[SPARK-56803][SQL] Add bulk read+narrow path for INT64 DECIMAL to 32-bit Decimal Parquet vector updater ### What changes were proposed in this pull request? Extend the bulk read+widen pattern introduced in SPARK-56791 to `DowncastLongUpdater` (parquet INT64 + DECIMAL(p<=9) read into a Spark 32-bit `DecimalType`). A new `readLongsAsInts` default method on `VectorizedValuesReader` does the per-row fallback. `VectorizedPlainValuesReader` overrides it to fetch source bytes once via `getBuffer(total * 8)` and run a tight in-method conversion loop. `DowncastLongUpdater.readValues` becomes a one-line delegation. The narrowing is Java's primitive long-to-int cast (`(int) buffer.getLong()`), which discards the high 32 bits; this is non-lossy in practice because Parquet's DECIMAL(p<=9) encoding bounds the value range to `[-999_999_999, 999_999_999]`. ### Why are the changes needed? `DowncastLongUpdater.readValues` allocates a fresh `ByteBuffer` slice inside `getBuffer(8)` for every element on the legacy path, and that allocation dominates the loop. Collapsing N allocations into one is the same win SPARK-56791 delivered for the INT32 -> Long sibling, with the gain again amplifying on newer JDKs where escape analysis better optimizes the tight loop: | JDK | Baseline | After | Speedup | |----:|---------:|----------:|--------:| | 17 | 487.0 M/s | 1287.3 M/s | 2.64x | | 21 | 455.6 M/s | 5828.5 M/s | 12.79x | | 25 | 455.5 M/s | 6568.8 M/s | 14.42x | Peer Updaters in the same benchmark group hold within run-to-run noise, confirming the change is local to `DowncastLongUpdater`. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? New unit tests in `ParquetVectorUpdaterSuite` cover boundary batch lengths (0, 1, 7, 8, 9, 17, 1024, 4097), the singular `readValue` path, and sign preservation for in-range INT64 values that bound the DECIMAL(9, _) wire format. A new end-to-end test in `ParquetIOSuite` uses `parquet-mr`'s low-level API (`MessageTypeParser` + `SimpleGroup`) to write a file with `int64 v (DECIMAL(9, 2))` — Spark's own writer produces INT32-backed storage for precision <= 9, so this wire format only arises from other writers (Hive, Impala). The test reads the file back as `DecimalType(9, 2)` and confirms the values round-trip exactly. Benchmark results on JDK 17, 21, and 25 are committed on the branch. ### Was this patch authored or co-authored using generative AI tooling? Generated-by: Claude Code Closes #55853 from LuciferYang/SPARK-56803-downcast-long. Authored-by: YangJie <yangjie01@baidu.com> Signed-off-by: yangjie01 <yangjie01@baidu.com>
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R (Deprecated), and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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Spark is built using Apache Maven. To build Spark and its example programs, run:
./build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
More detailed documentation is available from the project site, at “Building Spark”.
For general development tips, including info on developing Spark using an IDE, see “Useful Developer Tools”.
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1,000,000,000:
scala> spark.range(1000 * 1000 * 1000).count()
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1,000,000,000:
>>> spark.range(1000 * 1000 * 1000).count()
Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be spark:// URL, “yarn” to run on YARN, and “local” to run locally with one thread, or “local[N]” to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at “Specifying the Hadoop Version and Enabling YARN” for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
