When I was hiring data scientists for a previous job, my favorite tricky question was "what stack/architecture would you build" with the somewhat detailed requirements of "6 TiB of data" in sight. I was careful not to require overly complicated sums, I simply said it's MAX 6TiB
I patiently listened to all the big query hadoop habla-blabla, even asked questions about the financials (hardware/software/license BOM) and many of them came up with astonishing tens of thousands of dollars yearly.
The winner of course was the guy who understood that 6TiB is what 6 of us in the room could store on our smart phones, or a $199 enterprise HDD (or three of them for redundancy), and it could be loaded (multiple times) to memory as CSV and simply run awk scripts on it.
I am prone to the same fallacy: when I learn how to use a hammer, everything looks like a nail. Yet, not understanding the scale of "real" big data was a no-go in my eyes when hiring.
Blows my mind. I am a backend programmer and a semi-decent sysadmin and I would have immediately told you: "make a ZFS or BCacheFS pool with 20-30% redundancy bits and just go wild with CLI programs, I know dozens that work on CSV and XML, what's the problem?".
And I am not a specialized data scientist. But with time I am wondering if such a thing even exists... being a good backender / sysadmin and knowing a lot of CLI tools has always seemed to do the job for me just fine (though granted I never actually managed a data lake, so I am likely over-simplifying it).
Lol. Data management is about safety, auditablity, access control, knowledge sharing and who bunch of other stuff. I would've immediately shown you the door as someone who i cannot trust data with.
No need to act smug and superior, especially since nothing about OP's plan here actually precludes having all the nice things you mentioned, or even having them inside $your_favorite_enterprise_environment.
You risk coming across as a person who feels threatened by simple solutions, perhaps someone who wants to spend $500k in vendor subscriptions every year for simple and/or imaginary problems... exactly the type of thing TFA talks about.
But I'll ask the question.. why do you think safety, auditablity, access control, and knowledge sharing are incompatible with CLI tools and a specific choice of file system? What's your preferred alternative? Are you sticking with that alternative regardless of how often the work load runs, how often it changes, and whether the data fits in memory or requires a cluster?
I responded with the same tone that gp responded with. "blows my mind" ( that people can be so stupid) .
The classic putting words in people's mouths technique it is then. The good old straw man.
If you really must know: I said "blows my mind [that people don't try simpler and proven solutions FIRST]".
I don't know what do you have to gain to come here and pretend to be in my head. Now here's another thing that blows my mind.
Well why don't people do that according to you ?
Its not 'mind blowing' to me because you can never guess what angle interviewer is coming at you. Especially when they use the words like ' data stack'.
I don't know why and this is why I said it's mind-blowing. Because to me trying stuff that can work on most laptops comes naturally in my head as the first viable solution.
As for interviews, sure, they have all sorts of traps. It really depends on the format and the role. Since I already disclaimed that I am not actual data scientist and just a seasoned dev who can make some magic happen without a dedicated data team (if/when the need arises) then I wouldn't even be in a data scientist interview in the first place. ¯\_(ツ)_/¯
Thats fair. My comment wasn't directed at you. I was trying to be smart and write an inverse of original comment. Where I as an interviewer was looking for a proper 'data stack' and interviewee responded with a bespoke solution.
"not understanding the scale of "real" big data was a no-go in my eyes when hiring."
Sure, okay, I get it. My point was more like "Have you tried this obvious thing first that a lot of devs can do for you without too much hassle?". If I were to try for a dedicated data scientist position then I'd have done homework.
Why would you guess in that situation though?
It’s an interview, there’s at least 1 person talking to you — you should talk to them, ask them questions, share your thoughts. If you talking to them is a red flag, then high chances that you wouldn’t want to work there anyway.
Another comment mentions this classic meme:
A lot of industry work really does fall into this category, and it's not controversial to say that going the wrong way on this thing is mind-blowing. More than not being controversial, it's not confrontational, because his comment was essentially re: the industry, whereas your comment is directed at a person.
Drive by sniping where it's obvious you don't even care to debate the tech itself might get you a few "sick burn, bro" back-slaps from certain crowds, or the FUD approach might get traction with some in management, but overall it's not worth it. You don't sound smart or even professional, just nervous and afraid of every approach that you're not already intimately familiar with.
i repurposed the parent comment
"not understanding the scale of "real" big data was a no-go in my eyes when hiring." , "real winner" ect.
But yea you are right. I shouldn't have directed it at commenter. I was miffed at interviewers who use "tricky questions" and expect people to read their minds and come up with their preconceived solution.
Abstractly, "safety, auditablity, access control, knowledge sharing" are about people reading and writing files: simplifying away complicated management systems improves security. The operating system should be good enough.
What about his answer prevents any of that? As stated the question didn't require any of what you outline here. ZFS will probably do a better job of protecting your data than almost any other filesystem out there so it's not a bad foundation to start with if you want to protect data.
Your entire post reeks of "I'm smarter than you" smugness while at the same time revealing no useful information or approaches. Near as I can tell no one should trust you with any data.
unlike "blows my mind" ?
Right. OP mentioned it was "tricky question" . What makes it tricky is that all those attributes are implicitly assumed. I wouldn't interview at google and tell them my "stack" is "load it on your laptop". I would never say that in an interview even if I think that's the right "stack" .
"blows my mind" is similar in tone yes. But I wasn't replying to the OP. Further the OP actually goes into some detail about how he would approach the problem. You do not.
You are assuming you know what the OP meant by tricky question. And your assumption contradicts the rest of the OP's post regarding what he considered good answers to the question and why.
Honest question: was "blows my mind" so offensive? Thought it was quite obvious I meant that "it blows my mind people don't try the simpler stuff first, especially having in mind that it works for much bigger percentage than cloud providers would have you believe"?
I guess it wasn't but even if so, it would be legitimately baffling how people manage to project so much negativity in three words that are slightly tongue-in-cheek casual comment on the state of affairs in an area whose value is not always clear (in my observations, only after you start having 20+ data sources it starts to pay off to have dedicated data team; I've been in teams only 3-4 devs and we still managed to have 15-ish data dashboards for the executives without too much cursing).
An anecdote, surely, but what isn't?
I generally don't find that sort of thing offensive when combined with useful alternative approaches like your post provided. However the phrase does come with a connotation that you are surprised by a lack of knowledge or skill in others. That can be taken as smug or elitist by someone in the wrong frame of mind.
Thank you, that's helpful.
I already qualified my statement quite well by stating my background but if it makes you feel better then sure, show me the door. :)
I was never a data scientist, just a guy who helped whenever it was necessary.
No. You qualified it with "blows my mind" . Why would it 'blow your mind' if you don't have any data background.
He didn't say he didn't have any data background. He's clearly worked with data on several occasions as needed.
Are you trolling? Did you miss the part where I said I worked with data but wouldn't say I'm a professional data scientist?
This negative cherry picking does not do your image any favors.
this is how you know when someone takes themself too seriously
buddy, you're just rolling off buzzwords and lording it over other people
buddy you suffer from NIH syndrome upset that no one wants your 'hacks'.
Edit: for above comment.
My comment wasn't directed at parent. I was trying to be smart and write an inverse of original comment. Opposite scenario Where I as an interviewer was looking for a proper 'data stack' and interviewee responded with a bespoke solution.
"not understanding the scale of "real" big data was a no-go in my eyes when hiring."
i was trying to point out that you can never know where the interviewer is coming from. Unless i know interviewer personally i would bias towards playing it safe and go with 'enterpisey stack'
To be fair on candidates, CLI programs create technical debt the moment they're written.
A good answer that strikes a balance between size of data, latency and frequency requirements is a candidate who is able to show that they can choose the right tool that the next person will be comfortable with.
True on the premise, yep, though I'm not sure how using CLI programs like LEGO blocks creates a tech debt?
I remember replacing a CLI program built like Lego blocks. It was 90-100 LEGO blocks, written over the course of decades, in: Cobol; Fortran; C; Java; Bash; and Perl, and the Legos "connected" with environmental variables. Nobody wanted to touch it lest they break it. Sometimes it's possible to do things too smartly. Apache Spark runs locally (and via CLI).
No no, I didn't mean that at all. I meant a script using well-known CLI programs.
Obviously organically grown Frankenstein programs are a huge liability, I think every reasonable techie agrees on that.
Well your little CLI-query is suddenly in production and then... it easily escalates.
I already said I never managed a data lake and simply got stuff when it was needed but if you need to criticize then by all means, go wild.
True but it's typically less debt than anything involving a gui, pricetag, or separate server.
...or put it into SQLite for extra blazing fastness! No kidding.
That's included in CLI tools. Also duckdb and clickhouse-local are amazing.
I need to learn more about the latter for some log processing...
Log files aren’t data. That’s your first problem. But that’s the only thing that most people have that generates more bytes than can fit on screen in a single spreadsheet.
Of course they are. They just aren't always structured nicely.
Everything is data if you are brave enough.
clickhouse-local had been astonishingly fast for operating on many GB of local CSVs.
I had a heck of a time running the server locally before I discovered the CLI.
> But with time I am wondering if such a thing even exists
Check out "data science at the command line":
https://jeroenjanssens.com/dsatcl/
One thing that may have an impact on the answers: you are hiring them, so I assume they are passing a technical interview. So they expect that you want to check their understanding of the technical stack.
I would not conclude that they over-engineer everything they do from such an answer, but rather just that they got tricked in this very artificial situation where you are in a dominant position and ask trick questions.
I was recently in a technical interview with an interviewer roughly my age and my experience, and I messed up. That's the game, I get it. But the interviewer got judgemental towards my (admittedly bad) answers. I am absolutely certain that were the roles inverted, I could choose a topic I know better than him and get him in a similarly bad position. But in this case, he was in the dominant position and he chose to make me feel bad.
My point, I guess, is this: when you are the interviewer, be extra careful not to abuse your dominant position, because it is probably counter-productive for your company (and it is just not nice for the human being in front of you).
From the point of view of the interviewee, it's impossible to guess if they expect you to answer "no need for big data" or if they expect you to answer "the company is aiming for exponential growth so disregard the 6TB limit and architect for scalability"
FWIW, it's a 2.5 second extra to say "Although you don't need big data, but if you insist, ..." and gimme the hadoop answer.
Sure, but as you said yourself: it's a trick question. How often does the employee have to answer trick questions without having any time to think in the actual job?
As an interviewer, why not asking: "how would you do that in a setup that doesn't have much data and doesn't need to scale, and then how would you do it if it had a ton of data and a big need to scale?". There is no trick here, do you feel you lose information about the interviewee?
Trick questions (although not known as such at the time) are the basis of most of the work we do? XY problem is a thing for a reason, and I cannot count the number of times my teams and I have ratholed on something complex only to realize we were solving for the wrong problem, i.e. A trick question.
As a sibling puts it though, it's a matter of level. Senior/staff and above? Yeah, that's mostly what you do. Lower than that, then you should be able to mostly trust those upper folks to have seen through the trick.
I don't know about you, but in my work, I always have more than 3 seconds to find a solution. I can slowly think about the problem, sleep on it, read about it, try stuff, think about it while running, etc. I usually do at least some of those for new problems.
Then of course there is a bunch of stuff that is not challenging and for which I can start coding right away.
In an interview, those trick questions will just show you who already has experience with the problem you mentioned and who doesn't. It doesn't say at all (IMO) how good the interviewee is at tackling challenging problem. The question then is: do you want to hire someone who is good at solving challenging problems, or someone who already knows how to solve the one problem you are hiring them for?
[delayed]
Depends on the level you're hiring for. At a certain point, the candidate needs to be able to identify the right tool for the job, including when that tool is not the usual big data tools but a simple script.
[delayed]
Once had a coworker write a long proposal to rewrite some big old application from Python to Go. I threw in a single comment: why don't we use the existing code as a separate executable?
Turns out he was laid off and my suggestion was used.
(Okay, I'm being silly, the layoff was a coincidence)
Is this like interviewing for a chef position for a fancy restaurant and when asked how to perfectly cook a steak, you preface it with “well you can either go to McDonald’s and get a burger, or…”
It may not be reasonable to suggest that in a role that traditionally uses big data tools
I see it more like "it's 11pm and a family member suddenly wants to eat a steak at home, what would you do?"
The person who says "I'm going drive back to the restaurant and take my professional equipment home to cook the steak" is probably offering the wrong answer.
I'm obviously not a professional cook, but presumably the ability to improvise with whatever tools you currently have is a desirable skill.
Hmm I would say that the equivalent to your 11pm question is more something like "your sister wants to backup her holiday pictures on the cloud, how do you design it?". The person who says "I ask her 10 millions to build a data center" is probably offering the wrong answer :-).
I think more like, how would you prepare and cook the best five course gala dinner for only $10. That requires true skill.
Idk, in this instance I feel pretty strongly that cloud, and solutions with unecessary overhead, are the fast food. The article proposes not eating it all the time.
I’m not sure if you are referencing it intentionally or not, but some chefs (Gordon Ramsey for one) will ask an interviewee to make some scrambled eggs; something not super niche or specialized but enough to see what their technique is.
It is a sort of “interview hack” example that’s been used to emphasize the idea of a simple unspecialized skill-test that went around a while ago. I guess upcoming chefs probably practice egg scrambling nowadays, ruining the value of the test. But maybe they could ask to make a bit of steak now.
That's great, but it's really just desiderata about you and your personal situation.
E.g., if a HN'er takes this as advice they're just as likely to be gated by some other interviewer who interprets hedging as a smell.
I believe the posters above are essentially saying: you, the interviewer, can take the 2.5 seconds to ask the follow up, "... and if we're not immediately optimizing for scalability?" Then take that data into account when doing your assessment instead of attempting to optimize based on a single gate.
Edit: clarification
This is the crux of it. Another interviewer would’ve marked “run on a local machine with a big SSD” - as: this fool doesn’t know enough about distributed systems and just runs toy projects on one machine
That is what I think interviewers think when I don’t immediately bring up kubernetes and sqs in an architecture interview
depending on the shop? For some kinds of tasks, jumping to kubernets right away would be a minus during interview.
If people in high stakes environments interpret hedging as a smell - run from that company as fast as you can.
Hedging is a natural adult reasoning process. Do you really want to work with someone who doesn't understand that?
I once killed the deployment of a big data team in a large bank when I laid out in excruciating details exactly what they'd have to deal with during an interview.
Last I heard theyd promoted one unix guy on the inside to baby sit a bunch of chron jobs on the biggest server they could find.
It doesn't matter. The answer should be "It depends, what are the circumstances - do we expect high growth in the future? Is it gonna stay around 6TB? How and by whom will it be used and what for?"
Or, if you can guess what the interviewer is aiming for, state the assumption and go from there "If we assume it's gonna stay at <10TB for the next couple of years or even longer, then..."
Then the interviewer can interrupt and change the assumptions to his needs.
You shouldn’t guess what they expect, you should say what you think is right, and why. Do you want to work at a company where you would fail an interview due to making a correct technical assessment? And even if the guess is right, as an interviewer I would be more impressed by an applicant that will give justified reasons for a different answer than what I expected.
It's almost a law "all technical discussions devolve into interview mind games", this industry has a serious interview/hiring problem.
.parquet files are completely underrated, many people still do not know about the format!
.parquet preserves data types (unlike CSV)
They are 10x smaller than CSV. So 600GB instead of 6TB.
They are 50x faster to read than CSV
They are an "open standard" from Apache Foundation
Of course, you can't peek inside them as easily as you can a CSV. But, the tradeoffs are worth it!
Please promote the use of .parquet files! Make .parquet files available for download everywhere .csv is available!
I actually benchmarked this and duckdb CSV reader is faster than parquet reader.
I would love to see the benchmarks. That is not my experience, except in the rare case of a linear read (in which CSV is much easier to parse).
CSV underperforms in almost every other domain, like joins, aggregations, filters. Parquet lets you do that lazily without reading the entire Parquet dataset into memory.
Yes, I think duckdb only reads CSV, then projects necessary data into internal format (which is probably more efficient than parquet, again based on my benchmarks), and does all ops (joins, aggregations) on that format.
Yes, it does that, assuming you read in the entire CSV, which works for CSVs that fit in memory.
With Parquet you almost never read in the entire dataset and it's fast on all the projections, joins, etc. while living on disk.
what? Why CSV is required to fit in memory in this case? I tested CSVs which are far larger than memory, and it works just fine.
For how many rows?
10B
Parquet is underdesigned. Some parts of it do not scale well.
I believe that Parquet files have rather monolithic metadata at the end and it has 4G max size limit. 600 columns (it is realistic, believe me), and we are at slightly less than 7.2 millions row groups. Give each row group 8K rows and we are limited to 60 billion rows total. It is not much.
The flatness of the file metadata require external data structures to handle it more or less well. You cannot just mmap it and be good. This external data structure most probably will take as much memory as file metadata, or even more. So, 4G+ of your RAM will be, well, used slightly inefficiently.
(block-run-mapped log structured merge tree in one file can be as compact as parquet file and allow for very efficient memory mapped operations without additional data structures)
Thus, while parqet is a step, I am not sure it is a step in definitely right direction. Some aspects of it are good, some are not that good.
Why would you need 7.2 mil row groups?
Row group size when stored in HDFS is usually equal to HDFS bock size by default, which is 128MB
7.2 mil * 128MB ~ 1PB
You have a single parquet file 1PB in size?
What format would you recommend instead?
Nobody is forcing you to use a single Parquet file.
some critiques of parquet by andy pavlo
https://www.vldb.org/pvldb/vol17/p148-zeng.pdf
Parquet is not a database, it's a storage format that allows efficient column reads so you can get just the data you need without having to parse and read the whole file.
Most tools can run queries across parquet files.
Like everything, it has its strengths and weaknesses, but in most cases, it has better trade-offs over CSV if you have more than a few thousand rows.
Please promote the use of .parquet files!
Maybe laterParquet is a file format, not a piece of software. 'apt install csv' doesn't make any sense either.
There is no support for parquet in Debian, by contrast
If you want to shine with snide remarks, you should at least understand the point being made:
Third consecutive time in 86 days that you mention .parquet files. I am out of my element here, but it's a bit weird
Sometimes when people discover or extensively use something they are eager to share in contexts they think are relevant. There is an issue when those contexts become too broad.
3 times across 3 months is hardly astroturfing for big parquet territory.
FWIW I am the same. I tend to recommend BigQuery and AWS/Athena in various posts. Many times paired with Parquet.
But it is because it makes a lot of things much simpler, and that a lot of people have not realized that. Tooling is moving fast in this space, it is not 2004 anymore.
His arguments are still valid and 86 days is a pretty long time.
Why is .parquet better than protobuf?
Parquet is columnar storage, which is much faster for querying. And typically for protobuf you deserialize each row, which has a performance cost - you need to deserialize the whole message, and can't get just the field you want.
So, of you want to query a giant collection of protobufs, you end up reading and deserializing every record. For parquet, you get much closer to only reading what you need.
I can appreciate the vertical scaling solution, but to be honest, this is the wrong solution for almost all use cases - consumers of the data don't want awk, and even if they did, spooling over 6TB for every kinda of query without partitioning or column storage is gonna be slow on a single cpu - always.
I've generally liked BigQuery for this type of stuff - the console interface is good enough for ad-hoc stuff, you can connect a plethora of other tooling to it (Metabase, Tableau, etc). And if partitioned correctly, it shouldn't be too expensive - add in rollup tables if that becomes a problem.
A moderately powerful desktop processor has memory bandwidth of over 50TB/s so yeah it'll take a couple of minutes sure.
The slow part of using awk is waiting for the disk to spin over the magnetic head.
And most laptops have 4 CPU cores these days, and a multiprocess operating system, so you don’t have to wait for random access on a spinning plate to find every bit in order, you can simply have multiple awk commands running in parallel.
Awk is most certainly a better user interface than whatever custom BrandQL you have to use in a textarea in a browser served from localhost:randomport
If we're talking about 6 TB of data:
- You can upgrade to 8 TB of storage on a 16-inch MacBook Pro for $2,200, and the lowest spec has 12 CPU cores. With up to 400 GB/s of memory bandwidth, it's truly a case of "your big data problem easily fits on my laptop".
- Contemporary motherboards have 4 to 5 M.2 slots, so you could today build a 12 TB RAID 5 setup of 4 TB Samsung 990 PRO NVMe drives for ~ 4 x $326 = $1,304. Probably in a year or two there will be 8 TB NVMe's readily available.
Flash memory is cheap in 2024!
You can go further.
There are relatively cheap adapter boards which let you stick 4 M.2 drives in a single PCIe x16 slot; you can usually configure a x16 slot to be bifurcated (quadfurcated) as 4 x (x4).
To pick a motherboard at quasi-random:
Tyan HX S8050. Two M.2 on the motherboard.
20 M.2 drives in quadfurcated adapter cards in the 5 PCIe x16 slots
And you can connect another 6 NVMe x4 devices to the MCIO ports.
You might also be able to hook up another 2 to the SFF-8643 connectors.
This gives you a grand total of 28-30 x4 NVME devices on one not particularly exotic motherboard, using most of the 128 regular PCIe lanes available from the CPU socket.
I haven't been using spinning disks for perf critical tasks for a looong time... but if I recall correctly, using multiple processes to access the data is usually counter-productive since the disk has to keep repositioning its read heads to serve the different processes reading from different positions.
Ideally if the data is laid out optimally on the spinning disk, a single process reading the data would result in a mostly-sequential read with much less time wasted on read head repositioning seeks.
In the odd case where the HDD throughput is greater than a single-threaded CPU processing for whatever reason (eg. you're using a slow language and complicated processing logic?), you can use one optimized process to just read the raw data, and distribute the CPU processing to some other worker pool.
Running awk on an in-memory CSV will come nowhere even close to the memory bandwidth your machine is capable of.
And here we see this strange thing that data science people does in forgetting that 6TB is small change for any SQL server worth it's salt.
Just dump it into Oracle, postgre, mssql, or mysql and be amazed by the kind of things you can do with 30year old data analysis technology on an modern computer.
you wouldn't have been a 'winner' per OP. real answer is loading it on their phones not on sqlserver or whatever.
To be honest OP is kind of making the same mistake in assuming that the only real alternatives is "new data science products" and old school scripting exists as valuable tools.
The extend people goes to to not recognize how much the people creating the SQL language and the relational database engines we now take for granted actually knew what they were doing, are a bit of an mystery to me.
The right answer to any query that can be defined in SQL is pretty much always an SQL engine even if it's just sqlite running on an laptop. But somehow people seems to keep comming up with reasons not to use SQL.
Once you understand that 6tb fits on a hard drive, you can just as well put it in a run-of-the-mill pg instance, which metabase will reference just as easily. Hell, metabase is fine with even a csv file...
I worked in a large company that had a remote desktop instance with 256gb ram running a PG instance that analysts would log in to to do analysis. I used to think it was a joke of setup for such a large company.
I later moved to a company with a fairly sophisticated setup with Databricks. While Databricks offered some QoL improvements, it didn't magically make all my queries run quickly, and it didn't allow me anything that I couldn't have done on the remote desktop setup.
Hes hiring data scientists not building a service though. This might realistically be a one off analysis for those 6tb. At which point you are happy your data scientists has returned statistical information instead of spending another week making sure the pipeline works if someone puts a greek character in a field.
Even if I'm doing a one off, depending on the task it can be easier/faster/more reliable to load 6TiB into a big query table than waiting hours for some task to complete and fiddling with parallelism and memory management.
It's a couple hundred bucks a month and $36 to query the entire dataset, after partitioning thats not terrible.
you can scale vertically with a much better tech than awk.
enter duckdb with columnar vectorized execution and full SQL support. :-)
disclaimer: i work with the author at motherduck and we make a data warehouse powered by duckdb
I agree with this. BigQuery or AWS s3/Athena.
You shouldn't have to set up a cluster for data jobs these days.
And it kind of points out the reason for going with a data scientist with the toolset he has in mind instead of optimizing for a commandline/embedded programmer.
The tools will evolve in the direction of the data scientist, while the embedded approach is a dead end in lots of ways.
You may have outsmarted some of your candidates, but you would have hired a person not suited for the job long term.
You have 6 TiB of ram?
You can have 8TB RAM in a 2U box for under 100K. grab a couple and it will save you millions a year compared to over-engineered bigdata setup.
Bigquery and snowflake are software. They come with a sql engine, data governance, integration with your ldap, auditing. Loading data into snowflake isn't overegineering. What you described is over-engineering.
No business is passing 6tb data around on their laptops.
So is ClickHouse your point being ? Please point out what a server being able to have 8TB of RAM has to do with laptops.
You don’t need that much ram to use mmap(2)
To be fair, mmap doesn't put your data in RAM, it presents it as though it was in RAM and has the OS deal with whether or not it actually is.
https://yourdatafitsinram.net/
Was posted as https://news.ycombinator.com/item?id=9581862 in 2015
The "(multiple times)" part probably means batching or streaming.
But yeah, they might have that much RAM. At a rather small company I was at we had a third of it in the virtualisation cluster. I routinely put customer databases in the hundreds of gigabytes into RAM to do bug triage and fixing.
Indeed, what I meant to say is that you can load it in multiple batches. However, now thinking, I did play around with servers of TiBs of memory :-)
If you're one of the public clouds targeting SAP use cases, you probably have some machines with 12TB [0, 1, 2].
[0] https://aws.amazon.com/blogs/aws/now-available-amazon-ec2-hi...
[1] https://cloud.google.com/blog/products/sap-google-cloud/anno...
[2] https://azure.microsoft.com/en-us/updates/azure-mv2-series-v...
I personally don't but our computer cluster at work as around 50,000 CPU cores. I can request specific configurations through LSF and there are at least 100 machines with over 4TB RAM and that was 3 years ago. By now there are probably machines with more than that. Those machines are usually reserved for specific tasks that I don't do but if I really needed it I could get approval.
If my business depended on it? I can click a few buttons and have a 8TiB Supermicro server on my doorstep in a few days if I wanted to colo that. EC2 High Memory instances offer 3, 6, 9, 12, 18, and 24 TiB of memory in an instance if that's the kind of service you want. Azure Mv2 also does 2850 - 11400GiB.
So yes, if need to be, I have 6 TiB of RAM.
We are decomming our 5-year old 4TB systems this year and could have been ordered with more
How would six terabytes fit into memory?
It seems like it would get a lot of swap thrashing if you had multiple processes operating on disorganized data.
I'm not really a data scientist and I've never worked on data that size so I'm probably wrong.
What device do you have in mind? I've seen places use 2TB RAM servers, and that was years ago, and it isn't even that expensive (can get those for about $5K or so).
Currently HP allows "up to 48 DIMM slots which support up to 6 TB for 2933 MT/s DDR4 HPE SmartMemory".
Close enough to fit the OS, the userland, and 6 TiB of data with some light compression.
Why would you have "disorganized data"? Or "multiple processes" for that matter? The OP mentions processing the data with something as simple as awk scripts.
I mean if you're doing data science the data is not always organized and of course you would want multi-processing.
1 TB of memory is like 5 grand from a quick Google search then you probably need specialized motherboards.
Not necessarily - I might not want it or need it. It's a few TB, it can be on a fast HD, on an even faster SSD, or even in memory. I can crunch them quite fast even with basic linear scripts/tools.
And organized could just mean some massaging or just having them in csv format.
This is already the same rushed notions about "needing this" and "must have that" that the OP describes people jumping to, that leads them to suggest huge setups, distributed processing, multi-machine infrastructure, for use cases and data sizes that could fit on a single server with redundancy and be done it.
DHH has often written about this for their Basecamp needs (scalling vertically where others scale horizontally having worked for them for most of their operation), there's also this classic post: https://adamdrake.com/command-line-tools-can-be-235x-faster-...
Not that specialized, I've work with server deployments (HP) with 1, 1.5 and 2TB RAM (and > 100 cores), it's trivial to get.
And 5 or even 30 grand would still be cheaper (and more effective and simpler) than the "big data" setups some of those candidates have in mind.
Yeah I agree about over engineering.
Im just trying to understand the parent to my original comment.
How would running awk for analysis on 6TB of data work quickly and efficiently?
They say it would go into memory but its not clear to me how that would work as would still have paging and thrashing issues if the data didnt have often used sections of the data.
am I overthinking it and they were they just referring to buying a big ass Ram machine?
“How would six terabytes fit into memory?”
A better question would be:
Why would anyone stream 6 terabytes of data over the internet?
In 2010 the answer was: because we can’t fit that much data in a single computer, and we can’t get accounting or security to approve a $10k purchase order to build a local cluster, so we need to pay Amazon the same amount every month to give our ever expanding DevOps team something to do with all their billable hours.
That may not be the case anymore, but our devops team is bigger than ever, and they still need something to do with their time.
Well yeah streaming to the cloud to work around budget issues is a while nother convo haha.
There are machines that can fit that and more: https://yourdatafitsinram.net/
I'm not advocating that this is generally a good or bad idea, or even economical, but it's possible.
I'm trying to understand what the person I'm replying to had in mind when they said fit six terabytes in memory and search with awk.
is this what they were referring to just by a big ass Ram machine?
6 TB does not fit in memory. However, with a good storage engine and fast storage this easily fits within the parameters of workloads that have memory-like performance. The main caveat is that if you are letting the kernel swap that for you then you are going to have a bad day, it needs to be done in user space to get that performance which constrains your choices.
It would easy fit in ram: https://yourdatafitsinram.net/
Now, you have to consider the cost it takes for you whole team to learn how to use AWK instead of SQL. Then you do these TCO calculations and revert back to the BigQuery solution.
About $20/month for chatgpt or similar copilot, which really they should reach for independently anyhow.
And since the data scientist cannot verify the very complex AWK output that should be 100% compatible with his SQL query, he relies on the GPT output for business-critical analysis.
Only if your testing frameworks are inadequate. But I belive you could be missing or mistaken on how code generation successfully integrates into a developer and data scientist's work flow.
Why not take a few days to get familiar with AWK, a skill which will last a lifetime? Like SQL, it really isn't so bad.
It is easier to write complex queries in SQL instead of AWK. I know both AWK and SQL, and I find SQL much easier for complex data analysis, including JOINS, subqueries, window functions, etc. Of course, your mileage may vary, but I think most data scientists will be much more comfortable with SQL.
Many people have noted how when using LLMs for things like this, the person’s ultimate knowledge of the topic is less than it would’ve otherwise been.
This effect then forces the person to be reliant on the LLM for answering all questions, and they’ll be less capable of figuring out more complex issues in the topic.
$20/mth is a siren’s call to introduce such a dependency to critical systems.
For someone who is comfortable with sql we are talking minutes to hours to figure out awk well enough to see how its used or use it.
It is not only about whether people can figure it out awk. It is also about how supportable the solution is. SQL provides many features specifically to support complex querying and is much more accessible to most people - you can't reasonably expect your business analysts to do complex analysis using awk.
Not only that, it provides a useful separation from the storage format so you can use it to query a flat file exposed as table using Apache Drill or a file on s3 exposed by Athena or data in an actual table stored in a database and so on. The flexibility is terrific.
Not necessarily. I always try to write to disk first, usually in a rotating compressed format if possible. Then, based on something like a queue, cron, or inotify, other tasks occur, such as processing and database logging. You still end up at the same place, and this approach works really well with tools like jq when the raw data is in jsonl format.
The only time this becomes an issue is when the data needs to be processed as close to real-time as possible. In those instances, I still tend to log the raw data to disk in another thread.
With the exception of regexes- which any programmer or data analyst ought to develop some familiarity with anyway- you can describe the entirety of AWK on a few sheets of paper. It's a versatile, performant, and enduring data-handling tool that is already installed on all your servers. You would be hard-pressed to find a better investment in technical training.
I ask a similar question on screens. Almost no one gives a good answer. They describe elaborate architectures for data that fits in memory, handily.
I think that’s the way we were taught in college / grad school. If the premise of the class is relational databases, the professor says, for the purpose of this course, assume the data does not fit in memory. Additionally, assume that some normalization is necessary and a hard requirement.
Problem is most students don’t listen to the first part “for the purpose of this course”. The professor does not elaborate because that is beyond the scope of the course.
FWIW if they were juniors, I would've continued the interview and direct them with further questions, and observer their flow of thinking to decide if they are good candidates to pursue further.
But no, this particular person had been working professionally for decades (in fact, he was much older than me).
Yeah. I don’t even bother asking juniors this. At that level I expect that training will be part of the job, so it’s not a useful screener.
I took a Hadoop class. We learned hadoop and were told by the instructor we probably wouldn’t’t need it, and learned some other Java processing techniques (streams etc)
People can always find excuses to boot candidates.
I would just back-track from a shipped product date, and try to guess who we needed to get there... given the scope of requirements.
Generally, process people from a commercially "institutionalized" role are useless for solving unknown challenges. They will leave something like an SAP, C#, or MatLab steaming pile right in the middle of the IT ecosystem.
One could check out Aerospike rather than try to write their own version (the dynamic scaling capabilities are very economical once setup right.)
Best of luck, =3
https://x.com/garybernhardt/status/600783770925420546 (Gary Bernhardt of WAT fame):
This is from 2015...
I wonder if it's fair to revise this to 'your data set fits on NVME drives' these days. Astonishing how fast and how much storage you can get these days.
Based on a very brief search: Samsung's fastest NVME drives [0] could maybe keep up with the slowest DDR2 [1]. DDR5 is several orders of magnitude faster than both [2]. Maybe in a decade you can hit 2008 speeds, but I wouldn't consider updating the phrase before then (and probably not after, either).
[0] https://www.tomshardware.com/reviews/samsung-980-m2-nvme-ssd...
[1] https://www.tomshardware.com/reviews/ram-speed-tests,1807-3....
[2] https://en.wikipedia.org/wiki/DDR5_SDRAM
The statement was "fits on", not "matches the speed of".
You can always check available ram: https://yourdatafitsinram.net/
https://yourdatafitsinram.net/
What kind of business just has a static set of 6TiB data that people are loading on their laptops.
You tricked candidates with your nonsensical scenario. Hate smartass interviewers like this that are trying some gotcha to feel smug about themselves.
Most candidates don't feel comfortable telling ppl 'just load on your laptops' even if they think thats sensible. They want to present a 'professional solution', esp when you tricked them with the word 'stack'. which is how most of them prbly perceived your trick question.
This comment is so infuriating to me. Why be assholes to each other when world is already full of them.
I disagree with your take. Your surly rejoinder aside, the parent commenter identifies an area where senior level knowledge and process appropriately assess a problem. Not every job interview is satisfying checklist of prior experience or training, but rather assessing how well that skillset will fit the needed domain.
In my view, it's an appropriate question.
What did you gather as 'needed domain' from that comment. 'needed domain' is often implicit, its not a blank slate. candidates assume all sorts of 'needed domain' even before the interview starts, if i am interviewing at bank I wouldn't suggest 'load it on your laptops' as my 'stack'.
OP even mentioned that it his favorite 'tricky question' . It would def trick me because they used the word 'stack' which has specific meaning in the industry. There are even websites dedicated to 'stack's https://stackshare.io/instacart/instacart
Well put. Whoever asked this question is undoubtedly a nightmare to work with. Your data is the engine that drives your business and its margin improvements, so why hamstring yourself with a 'clever' cost saving but ultimately unwieldy solution that makes it harder to draw insight (or build models/pipelines) from?
Penny wise and pound foolish, plus a dash of NIH syndrome. When you're the only company doing something a particular way (and you're not Amazon-scale), you're probably not as clever as you think.
Big data companies or those that work with lots of data.
The largest dataset I worked with was about 60TB
While that didn't fit in ram most people would just load the sample data into the cluster when I told them it would be faster to load 5% locally and work off that.
Most business have static sets of data that people load on their PCs. (Why do you assume laptops?)
The only weird part of that question is that 6TiB is so big it's not realistic.
I agree that keeping data local is great and should be the first option when possible. It works great on 10GB or even 100GB, but after that starts to matter what you optimize for because you start seeing execution bottlenecks.
To mitigate these bottlenecks you get fancy hardware (e.g oracle appliance) or you scale out (and get TCO/performance gains from separating storage and compute - which is how Snowflake sold 3x cheaper compared to appliances when they came out).
I believe that Trino on HDFS would be able to finish faster than awk on 6 enterprise disks for 6TB data.
In conclusion I would say that we should keep data local if possible but 6TB is getting into the realm where Big Data tech starts to be useful if you do it a lot.
The point of the article is 99.99% of businesses never pass even the 10 Gb point though.
I agree with the theme of the article. My reply was to parent comment which has a 6 TB working set.
I wouldn't underestimate how much a modern machine with a bunch of RAM and SSDs can do vs HDFS. This post[1] is now 10 years old and has find + awk running an analysis in 12 seconds (at speed roughly equal to his hard drive) vs Hadoop taking 26 minutes. I've had similar experiences with much bigger datasets at work (think years of per-second manufacturing data across 10ks of sensors).
I get that that post is only on 3.5GB, but, consumer SSDs are now much faster at 7.5GB/s vs 270MB/s HDD back when the article was written. Even with only mildly optimised solutions, people are churning through the 1 billion rows (±12GB) challenge in seconds as well. And, if you have the data in memory (not impossible) your bottlenecks won't even be reading speed.
[1]: https://adamdrake.com/command-line-tools-can-be-235x-faster-...
Plenty of people get offended if you tell them that their data isn’t really “big data”. A few years ago I had a discussion with one of my directors about a system IT had built for us with Hadoop, API gateways, multiple developers and hundreds of thousands of yearly cost. I told him that at our scale (now and any foreseeable future) I could easily run the whole thing on a USB drive attached to his laptop and a few python scripts. He looked really annoyed and I was never involved again with this project.
I think it’s part of the BS cycle that’s prevalent in companies. You can’t admit that you are doing something simple.
In most non-tech companies, it comes down to the motive of the manager and in most cases it is expansion of reporting line and grabbing as much budget as possible. Using "simple" solutions runs counter to this central motivation.
- the manager wants expansion
- the developers want to get experience in a fancy stack to build up their resume
Everyone benefits from the collective hallucination
This is also true of tech companies. Witness how the "GenAI" hammer is being used right now at MS, Google, Meta, etc.
I think I've written about it here before, but I imported ≈1 TB of logs into DuckDB (which compressed it to fit in RAM of my laptop) and was done with my analysis before the data science team had even ingested everything into their spark cluster.
(On the other hand, I wouldn't really want the average business analyst walking around with all our customer data on their laptops all the time. And by the time you have a proper ACL system with audit logs and some nice way to share analyses that updates in real time as new data is ingested, the Big Data Solution™ probably have a lower TCO...)
you probably didn't do joins for example on your dataset, because DuckDB is OOMing on them if they don't fit memory.
I doubt it. The common Big Data Solutions manage to have a very high TCO, where the least relevant share is spent on hardware and software. Most of its cost comes from reliability engineering and UI issues (because managing that "proper ACL" that doesn't fit your business is a hell of a problem that nobody will get right).
How could anyone answer this without knowing how the data is to be used (query patterns, concurrent readers, writes/updates, latency, etc)?
Awk may be right for some scenarios, but without specifics it can't be a correct answer.
Those are very appropriate follow up questions I think. If someone tasks you to deal with 6 TiB of data, it is very appropriate to ask enough questions until you can provide a good solution, far better than to assume the questions are unknowable and blindly architect for all use cases.
It depends on what you want to do with the data. It can be easier to just stick nicely-compressed columnar Parquets in S3 (and run arbitrarily complex SQL on them using Athena or Presto) than to try to achieve the same with shell-scripting on CSVs.
how exactly is this solution easier than putting the very Parquet files on a classic filesystem. Why does the easy solution require an amazon-subscription?
This is a great test / question. More generally, it tests knowledge with basic linux tooling and mindset as well as experience level with data sizes. 6TiB really isn't that much data these days, depending on context and storage format, etc. of course.
It could be a great question if you clarify the goals. As it stands it’s “here’s a problem, but secretly I have hidden constraints in my head you must guess correctly”.
The OPs desired solution could have been found from probably some of those other candidates if asked “here is the challenge, solve in most McGuyver way possible”. Because if you change the second part, the correct answer changes.
“Here is a challenge, solve in the most accurate, verifiable way possible”
“Here is a challenge, solve in a way that enables collaboration”
“Here is a challenge, 6TiB but always changing”
^ These are data science questions much more than the question he was asking. The answer in this case is that you’re not actually looking for a data scientist.
Problem is possibly that most people with that sort of hands-on intuition for data don't see themselves as data scientists and wouldn't apply for such a position.
It's a specialist role, and most people with the skills you seek are generalists.
Yeah it’s not really what you should be hiring a data scientist to do. I’m of the opinion that if you don’t have a data engineer, you probably don’t need a data scientist. And not knowing who you need for a job causes a lot of confusion in interviews.
In my context 99% of the problem is the ETL, nothing to do with complex technology. I see people stuck when they need to get this from different sources in different technologies and/or APIs.
I have lived through the hype of Big data it was a time of HDFS+HTable I guess and Hapoop etc.
One can't go wrong with DuckDB+SQLite+Open/Elasticsearch either with 6 to 8 even 10 TB of data.
[0]. https://duckdb.org/
It's astonishing how shit the cloud is compared to boring-ass pedestrian technology.
For example, just logging stuff into a large text file is so much easier, performant and searchable that using AWS CloudWatch, presumably written by some of the smartest programmers who ever lived.
On another note I was once asked to create a big data-ish object DB, and me, knowing nothing about the domain, and a bit of benchmarking, decided to just use zstd-compressed json streams with a separate index in an sql table. I'm sure any professional would recoil at it in horror, but it could do literally gigabytes/sec retrieval or deserialization on consumer grade hardware.
I'm not even in data science, but I am a slight data hoarder. And heck even I'd just say throw that data on a drive and have a backup in the cloud and on a cold hard drive.
If you look at the article the data space is more commonly 10GB which matches my experience. For these sizes definitely simple tools are enough.
My smartphone cannot store 1TiB. <shrug>
I'm on some reddit tech forums and people will say "I need help storing a huge amount of data!" and people start offering replies for servers that store petabytes.
My question is always "How much data do you actually have?" Many times you they reply with 500GB or 2TB. I tell that that isn't much data when you can get 1TB micro SD card the size of a fingernail or a 24TB hard drive.
My feeling is that if you really need to store petabytes of data that you aren't going to ask how to do it on reddit. If you need to store petabytes you will have an IT team and substantial budget and vendors that can figure it out.
Even if a 6 terabyte CSV file does fit in RAM, the only thing you should do with it is convert it to another format (even if that's just the in-memory representation of some program). CSV stops working well at billions of records. There is no way to find an arbitrary record because records are lines and lines are not fixed-size. You can sort it one way and use binary search to find something in it in semi-reasonable time but re-sorting it a different way will take hours. You also can't insert into it while preserving the sort without rewriting half the file on average. You don't need Hadoop for 6 TB but, assuming this is live data that changes and needs regular analysis, you do need something that actually works at that size.
This feels representative of so many of our problems in tech, overengineering, over-"producting," over-proprietary-ing, etc.
Deep centralization at the expense of simplicity and true redundancy; like renting a laser cutter when you need a boxcutter, a pair of scissors, and the occasional toenail clipper.
As a point of reference, I routinely do fast-twitch analytics on tens of TB on a single, fractional VM. Getting the data in is essentially wire speed. You won't do that on Spark or similar but in the analytics world people consistently underestimate what their hardware is capable of by something like two orders of magnitude.
That said, most open source tools have terrible performance and efficiency on large, fast hardware. This contributes to the intuition that you need to throw hardware at the problem even for relatively small problems.
In 2024, "big data" doesn't really start until you are in the petabyte range.
is not somewhat detailed requirements, as it depends quite a bit on the nature of the data.
And how many data scientists are familiar with using awk scripts? If you’re the only one then you’ll have failed at scaling the data science team.
Huh? How are you proposing loading a 6TB CSV into memory multiple times? And then processing with awk, which generally streams one a line at a time.
Obviously we can get boxes with multiple terabytes of RAM for $50-200/hr on-demand but nobody is doing that and then also using awk. They’re loading the data into clickhouse or duckdb (at which point the ram requirement is probably 64-128GB)
I feel like this is an anecdotal story that has mixed up sizes and tools for dramatic effect.
The problem with your question is that they are there to show off their knowledge. I failed a tech interview once, question was build a web page/back end/db that allows people to order let's say widgets, that will scale huge. I went the simpleton answer route, all you need is Rails, a redis cache and an AWS provisioned relational DB, solve the big problems later if you get there sort of thing. Turns out they wanted to hear all about microservices and sharding.
Wait, how would you split 6 TiB across 6 phones, how would you handle the queries? How long will the data live, do you need to handle schema changes, and how? And what is the cost of a machine with 15 or 20 TiB of RAM (you said it fits in memory multiple times, right?) - isn’t the drive cost irrelevant here? How many requests per second did you specify? Isn’t that possibly way more important than data size? Awk on 6 TiB, even in memory, isn’t very fast. You might need some indexing, which suddenly pushes your memory requirement above 6 TiB, no? Do you need migrations or backups or redundancy? Those could increase your data size by multiples. I’d expect a question that specified a small data size to be asking me to estimate the real data size, which could easily be 100 TiB or more.
The funny thing is that is exactly the place I want to work at. I've only found one company so far and the owner sold during the pandemic. So far my experience is that amount of companies/people that want what you describe is incredibly low.
I wrote a comment on here the other day that some place I was trying to do work for was using $11k USD a month on a BigQuery DB that had 375MB of source data. My advice was basically you need to hire a data scientist that knows what they are doing. They were not interested and would rather just band-aid the situation for a "cheap" employee. Despite the fact their GCP bill could pay for a skilled employee.
As I've seen it for the last year job hunting most places don't want good people. They want replaceable people.
I can’t really think of a product with the requirement of max 6TiB data. If the data is big as TiB, most products have 100x TiB rather than a few ones.
There’d still have to be some further questions, right? I guess if you store it on the interview group’s cellphones you’ll have to plan on what to do if somebody leaves or the interview room is hit by a meteor, if you plan to store it in ram on a server you’ll need some plan for power outages.
If it's not a very write heavy workload but you'd still want to be able to look things up, wouldn't something like SQLite be a good choice, up to 281 TB: https://www.sqlite.org/limits.html
It even has basic JSON support, if you're up against some freeform JSON and not all of your data neatly fits into a schema: https://sqlite.org/json1.html
A step up from that would be PostgreSQL running in a container: giving you the support for all sorts of workloads, more advanced extensions for pretty much anything you might ever want to do, from geospatial data with PostGIS, to something like pgvector, timescaledb etc., while still having a plethora of drivers and still not making your drown in complexity and having no issues with a few dozen/hundred TB of data.
Either of those would be something that most people on the market know, neither will make anyone want to pull their hair out and they'll give you the benefit of both quick data writes/retrieval, as well as querying. Not that everything needs or can even work with a relational database, but it's still an okay tool to reach for past trivial file storage needs. Plus, you have to build a bit less of whatever functionality you might need around the data you store, in addition to there even being nice options for transparent compression.
If you were hiring me for a data engineering role and asked me how to store and query 6 TiB, I'd say you don't need my skills, you've probably got a Postgres person already.
I am a big fan of these simplistic solutions. In my own area, it was incredibly frustrating as what we needed was a database with a smaller subset of the most recent information from our main long-term storage database for back end users to do important one-off analysis with. This should've been fairly cheap, but of course the IT director architect guy wanted to pad his resume and turn it all into multi-million project with 100 bells and whistles that nobody wanted.
I dont know anything but when doing that I always end up next Thursday having the same with 4TB and the next with 17 at which point I regret picking a solution that fit so exactly.