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Making Postgres great again - Colette Biervliet-Schranz
The document discusses the decision to transition from Elasticsearch to a PostgreSQL solution for search functionality in a system handling large volumes of orders. It addresses the effectiveness of PostgreSQL in meeting the performance needs of various seller categories, highlighting good response times and filtering capabilities. Future considerations and gratitude to the team are also mentioned, reflecting on the positive outcomes of this change.
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Making Postgres great again - Colette Biervliet-Schranz
- 1. Making Postgres GreatAgain: Why We Ditched Elasticsearch in Favor of a Postgres Solution Colette Biervliet-Schranz Team POW 1
- 2. 2 Is Postgres goodenough? ??? IDK
- 3. ❖ Our usecase ❖ Postgres Research and Results ❖ Looking ahead 3 Agenda
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- 6. 6 Current Search onOrders Things you can search for: 467144359 790356082 734724846 890267385 153858964 857894655 105735956 937485025 183649573 265037950 729473560 385620718 275936590 274650374 104836395 690376403 306429853 844377449 The possibilities are endless!
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- 12. ❖ “Small” sellerhas 10 orders/year ❖ “Medium” seller has 500 orders/year ❖ “Large” seller has 10,000 orders/year 12 Who is searching?
- 13. 13 To elasticsearch ornot to elasticsearch...
- 14. ❖ Our usecase ❖ Postgres Research and Results ❖ Looking ahead 14 Agenda
- 15. 15 Test Data Set ❖50 Million orders in total ❖ 50k open orders ❖ 5 million (10%) LvB ❖ 45 million (90%) LvR ❖ 2000 sellers ❖ 10,000 products ❖ Each seller had about 25,000 orders ❖ REMEMBER: “average” seller has ~1,000/year
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- 17. 17 REMEMBER ❖ 13 requests/second ❖26 requests/second
- 18. 18 Results If you’re readingthis, you’ve got very good eyes! Congrats!
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- 21. 21 Is Postgres goodenough?
- 22. 22 Is Postgres goodenough? With an average response time of 141 ms, YES!
- 23. ❖ Our usecase ❖ Postgres Research and Results ❖ Looking ahead 23 Agenda
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- 25. Special Thanks To mysupportive teammates !!! 25 Yay Leon Andrey Eduardo
- 26. Thanks! Til next timebol.com