STORE Achieves +10,000 Transactions-Per-Second with Burst Traffic and 21 Validator Nodes

Test Summary - What We Are Trying to Learn

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STORE’s mission is to become zero-fee payment infrastructure for the global internet. The Dynamic Proof of Stake (DyPoS) consensus engine powering the STORE infrastructure is designed to process thousands of transactions per second. When transactions arrive continuously but at lower rates, the consensus engine is capable of handling the incoming transactions, but how does it behave when transactions come in bursts? When STORE is used as the payment platform by merchants and app developers, the transactions are likely to come in bursts from multiple sources. So, we need to characterize the behavior of the consensus engine under such circumstances.

1/ This thread compares @storecoin with @blockstack. The request for this comparison came from here — https://t.co/WboUHs8NFc
— $STORE Engineering (@STOREdevs) July 16, 2019

2/ The comparison is based on the following resources on @blockstack -- a) their website — https://t.co/JIy18SBLVP, b) their white paper — https://t.co/bLvMDE3JkN, and c) Gaia storage system —https://t.co/pHCliE5LQ4.
— $STORE Engineering (@STOREdevs) July 16, 2019

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The consensus efficiency is related to transaction volume, but the relationship is not constant across different loads. Up to a threshold, consensus efficiency is directly proportional to transaction volume. This is the threshold at which the validator nodes are utilized to their full capacity. Once this threshold is breached, the consensus efficiency is inversely proportional to transaction volume.
The consensus efficiency is related to transaction volume, but the relationship is not constant across different loads. Up to a threshold, consensus efficiency is directly proportional to transaction volume. This is the threshold at which the validator nodes are utilized to their full capacity. Once this threshold is breached, the consensus efficiency is inversely proportional to transaction volume.
The consensus efficiency is related to transaction volume, but the relationship is not constant across different loads. Up to a threshold, consensus efficiency is directly proportional to transaction volume. This is the threshold at which the validator nodes are utilized to their full capacity. Once this threshold is breached, the consensus efficiency is inversely proportional to transaction volume.
The consensus efficiency is related to transaction volume, but the relationship is not constant across different loads. Up to a threshold, consensus efficiency is directly proportional to transaction volume. This is the threshold at which the validator nodes are utilized to their full capacity. Once this threshold is breached, the consensus efficiency is inversely proportional to transaction volume.

The consensus efficiency is related to transaction volume, but the relationship is not constant across different loads. Up to a threshold, consensus efficiency is directly proportional to transaction volume. This is the threshold at which the validator nodes are utilized to their full capacity. Once this threshold is breached, the consensus efficiency is inversely proportional to transaction volume.
The consensus efficiency is related to transaction volume, but the relationship is not constant across different loads. Up to a threshold, consensus efficiency is directly proportional to transaction volume. This is the threshold at which the validator nodes are utilized to their full capacity. Once this threshold is breached, the consensus efficiency is inversely proportional to transaction volume.