5 Indexing Rewards Proportions
Now that we have a list of subgraphs we have synced, we want to figure out which ones offer the best indexing rewards returns. To do this, we want to pull in the number of allocated tokens on each subgraph deployment, as well as the curation signal. From there, we will be able to figure out which ones have the largest amount of signal relative to the allocation sizes, and maximize our returns.
This is the base query we will use:
query = '
query MyQuery($created_at_gt: Int!) {
subgraphDeployments(
first: 1000
orderBy: createdAt
orderDirection: asc
where: {activeSubgraphCount_gt: 0, deniedAt: 0, createdAt_gt: $created_at_gt}
) {
ipfsHash
indexerAllocations(where: {poi: null}) {
allocatedTokens
indexer {
id
}
}
signalledTokens
createdAt
deprecatedSubgraphCount
}
}'Next we initialize an empty dataset with tibble(), and initialize created_at_gt to 0. The GraphQL endpoints are limited to a maximum value of 1,000 rows at a time, so we will want to extract the data in parts and construct a complete dataset. There will never be more than 1,000 subgraphs created in the exact same second provided by createdAt, so we can use this as a filter to build a full dataset.
At the start, the filter will be set to 0, which will give us the first 1,000 created subgraphs. After that point, we can get the maximum createdAt value, and use that to only get subgraphs created after the maximum timestamp from the previous batch. This way we build a full dataset without pulling duplicate data.
The implementation isn’t overly important. The only important thing to understand is that we are pulling the signal and allocation sizes for each subgraph that exists in this step:
# initialize empty dataset
rewards_info = tibble()
# set created_at_gt variable to 0 to start before we overwrite it with the new maximum as data is pulled
variables = list(created_at_gt = 0)
# keep repeating the same query until we
repeat {
# Send POST request to the GraphQL API
response = POST(url, body = list(query = query, variables = variables), encode = "json")
# Parse the JSON response
content = content(response, "text", encoding='UTF-8')
json_data = fromJSON(content, flatten = TRUE)
data = json_data$data$subgraphDeployments
# if we are out of rows to collect, break the repetition and move on to the next step
if (length(data) == 0) {
break
}
# Process the data
processed_data = tibble(data) %>%
# calculate and clean up columns needed. Start by calculating count of indexers per subgraph
mutate(unique_indexers = map_int(indexerAllocations, ~length(unique(.x$indexer.id))),
# allocated tokens per subgraph
total_allocated_tokens = map_dbl(indexerAllocations, ~sum(as.numeric(.x$allocatedTokens))) / 10^18,
# signalled tokens per subgraph
signalled_tokens = as.numeric(signalledTokens) / 10^18,
# convert created at timestamp to datetime
created_at = as.POSIXct(as.numeric(createdAt), origin = "1970-01-01"),
# track whether the subgraph is deprecated
deprecated_subgraph_count = deprecatedSubgraphCount) %>%
# rename ipfsHash to deployment and select only the columns needed
select(deployment = ipfsHash, unique_indexers, total_allocated_tokens, signalled_tokens,
created_at, deprecated_subgraph_count)
# union the newly pulled data to the full dataset
rewards_info = bind_rows(rewards_info, processed_data)
# if less than 1,000 rows are collected this means there's no more data to collect and can move on
if (nrow(processed_data) < 1000) {
break
}
# set the new maximum subgraph creation time to use for the next data pull
variables$created_at_gt = max(as.numeric(data$createdAt))
}
# Show and confirm full row count
paste("Fetched indexer counts for", nrow(rewards_info), "subgraphs")## [1] "Fetched indexer counts for 9134 subgraphs"# join rewards info to the list of synced subgraphs
synced_subgraphs = synced_subgraphs %>%
left_join(rewards_info, by = 'deployment') %>%
select(deployment, unique_indexers, signalled_tokens, total_allocated_tokens) %>%
mutate(rewards_proportion = signalled_tokens / total_allocated_tokens)## deployment unique_indexers signalled_tokens total_allocated_tokens
## 1 QmZWfqD8Ve9u19nvC6sBGHChDGAm78KquzNfaPSyKMNNaX 5 9801.073614 4232407.0
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## 17 QmQQsMHsa2oiumsGkB1wPnkRsBQhH1y3JhVEmRZ7yMcedW NA NA NA
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## 148 QmTsgX4C2sAmvDg6C3pKVxKpHGkF74vrNGJqGCCZ3qJP2f 1 3.770774 0.0
## 149 QmRGr89rJAfDTHhqx7FXeY2rKVkCtgoqisnXGWXJoC2v4d 8 8317.197286 3034921.6
## 150 QmVFGoBvTa2YWE5QCDXFg1EeaYHKvds9voYQGd2PWRPdrZ 13 4901.496016 1449905.0
## 151 QmYsSBrUCrPMkFAASNEP4h6CeqL2viYqWy4gH1Sxnftf5J 12 2973.326164 930011.9
## 152 Qme4i64hYrHrAgwuHXfnMhkyeYRkT89uXDuvHQMxX2woeR 1 1.000000 0.0
## 153 QmY3km7en3EKFN36tGDp1rgpRJ6VicFYsCUEHjSciHvzPR 13 2079.000289 618035.0
## 154 Qmaa4sr6PG4agPMTZfpRnALEhVJ5argjQ41UJ5pELnwU7Z 8 1360.448784 502558.0
## 155 QmbWhGfCrewoQvwJ7GSvAm51AX8Z3rb8q3fsJNvAFB9svz 1 1.000000 0.0
## 156 QmYAafEPam85rVLcDRZqGXUmwrfj34GfZ91WK751LsphrC 13 3174.168800 955039.0
## 157 Qmaxcs5FKtvm2hgKRFPSfoQf9w4r5squ3XixKG6YwG1R14 11 999.387368 305959.5
## 158 QmQfH6zEaoHns9d3jNwko8QXmb8xVofQKPdCLop6qKMUnn 1 5.900000 0.0
## 159 QmbnwSqo4RSDUuP7Kf5WAtj4DLCWFDhyhTRPM2drthaGVQ 9 4913.554881 1606303.0
## 160 QmWiGJfHJq3cdPzJrtoiraAJ4CymF5yTW2JZM6pMqGtifd 1 1.000000 0.0
## 161 QmVEWdQQTJNoURcdefRQm8JEZo7TFtLA1dwkMRrBs2585Z 2 10.164842 1.0
## 162 QmVpFJ5tfc9YPDS8TaRj2ocsq9CGKYhRem2HMbCRWMBLRE 3 1980.474672 617832.0
## 163 QmaL7SDPYXmad6VwKzS3f3q6URqwWWzy9LoaMrimi2wynY 9 9935.115977 4290290.4
## 164 Qma9DHbg3q1QLbUwnbuM6s16c2eMY4KviLs4MzvzV7TkDV 1 1.000000 0.0
## 165 QmcoF5wVuvNUTHNP8JozPQQfmxHhyKPVK1ajN6HhY3PB1R 7 546.478289 166501.3
## 166 QmfUgbmMzMtFZ1ZEjuF9dBKCaFybHj2VkSozNBAT2A6QLZ 1 1.000000 0.0
## 167 QmbrFgsMRiVSFLdpT5NveyioE1L6P5RCgy5eNYQvufhuLp 1 39.813206 0.0
## 168 QmRuPq9vmTwDPEQvUaoMGvB3zEPc5S3J1wuJPoW5WMTuVL 11 507.281153 135824.0
## 169 QmVtNnXse2zWVRsfNGW8SxvipS9SVwC7g9qyQMHvUQbqAP 5 9472.496760 4090517.3
## 170 QmRAbgoZ2mBpxqj4Z32KFaso8rsDhdm8KcwEQhWMzD8bTN 2 13.878682 1.0
## 171 QmXUTLBibdxWtk37dxJLcchSZfRz7FxKHdqrQwc6y5zwas 1 5.880600 0.0
## 172 QmYzXQTUFhqwnDFxS1ya7xjMSHZmvu7VdM8xRtHXmTrNs9 6 1079.110830 326124.0
## 173 QmcADSmM91yAFC45g6cuc5UBP2d3g7Q3FzqmSFLD5Lu9sK 8 5941.010933 1926138.0
## 174 QmXZiV6S13ha6QXq4dmaM3TB4CHcDxBMvGexSNu9Kc28EH 18 63192.484713 36449331.2
## 175 QmYPnsfXt9W6dMqq6PPfhfEZ6bXv8Tti7fqtSh9EpnLfBe 6 1981.000015 609891.2
## 176 QmbjYuCtfduXuckjswRHf9qdT1H7J6HNSKtVSijYizXsBh 8 486.375619 145990.0
## 177 QmSDPnHzyW8yfnuhB423ssVY5r4bQrr5C1rXT8qMroNgmv 17 1641.189683 478765.0
## 178 QmSdc5FFbLzF436KEqYjCn3BLL5HN69ASNucTFnFqtGUUn 1 1.000000 0.0
## 179 QmUS8ekFtGby8TQhxt7i3RoQySb1QVtKW4VBU2CG2pS9Eh 6 909.267469 279771.0
## 180 QmeHZcGmj7wrJMHRrPVDGiVaprs7t21n1jjeUHhPUeMRcA 1 2.161817 0.0
## 181 QmT4t8qY58x1M6wyGrY4DmMsxa6oEYWurP1TULL1Ca3GTc 1 2.970000 0.0
## 182 QmcUSTRRpeyfETY1qTD9HzDfGdxeo7YqNfG9VAvYYTBRzv 7 6656.673943 2423390.8
## 183 QmZqKd3ZvqwWcLsydUdQoW3gEJS9KthEEEqBVKC9qkLXbe 1 1.000000 0.0
## 184 QmcYjagkzh7KiFKoLzeXs5SGynz7iMPVRnSdVPYiCUrLGq 6 41976.391649 25050877.7
## 185 QmVagvbwFHWGEBUxhXW1DZWFNvvwYAcUvbJGVorGXetsc7 1 1.000000 0.0
## 186 QmdX1F96DH26zMfyCnp8uVNbbJUJiq7UAjXeVvm2aDvwwr 1 1.000000 0.0
## 187 QmbPUyAyzyVVFpaJCS7T9g6imVwp9LRJLP9ZWumwUjQeKj 1 1.000000 0.0
## 188 QmYRXhV2rQ8YdJLcaATXdKqL9MkRTrSVAFyfXGCPutBSHC NA NA NA
## 189 QmaRd3T8BTfPMNMrxo9uehkZFuHeqavLxUhAXTnQ3xPbGj 2 68.328874 18770.0
## 190 Qme5nGiCCDJcDtA5xoJ4iPAtjQgBfLekqRvxXesKCiH8XX 17 1577.792110 470475.0
## 191 QmXtiD3cPJ51x4cyzCy51Cw8f8V64AwoejRWZyR8EtaGyo 8 1428.978583 386596.0
## 192 QmNeyrYWm2HgmJ1EUToNfacWUaiDmTnV32EJa9TvndVg9f 1 5.940000 0.0
## 193 QmWoG43UWCqZpM3ZCvdvjAfeNuYxs3kEeME9aCytkSZAGE 11 2971.009187 896108.0
## 194 QmYWvmm6rxvAk8E3cA6iXPhC6ETBLJFuEw8maYJ7YV9ATx 10 20447.448637 8824698.7
## 195 Qmck1XDhZcCYCsdKYXy2DS7CS3kQfEHT67NVan5cfCmLC1 1 27.432854 0.0
## 196 Qmb1kpm8RzXRQUD8gBHSuPqd8HAkcQds577cauahwejDp9 1 52.663057 0.0
## 197 QmfE7XHYSeZ6ptESATYb1FtW4sqtWxiVCjXTbPY98ajqBe 1 1.000000 0.0
## 198 QmcN5d7N4Xe6tL9pW9U71jzo9sPSuioaJeCqbbAC1PhWQD 2 20.207914 2000.0
## 199 QmQSNjw39ij4nmjNmpsC6t3fkBvjdfMujK52bkRHExuyM6 2 4.962533 1.0
## 200 QmbcPkYbBDageNbGCvzGkAggLrmRygabbTKbbvknkoTeuW 10 396.000048 100151.0
## rewards_proportion
## 1 0.002315721
## 2 0.001706691
## 3 0.003308895
## 4 0.003067800
## 5 0.003322641
## 6 0.003266404
## 7 0.003481538
## 8 0.003194710
## 9 0.003326646
## 10 0.003385446
## 11 0.003194384
## 12 0.003498598
## 13 0.003308902
## 14 0.003181254
## 15 0.001998240
## 16 0.003120427
## 17 NA
## 18 0.003194431
## 19 0.003095121
## 20 Inf
## 21 Inf
## 22 Inf
## 23 Inf
## 24 0.003730569
## 25 Inf
## 26 NaN
## 27 Inf
## 28 Inf
## 29 Inf
## 30 Inf
## 31 Inf
## 32 0.010000000
## 33 Inf
## 34 Inf
## 35 NA
## 36 0.003321819
## 37 Inf
## 38 Inf
## 39 Inf
## 40 Inf
## 41 0.003320510
## 42 0.005570769
## 43 Inf
## 44 NA
## 45 Inf
## 46 0.006680580
## 47 Inf
## 48 Inf
## 49 Inf
## 50 0.006840655
## 51 Inf
## 52 Inf
## 53 Inf
## 54 0.005550501
## 55 Inf
## 56 Inf
## 57 NA
## 58 Inf
## 59 Inf
## 60 0.003230159
## 61 0.003868034
## 62 0.008287268
## 63 0.003403478
## 64 Inf
## 65 Inf
## 66 Inf
## 67 Inf
## 68 0.003640526
## 69 Inf
## 70 0.004872746
## 71 0.003477552
## 72 Inf
## 73 Inf
## 74 Inf
## 75 0.003531027
## 76 Inf
## 77 0.003285470
## 78 Inf
## 79 Inf
## 80 0.003059566
## 81 0.010000000
## 82 0.002637764
## 83 0.003260440
## 84 Inf
## 85 Inf
## 86 0.003558096
## 87 0.003142382
## 88 0.003326903
## 89 0.003064577
## 90 Inf
## 91 0.003420209
## 92 0.002542345
## 93 NA
## 94 0.003411720
## 95 0.004398461
## 96 0.003042280
## 97 0.003037306
## 98 Inf
## 99 0.003250590
## 100 Inf
## 101 0.004204966
## 102 0.002700271
## 103 NA
## 104 Inf
## 105 0.003302231
## 106 0.002391808
## 107 0.002988389
## 108 0.003358119
## 109 0.003008104
## 110 Inf
## 111 0.004663081
## 112 0.002687733
## 113 0.002240545
## 114 0.003093887
## 115 0.002768857
## 116 Inf
## 117 0.003704723
## 118 0.014457881
## 119 Inf
## 120 Inf
## 121 0.008691222
## 122 0.002914825
## 123 Inf
## 124 0.003439660
## 125 0.002981095
## 126 0.009900000
## 127 11.892535565
## 128 Inf
## 129 0.011598723
## 130 0.002580903
## 131 0.003192484
## 132 Inf
## 133 0.003908547
## 134 0.003308896
## 135 0.003816992
## 136 Inf
## 137 Inf
## 138 NA
## 139 0.003609245
## 140 0.009900000
## 141 0.002951960
## 142 0.003431573
## 143 0.003182851
## 144 0.002893281
## 145 0.002728750
## 146 0.002311756
## 147 0.001859529
## 148 Inf
## 149 0.002740498
## 150 0.003380564
## 151 0.003197084
## 152 Inf
## 153 0.003363888
## 154 0.002707048
## 155 Inf
## 156 0.003323601
## 157 0.003266404
## 158 Inf
## 159 0.003058922
## 160 Inf
## 161 10.164842165
## 162 0.003205523
## 163 0.002315721
## 164 Inf
## 165 0.003282126
## 166 Inf
## 167 Inf
## 168 0.003734842
## 169 0.002315721
## 170 13.878682017
## 171 Inf
## 172 0.003308897
## 173 0.003084416
## 174 0.001733708
## 175 0.003248120
## 176 0.003331568
## 177 0.003427965
## 178 Inf
## 179 0.003250042
## 180 Inf
## 181 Inf
## 182 0.002746843
## 183 Inf
## 184 0.001675646
## 185 Inf
## 186 Inf
## 187 Inf
## 188 NA
## 189 0.003640324
## 190 0.003353615
## 191 0.003696310
## 192 Inf
## 193 0.003315459
## 194 0.002317070
## 195 Inf
## 196 Inf
## 197 Inf
## 198 0.010103957
## 199 4.962532739
## 200 0.003954030
## [ reached 'max' / getOption("max.print") -- omitted 68 rows ]Nice! Next, we want to also consider how many queries are served on each subgraph before we allocate towards them, which we will do in the next section