ORC Specification v0

This version of the file format was originally released as part of Hive 0.11.


Hive’s RCFile was the standard format for storing tabular data in Hadoop for several years. However, RCFile has limitations because it treats each column as a binary blob without semantics. In Hive 0.11 we added a new file format named Optimized Row Columnar (ORC) file that uses and retains the type information from the table definition. ORC uses type specific readers and writers that provide light weight compression techniques such as dictionary encoding, bit packing, delta encoding, and run length encoding – resulting in dramatically smaller files. Additionally, ORC can apply generic compression using zlib, or Snappy on top of the lightweight compression for even smaller files. However, storage savings are only part of the gain. ORC supports projection, which selects subsets of the columns for reading, so that queries reading only one column read only the required bytes. Furthermore, ORC files include light weight indexes that include the minimum and maximum values for each column in each set of 10,000 rows and the entire file. Using pushdown filters from Hive, the file reader can skip entire sets of rows that aren’t important for this query.

ORC file structure

File Tail

Since HDFS does not support changing the data in a file after it is written, ORC stores the top level index at the end of the file. The overall structure of the file is given in the figure above. The file’s tail consists of 3 parts; the file metadata, file footer and postscript.

The metadata for ORC is stored using Protocol Buffers, which provides the ability to add new fields without breaking readers. This document incorporates the Protobuf definition from the ORC source code and the reader is encouraged to review the Protobuf encoding if they need to understand the byte-level encoding


The Postscript section provides the necessary information to interpret the rest of the file including the length of the file’s Footer and Metadata sections, the version of the file, and the kind of general compression used (eg. none, zlib, or snappy). The Postscript is never compressed and ends one byte before the end of the file. The version stored in the Postscript is the lowest version of Hive that is guaranteed to be able to read the file and it stored as a sequence of the major and minor version. This version is stored as [0, 11].

The process of reading an ORC file works backwards through the file. Rather than making multiple short reads, the ORC reader reads the last 16k bytes of the file with the hope that it will contain both the Footer and Postscript sections. The final byte of the file contains the serialized length of the Postscript, which must be less than 256 bytes. Once the Postscript is parsed, the compressed serialized length of the Footer is known and it can be decompressed and parsed.

message PostScript {
 // the length of the footer section in bytes
 optional uint64 footerLength = 1;
 // the kind of generic compression used
 optional CompressionKind compression = 2;
 // the maximum size of each compression chunk
 optional uint64 compressionBlockSize = 3;
 // the version of the writer
 repeated uint32 version = 4 [packed = true];
 // the length of the metadata section in bytes
 optional uint64 metadataLength = 5;
 // the fixed string "ORC"
 optional string magic = 8000;
enum CompressionKind {
 NONE = 0;
 ZLIB = 1;
 SNAPPY = 2;
 LZO = 3;
 LZ4 = 4;
 ZSTD = 5;

The Footer section contains the layout of the body of the file, the type schema information, the number of rows, and the statistics about each of the columns.

The file is broken in to three parts- Header, Body, and Tail. The Header consists of the bytes “ORC’’ to support tools that want to scan the front of the file to determine the type of the file. The Body contains the rows and indexes, and the Tail gives the file level information as described in this section.

message Footer {
 // the length of the file header in bytes (always 3)
 optional uint64 headerLength = 1;
 // the length of the file header and body in bytes
 optional uint64 contentLength = 2;
 // the information about the stripes
 repeated StripeInformation stripes = 3;
 // the schema information
 repeated Type types = 4;
 // the user metadata that was added
 repeated UserMetadataItem metadata = 5;
 // the total number of rows in the file
 optional uint64 numberOfRows = 6;
 // the statistics of each column across the file
 repeated ColumnStatistics statistics = 7;
 // the maximum number of rows in each index entry
 optional uint32 rowIndexStride = 8;

Stripe Information

The body of the file is divided into stripes. Each stripe is self contained and may be read using only its own bytes combined with the file’s Footer and Postscript. Each stripe contains only entire rows so that rows never straddle stripe boundaries. Stripes have three sections: a set of indexes for the rows within the stripe, the data itself, and a stripe footer. Both the indexes and the data sections are divided by columns so that only the data for the required columns needs to be read.

message StripeInformation {
 // the start of the stripe within the file
 optional uint64 offset = 1;
 // the length of the indexes in bytes
 optional uint64 indexLength = 2;
 // the length of the data in bytes
 optional uint64 dataLength = 3;
 // the length of the footer in bytes
 optional uint64 footerLength = 4;
 // the number of rows in the stripe
 optional uint64 numberOfRows = 5;

Type Information

All of the rows in an ORC file must have the same schema. Logically the schema is expressed as a tree as in the figure below, where the compound types have subcolumns under them.

ORC column structure

The equivalent Hive DDL would be:

create table Foobar (
 myInt int,
 myMap map<string,
 struct<myString : string,
 myDouble: double>>,
 myTime timestamp

The type tree is flattened in to a list via a pre-order traversal where each type is assigned the next id. Clearly the root of the type tree is always type id 0. Compound types have a field named subtypes that contains the list of their children’s type ids.

message Type {
 enum Kind {
 BYTE = 1;
 SHORT = 2;
 INT = 3;
 LONG = 4;
 FLOAT = 5;
 DOUBLE = 6;
 STRING = 7;
 BINARY = 8;
 LIST = 10;
 MAP = 11;
 STRUCT = 12;
 UNION = 13;
 DECIMAL = 14;
 DATE = 15;
 VARCHAR = 16;
 CHAR = 17;
 // the kind of this type
 required Kind kind = 1;
 // the type ids of any subcolumns for list, map, struct, or union
 repeated uint32 subtypes = 2 [packed=true];
 // the list of field names for struct
 repeated string fieldNames = 3;
 // the maximum length of the type for varchar or char in UTF-8 characters
 optional uint32 maximumLength = 4;
 // the precision and scale for decimal
 optional uint32 precision = 5;
 optional uint32 scale = 6;

Column Statistics

The goal of the column statistics is that for each column, the writer records the count and depending on the type other useful fields. For most of the primitive types, it records the minimum and maximum values; and for numeric types it additionally stores the sum. From Hive 1.1.0 onwards, the column statistics will also record if there are any null values within the row group by setting the hasNull flag. The hasNull flag is used by ORC’s predicate pushdown to better answer ‘IS NULL’ queries.

message ColumnStatistics {
 // the number of values
 optional uint64 numberOfValues = 1;
 // At most one of these has a value for any column
 optional IntegerStatistics intStatistics = 2;
 optional DoubleStatistics doubleStatistics = 3;
 optional StringStatistics stringStatistics = 4;
 optional BucketStatistics bucketStatistics = 5;
 optional DecimalStatistics decimalStatistics = 6;
 optional DateStatistics dateStatistics = 7;
 optional BinaryStatistics binaryStatistics = 8;
 optional TimestampStatistics timestampStatistics = 9;
 optional bool hasNull = 10;

For integer types (tinyint, smallint, int, bigint), the column statistics includes the minimum, maximum, and sum. If the sum overflows long at any point during the calculation, no sum is recorded.

message IntegerStatistics {
 optional sint64 minimum = 1;
 optional sint64 maximum = 2;
 optional sint64 sum = 3;

For floating point types (float, double), the column statistics include the minimum, maximum, and sum. If the sum overflows a double, no sum is recorded.

message DoubleStatistics {
 optional double minimum = 1;
 optional double maximum = 2;
 optional double sum = 3;

For strings, the minimum value, maximum value, and the sum of the lengths of the values are recorded.

message StringStatistics {
 optional string minimum = 1;
 optional string maximum = 2;
 // sum will store the total length of all strings
 optional sint64 sum = 3;

For booleans, the statistics include the count of false and true values.

message BucketStatistics {
 repeated uint64 count = 1 [packed=true];

For decimals, the minimum, maximum, and sum are stored.

message DecimalStatistics {
 optional string minimum = 1;
 optional string maximum = 2;
 optional string sum = 3;

Date columns record the minimum and maximum values as the number of days since the UNIX epoch (1/1/1970 in UTC).

message DateStatistics {
 // min,max values saved as days since epoch
 optional sint32 minimum = 1;
 optional sint32 maximum = 2;

Timestamp columns record the minimum and maximum values as the number of milliseconds since the epoch (1/1/2015).

message TimestampStatistics {
 // min,max values saved as milliseconds since epoch
 optional sint64 minimum = 1;
 optional sint64 maximum = 2;

Binary columns store the aggregate number of bytes across all of the values.

message BinaryStatistics {
 // sum will store the total binary blob length
 optional sint64 sum = 1;

User Metadata

The user can add arbitrary key/value pairs to an ORC file as it is written. The contents of the keys and values are completely application defined, but the key is a string and the value is binary. Care should be taken by applications to make sure that their keys are unique and in general should be prefixed with an organization code.

message UserMetadataItem {
 // the user defined key
 required string name = 1;
 // the user defined binary value
 required bytes value = 2;

File Metadata

The file Metadata section contains column statistics at the stripe level granularity. These statistics enable input split elimination based on the predicate push-down evaluated per a stripe.

message StripeStatistics {
 repeated ColumnStatistics colStats = 1;
message Metadata {
 repeated StripeStatistics stripeStats = 1;


If the ORC file writer selects a generic compression codec (zlib or snappy), every part of the ORC file except for the Postscript is compressed with that codec. However, one of the requirements for ORC is that the reader be able to skip over compressed bytes without decompressing the entire stream. To manage this, ORC writes compressed streams in chunks with headers as in the figure below. To handle uncompressable data, if the compressed data is larger than the original, the original is stored and the isOriginal flag is set. Each header is 3 bytes long with (compressedLength * 2 + isOriginal) stored as a little endian value. For example, the header for a chunk that compressed to 100,000 bytes would be [0x40, 0x0d, 0x03]. The header for 5 bytes that did not compress would be [0x0b, 0x00, 0x00]. Each compression chunk is compressed independently so that as long as a decompressor starts at the top of a header, it can start decompressing without the previous bytes.

compression streams

The default compression chunk size is 256K, but writers can choose their own value. Larger chunks lead to better compression, but require more memory. The chunk size is recorded in the Postscript so that readers can allocate appropriately sized buffers. Readers are guaranteed that no chunk will expand to more than the compression chunk size.

ORC files without generic compression write each stream directly with no headers.

Run Length Encoding

Base 128 Varint

Variable width integer encodings take advantage of the fact that most numbers are small and that having smaller encodings for small numbers shrinks the overall size of the data. ORC uses the varint format from Protocol Buffers, which writes data in little endian format using the low 7 bits of each byte. The high bit in each byte is set if the number continues into the next byte.

Unsigned Original Serialized
0 0x00
1 0x01
127 0x7f
128 0x80, 0x01
129 0x81, 0x01
16,383 0xff, 0x7f
16,384 0x80, 0x80, 0x01
16,385 0x81, 0x80, 0x01

For signed integer types, the number is converted into an unsigned number using a zigzag encoding. Zigzag encoding moves the sign bit to the least significant bit using the expression (val « 1) ^ (val » 63) and derives its name from the fact that positive and negative numbers alternate once encoded. The unsigned number is then serialized as above.

Signed Original Unsigned
0 0
-1 1
1 2
-2 3
2 4

Byte Run Length Encoding

For byte streams, ORC uses a very light weight encoding of identical values.

  • Run - a sequence of at least 3 identical values
  • Literals - a sequence of non-identical values

The first byte of each group of values is a header that determines whether it is a run (value between 0 to 127) or literal list (value between -128 to -1). For runs, the control byte is the length of the run minus the length of the minimal run (3) and the control byte for literal lists is the negative length of the list. For example, a hundred 0’s is encoded as [0x61, 0x00] and the sequence 0x44, 0x45 would be encoded as [0xfe, 0x44, 0x45]. The next group can choose either of the encodings.

Boolean Run Length Encoding

For encoding boolean types, the bits are put in the bytes from most significant to least significant. The bytes are encoded using byte run length encoding as described in the previous section. For example, the byte sequence [0xff, 0x80] would be one true followed by seven false values.

Integer Run Length Encoding, version 1

ORC v0 files use Run Length Encoding version 1 (RLEv1), which provides a lightweight compression of signed or unsigned integer sequences. RLEv1 has two sub-encodings:

  • Run - a sequence of values that differ by a small fixed delta
  • Literals - a sequence of varint encoded values

Runs start with an initial byte of 0x00 to 0x7f, which encodes the length of the run - 3. A second byte provides the fixed delta in the range of -128 to 127. Finally, the first value of the run is encoded as a base 128 varint.

For example, if the sequence is 100 instances of 7 the encoding would start with 100 - 3, followed by a delta of 0, and a varint of 7 for an encoding of [0x61, 0x00, 0x07]. To encode the sequence of numbers running from 100 to 1, the first byte is 100 - 3, the delta is -1, and the varint is 100 for an encoding of [0x61, 0xff, 0x64].

Literals start with an initial byte of 0x80 to 0xff, which corresponds to the negative of number of literals in the sequence. Following the header byte, the list of N varints is encoded. Thus, if there are no runs, the overhead is 1 byte for each 128 integers. Numbers [2, 3, 6, 7, 11] would be encoded as [0xfb, 0x02, 0x03, 0x06, 0x07, 0xb].


The body of ORC files consists of a series of stripes. Stripes are large (typically ~200MB) and independent of each other and are often processed by different tasks. The defining characteristic for columnar storage formats is that the data for each column is stored separately and that reading data out of the file should be proportional to the number of columns read.

In ORC files, each column is stored in several streams that are stored next to each other in the file. For example, an integer column is represented as two streams PRESENT, which uses one with a bit per value recording if the value is non-null, and DATA, which records the non-null values. If all of a column’s values in a stripe are non-null, the PRESENT stream is omitted from the stripe. For binary data, ORC uses three streams PRESENT, DATA, and LENGTH, which stores the length of each value. The details of each type will be presented in the following subsections.

There is a general order for index and data streams:

  • Index streams are always placed together in the beginning of the stripe.
  • Data streams are placed together after index streams (if any).
  • Inside index streams or data streams, the unencrypted streams should be placed first and then followed by streams grouped by each encryption variant.

There is no fixed order within each unencrypted or encryption variant in the index and data streams:

  • Different stream kinds of the same column can be placed in any order.
  • Streams from different columns can even be placed in any order. To get the precise information (a.k.a stream kind, column id and location) of a stream within a stripe, the streams field in the StripeFooter described below is the single source of truth.

In the example of the integer column mentioned above, the order of the PRESENT stream and the DATA stream cannot be determined in advance. We need to get the precise information by StripeFooter.

The stripe footer contains the encoding of each column and the directory of the streams including their location.

message StripeFooter {
 // the location of each stream
 repeated Stream streams = 1;
 // the encoding of each column
 repeated ColumnEncoding columns = 2;

To describe each stream, ORC stores the kind of stream, the column id, and the stream’s size in bytes. The details of what is stored in each stream depends on the type and encoding of the column.

message Stream {
 enum Kind {
 // boolean stream of whether the next value is non-null
 // the primary data stream
 DATA = 1;
 // the length of each value for variable length data
 LENGTH = 2;
 // the dictionary blob
 // deprecated prior to Hive 0.11
 // It was used to store the number of instances of each value in the
 // dictionary
 // a secondary data stream
 // the index for seeking to particular row groups
 required Kind kind = 1;
 // the column id
 optional uint32 column = 2;
 // the number of bytes in the file
 optional uint64 length = 3;

Depending on their type several options for encoding are possible. The encodings are divided into direct or dictionary-based categories and further refined as to whether they use RLE v1 or v2.

message ColumnEncoding {
 enum Kind {
 // the encoding is mapped directly to the stream using RLE v1
 DIRECT = 0;
 // the encoding uses a dictionary of unique values using RLE v1
 // the encoding is direct using RLE v2
 required Kind kind = 1;
 // for dictionary encodings, record the size of the dictionary
 optional uint32 dictionarySize = 2;

Column Encodings

SmallInt, Int, and BigInt Columns

All of the 16, 32, and 64 bit integer column types use the same set of potential encodings, which is basically whether they use RLE v1 or v2. If the PRESENT stream is not included, all of the values are present. For values that have false bits in the present stream, no values are included in the data stream.

Encoding Stream Kind Optional Contents
  DATA No Signed Integer RLE v1

Note that the order of the Stream is not fixed. It also applies to other Column types.

Float and Double Columns

Floating point types are stored using IEEE 754 floating point bit layout. Float columns use 4 bytes per value and double columns use 8 bytes.

Encoding Stream Kind Optional Contents
  DATA No IEEE 754 floating point representation

String, Char, and VarChar Columns

String, char, and varchar columns may be encoded either using a dictionary encoding or a direct encoding. A direct encoding should be preferred when there are many distinct values. In all of the encodings, the PRESENT stream encodes whether the value is null. The Java ORC writer automatically picks the encoding after the first row group (10,000 rows).

For direct encoding the UTF-8 bytes are saved in the DATA stream and the length of each value is written into the LENGTH stream. In direct encoding, if the values were [“Nevada”, “California”]; the DATA would be “NevadaCalifornia” and the LENGTH would be [6, 10].

For dictionary encodings the dictionary is sorted (in lexicographical order of bytes in the UTF-8 encodings) and UTF-8 bytes of each unique value are placed into DICTIONARY_DATA. The length of each item in the dictionary is put into the LENGTH stream. The DATA stream consists of the sequence of references to the dictionary elements.

In dictionary encoding, if the values were [“Nevada”, “California”, “Nevada”, “California”, and “Florida”]; the DICTIONARY_DATA would be “CaliforniaFloridaNevada” and LENGTH would be [10, 7, 6]. The DATA would be [2, 0, 2, 0, 1].

Encoding Stream Kind Optional Contents
  DATA No String contents
  LENGTH No Unsigned Integer RLE v1
  DATA No Unsigned Integer RLE v1
  DICTIONARY_DATA No String contents
  LENGTH No Unsigned Integer RLE v1

Boolean Columns

Boolean columns are rare, but have a simple encoding.

Encoding Stream Kind Optional Contents
  DATA No Boolean RLE

TinyInt Columns

TinyInt (byte) columns use byte run length encoding.

Encoding Stream Kind Optional Contents
  DATA No Byte RLE

Binary Columns

Binary data is encoded with a PRESENT stream, a DATA stream that records the contents, and a LENGTH stream that records the number of bytes per a value.

Encoding Stream Kind Optional Contents
  DATA No String contents
  LENGTH No Unsigned Integer RLE v1

Decimal Columns

Decimal was introduced in Hive 0.11 with infinite precision (the total number of digits). In Hive 0.13, the definition was change to limit the precision to a maximum of 38 digits, which conveniently uses 127 bits plus a sign bit. The current encoding of decimal columns stores the integer representation of the value as an unbounded length zigzag encoded base 128 varint. The scale is stored in the SECONDARY stream as a signed integer.

Encoding Stream Kind Optional Contents
  DATA No Unbounded base 128 varints
  SECONDARY No Signed Integer RLE v1

Date Columns

Date data is encoded with a PRESENT stream, a DATA stream that records the number of days after January 1, 1970 in UTC.

Encoding Stream Kind Optional Contents
  DATA No Signed Integer RLE v1

Timestamp Columns

Timestamp records times down to nanoseconds as a PRESENT stream that records non-null values, a DATA stream that records the number of seconds after 1 January 2015, and a SECONDARY stream that records the number of nanoseconds.

Because the number of nanoseconds often has a large number of trailing zeros, the number has trailing decimal zero digits removed and the last three bits are used to record how many zeros were removed. if the trailing zeros are more than 2. Thus 1000 nanoseconds would be serialized as 0x0a and 100000 would be serialized as 0x0c.

Encoding Stream Kind Optional Contents
  DATA No Signed Integer RLE v1
  SECONDARY No Unsigned Integer RLE v1

Struct Columns

Structs have no data themselves and delegate everything to their child columns except for their PRESENT stream. They have a child column for each of the fields.

Encoding Stream Kind Optional Contents

List Columns

Lists are encoded as the PRESENT stream and a length stream with number of items in each list. They have a single child column for the element values.

Encoding Stream Kind Optional Contents
  LENGTH No Unsigned Integer RLE v1

Map Columns

Maps are encoded as the PRESENT stream and a length stream with number of items in each map. They have a child column for the key and another child column for the value.

Encoding Stream Kind Optional Contents
  LENGTH No Unsigned Integer RLE v1

Union Columns

Unions are encoded as the PRESENT stream and a tag stream that controls which potential variant is used. They have a child column for each variant of the union. Currently ORC union types are limited to 256 variants, which matches the Hive type model.

Encoding Stream Kind Optional Contents


Row Group Index

The row group indexes consist of a ROW_INDEX stream for each primitive column that has an entry for each row group. Row groups are controlled by the writer and default to 10,000 rows. Each RowIndexEntry gives the position of each stream for the column and the statistics for that row group.

The index streams are placed at the front of the stripe, because in the default case of streaming they do not need to be read. They are only loaded when either predicate push down is being used or the reader seeks to a particular row.

message RowIndexEntry {
 repeated uint64 positions = 1 [packed=true];
 optional ColumnStatistics statistics = 2;
message RowIndex {
 repeated RowIndexEntry entry = 1;

To record positions, each stream needs a sequence of numbers. For uncompressed streams, the position is the byte offset of the RLE run’s start location followed by the number of values that need to be consumed from the run. In compressed streams, the first number is the start of the compression chunk in the stream, followed by the number of decompressed bytes that need to be consumed, and finally the number of values consumed in the RLE.

For columns with multiple streams, the sequences of positions in each stream are concatenated. That was an unfortunate decision on my part that we should fix at some point, because it makes code that uses the indexes error-prone.

Because dictionaries are accessed randomly, there is not a position to record for the dictionary and the entire dictionary must be read even if only part of a stripe is being read.

Note that for columns with multiple streams, the order of stream positions in the RowIndex is fixed, which may be different to the actual data stream placement, and it is the same as Column Encodings section we described above.