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Moonriver Summary (Daily)

Source: moonriver.polkaholic.io

Relay Chain: kusama Para ID: 2023

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 5,218,806 5,225,761 6,956 63 43 955 482,771 542,718 6,788 ($206,720.09) 20 ($4,619.77) 9 ($17,686.94) 24 21
2023-09-29 5,211,776 5,218,805 7,030 73 57 799 481,819 505,781 6,585 ($308,282.76) 4 ($31.32) 14 ($4,615.09) 5 15
2023-09-28 5,204,621 5,211,775 7,155 122 69 911 481,027 579,046 8,292 ($346,209.95) 10 ($639.58) 17 ($1,622.58) 18 28
2023-09-27 5,197,445 5,204,620 7,176 59 40 983 480,126 569,137 7,920 ($199,091.98) 18 ($2,296.29) 20 ($2,221.10) 20 22
2023-09-26 5,190,288 5,197,444 7,157 68 34 782 479,148 527,070 6,160 ($2,695,352.95) 15 ($32,239.56) 14 ($5,215.29) 21 25
2023-09-25 5,183,152 5,190,287 7,136 92 65 750 478,375 634,881 7,903 ($2,063,544.23) 39 ($18,433.11) 52 ($8,694.73) 58 56
2023-09-24 5,176,002 5,183,151 7,150 59 36 1,103 477,633 589,668 7,506 ($1,147,094.64) 8 ($132.67) 10 ($469.10) 16 15
2023-09-23 5,168,855 5,176,001 7,147 48 31 1,185 476,537 549,076 7,356 ($284,377.44) 20 ($9,212.99) 11 ($873.04) 25 11
2023-09-22 5,161,689 5,168,854 7,166 88 50 1,164 475,359 563,333 8,082 ($141,182.51) 10 ($249.70) 10 ($1,068.98) 21 29
2023-09-21 5,154,527 5,161,688 7,162 69 42 2,280 1,016 474,204 593,265 6,986 ($264,781.21) 9 ($358.76) 34 ($2,433.40) 38 35
2023-09-20 5,147,363 5,154,526 7,164 69 46 2,168 919 473,195 530,514 6,259 ($250,820.84) 14 ($1,947.34) 40 ($6,435.17) 46 45
2023-09-19 5,140,221 5,147,362 7,142 70 46 1,042 472,292 542,247 6,861 ($913,131.84) 8 ($354.46) 37 ($1,435.91) 38 40
2023-09-18 5,133,064 5,140,220 7,157 65 45 1,012 471,261 532,670 6,767 ($218,676.93) 7 ($236.67) 43 ($1,392.35) 57 58
2023-09-17 5,125,937 5,133,063 7,127 58 41 1,367 470,258 552,173 8,669 ($119,391.94) 3 ($19.48) 32 ($1,084.64) 50 47
2023-09-16 5,118,829 5,125,936 7,108 51 38 2,547 1,663 468,901 484,955 6,474 ($169,424.65) 5 ($218.45) 38 ($2,874.80) 54 50
2023-09-15 5,111,727 5,118,828 7,102 78 52 8,424 467,244 458,550 5,201 ($427,295.24) 11 ($314.09) 47 ($3,992.71) 78 70
2023-09-14 5,104,604 5,111,726 7,123 72 44 1,310 458,824 481,005 5,630 ($182,713.74) 18 ($1,201.25) 34 ($3,696.49) 66 58
2023-09-13 5,097,470 5,104,603 7,134 71 48 2,771 464,864 496,734 8,693 ($3,929,765.54) 6 ($977.16) 39 ($3,182.42) 56 55
2023-09-12 5,090,335 5,097,469 7,135 80 56 1,434 462,097 503,631 7,047 ($177,325.03) 15 ($969.26) 43 ($22,266.86) 46 52
2023-09-11 5,083,203 5,090,334 7,132 91 64 1,055 460,665 579,834 7,674 ($1,206,923.04) 70 ($3,016.23) 50 ($7,537.05) 111 119
2023-09-10 5,076,045 5,083,202 7,158 58 39 1,702 459,621 510,698 6,980 ($483,472.87) 5 ($1,055.60) 32 ($2,238.22) 53 45
2023-09-09 5,068,912 5,076,044 7,133 48 37 1,285 457,932 494,752 5,754 ($50,136.01) 6 ($276.40) 34 ($2,176.53) 54 46
2023-09-08 5,061,754 5,068,911 7,158 61 45 1,285 456,652 520,106 6,948 ($431,970.67) 14 ($1,656.02) 37 ($4,720.45) 46 45
2023-09-07 5,054,604 5,061,753 7,150 59 40 1,695 455,373 553,280 8,734 ($332,143.84) 9 ($809.76) 47 ($1,869.21) 53 51
2023-09-06 5,047,443 5,054,603 7,161 87 61 2,498 453,700 638,617 13,570 ($1,215,674.23) 30 ($1,077.19) 37 ($8,801.42) 56 61
2023-09-05 5,040,282 5,047,442 7,161 65 40 2,483 451,210 884,586 12,386 ($196,597.08) 11 ($311.72) 32 ($4,208.53) 36 37
2023-09-04 5,033,133 5,040,281 7,149 101 54 1,518 448,737 648,522 7,439 ($229,034.80) 15 ($1,127.76) 33 ($1,831.03) 37 35
2023-09-03 5,026,006 5,033,132 7,127 69 44 1,592 447,225 541,259 7,393 ($160,335.20) 7 ($1,933.67) 36 ($2,811.16) 38 36
2023-09-02 5,018,922 5,026,005 7,084 81 51 1,047 445,637 646,275 7,114 ($778,604.82) 34 ($10,819.14) 61 ($6,565.05) 70 66
2023-09-01 5,015,245 5,018,921 3,677 73 43 877 444,597 363,232 5,631 ($52,282.33) 9 ($1,206.01) 25 ($2,066.69) 21 25

Sample Queries:

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

Blocks

Schema

SELECT date(block_time) as logDT, MIN(number) startBN, MAX(number) endBN, COUNT(*) numBlocks 
 FROM `bigquery-public-data.crypto_kusama.blocks2023`  
 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_kusama.extrinsics2023`  
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_kusama.accountsactive2023` 
 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_kusama.accountspassive2023` 
 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_kusama.accountsnew2023` 
 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_kusama.balances2023` 
 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_kusama.events2023` 
 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_kusama.transfers2023` 
 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_kusama.xcmtransfers` 
 where destination_para_id = 2023 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_kusama.xcmtransfers` 
 where origination_para_id = 2023 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_kusama.xcm` 
 where destination_para_id = 2023 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_kusama.xcm` 
 where origination_para_id = 2023 and LAST_DAY(date(origination_ts)) = "2023-09-30" 
 group by logDT 
order by logDT

Report source: https://cdn.polkaholic.io/substrate-etl/kusama/2023.json | See Definitions for details