ORC File Format

ORC File Format

The Optimized Row Columnar (ORC) file format provides a highly efficient way to store Hive data. It was designed to overcome limitations of the other Hive file formats. Using ORC files improves performance when Hive is reading, writing, and processing data.

Compared with RCFile format, for example, ORC file format has many advantages such as:

  • a single file as the output of each task, which reduces the NameNode's load
  • Hive type support including datetime, decimal, and the complex types (struct, list, map, and union)
  • light-weight indexes stored within the file
    • skip row groups that don't pass predicate filtering
    • seek to a given row
  • block-mode compression based on data type
    • run-length encoding for integer columns
    • dictionary encoding for string columns
  • concurrent reads of the same file using separate RecordReaders
  • ability to split files without scanning for markers
  • bound the amount of memory needed for reading or writing
  • metadata stored using Protocol Buffers, which allows addition and removal of fields

File Structure

An ORC file contains groups of row data called stripes, along with auxiliary information in a file footer. At the end of the file a postscript holds compression parameters and the size of the compressed footer.

The default stripe size is 250 MB. Large stripe sizes enable large, efficient reads from HDFS.

The file footer contains a list of stripes in the file, the number of rows per stripe, and each column's data type. It also contains column-level aggregates count, min, max, and sum.

This diagram illustrates the ORC file structure:

Stripe Structure

As shown in the diagram, each stripe in an ORC file holds index data, row data, and a stripe footer.

The stripe footer contains a directory of stream locations. Row data is used in table scans.

Index data includes min and max values for each column and the row positions within each column (A bit field or bloom filter could also be included.) Row index entries provide offsets that enable eeking to the right compression block and byte within a decompressed block.

Having relatively frequent row index entries enables row-skipping within a stripe for rapid reads, despite large stripe sizes. By default every 10,000 rows can be skipped.

With the ability to skip large sets of rows based on filter predicates, you can sort a table on its secondary keys to achieve a big reduction in execution time. For example, if the primary partition is transaction date, the table can be sorted on state, zip code, and last name. Then looking for records in one state will skip the records of all other states.

Hive QL Syntax

File formats are specified at the table (or partition) level. You can specify the ORC file format with Hive QL statements such as these:

  • CREATE TABLE ... STORED AS ORC
  • ALTER TABLE ... [PARTITION partition_spec] SET FILEFORMAT ORC

    Note: This statement only works on partitioned tables. If you apply it to flat tables, it can cause query errors.

  • SET hive.default.fileformat=Orc

The parameters are all placed in the TBLPROPERTIES. They are:

Key Default Notes
orc.compress ZLIB high level compression (one of NONE, ZLIB, SNAPPY)
orc.compress.size 262,144 number of bytes in each compression chunk
orc.stripe.size 268435456 number of bytes in each stripe
orc.row.index.stride 10,000 number of rows between index entries (must be >= 1000)
orc.create.index true whether to create row indexes

For example, creating an ORC stored table without compression:

create table Addresses ( name string, street string, city string, state string, zip int ) stored as orc tblproperties ("orc.compress"="NONE");

Serialization and Compression

The serialization of column data in an ORC file depends on whether the data type is integer or string.

Integer Column Serialization

Integer columns are serialized in two streams.

  1. present bit stream: is the value non-null?
  2. data stream: a stream of integers

Integer data is serialized in a way that takes advantage of the common distribution of numbers:

  • Integers are encoded using a variable-width encoding that has fewer bytes for small integers.
  • Repeated values are run-length encoded.
  • Values that differ by a constant in the range (-128 to 127) are run-length encoded.

The variable-width encoding is based on Google's protocol buffers and uses the high bit to represent whether this byte is not the last and the lower 7 bits to encode data. To encode negative numbers, a zigzag encoding is used where 0, -1, 1, -2, and 2 map into 0, 1, 2, 3, 4, and 5 respectively.

Each set of numbers is encoded this way:

  • If the first byte (b0) is negative:
    • -b0 variable-length integers follow.
  • If the first byte (b0) is positive:
    • it represents b0 + 3 repeated integers
    • the second byte (-128 to +127) is added between each repetition
    • 1 variable-length integer.

In run-length encoding, the first byte specifies run length and whether the values are literals or duplicates. Duplicates can step by -128 to +128. Run-length encoding uses protobuf style variable-length integers.

String Column Serialization

Serialization of string columns uses a dictionary to form unique column values The dictionary is sorted to speed up predicate filtering and improve compression ratios.

String columns are serialized in four streams.

  1. present bit stream: is the value non-null?
  2. dictionary data: the bytes for the strings
  3. dictionary length: the length of each entry
  4. row data: the row values

Both the dictionary length and the row values are run length encoded streams of integers.

Compression

Streams are compressed using a codec, which is specified as a table property for all streams in that table To optimize memory use, compression is done incrementally as each block is produced. Compressed blocks can be jumped over without first having to be decompressed for scanning. Positions in the stream are represented by a block start location and an offset into the block.

The codec can be Snappy, Zlib, or none.