Postgresql updating millions of rows Aspergers hookups

25-Aug-2016 07:46

Use window functions (or analytic functions if you’re coming from an Oracle background) to efficiently return this data, and use the portion (in this example, we’re marking this batch of rows as being “backfilled” as part of our data migration, presumably to differentiate them from other rows that already existed prior).This is useful for transforming data, analogous to how the higher-order map function works in many programming languages, and replaces the need for a PL/pg SQL loop except in more complicated scenarios.Note: If you have a very large volume of data, I would recommend transforming the data on disk first, then performing a bulk insert or CTE onto customers.A more standard way to do this would be to include a sub-select, but sometimes the join style is easier to write and even faster to execute.Fortunately, you have elected to use Postgre SQL or some other industrial-strength RDBMS as your database workhorse of choice, and you will soon have the necessary tools to nail down these newfound requirements with ease.We love using Postgres at Tilt, and it makes our lives easier every day.Here’s a concrete example of how we can safely upgrade our most trusted “moderator” users to the “admin” role on a large internet forum, without contending with concurrent writes or incurring substantial I/O all at once: immediately locks the rows being retrieved in the first step (as if they were to be updated), and this prevents them from being modified or deleted by other transactions until the current transaction ends. I tried to keep this post fairly concise, but for the boldest of explorers, here is an excellent wiki page containing many other Postgres gems that you also may not have known about.As you can see, with so many powerful data manipulation functions at our disposal, the possibilities are truly endless.

A common heuristic for indexing database tables is to index the columns you use for sorting, filtering, joining, or grouping.It’s stable, extensible, supports high volume, and has so many advanced features that keep getting better with each new release (JSONB, Lateral Joins, Materialized Views, and Foreign Data Wrappers just to name a few).I could talk about the exciting future of Postgres all day, but for now, I’ll share some examples of how it has impacted us directly here at Tilt.Finally, we may only want to lock and process so many rows at once during a particularly sensitive data migration – here’s how you can safely dispatch a large number of updates.I recently learned this from rosser, who shared this technique with everyone on Hacker News. Hopefully you will have gleaned a thing or two from this post, and are now ready to venture out and write some robust and awesome data migrations of your own. Voila: sensible defaults, better shell and overall Postgres experience.

A common heuristic for indexing database tables is to index the columns you use for sorting, filtering, joining, or grouping.It’s stable, extensible, supports high volume, and has so many advanced features that keep getting better with each new release (JSONB, Lateral Joins, Materialized Views, and Foreign Data Wrappers just to name a few).I could talk about the exciting future of Postgres all day, but for now, I’ll share some examples of how it has impacted us directly here at Tilt.Finally, we may only want to lock and process so many rows at once during a particularly sensitive data migration – here’s how you can safely dispatch a large number of updates.I recently learned this from rosser, who shared this technique with everyone on Hacker News. Hopefully you will have gleaned a thing or two from this post, and are now ready to venture out and write some robust and awesome data migrations of your own. Voila: sensible defaults, better shell and overall Postgres experience.Oh, and no users are allowed to log in for the duration of this data migration.