flipside

    Flow_Events Event Examples
    DiamondFlow_Events Event Examples
    Updated 2025-06-16
    SELECT
    DATE_TRUNC('day', block_timestamp) as date,
    event_contract,
    event_type,
    COUNT(tx_id) as daily_event_count,
    SUM(COUNT(DISTINCT tx_id)) OVER (ORDER BY DATE_TRUNC('day', block_timestamp) ASC) as running_total,
    AVG(COUNT(tx_id)) OVER (
    ORDER BY DATE_TRUNC('day', block_timestamp)
    ROWS BETWEEN 27 PRECEDING AND CURRENT ROW
    ) as four_week_rolling_avg
    FROM flow.core.fact_events
    WHERE
    event_contract LIKE '%' || '{{ContractAddress}}' || '%'
    AND event_type LIKE '%Mint%'
    AND CASE '{{Timeframe}}'
    WHEN 'Custom Timeframe' THEN block_timestamp >= '{{Start}}' AND block_timestamp <= '{{End}}'
    WHEN '7D' THEN block_timestamp >= DATEADD('day', -7, CURRENT_TIMESTAMP())
    WHEN '14D' THEN block_timestamp >= DATEADD('day', -30, CURRENT_TIMESTAMP())
    WHEN '30D' THEN block_timestamp >= DATEADD('day', -30, CURRENT_TIMESTAMP())
    WHEN '90D' THEN block_timestamp >= DATEADD('day', -90, CURRENT_TIMESTAMP())
    WHEN '6M' THEN block_timestamp >= DATEADD('month', -6, CURRENT_TIMESTAMP())
    WHEN '1Y' THEN block_timestamp >= DATEADD('year', -1, CURRENT_TIMESTAMP())
    WHEN 'ALL' THEN 1=1
    WHEN '2025' THEN block_timestamp >= '2025-01-01' AND block_timestamp <= '2025-12-31'
    WHEN '2024' THEN block_timestamp >= '2024-01-01' AND block_timestamp <= '2024-12-31'
    WHEN '2023' THEN block_timestamp >= '2023-01-01' AND block_timestamp <= '2023-12-31'
    WHEN '2022' THEN block_timestamp >= '2022-01-01' AND block_timestamp <= '2022-12-31'
    WHEN '2021' THEN block_timestamp >= '2021-01-01' AND block_timestamp <= '2021-12-31'
    WHEN '2020' THEN block_timestamp >= '2020-01-01' AND block_timestamp <= '2020-12-31'
    ELSE block_timestamp >= DATEADD('day', -30, CURRENT_TIMESTAMP()) -- Default to 30 days
    END
    GROUP BY
    1, 2, 3
    ORDER BY
    date DESC;
    Last run: about 2 months ago
    DATE
    EVENT_CONTRACT
    EVENT_TYPE
    DAILY_EVENT_COUNT
    RUNNING_TOTAL
    FOUR_WEEK_ROLLING_AVG
    1
    		2025-04-11 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted100299411568.28
    2
    		2025-04-08 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted56010299212046.125
    3
    		2025-04-04 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted226243110134.652
    4
    		2025-03-25 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted4588242710585.045
    5
    		2025-03-18 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted1612237610870.619
    6
    		2025-03-12 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted1092235811333.55
    7
    		2025-03-05 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted1872234411872.578
    8
    		2025-03-03 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted291232412428.166
    9
    		2025-02-26 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted1352231913142.117
    10
    		2025-02-24 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted91230313879
    11
    		2025-02-19 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted300230214798.2
    12
    		2025-02-18 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted282229615833.785
    13
    		2025-02-13 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted7636229017030.076
    14
    		2025-02-08 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted95220717812.916
    15
    		2025-02-06 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted44288220519423.636
    16
    		2025-01-31 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted308175716937.2
    17
    		2025-01-30 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted12670175318784.888
    18
    		2025-01-29 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted10000161619549.25
    19
    		2025-01-24 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted453151620913.428
    20
    		2025-01-22 00:00:00.000A.e4cf4bdc1751c65d.AllDayMomentNFTMinted52084151024323.5
    25
    2KB
    14s