The number shouldīe carefully chosen to minimize overhead and avoid OOMs in writing data. The number of rows to include in an orc vectorized writer batch. The number shouldīe carefully chosen to minimize overhead and avoid OOMs in reading data. The number of rows to include in an orc vectorized reader batch. If false,Ī new non-vectorized ORC reader is used in native implementation.įor hive implementation, this is ignored. hive means the ORC libraryĮnables vectorized orc decoding in native implementation. This behavior is controlled by the configuration, and is turned on by default. For CTAS statement, only non-partitioned Hive metastore ORC tables are converted. When reading from Hive metastore ORC tables and inserting to Hive metastore ORC tables, Spark SQL will try to use its own ORC support instead of Hive SerDe for better performance. mask "nullify:ssn sha256:email" ) Hive metastore ORC table conversion Of Zstandard compression in ORC files on both Hadoop versions.ĬREATE TABLE encrypted ( ssn STRING, email STRING, name STRING ) USING ORC OPTIONS ( hadoop. setting the global SQL option to true.setting data source option mergeSchema to true when reading ORC files, or.Since schema merging is a relatively expensive operation, and is not a necessity in most cases, we Source is now able to automatically detect this case and merge schemas of all these files. Up with multiple ORC files with different but mutually compatible schemas. Users can start withĪ simple schema, and gradually add more columns to the schema as needed. Like Protocol Buffer, Avro, and Thrift, ORC also supports schema evolution. The vectorized reader is used when is also set to true, and is turned on by default. The vectorized reader is used for the native ORC tables (e.g., the ones created using the clause USING ORC) when is set to native and is set to true.įor the Hive ORC serde tables (e.g., the ones created using the clause USING HIVE OPTIONS (fileFormat 'ORC')), Native implementation supports a vectorized ORC reader and has been the default ORC implementation since Spark 2.3. Since Spark 3.1.0, SPARK-33480 removes this difference by supporting CHAR/VARCHAR from Spark-side. hive implementation is designed to follow Hive’s behavior and uses Hive SerDe.įor example, historically, native implementation handles CHAR/VARCHAR with Spark’s native String while hive implementation handles it via Hive CHAR/VARCHAR.native implementation is designed to follow Spark’s data source behavior like Parquet.Two implementations share most functionalities with different design goals. Spark supports two ORC implementations ( native and hive) which is controlled by. Apache ORC is a columnar format which has more advanced features like native zstd compression, bloom filter and columnar encryption.
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