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bifrost Summary (Daily)

Source: bifrost.polkaholic.io

Relay Chain: polkadot Para ID: 2030

Daily Summary for Month ending in 2023-02-28

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
2023-02-28 1,835,323 1,842,353 7,031 288 96 21 6 3,822 16,767 223 ($62,659.07) 43 ($53,826.90) 18 ($11,393.77) 43 35
2023-02-27 1,828,287 1,835,322 7,036 319 92 18 3 3,817 16,915 242 ($29,107.96) 34 ($23,629.82) 18 ($4,554.36) 35 31
2023-02-26 1,821,259 1,828,286 7,028 307 82 19 8 3,814 16,883 250 ($16,801.79) 41 ($16,735.92) 22 ($6,923.46) 41 40
2023-02-25 1,814,233 1,821,258 7,026 254 79 30 8 3,806 16,419 220 ($30,852.79) 30 ($16,049.52) 12 ($7,286.08) 30 22
2023-02-24 1,807,236 1,814,232 6,997 284 81 19 3 3,798 16,635 243 ($8,406.53) 35 ($4,693.24) 15 ($5,908.81) 35 25
2023-02-23 1,800,215 1,807,235 7,021 296 86 23 4 3,795 16,708 219 ($13,197.33) 37 ($2,231.63) 23 ($13,313.86) 37 34
2023-02-22 1,793,181 1,800,214 7,034 351 103 24 7 3,791 17,298 321 ($68,243.08) 49 ($37,507.24) 33 ($23,486.37) 49 45
2023-02-21 1,786,136 1,793,180 7,045 382 94 23 13 3,784 17,556 246 ($13,332.29) 29 ($6,272.67) 18 ($4,037.39) 29 39
2023-02-20 1,779,118 1,786,135 7,018 338 94 23 9 3,771 17,131 254 ($15,065.54) 37 ($6,248.59) 24 ($2,813.92) 37 39
2023-02-19 1,772,108 1,779,117 7,010 426 86 25 6 3,762 18,067 525 ($59,092.56) 37 ($16,361.57) 24 ($6,584.41) 37 38
2023-02-18 1,765,053 1,772,107 7,055 365 103 26 6 3,757 17,613 424 ($41,529.55) 37 ($25,870.13) 22 ($3,183.81) 37 36
2023-02-17 1,758,035 1,765,052 7,018 365 86 26 8 3,751 17,239 333 ($5,842.73) 27 ($2,190.24) 39 ($2,445.38) 27 53
2023-02-16 1,751,009 1,758,034 7,026 554 127 26 12 3,743 19,166 618 ($33,096.32) 60 ($7,692.86) 41 ($31,018.11) 60 56
2023-02-15 1,743,949 1,751,008 7,060 380 93 26 10 3,733 17,730 414 ($213,021.01) 47 ($165,594.79) 30 ($8,596.21) 47 40
2023-02-14 1,736,897 1,743,948 7,052 583 98 23 7 3,723 18,633 325 ($35,504.19) 38 ($8,621.73) 34 ($15,294.75) 38 49
2023-02-13 1,729,827 1,736,896 7,070 378 107 25 10 3,716 17,715 394 ($25,780.87) 46 ($18,866.33) 40 ($5,102.48) 50 52
2023-02-12 1,722,753 1,729,826 7,074 368 104 26 17 3,707 17,796 432 ($104,737.47) 61 ($85,102.64) 10 ($16,100.24) 61 20
2023-02-11 1,715,670 1,722,752 7,083 386 99 29 12 3,690 17,678 313 ($22,710.95) 40 ($14,938.19) 15 ($5,441.31) 40 34
2023-02-10 1,708,639 1,715,669 7,031 466 115 24 15 3,678 18,406 435 ($52,175.43) 70 ($35,362.43) 28 ($4,635.04) 70 47
2023-02-09 1,701,562 1,708,638 7,077 495 113 29 13 3,664 18,578 437 ($23,895.20) 66 ($11,721.17) 44 ($25,104.44) 66 62
2023-02-08 1,694,559 1,701,561 7,003 516 125 31 20 3,652 18,620 560 ($435,430.88) 44 ($183,386.24) 26 ($36,194.73) 44 41
2023-02-07 1,687,542 1,694,558 7,017 552 147 32 23 3,633 19,128 556 ($148,408.40) 76 ($129,845.95) 23 ($33,899.55) 76 34
2023-02-06 1,680,491 1,687,541 7,051 710 179 22 8 3,612 20,598 721 ($74,823.61) 68 ($10,358.59) 29 ($3,553.99) 68 43
2023-02-05 1,673,424 1,680,490 7,067 365 103 23 8 3,609 17,537 313 ($35,732.34) 35 ($21,595.27) 9 ($1,004.97) 35 23
2023-02-04 1,666,388 1,673,423 7,036 535 145 21 14 3,605 19,134 524 ($330,856.92) 72 ($229,957.42) 13 ($4,148.77) 72 33
2023-02-03 1,659,339 1,666,387 7,049 394 104 24 16 3,596 17,861 352 ($38,260.71) 49 ($17,014.69) 21 ($581.96) 49 39
2023-02-02 1,652,290 1,659,338 7,049 345 92 19 8 3,582 17,317 283 ($52,556.05) 39 ($46,672.54) 18 ($3,745.17) 39 36
2023-02-01 1,645,275 1,652,289 7,015 358 89 26 16 3,581 17,242 234 ($11,409.39) 40 ($9,795.66) 30 ($1,744.08) 40 50

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)) = "2023-02-28" 
 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)) = "2023-02-28" 
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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 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)) = "2023-02-28" 
 group by logDT 
order by logDT

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