On February 16th 2018 the Ethereum Community fund was announced, over $100 million dollars to be given as grants to fund development of blockchains that scale. The race for scalable blockchain infrastructure has begun. I originally started Ellipticoin because I didn’t like how much energy Proof of Work burns and I wanted to use existing tools to build smart contracts and Dapps. But I realize building a blockchain that scales is also desperately needed in the space. A transaction on any blockchain network has to go through a number of steps before it’s confirmed. If the blockchain is programmable like in the case Ethereum and Ellipticoin, one of those steps is executing the transaction in a Virtual Machine. Making that step fast is key to building a scalable blockchain. The Ellipticoin network isn’t live yet but I do have enough built to start benchmarking the Virtual Machine. This post is about how I got the Ellipticoin Virtual Machine to scale from 10 transactions per second to 4 thousand transactions per second.

The first thing I did was I set up a simple benchmark test in the Ellipticoin Blacksmith Node.

  def run(_) do
    constructor(@sender, 1000000000)
      "base_token_transfer"    => fn -> transfer(1, @receiver) end,
    }, time: 1)
    {:ok, balance}  = balance(@receiver)
    IO.puts "Receiver's balance after benchmark #{Cbor.decode(balance)}"

I used Benchee which runs code repeatedly for a set interval of time and outputs statistics on how long each run took. The benchmark is pretty simple. First it loads up the “sender” with 1000000000 tokens. Then benchee runs the transfer function repeatedly for 1 second. Once the time is up Benchee prints out some statistics about how long each transfer took. For good measure I printed out the balance of the recipient at the end just to make sure they got all their tokens.

The token contract I ran is written in Rust and compiled to WebAssembly.

use alloc::vec::Vec;
use error::{self, Error};

use blockchain::*;

pub struct BaseToken<T: BlockChain>  {
    pub blockchain: T

impl <B> BaseToken<B> where B: BlockChain {
    pub fn constructor(&self, initial_supply: u64) {
        self.write(self.sender(), initial_supply);

    pub fn balance_of(&self, address: Vec<u8>) -> u64 {

    pub fn transfer(&self, receiver_address: Vec<u8>, amount: u64)  -> Result<(), Error> {
        let sender_balance = self.read(&self.sender());
        let receiver_balance = self.read(&receiver_address);

        if sender_balance > amount {
            self.write(self.sender(), sender_balance - amount);
            self.write(receiver_address, receiver_balance + amount);
        } else {

    fn sender(&self) -> Vec<u8> {

    fn read(&self, key: &Vec<u8>) -> u64 {

    fn write(&self, key: Vec<u8>, value: u64) {
        self.blockchain.write_u64(key, value)

It does everything you’d expect a basic token contract to do. First it checks if the sender has enough tokens to send. If they do it subtracts the amount from the sender’s balance and adds it to the recipient’s balance. If the sender doesn’t have enough tokens it returns an error.

When I ran then benchmark before any optimization each iteration took on average 97.7 milliseconds which comes out to 10.24 iterations per second on average. Currently the Ethereum network processes about 15 transactions per second so I still had a ways to go.

ellipticoin-blacksmith-node master % mix benchmark

Name                   ips       average     deviation  median         99th %

base_token_transfer   10.24     97.70 ms     ±1.65%    98.02 ms      100.13 ms

Receiver's balance after benchmark 10

The code running inside the VM is pretty simple. It does one comparison between integers (checking the senders balance) and updates two values in the state database (backed by Rocksdb). A lot of this time was spent reading the the contract code and initializing the virtual machine. Running these operations natively would be much quicker. So that’s what I did.

If we know the inner logic of a contract and know what incoming calls to that contract look like we can intercept the calls before the VM is started and run the logic natively.

Erlang and therefore Elixir have a fast and powerful feature called binary pattern matching. This was perfect for matching against only the transactions I wanted and at the same time extracting all information relevant to that transaction.

The code looks a little funny but what it’s doing is pretty simple. Basically it’s matching against the transaction data and if the transaction contains a certain string of bytes then we know it’s a call to the “transfer” function. If it is, in fact, a call to the transfer function, then run the logic of the transfer function natively, otherwise, we will need to run the transaction in the external VM.

Making that change made transactions go through 70 times faster! Each one ran in 1.38 milliseconds on average which give us 724 runs per second.

Name                   ips          average   deviation   median      99th %
base_token_transfer   724.65        1.38 ms   ±55.42%     1.24 ms     6.41 ms

Pretty good!

We were still reading and writing to disk in each iteration of the transfer function though. This could be improved. It would be much faster if we could be reading and writing to memory instead. So that’s what I did.

It was actually pretty easy to switch from storing state on disk to storing it in memory as well. All I had to do was swap the read and write functions from Rocksdb to Redis. Now you might be saying: “Mason, you can’t store blockchain state in Redis it won’t have strong persistence guarantees.” You’d be right that Rocksdb has much stronger guarantees that you won’t lose data in case of a power outage. But if a blockchain is spread across hundreds or thousands of machines you can rely on other nodes to recover from catastrophic failure. Storing data in memory is also a concerning for centralization. Memory is much more expensive than disk space and it’s possible that the blockchain size gets so large that you wouldn’t be able to run a full node on a consumer laptop. This is a tradeoff I’m willing to make. Currently you can rent a r4.2xlarge from AWS for under $400/mo which has 61 gigabytes of RAM. This cost would only apply to staking nodes which maintain consensus. Rent would be paid to the staking nodes to avoid the chain from perpetually growing larger. This is also being considered for the Ethereum network. Clients would then connect to edge nodes to interact with the network. I do want Ellipticoin to be sufficiently decentralized but I’m willing to make certain decentralization trade-offs in favor for performance. Currently I think the decentralization trade-off is worth it for storing state in memory.

So how fast is?

Pretty fast:

Name                    ips        average   deviation   median    99th %
base_token_transfer   4.82 K      207.31 μs   ±8.74%     203 μs   285.17 μs

207.31 μs per transaction gives us 4.82 K transaction per second which is getting toward where we need to be for a scalable blockchain. If you’d like to download and run these benchmarks yourself you can clone the blacksmith node repo and run mix benchmark.

I think the main bottleneck is at this point is Elixir’s GenServer which is currently being called every time a transcation is run. Theoretically I could rewrite the Blacksmith node in Rust at which point our bottleneck would probably be Redis. 🚀😳

Note that these transaction speeds are only for optimistic native functions that will have to be built into the nodes. I’d imagine to start you could build in native functionality for all Ellipticoin tokens. Other native contracts could be added as well when needed. If, for example, a certain cat trading game exploded in popularity you could implement it’s functionality natively in the nodes. Then all those transactions would be much faster and wouldn’t clog the network. If the original smart contracts are written in Rust this shouldn’t be too hard though. If a node was written in Rust it could even share the same code!

I still have a long way to go on getting the Ellipticoin network up and running and more research to do on scaling, but I’m happy to be sharing my findings. I think the idea of optimistic native functions are an easy way to get a big performance boost on commonly used transaction types at little cost. I’m still not sure storing blockchain state in memory is the best way to go, but the performance gains definitely make it worth considering. I’ve also been thinking about having a “hot chain” that’s stored in memory that could be used for trading and what not and a “cold chain” that’s backed by hard drives that can be used as a store of value. If you have any feedback on these ideas or anything else come say hi in the Ellipticoin Telegram Room!

Back to work…


Thanks to Geoff Hayes for editing and giving feedback on earlier drafts of this post.