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GossipSub

Key metrics on the Ethereum pubsub network

Bandwidth consumption statistics, Message bandwidth consumption, Number of duplicates, Block arrival times.

Bandwidth

Bandwidth consumption breakdown per direction and message type

Inbound Outbound Bandwidth Usage

Inbound Outbound Bandwidth Usage

Tracks the directional flow of network traffic through the GossipSub protocol, showing both inbound and outbound bandwidth usage measured in kilobytes per second. Data is aggregated in 5-minute intervals to provide a clear view of network communication patterns. This helps identify peak usage periods, network congestion, and the balance between data consumption and production across the Ethereum network.

Bandwidth Usage in Kilobytes/s agg. in 5m intervals

Bandwidth Usage by Message Type

Bandwidth Usage by Message Type

Breaks down GossipSub bandwidth usage by message type, showing how different kinds of network messages (blocks, transactions, metadata, etc.) contribute to overall network traffic. This analysis helps identify which message types consume the most bandwidth and reveals communication patterns within the Ethereum network. Use the direction filter to focus on inbound or outbound traffic specifically.

Insights into the share of each messages bandwidth consumption

Bandwidth Usage Distribution

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Block Arrival Times

Block arrival times per geographic region (continent) for every client implementation and for different percentiles.

Block Arrival Times Filtered

Block Arrival Times Filtered

The plot shows the minimum, average, P50 and P95 values of the block arrival times aggregated over a range of 4 epochs (i.e., 1536 seconds). The dropdown menus help filter by geographic region and agent type (i.e., client implementation). The block arrival time is calculated based on the difference between when our node received the message and the slot start time (when the block is supposed to have been created).

Aggregated Block Arrival Times Filtered by Continent and Agent Type.

Block Arrival Times Grouped

Block Arrival Times Grouped

The plot shows the block arrival time grouped by geographic region and agent. Arrival times are aggregated over a range of 4 epochs (i.e., 1536 seconds). The extra dropdown shows several percentiles. The block arrival time is calculated based on the difference between when our node received the message and the slot start time (when the block is supposed to have been created).

Aggregated Block Arrival Times Grouped by Continent and Agent Type.

Msg Arrival Times CDF

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Message Size / Duplicate Count Correlation

Message Size / Duplicate Count Correlation

Under the same network bandwidth, larger messages take longer to be fully transmitted between peers. Since a peer can only detect a message duplicate after downloading it completely, the likelihood of receiving duplicates increases with message size. This heatmap shows the correlation between message size and duplicate count, showing the number of messages per topic aggregated by "message size" and "number of duplicates" ranges.

Relation between message size and the likelihood of receiving duplicates

Duplicates

Duplicate message statistics per topic, geographic distribution and client implementation

Duplicates Per Topic CDF

Duplicates Per Topic CDF

This graph shows the cumulative distribution function of the number of duplicate messages per topic during the given day. When you hove over the graph, the number of samples represent the number of unique messages, identified by their message IDs, that were received during that day. The point on the x-axis show show often we have received the same message and the y-axis shows where this message falls in the overall distribution of received duplicates.

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Time to First Duplicate

CDF of how long it took for the first duplicate to arrive.

Duplicate Message Sizes Per Topic CDF

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Msg Size / Count Correlation

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Related Work

Blog posts, papers and talks by the ProbeLab team about Ethereum.

From rawpixel.com / Freepik
Blog post

ProbeLab's 2025 Review and 2026 Outlook

What we've achieved in 2025 and what we're targeting in 2026

MIDDLEWARE '25 Conference Paper
Paper

PANDAS: Peer-to-peer, Adaptive Networking Allowing Data Availability Sampling within Ethereum Consensus Timebounds

Layer-2 protocols such as rollups can help address Ethereum's throughput limits. An efficient data availability layer is key for layer-2 support in Ethereum, but broadcast …

SIGMETRICS '26 Conference Paper
Paper

Multiple Sides of 36 Coins: Measuring Peer-to-Peer Infrastructure Across Cryptocurrencies

Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer (P2P) …

Ethereum Logo Blog Hero
Blog post

Behind the Scenes of Ethereum's Pectra Upgrade: A Data-Driven Analysis

The Pectra Upgrade in Numbers from the P2P Network.

Springer Cluster Computing Journal Article
Paper

The Impact of Connectivity and Software in Ethereum Validator Performance

Modern public blockchains like Ethereum rely on p2p networks to run distributed and censorship-resistant applications. With its wide adoption, it operates as a highly critical …

EthResearch Technical Report
Paper

Empirical Blob Sidecar Hit Rate Based on Local EL’s Mempool

This empirical study measures blob sidecar availability in Ethereum's execution layer (EL) mempool to assess the feasibility of distributed block building for reducing validator bandwidth …