Numerical compression is harder than it looks
If you are looking at Quasar compression, you probably have data that keeps growing, costs real money to move, and still needs to remain online because people query it.
At this scale, compression can turn an impossible workload into a manageable one.
Delta4C: How Quasar compresses better
On realistic telemetry, Delta4C achieves compression ratios roughly 8–12× those of general-purpose compressors. On highly structured data, it can achieve orders-of-magnitude gains while sustaining multi-GiB/s throughput.
Delta4C: How Quasar compressed better
Delta4C is Quasar’s lossless compression engine for numerical data. It works on integers and floating-point values, and recovers the original data bit for bit.
The core idea is simple: do not assume one compression strategy will fit the data.
Delta4C first analyzes each block quickly enough to stay compatible with live ingestion. It looks at value distributions, deltas, repetition, entropy, and bit-level structure, including floating-point layout.
Then it splits decisions by block, so one noisy region does not force the wrong strategy on the rest of the dataset.
Finally, Delta4C composes the compression pipeline dynamically. A block may benefit from delta or delta-delta encoding, run-length encoding, bit-packing, dictionary compression, floating-point transcoding, or a combination of stages. Delta4C applies the stages that help and skips the ones that do not.
On realistic telemetry, Delta4C delivers roughly 8-12X higher compression than general-purpose compressors. On highly structured data, it can deliver orders-of-magnitude gains while sustaining multi-GiB/s throughput.