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bifrost Summary (Daily)

Source: bifrost.polkaholic.io

Relay Chain: polkadot Para ID: 2030

Daily Summary for Month ending in 2023-09-30

Date Start Block End Block # Blocks # Extrinsics # Active Accounts # Passive Accounts # New Accounts # Addresses # Events # Transfers ($USD) # XCM Transfers In ($USD) # XCM Transfers Out ($USD) # XCM In # XCM Out Issues
2023-09-30 3,169,312 3,175,499 6,188 253 79 26 9 5,276 15,045 247 ($18.89) 14 ($16,438.09) 2 ($259.63) 36 16
2023-09-29 3,163,289 3,169,311 6,023 287 69 22 2 5,267 15,111 277 ($2,199.56) 30 ($7,139.79) 2 ($1,755.68) 42 28
2023-09-28 3,157,247 3,163,288 6,042 299 74 37 6 5,265 15,381 279 ($4,376.24) 24 ($2,946.83) 4 ($7,758.45) 50 39
2023-09-27 3,151,208 3,157,246 6,039 369 86 27 6 5,259 16,100 457 ($3,925.75) 35 ($11,554.29) 5 ($1,075.13) 43 38
2023-09-26 3,144,951 3,151,207 6,257 254 71 32 3 5,255 15,288 184 ($49,610.06) 24 ($19,581.14) 6 ($3,055.97) 35 30
2023-09-25 3,138,759 3,144,950 6,192 243 77 29 5 5,252 14,996 192 ($1,267.35) 14 ($3,763.61) 4 ($796.55) 30 39
2023-09-24 3,132,497 3,138,758 6,262 220 61 24 6 5,247 14,852 138 ($821.30) 20 ($195,208.82) 4 ($1,088.90) 23 25
2023-09-23 3,126,141 3,132,496 6,356 241 59 28 7 5,241 15,346 179 ($408,286.54) 18 ($399,738.27) 1 ($487.92) 22 26
2023-09-22 3,119,821 3,126,140 6,320 271 68 28 3 5,236 15,606 215 ($9,978.87) 19 ($1,447.15) 5 ($495.78) 26 25
2023-09-21 3,113,390 3,119,820 6,431 253 71 28 6 5,233 15,571 196 ($14,756.12) 21 ($8,531.39) 1 ($4.66) 34 30
2023-09-20 3,107,326 3,113,389 6,064 267 63 23 3 5,227 14,926 187 ($11,460.32) 20 ($2,995.68) 4 ($7,978.79) 30 27
2023-09-19 3,101,204 3,107,325 6,122 557 80 30 6 5,225 17,995 758 ($21,378.71) 29 ($9,911.03) 4 ($11,205.60) 36 29
2023-09-18 3,095,108 3,101,203 6,096 3,380 85 8 5,219 46,090 6,340 ($29,082.37) 27 ($12,347.19) 9 ($1,826.52) 53 49
2023-09-17 3,089,168 3,095,107 5,940 3,161 65 26 6 5,211 43,540 5,950 ($8,832.15) 18 ($3,819.84) 1 ($4.96) 23 24
2023-09-16 3,083,097 3,089,167 6,071 1,267 84 29 8 5,205 24,797 1,990 ($21,099.23) 33 ($12,205.81) 6 ($3,490.42) 39 36
2023-09-15 3,077,045 3,083,096 6,052 389 85 33 8 5,197 16,181 303 ($63,674.53) 23 ($27,009.66) 2 ($957.59) 51 53
2023-09-14 3,070,896 3,077,044 6,149 334 82 30 5,189 15,772 250 ($18,519.01) 27 ($7,956.51) 2 ($33.02) 43 34
2023-09-13 3,064,824 3,070,895 6,072 350 96 24 4 3,329 15,876 291 ($25,648.87) 36 ($10,620.78) 1 ($3.82) 47 37
2023-09-12 3,058,657 3,064,823 6,167 396 94 26 5 5,173 16,688 380 ($33,267.57) 56 ($16,786.20) 1 ($110.88) 67 52
2023-09-11 3,052,460 3,058,656 6,197 332 91 31 9 5,169 16,009 269 ($242,707.47) 33 ($97,351.99) 1 ($790.39) 44 33
2023-09-10 3,046,299 3,052,459 6,161 262 55 19 2 5,161 15,040 155 ($11,001.43) 16 ($3,741.76) 26 28
2023-09-09 3,040,149 3,046,298 6,150 246 57 17 6 5,159 14,913 192 ($37,142.95) 16 ($1,330.68) 20 26
2023-09-08 3,034,037 3,040,148 6,112 243 62 23 3 5,155 14,685 138 ($35,552.69) 16 ($16,751.23) 19 15
2023-09-07 3,027,906 3,034,036 6,131 219 68 22 8 5,152 14,569 166 ($37,018.35) 24 ($22,015.15) 1 ($294.56) 31 17
2023-09-06 3,021,802 3,027,905 6,104 264 77 24 7 5,145 14,951 180 ($9,604.50) 20 ($1,768.21) 3 ($613.83) 28 21
2023-09-05 3,015,719 3,021,801 6,083 367 104 31 13 5,139 16,141 343 ($44,120.14) 37 ($8,232.23) 1 ($7.55) 52 44
2023-09-04 3,009,818 3,015,718 5,901 316 80 25 13 5,126 15,415 260 ($24,917.44) 44 ($43,051.48) 2 ($1.96) 70 40
2023-09-03 3,003,782 3,009,817 6,036 234 54 18 4 5,113 14,508 120 ($13,526.68) 22 ($9,338.06) 28 26
2023-09-02 2,997,800 3,003,781 5,982 260 62 21 5 5,109 14,638 142 ($10,777.92) 23 ($7,110.59) 29 25
2023-09-01 2,991,834 2,997,799 5,966 298 73 28 9 5,104 14,946 193 ($36,590.28) 19 ($7,334.85) 2 ($1.95) 31 44

Sample Queries:

You can generate the above summary data using the following queries using the public dataset bigquery-public-data.crypto_polkadot in Google BigQuery:

Blocks

Schema

SELECT date(block_time) as logDT, MIN(number) startBN, MAX(number) endBN, COUNT(*) numBlocks 
 FROM `bigquery-public-data.crypto_polkadot.blocks2030`  
 where LAST_DAY(date(block_time)) = "2023-09-30" 
 group by logDT 
 order by logDT

Signed Extrinsics

Schema

SELECT date(block_time) as logDT, 
COUNT(*) numSignedExtrinsics 
FROM `bigquery-public-data.crypto_polkadot.extrinsics2030`  
where signed and LAST_DAY(date(block_time)) = "2023-09-30" 
group by logDT 
order by logDT

Active Accounts

Schema

SELECT date(ts) as logDT, 
 COUNT(*) numActiveAccounts 
 FROM `bigquery-public-data.crypto_polkadot.accountsactive2030` 
 where LAST_DAY(date(ts)) = "2023-09-30" 
 group by logDT 
 order by logDT

Passive Accounts

Schema

SELECT date(ts) as logDT, 
 COUNT(*) numPassiveAccounts 
 FROM `bigquery-public-data.crypto_polkadot.accountspassive2030` 
 where LAST_DAY(date(ts)) = "2023-09-30" 
 group by logDT 
 order by logDT

New Accounts

Schema

SELECT date(ts) as logDT, 
 COUNT(*) numNewAccounts 
 FROM `bigquery-public-data.crypto_polkadot.accountsnew2030` 
 where LAST_DAY(date(ts)) = "2023-09-30" 
 group by logDT
 order by logDT

Addresses with Balances

Schema

SELECT date(ts) as logDT,
 COUNT(distinct address_pubkey) numAddress 
 FROM `bigquery-public-data.crypto_polkadot.balances2030` 
 where LAST_DAY(date(ts)) = "2023-09-30" 
 group by logDT 
 order by logDT

Events

Schema

SELECT date(block_time) as logDT, 
 COUNT(*) numEvents 
 FROM `bigquery-public-data.crypto_polkadot.events2030` 
 where LAST_DAY(date(block_time)) = "2023-09-30" 
 group by logDT 
 order by logDT

Transfers:

Schema

SELECT date(block_time) as logDT, 
 COUNT(*) numEvents 
 FROM `bigquery-public-data.crypto_polkadot.transfers2030` 
 where LAST_DAY(date(block_time)) = "2023-09-30" 
 group by logDT 
 order by logDT

XCM Transfers In:

Schema

SELECT date(origination_ts) as logDT, 
 COUNT(*) numXCMTransfersOut 
 FROM `bigquery-public-data.crypto_polkadot.xcmtransfers` 
 where destination_para_id = 2030 and LAST_DAY(date(origination_ts)) = "2023-09-30" 
 group by logDT order by logDT

XCM Transfers Out:

Schema

SELECT date(origination_ts) as logDT, 
 COUNT(*) numXCMTransfersIn 
 FROM `bigquery-public-data.crypto_polkadot.xcmtransfers` 
 where origination_para_id = 2030 and LAST_DAY(date(origination_ts)) = "2023-09-30" 
 group by logDT 
order by logDT

XCM Messages In:

Schema

SELECT date(origination_ts) as logDT, 
 COUNT(*) numXCMMessagesOut 
 FROM `bigquery-public-data.crypto_polkadot.xcm` 
 where destination_para_id = 2030 and LAST_DAY(date(origination_ts)) = "2023-09-30" 
 group by logDT order by logDT

XCM Messages Out:

Schema

SELECT date(origination_ts) as logDT, 
 COUNT(*) numXCMMessagesIn 
 FROM `bigquery-public-data.crypto_polkadot.xcm` 
 where origination_para_id = 2030 and LAST_DAY(date(origination_ts)) = "2023-09-30" 
 group by logDT 
order by logDT

Report source: https://cdn.polkaholic.io/substrate-etl/polkadot/2030.json | See Definitions for details