> Consulting service: you bring your big data problems to me, I say "your data set fits in RAM", you pay me $10,000 for saving you $500,000.
This is from 2015...
[0] https://www.tomshardware.com/reviews/samsung-980-m2-nvme-ssd...
[1] https://www.tomshardware.com/reviews/ram-speed-tests,1807-3....
Once the index is too big to have in one place, thing get more complicated.
You want something like PM1735: PCIe 4.0 x8, up to 8000 MB/s sequential read.
And while DDR5 is surely faster the question is what the data access patterns are there.
In almost all cases (ie mix of random access, occasional sequential reads) just reading from the NVMe drive would be faster than loading to RAM and reading from there. In some cases you would spend more time processing the data than reading it.
PS all these RAM bandwidth rates are good for the sequential access, as you go random access the bandwidth drops.
https://semiconductor.samsung.com/ssd/enterprise-ssd/pm1733-...
The value of our data skills are getting eroded!
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.
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
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.
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!
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.
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.
SQL should be fine for that.
Actually, I have a feeling that the awk solution will struggle if there are many unique keys.
For example if they in that dataset have a million customers and want to extract the top 10. Then there is an intermediate map stage that will be storage or memory consuming.
It is like matrix multiplication. Calculating the dot product is trivial, but when the matrix has n:m dimensions and n,m starts to grow, it becomes more and more resource heavy. And then the laptop will not be able to handle it.
(in the example, m is the number of rows, and n is the number of unique customers. The dot product is just a sum over one dimension, while the group by customer id is the tricky part)
It's a couple hundred bucks a month and $36 to query the entire dataset, after partitioning thats not terrible.
You can always save an intermediate data set partitioned and massaged into whatever format makes subsequent queries easy, but that's usually application-dependent, and so you want that control over how you actually store your intermediate results.
If you only needed this once, the BQ approach requires very little setup and many places already have a billing account. If this is recurring then you need to figure out what the ownership plan of the hard drive is (what's it connected to, who updates this computer, what happens when it goes down, etc.).
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.
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.
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.
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
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).
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.
Obviously organically grown Frankenstein programs are a huge liability, I think every reasonable techie agrees on that.
Check out "data science at the command line":
...or put it into SQLite for extra blazing fastness! No kidding.
I had a heck of a time running the server locally before I discovered the CLI.
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) .
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'.
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.
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)_/¯
"not understanding the scale of "real" big data was a no-go in my eyes when hiring."
> Consulting service: you bring your big data problems to me, I say "your data set fits in RAM", you pay me $10,000 for saving you $500,000.
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.
"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.
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" ?
> As stated the question didn't require any of what you outline here.
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" .
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.
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 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.
This negative cherry picking does not do your image any favors.
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'
buddy, you're just rolling off buzzwords and lording it over other people
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.
>It seems like it would get a lot of swap thrashing if you had multiple processes operating on disorganized data.
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.
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.
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-...
>1 TB of memory is like 5 grand from a quick Google search then you probably need specialized motherboards.
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.
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?
In that 6TB is not that huge of an amount
That's their total dataset, and there's no "real time" requirement.
They can start a batch process, process the data, and be done with it.
Here's an example of someone using awk (read further down for the relevant section):
https://livefreeordichotomize.com/posts/2019-06-04-using-awk...
"I was now able to process a whole 5 terabyte batch in just a few hours."
>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
There's no need to have paging and thrashing issues if you can fit all (or even most) of your data in memory. And you can always also split, process, and aggregate partial results.
>am I overthinking it and they were they just referring to buying a big ass Ram machine?
Yeah, they said one can buy a machine with several TB of memory.
I'm not advocating that this is generally a good or bad idea, or even economical, but it's possible.
is this what they were referring to just by a big ass Ram machine?
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 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-...
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.
But no, this particular person had been working professionally for decades (in fact, he was much older than me).
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
It's a specialist role, and most people with the skills you seek are generalists.
So yes, if need to be, I have 6 TiB of RAM.
No business is passing 6tb data around on their laptops.
And how that price would compare to the equivalent big data solution in the cloud.
[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...
It’ll be slower, perhaps by a lot, but most “big data” stuff is already so god damned slow that mmap probably still beats it, while being immeasurably simpler and cheaper.
For CSV it flat out doesn't matter what you do since the format is so inefficient and needs to be read start to finish, but something like parquet probably benefits from explicit read syscalls, since it's block based and highly structured, where you can predict the read patterns much better than the kernel can.
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.
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.
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.
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.
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.
Why would use use bigquery just to get SQL?
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.
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.
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.
In my view, it's an appropriate question.
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
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.
You don't get extra credit points for mind-reading, if anything you get more esteem for requirements-gathering, which would lead you towards either a professional solution or a laptop solution: whichever fits the business needs.
It might be a totally unreasonable question if it's provided context-free as a form on a screen, but it is a perfectly reasonable conversation-starter in an interview.
In terms of maturity of solution and diversity of downstream applications you'll go much further with BigQuery/Athena (at comically low cost) for this amount of data than some cobbled together "local" solution.
I thoroughly agree with the author but the comments in this thread are an indication of people who haven't actually had to do meaningful ongoing work with modest amounts of data if they're suggesting just storing it as plain text on personal devices.
I'm not advocating for complicated or expensive solutions here, BigQuery and Athena are very low complexity compared to any of the Hadoop et-al tooling (yes Athena is Trino is in the family, but it is managed and dirt cheap).
(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...)
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).
I'm not sure there is a way to get this right unless there is a programmatic integration into the org chart, and ability to describe and parse in a declarative language the organizational rules of who has access to what, when, under what auth, etc. It has otherwise been for me an exercise in watching massive amounts of toil manually interpreting between the SOT of the org chart and all the other applications mediated by many manual approval policies and procedures. And at every client I've posed this to, I've always been denied that programmatic access for integration.
A lot of sites try to avoid this by designing ACL's around certain activity or data domains because those are more stable than organizations, but this breaks down when you get to the fine-grained levels of the ACL's so we get capped benefits from this approach.
I'd love to hear how others solve this in large (10K+ staff) organizations that frequently change around teams.
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.
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).
Or, if you guess what the interview 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.
Even if it would say "it will stay at 6TiB" I would probably prefer a senior candidate to briefly question it, such as "It is surprising that we know it will stay at 6TiB and if this were a real project I'd try to at least sanitycheck that this requirement is really correct, but for now I'll assume this is a given..."
At least if, as the interviewer, I told them to treat the challenge similar to a real request/project and not to not-question the given numbers etc.
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
The few times I've ignored red flags from a company in an interview I've been in a world of pain afterwards.
Give both answers, and explain why the obvious "hard" answer is wrong
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?
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?
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?
You may say "yeah but you just have to think out loud, that's what the interviewer wants". But again that's not how I work. If the interviewer wants to see me design a system, they should watch me read documentation for hours, then think about it while running, and read again, draw a quick thing, etc.
Turns out he was laid off and my suggestion was used.
(Okay, I'm being silly, the layoff was a coincidence)
On the other hand, "hey I have 6TiB data, please prepare to analyze it, feel free to ask any questions for clarification but I may not know the answers" is much more representative of a real-life task.
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 may not be reasonable to suggest that in a role that traditionally uses big data tools
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.
What would be the equivalent for a technical interview? Perhaps: Implement a generic linked list or dynamic array.
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.
How much do they pay? How long has it been since my last proper meal? How long until my rent is due?
I had an interview "design an app store". I tried asking, ok an app store has a ton of components, which part of the app store are you asking exactly? The response I got was "Have you ever used an app store? Design an app store". Umm ok.
But now as a senior, I have the same questions and answers regardless if I’m being interviewed or not.
DING DING DING!
About 30% of all interviews I've had in my career the person doing the technical interview was using it as a means to stroke their ego. I got the impression they don't get the power they want there and this was a brief reprieve where they are "the expert" and get to thumbs up or thumbs down in their kingly intellectual facade.
TBF, I personally cut them some slack because they may actually be right about a lot and not given the authority to do what they know is right.
Still, if we're subjected to AI resume filtering, then how about we replace the technical interview process with AI too and eliminate the BS ego trips?
Tell me this isn't big data, and then, if you must, tell me about hadoop (or whatever big data is)
.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 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.
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.
A former colleague of mine is now working on a memory-mapped log-structured merge tree implementation and it can be a good alternative. LSM provides elasticity, one can store as much data as one needs, it is static, thus it can be compressed as well as Parquet-stored data, memory mapping and implicit indexing of data do not require additional data structures.
Something like LevelDB and/or RocksDB can provide most of that, especially when used in covering index [1] mode.
[1] https://www.sqlite.org/queryplanner.html#_covering_indexes
But nobody tells me that I can hit a hard limit and then I need a second Parquet file and should have some code for that.
The situation looks to me as if my "Favorite DB server" supports, say, only 1.9 billions records per table and if I hit that limit I need a second instance of my "Favorite DB server" just for that unfortunate table. And it is not documented anywhere.
"Dictionary Encoding is effective across data types (even for floating-point values) because most real-world data have low NDV ratios. Future formats should continue to apply the technique aggressively, as in Parquet."
So this is not critique, but assessment. And Parquet has some interesting design decisions I did not know about.
So, let me thank you again. ;)
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?
You can have compressed Parquet columns with 8192 entries being a couple of tens bytes in size. 600 columns in a row group is then 12K bytes or so, leading us to 100GB file, not a petabyte. Four orders of magnitude of difference between your assessment and mine.
I actually benchmarked this and duckdb CSV reader is faster than parquet reader.
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.
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.
The parquet file has metadata that allows duckdb to only read the parts that are actually used, reducing total amount of data read from disk/network.
this makes sense, and what I hoped to have. But in reality looks like parsing CSV string works faster than bloated and overengineered parquet format with libs.
Anecdotally having worked with large CSVs and large on-disk Parquet datasets, my experience is the opposite of yours. My DuckDB queries operate directly on Parquet on disk and never load the entire dataset, and is always much faster than the equivalent operation on CSV files.
I think your experience might be due to -- what it sounds like -- parsing the entire CSV into memory first (CREATE TABLE) and then processing after. That is not an apples-to-apples comparison because we usually don't do this with Parquet -- there's no CREATE TABLE step. At most there's a CREATE VIEW, which is lazy.
I've seen your comments bashing Parquet in DuckDB multiple times, and I think you might be doing something wrong.
original discussion was about CSV vs parquet "reader" part, so this is exactly apple to apple testing, easy to benchmark and I stand my ground. What you are doing downstream, it is another question which is not possible to discuss because no code for your logic is available.
> I've seen your comments bashing Parquet in DuckDB multiple times, and I think you might be doing something wrong.
like running one command from DuckDB doc.
Also, I am not "bashing", I just state that CSV reader is faster.
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.
Dremel was pretty revolutionary when it came out in 2006 - you could run ad-hoc analyses in seconds that previously would've taken a couple days of coding & execution time. Parquet is awesome for the same reasons.
3 times across 3 months is hardly astroturfing for big parquet territory.
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.
apt-cache search parquet
<nada>
$ apt-cache search csv | wc -l
225
$ apt-cache search parquet | wc -l
0 apt-cache search csv | wc -l
259how? lossless compression? under what scenario?
vague headlines like this just beg more questions
Take Apache Iceberg for example. It is essentially a specification to how to store parquet files for efficient use and exploration of data, but the only implementation... depends on Apache Spark!
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.
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.
One can't go wrong with DuckDB+SQLite+Open/Elasticsearch either with 6 to 8 even 10 TB of data.
[0]. https://duckdb.org/
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.
I think it’s part of the BS cycle that’s prevalent in companies. You can’t admit that you are doing something simple.
- the developers want to get experience in a fancy stack to build up their resume
Everyone benefits from the collective hallucination
Data organisation can be more of an issue, but the general issue with this is often a lack of internal discipline on the data owners to carefully manage their data. But that isn't nearly as attractive for management. Putting 'brought in new cloud vendor / technology' looks better than 'improved data organisation' on a CV even if the new vendor was a waste of money.
It’s like a reverse signal.
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.
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.
Ramdisk would work.
Awk is imo a poor solution. I use awk all the time and I would never use it for something like this. Why not just use postgres. Its a lot more powerful, easy to setup and you get SQL which is extremely powerful. Normally I might even go with sqllite but for me 6TB is too much for sqllite.
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.
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.
What do you use?
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.
is not somewhat detailed requirements, as it depends quite a bit on the nature of the data.
You didn't, and indeed you have a point (missing specification of expected queries), so I expand it as a response here.
Among the MANY requirements I shared with the candidate, only one was the 6TiB. Another one was that it was going to be serving as part of the backend of an internal banking knowledge base, with at maximum 100 request a day (definitely not 10k people using it).
To all the upset data infrastructure wizards here: calm down. It was a banking startup, with an experimental project, and we needed the sober thinker generalist, who can deliver solutions to real *small scale* problems, and not the one who was the winner on the buzzword bingo.
HTH.
Processing and querying it is trickier.
Why?
That's the boring solution. If you don't have a use case, what kind of queries you would run then opt for maximum flexibility with the minimum setup of a managed solution.
If cost is prohibitive on the long run, you can figure out a more tailored solution based on the revealed preferences.
Fiddling with CSVs is the DWH version of the legendary "Dropbox HN commenter".
Whereas the "right answer" (I had a man on the inside) was to describe some wild tall and wide event based distributed system. For some nominal request volume that was nowhere near the limits of postgres. And they didn't even care if you solved the actual hard distributed system problems that would arise like distributed transactions etc.
Anyway, I said I failed the interview, really they failed my filter because if they want me to ignore pragmaticism and blindly regurgitate a YouTube video on "system design" FAANG interview prep, then I don't want to work there anyway.
That's quite a bit..
External or internal? Any examples?
"... it could be loaded (multimple times) to memory"
All 6TiB at once, or loaded in chunks?
I gave him the "find the phone numbers in 50,000 html files" question, and he decided to write a huge program with an ad-hoc state machine. When I asked how long it would take to write the program, he said he'd have to hit N files, with M lines per file, so... I interrupted him: no, WRITE. How long to WRITE the program? Oh. He estimated it at 5 days of work. At this point I was 50% ready to throw him out.
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.