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This version of the file format was originally released as part ofHive 0.12.

Hive's RCFile was the standard format for storing tabular data inHadoop for several years. However, RCFile has limitations because ittreats each column as a binary blob without semantics. In Hive 0.11 weadded a new file format named Optimized Row Columnar (ORC) file thatuses and retains the type information from the table definition. ORCuses type specific readers and writers that provide light weightcompression techniques such as dictionary encoding, bit packing, deltaencoding, and run length encoding – resulting in dramatically smallerfiles. Additionally, ORC can apply generic compression using zlib, orSnappy on top of the lightweight compression for even smallerfiles. However, storage savings are only part of the gain. ORCsupports projection, which selects subsets of the columns for reading,so that queries reading only one column read only the requiredbytes. Furthermore, ORC files include light weight indexes thatinclude the minimum and maximum values for each column in each set of10,000 rows and the entire file. Using pushdown filters from Hive, thefile reader can skip entire sets of rows that aren't important forthis query.

Since HDFS does not support changing the data in a file after it iswritten, ORC stores the top level index at the end of the file. Theoverall structure of the file is given in the figure above. Thefile's tail consists of 3 parts; the file metadata, file footer andpostscript.

The metadata for ORC is stored usingProtocol Buffers, which providesthe ability to add new fields without breaking readers. This documentincorporates the Protobuf definition from theORC source code and thereader is encouraged to review the Protobuf encoding if they need tounderstand the byte-level encoding

The sections of the file tail are (and their protobuf message type):

• encrypted stripe statistics: list of ColumnarStripeStatistics
• stripe statistics: Metadata
• footer: Footer
• postscript: PostScript
• psLen: byte

Postscript

The Postscript section provides the necessary information to interpretthe rest of the file including the length of the file's Footer andMetadata sections, the version of the file, and the kind of generalcompression used (eg. none, zlib, or snappy). The Postscript is nevercompressed and ends one byte before the end of the file. The versionstored in the Postscript is the lowest version of Hive that isguaranteed to be able to read the file and it stored as a sequence ofthe major and minor version. This file version is encoded as [0,12].

The process of reading an ORC file works backwards through thefile. Rather than making multiple short reads, the ORC reader readsthe last 16k bytes of the file with the hope that it will contain boththe Footer and Postscript sections. The final byte of the filecontains the serialized length of the Postscript, which must be lessthan 256 bytes. Once the Postscript is parsed, the compressedserialized length of the Footer is known and it can be decompressedand parsed.

The Footer section contains the layout of the body of the file, thetype schema information, the number of rows, and the statistics abouteach of the columns.

The file is broken in to three parts- Header, Body, and Tail. TheHeader consists of the bytes 'ORC'' to support tools that want toscan the front of the file to determine the type of the file. The Bodycontains the rows and indexes, and the Tail gives the file levelinformation as described in this section.

Stripe Information

The body of the file is divided into stripes. Each stripe is selfcontained and may be read using only its own bytes combined with thefile's Footer and Postscript. Each stripe contains only entire rows sothat rows never straddle stripe boundaries. Stripes have threesections: a set of indexes for the rows within the stripe, the dataitself, and a stripe footer. Both the indexes and the data sectionsare divided by columns so that only the data for the required columnsneeds to be read.

The encryptStripeId and encryptedLocalKeys support columnencryption. They are set on the first stripe of each ORC file withcolumn encryption and not set after that. For a stripe with the valuesset, the reader should use those values for that stripe. Subsequentstripes use the previous encryptStripeId + 1 and the same keys.

The current ORC merging code merges entire files, and thus the readerwill get the correct values on what was the first stripe and continueon. If we develop a merge tool that reorders stripes or does partialmerges, these values will need to be set correctly by that tool.

Type Information

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

The equivalent Hive DDL would be:

The type tree is flattened in to a list via a pre-order traversalwhere each type is assigned the next id. Clearly the root of the typetree is always type id 0. Compound types have a field named subtypesthat contains the list of their children's type ids.

Column Statistics

The goal of the column statistics is that for each column, the writerrecords the count and depending on the type other useful fields. Formost of the primitive types, it records the minimum and maximumvalues; and for numeric types it additionally stores the sum.From Hive 1.1.0 onwards, the column statistics will also record ifthere 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.

For integer types (tinyint, smallint, int, bigint), the columnstatistics includes the minimum, maximum, and sum. If the sumoverflows long at any point during the calculation, no sum isrecorded.

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

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

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

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

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

Timestamp columns record the minimum and maximum values as the number ofmilliseconds since the UNIX epoch (1/1/1970 00:00:00). Before ORC-135, thelocal timezone offset was included and they were stored as minimum andmaximum. After ORC-135, the timestamp is adjusted to UTC before beingconverted to milliseconds and stored in minimumUtc and maximumUtc.

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

User Metadata

The user can add arbitrary key/value pairs to an ORC file as it iswritten. The contents of the keys and values are completelyapplication defined, but the key is a string and the value isbinary. Care should be taken by applications to make sure that theirkeys are unique and in general should be prefixed with an organizationcode.

File Metadata

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

ORC as of Apache ORC 1.6 supports column encryption where the data andstatistics of specific columns are encrypted on disk. Columnencryption provides fine-grain column level security even when manyusers have access to the file itself. The encryption is transparent tothe user and the writer only needs to define which columns andencryption keys to use. When reading an ORC file, if the user hasaccess to the keys, they will get the real data. If they do not havethe keys, they will get the masked data.

Each encrypted column in each file will have a random local keygenerated for it. Thus, even though all of the decryption happenslocally in the reader, a malicious user that stores the key onlyenables access that column in that file. The local keys are encryptedby the Hadoop or Ranger Key Management Server (KMS). The encryptedlocal keys are stored in the file footer's StripeInformation.

When ORC is using the Hadoop or Ranger KMS, it generates a random encryptedlocal key (16 or 32 bytes for 128 or 256 bit AES respectively). Using thefirst 16 bytes as the IV, it uses AES/CTR to decrypt the local key.

With the AWS KMS, the GenerateDataKey method is used to create a new localkey and the Decrypt method is used to decrypt it.

Data Masks

The user's data is statically masked before writing the unencryptedvariant. Because the masking was done statically when the file waswritten, the information about the masking is just informational. Barsoom 2 1 download free.

The three standard masks are:

• nullify - all values become null
• redact - replace characters with constants such as X or 9
• sha256 - replace string with the SHA 256 of the value

The default is nullify, but masks may be defined by the user. Masksare not allowed to change the type of the column, just the values.

Encryption Keys

In addition to the encrypted local keys, which are stored in thefooter's StripeInformation, the file also needs to describe the masterkey that was used to encrypt the local keys. The master keys aredescribed by name, their version, and the encryption algorithm.

The encryption algorithm is stored using an enumeration and sinceProtoBuf uses the 0 value as a default, we added an unused value. Apocalipsis wormwood edition 2 0. Thatensures that if we add a new algorithm that old readers will getUNKNOWN_ENCRYPTION instead of a real value.

Encryption Variants

Each encrypted column is written as two variants:

• encrypted unmasked - for users with access to the key
• unencrypted masked - for all other users

The changes to the format were done so that old ORC readers will readthe masked unencrypted data. Encryption variants encrypt a subtree ofcolumns and use a single local key. The initial version of encryptionsupport only allows the two variants, but this may be extended laterand thus readers should use the first variant of a column that thereader has access to.

Each variant stores stripe and file statistics separately. The filestatistics are serialized as a FileStatistics, compressed, encryptedand stored in the EncryptionVariant.fileStatistics.

The stripe statistics for each column are serialized asColumnarStripeStatistics, compressed, encrypted and stored in a streamof kind STRIPE_STATISTICS. By making the column stripe statisticsindependent of each other, the reader only reads and parses thecolumns contained in the SARG.

Stream Encryption

Our encryption is done using AES/CTR. CTR is a mode that has some verynice properties for us:

• It is seeded so that identical data is encrypted differently.
• It does not require padding the stream to the cipher length.
• It allows readers to seek in to a stream.
• The IV does not need to be randomly generated.

To ensure that we don't reuse IV, we set the IV as:

• bytes 0 to 2 - column id
• bytes 3 to 4 - stream kind
• bytes 5 to 7 - stripe id
• bytes 8 to 15 - cipher block counter

However, it is critical for CTR that we never reuse an initializationvector (IV) with the same local key.

For data in the footer, use the number of stripes in the file as thestripe id. This guarantees when we write an intermediate footer in toa file that we don't use the same IV.

Additionally, we never reuse a local key for new data. For example, whenmerging files, we don't reuse local key from the input files for the newfile tail, but always generate a new local key.

If the ORC file writer selects a generic compression codec (zlib orsnappy), every part of the ORC file except for the Postscript iscompressed with that codec. However, one of the requirements for ORCis that the reader be able to skip over compressed bytes withoutdecompressing the entire stream. To manage this, ORC writes compressedstreams in chunks with headers as in the figure below.To handle uncompressable data, if the compressed data is larger thanthe original, the original is stored and the isOriginal flag isset. Each header is 3 bytes long with (compressedLength * 2 +isOriginal) stored as a little endian value. For example, the headerfor 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 sothat as long as a decompressor starts at the top of a header, it canstart decompressing without the previous bytes.

The default compression chunk size is 256K, but writers can choosetheir own value. Larger chunks lead to better compression, but requiremore memory. The chunk size is recorded in the Postscript so thatreaders can allocate appropriately sized buffers. Readers areguaranteed that no chunk will expand to more than the compression chunksize.

ORC files without generic compression write each stream directlywith no headers.

Base 128 Varint

Variable width integer encodings take advantage of the fact that mostnumbers are small and that having smaller encodings for small numbersshrinks the overall size of the data. ORC uses the varint format fromProtocol Buffers, which writes data in little endian format using thelow 7 bits of each byte. The high bit in each byte is set if thenumber continues into the next byte.

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

The default compression chunk size is 256K, but writers can choosetheir own value. Larger chunks lead to better compression, but requiremore memory. The chunk size is recorded in the Postscript so thatreaders can allocate appropriately sized buffers. Readers areguaranteed that no chunk will expand to more than the compression chunksize.

ORC files without generic compression write each stream directlywith no headers.

Base 128 Varint

Variable width integer encodings take advantage of the fact that mostnumbers are small and that having smaller encodings for small numbersshrinks the overall size of the data. ORC uses the varint format fromProtocol Buffers, which writes data in little endian format using thelow 7 bits of each byte. The high bit in each byte is set if thenumber continues into the next byte.

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

Byte Run Length Encoding

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

• 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 determineswhether it is a run (value between 0 to 127) or literal list (valuebetween -128 to -1). For runs, the control byte is the length of therun minus the length of the minimal run (3) and the control byte forliteral lists is the negative length of the list. For example, ahundred 0's is encoded as [0x61, 0x00] and the sequence 0x44, 0x45would be encoded as [0xfe, 0x44, 0x45]. The next group can chooseeither of the encodings.

Boolean Run Length Encoding

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

Integer Run Length Encoding, version 1

In Hive 0.11 ORC files used Run Length Encoding version 1 (RLEv1),which provides a lightweight compression of signed or unsigned integersequences. 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 thelength of the run - 3. A second byte provides the fixed delta in therange of -128 to 127. Finally, the first value of the run is encodedas a base 128 varint.

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For example, if the sequence is 100 instances of 7 the encoding wouldstart with 100 - 3, followed by a delta of 0, and a varint of 7 foran encoding of [0x61, 0x00, 0x07]. To encode the sequence of numbersrunning 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 correspondsto the negative of number of literals in the sequence. Following theheader byte, the list of N varints is encoded. Thus, if there areno runs, the overhead is 1 byte for each 128 integers. The first 5prime numbers [2, 3, 4, 7, 11] would encoded as [0xfb, 0x02, 0x03,0x04, 0x07, 0xb].

Integer Run Length Encoding, version 2

In Hive 0.12, ORC introduced Run Length Encoding version 2 (RLEv2),which has improved compression and fixed bit width encodings forfaster expansion. RLEv2 uses four sub-encodings based on the data:

• Short Repeat - used for short sequences with repeated values
• Direct - used for random sequences with a fixed bit width
• Patched Base - used for random sequences with a variable bit width
• Delta - used for monotonically increasing or decreasing sequences

Short Repeat

The short repeat encoding is used for short repeating integersequences with the goal of minimizing the overhead of the header. Allof the bits listed in the header are from the first byte to the lastand from most significant bit to least significant bit. If the type issigned, the value is zigzag encoded.

• 1 byte header
  • 2 bits for encoding type (0)
  • 3 bits for width (W) of repeating value (1 to 8 bytes)
  • 3 bits for repeat count (3 to 10 values)
• W bytes in big endian format, which is zigzag encoded if they typeis signed

The unsigned sequence of [10000, 10000, 10000, 10000, 10000] would beserialized with short repeat encoding (0), a width of 2 bytes (1), andrepeat count of 5 (2) as [0x0a, 0x27, 0x10].

Direct

The direct encoding is used for integer sequences whose values have arelatively constant bit width. It encodes the values directly using afixed width big endian encoding. The width of the values is encodedusing the table below.

The 5 bit width encoding table for RLEv2:

• 2 bytes header
  • 2 bits for encoding type (1)
  • 5 bits for encoded width (W) of values (1 to 64 bits) using the 5 bitwidth encoding table
  • 9 bits for length (L) (1 to 512 values)
• W * L bits (padded to the next byte) encoded in big endian format, which iszigzag encoding if the type is signed

The unsigned sequence of [23713, 43806, 57005, 48879] would beserialized with direct encoding (1), a width of 16 bits (15), andlength of 4 (3) as [0x5e, 0x03, 0x5c, 0xa1, 0xab, 0x1e, 0xde, 0xad,0xbe, 0xef].

Patched Base

The patched base encoding is used for integer sequences whose bitwidths varies a lot. The minimum signed value of the sequence is foundand subtracted from the other values. The bit width of those adjustedvalues is analyzed and the 90 percentile of the bit width is chosenas W. The 10% of values larger than W use patches from a patch listto set the additional bits. Patches are encoded as a list of gaps inthe index values and the additional value bits.

• 4 bytes header
  • 2 bits for encoding type (2)
  • 5 bits for encoded width (W) of values (1 to 64 bits) using the 5 bit width encoding table
  • 9 bits for length (L) (1 to 512 values)
  • 3 bits for base value width (BW) (1 to 8 bytes)
  • 5 bits for patch width (PW) (1 to 64 bits) using the 5 bit widthencoding table
  • 3 bits for patch gap width (PGW) (1 to 8 bits)
  • 5 bits for patch list length (PLL) (0 to 31 patches)
• Base value (BW bytes) - The base value is stored as a big endian valuewith negative values marked by the most significant bit set. If it thatbit is set, the entire value is negated.
• Data values (W * L bits padded to the byte) - A sequence of W bit positivevalues that are added to the base value.
• Patch list (PLL * (PGW + PW) bytes) - A list of patches for valuesthat didn't fit within W bits. Each entry in the list consists of agap, which is the number of elements skipped from the previouspatch, and a patch value. Patches are applied by logically or'ingthe data values with the relevant patch shifted W bits left. If apatch is 0, it was introduced to skip over more than 255 items. Thecombined length of each patch (PGW + PW) must be less or equal to64.

The unsigned sequence of [2030, 2000, 2020, 1000000, 2040, 2050, 2060, 2070,2080, 2090, 2100, 2110, 2120, 2130, 2140, 2150, 2160, 2170, 2180, 2190]has a minimum of 2000, which makes the adjustedsequence [30, 0, 20, 998000, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,150, 160, 170, 180, 190]. It has anencoding of patched base (2), a bit width of 8 (7), a length of 20(19), a base value width of 2 bytes (1), a patch width of 12 bits (11),patch gap width of 2 bits (1), and a patch list length of 1 (1). Thebase value is 2000 and the combined result is [0x8e, 0x13, 0x2b, 0x21, 0x07,0xd0, 0x1e, 0x00, 0x14, 0x70, 0x28, 0x32, 0x3c, 0x46, 0x50, 0x5a, 0x64, 0x6e,0x78, 0x82, 0x8c, 0x96, 0xa0, 0xaa, 0xb4, 0xbe, 0xfc, 0xe8]

Delta

The Delta encoding is used for monotonically increasing or decreasingsequences. The first two numbers in the sequence can not be identical,because the encoding is using the sign of the first delta to determineif the series is increasing or decreasing.

• 2 bytes header
  • 2 bits for encoding type (3)
  • 5 bits for encoded width (W) of deltas (0 to 64 bits) using the 5 bitwidth encoding table
  • 9 bits for run length (L) (1 to 512 values)
• Base value - encoded as (signed or unsigned) varint
• Delta base - encoded as signed varint
• Delta values (W * (L - 2)) bytes - encode each delta after the firstone. If the delta base is positive, the sequence is increasing and if it isnegative the sequence is decreasing.

The unsigned sequence of [2, 3, 5, 7, 11, 13, 17, 19, 23, 29] would beserialized with delta encoding (3), a width of 4 bits (3), length of10 (9), a base of 2 (2), and first delta of 1 (2). The resultingsequence is [0xc6, 0x09, 0x02, 0x02, 0x22, 0x42, 0x42, 0x46].

The body of ORC files consists of a series of stripes. Stripes arelarge (typically ~200MB) and independent of each other and are oftenprocessed by different tasks. The defining characteristic for columnarstorage formats is that the data for each column is stored separatelyand that reading data out of the file should be proportional to thenumber of columns read.

In ORC files, each column is stored in several streams that are storednext to each other in the file. For example, an integer column isrepresented as two streams PRESENT, which uses one with a bit pervalue recording if the value is non-null, and DATA, which records thenon-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, ORCuses three streams PRESENT, DATA, and LENGTH, which stores the lengthof each value. The details of each type will be presented in thefollowing subsections.

The layout of each stripe looks like:

• index streams
  • unencrypted
  • encryption variant 1.N
• data streams
  • unencrypted
  • encryption variant 1.N
• stripe footer

Stripe Footer

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

If the file includes encrypted columns, those streams and columnencodings are stored separately in a StripeEncryptionVariant per anencryption variant. Additionally, the StripeFooter will contain twoadditional virtual streams ENCRYPTED_INDEX and ENCRYPTED_DATA thatallocate the space that is used by the encryption variants to storethe encrypted index and data streams.

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 streamdepends on the type and encoding of the column.

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

SmallInt, Int, and BigInt Columns

All of the 16, 32, and 64 bit integer column types use the same set ofpotential encodings, which is basically whether they use RLE v1 orv2. If the PRESENT stream is not included, all of the values arepresent. For values that have false bits in the present stream, novalues are included in the data stream.

Float and Double Columns

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

String, Char, and VarChar Columns

String, char, and varchar columns may be encoded either using adictionary encoding or a direct encoding. A direct encoding should bepreferred when there are many distinct values. In all of theencodings, the PRESENT stream encodes whether the value is null. TheJava ORC writer automatically picks the encoding after the first rowgroup (10,000 rows).

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

For dictionary encodings the dictionary is sorted and UTF-8 bytes ofeach unique value are placed into DICTIONARY_DATA. The length of eachitem in the dictionary is put into the LENGTH stream. The DATA streamconsists of the sequence of references to the dictionary elements.

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

Boolean Columns

Boolean columns are rare, but have a simple encoding.

TinyInt Columns

TinyInt (byte) columns use byte run length encoding.

Binary Columns

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

Decimal Columns

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

Date Columns

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

Timestamp Columns

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

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

Struct Columns

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

List Columns

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

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Map Columns

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

Union Columns

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

Row Group Index

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

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

To record positions, each stream needs a sequence of numbers. Foruncompressed streams, the position is the byte offset of the RLE run'sstart location followed by the number of values that need to beconsumed from the run. In compressed streams, the first number is thestart of the compression chunk in the stream, followed by the numberof decompressed bytes that need to be consumed, and finally the numberof values consumed in the RLE.

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

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

Bloom Filter Index

Bloom Filters are added to ORC indexes from Hive 1.2.0 onwards.Predicate pushdown can make use of bloom filters to better prunethe row groups that do not satisfy the filter condition.The bloom filter indexes consist of a BLOOM_FILTER stream for eachcolumn specified through ‘orc.bloom.filter.columns' table properties.A BLOOM_FILTER stream records a bloom filter entry for each rowgroup (default to 10,000 rows) in a column. Only the row groups thatsatisfy min/max row index evaluation will be evaluated against thebloom filter index.

Each bloom filter entry stores the number of hash functions (‘k') usedand the bitset backing the bloom filter. The original encoding (preORC-101) of bloom filters used the bitset field encoded as a repeatingsequence of longs in the bitset field with a little endian encoding(0x1 is bit 0 and 0x2 is bit 1.) After ORC-101, the encoding is asequence of bytes with a little endian encoding in the utf8bitset field.

Bloom filter internally uses two different hash functions to map a keyto a position in the bit set. For tinyint, smallint, int, bigint, floatand double types, Thomas Wang's 64-bit integer hash function is used.Doubles are converted to IEEE-754 64 bit representation (using Java'sDouble.doubleToLongBits(double)). Floats are as converted to double(using Java's float to double cast). All these primitive typesare cast to long base type before being passed on to the hash function.For strings and binary types, Murmur3 64 bit hash algorithm is used.The 64 bit variant of Murmur3 considers only the most significant8 bytes of Murmur3 128-bit algorithm. The 64 bit hashcode generatedfrom the above algorithms is used as a base to derive ‘k' differenthash functions. We use the idea mentioned in the paper 'Less Hashing,Same Performance: Building a Better Bloom Filter' by Kirsch et. al. toquickly compute the k hashcodes.

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The algorithm for computing k hashcodes and setting the bit positionin a bloom filter is as follows:

1. Get 64 bit base hash code from Murmur3 or Thomas Wang's hash algorithm.
2. Split the above hashcode into two 32-bit hashcodes (say hash1 and hash2).
3. k'th hashcode is obtained by (where k > 0):
   • combinedHash = hash1 + (k * hash2)
4. If combinedHash is negative flip all the bits:
   • combinedHash = ~combinedHash
5. Bit set position is obtained by performing modulo with m:
   • position = combinedHash % m
6. Set the position in bit set. The LSB 6 bits identifies the long indexwithin bitset and bit position within the long uses little endian order.
   • bitset[position »> 6] |= (1L « position);

Bloom filter streams are interlaced with row group indexes. This placementmakes it convenient to read the bloom filter stream and row index streamtogether in single read operation.

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1. Understanding an RSS Document

Understanding an RSS Document

Let's begin by taking a look at an RSS 2.0 document. To create your own RSS document, you need to know the names of the various parts of such a document.

Start composing your RSS document by opening an RSS creator program such as NewzAlert Composer and giving your RSS feed a name—'Steve's News' in the example shown here (Figure 3.1). This is the RSS feed name that appears in the RSS reader (Figure 3.2).

Figure 3.1 NewzAlert Composer provides property information about your feed.

Figure 3.2 Here's the NewzAlert feed displayed in RSSReader.

The name of the actual RSS document is news.xml, and like all XML documents, this one starts with an XML declaration:

The next line of code in the RSS document is called the document element because it contains all other elements in the document. Thus, it is here that the document specifies that its version is RSS 2.0.

As you can see, the element looks much like the HTML elements you're already familiar with. (For more about how XML elements work, see Chapter 4.)

Inside the element is a element. A channel element, referred to as the channel in an RSS document, provides details about the feed you're creating.

You'll see many different elements inside an RSS element. When you're creating your own RSS feeds using a tool like NewzAlert Composer, these are the elements you need to know about:

• The </tt> element contains the title of the feed. This is the name that will appear in the feeds window.</li><li>The <tt><link></tt> element contains a link for your feed. Usually this is a link to your home page for anyone reading your feed who wants more information.</li><li>The <tt><description></tt> element explains what your feed is all about.</li><li>The <tt><language></tt> element indicates the language the feed is in. For example, <em>en-us</em> means U.S. English (see Chapter 4).</li><li>The <tt><image></tt> element holds the URL of an image for your feed (see Figure 3.2).</li><li>The <tt><copyright></tt> element provides copyright information, if you want to add it.</li><li>The <tt><managing Editor></tt> element holds the name and/or email of the person responsible for the feed.</li></ul><p>Here's what these elements for our news.xml code look like as stored by NewzAlert Composer:</p><p>Each of the items in the feed is stored in an <tt><item></tt> element. An <tt><item></tt> element contains these elements:</p><ul><li>The <tt><title></tt> element is the text that will appear in the titles window of an RSS reader.</li><li>The <tt><description></tt> element will appear in an RSS reader's description window when the user clicks the item's title in the title window.</li><li>The <tt><pubDate></tt> element holds the date the item was published.</li><li>The <tt><link></tt> element holds the URL to get more information about the item.</li></ul><p>Here's what these elements look like in the single <item> element:</p><p>Now that you have an overview of how an RSS 2.0 document works, it's time to start creating an RSS feed.</p><br><br><br><br>