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B-tree database lookups are way slower without a memory cache in place + a good replacement policy for OLTP. MySQL uses a segmented LRU whereas Postgres uses a clock-sweep algorithm. Both good choices for different reasons.

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Ben Dicken

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51,744 просмотров • 6 месяцев назад •via X (Twitter)

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DBngin is such a good little app for managing database local DB servers. Highly recommended if your app needs a MySQL, Postgres or Redis DB
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Kicking off Earth Week with a big question: Is crypto good for the environment? 🌍 Bitcoin uses more energy than Finland — but there’s a better way. We break it down in the latest Stronghold 101. 🎥 Watch:
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India is a country which can understand the implication of a good which comes in our way, although a good is never unmitigated, a good is never without riders, a good is never - on its own - good, it is for us all to use in such a way that it is for the common good. I think the AI is something which we should know how to keep well under our reins and serve for the common good. - Smt Nirmala Sitharaman in New Delhi.
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Cleotrapa finally exposes Ice Spice in a 2 series video. “She’s not a good human being..this girl uses me for her rollout, everytime we hangout she drops a song the next day” + more
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Being a candidate and governing requires different skillsets. Debates are not a good way to pick a president. Instead of these gotcha sound bite performances, let's sit down with each candidate for an hour long, substantive policy discussion. Who can bring us together again?
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Database table size impacts performance in more ways than one: a) B-tree depth. Using 8k pages and a 16b uuid: 1 level = ~370 rows 2 levels = ~138k rows 3 levels = ~50m rows 4 levels = ~20b rows The lookup cost on a table with 100k rows is not the same as one with 1b rows. This can apply both to the table itself (MySQL cluster index) as well as the indexes. Sometimes a single query requires many of them. b) Small table → fits in RAM → fast reads. The larger the table, the more likely to read from disk plus churn the cache. c) # of indexes. Each adds maintenance overhead for insertions, and for Postgres vacuum overhead as well. Keep an eye on this! It's useful to take regular stock of your tables + indexes. Clean bloat. Remove unused indexes. Partition if needed.
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