James Ray edited this page Aug 22, 2018 · 56 revisions



Block Processing

The point of this benchmark is to give a controlled time trial for processing blocks including all of the guff that goes with block processing including PoW verification, transaction signature checking, EVM code execution, receipt verification, uncle validation and database population. In order to mitigate biases one way or another for each of those aspects in the benchmark, we use the first 1,000,000 blocks of the Frontier mainnet chain.


There are probably several ways of effecting this benchmark for each client (e.g. cpp-ethereum has the --import feature), but for now we're settling on a simple "sync from localhost" method.

This involves setting up a client synced up to block 1,000,000 of the Frontier network (we used eth, specifically, but in principle it shouldn't matter which client is used). Following this, the client to be benchmarked is executed on the same machine with a clean database in the full block processing mode. It is initialised with the former client's address and allowed to synchronise. The process is timed until it eventually imports block 1,000,000.

We did all tests on a standard Digital Ocean 4GB droplet running Ubuntu 14.04.3 x64.


To automate the method described above, the following library has been created:

To benchmark a client, first, a "master" node (e.g. geth) has to be installed and synced up from the network up to a certain point (e.g. 1m blocks). Then, the master client should be restarted in "no-discovery" mode, e.g.:

geth --datadir /tmp/geth-master --nodiscover --port 30305 console

Next, the client to be benchmarked has to be installed and configured, if necessary.

The benchmarking script has to be passed the "master" node's enode address along with other optional arguments (run bin/run-bench -h for more info).

Command for benchmarking Parity:

bin/run-bench --rm -e <master enode> parity

Note: since the main purpose of this utility is to benchmark full blockchain processing times, certain optimisation options like geth --fast have been intentionally turned off.


-- Eth EthereumJ Geth Parity
Time 4h 33m 7h 7m 8h 43m 2h 31m
CPU (avg) 123% 90% 70% 107%
Memory (avg) 921MB 3.168GB 1.5GB 365MB

The Trie

The point of this is to give a controlled test between clients that reflects typical on-chain situations. We do this by defining a common dataset of key/value pairs for insertion into the trie and root-calculation. There are two modes to reflect the two use cases of the trie in Ethereum:

  • Persistent Tries: The secure tries for storage and state. These have their nodes stored permanently in a backing store and, periodically, are updated a number of times before a new root is taken and used in either the receipt or the account's storage_root field.
  • Ephemeral Tries: The receipt and transaction tries. These tries do not require that their nodes be stored permanently, but rather are used primarily to determine their root, which is placed in the header under receipts_root and transactions_root fields.

Persistent Tries

For this benchmark, the dataset is inserted into the trie with root hashes being computed at specific intervals ("era_size"). This number represents the number of update operations per root calculation.

Ephemeral Tries

For this benchmark, the important thing is to take all data and determine the root; no roots need be computed along the way and there is no assertion that any state used when determining the root be required. In this sense era_size is equivalent to the dataset size.

Standard Dataset

Sensible implementations may precompute the dataset to avoid the additional burden of SHA3 computation at benchmark time.

from ethereum import trie, db, utils
import time

ROUNDS = 1000
a = time.time()
t = trie.Trie(db.EphemDB())
seed = '\x00' * 32

for i in range(ROUNDS):
    seed = utils.sha3(seed)
    mykey = seed[:MIN_COUNT + ord(seed[-1]) % (MAX_COUNT + 1 - MIN_COUNT)]
        t.update(mykey, mykey)
        seed = utils.sha3(seed)
        myval = seed[-1] if ord(seed[0]) % 2 else seed
        t.update(mykey, myval)
    if i % ERA_SIZE == 0
        seed = t.root()
print time.time() - a 
print t.root_hash.encode('hex')

The final trie root after 1000 rounds should be 36f6...93a3 for SYMMETRIC = True and da8a...0ca4 for False.


Tests run on:

Linux gav-MacBookPro 4.4.0-040400rc7-lowlatency #201512272230 SMP PREEMPT Mon Dec 28 03:36:57 UTC 2015 x86_64 x86_64 x86_64 GNU/Linux


model name	: Intel(R) Core(TM) i7-4980HQ CPU @ 2.80GHz
cpu MHz		: 3195.609
cache size	: 6144 KB
bogomips	: 5587.06


cd libweb3core
cmake -DCMAKE_BUILD_TYPE=Release
make -j8
sudo nice -n -19 ./libweb3core/bench/bench trie


cd util && cargo bench

CPython / PyPy

pip install ethereum


sudo nice -n -19 godep go test  -run=- -bench=Std ./trie


git clone`
cd merkle-patricia-tree
npm install
node ./benchmarks/random.js


All times in ms.

Test ID is given as pair_count-era_size-key_size-value_type, where valid value_types are ran (SYMMETRIC = False) and mir (SYMMETRIC = True).

Persistent Trie Benchmark

The standard secure-trie test is 1k-9-32-ran. ran is used as value_type as it better reflects the kinds of values that are typically written in the blockchain. The 9 that is used as era_size is determined empirically from the Frontier mainnet: the mean number of insertions per commit on all secure tries from block #0 to #900,000 is 9.35 (TODO: determine median & quartiles - they're likely to be better indicators on real world performance).

Client Time SHA3s
C++ 23.8 8469
CPython 369 7079
PyPy 45
Go 23.6
Pure JS 374
Parity 11.6 4221

Other results (values other than 9 inserts/commit)

Test ID C++ CPython PyPy Go JS
1k-3-32-ran 23.8 369 45 45.7 388
1k-5-32-ran 23.8 369 41 28.5 374

Ephemeral Trie Benchmark

For this benchmark, 1k inserts is quite reasonable; we naturally avoid doing any "partially-complete" root determination. TODO: redo benchmarks with two byte key size & 32-byte value size to better reflect the insecure trie contents.

Test ID C++ CPython PyPy Go JS Parity
1k-1k-32-ran 23.8 369 46 10.0 389 1.634
1k-1k-32-mir 23.9 294 45 8.0 382 1.529

Note: PyPy times were measured on 1.7 GHz i7

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