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Tue, 19 May 2026 14:27:28 +0000 From: Martin Mueller To: "pgsql-general@postgresql.org" Subject: scaling up from t1n to 60 million records Thread-Topic: scaling up from t1n to 60 million records Thread-Index: AQHc55nMCqa1OUuiTk+CoVWmCRqQbA== Date: Tue, 19 May 2026 14:27:28 +0000 Message-ID: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-ms-reactions: allow x-ms-publictraffictype: Email x-ms-traffictypediagnostic: CY8PR05MB10108:EE_|SA3PR05MB10419:EE_ x-ms-office365-filtering-correlation-id: de8e966b-7ef2-4684-0e20-08deb5b2c10c x-ms-exchange-senderadcheck: 1 x-ms-exchange-antispam-relay: 0 x-microsoft-antispam: BCL:0;ARA:13230040|366016|1800799024|786006|376014|8096899003|56012099003|18002099003|11063799006|38070700021; x-microsoft-antispam-message-info: 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 x-forefront-antispam-report: CIP:255.255.255.255;CTRY:;LANG:en;SCL:1;SRV:;IPV:NLI;SFV:NSPM;H:CY8PR05MB10108.namprd05.prod.outlook.com;PTR:;CAT:NONE;SFS:(13230040)(366016)(1800799024)(786006)(376014)(8096899003)(56012099003)(18002099003)(11063799006)(38070700021);DIR:OUT;SFP:1102; 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charset="us-ascii" Content-Transfer-Encoding: quoted-printable I use Postgres with a GUI frontend (Aquafold) as a very large spreadsheet o= n steroids that analyzes rare or defective spellings in a corpus of 65,000 = texts and1.5 billion words. I typically extract data from the corpus with= python scripts, turn them into tables and load them into the database. On my Mac with 32 GB of memory performance is OK with queries that typicall= y within seconds extract data rows from tables with up to ten million rows= . If the result set is large, I suspect that most of time machine's time i= s spent displaying result sets. I have used indexing sparingly. While it he= lps, the time savings often don't matter much. I am thinking about scaling up to table with about 60 million rows. Are th= ere things to do or watch out for? Or should I proceed on the assumption th= at that 60 million records are within scope and that the added timecost is = roughly linear? Martin Mueller Professor emeritus of English and Classics Northwestern University --_000_CY8PR05MB1010861EAD48ED098786C9690C4002CY8PR05MB10108na_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

I use Postgres with a= GUI frontend (Aquafold) as a very large spreadsheet on steroids that analy= zes rare or defective spellings in a corpus of 65,000 texts and1.5 billion = words.  I typically extract  data from the corpus with python scripts, turn them into tables and load them i= nto the database.


On my Mac with 32 GB = of memory performance is OK with queries that typically within seconds extr= act data rows from tables  with up to ten million rows.  If the r= esult set is large, I suspect that most of time machine's time is spent displaying result sets. I have used indexing spari= ngly. While it helps, the time savings often don't matter much. 


I am thinking about s= caling up to table with about 60 million rows.  Are there things to do= or watch out for? Or should I proceed on the assumption that that 60 milli= on records are within scope and that the added timecost is roughly linear?

 

Martin M= ueller

Professo= r emeritus of English and Classics

Northwes= tern University

 

 

 

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