13 ms·

Ask HN: Server-side web apps – is the database still a bottleneck?

• winkeltripel 7y ago
  Yarp, it is.
• rcavezza 7y ago
  Based on my experience, it's the most typically seen bottleneck. But bottlenecks change over time. You notice slowness somewhere in a website action and you dig into it. Sometimes, it's a new database table that isn't optimized with correct indexing. Sometimes, you end up doing an n^2 or n^3 operation on the backend (sometimes it's on the frontend). Sometimes, you're data is growing at a speed that your single monolith database isn't cutting it and you need to shard or consider a different type of storage. Bottlenecks change all the time. You fix one, and now suddenly, the bottleneck lies somewhere else. There's almost always a bottleneck somewhere. A database bottleneck is relatively common, though. From my experience, the bottleneck is mostly at some type of Big-O relevant scale in the code or database that using java vs ruby vs python doesn't make a huge difference. However, if you're in a situation where speed is absolutely crucial - like high frequency trading (where milliseconds mean millions of dollars) - well, that opens a completely different can of worms.
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  • mandeepj 7y ago
    Just think about the architecture of NYSE. They are supporting billions of trades per day from so many decades.
    • chenster 7y ago
      And there's Kdb+ https://en.wikipedia.org/wiki/Kdb%2B https://en.wikipedia.org/wiki/Kdb%2B
• BjoernKW 7y ago
  That largely depends on the usage patterns of the app in question. Write operations can be particularly expensive, read operations much less so, especially with application-level caching. Even as for write operations or notoriously expensive read operations such as full table scans modern RDBMS are optimised to a degree that for most applications database performance shouldn't be an issue. While traditional hard drives competed with the network for the title of "system component with the lowest bandwidth" (see http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keyn...) accessing data on an SSD is way faster than accessing a resource via network. So, today the technical bottleneck (as in "component with the lowest bandwidth") in a typical web app system architecture is the network.
• yakshaving_jgt 7y ago
  Yes, the database is still a bottleneck, but that depends on the size of the data and what you're querying for. If your data fits comfortably in memory (and so do any operations you perform over it), then performance is not really an issue. For many businesses — probably the vast majority — all the data could just sit in memory.
• GrumpyNl 7y ago
  Biggest bottleneck these days are poorly written sqls and badly optimized db's. Hardware isn't really an issue anymore.
  • godzilla82 7y ago
    Also, poorly written application logic.
• patsplat 7y ago
  Persistence is still slow, but is no longer a major bottleneck. SSDs are faster, but io and network is still slow. What has changed it is much easier for the client to be continually in touch with the server so that by the time the next "page" is loaded all necessary data has been retrieved. This way the database is no longer the same obvious bottleneck.
• felixge 7y ago
  In my experience yes. Example: We struggled with some read-queries taking a long time when aggregating over millions of rows. To solve this, we used triggers to pre-compute aggregate groups in real-time during write, which allowed us to easily achieve sub-second response times. However, this caused a lot of write amplification and now this is becoming a bottleneck. We could probably solve some of these problems by looking into column stores, but that carries it's own downsides (operational complexity, real-time data loading, etc.). Or we could buy faster disks, but that gets expensive quickly. YMMV but I certainly still experience more database/disk related bottlenecks than application-level ones.
  • sparkling 7y ago
    SQL in my experience is not designed for workloads where you want to compute together/return a ton of values from a ton of rows at once. Its good where you need to cherry-pick just a few rows belonging to one user from a databses of millions of row (e.g. CRUD operations). tl;dr: simple CRUD operations on few rows scale fine data-science/analytics stuff involving millions of in one query scale rather badly
    • felixge 7y ago
      SQL isn't the problem IMO. The problem is that the implementation we're using (PostgreSQL) is a row-store which is indeed more optimal for the kind of operations you mention. However, SQL isn't limited to row-stores. There are column-store implementations that are quite amazing for aggregate queries, e.g. Clickhouse. Using one of those would very likely work for us, but my understanding is that loading data into them in real-time is problematic.
      • kwillets 7y ago
        How close is real time? Seconds? Minutes? Also, what type of aggregate?
  • granshaw 7y ago
    Could you do something like computing the aggregate groups in background jobs instead, perhaps staggered, to take that out of the critical path?
• dorfsmay 7y ago
  The reason the database is the bottleneck is that in traditional architectures everything can be duplicated and run in parallel except writing to the database which has to be done serially one write at a time. How fast you can write (and commit to "permanent" storage like disc and SSD) is constrained by hardware. There are ways to alleviate this issue but they have different trade offs. One of the most used pattern is "eventual consistency". You might want to read about the CAP theorem if you have not heard about it: https://en.wikipedia.org/wiki/CAP_theorem https://en.wikipedia.org/wiki/CAP_theorem
  • wbl 7y ago
    Databases can handle multiple concurrent writes just fine and have since the 1970's.
    • madmax96 7y ago
      I think that parent was trying to state that maintaining sequential consistency is a bottleneck for traditional databases.
      • mcguire 7y ago
        And that is true if you assume significant contention. Which is not a safe assumption.
        • dorfsmay 7y ago
          If there is no contention, then what is the problem you cannot easily architect around in 2019?
          • mcguire 7y ago
            Latency.
    • ericpauley 7y ago
      Not a charitable interpretation of the parent comment. Even though databases allow concurrent transactions, commiting these transactions with complete sequential consistency is still an inherently serial process, at least insofar as it cannot be generally split across machines.
• deleted 7y ago
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• deleted 7y ago
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• onion2k 7y ago
  Surely this depends entirely on the app. Is the database a bottle neck for a CRM app that deals with millions of customer records? Probably because everything else is incredibly lightweight. Is it the bottleneck for an app that transcodes video on the server? Absolutely not. And so on, for every imaginable app.
  • unilynx 7y ago
    But the database is often the hardest thing to scale. Scaling up transcoding is childs play by comparison.
• godzilla82 7y ago
  In my experience with enterprise applications, we hit database bottlenecks only if you are doing aggregate operations over millions of records or firing 'like' queries over varchar(max) columns without full text indexes. Or generating reports with joins over multiple million record tables. For most crud operations any production ready database will not bottle neck before you have to fine tune your application server and application itself. Non thread safe application code and libraries have caused more performance issues than large join queries.
• cc81 7y ago
  It is probably one of the more common together with loading too much JavaScript and image content. However even with that said a modern SQL database can handle A LOT of traffic and I've had discussions about internal CRUD applications with a couple of thousand users at most where people say we must use a document database because relational databases does not scale. Insane. Of course people can use document databases and similar where it fits but you get a lot of nice things with a traditional rdmbs that you might miss later.
  • Cthulhu_ 7y ago
    I haven't had a discussion about performance in years, weirdly enough, but people did default to the scalable solutions - even before knowing whether they needed it. Think new applications built using microservices. They also spent nearly two years before releasing something with a fraction of the features of the system it was supposed to replace. If someone mentions a need for a technology choice based on performance, they had better come up with real numbers. Until they do, clarity, correctness and consistency should be the primary metrics. Don't solve a performance problem before you have the luxury of having a performance problem.
    • specialist 7y ago
      Premature scaling is akin to premature optimization. On one of my recent projects, the highest use data was stored in DynamoDB. The entire dataset could trivially fit in RAM. So much wasted effort. The org also over used Spark, event streams, and elasticsearch. One could argue (rationalize) that using DynamoDB, for instance, for the small stuff builds team competency applicable to the big stuff. Or that using one set of tools reduces dependencies. I'd have to see some case studies. Because from where I was sitting, 90% of our maintenance costs were from mitigating bad sacred cow design decisions. I've worked on similar metastatic tech debt based on NoSQL. Much as I love Redis, it's not a duck (fly, swim, walk).
      • cc81 7y ago
        I can understand the competency stuff. I was really blown away with React when that arrived and used it early, probably too early. React itself has been remarkably stable and consistent but everything around was chaotic for a couple of years. Anyway, we went all in in the department and builds all our front ends with React. I would argue it is a great fit for around half our applications as they are more complex but the rest could easily have been built with very little javascript enhancement. However now people can move between projects more easily and building something with React when you are comfortable with it and has a nice base is not slower than something else. So I understand that people want to try new things but damn I wonder if I sounded the same when I wanted to start using React. - "We can just use couchdb because then we won't have any problems with scale and we will never have to have any downtime because schema updates". ....yeah but that comes at a cost. You need to handle those schema updates in code. You need to handle those conflicts that arise from eventual consistency in code. Anyway, I like the idea where you have a limited innovation budget where you can go for a new type of db but then you select other technologies that you are comfortable with and are true and tested. Not "Yeah, we will build this in a completely new way and we have no experience with docker, kubernetes, nodejs, document databases, vue.js, Elastic stack, prometheus or kibana" but this modern way of building things means everything will go much faster. Sometimes you have to go that route but the learning time can be pretty rough.
• SimonPStevens 7y ago
  It's hugely dependant on the app and type of data it's processing. Our primary apps deal with trading data at 15/30 minute intervals over multiple years. It's not uncommon to have millions of rows involved in a process. Yes, nearly all of our performance problems boil down to the database. The majority of the time spent on the server is on data access. Basic CRUD on small data wouldn't be the same.
• viraptor 7y ago
  It really depends on the app, but if you're asking about a typical situation of a typical crud app, then yes, db is still likely the slowest part. SSD doesn't change this, because a typical crud app fits completely in memory and is 99% buffered reads anyway.
  • chenster 7y ago
    Is buffered read a database feature or the application?
    • viraptor 7y ago
      Yes. Either. Depends on your application. It may be a feature of orm caching as well.
    • deleted 7y ago
      [deleted]
• hu3 7y ago
  In general yes, database will be your bottleneck. In reality, when database becomes a bottleneck it means you're probably making enough that you can just throw more money at it.
  • talaketu 7y ago
    Let me know when this happens - I'll stand in front of it.
• Cthulhu_ 7y ago
  One important thing that has changed since I learned of the "databases are slow" is that they can now be "as-a-service", with services like Amazon Aurora; from the outside interface they're just SQL databases, but underneath they can scale up a lot with minimal effort or knowledge. Likewise, you can have your database of choice and put it on a server which you can scale up or down. Not sure how the migration for those works though.
  • noobiemcfoob 7y ago
    The serverless database options still remain the bottleneck though. They are still often the slowest aspect of any logic path if not also the most expensive.
• noobiemcfoob 7y ago
  Memory access of any type is often the slowest. This might just be a general law of computing regardless of level of the stack at this point. In the serverless world, memory accesses are the most expensive with writes being the most expensive of all (where expensive == slow AND literally expensive)
  • mi100hael 7y ago
    Do you have an example of that being the case? Memory is usually pretty quick compared to disk & network.
    • noobiemcfoob 7y ago
      I mean memory, storage in a broad sense. Not a statement about in RAM or cache, though I see the confusion in retrospect. Having code access some sort of storage (better word than memory) is always the most expensive part.
• beat 7y ago
  Anything can be your bottleneck. Fix one bottleneck, find the next one. That said, databases are often the bottleneck. And with web apps, "the speed of your server-side programming language" is almost never the bottleneck - although the architectural limitations of your framework may be.
  • asark 7y ago
    I've seen a lot of cases where "the speed of your server-side programming language" was, in fact, the problem—because the developer was doing stuff in it that they should have told their DB to do for them. Basically the speed of the server-side programming language doesn't matter only as long as you're very careful not to, you know, use it to do anything. Which is a realistic goal in many applications, by leaning on one or more DBs more heavily.
• mcguire 7y ago
  The data is a bit old, but I don't think the relative order or rough magnitude has changed. https://gist.github.com/jboner/2841832 https://gist.github.com/jboner/2841832
• namelosw 7y ago
  In short, yes. The database is the bottleneck because it's much harder to scale than applications. The path of evolution in the industry: 1. Stateful application - usually only 1 server, not distributed at all. It's very hard to scale. 2. An obvious solution is to make application stateless and having a centralized state. Then the application is very easy to scale and operate. And databases slowly became the bottleneck because there are much more applications than databases. 3. Then not so obvious solution is to split your problem domain into solution contexts, where every context become an application and each application have a database to talk to. So the databases are still not really distributed but it's sort of distributed by your sub-domain of business. (I think it's the industry mainstream or becoming mainstream now) 4. Then the non-obvious solution is to have truly distributed states split across your application. Basically, the goal is you can distribute any object across a whole lot cluster, in a more or less transparency way which lets you treat the cluster as a whole without concern about individual machines. (There are some cutting edge stuff yet to become mainstream, like Orleans / Lasp-lang or just cluster/shared actor like Akka, or just distributed Erlang, etc) It's an interesting example that short-term solution is in a totally different direction from the powerful solution. To address this in short-term is to extract and centralize the state, while to address this in the long-term is to truly distribute the state.
  • jchrisa 7y ago
    One example of #4 that is making headway in production systems is the Calvin protocol (a RAFT based write-ahead-log with lock-free deterministic transaction application). It's implemented by my employer FaunaDB. You can learn more here https://fauna.com/blog/consistency-without-clocks-faunadb-transaction-protocol https://fauna.com/blog/consistency-without-clocks-faunadb-tr...
  • dranka 7y ago
    A variant of no 3 is used for some large scale telecom equipment supporting multi millions of attached users with serious uptime requirements. State is distributed and handled locally and replicated to a sibling node for fallback protection in case a node goes down. The replication is dynamic so new sibling relationships are established when nodes come and go. There is also some more persistent state (like billing information) that is used to recover in case of total node failure but a lot of the transient state can be rebuilt when users are forced to reattach
• kid64 7y ago
  This is a ridiculous conversation. Disks and RAM will always occupy their respective positions in the storage hierarchy, so databases (assuming stored on disks) will always be the bottleneck.
• fulafel 7y ago
  I thought this promising title was going to be about a new trend called "server-side web apps" and how they reduce DB round trips. Oh well :)
  • asark 7y ago
    "One cool, super-advanced trick you can use to reduce latency for simple queries by 95%!" Then you read the article and it's just running the app and DB on the same physical server, and communicating over local sockets rather than a TCP/IP stack through various virtualization layers and across several networks, real and virtual—so, partying like it's 1999.
• hedora 7y ago
  Assuming everything is designed to scale linearly to some point well above the current workload, a better way to phrase this is to ask what percentage of your hardware spend goes to compute, network and storage machines. Note that a single postgres instance with one core and 1gb of ram on 1 ssd can “scale linearly” by my definition, since you can easily double, triple, etc all those specs. On the other hand, a fully populated data center running scale out software at 100% power capacity can’t scale linearly anymore, because you can’t upgrade it anymore. For that data center, power or maybe space is the bottleneck. Short answer to the question you asked: SSD’s are not going to be the bottleneck for software that’s migrating off disk, and the database probably won’t either. Hardware trends mean the storage and database just got 10-100x faster, while the business logic maybe doubled in speed. If the system was well balanced before, that means no one spent then-unnecessary effort on now-necessary optimizations on the compute side.
• RocketSyntax 7y ago
  It does feel weird pulling an entire database into a docker container... I don't know what the path looks like to make that a cluster.
• jandrewrogers 7y ago
  I've spent my career answering this question at myriad companies. In a few cases, the database was actually a bottleneck. In the vast majority of cases, the database was just being used very poorly and was no longer a bottleneck once used correctly. The exceptions to this usually involve exotic data models, which isn't most web apps. Of the few cases where the database was the bottleneck in practice, I cannot think of a single cases where it should have been the bottleneck in theory. The limitations were always a property of the specific database implementation being used, not intrinsic to databases as a class of software. For example, most open source databases have notoriously poor storage throughput as a product of their architecture; faster storage definitely helps but it often solves a problem that may not exist in other database implementations. In these cases the problem is an architectural mismatch. Sometimes architectural mismatch is an engineering problem. A handful of workloads and data models are currently only possible with custom database engines e.g. high-throughput mixed workloads or data models that require continuous load shifting. As a practical matter, the exotic cases that require a custom database engine are also the cases where no company should try to build it themselves without a deep expert -- they are considered technically exotic for good reasons.
• d0m 7y ago
  Nowadays, in most system architectures, app servers are stateless and can be scaled horizontally. But something needs to hold the state, and that thing will be the bottleneck. Yes, you can horizontally scale some databases, but distributed state is very tricky to get right and will have performance trade-off.
• orthecreedence 7y ago
  What are you doing? How many writes/s? How much processing are you doing in the app server? Do you have a queue you can offload work onto? Your question is a bit open-ended. Scaling your database is generally the hardest part. Any jerk can put 100 app servers behind a load balancer. Scaling data is significantly harder, especially when it comes to writes. There are options to get around this, but they come with compromises. So, really, it depends on what you're doing. In general, use things you're comfortable with. Is Python slower than Rust? Sure. But do you need 2ms response times from your app server or is 10ms fine, especially when latency between the user and your first load balancer is likely 3-5x that number? Build it, test it, if it's too slow, cache it. If you can't cache it, break out the offending piece and optimize it or put it into a faster language.
• idlewords 7y ago
  Never underestimate the power of a properly configured server with 1TB of RAM running MySQL off of SSD. In my experience, people used to cloud services misjudge the power of correctly tuned modern hardware by two-three orders of magnitude. Giving a general answer to your question is hard in the absence of further information.
  • bostik 7y ago
    Heh, most systems could fit their entire working dataset in 1TB and never worry about I/O. Except for logs, of course. Those things are incredible disk space hogs. Also, serialising data to logs can surprisingly expensive. (Our ES logging cluster has tens to low hundreds of TB of storage, and it expires data after roughly 40 days.)
  • GFischer 7y ago
    I went the opposite way, from an employer with an insane dedicated server to using cloud services, and I was appalled at how weak the cloud SQL servers are. Of course, they don´t need hardware expenses, licensing is factored in, administration is orders of magnitude easier and cheaper, so the tradeoff is usually worth it.
• esaym 7y ago
  For simple "web apps", typically its a poor understanding of SQL that is the bottle neck. Since most web frameworks include an ORM, many developers don't even know much SQL any more and treat the database as a simple 'object' that you can call methods on and iterate through. Case in point: you're building a dash board for a web based store to show details about the 5 most recent orders. But each order references 5 other items (as a simple example). And each item references an SKU# that can come from 5 manufacturers. So an optimized query set from an ORM will end up querying the DB like this: First it selects the 5 rows from the order table, but since you left out any join clauses, it then runs 5 more selects on each of the rows returned from the order table to pull the related rows from the items table. So we are now up to 6 queries. But each row of the item table references 5 manufacturers (again, as a simple schema example). So each of the 'item' 'objects' (5 per order) has to then make a query to fetch the sku manufacturer. So that is 25 more select statement queries on top of the original 6. So using the ORM properly and building out the right join clauses, you would have made 1 query. Instead you made 31. Now lets say that DB is not on the same machine, but a different machine on the network. Throw in 2ms - 10ms latency per query and now you are looking at 62 - 310ms of latency to fetch your data. And that is just for 5 'rows' of data. Do the math for a more realistic dataset, 500-1000 rows or so. Now you're looking at minutes of latency for your data to come from the DB. The worse case I've seen recently was this exact case. To generate an inventory page to show an html table with about 300 rows, the DB was being queried over 300,000 times with a page load of about 7 minutes (and getting longer everyday). I added the proper join clauses to the ORM and one query was issued which would return the result set in about 700ms which put the page load time to about 1 second. A 1 second page load imo is still not very good. But it seemed to impress everyone that with 1 hour of work, a page load went from from 7 minutes to 1 second.... For what ever reason, most people don't understand this anymore. Instead of learning SQL, the line of thought is to upgrade the machine, move to a 'no-sql' DB, introduce some caching layer, and many number of other ideas. But "lets make sure we are querying the DB instead of iterating through it" is never discussed.
  • iaabtpbtpnn 7y ago
    You've done a great job of explaining the type of DB interactions that most frequently cause performance issues at my workplace. I don't understand why developers are so unwilling to just learn SQL.
• owaislone 7y ago
  Yes, for the most part, it still is. It's not always disk though. I can be CPU or memory but most of the time it is the database that falls short of it.
• jerf 7y ago
  "Therefore, the speed of your server-side programming language is not important (or less important)." I view this statement as a prediction, in the scientific sense: "If you re-implement a program in a faster language, but have the same database access, the user will not have any visible performance improvements." Like almost anything else, the answer is, "it depends". It is often true. However, I have falsified it in some specific instances. It is definitely possible to take a "scripting language", which ranges in the 20x-50x slower than C (a rough, but adequate, approximation of "optimal"), then layer it in a couple more layers of abstraction that may be really nifty and cool and awesome but also adds another 10x to the slowdown (or more... if you're not careful, this term can be unboundedly large), and get to the point that simply generating an HTML page takes a full second of a 3GHz's processor time. I have replaced things that did non-trivial database work with something in a compiled language and gone from "noticeably sluggish" to "essentially instant" without all that much effort. In one of those cases, significant effort had been expended trying to figure out why the database was so slow, presumably because of the prevalence of this meme, when even the first glance at the output of a profiling tool said that was not where the problem was. Performance is rarely the first thing to worry about, but I find it's something you do want to at least keep in mind while developing. If you do at least keep it in mind, I suspect the saying is generally true, and the raw speed of the underlying server language won't bother you much. But you are definitely much closer to the point where performance becomes problematic than you start at with a faster language, so you need to be that much more vigilant. People who are lulled into a false sense of security by the idea that the database is always the bottleneck can get blindsided by performance issues in their app. I think the meme overall is damaging. It's not true enough to make it a core, unquestioned assumption that you can rely on, and if it can't be that, then you might as well simply use the correct answer, which is to use a real profiler and look at the real data. I pretty much always through at least a basic profile pass at any code that isn't just brute scripting.
  • Chyzwar 7y ago
    Most web application code is IO bound. You will get better performance by using more productive language to iterate and create better architecture. Github(Ruby) is noticeably faster than Bitbucket(Java) because engineering time spent on what matters. I think most slowness is coming from bloated apps trying to push too much code/data over the crappy network. Additionaly it is easy to misconfigure AWS infrastructure.
• guggle 7y ago
  Yes, any server-side language will do. But when you read "database is the most common bottleneck", you could frequently replace it with "database is the least well understood component in the stack". As soon as you stop using the database as a dumb store, you start to realise that your db can handle a lot more than you thought. One has to go beyond what the average ORM offers.
• tombert 7y ago
  Even if your disk speed is infinitely fast, or your using an in-memory thing like Redis, hitting the DB is almost always going to be slow-ish due to network latency. Redis is awesome badass rockstar tech, but I try and avoid it for any single-processe thing, instead opting for a tried-and-true global (thread-safe) hash table. Network packets aren't free, and no matter how cool your database is, I don't see you avoiding that. Not to mention that there is a cost to (de)serializing data, which you don't pay if you use something built into the language itself. I'm not saying to go drop Redis (or any database/cache), just to keep this in mind.
• tracker1 7y ago
  Often, yes... it will largely depend on what you are doing, how cacheable things are, and how large your data set is. If you are largely static and most of your content is tethered to fixed keys that can map from URL patterns, then you could through a large redis cache in front and it wouldn't be the bottleneck... or enough ram to cache on-system. There are more details to how much your disk or database are your bottleneck, but in almost every case, they are still much slower than your CPU or your network interface (that may be a bottleneck in some cases),
• imtringued 7y ago
  From fastest to slowest: web server > database > synchronous database connection. The bottleneck in my code is rarely the database but rather the interface that is used to it. I'm sure if you had thousands of users running high latency but low utilization queries you could reach the actual limit of the database. 99% of the performance problems I experience involve loading data from systems that don't let me access it directly and instead the data is simply dumped and my application has to load the dump. When you have to delete and reinsert 300000 rows every day and use the naive way of INSERTing the data then you will wait several minutes because the synchronous nature of SQL means that the client has to wait for a response on every statement. An easy way out is to use non-standard features like COPY or INSERT INTO x FROM unnest(...) in postgres. Except they aren't really pretty to use. Your 1 line Domain.save() suddenly turns into a 30 line query just for inserting some data. I still prefer the former for smaller datasets even if it means waiting 2 minutes on an initial import that only happens once because that can be quickly implemented and then you can simply throw the code away if you need something better.