Comments (3)
If I may...
It is a multidimensional problem. Some cars go fast, other cars are strong, other cars save gaz... No matter which car you pick, some other car is going to be better according to some criterion.
The same applies to compression. There are various trade-offs and no one compression technique is best at all things.
It makes it complicated, but then that is why engineers are well paid.
from turbopfor-integer-compression.
Thanks so much for the clarification :)
It is amazing that TPF is so much faster than 7z!
Thanks for the wonderful work :)
from turbopfor-integer-compression.
Integer compression is not designed to have the best compression ratio, but mainly for speed.
7z is very slow, compression is in the range 1MB/s and decompression speed in the range of 50MB/s. TurboPFor compression and decompression speed is > 1GB/s. The use case for integer compression is not archiving but in memory databases, seach engines,... where speed is more important than compression ratio.
from turbopfor-integer-compression.
Related Issues (20)
- Benchmark: TurboTranspose+iccodecs vs Quantile Compression
- Turbopfor 256 performs worse than Turbopfor 128 HOT 5
- macos 13.3.1 m1 build issue HOT 8
- D1 Differential Coding HOT 2
- Boundary check in idxqry.C HOT 3
- Benchmark: TurboPFor Integer Compression on APPLE M1
- Just some questions about TurboPFor Implementation HOT 3
- p4ddec32 HOT 1
- Cross-compiling for iOS HOT 1
- python support HOT 1
- small array compression HOT 3
- negative ints? HOT 1
- Streaming Data HOT 3
- icapp I and J arguments HOT 1
- -E option HOT 2
- fpxenc8 error
- Is lzturbo dead?
- Messy project management, fixes randomly reverted HOT 10
- vlccomp32, vhicomp32 corrupt memory for small input buffers
- Cargo publish Rust crate (probably renamed turbo-pfor-sys) HOT 2
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from turbopfor-integer-compression.