This is a collection of compression algorithms that are used to compress data of different types. The algorithms are optimized for time-series use-cases; many of them assume that adjacent rows will have "similar" values.
Each compression algorithm the API is divided into two parts: a compressor and a decompression iterator. The compressor is used to compress new data.
<algorithm name>_compressor_alloc- creates the compressor<algorithm_name>_compressor_append_null- appends a null<algorithm_name>_compressor_append_value- appends a non-null value<agorithm_name>_compressor_finish- finalizes the compression and returns the compressed data
Data can be read back out using the decompression iterator. An iterator can operate backwards or forwards. There is no random access. The api is
<algorithm_name>_decompression_iterator_from_datum_<forward|reverse>- create a new DatumIterator in the forward or reverse direction.- a DatumIterator has a function pointer called
try_nextthat returns the nextDecompressResult.
A DecompressResult can either be a decompressed value datum, null, or a done marker to indicate that the iterator is done.
Each decompression algorithm also contains send and recv function to get the external binary representations.
CompressionAlgorithmDefinition is a structure that defines function pointers to get forward and reverse iterators
as well as send and recv functions. The definitions array in compression.c contains a CompressionAlgorithmDefinition
for each compression algorithm.
The simple8b rle algorithm is a building block for many of the compression algorithms.
It compresses a series of uint64 values. It compresses the data by packing the values into the least
amount of bits necessary for the magnitude of the int values, using run-length-encoding for large numbers of repeated values,
A complete description is in the header file. Note that this is a header-only implementation as performance
is paramount here as it is used as a primitive in all the other compression algorithms.
for each integer, it takes the delta-of-deltas with the pervious integer, zigzag encodes this deltadelta, then finally simple8b_rle encodes this zigzagged result. This algorithm performs very well when the magnitude of the delta between adjacent values tends not to vary much, and is optimal for fixed rate-of-change.
gorilla encodes floats using the Facebook gorilla algorithm. It stores the
compressed xors of adjacent values. It is one of the few simple algorithms
that compresses floating point numbers reasonably well.
The dictionary mechanism stores data in two parts: a "dictionary" storing each unique value in the dataset (stored as an array, see below) and simple8b_rle compressed list of indexes into the dictionary, ordered by row. This scheme can store any type of data, but will only be a space improvement if the data set is of relatively low cardinality.
The array "compression" method simply stores the data in an array-like structure and does not actually compress it (though TOAST-based compression can be applied on top). It is the compression mechanism used when no other compression mechanism works. It can store any type of data.
Chunks will be merged during compression if we specify the compress_chunk_time_interval parameter.
This value will be used to merge chunks adjacent on the time dimension if possible. This allows usage
of smaller chunk intervals which are rolled into bigger compressed chunks.
Compression itself is altered by changing the destination compressed chunk from a newly created one to an already existing chunk which satisfies the necessary requirements (is adjacent to the compressed chunk and chunk interval can be increased not to go over compress chunk time interval).
After compression completes, catalog is updated by dropping the compressed chunk and increasing the chunk interval of the adjacent chunk to include its time dimension slice. Chunk constraints are updated as necessary.
When merging such chunks, due to the nature of sequence number ordering, we will inherently be left with chunks where the sequence numbers are not correctly ordered. In order to mitigate this issue, chunks are recompressed immediately. This has obvious performance implications which might make merging chunks not optimal for certain setups.