{"id":9606,"date":"2026-01-09T07:02:24","date_gmt":"2026-01-09T07:02:24","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2026\/01\/09\/faster-is-not-always-better-choosing-the-right-postgresql-insert-strategy-in-python-benchmarks\/"},"modified":"2026-01-09T07:02:24","modified_gmt":"2026-01-09T07:02:24","slug":"faster-is-not-always-better-choosing-the-right-postgresql-insert-strategy-in-python-benchmarks","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2026\/01\/09\/faster-is-not-always-better-choosing-the-right-postgresql-insert-strategy-in-python-benchmarks\/","title":{"rendered":"Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks)"},"content":{"rendered":"<p>    Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks)<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>PostgreSQL is fast. Whether your Python code can or should keep up depends on context. This article compares and benchmarks various insert strategies, focusing not on micro-benchmarks but on trade-offs between safety, abstraction, and throughput \u2014 and choosing the right tool for the job.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/faster-is-not-always-better-choosing-the-right-postgresql-insert-strategy-in-python-benchmarks\/\">Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks)<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Mike Huls<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/faster-is-not-always-better-choosing-the-right-postgresql-insert-strategy-in-python-benchmarks\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks) PostgreSQL is fast. Whether your Python code can or should keep up depends on context. This article compares and benchmarks various insert strategies, focusing not on micro-benchmarks but on trade-offs between safety, abstraction, and throughput \u2014 and choosing the right tool [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,401,83,1805,4573,400,160],"tags":[1684,1473,395],"class_list":["post-9606","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-data-engineering","category-data-science","category-databases","category-postgres","category-postgresql","category-programming","tag-benchmarks","tag-choosing","tag-right"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9606"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=9606"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9606\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=9606"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=9606"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=9606"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}