Try Quests to earn SOLPolygon Block Performance
In this dashboard we will focus on block perfomance metrics of Polygon blockchain:
- maximum and minimum recorded time between two blocks
- average time between blocks
- transactions in a block on average
Polygon blockchain will be compared with other L2: Optimism and with Solana blockchain.
Also we will find out, is there a correlation in block performance between Polygon and other 2 networks.
- Fact_blocks Flipside’s tables from Polygon, Solana and Optimism are used
- To calculate the time between transactions, window fuction LAG (value from previous row) is used
- Optimism blockchain’s blocks contain just one transaction each, so to compare Optimism with other networks, I will group Optimism transactions which have the same blocks timestamp, as it would belong to one block.
- To establish a correlation between Polygon and other networks in block performance, we will use the most fluctuating metric: maximum time between blocks.
Polygon blocks contain more transactions than Optimism blocks. Solana blocks have very large number of transactions (from 1600 to 2200 txs per block) because of different architecture.
All 3 networks have stable figures for minimum time between blocks: 2 seconds on Polygon, 15 seconds on Optimism, near 0 on Solana.
We see big delays on Solana on some days (up to 88 seconds between blocks). Polygon network outperforms Solana and Optimism when we compare maximum time between blocks.
Average time figures are rather stable for all 3 networks. Solana network is fastest.
At last, we will find out, is there a correlation in block performance between Polygon and other 2 networks. We will use the most fluctuating metric: maximum time between blocks. To establish a relation between metrics, we need to calculate the Pearson Correlation Coefficient. It’s a widely used method of determining the strength of the relationship between two sets of numbers. This coefficient is calculated as a number between -1 and 1. 1 being the strongest possible positive correlation and -1 being the strongest possible negative correlation.
There’s a positive correlation of maximum time between blocks. It means that there are common factors that influence Polygon and other networks. These factors
cause delays.
