Latest Tupelo Performance Data

We take performance seriously.

The best way we can live that is by transparently sharing what we have measured, how the test network has performed and what changes are driving performance improvements.

We are currently testing on 21 and 100 signing node clusters. We have been expanding the number of AWS regions to better model latency. The number of regions and instance type are noted with the results below.

We will continue to share these numbers as they become available.


May 17th - Latest update

Substantial performance improvements upgrading libp2p pubsub, fixing a problem with rapid subscriptions, and improving our benchmarking methodology.

Signers Throughput Finality (mean) Finality (P95)
21 200 tx/sec 197 ms 290 ms
100 200 tx/sec 423 ms 664 ms

AWS Regions: 8
AWS Instance Types: c5.xlarge (4cpu 8gb ram)


April 19th

Substantial performance improvements through consensus streamlining including optimizing subscriptions and improved bootstrapping.

Signers Throughput Finality (mean) Finality (P95)
21 200 tx/sec 839 ms 1381 ms
100 200 tx/sec 831 ms 1739 ms

AWS Regions: 8
AWS Instance Types: c5.xlarge (4cpu 8gb ram)


January 6th

The workflow was refactored to use actor model ProtoActor. More of the state was moved to be held in-memory.

Signers Throughput Finality (mean) Finality (P95)
21 200 tx/sec 1814 ms 2367 ms
100 200 tx/sec 4612 ms 6104 ms

AWS Regions: 8
AWS Instance Types: c5.xlarge (4cpu 8gb ram)


December 11th Performance Test

Message ingress was moved to an in memory queue (from disk). Signature checking was parallelized.

Signers Throughput Finality (mean) Finality (P95)
21 50 tx/sec 1218 ms 1791 ms
100 25 tx/sec 3662 ms 4525 ms

AWS Regions: 8
AWS Instance Types: c5.xlarge (4cpu 8gb ram)


December 4th Performance Test

Transaction/state syncing between nodes was changed to an IBF (Invertible Bloom Filter).

Signers Throughput Finality (mean) Finality (P95)
21 50 tx/sec 1147 ms 2320 ms
100 25 tx/sec 12143 ms 19434 ms

AWS Regions: 3
AWS Instance Types: c5.xlarge (4cpu 8gb ram)