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ReportHi everyone,
I've built a scheduled flow that grabs tweets (rows of data) from an excel file (that another flow or set of flows populates), filters them based on date and time, and sends an email. In an ideal world, this email will only contain tweets posted within the last two hours.
I ran into an issue where the ODATA filter doesn't want to allow a "greater than" operation on the twitter posted time, so I've had to split my filters between "posted on date" and "posted time", where the date is filtered via ODATA when the rows are imported, and the time is done via a "filter array" operation. Any thoughts/critiques on this approach are welcome - I feel like this workaround is probably where the bug is somewhere.
For some reason, this flow will operate as intended, but only in the evenings for a 4 hour time span (the 6pm and 8 pm emails in US Central Time). The tweet posted times are in UTC, and the conversion columns in the Excel just split the tweet posted times into date and time segments, so are also in UTC... so the filters should be good to go at all times.
I'm stuck on why this flow sends empty tables from 12 am to 4 pm Central Time, then works until about 9 pm, then starts sending empty tables again.
For reference, here's a tweet from my excel file:
| name | Justin L. Williams |
| username | jwwilliamstrial |
| tweettttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt | Investigators release preliminary report on pipeline explosion in Corpus Christi Ship Channel https://t.co/Fj9PIjOk8Q |
| DatePosted | 09/29/2020 |
| Time Posted | 05:39:04 PM |
| Tweet URL | https://twitter.com/jwwilliamstrial/status/1311008571023405056 |
| user friends | 131 |
| user followers | 34 |
| tweet favorites | FALSE |
| keyword | explosion |
| user location | Corpus Christi, Texas |
| posted | 2020-09-29T18:22:55.000Z |
| user description | Partner at Williams Attorneys P. L. L. C. Former partner at at COLE, EASLEY, SCIBA & WILLIAMS. Tries cases for injured parties y |
| TimePostedCentralUS | 1:16:06 AM |
And the flow:
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