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To wit: I made it through a master's in CS without a database class. This reminds me of the famous Rob Pike quote: "Data dominates. If you've chosen the right

To wit: I made it through a master's in CS without a database class. This reminds me of the famous Rob Pike quote: "Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming." I've often found that if I'm coding something and the code starts looking increasingly gnarly, that rethinking the data structures / data model will clean up the code.

AlphaSite 6y ago
Although it says data structures, isn’t it more important to talk about the structure of the data than the choice of data structure?
bshipp 6y ago
every one of my data scraping projects is littered with files titled "database.db.bak1", "database.db.bak2".... for this exact reason. "Oh I scraped 1000 pages and realized I missed an entire nested data structure....better blow away the dB and try again.
gautamdivgi 6y ago
We had two books when I was doing my bachelors in CS many moons ago (late 90's). One was by Silberschatz & Galvin and the other by Ullman. The first one was used for relational algebra, various normal forms and introducing table design, keys, constraints,etc. The second was used to teach the theory for internals on how a DB is implemented - B+ trees, deadlock handling, etc, etc. That was a hard course and it was mandatory. I can see not electing to have databases as a course for masters though. Masters is to allow you to specialize and be more choice driven.
andrewl 6y ago
On that topic, Fred Brooks, author of The Mythical Man Month, said "Show me your flowchart and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowchart; it'll be obvious."
slver 6y ago
"Data structures, not algorithms, are central to programming. Can we unify this to simply "data structures are essential to algorithms". It's really weird to put these in opposition, when algorithm with no data and data without algorithm make no sense. Data structures are encoded with use cases in mind, those use cases at least at the very low level are their algorithms.
- Twisol 6y ago
  I think programming is more than just algorithms. System design and architecture have a real impact on the longevity and maintainability of your system, and I'd argue that data models (not structures per se, the distinction being logical vs. physical) are foundational to a good design. In contrast, algorithms play a much more focused role. Put differently: > Data structures are encoded with use cases in mind, those use cases at least at the very low level are their algorithms. As you've lampshaded, it's the use cases that are most fundamental. The data model should be designed to serve those use cases. The data structures and algorithms reflect the physical reality of the logical data model.
- goto11 6y ago
  In the context of persistent databases, the schema is more important than the algorithms. You can change the algorithms on the fly, but the schema is fundamental. The strength of the relational model is it is separated from any specific algorithm.
  - slver 6y ago
    There are underlying algorithms and data structures in databases (b-tree, indexing, WAL etc.) and then there are application level data structures and algorithms, like the schema and how you use it in your app. So if we see data and algorithms disconnected, we're just thinking about different architectural layers.
macando 6y ago
> "Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming." I'm saving this quote. > I've often found that if I'm coding something and the code starts looking increasingly gnarly that rethinking the data structures / data model will clean up the code. I've faced this over and over again. Writing algorithms is hard when the underlying data is not optimal. It simply invites writing complicated code and workarounds.
- Akronymus 6y ago
  > I've faced this over and over again. Writing algorithms is hard when the underlying data is not optimal. It simply invites writing complicated code and workarounds. This is actually one of the reasons I am moving more and more to functional programming for myself. I find that modeling the data as just data makes it easier to find a good representation. And that it usually results in simpler algorithms.

AlphaSite 6y ago

Although it says data structures, isn’t it more important to talk about the structure of the data than the choice of data structure?

bshipp 6y ago

every one of my data scraping projects is littered with files titled "database.db.bak1", "database.db.bak2".... for this exact reason. "Oh I scraped 1000 pages and realized I missed an entire nested data structure....better blow away the dB and try again.

gautamdivgi 6y ago

We had two books when I was doing my bachelors in CS many moons ago (late 90's). One was by Silberschatz & Galvin and the other by Ullman. The first one was used for relational algebra, various normal forms and introducing table design, keys, constraints,etc. The second was used to teach the theory for internals on how a DB is implemented - B+ trees, deadlock handling, etc, etc. That was a hard course and it was mandatory. I can see not electing to have databases as a course for masters though. Masters is to allow you to specialize and be more choice driven.

andrewl 6y ago

On that topic, Fred Brooks, author of The Mythical Man Month, said "Show me your flowchart and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowchart; it'll be obvious."

slver 6y ago

"Data structures, not algorithms, are central to programming. Can we unify this to simply "data structures are essential to algorithms". It's really weird to put these in opposition, when algorithm with no data and data without algorithm make no sense. Data structures are encoded with use cases in mind, those use cases at least at the very low level are their algorithms.

Twisol 6y ago
I think programming is more than just algorithms. System design and architecture have a real impact on the longevity and maintainability of your system, and I'd argue that data models (not structures per se, the distinction being logical vs. physical) are foundational to a good design. In contrast, algorithms play a much more focused role. Put differently: > Data structures are encoded with use cases in mind, those use cases at least at the very low level are their algorithms. As you've lampshaded, it's the use cases that are most fundamental. The data model should be designed to serve those use cases. The data structures and algorithms reflect the physical reality of the logical data model.
goto11 6y ago
In the context of persistent databases, the schema is more important than the algorithms. You can change the algorithms on the fly, but the schema is fundamental. The strength of the relational model is it is separated from any specific algorithm.
- slver 6y ago
  There are underlying algorithms and data structures in databases (b-tree, indexing, WAL etc.) and then there are application level data structures and algorithms, like the schema and how you use it in your app. So if we see data and algorithms disconnected, we're just thinking about different architectural layers.

macando 6y ago

> "Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming." I'm saving this quote. > I've often found that if I'm coding something and the code starts looking increasingly gnarly that rethinking the data structures / data model will clean up the code. I've faced this over and over again. Writing algorithms is hard when the underlying data is not optimal. It simply invites writing complicated code and workarounds.

Akronymus 6y ago
> I've faced this over and over again. Writing algorithms is hard when the underlying data is not optimal. It simply invites writing complicated code and workarounds. This is actually one of the reasons I am moving more and more to functional programming for myself. I find that modeling the data as just data makes it easier to find a good representation. And that it usually results in simpler algorithms.