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Each client saw the same blocks, the same attestations, and the same cutoff rules. We then compared how each implementation classified each slot."}),"\n",(0,n.jsx)(t.p,{children:"This post describes the simulator, the dataset, the infrastru
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The orchestrator is responsible for fetching blocks and attestations, while each client implementation is responsible for applying its FCR logic. That keeps the multi-client simulator easier to reason about, and it means we can swap or replay datasources without reimplementing data loading separately in every client."}),"\n",(0,n.jsx)(t.p,{children:'We also "warm" the beacon nodes before the simulation run to hydrate their fork choice store and any caches. For the run below, we warmed each beacon node with 10 epochs worth of attestations.'}),"\n",(0,n.jsx)("img",{src:"/img/blog/fcr-simulator/diagram-slot-loop.png",alt:"Diagram: orchestrator slot loop driving an engine subprocess"}),"\n",(0,n.jsx)(t.h2,{id:"the-data",children:"The data"}),"\n",(0,n.jsx)(t.p,{children:"The simulator uses data we already had from other ethpandaops infrastructure:"}),"\n",(0,n.jsxs)(t.ul,{children:["\n",(0,n.jsxs)(t.li,{children:[(0,n.jsx)(t.strong,{children:"Blocks"}),"\n",(0,n.jsxs)(t.ul,{children:["\n",(0,n.jsxs)(t.li,{children:["Canonical blocks from ",(0,n.jsx)(t.code,{children:"era"})," files, using the Nimbus team\u2019s mirror"]}),"\n",(0,n.jsxs)(t.li,{children:["Orphaned blocks from ",(0,n.jsx)(t.a,{href:"https://tracoor.mainnet.ethpandaops.io",children:"ethpandaops/tracoor"})]}),"\n"]}),"\n"]}),"\n",(0,n.jsxs)(t.li,{children:[(0,n.jsx)(t.strong,{children:"Attestations"}),"\n",(0,n.jsxs)(t.ul,{children:["\n",(0,n.jsxs)(t.li,{children:["Single Attestations from ",(0,n.jsx)(t.a,{href:"https://github.com/ethpandaops/xatu",children:"ethpandaops/xatu"})," that voted for each block before the 12 second cutoff"]}),"\n"]}),"\n"]}),"\n"]}),"\n",(0,n.jsxs)(t.p,{children:[(0,n.jsx)(t.strong,{children:"The attestation dataset is not a perfect recreation of what a beacon node would have experienced at the time"}),". For some slots, this view of the network may report more attestations than your average beacon node. Conversely, for other slots, this view may have less attestations. This data also completely ignores ",(0,n.jsx)(t.em,{children:"aggregate"})," attestations. We assume that attestation aggregation is steady and reliable on Mainnet as this is where most nodes would get 62/64 of their attestations from."]}),"\n",(0,n.jsx)(t.h2,{id:"running-the-simulations",children:"Running the simulations"}),"\n",(0,n.jsx)(t.p,{children:"Replaying a year of mainnet through fork choice one slot at a time would be too slow to be useful. The slot state transition is expensive, and repeated queries against the Xatu database would add more overhead."}),"\n",(0,n.jsx)(t.p,{children:"To make the experiment practical, we ran the replay as a sharded Kubernetes job. We also ran a one-off query against Xatu and stored the result in our in-house S3 cluster. This gave every run the same input data and avoided repeatedly querying the database during simulation."}),"\n",(0,n.jsx)("img",{src:"/img/blog/fcr-simulator/diagram-sharded-replay.png",alt:"Diagram: sharded replay topology across the k3s cluster"}),"\n",(0,n.jsx)(t.p,{children:"We ran the simulations in 1 to 3 month epoch ranges. This was mostly a practical choice: if a job failed near the end of a long run, we wanted to limit how much work could be lost or need to be discarded."}),"\n",(0,n.jsxs)(t.p,{children:["After benchmarking, we ran 96 beacon nodes across 6 ",(0,n.jsx)(t.a,{href:"https://www.hetzner.com/dedicated-rootserver/ax162-r/configurator/",children:"Hetzner AX162"})," servers. Each server ran 16 beacon nodes. We also mounted each client database into RAMFS to minimize the expensive disk reads and writes."]}),"\n",(0,n.jsx)(t.p,{children:(0,n.jsx)(t.strong,{children:"The workload was heavily memory bound, but this setup still gave us a roughly 25x to 35x speedup compared with a sequential replay."})}),"\n",(0,n.jsx)(t.h2,{id:"results",children:"Results"}),"\n",(0,n.jsx)(t.admonition,{title:"Caveat",type:"warning",children:(0,n.jsx)(t.p,{children:"About 31 hours of slots, or 0.36% of the dataset, are excluded from every chart and number below. This consists of 9 Xatu pipeline issues totalling about 22 hours, and one 8.4 hour simulator feeder bug on 2026-04-01."})}),"\n",(0,n.jsx)(t.h3,{id:"coverage",children:"Coverage"}),"\n",(0,n.jsxs)(t.table,{children:[(0,n.jsx)(t.thead,{children:(0,n.jsxs)(t.tr,{children:[(0,n.jsx)(t.th,{children:"Client"}),(0,n.jsx)(t.th,{children:"Epoch range"}),(0,n.jsx)(t.th,{children:"Dates (UTC)"}),(0,n.jsx)(t.th,{children:"Slots"})]})}),(0,n.jsxs)(t.tbody,{children:[(0,n.jsxs)(t.tr,{children:[(0,n.jsx)(t.td,{children:"Lighthouse"}),(0,n.jsx)(t.td,{children:"369,000 to 449,000"}),(0,n.jsx)(t.td,{children:"2025-05-29 to 2026-05-20"}),(0,n.jsx)(t.td,{children:"2,560,000"})]}),(0,n.jsxs)(t.tr,{children:[(0,n.jsx)(t.td,{children:"Lodestar"}),(0,n.jsx)(t.td,{children:"417,500 to 449,000"}
1),(0,n.jsx)(t.td,{children:"2025-12-31 to 2026-05-20"}),(0,n.jsx)(t.td,{children:"1,008,000"})]}),(0,n.jsxs)(t.tr,{children:[(0,n.jsx)(t.td,{children:"Grandine"}),(0,n.jsx)(t.td,{children:"423,800 to 449,000"}),(0,n.jsx)(t.td,{children:"2026-01-28 to 2026-05-20"}),(0,n.jsx)(t.td,{children:"806,400"})]}),(0,n.jsxs)(t.tr,{children:[(0,n.jsx)(t.td,{children:"Teku"}),(0,n.jsx)(t.td,{children:"423,800 to 449,000"}),(0,n.jsx)(t.td,{children:"2026-01-28 to 2026-05-20"}),(0,n.jsx)(t.td,{children:"806,400"})]})]})]}),"\n",(0,n.jsx)(o.A,{}),"\n",(0,n.jsx)(l.A,{}),"\n",(0,n.jsx)(r.A,{}),"\n",(0,n.jsx)(t.h3,{id:"what-we-found",children:"What we found"}),"\n",(0,n.jsxs)(t.p,{children:["The most important number first: ",(0,n.jsx)(t.strong,{children:"zero false confirmations"}),". Across 5.15 million slot evaluations and all four clients, no implementation ever fast-confirmed a block that turned out to be non-canonical on the finalized chain. The simulator checks each engine's ",(0,n.jsx)(t.code,{children:"confirmed_root"})," against the ERA-file canonical block at that slot during the merge step, and across the entire 12-month period (including the December 4 2025 Fusaka mainnet instability) there are zero mismatches."]}),"\n",(0,n.jsxs)(t.p,{children:["Headline rate: across 800,000 mainnet slots in the comparable window, roughly ",(0,n.jsx)(t.strong,{children:"96 out of every 100 slots would have been fast-confirmed within 12 seconds"}),"."]}),"\n",(0,n.jsxs)(t.p,{children:["Three of the four clients agree almost exactly on which slots qualify. Lighthouse and Grandine report identical 1-slot rates to four decimal places (",(0,n.jsx)(t.strong,{children:"95.8165%"}),") and agree on every single slot in the shared window. Lodestar lands at ",(0,n.jsx)(t.strong,{children:"95.8164%"}),", which is a single-slot difference across ~800k. Effectively identical."]}),"\n",(0,n.jsxs)(t.p,{children:[(0,n.jsx)(t.strong,{children:"Teku is the odd one out."})," It reports ",(0,n.jsx)(t.strong,{children:"97.3926%"}),", marking ~12,500 ",(0,n.jsx)(t.em,{children:"more"})," slots as fast-confirmed than the others over the same window."]}),"\n",(0,n.jsx)(t.h2,{id:"reproducibility",children:"Reproducibility"}),"\n",(0,n.jsx)(t.p,{children:"Every CSV in the v0.2.0 release is pinned to a specific engine image tag. Each engine image tag is pinned to an upstream git ref."}),"\n",(0,n.jsxs)(t.p,{children:["The full mapping is in the release ",(0,n.jsx)(t.code,{children:"METADATA.json"}),", including:"]}),"\n",(0,n.jsxs)(t.ul,{children:["\n",(0,n.jsx)(t.li,{children:"schema"}),"\n",(0,n.jsx)(t.li,{children:"run parameters"}),"\n",(0,n.jsx)(t.li,{children:"attestation source mode"}),"\n",(0,n.jsx)(t.li,{children:"per-CSV image SHA"}),"\n",(0,n.jsx)(t.li,{children:"per-engine upstream commit"}),"\n"]}),"\n",(0,n.jsx)(t.p,{children:"You can download the dataset with:"}),"\n",(0,n.jsx)(t.pre,{children:(0,n.jsx)(t.code,{className:"language-bash",children:"curl -L https://github.com/ethpandaops/fcr-simulator/releases/download/v0.2.0/fcr-data-2026-05-26.tar -o fcr-data.tar\ntar -xf fcr-data.tar\n"})}),"\n",(0,n.jsxs)(t.p,{children:["The orchestrator and engines are available at ",(0,n.jsx)(t.a,{href:"https://github.com/ethpandaops/fcr-simulator",children:"https://github.com/ethpandaops/fcr-simulator"})]}),"\n",(0,n.jsx)(t.p,{children:"Love,"}),"\n",(0,n.jsx)(t.p,{children:"EthPandaOps Team"})]})}function p(e={}){const{wrapper:t}={...(0,i.R)(),...e.components};return t?(0,n.jsx)(t,{...e,children:(0,n.jsx)(m,{...e})}):m(e)}},40160(e,t,a){a.d(t,{VO:()=>s,by:()=>n,sU:()=>i});const s=["lighthouse","lodestar","grandine","teku"],n={lighthouse:"Lighthouse",lodestar:"Lodestar",grandine:"Grandine",teku:"Teku"},i={lighthouse:"#4d8df6",lodestar:"#f59e0b",grandine:"#a855f7",teku:"#ef4444"}},46414(e,t,a){a.d(t,{A:()=>s});const s={card:"card_ruE0",header:"header_zR3_",title:"title_uVkw",subtitle:"subtitle_VnlO",controls:"controls_D4lI",controlGroup:"controlGroup_gr5n",controlLabel:"controlLabel_ZWVl",controlButton:"controlButton_DGeQ",controlButtonActive:"controlButtonActive_JoZb",legend:"legend_blfK",legendItem:"legendItem_Z68t",legendItemOff:"legendItemOff_YMPg",legendDot:"legendDot_Fwds",tooltip:"tooltip_Rb8y",tooltipTitle:"tooltipTitle_xHdU",tooltipRow:"tooltipRow_maAY",tooltipKey:"tooltipKey_a7r6",tooltipValue:"tooltipValue_hZS4",footnote:"footnote_vBYq",chartWrap:"chartWrap_QUeW",percentileTable:"percentileTable_WFC9",percentileCell:"percentileCell_WNL0",percentileCellHead:"percentileCellHead_JIaP",percentileValue:"percentileValue_mV_K",fallback:"fallback_nFhQ"}},85339(e){e.exports=JSON.parse('{"permalink":"/posts/fcr-simulator","source":"@site/blog/2026-05-26-fcr-simulator/index.md","title":"Replaying a year of mainnet with the Fast Confirmation Rule","description":"We replayed 12 months of mainnet beacon chain history through 4 consensus-layer implementations of the Fast Confirmation Rule.","date":"2026-05-26T00:00:00.000Z","tags":[{"inline":true,"label":"fcr","permalink":"/posts/tags/fcr"},{"inline":true,"label":"consensus-layer","permalink":"/posts/tags/consensus-layer"},{"inline":true,"label":"xatu","permalink":"/posts/tags/xatu"},{"inline":true,"label":"kubernetes","permalink":"/posts/tags/kubernetes"},{"inline":true,"label":"data","permalink":"/posts/tags/data"}],"readingTime":5.13,"hasTruncateMarker":false,"authors":[{"name":"samcm","title":"DevOps Engineer","bio":"DevOps Engineer","url":"https://github.com/samcm","github":"https://github.com/samcm","t
1witter":"https://x.com/samcmAU","imageURL":"/img/team/samcm.jpeg","key":"samcm","page":null},{"name":"savid \ud83d\ude82","title":"DevOps Engineer","bio":"DevOps Engineer","url":"https://github.com/savid","github":"https://github.com/savid","twitter":"https://x.com/savid","imageURL":"/img/team/savid.jpeg","key":"savid","page":null}],"frontMatter":{"slug":"fcr-simulator","title":"Replaying a year of mainnet with the Fast Confirmation Rule","authors":["samcm","savid"],"description":"We replayed 12 months of mainnet beacon chain history through 4 consensus-layer implementations of the Fast Confirmation Rule.","tags":["fcr","consensus-layer","xatu","kubernetes","data"],"image":"/img/blog/fcr-simulator/header.png","githubRepos":[{"name":"fcr-simulator","url":"https://github.com/ethpandaops/fcr-simulator"}]},"unlisted":false,"prevItem":{"title":"Panda: one tool for the whole data stack","permalink":"/posts/panda"},"nextItem":{"title":"Validator Report: Investigate Validator Performance","permalink":"/posts/validator-report"}}')}}]);
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