Hi, All.
Apache Spark always has been a fast and general engine, and
supports Apache ORC inside `sql/hive` module with Hive dependency since Spark
1.4.X (SPARK-2883).
However, there are many open issues about `Feature parity for ORC with Parquet
(SPARK-20901)` as of today.
With new Apache ORC 1.4 (released 8th May), Apache Spark is able to get the
following benefits.
- Usability:
* Users can use `ORC` data sources without hive module (-Phive) like
`Parquet` format.
- Stability & Maintanability:
* ORC 1.4 already has many fixes.
* In the future, Spark can upgrade ORC library independently from Hive
(similar to Parquet library, too)
* Eventually, reduce the dependecy on old Hive 1.2.1.
- Speed:
* Last but not least, Spark can use both Spark `ColumnarBatch` and ORC
`RowBatch` together
which means full vectorization support.
First of all, I'd love to improve Apache Spark in the following steps in the
time frame of Spark 2.3.
- SPARK-21422: Depend on Apache ORC 1.4.0
- SPARK-20682: Add a new faster ORC data source based on Apache ORC
- SPARK-20728: Make ORCFileFormat configurable between sql/hive and sql/core
- SPARK-16060: Vectorized Orc Reader
I’ve made above PRs since 9th May, the day after Apache ORC 1.4 release,
but the PRs seems to need more attention of PMC since this is an important
change.
Since the discussion on Apache Spark 2.3 cadence is already started this week,
I thought it’s a best time to ask you about this.
Could anyone of you help me to proceed ORC improvement in Apache Spark
community?
Please visit the minimal PR and JIRA issue as a starter.
* https://github.com/apache/spark/pull/18640
* https://issues.apache.org/jira/browse/SPARK-21422
Thank you in advance.
Bests,
Dongjoon Hyun.
