As I promised to discuss about the 4 key innovations in blockchain architecture of Somnia that makes it achieve Highspeed, this is the fourth article in the
series, and it is the most interesting one. The remaining pieces now come
together. Thank you for your inteerest, So let’s dive in.
The data flood problem at real scale
At six figures of transactions per second, raw bytes become the bottleneck. Even a plain ERC-20 transfer weighs around a couple hundred bytes once signatures and metadata are counted. Push that to a million transfers per second and outbound traffic lands near gigabit links that the public internet will not carry reliably between globally distributed validators. Without a better plan, a chain must centralize its topology or throttle throughput. Advanced compression changes the physics by shrinking what has to move on the wire while keeping verifiability intact.
Information theory does the heavy lifting
Not every byte is equally surprising. In live markets, who transacts, which contract gets called, and which parameters appear follow sharp, power law shaped distributions. A small set of accounts and methods dominate activity during spikes. Information theory says cost in bits tracks the negative log of probability. If a contract appears in 10 percent of calls, its address can be encoded in just a few bits rather than 20 bytes. Across a busy surge, that asymmetry compounds. By modeling these distributions online, the network replaces bulky literals with tiny symbols and reconstructs the original values at the other end with no loss.
Why streaming beats block compression for chains
Traditional block compression treats each payload as an island. It helps, but it ignores the shared history that makes live traffic predictable. Streaming compression exploits that shared past. Sender and receiver advance through the same byte stream and build an implicit dictionary on the fly. Instead of resending an address, the encoder can point back to where it appeared megabytes ago. Ratios improve dramatically. Classic blockchains cannot use this because each block is proposed by a different node and the stream resets. Somnia’s MultiStream design solves that by letting each validator publish a continuous data chain. The same process compresses its own stream, the peer reconstructs it in the same order, and the compression window never tears. Production stays decoupled from consensus, so the network gets streaming grade ratios without sacrificing safety.
Uncompressible parts handled the right way
Two fields resist compression by design. Hashes behave like uniform noise under any bit change and so do signatures. The answer is to avoid sending what can be derived and to aggregate what cannot be changed. Transaction hashes are deterministic functions of the payload, so receivers recompute them locally instead of reading them off the wire. Signatures are trickier because each transaction needs a valid signature, yet their entropy is irreducible. Somnia adopts BLS signature aggregation, allowing hundreds or thousands of signatures to collapse into one constant size proof for a batch. Verification cost for the batch approaches the cost of checking a single signature and the bytes that would have carried individual signatures disappear from the stream. Compression ratios improve and signature checks stop being the dominant CPU tax during surges.
Bandwidth symmetry keeps every peer inside budget
Another hidden limiter in classic designs is asymmetric upload pressure. When one leader must blast a whole block’s payload to every peer, required upstream bandwidth scales with peer count and block rate. That single socket becomes the choke point. Somnia evens the profile. Each validator continually publishes only its own shard of data at a steady rate and downloads everyone else’s shards at the same steady rate. Total network traffic is unchanged, but the per peer envelope is symmetric and constant. No validator needs to push faster than the chain’s aggregate bandwidth, but all of them contribute continuously. This makes aggregate throughput track the real capabilities of peers rather than the luck of which node is leader.
From bytes to confirmations under crowd pressure
The real test is not quiet days, it is the moments when attention spikes. Streaming compression squeezes redundant addresses, methods, and parameters into symbols. Hashes never cross the wire. BLS turns a forest of signatures into a single leaf. The steady state bandwidth schedule prevents one node from becoming a bottleneck. Meanwhile, MultiStream consensus anchors only the tips of the data chains, so agreement rounds are small even when the published payload is huge. Execution then consumes a globally ordered stream that remains compact and cache friendly. The outcome is concrete. Blocks clear on time, fees do not spiral, and users see confirmations at the pace of the hardware, not the quirks of the network.
The finish line for SOMI’s architecture
With single core acceleration, IceDB’s deterministic storage, MultiStream consensus, and now advanced compression, SOMI’s stack aligns around the same principle. Design for correlated surges, not for textbook independence. Compress what is predictable, derive what is reproducible, aggregate what cannot be shrunk, and spread bandwidth responsibilities evenly. This fourth innovation turns raw throughput into end to end responsiveness on real networks. It is the difference between a lab demo and a chain that holds up when everyone shows up at once.
