Speaker 1: Welcome to the deep Dive. We're of the show that
Speaker 1: cuts through the noise, getting straight to the fascinating core
Speaker 1: of well complex topics. That's the plan, and today we
Speaker 1: are diving headfirst into the fundamentals of computer science. We're
Speaker 1: drawing insights from computer science distilled by a Watston Fererrafilo.
Speaker 2: A great resource, absolutely.
Speaker 1: And our mission isn't just for coders, right, It's for
Speaker 1: anyone who wants to sharpen their computational.
Speaker 2: Thinking exactly to really understand the invisible forces powering our
Speaker 2: digital world.
Speaker 1: And you know it goes way beyond just the machines.
Speaker 2: Themselves, it really does. There's that famous quote from Esger Dikstra,
Speaker 2: a real pioneer. Oh yeah, he said, computer science is
Speaker 2: not about machines in the same way that astronomy is
Speaker 2: not about telescopes.
Speaker 1: Huh. I like that.
Speaker 2: It's fundamentally about problem solving. It's a systematic way of
Speaker 2: thinking that helps us break down incredibly complex challenges.
Speaker 1: That quote perfectly sets the stage, doesn't it. So you
Speaker 1: listening are about to take a shortcut sort of to
Speaker 1: being truly well informed about these foundations, the stuff that
Speaker 1: builds everything from your phone apps to global networks. Okay,
Speaker 1: so if computer science isn't just about the physical machines,
Speaker 1: where do we even begin when we talk about the
Speaker 1: art of solving computational problems?
Speaker 2: Well, we begin right at the start, taking a big,
Speaker 2: maybe fuzzy idea and breaking.
Speaker 1: It down into smaller pieces.
Speaker 2: Exactly definable chunks. Translating a human thought into something that
Speaker 2: you know could eventually be processed step by step.
Speaker 1: Okay, that makes a lot of sense. So what tools
Speaker 1: do we use for that initial modeling of ideas well?
Speaker 2: Visualizing the process is a great start. I think of flow.
Speaker 1: Charts, right, like drawing it out.
Speaker 2: Yeah, the book mentioned how wikipedians actually used a flow
Speaker 2: chart to map out their whole collaborative editing process.
Speaker 1: Really for human interaction.
Speaker 2: Yep, showing how discussions, edits agreements would flow. It's powerful
Speaker 2: for complex interactions, not just code or you know, a
Speaker 2: simpler example just mapping the steps to find the biggest
Speaker 2: of three numbers.
Speaker 1: I can see how that would clarify things. Okay, what
Speaker 1: about pseudo code. I've had that term thrown around. Ugh.
Speaker 2: Pseudocode another fantastic tool. It's like human friendly code, so
Speaker 2: not actual programming language. It completely disregards strict syntax rules.
Speaker 2: You just write out the steps in plain English or
Speaker 2: whatever works for you.
Speaker 1: So flow chart, pseudocode, they're basically tools for thinking clearly.
Speaker 2: Then that's it, helping you visualize and refine the process
Speaker 2: before you even think about writing actual rentable code, structuring
Speaker 2: your thoughts.
Speaker 1: Logically, and speaking of logic, the book makes this interesting point.
Speaker 1: Coders work with logic so much it messes their minds,
Speaker 1: but often use it unknowingly.
Speaker 2: Uh huh, Yeah, there's some truth to that.
Speaker 1: So what's the benefit of understanding logic consciously?
Speaker 2: It's about precision. Formal logic lets us work with definite
Speaker 2: true or false statements black and white pretty much. Yeah,
Speaker 2: understanding logical operators A and D or xor conditional negation.
Speaker 2: It's all boole.
Speaker 1: Algebra really, giving us rules to simplify things exactly.
Speaker 2: The source has a great example the hot server problem.
Speaker 2: A server crashes if it's overheating and the AC is off, okay,
Speaker 2: or if it's overheating and the Chassi cooler fails.
Speaker 1: Right, So you'd write that out as a logical expression.
Speaker 2: Precisely, maybe overheating andy acoff or overheating and de cooler failed.
Speaker 1: What's complicated, But here's the power.
Speaker 2: Using logic rules like distributivity from Boolean algebra, you simplify
Speaker 2: that it becomes overheating and d acoff or cooler failed.
Speaker 2: Much clearer, right.
Speaker 1: Yeah, that's much simpler. You see immediately overheating is key
Speaker 1: plus one of the cooling failures.
Speaker 2: Exactly. This kind of simplification saves a ton of effort
Speaker 2: in complex systems. It gives you the precise conditions.
Speaker 1: Okay, great illustration, And to analyze these logical models you
Speaker 1: mentioned truth tables.
Speaker 2: Ah, yes, truth tables are like comprehensive diagnostic tools. Oh
Speaker 2: so they lay out every single possible combination of inputs
Speaker 2: truefall for each condition and show the outcome for each one.
Speaker 1: Check every scenario.
Speaker 2: Yep. The book's fragile system example about designing a database
Speaker 2: had four variables that means sixteen possible states. A truth
Speaker 2: table maps mole out.
Speaker 1: Ensures you don't miss any weird.
Speaker 2: Edge cases precisely. It's systematic.
Speaker 1: Now, what's really fascinating here is how this abstract logic
Speaker 1: connects directly to the hardware. Right, how do true false
Speaker 1: becomes something a machine understands?
Speaker 2: It's kind of brilliant in its simplicity, True and false
Speaker 2: become high or low voltage signals signals right, right, And
Speaker 2: these logical operations an D or not T are performed
Speaker 2: by tiny electrical circuits. We call them logic gates.
Speaker 1: Got it.
Speaker 2: The book even shows how you combine these gates to
Speaker 2: actually some numbers, like how two plus three weekls five
Speaker 2: works at the binary level.
Speaker 1: Wow, so's the CPU, the brain of the computer.
Speaker 2: It's working. Principles are still fundamentally based on this Boolean algebra.
Speaker 2: It's millions billions of these tiny switches manipulating voltages.
Speaker 1: Incredible. Okay, So logic defines conditions. What about the sheer
Speaker 1: scale of possibilities that brings us to counting? Why is
Speaker 1: something basic counting so vital here?
Speaker 2: Because it builds your intuition, your gut feeling for how
Speaker 2: possibilities combine and often explode explode. Yeah, it's not about
Speaker 2: memorizing formulas, but grasping the principles, like cracking a simple
Speaker 2: pin two digits one letter.
Speaker 1: Okay, so ten choices, ten choices, twenty six choices, right.
Speaker 2: You multiply them ten by ten by twenty six, it's
Speaker 2: twenty six hundred possibilities, or the team building example. Twenty
Speaker 2: three candidates each can be in or out.
Speaker 1: So two choices for each of the twenty three.
Speaker 2: That's two to the power of twenty three possible teams.
Speaker 2: The numbers grow fast.
Speaker 1: And sometimes they grow astronomically fast, like with permutations right
Speaker 1: where order matters exactly.
Speaker 2: Permutations are about the number of ways to order things.
Speaker 2: The classic traveling salesman.
Speaker 1: Problem finding the shortest route.
Speaker 2: Yeah, just fifteen cities, yeh. Try to take every possible
Speaker 2: route order you're looking at over one point three trillion
Speaker 2: routes trillion. Wow, And if each calculation takes a microsecond,
Speaker 2: that's fifteen days of compute time for twenty cities. It
Speaker 2: jumps to seventy seven thousand years.
Speaker 1: That's insane.
Speaker 2: The precious tune example too, thirteen note six note melodies,
Speaker 2: over one point two million possibilities. It shows that factorials
Speaker 2: just make things explode computationally staggering.
Speaker 1: Okay, what if the order doesn't matter? Like in combination.
Speaker 2: Combinations are about selecting items where arrangement isn't important, like
Speaker 2: the chess queen's.
Speaker 1: Problem placing eight queens so none attack each other.
Speaker 2: Right, if we were just choosing eight squares out of
Speaker 2: sixty four, ignoring the attack rules for a moment, that's
Speaker 2: sixty four, choose eight. It was about four point four
Speaker 2: billion ways.
Speaker 1: Still huge, but less than the traveling salesman for fifteen cities.
Speaker 2: Often significantly smaller than permutations. Yeah yeah, but still massive
Speaker 2: numbers to deal with.
Speaker 1: And then there are sums. I remember Gauss's trick for
Speaker 1: adding one to one hundred. How does that play in?
Speaker 2: It's a classic shortcut, right, Yeah. That one plus two
Speaker 2: plus ro plus and n plus one two formula the
Speaker 2: flying cheap airline ticket problem. And the source uses this
Speaker 2: finding the cheapest flight over thirty days. You check every
Speaker 2: pair of star.
Speaker 1: Ten dates, so day one to day two, day one
Speaker 1: to day three up to day twenty ninety day thirty exactly.
Speaker 2: That's a sum one plus two plus b L plus
Speaker 2: twenty nine, which Gauss's trick tells you as four hundred
Speaker 2: and thirty five pairs. Shows how even simple sums lead
Speaker 2: to lots of operations.
Speaker 1: And this leads into probability, too, doesn't it.
Speaker 2: Absolutely. Probability concepts are critical for managing uncertainty thinking about
Speaker 2: independent events. Hey, like backing up data using three cheap
Speaker 2: discs might actually give you a lower overall failure risk
Speaker 2: than one super expensive disc if their failures are independent,
Speaker 2: interesting trade off.
Speaker 1: Or mutually exclusive events like choosing one subscription plan. And
Speaker 1: understanding this helps avoid things like the gambler's fallacy, believing.
Speaker 2: A coin is due for heads after a run of tails.
Speaker 1: Yeah, past independent events just don't affect future ones. It
Speaker 1: seems obvious, but it trips people up. So okay, with modeling,
Speaker 1: logic and counting, we can define and understand problems. But
Speaker 1: we have a solution. How do we know if it's
Speaker 1: actually good or you know, fast?
Speaker 2: Yeah, that feels like the crucial next step. It brings
Speaker 2: us to complexity. The book asks sorting twenty six cards,
Speaker 2: how long does it take? What if you have fifty
Speaker 2: two cards? Does it just take twice as long?
Speaker 1: It's often more nuanced than that, and it's absolutely critical.
Speaker 1: We talk about time complexity, usually written as T on
Speaker 1: N T O N.
Speaker 2: Yeah.
Speaker 1: It measures how the number of operations and algorithm performs
Speaker 1: scales with the input size N, and we usually focus
Speaker 1: on the worst case.
Speaker 2: Planning for the worst makes sense.
Speaker 1: And the standard way to talk about this growth is
Speaker 1: big O notation. It focuses on the dominant term how
Speaker 1: it scales, like.
Speaker 2: The biggest factor at n gets large exactly. For example,
Speaker 2: a simple method like selection sword is O N two
Speaker 2: O of n squared, meaning meaning if you double the
Speaker 2: input size n, the time it takes roughly quadruples. Because
Speaker 2: of that N squared factor, it grows quadratically.
Speaker 1: Ouch. And here's where we get that aha moment, right.
Speaker 1: Comparing something like om log in to O N two.
Speaker 1: The difference is huge.
Speaker 2: It's absolutely mass especially for large inputs. The source has
Speaker 2: a table. It's shocking.
Speaker 1: Give us an idea.
Speaker 2: Okay, for a million items, an O N two algorithm
Speaker 2: might literally take years to rent years. Yeah, But an
Speaker 2: O N log n algorithm for the same million items,
Speaker 2: it might take.
Speaker 1: Minutes minutes versus years. That's incredible.
Speaker 2: That log in factor makes a profound difference. It often
Speaker 2: comes from algorithms that repeatedly cut the problem size in half.
Speaker 2: It's why finding efficient algorithms is so important.
Speaker 1: So O N two is bad for big inputs, O
Speaker 1: en log in is much better. What about even worse
Speaker 1: things like exponential algorithms?
Speaker 2: Ah, those are the scary ones, things like two oh
Speaker 2: of two. To the end's they grow so incredibly fast
Speaker 2: they're considered not runnable for anything but really tiny inputs.
Speaker 2: Even supercomputers can't handle them. Once NN gets even moderately.
Speaker 1: Large, like the traveling salesman problem again exactly.
Speaker 2: That often falls into this category, along with other famously
Speaker 2: hard problems called NP complete problems like the knapsack problem.
Speaker 1: And isn't there a prize for solving one of those
Speaker 1: efficient Huh?
Speaker 2: Yes? Yeah, that's the million dollar prize from the Clay
Speaker 2: Mathematics Institute. If you find a non exponential algorithm for
Speaker 2: any NP complete problem, you solve a major open question
Speaker 2: in computer science.
Speaker 1: Wow. Okay, So knowing this, how do we devise clever
Speaker 1: strategies to solve problems more efficiently? Like the quote says,
Speaker 1: if you find a good move, look for a better one.
Speaker 2: Right, we need strategies.
Speaker 1: Let's start simple iteration just using loops right yep.
Speaker 2: Repeating a process like merging two sorted lists you just
Speaker 2: iteratively compare the front items simple and effective.
Speaker 1: But iteration can lead to bad complexity too.
Speaker 2: It can if you have nested loops, like when generating
Speaker 2: a power set all possible subsets.
Speaker 1: Of items like all combinations pizza toppings sort of.
Speaker 2: Yeah, that can quickly lead to O two end complexity
Speaker 2: exponential Again. Then there's recursion, which can be quite elegant.
Speaker 1: Where a function calls itself.
Speaker 2: Exactly, it calls clones of itself to solve smaller versions
Speaker 2: of the same problem. Really intuitive for things like calculating
Speaker 2: fibinih numbers or checking if a word is a palindrome.
Speaker 1: Simpler code often often.
Speaker 2: Yes, but there's a trade off. Recursion can have computational overhead.
Speaker 2: The computer needs to keep track of all those calls,
Speaker 2: which can use more memory in CPU time than a
Speaker 2: straightforward iterative loop.
Speaker 1: Simplicity versus performance a classic dilemma. It often is what
Speaker 1: about just trying everything? Brute force?
Speaker 2: That's exactly what it sounds like. Inspecting all possible solutions
Speaker 2: seems inefficient. Often it is very naive, but sometimes it's
Speaker 2: the only direct way with the starting point. For the
Speaker 2: best trade problem finding the best days to buy and
Speaker 2: sell gold, say.
Speaker 1: You check every possible by day and sell day pair.
Speaker 2: Yep, that's o N two. For the knapsack problem, checking
Speaker 2: every combination of items that's two n A two quickly
Speaker 2: becomes impossible.
Speaker 1: So brute force is often too slow. How do we
Speaker 1: make it smarter? Can we avoid checking everything?
Speaker 2: Absolutely? A key optimization is backtracking.
Speaker 1: How does that work?
Speaker 2: Think of the eight queen puzzle again. Instead of placing
Speaker 2: queens randomly, backtracking places them one by one, ensuring each
Speaker 2: placement is valid so far.
Speaker 1: And if it hits a dead end, if.
Speaker 2: It places a queen and realizes there's no valid spot
Speaker 2: for the next one, it rolls back its last move,
Speaker 2: takes the queen off, and tries a different spot in
Speaker 2: the previous.
Speaker 1: Row, failing early. Like you said, exactly, Yeah.
Speaker 2: It prunes entire branches of the search tree that can't
Speaker 2: possibly lead to a solution, cuts down the search space dramatically.
Speaker 1: That's clever. But what if even backtracking is too slow?
Speaker 1: Or maybe we just need a good enough answer, not
Speaker 1: the absolute perfect one.
Speaker 2: Right. That's where heuristics come in. They're like rules of thumb.
Speaker 1: Or shortcuts, not guaranteed to be optimal.
Speaker 2: Not guaranteed no, but they often lead to a pretty
Speaker 2: good solution much much faster. A common one is the
Speaker 2: greedy approach. Yeah. For the knapsack problem, a greedy approach
Speaker 2: might just be keep packing the most valuable items per
Speaker 2: pound until the bag is full, don't overthink future possibilities.
Speaker 1: Simple does it work?
Speaker 2: Sometimes well. For the traveling salesman, a greedy heuristic might
Speaker 2: be always go to the nearest unvisited city next. Not
Speaker 2: optimal but fast, and sometimes like connecting settlements with minimum
Speaker 2: wire power grid problem, the greedy approach actually is the
Speaker 2: optimal solution.
Speaker 1: Interesting, Okay. One more strategy branch and bound. How does
Speaker 1: that fit? In?
Speaker 2: Branch and bound is a more sophisticated strategy, usually for
Speaker 2: optimization problems where you want the absolute best solution.
Speaker 1: Like backtracking, but smarter kind of.
Speaker 2: It systematically searches dividing the problem up. But the key
Speaker 2: is it uses bounds, estimates of the best possible outcome
Speaker 2: in a given branch.
Speaker 1: Bounds.
Speaker 2: Yeah, Like for the knapsack problem, you could calculate an
Speaker 2: optimistic upper bound on the profit you could get from
Speaker 2: a partial solution, for example, allowing fractions of items. If
Speaker 2: that upper bound is still worse than a complete solution
Speaker 2: you've already found.
Speaker 1: Elsewhere, you just ditch that whole branch exactly.
Speaker 2: You prune it, knowing it can't possibly lead to the
Speaker 2: best overall answer. This raise is in shortant question, how
Speaker 2: can code go from super slow to reasonably paced. This
Speaker 2: kind of intelligent pruning is often the key. It lets
Speaker 2: you find the optimal solution way faster than simple brute
Speaker 2: force or basic backtracking.
Speaker 1: Okay, so we've got ways to think about problems, ways
Speaker 1: to strategize for efficiency. But what about the data itself?
Speaker 1: The information these algorithms work on. How do we manage that.
Speaker 2: Great question that brings us to organizing the data effectively?
Speaker 1: Which starts with abstract data types or ADTs. The book
Speaker 1: uses a card dashboard analogy.
Speaker 2: Yeah, it's a good one. The dashboard hides all the
Speaker 2: complex engine mechanics, right, you just use the control speed fuel.
Speaker 1: So ADT's hide the data details exactly if.
Speaker 2: They define a set of operations like push, pop, find
Speaker 2: without specifying how those operations are implemented or how the
Speaker 2: data is stored underneath. What's the advantage Simpler code flexibility.
Speaker 2: You can swap out the underline implementation later without changing
Speaker 2: the code that uses the ADT Reasonability, better organization, fewer bugs,
Speaker 2: lots of benefits. Interact with the interface, not the messy internals.
Speaker 2: And there are several common ADTs we use all the time,
Speaker 2: like a stack YEP, a stack is lifo last in,
Speaker 2: first out. Think of the undoe button in your text editor.
Speaker 1: The last thing you did gets undone first, like a stack.
Speaker 2: Of plates exactly. Then there's a queue which is fifo
Speaker 2: first in, first out, like a line for coffee or
Speaker 2: print jobs waiting for.
Speaker 1: The printer first come for served.
Speaker 2: Right. A priority queue is similar, but items have priorities.
Speaker 2: Higher priority items jump the queue like in an er.
Speaker 1: Makes sense.
Speaker 2: A list lets you insert or remove items anywhere. A
Speaker 2: map or dictionary stores key value pairs like a word
Speaker 2: in its definition, and a set holds unique items, like
Speaker 2: a collection of unique user names.
Speaker 1: So those are the abstract ideas. What about the actual
Speaker 1: data structures, the concrete ways data is organized in memory?
Speaker 2: Right, this is the implementation layer, like a rayse arrays
Speaker 2: or fundamental data storage side by side in memory. This
Speaker 2: gives you instant access. Oh one, if you know the index,
Speaker 2: the position that they're rigid, Yeah, fixed size. Usually resizing
Speaker 2: can be slow because you might have to copy everything.
Speaker 1: Then you have linked lists, chains of items.
Speaker 2: Cans of cells. Yeah yeah, they don't need to be
Speaker 2: contiguous in memory, very flexible for adding or removing items,
Speaker 2: just relink the pointers. But finding an item takes time,
Speaker 2: oh when because you might have to follow the chain
Speaker 2: from the start.
Speaker 1: Trade offs again, speed versus flexibility always trade offs.
Speaker 2: Then we get into trees. Hierarchical structures like a family
Speaker 2: tree sort of binary search trees are really important. They
Speaker 2: keep data sorted in a way that allows very fast
Speaker 2: searching how fast O log in average. If the tree
Speaker 2: is balanced, that means it doesn't get too lopsided. Keeping
Speaker 2: trees balanced is key, which leads to self balancing trees
Speaker 2: like avl or red black trees and things like B
Speaker 2: trees are optimalist for storing tree structures on disk.
Speaker 1: Okay, and one more structure. Hash tables. You said they're
Speaker 1: like magic.
Speaker 2: They feel like magic. Sometimes they allow finding items in
Speaker 2: one time on average constant time. They use hash function,
Speaker 2: a special calculation to convert the item's key directly into
Speaker 2: a memory address or close.
Speaker 1: To it, so you jump right to the data pretty much.
Speaker 2: The main challenge is hash collisions when different keys hash
Speaker 2: to the same spot. There are ways to handle that,
Speaker 2: but yeah, hash tables are behind maps and sets when
Speaker 2: you need that super fast access.
Speaker 1: Okay, stepping back from individual structures, managing huge amounts of
Speaker 1: data brings us to databases and relational databases have been
Speaker 1: king for a long time. How do they work?
Speaker 2: They organize data into tables think spreadsheets basically with rows
Speaker 2: and columns with strict rules, very strict rules defined by
Speaker 2: a schema. It dictates what kind of data goes in
Speaker 2: each column field for each rope entry. But the real
Speaker 2: power isn't just the tables. It's the relationships between tables.
Speaker 1: How do you link them?
Speaker 2: Using primary keys unique IDs for each row and a table,
Speaker 2: and foreign keys which are references to primary keys in
Speaker 2: other tables.
Speaker 1: Ah, So you don't repeat information.
Speaker 2: Exactly, You avoid data duplication. I don't store a customer's
Speaker 2: full address with every single order they place. You have
Speaker 2: a customer's table and an order's table linked by a
Speaker 2: customer id. This process is called normalization. It keeps data
Speaker 2: consistent and reduces redundancy.
Speaker 1: And we talk to these databases using SQL right structured query.
Speaker 2: Language, that's the standard language. Yeah.
Speaker 1: Key commands are like select from.
Speaker 2: Where, order by for sorting, group by why for aggregation,
Speaker 2: and crucially join what is join do? Join lets you
Speaker 2: combine data from multiple related tables based on those keys
Speaker 2: we talked about, like getting customer names alongside their order details.
Speaker 2: It's incredibly powerful.
Speaker 1: That also maybe slow it can be.
Speaker 2: Joining very large tables is computationally expensive. That's kind of
Speaker 2: the power and weakness of the relational model. So to
Speaker 2: speed things up, we use indexing.
Speaker 1: Like an index in a book similar idea.
Speaker 2: It's a separate data structure, often a self balancing tree
Speaker 2: that maps index values like IDs to their actual location
Speaker 2: in the table.
Speaker 1: So queries are faster, much faster.
Speaker 2: O log instead of scanning the whole table. Sorting is
Speaker 2: faster too, But indexes have costs, take up space, and
Speaker 2: they slow down updates because the index needs to be
Speaker 2: updated too.
Speaker 1: So don't just index everything.
Speaker 2: Definitely not, use tools to see where indexes actually help.
Speaker 2: And finally, transactions are vital why they ensure operations happen
Speaker 2: completely or not at all. Think transferring money. You need
Speaker 2: to debit one account and credit another. A transaction bundles
Speaker 2: those together. If anything fails midway, the whole thing rolls back.
Speaker 2: Prevents data inconsistencies, right, crucial.
Speaker 1: For things like banking. Okay, but relational isn't the only
Speaker 1: game in town anymore. We hear a lot about no
Speaker 1: SQL databases. How are they different?
Speaker 2: They generally bitch the rigid table structure and relationships for
Speaker 2: more flexibility.
Speaker 1: Like documents stores.
Speaker 2: Yeah, popular ones like Mango dB. Data is stored in
Speaker 2: flexible documents, often jason like grouped in collections, no fixed schema.
Speaker 2: Usually data might be duplicated intentionally for faster reads.
Speaker 1: So different philosophy than normal is very.
Speaker 2: Different, more application centric than data centric. Then you have
Speaker 2: key value.
Speaker 1: Stores, simple key simple value.
Speaker 2: The simplest think REDTIS or memcached mainly used for cashing,
Speaker 2: getting that one access speed.
Speaker 1: And graph databases.
Speaker 2: Those are cool designed specifically for networked data. Nodes represent
Speaker 2: entities like people, Edges represent relationships like friendships. If your
Speaker 2: data looks like a network, a graph database like neo
Speaker 2: fog is often the most natural.
Speaker 1: Fit, and no sequels is often linked with big data.
Speaker 2: Often yeah the three v's volume, velocity, variety, no sequels.
Speaker 2: Flexibility can handle massive, fast changing unstructured data better than
Speaker 2: rigid relational models, sometimes.
Speaker 1: So segle versus no SQL.
Speaker 2: It's a trade off, big trade off. Relational prioritizes data
Speaker 2: consistency via normalization and transactions. No SQL often prioritizes flexibility,
Speaker 2: speed and scalability. But consistency might need more careful handling
Speaker 2: by the application developer, and.
Speaker 1: For truly massive scale databases can be running on multiple
Speaker 1: machines YEP to.
Speaker 2: Handle huge loads or ensure high availability. This involves techniques
Speaker 2: like replication and charting copying data. Single master replication has
Speaker 2: one machine handle rights, others handle reads, good for read
Speaker 2: heavy loads. Multi master lets multiple machines handle rights better
Speaker 2: for right scaling, but more complex and charting. That's splitting
Speaker 2: the data itself across multiple machines like customers am go
Speaker 2: on server one and Z on server two. Often combined
Speaker 2: with replication, but distribution introduces a huge challenge data consistency.
Speaker 1: Making sure all the machines agree exactly.
Speaker 2: There's a fundamental trade off between consistency and performance availability.
Speaker 2: The movie ticket example, two people by the last ticket
Speaker 2: via different server simultaneously. How do you resolve that instantly
Speaker 2: without slowing everything down tricky. Many systems use eventual consistency,
Speaker 2: meaning the data will eventually become consistent across all nodes,
Speaker 2: but there might be brief periods where different users see
Speaker 2: slightly different data. It's a design choice.
Speaker 1: We also briefly touched on GIS and serialization formats.
Speaker 2: Right, gis for spatial data maps, locations, nearest hospital queries,
Speaker 2: and serialization formats like SQL, dumps, XML, CSV, and especially JSON,
Speaker 2: which is kind of becoming the universal standard for exchanging
Speaker 2: data between systems.
Speaker 1: Okay, pew, we have the thought process, the efficiency strategies,
Speaker 1: ways to manage data, But how does it all actually
Speaker 1: run on the metal? How do computers work under the hood?
Speaker 2: Right, let's get down to the hardware, the computer architecture
Speaker 2: CPU and.
Speaker 1: RAM SO RAM random access memory. What is it?
Speaker 2: Fundamentally, it's basically a grid of millions or billions of
Speaker 2: tiny memory cells. Each cell has a unique numerical.
Speaker 1: Address and you access them.
Speaker 2: Using electrical signals on buses and address PUS specifies which
Speaker 2: cell and a data bus carries the actual data as
Speaker 2: binary ones and narlows represented by high low voltage to
Speaker 2: be read written. And the CPU central processing unit is
Speaker 2: the brain doing the work.
Speaker 1: What does it do?
Speaker 2: It has its own suit profast internal storage called registers,
Speaker 2: and it understands a specific set of simple commands. It's
Speaker 2: instruction set, things like ad number eight to number B,
Speaker 2: move data from memory location x to register y compare
Speaker 2: two numbers.
Speaker 1: So computer code deep down is just a sequence of
Speaker 1: these simple instructions.
Speaker 2: Exactly a sequence of numbers representing the CPU operations. The
Speaker 2: CPU runs in a constant fetch execute cycle fetch execute YEP,
Speaker 2: guided by the program counter PC, which holds the memory
Speaker 2: address of the next instruction. The CPU fetches that instruction
Speaker 2: from RAM, decodes it, executes it, maybe fetching data from RAM,
Speaker 2: doing math, storing results back, and then moves the PC
Speaker 2: to the next instruction, over and over, billions of times
Speaker 2: a second.
Speaker 1: It's kind of amazing. Everything we do browsing, gaming spreadsheets
Speaker 1: boils down to that cycle executing simple math and data
Speaker 1: moves pretty much.
Speaker 2: Yeah, the old Space Invaders game around three thousand machine
Speaker 2: instructions on a two miller heard CPU. Modern CPUs do
Speaker 2: vastly more, much.
Speaker 1: Faster, and different CPUs speak different languages.
Speaker 2: Right architectures, right CPU architectures like by eighty six. Most
Speaker 2: PCs MAX or arm phones, tablets New or Max have
Speaker 2: different instruction sets. Code compiled for one won't run on
Speaker 2: the other.
Speaker 1: And there's Indian this too, big Indian versus little Indian.
Speaker 1: How numbers are stored.
Speaker 2: Yeah, most CPUs today are a little Indian. Least significant
Speaker 2: bite first. But interestingly, network traffic often uses big Indian,
Speaker 2: a legacy from early router hardware.
Speaker 1: So how do we get from say, Python or C
Speaker 1: plus plus code to those specific CPU instructions. That's the
Speaker 1: job of a compiler, right.
Speaker 2: The compiler is that magic program that translates high level,
Speaker 2: human readable code into low level machine code for a
Speaker 2: specific CPU architecture.
Speaker 1: Is it true any code can be boiled down to
Speaker 1: simple moves?
Speaker 2: Theoretically yes, based on Turing completeness, It's been shown that
Speaker 2: even just a single instruction type like mov moved data,
Speaker 2: if used cleverly, is powerful enough to compute anything computable.
Speaker 2: And programs don't just talk to this CPU. They need
Speaker 2: the operating system too.
Speaker 1: Right Windows, Mac, OS, Linux, for things like reading files,
Speaker 1: getting keyboard input, drawing on the screen. Programs make system
Speaker 1: calls to the OS for these services.
Speaker 2: So compiled code targets both a CPU and an.
Speaker 1: OS, and compiles are smart. They optimize the code.
Speaker 2: Oh yeah, good compilers do incredible optimizations, rearranging instructions, eliminating
Speaker 2: redundant steps, making the machine code much faster than the
Speaker 2: naive translation.
Speaker 1: So the advice is write clean code, don't prematurely optimize yourself.
Speaker 2: Generally yes, focus on clarity. If you have performance problems,
Speaker 2: use profiling tools to find the real bottlenecks, then optimize
Speaker 2: those specific parts. Let the compiler handle the general stuff.
Speaker 1: What about languages like JavaScript or Python? They don't always
Speaker 1: use compilers upfront, right.
Speaker 2: Those are often scripting languages that use interpreters, and interpreter
Speaker 2: reads and executes the code line by line or translates
Speaker 2: it on the fly.
Speaker 1: Slower than compiled code.
Speaker 2: Usually yes, yeah, but often faster for development you don't
Speaker 2: have long compile step. Some languages like Go try to
Speaker 2: bridge the gap with super fast compilation, and.
Speaker 1: People can reverse this. Look at the machine code.
Speaker 2: Yep, disassembly and reverse engineering, taking the binary machine code
Speaker 2: and trying to figure out the original logic. Hackers do
Speaker 2: it to bypass licenses. Security researchers do it to find vulnerabilities,
Speaker 2: like with stucksnet.
Speaker 1: Which highlights the value of open source software.
Speaker 2: Exactly with open source like Linux, many eyes can scrutinize
Speaker 2: the source code for flaws, which arguably leads to better
Speaker 2: security than closed source systems. Or only the vendor sees
Speaker 2: the code, okay.
Speaker 1: Last piece of the hardware puzzle, the memory hierarchy. You
Speaker 1: mentioned caches earlier. Why do we need different types of
Speaker 1: memory Because.
Speaker 2: Of the processor memory gap. CPUs got incredibly fast, much
Speaker 2: faster than main memory RAM. If the CPU had to
Speaker 2: wait for RAM every single time it needed data, it
Speaker 2: would spend most of its time idle.
Speaker 1: So cash's L one, L two, L three bridge that
Speaker 1: gap precisely.
Speaker 2: They are small, extremely fast memory banks right on or
Speaker 2: very near the CPU chip. They store copies of recently
Speaker 2: used data from RAM.
Speaker 1: How do they know what data to store?
Speaker 2: The exploit locality temporal locality. If you just use some data,
Speaker 2: you'll probably use it again soon, spatial locality. If you
Speaker 2: just use data at address X, you'll probably use data
Speaker 2: near X soon.
Speaker 1: So the CASH keeps that nearby recent data close exactly.
Speaker 2: Accessing the OL one CASH might take say ten CPU cycles,
Speaker 2: Axasing RAM might take a thousand cycles. Cashes make a
Speaker 2: massive difference by reducing that waiting time.
Speaker 1: And then below RAM and the higher tricky is secondary
Speaker 1: memory like hard disks or SSDs.
Speaker 2: Right, primary memory is RAM, volatile fast. Secondary memory is
Speaker 2: non volatile storage hard discs, HDDs, solid state drives as eight.
Speaker 1: Difference is huge.
Speaker 2: Again, dramatic RAM is maybe a thousand cycles away a
Speaker 2: traditional hard disc maybe a million cycles. SSDs are much
Speaker 2: faster than hgds, but still way slower.
Speaker 1: Than RAM, and if you run out of RAM.
Speaker 2: The system starts trashing constantly swapping data between RAM and
Speaker 2: the much slower disc. The computer becomes practically unusable. Insufficient
Speaker 2: RAM is a huge performance killer, maybe a top cause
Speaker 2: of server failure. Wow, this hierarchy extends further external drives,
Speaker 2: tape backups, per schary storage, each level bigger, cheaper, but slower,
Speaker 2: and the key insight identifying the data your application uses
Speaker 2: most frequently and making that data faster to access by
Speaker 2: loading it into RAM or leveraging caches effectively is one
Speaker 2: of the biggest levers you have for improving program speed.
Speaker 1: Okay, wow, we have really covered a ton of ground today.
Speaker 1: I mean, from pure logic and problem solving.
Speaker 2: Through designing efficient algorithms, managing data structures and databases.
Speaker 1: Right down to the CPU cycles and memory hierarchy.
Speaker 2: It's quite a journey, it really is, and hopefully shows
Speaker 2: that computer science is so much more than just typing code.
Speaker 1: It's a fundamental way of thinking, Isn't it Absolutely a
Speaker 1: powerful toolkit for approaching problems systematically, and that applies way
Speaker 1: beyond just computers. So you've peered behind the curtain, got
Speaker 1: the distilled essence. Hopefully, What does this all mean for you?
Speaker 2: Yeah?
Speaker 1: How can you use this now that you've got this
Speaker 1: lens of computational thinking? What complex problem in your own
Speaker 1: life could be? Anything? Work, project planning, something complicated.
Speaker 2: Even just organizing your thoughts?
Speaker 1: Right? Could you simplify it by applying one of these tools,
Speaker 1: breaking it down, logically, optimizing a process, organizing the information better.
Speaker 2: It's worth thinking about. These aren't just for computers.
Speaker 1: Definitely something to mall over. Yeah, we encourage you to
Speaker 1: keep exploring these ideas and of course keep tuning in
Speaker 1: for more deep dives.