foldU

8 ms·

31.
The Little Planner Chapter 4: A Pushdown Party(buttondown.email)
1 points by foldU 3y ago|0 comments
32.
The Little Planner Chapter 4: A Pushdown Party(buttondown.email)
4 points by foldU 3y ago|0 comments
33.
by foldU 3y ago
I had not heard of this term but it sounds like the same thing!
34.
SQL Scoping Is Surprisingly Subtle and Semantic(buttondown.email)
2 points by foldU 3y ago|0 comments
35.
SQL Scoping Is Surprisingly Subtle and Semantic(buttondown.email)
2 points by foldU 3y ago|0 comments
36.
SQL Scoping Is Surprisingly Subtle and Semantic(buttondown.email)
9 points by foldU 3y ago|0 comments
37.
by foldU 3y ago
whoops embarrassing. thank you for pointing that out!
38.
by foldU 3y ago
I had no idea, thanks!
39.
The Case of a Curious SQL Query(buttondown.email)
5 points by foldU 3y ago|0 comments
40.
The Case of a Curious SQL Query(buttondown.email)
9 points by foldU 3y ago|0 comments
41.
Representing Columns in Query Optimizers(buttondown.email)
3 points by foldU 3y ago|0 comments
42.
Why Are Query Plans Trees?(buttondown.email)
1 points by foldU 3y ago|0 comments
43.
Internal Affairs(buttondown.email)
4 points by foldU 3y ago|1 comments
44.
The Halloween Problem(buttondown.email)
3 points by foldU 3y ago|0 comments
45.
by foldU 3y ago
You are correct! I will fix it when I get home, thank you for the correction!
46.
Joins 13 Ways(justinjaffray.com)
531 points by foldU 3y ago|76 comments
47.
The CVM Algorithm for Estimating Distinct Elements in Streams [pdf](cs.stanford.edu)
3 points by foldU 3y ago|1 comments
48.
A Charming Algorithm for Count-Distinct(justinjaffray.com)
2 points by foldU 4y ago|0 comments
49.
by foldU 4y ago
This implementation incidentally uses a hash table, but any set-like data structure would work. This is different from things like HyperLogLog that actually depend on the hash values themselves in order to work.
50.
A Charming Algorithm for Count-Distinct(justinjaffray.com)
9 points by foldU 4y ago|2 comments
51.
Functional Dependencies and Orders(justinjaffray.com)
1 points by foldU 4y ago|0 comments
52.
Join: The Projection(justinjaffray.com)
4 points by foldU 4y ago|0 comments
53.
A gentle-ish introduction to worst-case optimal joins(justinjaffray.com)
92 points by foldU 4y ago|12 comments
54.
A Gentle(-Ish) Introduction to Worst-Case Optimal Joins(justinjaffray.com)
9 points by foldU 4y ago|0 comments
55.
by foldU 5y ago
Thanks so much for the thoughtful response! TigerBeetle seems very cool, I hope to dig in and learn more about it at some point. As an aside, your Twitter thread on macOS fsync behaviour was very helpful for me!
56.
Compaction(justinjaffray.com)
8 points by foldU 5y ago|0 comments
57.
by foldU 5y ago
Yes my intention was that it's more or less the reference version of that model, certainly databases like System R existed many years before Volcano :) I appreciate the correction.
58.
by foldU 5y ago
Both the linked papers (Neumann[0] and Shaikhha et al[1]) go into this a bit. I personally found figures 3 and 4 in the Neumann paper particularly helpful. [0] https://www.vldb.org/pvldb/vol4/p539-neumann.pdf [1]
59.
by foldU 5y ago
(I'm the author of this post) That's a great document I hadn't seen, thanks for sharing!
60.
Query Engines: Push vs. Pull(justinjaffray.com)
4 points by foldU 5y ago|0 comments

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