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 9169 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 QmPrUvv9vfDrBoZ2rU6CGeu23FijhHtnKiVFAvarVRHzYx 1 1.000000 0.0
## 2 QmTEQP9HPHsgQiKdNREVHS7F4waxEwCeq32eTU5hCRXgN4 1 1.000000 0.0
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## 5 QmcaboaEjatWFRe4fUcxXbajtEVJwr2emvdrA6VdjfXGyT NA NA NA
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## 149 QmPiXvLKgVwTngiFGfi6p4sA75WYsizgbbdNnGw6nyhNMX 2 58.806000 13300.0
## 150 QmZhm7MB9vuPkBeH5yNziz7jsUBZ8eHcAZYDo1n3EXjt9R 1 1.980000 0.0
## 151 Qmduy6o7VkWqJRgBqHUVVuPnowT6kAqWJmQWo5WecgKsV6 11 3433.186067 962298.0
## 152 QmWRpp5fQJLvq83ziAid4pKo6DKUWdFvF5F9o9EZnKuPvP 9 1462.758657 405373.0
## 153 QmPjeesdXSYBstNqJMDjqkpZP1CLtotR2y7Lom75JhBuG9 2 24.837083 4000.0
## 154 QmaTJb3mqt56BpjYzrH6YbQN1v38px64B3DMpXW7V4kVWD 1 1.000000 0.0
## 155 QmQBU2EcWk8X6KuTczQnqtPFyi49XKfvSQSZprn4RWhBZ5 16 12273.645516 4093716.3
## 156 Qma8qWnjNVyYvYYv7mR1baUDDrH3TUcXXUctqNXGYSp6LA 1 1.000000 0.0
## 157 QmQamfGnZ1bwt3Y1EzpSfxSG1HsdwpJfgpJGqdMMoPheFz 10 3191.220538 1009480.0
## 158 QmVgFruqJqEUpY4FaDt39vrWjNGN9YUbdnzkr81izro9DM 10 151.298044 158931.0
## 159 QmeWYQm1r39iZdKTzJYzNYHKmfNSWYEdq8cprPpfz44n88 3 18.887996 3100.0
## 160 QmdZDNidSJ1j8Ka2hQYjdiJZv3jhhqMG28MytKUDV5xwv8 1 1.000000 0.0
## 161 QmQYz9UE3uNhweSS4WQv9gW4HzYMSZZtE6tPuVRsyWLAsz 9 1980.000000 538499.0
## 162 QmSMf99YyUBJuSf2JroPDHDpNuR54esqq3emudg9vaoWKP 13 3235.891720 923215.7
## 163 QmVf3FcBasTsmATxekgL1QQKsK72CR9TG8zHwS1tJ5Tx4p 1 10.902607 0.0
## 164 QmS1nPRvYghS9UBrqFWP3W7XNnrrJCk9PR9CwiP82fiUGY 13 6400.511933 1594297.0
## 165 QmZ119nJYGVhsSSP8cCeBY1YpwoQCKBFhfvoxAy7fWs3Qi 3 65.347475 17017.0
## 166 QmbGxMtiEqy3j6DaPDABApKm9jKQZJQ6oNkbz1UtTDJxmR 1 2.687366 0.0
## 167 QmWSvJECwVS4PTvLJdde3peR5ZiRRN13durPzLd2otzJ6e 2 1.000000 100.0
## 168 QmVUMSVtJ4uAfUBBcgYJ9Ed43j27TZirZd7gzv6cUVqT6x 0 0.000000 0.0
## rewards_proportion
## 1 Inf
## 2 Inf
## 3 Inf
## 4 0.0032448319
## 5 NA
## 6 0.0045431816
## 7 Inf
## 8 0.0188642007
## 9 0.0036729241
## 10 0.0030473269
## 11 0.0054466781
## 12 0.0030304671
## 13 0.0043809061
## 14 Inf
## 15 0.0037358389
## 16 0.0033366043
## 17 0.0034942703
## 18 Inf
## 19 0.0035235684
## 20 Inf
## 21 0.0033181368
## 22 0.0033368066
## 23 Inf
## 24 Inf
## 25 Inf
## 26 0.0038721693
## 27 Inf
## 28 0.0034287358
## 29 Inf
## 30 0.0099000000
## 31 0.0036119728
## 32 0.0029751577
## 33 0.0032975798
## 34 Inf
## 35 Inf
## 36 Inf
## 37 Inf
## 38 Inf
## 39 0.0035169129
## 40 Inf
## 41 0.0099000000
## 42 0.0035916437
## 43 0.0025777743
## 44 Inf
## 45 Inf
## 46 0.0029995720
## 47 Inf
## 48 Inf
## 49 0.0035153574
## 50 Inf
## 51 0.0031821587
## 52 0.0032381932
## 53 Inf
## 54 Inf
## 55 Inf
## 56 Inf
## 57 0.0038632314
## 58 0.0030988509
## 59 0.0027656221
## 60 0.0029917036
## 61 Inf
## 62 0.0072289406
## 63 Inf
## 64 Inf
## 65 Inf
## 66 Inf
## 67 Inf
## 68 0.0086912219
## 69 0.0033116628
## 70 Inf
## 71 Inf
## 72 Inf
## 73 0.0100000000
## 74 0.0099000000
## 75 Inf
## 76 0.0032143711
## 77 Inf
## 78 0.0045918509
## 79 0.0039182412
## 80 Inf
## 81 Inf
## 82 Inf
## 83 Inf
## 84 Inf
## 85 0.0021102509
## 86 0.0037088529
## 87 Inf
## 88 Inf
## 89 Inf
## 90 0.0037098815
## 91 Inf
## 92 Inf
## 93 0.0034765445
## 94 Inf
## 95 Inf
## 96 0.0079626412
## 97 Inf
## 98 Inf
## 99 Inf
## 100 Inf
## 101 Inf
## 102 0.0016411765
## 103 0.0038873957
## 104 Inf
## 105 0.0040123185
## 106 0.0032997334
## 107 Inf
## 108 Inf
## 109 Inf
## 110 Inf
## 111 Inf
## 112 Inf
## 113 0.0034829144
## 114 Inf
## 115 Inf
## 116 Inf
## 117 Inf
## 118 0.0034686114
## 119 Inf
## 120 NA
## 121 0.0013289994
## 122 Inf
## 123 0.0101039572
## 124 0.0040941721
## 125 0.0034047714
## 126 0.0039234939
## 127 0.0207456093
## 128 Inf
## 129 Inf
## 130 Inf
## 131 0.0083400506
## 132 0.0033502462
## 133 Inf
## 134 0.0043376968
## 135 0.0038398448
## 136 Inf
## 137 0.0086439812
## 138 0.0097029900
## 139 Inf
## 140 Inf
## 141 Inf
## 142 Inf
## 143 Inf
## 144 0.0039989797
## 145 Inf
## 146 0.0194061244
## 147 Inf
## 148 Inf
## 149 0.0044215038
## 150 Inf
## 151 0.0035676953
## 152 0.0036084265
## 153 0.0062092707
## 154 Inf
## 155 0.0029981671
## 156 Inf
## 157 0.0031612519
## 158 0.0009519731
## 159 0.0060929019
## 160 Inf
## 161 0.0036768871
## 162 0.0035050223
## 163 Inf
## 164 0.0040146296
## 165 0.0038401290
## 166 Inf
## 167 0.0100000000
## 168 NaNNice! 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