@naturalsenoritas: Have you tried this before? Let me know in the comments. I start my day with this and walking bare feet (grounding) it truly gives my day a positive kick start 😂🤩#foryou #brain #lowtoxliving #depression #postiveenergy #positivity #positivehealing #focus #increaseimmunity #energy #creatorsearchinsights #naturalsenoritas #naturalsolutions #anxiety #sleeplessnight #imsomnia

Lizelle @ Natural Senoritas
Lizelle @ Natural Senoritas
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Region: ZA
Sunday 21 April 2024 10:02:11 GMT
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sa_bokkie
💫Louisa💫 :
🥰🥰🥰
2024-05-07 12:41:20
1
sa_bokkie
💫Louisa💫 :
sal nooit weer n antidepressants pil drink nie, wens meer mense wil dit doen
2024-05-07 12:48:23
1
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Same data. Different cache. Most people pick a cache by habit. Reach for Redis, move on. Here's the actual decision. ⚡ BEFORE ANY OF IT Is this read far more often than it changes? If not, stop. A cache on rarely-read or constantly-changing data buys you nothing and hands you a staleness bug for free. Every caching tree starts one question too late. 🌐 STATIC CONTENT — assets, images, JS, CSS, video → CDN. Cloudflare, Akamai, Fastly. Cached at the edge, physically near the user. 🔴 CHANGES OFTEN, SHARED — sessions, user data, API responses → Redis. Key-value, low latency. Set a TTL or set an eviction policy, or it never leaves. 🔵 COMPUTE-HEAVY, SAME PROCESS — models, lookups, config → In-memory. Just a dict in the process. Ultra fast, short-lived, dies when the process does. 🟣 EXPENSIVE QUERY, STABLE RESULT — reports, aggregates, analytics → Materialized view. Postgres, Snowflake, BigQuery, Databricks. Precomputed, refreshed on a schedule. ❌ AVOID Anything private in a CDN. Redis as your only copy of the data. In-memory across many instances — ten instances means ten different answers to the same question. A materialized view for live data. THE RULE: picking the store is the easy half. Knowing when to evict is the other. A cache is a bet that data won't change. Most cache bugs are that bet losing quietly, and nobody noticing for weeks. 📸 Screenshot this before your next system design interview. Follow @hackproduct ⚡ . . #systemdesign #systemdesigninterview #caching #redis #cdn
Same data. Different cache. Most people pick a cache by habit. Reach for Redis, move on. Here's the actual decision. ⚡ BEFORE ANY OF IT Is this read far more often than it changes? If not, stop. A cache on rarely-read or constantly-changing data buys you nothing and hands you a staleness bug for free. Every caching tree starts one question too late. 🌐 STATIC CONTENT — assets, images, JS, CSS, video → CDN. Cloudflare, Akamai, Fastly. Cached at the edge, physically near the user. 🔴 CHANGES OFTEN, SHARED — sessions, user data, API responses → Redis. Key-value, low latency. Set a TTL or set an eviction policy, or it never leaves. 🔵 COMPUTE-HEAVY, SAME PROCESS — models, lookups, config → In-memory. Just a dict in the process. Ultra fast, short-lived, dies when the process does. 🟣 EXPENSIVE QUERY, STABLE RESULT — reports, aggregates, analytics → Materialized view. Postgres, Snowflake, BigQuery, Databricks. Precomputed, refreshed on a schedule. ❌ AVOID Anything private in a CDN. Redis as your only copy of the data. In-memory across many instances — ten instances means ten different answers to the same question. A materialized view for live data. THE RULE: picking the store is the easy half. Knowing when to evict is the other. A cache is a bet that data won't change. Most cache bugs are that bet losing quietly, and nobody noticing for weeks. 📸 Screenshot this before your next system design interview. Follow @hackproduct ⚡ . . #systemdesign #systemdesigninterview #caching #redis #cdn

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