- **Overall performance (sum of 99)**
| Dimension | Vanilla Spark | KSHC Before | KSHC Now |
| ----------------- | ------------: | ----------: | ----------: |
| Total time | 2325.13 s | 3363.57 s | 2152.18 s |
| vs. Vanilla Spark | — | +44.66% | −7.44% |
| vs. KSHC Before | — | — | **−36.02%** |
- **DPP hit subset (73/99)**
DPP trigger was detected by matching `runtime partition filter` in the
driver logs.
```
3,4,5,6,7,8,10,11,12,13,14,15,17,18,19,20,23,25,26,27,29,30,31,32,33,
35,36,38,40,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,60,61,63,
64,65,66,67,68,69,70,71,72,74,75,77,78,79,80,81,83,85,86,87,89,91,92,
97,98
```
| Dimension | Vanilla Spark | KSHC Before | KSHC Now |
| ----------------- | ------------: | ----------: | ----------: |
| Subset total time | 1619.13 s | 2487.20 s | 1369.55 s |
| vs. Vanilla Spark | — | +53.61% | −15.41% |
| vs. KSHC Before | — | — | **−44.94%** |
On the DPP-hit subset, KSHC Now provides a 44.94% speedup over KSHC Before,
noticeably larger than the overall 36.02%, indicating the performance benefit
mainly comes from queries where DPP is triggered.
#### 4. Result correctness
Compared each of the 99 result files between KSHC Now and Vanilla Spark for
both ORC and Parquet. ORC: 94/99 byte-identical and 98/99
row-multiset-identical; Parquet: identical figures. The 4 row-order-only diffs
(q31/q65/q71/q79) come from queries whose `ORDER BY` clause does not totally
order the output. The single multiset diff (q39) is sub-ULP floating-point
rounding in `stddev`-style aggregates and is also present between KSHC Before
and Vanilla Spark, so it is unrelated to this PR. No correctness regression
introduced.
#### 5. Spark 4.0.1 benchmark
The same TPC-DS benchmark was also run against Spark 4.0.1 with KSHC.
Results align with the Spark 3.5.7 numbers: KSHC matches or outperforms the
native Hive path on DPP-eligible queries, and produces identical result sets.
Full Spark 4.0.1 benchmark result are omitted here to keep the report compact,
they can be shared on request.
### Was this patch authored or co-authored using generative AI tooling?
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Partially assisted by Claude Code (Claude Opus 4.7) for unit test, code
style fixes, and analysis of TPC-DS benchmark results. Core design and
implementation are human-authored.
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