Vertica Analytics Platform Version 9.2.x Documentation
Vertica supports various encoding and compression types, specified by the following
ENCODING parameter arguments:
- AUTO (default)
- Zstandard Compression
Valid Encoding for Numeric Data Types
Vertica supports the following encoding for numeric data types:
- Precision ≤ 18:
- Precision > 18:
AUTO encoding is ideal for sorted, many-valued columns such as primary keys. It is also suitable for general purpose applications for which no other encoding or compression scheme is applicable. Therefore, it serves as the default if no encoding/compression is specified.
|Column data type||Default encoding type|
|Lempel-Ziv-Oberhumer-based (LZO) compression|
|Compression scheme based on the delta between consecutive column values.|
The CPU requirements for this type are relatively small. In the worst case, data might expand by eight percent (8%) for LZO and twenty percent (20%) for integer data.
For each block of storage, Vertica compiles distinct column values into a dictionary and then stores the dictionary and a list of indexes to represent the data block.
BLOCK_DICT is ideal for few-valued, unsorted columnswhere saving space is more important than encoding speed. Certain kinds of data, such as stock prices, are typically few-valued within a localized area after the data is sorted, such as by stock symbol and timestamp, and are good candidates for BLOCK_DICT. By contrast, long CHAR/VARCHAR columns are not good candidates for BLOCK_DICT encoding.
CHAR and VARCHAR columns that contain 0x00 or 0xFF characters should not be encoded with BLOCK_DICT. Also, BINARY/VARBINARY columns do not support BLOCK_DICT encoding.
BLOCK_DICT encoding requires significantly higher CPU usage than default encoding schemes. The maximum data expansion is eight percent (8%).
This encoding type is similar to BLOCK_DICT except dictionary indexes are entropy coded. This encoding type requires significantly more CPU time to encode and decode and has a poorer worst-case performance. However, if the distribution of values is extremely skewed, using
BLOCK_DICT_COMP encoding can lead to space savings.
BZIP_COMP encoding uses the bzip2 compression algorithm on the block contents. See bzip web site for more information. This algorithm results in higher compression than the automatic LZO and gzip encoding; however, it requires more CPU time to compress. This algorithm is best used on large string columns such as VARCHAR, VARBINARY, CHAR, and BINARY. Choose this encoding type when you are willing to trade slower load speeds for higher data compression.
This compression scheme builds a dictionary of all deltas in the block and then stores indexes into the delta dictionary using entropy coding.
This scheme is ideal for sorted FLOAT and INTEGER-based (DATE/TIME/TIMESTAMP/INTERVAL) data columns with predictable sequences and only occasional sequence breaks, such as timestamps recorded at periodic intervals or primary keys. For example, the following sequence compresses well: 300, 600, 900, 1200, 1500, 600, 1200, 1800, 2400. The following sequence does not compress well: 1, 3, 6, 10, 15, 21, 28, 36, 45, 55.
If delta distribution is excellent, columns can be stored in less than one bit per row. However, this scheme is very CPU intensive. If you use this scheme on data with arbitrary deltas, it can cause significant data expansion.
This compression scheme is primarily used for floating-point data; it stores each value as a delta from the previous one.
This scheme is ideal for many-valued FLOAT columns that are sorted or confined to a range. Do not use this scheme for unsorted columns that contain NULL values, as the storage cost for representing a NULL value is high. This scheme has a high cost for both compression and decompression.
To determine if DELTARANGE_COMP is suitable for a particular set of data, compare it to other schemes. Be sure to use the same sort order as the projection, and select sample data that will be stored consecutively in the database.
For INTEGER and DATE/TIME/TIMESTAMP/INTERVAL columns, data is recorded as a difference from the smallest value in the data block. This encoding has no effect on other data types.
DELTAVAL is best used for many-valued, unsorted integer or integer-based columns. CPU requirements for this encoding type are minimal, and data never expands.
For INTEGER and DATE/TIME/TIMESTAMP/INTERVAL columns, and NUMERIC columns with 18 or fewer digits, data is recorded as the difference from the smallest value in the data block divided by the greatest common divisor (GCD) of all entries in the block. This encoding has no effect on other data types.
ENCODING GCDDELTA is best used for many-valued, unsorted, integer columns or integer-based columns, when the values are a multiple of a common factor. For example, timestamps are stored internally in microseconds, so data that is only precise to the millisecond are all multiples of 1000. The CPU requirements for decoding GCDDELTA encoding are minimal, and the data never expands, but GCDDELTA may take more encoding time than DELTAVAL.
This encoding type uses the gzip compression algorithm. See gzip web site for more information. This algorithm results in better compression than the automatic LZO compression, but lower compression than BZIP_COMP. It requires more CPU time to compress than LZO but less CPU time than BZIP_COMP. This algorithm is best used on large string columns such as VARCHAR, VARBINARY, CHAR, and BINARY. Use this encoding when you want a better compression than LZO, but at less CPU time than bzip2.
RLE (run length encoding) replaces sequences (runs) of identical values with a single pair that contains the value and number of occurrences. Therefore, it is best used for low cardinality columns that are present in the ORDER BY clause of a projection.
The Vertica execution engine processes RLE encoding run-by-run and the Vertica optimizer gives it preference. Use it only when run length is large, such as when low-cardinality columns are sorted.
The storage for RLE and AUTO encoding of CHAR/VARCHAR and BINARY/VARBINARY is always the same.
Vertica supports three ZSTD compression types:
ZSTD_COMPprovides high compression ratios. This encoding type has a higher compression than gzip. Use this when you want a better compression than gzip. For general use cases, use this or the
ZSTD_FAST_COMPuses the fastest compression level that the zstd library provides. It is the fastest encoding type of the zstd library, but takes up more space than the other two encoding types. For general use cases, use this or the
ZSTD_HIGH_COMPoffers the best compression in the zstd library. It is slower than the other two encoding types. Use this type when you need the best compression, with slower CPU time.
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