Spark Configuration

Table properties

Tables stored as ORC files use table properties to control their behavior. By using table properties, the table owner ensures that all clients store data with the same options.

Key Default Notes
orc.compress ZLIB high level compression = {NONE, ZLIB, SNAPPY, ZSTD}
orc.compress.size 262,144 compression chunk size
orc.stripe.size 67,108,864 memory buffer in bytes for writing
orc.row.index.stride 10,000 number of rows between index entries
orc.create.index true create indexes?
orc.bloom.filter.columns ”” comma separated list of column names
orc.bloom.filter.fpp 0.05 bloom filter false positive rate
orc.key.provider “hadoop” key provider
orc.encrypt ”” list of keys and columns to encrypt with
orc.mask ”” masks to apply to the encrypted columns

For example, to create an ORC table with Zstandard compression:

CREATE TABLE encrypted (
  ssn STRING,
  email STRING,
  name STRING
OPTIONS ( "kms://http@localhost:9600/kms",
  orc.key.provider "hadoop",
  orc.encrypt "pii:ssn,email",
  orc.mask "nullify:ssn;sha256:email"

Configuration properties

There are more Spark configuration properties related to ORC files:

Key Default Notes
spark.sql.orc.impl native The name of ORC implementation. It can be one of native or hive. native means the native ORC support. hive means the ORC library in Hive.
spark.sql.orc.enableVectorizedReader true Enables vectorized orc decoding in native implementation.
spark.sql.orc.mergeSchema false When true, the ORC data source merges schemas collected from all data files, otherwise the schema is picked from a random data file.
spark.sql.hive.convertMetastoreOrc true Spark SQL will use the Hive SerDe for ORC tables instead of the built-in support.