notebooks/2021/08/23/beacon_incident.ipynb

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   "source": [
    "Last Friday, 21st August 2021, a few node operators reported some unusual occurences of an increased number of missed attestations and dropped participation rates in the Beacon Chain. \n",
    "\n",
    "On some investigation by various client teams and other stakeholders, the root cause of this was found to be a slight increase in the number of orphaned blocks which was tracked down to a single Lido provider. [**Note**: here that a single Lido provider controls many validators and not just one or two. Given the amount of ETH staked using Lido, the number of validators controlled by this whitelisted Lido provider is expected to be really high]\n",
    "\n",
    "An increase in orphaned blocks would imply an increase in attestations that need to be put in other blocks, and blocks have a limit on the number of aggregates they can accommodate. This led to some attestations being dropped and validators being mildly affected for no fault of their own. \n",
    "\n",
    "[Ben Edgington](https://twitter.com/benjaminion_xyz) described the situation in great details in his [Twitter thread](https://twitter.com/benjaminion_xyz/status/1429044820383866896). Here is the description that he gives:"
   ]
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   "source": [
    ">I've just been writing up yesterday's little Eth2 beacon chain excitement for next week's What's New in Eth2.\n",
    ">I thought I'd give you a quick tweet-thread preview.\n",
    "\n",
    ">To be clear: the beacon chain was absolutely safe, no funds were at risk, no small animals were harmed.\n",
    ">It's only noteworthy because, after almost 9 months of near-perfect running, any kind of incident is quite interesting.\n",
    "\n",
    ">On Friday, my own validators missed two attestations. Missed attestations (votes that do not get on chain) happen, but are pretty rare. I noted it, but just assumed my ISP was acting up.\n",
    "\n",
    ">A little later, @Allnodes a big Teku user, reported to us a significant increase in missed attestions: from the normal zero, to 15 in 12 hours. I checked some Discord forums: a handful of other stakers were reporting similar. It smelt like a network-wide issue.\n",
    "\n",
    ">In addition, network participation (the proportion of total votes getting into blocks) was down by a percent or so. Not a big deal: 98% participation is totally fine, but still a hint that all was not perfect.\n",
    ">I pinged the other devs, and the brains got to work Brain\n",
    "\n",
    ">Nishant spotted that there were also unusual levels of orphaned blocks. These are blocks not included on chain, usually because they are produced too late. Also, the remaining blocks seemed to be unusually full of attestations (at capacity).\n",
    "\n",
    ">The issue is that there is limited space in blocks for attestations. If blocks are orphaned/missing, that increases the number of attestations that must be put in other blocks, and blocks have hard cap of 128 aggregates. They were overflowing.\n",
    "\n",
    ">The number of orphaned blocks was small, around one in a hundred or so, but the impact was noticeable. The reduced blockspace resulted in attestations being dropped and validators being (mildly) penalised through no fault of their own.\n",
    "\n",
    ">Most of the orphaned blocks were tracked down to a single Lido provider. The Lighthouse team got in touch, they fixed whatever misconfiguration was going on, and everything returned to normal, near-perfect running.\n",
    "\n",
    ">Again, this whole thing is a minor, almost trivial incident, and all is totally fine.\n",
    ">However, it concerns me that an issue at an operator running only around 1% of validators had an impact noticeable across the network.\n",
    "\n",
    ">It may be possible to decrease the impact of such outages by increasing the number of aggregate attestations included in blocks. It's currently 128, which is ok to keep the protocol safe, but results in innocent validators being penalised at times like this.\n",
    "\n",
    ">But increasing this would make blocks larger, which is also not desireable. I'm sure we will discuss the trade-offs in due course. It's not a critical issue by any means, but more noticeable as the number of validators increases and blocks get fuller.\n",
    "\n",
    ">The best remedy is not to use large staking pools, people!! The more diverse the network - clients, infrastructure, organisations - the less the impact of any single failure\n"
   ]
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   "source": [
    "It was also pointed out by PoS Ethereum Researcher [Danny Ryan](https://twitter.com/dannyryan) that another reason for this incident could be due to the fact that Prysm (The most widely used PoS Ethereum clients) does not refill mempool when blocks are reorg'd. (The Prysm team was very quick to [resolve this](https://t.co/NzigRzj9xV?amp=1)) \n",
    "\n",
    "What we observed, as Ben pointed out, could thus be beacuse of the clients that do refill the attestation pool not having enough space to include all the attestations that Prysm was dropping."
   ]
  },
  {
   "cell_type": "markdown",
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    "In this special edition of the Beacon Digest, we explore the impact of this incident on the Beacon Chain. We do so by analyzing the following metrics which we know are directly impacted because of the incident:\n",
    "\n",
    "- Attestation Count\n",
    "- Orphaned Blocks (Number)\n",
    "- Global Participation Rate \n",
    "\n",
    "In addition to this we also look into these metrics which may or may not have been indirectly affected by the incident:\n",
    "\n",
    "- Validator Slashings\n",
    "- Exit Counts\n",
    "\n",
    "\n",
    "These minor incidents are worth dissecting to understand better how the beacon chain, a system driven by tens and thousands of stakeholders accross the globe, behaves with a change in the action of a single large entity. We'll also see how quickly the network is able to recover from these minor hiccups.\n",
    "\n",
    "We observe these metrics from Epoch 58500 (Aug 18, 2021, 8:00 PM SGT) - Epoch 59560 (Aug 23, 2021, 1:04 PM SGT) to get a sense of how the network changed over time - before the incident, during the incident and after. \n",
    "\n",
    "Furthemore, we also dive into the attestation efficiency of 590 randomly selected Lido validators before and after the incident to see how these orphaned blocks affected the performance of the individual validators (in contrast to the impact on the overall network that we have been analyzing). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10d0748-5880-45a0-83a8-ac8f9dd19959",
   "metadata": {},
   "source": [
    "## Housekeeping - data collection "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4e24b2-949c-4a03-bf9a-d3684759fb87",
   "metadata": {},
   "source": [
    "As usual, we begin with collecting all the data we need in order to carry out our analysis!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "871244be-a44c-49f7-b6d3-35f5be4b25e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from web3 import Web3\n",
    "import json\n",
    "import requests\n",
    "import csv\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from time import *\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "pd.options.plotting.backend = \"plotly\"\n",
    "pio.renderers.default = \"plotly_mimetype+notebook_connected\"\n",
    "import plotly.graph_objects as go\n",
    "import math\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b68ddfe2-3757-4690-91ee-1c158ca0166d",
   "metadata": {},
   "outputs": [],
   "source": [
    "attestor_slashing = {}\n",
    "proposer_slashing = {}\n",
    "proposed_block_count = {}\n",
    "global_participation_rate = {}\n",
    "exit_counts = {}\n",
    "deposits = {}\n",
    "attestation_count = {}\n",
    "orphaned_blocks = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3af16e65-9bfa-4da3-8cb5-5a838244e7ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('api_key.txt', 'r') as api_file:\n",
    "#     api_key = api_file.read()\n",
    "\n",
    "for epoch in range(58500,59560):\n",
    "    x = requests.get('https://beaconcha.in/api/v1/epoch/{}?api_key={}'.format(epoch, api_key))\n",
    "    #Add a sleep to stay within the call rate limits\n",
    "    sleep(6)\n",
    "    \n",
    "    data = x.json()['data']\n",
    "    \n",
    "    attestor_slashing[epoch] = data['attesterslashingscount']\n",
    "    proposer_slashing[epoch] = data['proposerslashingscount']\n",
    "    proposed_block_count[epoch] = data['proposedblocks']\n",
    "    global_participation_rate[epoch] = data['globalparticipationrate']\n",
    "    exit_counts[epoch] = data['voluntaryexitscount']\n",
    "    deposits[epoch] = data['depositscount']\n",
    "    attestation_count[epoch] = data['attestationscount']\n",
    "    orphaned_blocks[epoch] = data['orphanedblocks']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe26c142-721d-4cc3-905d-735f63461ff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Writing the dictionaries to csv files\n",
    "\n",
    "with open('attestor_slashing.csv','w') as result_file:\n",
    "    wr = csv.writer(result_file, dialect='excel')\n",
    "    for key, value in attestor_slashing.items():\n",
    "        wr.writerow([key,value])\n",
    "        \n",
    "with open('proposer_slashing.csv','w') as result_file:\n",
    "    wr = csv.writer(result_file, dialect='excel')\n",
    "    for key, value in proposer_slashing.items():\n",
    "        wr.writerow([key,value])\n",
    "        \n",
    "with open('proposed_block_count.csv','w') as result_file:\n",
    "    wr = csv.writer(result_file, dialect='excel')\n",
    "    for key, value in proposed_block_count.items():\n",
    "        wr.writerow([key,value])\n",
    "        \n",
    "with open('global_participation_rate.csv','w') as result_file:\n",
    "    wr = csv.writer(result_file, dialect='excel')\n",
    "    for key, value in global_participation_rate.items():\n",
    "        wr.writerow([key,value])\n",
    "        \n",
    "with open('exit_counts.csv','w') as result_file:\n",
    "    wr = csv.writer(result_file, dialect='excel')\n",
    "    for key, value in exit_counts.items():\n",
    "        wr.writerow([key,value])\n",
    "        \n",
    "with open('deposits.csv','w') as result_file:\n",
    "    wr = csv.writer(result_file, dialect='excel')\n",
    "    for key, value in deposits.items():\n",
    "        wr.writerow([key,value])\n",
    "        \n",
    "with open('attestation_count.csv','w') as result_file:\n",
    "    wr = csv.writer(result_file, dialect='excel')\n",
    "    for key, value in attestation_count.items():\n",
    "        wr.writerow([key,value])\n",
    "        \n",
    "with open('orphaned_blocks.csv','w') as result_file:\n",
    "    wr = csv.writer(result_file, dialect='excel')\n",
    "    for key, value in orphaned_blocks.items():\n",
    "        wr.writerow([key,value])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "638f8a96-9f3c-4d0c-9191-f607529e4604",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>attestor_slashing</th>\n",
       "      <th>proposer_slashing</th>\n",
       "      <th>proposed_block_count</th>\n",
       "      <th>global_participation_rate</th>\n",
       "      <th>exit_counts</th>\n",
       "      <th>deposits</th>\n",
       "      <th>attestation_count</th>\n",
       "      <th>orphaned_blocks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58500</td>\n",
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       "      <td>97.931033</td>\n",
       "      <td>0</td>\n",
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       "      <td>58502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>98.627383</td>\n",
       "      <td>0</td>\n",
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       "      <td>58503</td>\n",
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       "      <td>2817</td>\n",
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       "      <td>58504</td>\n",
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       "      <td>32</td>\n",
       "      <td>99.045461</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>59554</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>98.328453</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>3715</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>59555</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>97.735530</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3347</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>59556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>97.726738</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3448</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>59557</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>99.317855</td>\n",
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       "      <td>2942</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>97.957760</td>\n",
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       "      <td>0</td>\n",
       "      <td>3035</td>\n",
       "      <td>0</td>\n",
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       "<p>1059 rows × 9 columns</p>\n",
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      "text/plain": [
       "      epoch  attestor_slashing  proposer_slashing  proposed_block_count  \\\n",
       "0     58500                  0                  0                    32   \n",
       "1     58501                  0                  0                    32   \n",
       "2     58502                  0                  0                    31   \n",
       "3     58503                  0                  0                    32   \n",
       "4     58504                  0                  0                    32   \n",
       "...     ...                ...                ...                   ...   \n",
       "1054  59554                  0                  0                    31   \n",
       "1055  59555                  0                  0                    32   \n",
       "1056  59556                  0                  0                    31   \n",
       "1057  59557                  0                  0                    32   \n",
       "1058  59558                  0                  0                    32   \n",
       "\n",
       "      global_participation_rate  exit_counts  deposits  attestation_count  \\\n",
       "0                     97.931033            0         0               3397   \n",
       "1                     96.418118            0         0               3272   \n",
       "2                     98.627383            0         0               3458   \n",
       "3                     99.394751            0         0               2817   \n",
       "4                     99.045461            0         0               2870   \n",
       "...                         ...          ...       ...                ...   \n",
       "1054                  98.328453            0        54               3715   \n",
       "1055                  97.735530            0         0               3347   \n",
       "1056                  97.726738            0         0               3448   \n",
       "1057                  99.317855            0         0               2942   \n",
       "1058                  97.957760            0         0               3035   \n",
       "\n",
       "      orphaned_blocks  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "...               ...  \n",
       "1054                0  \n",
       "1055                0  \n",
       "1056                0  \n",
       "1057                0  \n",
       "1058                0  \n",
       "\n",
       "[1059 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = [\"attestor_slashing\", \"proposer_slashing\", \"proposed_block_count\", \"global_participation_rate\", \"exit_counts\", \"deposits\", \"attestation_count\", \"orphaned_blocks\"]\n",
    "dfs = []\n",
    "\n",
    "for file in files:\n",
    "    df = pd.read_csv(f\"{file}.csv\", header=None, names=[\"epoch\", file])\n",
    "    df.dropna(subset=[\"epoch\"], inplace=True)\n",
    "    dfs += [df[[file]]] # We don't want the epoch column\n",
    "\n",
    "df = pd.concat([df[[\"epoch\"]]] + dfs, axis=1) # Concat everything, with the epoch column first\n",
    "df[\"global_participation_rate\"] *= 100 # Set to percentages\n",
    "df = df[df.epoch < max(df[\"epoch\"])] # Remove last epoch (queried too soon)\n",
    "df[\"epoch\"] = df[\"epoch\"].astype(int)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408cb767-d02f-4470-b1e0-01e530adf9cb",
   "metadata": {},
   "source": [
    "## Directly impacted metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7292296-760c-46b1-95d2-af4427ddfa99",
   "metadata": {},
   "source": [
    "### Attestation count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8319b7e2-8f81-47df-835c-71b9bd02c8e2",
   "metadata": {},
   "source": [
    "The first metric that we choose to analyze is attestation counts. One of the major implications of an increased number of orphaned blocks was that other blocks were overflowing with attestations, because of which it becomes interesting to see how the attestation counts changed over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e3882950-36a6-41be-af40-ba900cdb4873",
   "metadata": {},
   "outputs": [
    {
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         "x": [
          58500,
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          58502,
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          58506,
          58507,
          58508,
          58509,
          58510,
          58511,
          58512,
          58513,
          58514,
          58515,
          58516,
          58517,
          58518,
          58519,
          58520,
          58521,
          58522,
          58523,
          58524,
          58525,
          58526,
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          58528,
          58529,
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    "fig = px.area(df, x=\"epoch\", y=\"attestation_count\", title='Attestation Count', labels = {'epoch': 'Epoch', 'attestation_count': 'Attestation Count'})\n",
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    "We can see from the above graph that there was a sharp increase in the attestation counts from around epoch 58840 (Friday Aug 20, 2021, 8:16 AM SGT), after which it reaches its peak value. \n",
    "\n",
    "At its peak, the attestation count reached values as large as 4095 per epoch. Note that each block carries a maximum of 128 aggregated attestations, and there are at most 32 blocks per epoch, so the maximum amount of aggregates published during an epoch is 4098!\n",
    "\n",
    "From about Epoch 59110 (Saturday Aug 21, 2021, 1:04 PM SGT) we see a huge drop in the attestation count after which we observe a somewhat fluctuating behavior around a given range of values, consistent with the trend from before the increase. "
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    "Given that the root cause of all the strange occurences which were observed was attributed to the increased number of orphaned blocks, this analysis would not be complete without looking specifically into just how many blocks were orphaned."
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    "bucket_size = 200\n",
    "df[\"bucket\"] = np.floor_divide(df[\"epoch\"], bucket_size)\n",
    "df[\"n_bxs\"] = 1\n",
    "df_bucket = df[[\"bucket\", \"orphaned_blocks\", \"n_bxs\"]].groupby([\"bucket\", \"orphaned_blocks\"]).sum(\"n_bxs\").reset_index()\n",
    "df_bucket[\"epoch\"] = df_bucket.bucket.apply(\n",
    "    lambda bucket: f\"{max(min(df['epoch']), bucket * bucket_size)}-{min(max(df['epoch']), (bucket + 1) * bucket_size)}\"\n",
    ")\n",
    "\n",
    "fig = px.scatter(df_bucket, x = 'epoch', y = 'orphaned_blocks', size = 'n_bxs',\n",
    "                 labels={ \"orphaned_blocks\": \"Number of Orphaned Blocks\", \"epoch\": \"Epoch\", \"n_bxs\": \"Count\" },\n",
    "                 title = f\"Ophaned Blocks count in intervals of {bucket_size} Epochs\")\n",
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    "When we plot the orphaned block counts in intervals of 200 epochs, we can clearly deduce that out of all the epoch intervals in consideration, the intervals (58800 - 59000) and (59000 - 59200):\n",
    "\n",
    "- Have considerably lesser epochs with 0 orphaned blocks \n",
    "    - 132 and 141 vs what was otherwise around 185\n",
    "- Have considerably higher epochs with 1 orphaned block \n",
    "    - 56 and 43 vs what was otherwise around 12-16\n",
    "- Were the only intervals to have epochs with 2 orphaned blocks \n",
    "    - Counts were: 11 and 14"
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   "source": [
    "### Global participation rate"
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   "source": [
    "We continue our analysis by looking at how the participation rate was affected by this mini incident."
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   "source": [
    "bucket_size = 100\n",
    "df[\"bucket\"] = np.floor_divide(df[\"epoch\"], bucket_size)\n",
    "df[\"n_bxs\"] = 1\n",
    "bins = pd.IntervalIndex.from_tuples([(i,i+1) for i in range(int(min(df[\"global_participation_rate\"])), 100)][::-1])\n",
    "df[\"participation_bin\"] = pd.cut(df[\"global_participation_rate\"], bins)\n",
    "df_bucket = df[[\"bucket\", \"participation_bin\", \"n_bxs\"]].groupby([\"bucket\", \"participation_bin\"]).sum(\"n_bxs\").reset_index()\n",
    "epoch_names = df_bucket.bucket.apply(\n",
    "    lambda bucket: f\"{max(min(df['epoch']), bucket * bucket_size)}-{min(max(df['epoch']), (bucket + 1) * bucket_size)}\"\n",
    ")\n",
    "df_bucket[\"epoch\"] = epoch_names\n",
    "df_bucket = df_bucket[[\"participation_bin\", \"n_bxs\", \"epoch\"]].pivot(index=\"participation_bin\", columns=[\"epoch\"])\n",
    "\n",
    "px.imshow(\n",
    "    df_bucket.to_numpy(),\n",
    "    labels=dict(x=\"Epochs\", y=\"Participation rate\", color=\"Frequency\"),\n",
    "    x = sorted(list(set(epoch_names))),\n",
    "    y = [str(s) for s in np.flip(np.arange(int(min(df[\"global_participation_rate\"])), 100))],\n",
    "    color_continuous_scale='blues'\n",
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   "source": [
    "We see that the epoch intervals (58800 - 59000) and (59000 - 59200) continue to show anomalous behaviour* when compared to other intervals. Specifically in these ranges:\n",
    "\n",
    "- The participation rates of most epochs lie between 97% - 98% (compared to 98% - 99% which we see for most other epochs)\n",
    "- The number of epochs for which the participation rates were between of 96% - 97% is also considerably higher \n",
    "\n",
    "In fact specifically for the range (58800 - 59000) we see epochs for which the participation rate fell to values as low as 92.2 which is in general quite rare, see our previous Beacondigests."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b603d9-3c4f-4a0f-aed9-917d696b33f7",
   "metadata": {},
   "source": [
    "<sup>* Anomalous here refers to the fact that the participation rate is not only lower when compared to the other epochs in consideration but also to all other epochs that we have analysed ever since the inception of the Beacon Digest</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a34e51e-61bc-4cda-b9e3-7a8647e814d6",
   "metadata": {},
   "source": [
    "## Other metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b333cc1-6f4c-4495-b0bc-4b2305c79931",
   "metadata": {},
   "source": [
    "### Validator slashing incidents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e7d50c-c131-4ade-83a0-a994a6108d99",
   "metadata": {},
   "source": [
    "While there are no direct reports of this metric being affected by the overflowing attestations, we thought it would still be interesting to look into these to show how despite small occurrences such as this, most parts of this complex system called the Beacon Chain still continue to function as per usual. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76380508-704e-4ec3-9b62-a7bef9b63f65",
   "metadata": {},
   "source": [
    "#### Attester slashing"
   ]
  },
  {
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   "execution_count": 33,
   "id": "856dc7ee-e0dd-4a0b-a87b-1afd94a12aa7",
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Attester slashings\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"range\": [58500, 59558], \"title\": {\"text\": \"Epoch\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"temp\"}, \"visible\": false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('bab0fcad-cd50-4015-9280-98d705a89da7');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.default_rng(42)\n",
    "df['temp'] = rng.uniform(0, 10, len(df[\"epoch\"]))\n",
    "fig = px.scatter(\n",
    "    df[df.attestor_slashing > 0], x = 'epoch', y = 'temp', size = 'attestor_slashing',\n",
    "    size_max = 20, labels = {\"epoch\": \"Epoch\"},\n",
    "    title = f\"Attester slashings\"\n",
    ")\n",
    "fig.update_xaxes(range=(min(df[\"epoch\"]), max(df[\"epoch\"])))\n",
    "fig.update_yaxes(visible=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "403ab745-aaa1-413b-b5cd-32c9b73eac86",
   "metadata": {},
   "source": [
    "#### Proposer slashing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a96238ea-9c40-44e4-9a91-86245e9bfc7d",
   "metadata": {},
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = px.scatter(\n",
    "    df[df.proposer_slashing > 0], x = 'epoch', y = 'temp', size = 'proposer_slashing',\n",
    "    size_max = 20, labels = {\"epoch\": \"Epoch\"},\n",
    "    title = f\"Proposer slashings\"\n",
    ")\n",
    "fig.update_xaxes(range=(min(df[\"epoch\"]), max(df[\"epoch\"])))\n",
    "fig.update_yaxes(visible=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "093a69f4-5650-40e6-a32f-3f11e5988537",
   "metadata": {},
   "source": [
    "We note that there was 1 attester slashing event and 0 proposer slashing events for this range of epochs, both of which are not any really unusual in any way "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ec5094-fadd-4e4f-afbf-18412d319ffb",
   "metadata": {},
   "source": [
    "### Exit counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cf68d6-3ea0-45f3-9c33-d823011fba84",
   "metadata": {},
   "source": [
    "We also thought that it would be interesting to see if many validators or rather if any validator tried to initiaite a voluntarily exit given that there were a few validators who were, as Ben put it, being (mildly) penalised through no fault of their own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "447eea10-4ac5-43dd-a1ba-6821ad81e7f0",
   "metadata": {},
   "outputs": [
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\"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Epoch\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Number of exits\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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   "source": [
    "px.line(x=df['epoch'], y=df['exit_counts'], labels = {\"x\": \"Epoch\", \"y\": \"Number of exits\"}, title = \"Number of voluntary exits\")"
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "We observe that there were 0 voluntary exits for this range of epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0c615a-7f39-4721-a3a2-9db8817b676b",
   "metadata": {},
   "source": [
    "## Lido performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d040f2-b850-40a6-ab02-2a0bc22f887d",
   "metadata": {},
   "source": [
    "While so far our focus has been on mainly looking at how this incident has impacted the network as a whole, we now switch gears and look at how this has affected the long-term performance of 590 randomly selected Lido validators (Since the source of the incident was traced to a Lido operator) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87d1041-48ef-4c0d-a9f2-692925370078",
   "metadata": {},
   "source": [
    "### Attestation efficiency "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b74b551-da58-4a46-90c6-25db2b0cb2cc",
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   "source": [
    "The first metric that we look at is Attestation Effectiveness. While more orphaned blocks do not necessarily mean a decrease in the validator's efficiency, it will still be interesting to analyze whether Lido attestations were also not to the mark they were at during this period.  \n",
    "\n",
    "As explained in great detail in this [blog post by Attestant](https://www.attestant.io/posts/defining-attestation-effectiveness/), this metric gives us an idea of the usefulness of an attestation in terms of both block production and inclusion distance.\n",
    "\n",
    "The formula to calculate the attestation efficiency of a single attestation is:\n",
    "\n",
    "$$ \\frac{earliest\\ inclusion\\ slot-attestation\\ slot}{actual\\ inclusion\\ slot-attestation\\ slot} $$\n",
    "\n",
    "When we average this out for multiple attestations, we get a metric that is an indication of how \"effective\" the validator has been in performing their duties."
   ]
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  {
   "cell_type": "markdown",
   "id": "0d6c7ec3-929b-4e9d-ba5a-dcd306626ab6",
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    "We look at the average attestation efficiency of 590 randomly selected Lido validators:\n",
    "\n",
    "1) Based off the data we scraped on 8th August 2021\n",
    "2) Based off the data we scraped today (on 23rd August 2021)\n",
    "\n",
    "to see how it changed over the course of this incident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "72586e7b-780e-42cc-9c74-f4b33de34606",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['Validator Index', 'Attestation Efficiency']\n",
    "df1 = pd.read_csv('lido_effectiveness_0809.csv', header = None, names = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "843f9f34-f549-484c-a141-55e43f05585a",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['Validator Index', 'Attestation Efficiency']\n",
    "df2 = pd.read_csv('lido_effectiveness_0823.csv', header = None, names = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31148cf7-15e1-48da-a5c7-e6810c3af449",
   "metadata": {},
   "outputs": [],
   "source": [
    "nan_value = float(\"NaN\")\n",
    "df2.replace(\"\", nan_value, inplace=True)\n",
    "df2.dropna(subset = [\"Validator Index\"], inplace=True)\n",
    "df2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2ddf0c70-cd0f-4d63-b803-6f42ec0005f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary stats of efficiency before the incident\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    591.000000\n",
       "mean       0.993807\n",
       "std        0.013123\n",
       "min        0.916667\n",
       "25%        0.990099\n",
       "50%        1.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: Attestation Efficiency, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Summary stats of efficiency before the incident\")\n",
    "df1['Attestation Efficiency'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b8f53ec1-c6ac-4afb-8bd2-876512ed719f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary stats of efficiency after the incident\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    591.000000\n",
       "mean       0.992666\n",
       "std        0.017333\n",
       "min        0.847458\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: Attestation Efficiency, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Summary stats of efficiency after the incident\")\n",
    "df2['Attestation Efficiency'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2e1d0594-1614-4d8b-81b8-e12d3629dbbe",
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "fig = make_subplots(rows=2, cols=1)\n",
    "\n",
    "fig.append_trace(go.Violin(\n",
    "    name = 'Before',\n",
    "    x= df1['Attestation Efficiency']\n",
    "), row=1, col=1)\n",
    "\n",
    "fig.append_trace(go.Violin(\n",
    "    name = 'After',\n",
    "    x=df2['Attestation Efficiency'],\n",
    "), row=2, col=1)\n",
    "\n",
    "fig.update_layout(height=600, width=600, title_text=\"Attestation efficiency of 590 random Lido validators before and after the incident\")\n",
    "fig.update_xaxes(range=[0.84, 1])\n",
    "fig.update_xaxes(range=[0.84, 1.01])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c05f11-21b1-4a24-81ad-5f7ad9533e45",
   "metadata": {},
   "source": [
    "While the attestation efficiency of most validators continue to be between 0.95 - 1 even after the incident, the value for some validators went below 0.9 which was previously not the case. \n",
    "\n",
    "We also see from the summary stastics that the mean also fell down from 0.993807 to 0.992666. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9908ab99-de95-423d-aad6-993e2a9f1f18",
   "metadata": {},
   "source": [
    "Overall the attestation efficiencies of most validators still remain quite high while those of a few have fallen to relatively lesser values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d71059d-6149-4983-be0f-97958afce90a",
   "metadata": {},
   "source": [
    "### Validator earnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d5ff2f-c4ff-4793-ac9a-2784bbfa477a",
   "metadata": {},
   "source": [
    "To conclude our analysis, we look into the day-wise earnings of Lido validator #96325, which on 08/09 had an attestation efficiency of 1 and on 08/23 an attestation efficiency of about 0.87.\n",
    "\n",
    "We know that validator earnings are directly and negatively impacted by orphaned blocks, but we do not expect a large enough deviation to sound off an \"anomaly detector\", due to the high variability in rewards given the stochastic nature of block production.\n",
    "\n",
    "(If a validator is by chance called on to produce a block on some day, their earnings for that day will be a lot higher than on days when they were not called on to propose a block. But being called on to produce a block is probabilistic in nature)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "306e9068-b165-4929-9b67-d49857db7260",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = requests.get('https://beaconcha.in/api/v1/validator/stats/96325?api_key={}'.format(api_key))\n",
    "data = x.json()['data']\n",
    "earnings_dict = {}\n",
    "\n",
    "for i in data:\n",
    "    earnings_dict[i['day'] - 261] = i['end_balance'] - i['start_balance']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4f0ee8bc-2e57-48a4-ac84-510f5326a06d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_earnings = pd.DataFrame(list(earnings_dict.items()),columns = ['Days since incident','Earnings'])"
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\"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Earnings of validator #96325,\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"range\": [-15, 2], \"title\": {\"text\": \"Days since incident\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"range\": [5000000, 7000000], \"title\": {\"text\": \"Earnings\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e07a1c2a-46c2-416a-850e-af4feeba06de');\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "fig = px.area(df_earnings, x=\"Days since incident\", y=\"Earnings\", title='Earnings of validator #96325,')\n",
    "fig.update_xaxes(range=[-15, 2])\n",
    "fig.update_yaxes(range=[5000000, 7000000])\n",
    "fig.show()"
   ]
  },
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   "cell_type": "markdown",
   "id": "82e741ce-e858-4347-9858-2a96a4724e13",
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   "source": [
    "If we look at the earnings of the validator over a period of 15-days, we see that while there is a small dip in their earnings 1 day after the incident, as we hypothised the dip is not significant enough to sound off any alarm that something out of the blue has happened. \n",
    "\n",
    "This means that despite this small incident, people who have staked their ETH using Lido continue to earn rewards."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35666488-a8a3-48a8-a987-c6a8a37f0eb0",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de83f8ff-22c4-4203-a131-d7aa4b67ee50",
   "metadata": {},
   "source": [
    "Here are my closing thoughts before concluding this data analysis:\n",
    "    \n",
    "1) Kudos to the Prysmatic Labs team for their super quick response in coming up with a fix to properly reinsert re-org'ed attestation.\n",
    "\n",
    "2) As we saw in our analysis, even though metrics such as participation rate did drop, the fall was only by 1-2% allowing the system to be fully functional in near optimal conditions. Overall the network really did not take that big of a hit and was always up and running.\n",
    "\n",
    "3) Most of our notebooks thus far was focused on the extent of decentralization in the PoS Ethereum network, and this incident is a classic demonstration of why this is in fact very important. If the node operator whose blocks were being orphaned controlled only a small number of validators, its effects would not even be noticed by others in the network, but since it was one of Lido's which as of today 10.8% of all ETH staked on the network, the effects of one operator's hickups was felt by a significant number of validators. Decentralization (which is the heart of Blockchain systems) at its core aims to remove any single points of failure, thus compromising on this would essentially mean compromising on arguably one of the most important promises of distributed systems. \n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c8376497-5d4f-45f2-b8bc-7f615675e893",
   "metadata": {},
   "source": [
    "Beacon Chain incident - 21st August 2021"
   ]
  },
  {
   "cell_type": "raw",
   "id": "616f4ab6-7d0b-4567-8a22-c9551415a9fe",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "0374da12-681c-4ad3-8135-b055e29c47fc",
   "metadata": {},
   "source": [
    "// Authors\n",
    "let authorData = [\"shyam\"];"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d0ef6f1a-fdca-46b0-9889-9c97f61c669a",
   "metadata": {},
   "source": [
    "Many thanks to Barnabé Monnot and Caspar Schwarz-Schilling for their reviews and continued support for the project."
   ]
  }
 ],
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