9 Calculate New Allocations
- add 1,000 to all with at least 0.00001 in queries_proportion_per_indexer from rewards_info
- add 100 at a time to the best one
- re-calculate proportions and keep adding 100 at a time
- when a single one reaches 10% of total, stop adding to that one
- add to others using the same approach until out of available tokens
First, let’s set all new allocations to 0 as a start:
Next, let’s establish a maximum allocation size as 10% of our total stake. This helps spread our allocations across a larger number of subgraphs and avoid our APR from fluctuating as much after we have set the allocations:
## [1] 59317.47Before the next step, let’s make sure to fill in NAs with 0’s to avoid issues when calculating the new allocations:
synced_subgraphs = synced_subgraphs %>%
replace_na(list(
queries_per_indexer = 0,
unique_indexers = 0,
signalled_tokens = 0,
total_allocated_tokens = 0,
rewards_proportion = 0,
sum_queries = 0
))Now let’s start allocating some tokens. We will
synced_subgraphs %<>%
# for cases with more than 2 signal but less than 1000 in staked tokens, add a 1,000 allocation
mutate(new_allocation = case_when( signalled_tokens > 3 && total_allocated_tokens < 1000 ~ 1000,
# for cases with more than 50,000 queries per indexer, add 1000 allocation
queries_per_indexer > 50000 ~ 1000,
# for cases with less than 3 signal (but more than 0.5) and less than 1000 in staked tokens, add a 100 allocation
(signalled_tokens < 3 | signalled_tokens > 0.5) && total_allocated_tokens < 1000 ~ 100,
# everything else remains with the old new_allocation value
TRUE ~ new_allocation) )# calculate remaining tokens
remaining_tokens = available_tokens$available_stake - sum(synced_subgraphs$new_allocation,na.rm=T)
# now sort by best deals, and increment the top one by 100
synced_subgraphs %<>% arrange(desc(rewards_proportion))
# calculate new allocations - actual calculation -----------------------------------------------
data = synced_subgraphs
# Sort the data by rewards_proportion in descending order, but keep all rows
data = data %>%
arrange(desc(rewards_proportion)) %>%
select(deployment, signalled_tokens, total_allocated_tokens, rewards_proportion, queries_per_indexer, new_allocation)
# Calculate initial remaining tokens
remaining_tokens = available_tokens$available_stake - sum(data$new_allocation, na.rm=TRUE)
# Ensure remaining_tokens is not negative
remaining_tokens = max(remaining_tokens, 0)
# Function to calculate potential rewards_proportion
calculate_rewards_proportion = function(row, additional_allocation) {
(row$signalled_tokens) / (row$total_allocated_tokens + row$new_allocation + additional_allocation)
}
# Check if data is empty
if (nrow(data) == 0) {
print("Error: data is empty. Skipping allocation loop.")
} else {
while (remaining_tokens >= 100) {
# Print remaining tokens at the start of each iteration
# print(paste("Remaining tokens:", remaining_tokens))
# Calculate potential rewards_proportion for each row if we add 100 tokens
data$potential_rewards = mapply(calculate_rewards_proportion, split(data, 1:nrow(data)), 100)
# Find rows that haven't reached max_allocation
eligible_rows = which(data$new_allocation + 100 <= max_allocation)
# If no eligible rows, break the loop
if (length(eligible_rows) == 0) {
print("No eligible rows for allocation. Breaking loop.")
break
}
# Find the best eligible row
best_row = eligible_rows[which.max(data$potential_rewards[eligible_rows])]
# Allocate 100 tokens to the best row
data$new_allocation[best_row] = data$new_allocation[best_row] + 100
data$total_allocated_tokens[best_row] = data$total_allocated_tokens[best_row] + 100
remaining_tokens = remaining_tokens - 100
# Recalculate rewards_proportion for the updated row
data$rewards_proportion[best_row] = data$signalled_tokens[best_row] / data$total_allocated_tokens[best_row]
# Print information about the allocation
# print(paste("Allocated 100 tokens to row", best_row, ". New allocation:", data$new_allocation[best_row]))
}
}
# Print final allocation summary
print(paste("Total rows:", nrow(data)))## [1] "Total rows: 259"## [1] "Rows with non-zero allocation: 173"## [1] "Total allocated: 563900"## [1] "Remaining tokens: 74.7047393597895"# Ensure that synced_subgraphs is updated with the new allocations
synced_subgraphs %<>%
left_join(data %>% select(deployment, new_allocation), by = "deployment") %>%
mutate(new_allocation = coalesce(new_allocation.y, new_allocation.x)) %>%
select(-new_allocation.x, -new_allocation.y)# Final sorted dataset
final_allocations_data = data %>% arrange(desc(new_allocation)) %>% filter(new_allocation > 0)
# show final data
print(final_allocations_data)## deployment signalled_tokens total_allocated_tokens rewards_proportion
## 1 Qmduy6o7VkWqJRgBqHUVVuPnowT6kAqWJmQWo5WecgKsV6 3432.595602 967210.0 0.003548966
## 2 QmXQw8YADkc9LtcYxBCZq1NHJFNt6whZZn7Q45XEbsTttD 2911.511003 823947.0 0.003533614
## 3 QmdGmCh3dL4McHWLzkoLeoHUZZP9c6WcJpwhf7FRCoFxqN 2987.317418 852986.0 0.003502188
## 4 QmYodpkEm88nPwAi1QBnS8UVnidGGQTP3cmpB4Fp1y4Aex 3024.780525 875612.0 0.003454476
## 5 QmW8NKJZoYsu8v5KuknpSHdANJ6e97fWy1S9D5qMTPiP5q 1980.071765 569311.0 0.003478014
## 6 QmVHVUTkiTEdF7SijmwvjaGYcPYgwwBBnPd6f177bqFuhd 558.230664 146115.0 0.003820488
## 7 QmXtiD3cPJ51x4cyzCy51Cw8f8V64AwoejRWZyR8EtaGyo 1428.978583 405696.0 0.003522289
## 8 Qme1543TivTdAobAPuqaRVBqkU4aaxV5v9rzws9qHBUErt 3441.026189 1006763.9 0.003417908
## 9 QmQJDsmwXbFYmS2z9kqeJq4xdKzXT9DX9ZijBiR9Pd2nDA 994.635478 278481.0 0.003571646
## 10 QmcaswVo8QTsnAFG29wBEGfxEyfPBmazTxxW8XvczPTGsR 4910.217365 1445230.0 0.003397534
## 11 QmW4qESMa8dD6KBimm5jgTdNxqYciyNxkXN9G6r1xzPhEc 297.000000 70914.0 0.004188172
## 12 QmQFCuG8KpTCZfCP5ayGWQWFeQH5gDSnZsUxs8aevhEmWZ 100.000000 17619.0 0.005675691
## 13 QmYxJsyzaZPbwmukAzdiQyLLagxdB8RW7kK3tLbCRMAx7y 712.261660 201256.0 0.003539083
## 14 QmZisJKkgZji3d4Qd5XcNBBNh7ArxnNGWkJ4cbikvBFdL3 495.000000 137772.0 0.003592893
## 15 QmcK5HmdqJjhJh18g8cTog2FYP7497T2nz25ESaWdyXk1Z 2971.055532 875499.8 0.003393554
## 16 QmVeh3KUMBRNTTTZDipWFEdPrxnnnHz3yzyugRvJJwzXrY 97.029900 19500.0 0.004975892
## 17 QmWRpp5fQJLvq83ziAid4pKo6DKUWdFvF5F9o9EZnKuPvP 1461.556940 426155.0 0.003429637
## 18 QmbcPkYbBDageNbGCvzGkAggLrmRygabbTKbbvknkoTeuW 396.000048 109051.0 0.003631329
## 19 Qmb1kpm8RzXRQUD8gBHSuPqd8HAkcQds577cauahwejDp9 52.663057 7300.0 0.007214117
## 20 QmQsTaSDMfskRNLcWEzaxufUj1wTxL3MR9af6jtomsQMvr 72.289406 13300.0 0.005435294
## 21 QmV5UryFDWzTjugAMH4W6qdZwZeFvrggvB5vMPbYU1x8Xb 104.364025 22822.0 0.004572957
## 22 QmRuPq9vmTwDPEQvUaoMGvB3zEPc5S3J1wuJPoW5WMTuVL 507.281153 143024.0 0.003546825
## 23 QmRg3LG1pCZB9xWFS6z2iAwDwgSRta7JjCFTfdyVvHg9w1 147.675671 36083.0 0.004092666
## 24 QmaE9BZUMPpc9mcJPVbG9xnz6xWKDVKJeUyndcLWCi1nKr 82.872677 16800.0 0.004932897
## 25 QmXvdb33USYAq4XfPyAYZXDMHH4DrUtdfeGUjJejo4TEL7 618.507091 177095.0 0.003492516
## 26 QmaoWjQg4gFLV6LH3R9Db7cuppGMNjB4MG6V2gXzqfJWj7 135.042822 34060.0 0.003964851
## 27 QmbrFgsMRiVSFLdpT5NveyioE1L6P5RCgy5eNYQvufhuLp 39.813206 5900.0 0.006748001
## 28 QmSDPnHzyW8yfnuhB423ssVY5r4bQrr5C1rXT8qMroNgmv 1641.189683 483365.0 0.003395342
## 29 QmXvh2JpDWbxC3hbhzU1UMYYwWCozQobhvpaTPq5HongSP 68.919595 15275.0 0.004511921
## 30 QmVFGoBvTa2YWE5QCDXFg1EeaYHKvds9voYQGd2PWRPdrZ 4901.496016 1455105.0 0.003368483
## 31 QmfW5ePHUCrQ51fCcbJUvtKd7NRPeHei16cR8m1taHku8U 49.500000 9900.0 0.005000000
## 32 QmZzoueJuv3dWLFgGSCoK5A1UomR61BhhhbtD9nPT6i2cw 49.500000 9900.0 0.005000000
## 33 QmVgFruqJqEUpY4FaDt39vrWjNGN9YUbdnzkr81izro9DM 652.251226 189520.0 0.003441596
## 34 Qmck1XDhZcCYCsdKYXy2DS7CS3kQfEHT67NVan5cfCmLC1 27.432854 3600.0 0.007620237
## 35 QmQTGTovKTt3pni5iRyeWKYkUM2sttEahfDd5yhwqqiBkW 307.647585 87342.0 0.003522333
## 36 QmZPVq8VQWGp6K8WMmExuLEEnekzhG39tJttymoY4CZoAe 117.299100 31346.0 0.003742076
## 37 QmfQWmNKDBuZ9xrhk9oe4X9dB8svZSdJAeQEdva4KbaK25 1676.621257 496120.0 0.003379467
## 38 QmZ9woCuBdvfqn8MSQSe22ivuYFXw1EW5EgSeVd2tETTmy 97.034200 25613.0 0.003788475
## 39 QmeWYQm1r39iZdKTzJYzNYHKmfNSWYEdq8cprPpfz44n88 17.417662 2100.0 0.008294125
## 40 QmWGUDcQG1e8bjp9LRcyWCo4xfWcVCdgApi59sY6rgdRkS 23.197445 4000.0 0.005799361
## 41 Qmba5sBE59n6GatmbyBQPHEt6NhB7fmRPVEEzSLQeaaVoC 591.239722 173241.0 0.003412816
## 42 QmZtTFVqw46STD3uwHLD1gQWwn11NS7NaSw1tzZfKCgewG 1982.340978 588048.0 0.003371053
## 43 QmRAbgoZ2mBpxqj4Z32KFaso8rsDhdm8KcwEQhWMzD8bTN 13.878682 2101.0 0.006605751
## 44 QmPiXvLKgVwTngiFGfi6p4sA75WYsizgbbdNnGw6nyhNMX 58.806000 15400.0 0.003818571
## 45 QmcN5d7N4Xe6tL9pW9U71jzo9sPSuioaJeCqbbAC1PhWQD 20.207914 4000.0 0.005051979
## 46 QmWZrJghWFFqFaYteYmoFoBaMeancixDhU1qe2ZpSQ9Hir 12.545925 1900.0 0.006603118
## 47 Qma9bvmT39fCkG5woFkrXLebPUbbXGSBcXQD9Z3vArJyso 11.897579 1800.0 0.006609766
## 48 QmSxmGgUGwYskjkbzQZEjLC8jHbd1hjdcV8ifAiQRtDwZL 11.898762 1801.0 0.006606753
## 49 Qmc1mFtNdvykyysPZd8sAMz3tmvyeRWMDxpmggukTx7g1x 11.892538 1801.0 0.006603297
## 50 Qmap5Ycg96Cb4wikiraf4hQTRMRi2dERKVff9RPruaKnrd 11.892536 1801.0 0.006603296
## 51 QmPjeesdXSYBstNqJMDjqkpZP1CLtotR2y7Lom75JhBuG9 24.837083 5700.0 0.004357383
## 52 QmVf3FcBasTsmATxekgL1QQKsK72CR9TG8zHwS1tJ5Tx4p 10.902607 1601.0 0.006809873
## 53 QmcQe363orcX5u5DtLvjGrjwfCR89vfrA9YLjHWZc3zutD 10.638188 1600.0 0.006648868
## 54 QmdVmkErFM4RxYYTeoq786cnYkR41FmjPTpA7QzD8kfhA2 17.382444 3600.0 0.004828457
## 55 QmTScm43kN2xfdKK9h7nWs3a7c7ypjPjtKJe1NsAsCfnCL 9.850500 1500.0 0.006567000
## 56 QmVEWdQQTJNoURcdefRQm8JEZo7TFtLA1dwkMRrBs2585Z 10.164842 1501.0 0.006772047
## 57 QmZ119nJYGVhsSSP8cCeBY1YpwoQCKBFhfvoxAy7fWs3Qi 65.347475 18302.0 0.003570510
## 58 QmTsgX4C2sAmvDg6C3pKVxKpHGkF74vrNGJqGCCZ3qJP2f 3.770774 100.0 0.037707739
## 59 QmeHZcGmj7wrJMHRrPVDGiVaprs7t21n1jjeUHhPUeMRcA 2.161817 0.0 Inf
## 60 QmbGxMtiEqy3j6DaPDABApKm9jKQZJQ6oNkbz1UtTDJxmR 2.467061 0.0 Inf
## 61 QmcQSBBPpJgDcYrKyGy6oGP9BN7FcczRv32t1cfKbtWuRU 394.340542 117429.0 0.003358119
## 62 Qme5nGiCCDJcDtA5xoJ4iPAtjQgBfLekqRvxXesKCiH8XX 1577.792110 470475.0 0.003353615
## 63 QmSMf99YyUBJuSf2JroPDHDpNuR54esqq3emudg9vaoWKP 3231.514714 972059.7 0.003324399
## 64 QmYAafEPam85rVLcDRZqGXUmwrfj34GfZ91WK751LsphrC 3174.168800 955039.0 0.003323601
## 65 QmYNYWiVGHkRFjYMULxyECVgsaYfXDibEhjBx9sWTtsz9c 2471.881400 748549.0 0.003302231
## 66 QmQ6u3oHrvuMD2CcrWe2LuzagbFbxwqWfSWCvAuCnhgeLM 1211.346194 368698.0 0.003285470
## 67 QmUS8ekFtGby8TQhxt7i3RoQySb1QVtKW4VBU2CG2pS9Eh 909.267469 279771.0 0.003250042
## 68 QmcMk21Jdeng7SWv8yqhskhKbykqgH12vE64cnNhzSbXJ8 2044.087994 634392.5 0.003222119
## 69 QmQamfGnZ1bwt3Y1EzpSfxSG1HsdwpJfgpJGqdMMoPheFz 3182.515247 1017346.0 0.003128253
## 70 QmS1nPRvYghS9UBrqFWP3W7XNnrrJCk9PR9CwiP82fiUGY 3049.699219 985114.0 0.003095783
## 71 QmdorsAVanFXUyFQsfKaTJ6YykhH5PpJc9TG4mSBppPfsU 8300.725007 2682944.0 0.003093887
## 72 QmbnwSqo4RSDUuP7Kf5WAtj4DLCWFDhyhTRPM2drthaGVQ 4913.554881 1606303.0 0.003058922
## 73 Qmdv8zRyxPC7cmi5fpTdcViptimbCwaHVdb71sXPdWzUhL 768.657726 255529.0 0.003008104
## 74 Qmc4J3epkBZggHMA3dTSX5TPi4Eq2bn4JtZM17VDihMQiV 3237.492109 1083357.0 0.002988389
## 75 QmWXhLkz6fRJwLyFmgBKVu2NyMD6MqtGhcrt8bNcm1xpuU 6637.414759 2226502.4 0.002981095
## 76 Qmb27RY3RqP98UMKbTgScf6F7hhokfMuS9fV7VAtPiZHwF 8605.676263 2952381.4 0.002914825
## 77 QmQoJMZT4bLLUNvf9Z15VNub6Wk1oq6VcMm34omrrVZevs 1107.542897 400000.0 0.002768857
## 78 QmbzgPPc7pHwRetjTjGY7tfNG7sfrbkDpbhYFewtisZpvc 6186.104957 2267010.9 0.002728750
## 79 QmdkY9X6Wt3GXA67NYBMJ2NRX6rUsFyQkhk21cqGVZn1sf 19182.173948 7272134.3 0.002637764
## 80 QmcaboaEjatWFRe4fUcxXbajtEVJwr2emvdrA6VdjfXGyT 2749.110964 1065174.0 0.002580903
## 81 QmUhiH6Z5xo6o3GNzsSvqpGKLmCt6w5WzKQ1yHk6C8AA8S 2518.447688 1052947.3 0.002391808
## 82 QmYWvmm6rxvAk8E3cA6iXPhC6ETBLJFuEw8maYJ7YV9ATx 20447.448637 8824698.7 0.002317070
## 83 QmYXL6XeXyGC2DCnoQ45ApG68pi8irCZdRdtFx69FetRDd 18903.509363 8177120.0 0.002311756
## 84 QmXZiV6S13ha6QXq4dmaM3TB4CHcDxBMvGexSNu9Kc28EH 63192.484713 36449331.2 0.001733708
## 85 QmVUMSVtJ4uAfUBBcgYJ9Ed43j27TZirZd7gzv6cUVqT6x 0.000000 0.0 0.000000000
## 86 QmQfH6zEaoHns9d3jNwko8QXmb8xVofQKPdCLop6qKMUnn 5.900000 900.0 0.006555556
## 87 QmXUTLBibdxWtk37dxJLcchSZfRz7FxKHdqrQwc6y5zwas 5.880600 900.0 0.006534000
## 88 QmNeyrYWm2HgmJ1EUToNfacWUaiDmTnV32EJa9TvndVg9f 5.940000 900.0 0.006600000
## 89 QmaRd3T8BTfPMNMrxo9uehkZFuHeqavLxUhAXTnQ3xPbGj 68.328874 19570.0 0.003491511
## 90 QmcBxAXivw8bkNAhqavTzjEtbH8KSeS8NaSUiPU2fSbqWQ 4.395600 700.0 0.006279429
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## 92 QmY3km7en3EKFN36tGDp1rgpRJ6VicFYsCUEHjSciHvzPR 2079.000289 618735.0 0.003360082
## 93 QmcZaxZhpJ3HUKxtV1HD29TFf52GprC6HRbJkC8w6soizb 3.000000 400.0 0.007500000
## 94 QmeHJ8Wyk7HpAtFLSNNraVkoiqv78MheDJfLpbdGBb8U2L 3.000000 400.0 0.007500000
## 95 QmT4t8qY58x1M6wyGrY4DmMsxa6oEYWurP1TULL1Ca3GTc 2.970000 400.0 0.007425000
## 96 QmZhm7MB9vuPkBeH5yNziz7jsUBZ8eHcAZYDo1n3EXjt9R 1.980000 300.0 0.006600000
## 97 QmWMLVUQCzsqvQjjaAncpRdkjSVnRBJyZ92a3YAUj9Ynm8 2.000000 300.0 0.006666667
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## 99 QmNNwHNNs2MTtXnPbcLyRZ8KPnrg3CgXHfyMMVmx4qc3Rt 101.662307 30024.0 0.003386035
## 100 QmVQtVpxv9P3Lf1TtLDufqgnkw5RHCMubgsGTgq2orbCfU 118.925752 35158.0 0.003382609
## 101 QmcFx1aE7VhWXkQhcZyrzfm8AGkSzSWeid2FjTEkYBfKou 1.940612 300.0 0.006468708
## 102 QmZM8HcNZRZDNTKf4wGZmiSJRX5EQNaZcHrG2BpjqDLkUE 1.000000 100.0 0.010000000
## 103 QmaBvS459yzfu5ykA9eL6S8t1J8PeCm8n3112DVWqSNis5 1.000000 100.0 0.010000000
## 104 QmT8Y6WqcTD6iR6DbTzfwQUiar5FtuZnqJhT3t28dp4SXD 1.000000 100.0 0.010000000
## 105 QmaJcXuvGW3GST8UfjTHSXSpmJ4YupCXfyYq6K2L99FX1w 1.000000 100.0 0.010000000
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## 108 QmXYNUvUc9CriaaCfkevhjG8389GqYXPEM7S6zQTq1dB8P 1.000000 100.0 0.010000000
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## 117 QmSfKsoBUHcezd6uhhftvFAjLQm1JvbJLRPPGHRvzTYwna 1.000000 100.0 0.010000000
## 118 QmdiykT6LaGCiqPwrpZMsYzXh2PExBbnR3fcLj15q5cdXd 1.000000 100.0 0.010000000
## 119 QmWjKBaLENbsi2BPzUxpdiX7FUmw1eZqjEfLNv83RNb6YD 1.000000 100.0 0.010000000
## 120 Qmej11EGGzNGfbnEKbda3tHJogv9fwt2Rk3GRWNd8kY3Uq 1.000000 100.0 0.010000000
## 121 QmSj3cbVeNzXywVHQ1Ary5spgsa6LyrWcnhyRyLBPb4GKz 1.000000 100.0 0.010000000
## 122 QmTEQP9HPHsgQiKdNREVHS7F4waxEwCeq32eTU5hCRXgN4 1.000000 100.0 0.010000000
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## 124 QmSfYVdY2taCQ7DtLYXo19CkCdNWc8HN1oUuVjEJhoKqhz 1.000000 100.0 0.010000000
## 125 Qmb4YEj9qpoZd8iLVLWPdfVMsG3o5qBcFxZHXucGrUnd2w 1.000000 100.0 0.010000000
## 126 QmcBbQDQjqg2BJWLGGXmdgsFBMZyQ59KWS42Z9NMcXe1nu 1.000000 100.0 0.010000000
## 127 QmPHGCrBbDG2UX4kDxk4mMrC9cFKCYVQFWFoZVPDwuy31m 1.000000 100.0 0.010000000
## 128 QmP7KPubjkDA6EDr5rKNUbwzsEdGwNzr1wNRZctkfxix1x 1.000000 100.0 0.010000000
## 129 QmWNScL6xFVfFConNbNLobijUEGaWofqRewXgriD2QmhGZ 1.000000 100.0 0.010000000
## 130 QmdBdHWLMJGJYscE2SVstqTuAJxrjxvepWg1hEbKti4dYh 1.000000 100.0 0.010000000
## 131 QmPCJRrA1Fwbusu5oCu3zTaDoGzaPq3zc4FnhNdj2XDMFj 1.000000 100.0 0.010000000
## 132 QmXHxAEB493jUAcBVhwCd2odCdF2mh96GKndhaeHYGEoKG 1.000000 100.0 0.010000000
## 133 Qma4JUhxBgdoMGHs5MNnqWPbk2d3mE1SvqdPjRT8qwooyK 1.000000 100.0 0.010000000
## 134 QmevNFrcEkZcb7bji3xYuU5UWP7TZ9kurTJ4mW5VebGc9S 1.000000 100.0 0.010000000
## 135 QmaTJb3mqt56BpjYzrH6YbQN1v38px64B3DMpXW7V4kVWD 1.000000 100.0 0.010000000
## 136 QmdZDNidSJ1j8Ka2hQYjdiJZv3jhhqMG28MytKUDV5xwv8 1.000000 100.0 0.010000000
## 137 QmcBiwphyMWgxqM3YFDrtG1JFq2Bi8qKSAjxvfoStJDieU 1.000000 100.0 0.010000000
## 138 QmV4wyo47CoTejAVW6Cw4EVVc4pL4CWNmFWHwrwTsJrjiK 1.000000 100.0 0.010000000
## 139 QmatbdjawELyy69kyuG7TC8RKebsdgMoRpwX5ZYy9rURbC 1.000000 100.0 0.010000000
## 140 QmWdUm7HxFFXysSLGvFB1zjaSeYR8SPiLDo2M4gopdHajK 1.000000 100.0 0.010000000
## 141 QmS7wExrveyxpN5g6sfykDUkVHX7oGqvMr8Mw82SueXU8S 1.000000 100.0 0.010000000
## 142 QmbK8eiivKro8XT9xALjQSbDtcdTBPVYzZeC29gUua7P7F 1.000000 100.0 0.010000000
## queries_per_indexer new_allocation potential_rewards
## 1 1205.125000 55600 0.003356372
## 2 181.000000 43500 0.003356027
## 3 39516.272727 37100 0.003355835
## 4 37568.700000 25500 0.003356347
## 5 213.500000 20700 0.003355423
## 6 237148.333333 20200 0.003354449
## 7 431771.625000 20100 0.003355229
## 8 252.300000 18400 0.003356235
## 9 22801.500000 17900 0.003354803
## 10 10686.923077 17700 0.003356197
## 11 13.000000 17600 0.003351615
## 12 87430.500000 12200 0.003342358
## 13 11.400000 11000 0.003354092
## 14 3509.500000 9800 0.003352023
## 15 156.307692 9700 0.003355988
## 16 3203.000000 9500 0.003334361
## 17 122677.285714 9400 0.003354849
## 18 6.300000 8900 0.003354483
## 19 225390.000000 8300 0.003354335
## 20 43.500000 8300 0.003331309
## 21 218786.500000 8200 0.003353384
## 22 71765.545455 8200 0.003352285
## 23 64938.454545 7900 0.003349946
## 24 83585.500000 7800 0.003355169
## 25 35332.538462 7200 0.003354251
## 26 54463.363636 6100 0.003354268
## 27 50.000000 5900 0.003345648
## 28 103522.117647 5600 0.003355770
## 29 26.000000 5200 0.003349677
## 30 55.846154 5200 0.003356258
## 31 780.500000 4900 0.003322148
## 32 269.500000 4900 0.003322148
## 33 28748.727273 4800 0.003354857
## 34 176436.000000 4600 0.003305163
## 35 25437.181818 4300 0.003353400
## 36 1972.000000 3700 0.003337481
## 37 5500.000000 3500 0.003355121
## 38 2451.666667 3200 0.003356075
## 39 50194.500000 3000 0.003349550
## 40 61558.500000 3000 0.003267246
## 41 38804.600000 3000 0.003352821
## 42 7.142857 2500 0.003356214
## 43 27.000000 2100 0.003226850
## 44 74.000000 2100 0.003341250
## 45 24.000000 2000 0.003312773
## 46 54.000000 1900 0.003216904
## 47 51.000000 1800 0.003215562
## 48 28.000000 1800 0.003215013
## 49 23.500000 1800 0.003213331
## 50 27.000000 1800 0.003213330
## 51 4330.000000 1700 0.003311611
## 52 27.500000 1600 0.003302819
## 53 240187.500000 1600 0.003223693
## 54 22.500000 1600 0.003279706
## 55 1529.000000 1500 0.003177581
## 56 28.500000 1500 0.003277924
## 57 28.666667 1200 0.003333715
## 58 152483.000000 1100 0.002900595
## 59 108452.000000 1000 0.001965289
## 60 287201.000000 1000 0.002242782
## 61 126135.600000 1000 0.003326954
## 62 157502.647059 1000 0.003345793
## 63 126565.687500 1000 0.003320642
## 64 124488.153846 1000 0.003319778
## 65 114561.285714 1000 0.003297385
## 66 65933.166667 1000 0.003275697
## 67 231820.500000 1000 0.003237313
## 68 50866.266667 1000 0.003216542
## 69 230148.800000 1000 0.003124874
## 70 141770.230769 1000 0.003092330
## 71 180819.285714 1000 0.003092619
## 72 66396.777778 1000 0.003056828
## 73 128124.000000 1000 0.002995210
## 74 233633.444444 1000 0.002985358
## 75 436114.571429 1000 0.002979623
## 76 641672.388889 1000 0.002913740
## 77 182480.500000 1000 0.002761264
## 78 156356.214286 1000 0.002727426
## 79 1253062.117647 1000 0.002637365
## 80 936031.055556 1000 0.002578241
## 81 99344.882353 1000 0.002389312
## 82 212557.100000 1000 0.002316782
## 83 205762.625000 1000 0.002311445
## 84 712423.555556 1000 0.001733655
## 85 Inf 1000 0.000000000
## 86 49.000000 900 0.003105263
## 87 48.000000 900 0.003095053
## 88 50.000000 900 0.003126316
## 89 24.500000 800 0.003338001
## 90 48.000000 700 0.002930400
## 91 26.500000 700 0.003306151
## 92 4.461538 700 0.003355743
## 93 138.000000 400 0.003333333
## 94 138.000000 400 0.003333333
## 95 50.000000 400 0.003300000
## 96 51.000000 300 0.002828571
## 97 100.000000 300 0.002857143
## 98 102.000000 300 0.002857143
## 99 8129.800000 300 0.003341517
## 100 35569.272727 300 0.003344557
## 101 24.000000 200 0.003234354
## 102 60.000000 100 0.003333333
## 103 49.000000 100 0.003333333
## 104 55.000000 100 0.003333333
## 105 50.000000 100 0.003333333
## 106 50.000000 100 0.003333333
## 107 48.000000 100 0.003333333
## 108 48.000000 100 0.003333333
## 109 48.000000 100 0.003333333
## 110 50.000000 100 0.003333333
## 111 50.000000 100 0.003333333
## 112 51.000000 100 0.003333333
## 113 48.000000 100 0.003333333
## 114 52.000000 100 0.003333333
## 115 48.000000 100 0.003333333
## 116 50.000000 100 0.003333333
## 117 50.000000 100 0.003333333
## 118 48.000000 100 0.003333333
## 119 50.000000 100 0.003333333
## 120 47.000000 100 0.003333333
## 121 51.000000 100 0.003333333
## 122 50.000000 100 0.003333333
## 123 50.000000 100 0.003333333
## 124 50.000000 100 0.003333333
## 125 48.000000 100 0.003333333
## 126 48.000000 100 0.003333333
## 127 50.000000 100 0.003333333
## 128 50.000000 100 0.003333333
## 129 53.000000 100 0.003333333
## 130 49.000000 100 0.003333333
## 131 50.000000 100 0.003333333
## 132 50.000000 100 0.003333333
## 133 48.000000 100 0.003333333
## 134 49.000000 100 0.003333333
## 135 50.000000 100 0.003333333
## 136 44.000000 100 0.003333333
## 137 51.000000 100 0.003333333
## 138 51.000000 100 0.003333333
## 139 48.000000 100 0.003333333
## 140 50.000000 100 0.003333333
## 141 2287.000000 100 0.003333333
## 142 49.000000 100 0.003333333
## [ reached 'max' / getOption("max.print") -- omitted 31 rows ]