HN user

postila

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momjian.us 7y ago

Will Postgres live forever? [pdf]

postila
2pts0
blog.okmeter.io 7y ago

USE, RED and real-world PgBouncer monitoring

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2pts0
dwhsys.com 8y ago

Solving Data Science Tasks with Greenplum DB

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blog.timescale.com 8y ago

How to store time-series data in MongoDB, and why that’s a bad idea

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1pts0
blog.cloudflare.com 8y ago

HTTP Analytics for 6M requests per second using ClickHouse

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3pts0
bricklen.github.io 8y ago

PostgreSQL 10 Upgrade

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3pts0
write-skew.blogspot.com 8y ago

Parallel Hash for PostgreSQL

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www.youtube.com 8y ago

CMU 15-721 Advanced Database Systems [video]

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252pts17
wiki.postgresql.org 8y ago

Sudoku solver in recursive CTE (Postgres)

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1pts0
blog.jooq.org 8y ago

10 Cool SQL Optimisations That Do Not Depend on the Cost Model

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4pts0
h50146.www5.hpe.com 8y ago

PostgreSQL 10 New Features with Examples [pdf]

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23pts0
pgeoghegan.blogspot.com 8y ago

PostgreSQL Index bloat under a microscope

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3pts0
h50146.www5.hpe.com 9y ago

PostgreSQL 10 New Features. With Examples [pdf]

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3pts0
fortune.com 9y ago

Uber Will Merge with the ‘Google of Russia’ Yandex in 6 Countries

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1pts0
www.slideshare.net 9y ago

Advanced SQL in PostgreSQL

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6pts0
momjian.us 9y ago

PostgreSQL 10 Major Features

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csjournals.com 9y ago

Comparing Oracle Spatial and Postgres PostGIS

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github.com 9y ago

Pg_tsdtm – Distributed transaction management tools for PostgreSQL

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28pts2
pgconf.ru 9y ago

An Illustrated History of POSTGRES(QL) [pdf]

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2pts0
pgconf.ru 9y ago

Dynamic Compilation of SQL Queries in PostgreSQL Using LLVM JIT [pdf]

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2pts0
pgconf.ru 9y ago

Adaptive query optimization in PostgreSQL [pdf]

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2pts0
pgconf.ru 9y ago

RUM index and its applications (PostgreSQL) [pdf]

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2pts0
www.sai.msu.su 9y ago

JSON in Postgres, Present and Future [pdf]

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4pts0
www.crunchydata.com 9y ago

PostgreSQL 9.x Security Technical Implementation Guide [pdf]

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1pts1
penningpence.blogspot.com 9y ago

First Impressions – Google Cloud SQL for PostgreSQL

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22pts6
blog.2ndquadrant.com 9y ago

Postgres Autovacuum Tuning Basics

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8pts0
www.pgcon.org 9y ago

Queues in PostgreSQL (2016) [pdf]

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3pts0
blog.postgresql-consulting.com 9y ago

Deep dive into postgres stats: Introduction

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1pts0
techcrunch.com 9y ago

Trump is causing a political app boom, data shows

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3pts0
fosdem.org 9y ago

Graph Analytics on Massively Parallel Processing Databases

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2pts0
Gitlab 500 error 8 years ago

Better, automated tools for optimization are needed. Postgres ecosystem right now lacks a lot of things that enterprise systems have for decades.

I mean, in more recent Postgres versions, more things related to window functions are available – like CUBE, GROUPING SETS, etc. 9.3 was released back in 2013.

select version();

PostgreSQL 9.3.20 on x86_64-unknown-linux-gnu, compiled by gcc (Ubuntu 4.8.4-2ubuntu1~14.04.3) 4.8.4, 64-bi

I wonder why so old version is being used. I will be not supported by the community pretty soon.

+1. I also use *@mydomain.com feature in G Suite, and it's very convenient to understand which companies sell/pass email databases to others w/o my permission. In some cases, you need to reply from that "aliased" address -- in this case, I do go to the Settings, add an alias, got a confirmation code, and confirm it. Then this new "address" is available in GMail in drop-down "From:" menu when you write a new email.

username+anything@mydomain.com is also a useful feature (as well as u.s.er.nam.e@mydomain.com – dots are all ignored in GMail; some services don't allow "+" in email address field, so you can use finite number of variants with ".").

These little tricks make GMail convenient for geeks :)

[dead] 9 years ago

Funny: the error message says "integer divide by 0", so it's basically stack overflow error.

The very short video posted in CMAS news doesn't fully reflect the awesomeness of what's been done. The full recordings of one of the competition's days can be found here: https://www.youtube.com/watch?v=nyWBhcQJNY4

Truly fantastic video of freediving competition. It reminds me awesome NASA TV with their live streaming of spacewalks.

I've never seen so well-organized coverage of a freediving event: good video quality, deep camera going back and forth to 100+ meters, amazing live comments. Various styles, athletes' errors underwater and at the surface, and breathtaking mystery of free fall – the video carries all the details.

Good to see how the combination of modern technologies can be used to achieve such results with relatively low costs.

This looks like really good and well-timed reaction to Uber's case. And shame on Uber that it was absolutely not transparent, hiding its problems.

If you use FOSS, you must be open to communications with the Open Source community. There is a lot of ways to do it. If you're a giant, you can find resources to change the software and contribute with patches, but if you cannot do it, please at least speak openly (or at least privately to some core developers), explaining your needs and issues.

Using FOSS without that is the same as using pirate versions of Oracle or Microsoft products, without buying support subscription.

For some cases (like single HDD and low amount of RAM) it should be so, but for some cases(from "the whole table is already loaded to RAM" to "SSD" and to "a table is partitioned among multiple drives"), it must give very good improvements for analytical queries.

Many people smarter than me do think that caching [SQL] queries proved to be really bad idea (regardless of DBMS). Why caching API queries would be better?

Even if I would use query caching, I cannot imagine how it would help me to deal with really long queries (say, lacking proper indexes). Caching doesn't help when your query runs for 30 seconds – nobody will wait (and produce cache for others) so long.

Ok, but nested loop in so many situations is totally losing to other algorithms to join data (like merge join, hash join) when you deal with large datasets, right? So again, it will be inefficient by default, for so many cases.

There is a lot of cases when user needs just 20 rows from billions but getting them is very hard problem. One of such examples is well-known: it's twitter-like data model, where a user can "follow" thousands of others and you need to fetch top N recent posts from all that thousands streams.

In Postgres, straightforward approach to query such data is based on JOINs and it's absolutely inefficient. This can be dramatically optimized with recursive CTEs, arrays and loose index scan approach, but GraphQL by default it will do straightforward JOINs, right?

I hope GraphQL has (or will have soon) ways to overwrite/redefine queries, but again, it leads us to the same problems "patch driven development" that everyone hated in ORMs during decades. That's why I'm saying that GraphQL is "a new ORM", but it's more dangerous due to it's openness and proximity to web users, that's why it can bring even more dev and devops pain to the world that ORMs did during decades.

Yes but if in the past you could blame your internal developer for making slow SQL query, now these rich possibilities to perform really stressful queries to your DBMS are open to the public, right?

The main riddle to me is: in case of RDBMS in backend, how can we guess in advance which indexes are needed and how can we forbid/limit all "heavy" queries?

Does anybody considered this problem at all? (Giving too much flexibility to client and allowing non-optimal queries like joining several big tables or data collections w/o proper index support.) It's so weird that all materials I saw about GraphQL hushed up this question which is essential for the future of this technology.

And it's so similar to ORM's issues all the industry experienced past 20 years. But perhaps more dangerous due to the public nature of many APIs.