MongoDB queries don’t always return all matching documents
engineering.meteor.com
engineering.meteor.com
The fundamental problem is that MongoDB provides almost no stable semantics to build something deterministic and reliable on top of it.
That said. It is really, really easy to use.
That and some round robin dns entries should be good enough for prod use, yeah?
('course it's not webscale ready till it's deploayble as a docker container which contains critical nodejs code with a dependancy on leftpad.js...)
Not to mention that are NoSQL alternatives that have a better track record than Mongo like Cassandra.
Now they are considered two of the most rock solid NoSQL databases. The hatred towards MongoDB really is pretty irrational given just how popular the database is.
https://aphyr.com/posts/322-jepsen-mongodb-stale-reads
If it isn't already fixed, they need to fix it. I can't vouch for HBase, but Cassandra is stable and reliable now. Cassandra is also easier to scale.
But the difference is that Cassandra is reliably "broken" in those ways, and as a result there are ways of using it which don't lean heavily on those weaknesses. Such as writing only immutable data or isolating all data that will be used in paxos transactions into their own column families by convention, etc.
Cassandra more or less behaves exactly as it claims that it does. So you can do a somewhat thorough investigation of its system semantics and know what you can rely on and what you can't. MongoDB doesn't even uphold the system semantics it claims that it has, so it's just broken in weird and esoteric ways that you discover mostly by accident.
It has its potential flaws in being able to save the images/videos but please don't do it the disservice of likening it to MongoDB.
That is an unanswerable question since it's about everything. "NoSQL" is a huge variety of techniques - many of them yet to be invented, that only have one thing in common: "not SQL". From document storage over key/value storage to graph databases. Anyone who tells you what they think "about NoSQL" either has to redirect the question to become a useful one, or if they actually attempt to answer it take your popcorn and expect entertainment at best.
"Types and examples of NoSQL databases" - https://en.wikipedia.org/wiki/NoSQL#Types_and_examples_of_No...
Was there ever a time when it actually worked consistently well at something that was database shaped? Because I started dealing with it in ~2010 I think, and it wasn't a suitable database for anything other than toy projects or throw-away data back then, and while it's many versions newer, it still appears to be pretty fast and loose with its supposed system guarantees.
There was a point when they raised $100+ Million in funding that I thought they'd take that money and actually build a database. At least as recently as last Summer that wasn't a reality yet.
Not in any way, shape, or form. Keep in mind, as bad as it is now, it was vastly worse when it launched.
> I’ve hesitated to recommend RethinkDB in the past because prior to 2.1, an operator had to intervene to handle network or node failures. However, 2.1’s automatic failover converges reasonably quickly, and its claimed safety invariants appear to hold under partitions. I’m comfortable recommending it for users seeking a schema-less document store where inter-document consistency isn’t required. Users might also consider MongoDB, which recently introduced options for stronger read consistency similar to Rethink–the two also offer similar availability properties and data models.
Because some people can't stand having to work with SQL,migrations,schema and constraints, it's as simple as that ( That's not my opinion,that's just the rational behind MongoDB). Even if you use Postgres with the Json column type, you still need to write SQL queries and schemas.
In the context of analytics, it might make sense, I'm not a big data analyst, but I've seen MongoDB used to centralize logs.
Because I've seen Excel used as database with all kinds of macros and VBA scripts bolted-on/embedded to provide the workbook various shapes of stored-procedure and query capability... but, while sorta impressive in a "Holy crap, lol wut?" kind of way, I'm not sure any instance I observed of uses like that were actually good ideas. Full of epic cleverness and ingenuity? Definitely. A good idea? Probably not.
If you need to preserve data between the application versions then you still get all the headaches with MongoDb (either migrating the data or supporting multiple schema versions when you read the data, oh the fun!).
If you don't need to preserve data between the versions then you don't need to write migrations scripts in SQL, just scrape everything and pretend it's the first version of application.
I am trying to remember if I ever built a prototype that got rewritten. Probably not.
This would save you time on picking the db, schema changes or even migration changes in Mongo. You don't have to worry about bad documents from an earlier app revision.
I have ~40M documents, and it’s still a subset of what I’ll manage in production. It takes a few hours to load them in the database.
Of course, that doesn't mean indexes never help. See for example http://blog.2ndquadrant.com/jsonb-type-performance-postgresq....
I guess the workaround would be creating indexes on computed columns that query from the json data, together with changing one's queries to use that computed field. For example, with a json column storing names in various places, a computed column could collect all of them in an array. An index on that computed column will have good statistics.
Bottom-line: if you want your queries to run fast, you will have to tell your store what kind of data you have and what kind of queries you will run. Otherwise, there's little the store can do.
Having a traditional database with various constraints is a way to give that information. With json columns, you may have to do it in another way (for now).
/s
But its a really cool database (though I'm partial to rethinkdb now)
Me too. RethinkDB is my document database of choice these days. In my experience, its proven to be reliable and fast and the development team very responsive and helpful. They also seem quite mindful when it comes to new features and will delay things for years (eg auto-failover, which they now support but it took a while) if rushing it would impact quality.
That's what I want from a database: first and foremost it must be solid and not lose my data. Everything else (including high availability) can come after.
The thing is, if you actually try and write an app using MongoDB, you will rapidly find that you:
1) Have migrations (except they're going to be some scary ad hoc nodejs script that loop through your document store and modify fields on the fly).
2) Have schemas (except they'll be implicit and undocumented)
3) Constraints (except they'll be hidden inside your app logic, and violating them will cause data corruption).
The biggest lie about NoSQL databases is that they're schemaless. If you're EVER going to read the data back and do anything with it, it has a schema.
My first task in any project is to design the whole data model based on current requirements and while designing it I think of the interfaces and how would they read and write data (to refine requirements). Writing views and actions/APIs on top of well-formed data model then becomes a breeze.
My company used mongo for years before we got our shit together.
Schmemas were always implicit (until we got our shit together and started defining and enforcing them with Python Schematics).
Migrations were crazy scripts you run in prod or hacks you stick into your code to "transition".
And yes, surprise constrains left and right causing awful anti-patterns. One-character key names to save disk. Hashed values for indexed keys to save memory. Awkward structuring to improve query performance.
The worst part is, we now have tons of important data in these databases and almost no one understands the legacy crazy app logic that makes them tick.
That's actually a legit use case. Use MongoDB while you get your shit together. I use global variables while I'm noodling around in code. Eventually I refactor.
If an enforceable mechanism like that existed, I'd be a lot more confident in mocking things up. Stick SQLite in for the database, munge HTML and Javascript together, whatever—it's literally going to slap away the hand of anyone who tries to use it on a production workload, so why not?
(Going further, it'd be interesting to create some sort of quagmire of a software license, specifically for prototypeware, such that you'd be forced to rewrite all the prototype code instead of reusing even a hair of it in production. Maybe something like reassigning the IP to a trust, with the trust having an obligation to sue anyone and everyone who tries to create derivative works of the code they've been handed?)
The first problem is that every tool has demands, especially the limited ones, and you end up writing your application around those limits and demands, using platform-specific code that will have to be discarded and re-written come the migration.
The second problem is that these tools dictate design, and once you try migrating, you still have an application designed around the prototype tools, which make a lot of concessions and have design flaws because of that.
Finally, I've never understood the need for learning a specific tool, platform or language for "rapid prototyping". Use the tools you will use eventually, it's not that building something in, say, Java from scratch will take an order of magnitude more time and effort than building it on Node.js, despite all the hype, especially if you're a Java shop.
I think we're picturing different things here. You're picturing having software engineers make the prototype, and then having the same engineers do the final implementation. Meanwhile, I'm picturing two different teams, with different competencies—one who knows a prototyping toolchain backward and forward and is extremely productive in it, and the other who knows a solidly-architected platform just as well.
The classical pipeline in the animation industry is to have two separate "teams" of artists. One team does concept illustration and storyboarding, and the other does keyframe animation and in-betweening. The first of the two teams is essentially a team of prototypers. Their output is a product which stands on its own for internal evaluation purposes—but which isn't commercially viable "in production." (Nobody really wants to watch 1FPS sketches.) So, after the storyboarding is complete, the whole product is redone by the actual animators into the more familiar product of 24FPS tweened vector-lines or CGI model-joint movements.
The more familiar case of this for web development is where the "prototype" is a PSD file. Professional capital-D Designers are usually Photoshop experts—they're very productive in it, and can mock up something that can be evaluated for being "what the customer wants" quickly, with rapid iteration if it's not right. Once they've got the customer's sign-off, their output product—their prototype—can be tossed over to development staff to "make it work." (There are also an increasing number of interaction-design prototyping apps targeting the same set of designers, under the theory that they'll be able to become productive in quickly iterating the "feeling" of an app with a customer in the way they're already doing with the "look" of the app. I haven't met a designer that uses one of these professionally, but I think that's mostly because there aren't any of these yet well-known enough to be taught in art schools.)
But when it comes to workflow and use-case design, we don't really see the equivalent pipeline. Looking through the lens of separated "prototyper" and "engineer" roles, there are clearly tons of software-development tools that were intended to be used purely by "prototypers": Rails' view scaffolding, for example. But since this role isn't separate, these things get used by engineers, and sneered at, since, as you said, it's no more effort—when you're already an engineer—to just engineer the thing right from the beginning.
Interestingly, all of the true examples of workflow prototyping I can think of come from the specific domain of game development—but even there, nobody seems to realize that prototyping is the goal of these tools, and tries to misuse them as "production" tools. RPG Maker, seen as a tool for making a commercial RPG, is total crap. RPG Maker, seen as a tool for prototyping an RPG, is an excellent tool. Its output is effectively a sketch, a cartoon in the classical sense:
> The concept [of a cartoon] originated in the Middle Ages and first described a preparatory drawing for a piece of art, such as a painting, fresco, tapestry, or stained glass window.
A cartoon is a prototype used to communicate intent. Yes, you (as the producer of the finished piece) can cartoon together with a client to iterate on a proposal. But much more interestingly, a client can learn to cartoon on their own—and then, in place of a long design document, they can submit their cartoon to you. An RPG Maker game project is the best possible thing I could hope to receive as a design proposal from a client asking for me to make an RPG. It forces all the same decisions to be made that making the actual commercial game does—and thus embeds the answers to those decisions in the product—but it doesn't require the same skillset to create that the commercial game does, so the client can do it themselves. The prototyping tool, here, is doing the "iterating on a design together" job of the designer for them.
We do have one common prototyping tool in the software world—Excel. A complex Excel spreadsheet is a cartoon of a business process, that nearly anyone can make. We as engineers might hate them, because people generally have no sense of project organization when making them—but every project to convert an Excel "app" will take far less time than one that involves collecting the business requirements yourself. The decisions have already been made, and codified, into the spreadsheet. You don't have to sit there forcing the client to make them. The process of cartooning has forced them to do it themselves.
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To summarize: software prototyping tools aren't for engineers—if you have an engineer's mindset, you'll prototype at the speed of sound engineering practice, so prototype tools won't be any help to you; and you'll be more familiar with the production-quality tools anyway, so you'll be more productive in those than with the prototyping toolset.
But software prototyping tools definitely have uses: they can help designers to iterate on a "functional mock-up" to capture a client's intent; or they can even help clients to create those same mock-ups on their own. This is why "prototypeware" makes sense as software—but also why it should be self-limiting from being used in production. The prototype app wasn't created by someone with an engineering mindset—so there's no way it could end up well-engineered. Its purpose is to serve as a cartoon, a communication to an engineer; not to function in production on its own.
(Mind you, prototypeware could be made to function as an MVP in closed-alpha test scenarios, in the same way that the MVPs of many startups are actually backed by manual human action in their early stages. The point there is to test the correctness of the codified business process, rather than to support a production workload.)
I've just fixed up some code marked "proof of concept" that had been in production for a decade...
Admittedly some people's PoC work is better than what some consider to be release ready, but still this was not intended to be in that state for that long.
A refactoring does not change behavior, and can be perfomed in minor -- and in your example of a global variable, perhaps even trivial -- increments.
edit: typo
Any time the data schema(s) change, you need to migrate. I'll bet that even when sticking with the same database flavor you'll need to migrate a handful of times over the first few months. Requirements change, blah, blah. After the first couple migrations, you refactor to make that less painful. Eventually it might get to the point that your persistence layer is fairly abstracted and you can change databases without ripping apart everything else. Doesn't happen with every project, but sometimes.
Depends on the language, I suppose. I'm more productive with Python when I write everything procedurally and refactor into functions, classes, etc. every dozen lines or so. It's more fun than writing UML diagrams (and seems to produce better code, too!).
Or do you think so long that your head aches and your colleague Hephaestus splits you open to find a fully-formed cooperative multiple inheritance hierarchy?
...or get my data corrupted: "When MongoDB is all you have, it’s a cache with no backing store behind it. It will become inconsistent. Not eventually consistent — just plain, flat-out inconsistent, for all time. At that point, you have no options. Not even a nuclear one. You have no way to regenerate the data in a consistent state." http://www.sarahmei.com/blog/2013/11/11/why-you-should-never...
When you refactor or rewrite your code, you have the old code in version control, can write tests to confirm that it still works as expected, and there's no inherent time pressure.
If you pick an unreliable database and your data has been or is being lost and/or corrupted, it's more like a "try to stop the bleeding before the patient dies" situation.
That's not the time I want to be considering changing databases.
I agree with your last comment. I can't help but to laugh at people who think they would get away with designing a database with no schema. Schemaless for me meant that unless you enforce constraints, there won't be any.
There's a reason why there are ORMs even though Mongo drivers are sufficient for most cases.
1) I've always designed my data with future changes in mind. I often spend up to an hour thinking of possibilities of data that I want to store in a collection, before writing the schema. The flexibility i have with Mongo is that if I think I need a field but am unsure of the exact data type to store, i.e. is it a string or array of strings, or array of objects with strings? In that case I just leave the field as an object and change it later. The plus being that as long as I haven't stored anything with that field, I can always change its type without a 'migration script'.
I've only needed to 'migrate' by updating documents 4-5 time. When GeoJSON landed, and a few other times when I needed small changes to my data.
3) The only way I can think of enforcing constraints on < 3.2 is through indices, which is insufficient. Most ORMs do the enforcing. I've never needed to enforce them at an app level.
I've used MongoDB primarily for its Geo support, and JSON enabling me to get things done quicker relative to maintaining SQL tables. I've got a small but interesting use case, public transit. https://movinggauteng.co.za and https://rwt.to.
When I started with the projects, PostgreSQL + PostGIS felt like a black box, and I wanted something that would give me ease and flexibility. At the time hstore was the talk of the day, but seemed to not meet my needs.
It would now with JSON, but I'll stick with Mongo for now.
> Have migrations (except they're going to be some scary ad hoc nodejs script that loop through your document store and modify fields on the fly).
I literally just spent the better part of tonight AND yesterday evening dealing with one of these scripts. I had pulled down the production table to locally test the script (gross), but when I later ran it in the production environment, we'd somehow had an array sneak in to what was an object field. The whole thing just felt like a mess.
Because you can't just test it on one document and see if it works; you have no guarantee that all the documents will be identical. And if the migration script crashes halfway through... oh man.
Schema-issues and typing aside, I looked at MongoDB just long enough to find out there are no transactions, then ran away, quickly.
For a lot of tasks, I guess I would find MongoDB very useful, but lack of transactions is a complete deal breaker for me. Not having a real schema, referential integrity and all that makes them even more important, IMHO.
At work, I have had more than one quickly-hacked-together Perl script crash on me in the middle of a run. Having proper transactions has saved my butt repeatedly.
With a NoSQL database, you have an implicit schema, but it will only be enforced and fail at runtime - when your code expects a field but failed to find it, for instance.
With a dynamically typed language, you have implicit types, but only enforced at runtime - when your code expects a value to be an int but finds a boolean, for instance.
And both are fine, there is a need for both. I can see how the flexibility of being able to change, well, everything by just flipping a switch in your head ("this is an int now") might be helpful for, say, data exploration problems.
It's just that in a production environment, these features of NoSQL databases and dynamically typed languages turn into massive sources of problems and oh god, just don't.
Schemas don't change that much compared to code, having a strict schema enforced by the database saves you so much time and pain and downtime in the long run.
You always have a schema; where and how it's defined is the only question.
I despise mysql, but even it is better than mongo. At least with it I can easily transition the data to many different uses.
Also, and a point I find amusing is that many users of nosql claim schemaless and then go and write a layer on top of the datastore to enforce a schema. It would have been so much simpler to use a RDMS out the gate instead of badly implementing one.
You are giving the NoSQL crowd too much credit. Some abominations have no recognizable schema at all. The data store will just contain arbitrary dump of data which different developers decided their "schema" should be. The number of "columns" will vary, the "columns" will have arbitrary formats, so on and so forth.
If one developer decided to separate name into "first: John", "last: Doe", you will have that. If another decided to have "name: John Doe". That's what will be there. If one developer decided social security should be "SSN: 123-45-6789" and another decided it should "SSN: 123456780", well you are going to have fun cleaning up the data at the business or even application layer.
But that's not even the big issue with MongoDB. It's their lack of ACID compliance!
The real question is “How come these people are allowed anywhere near data stores?” SQL isn't ideal, but how many of the alternatives are better at protecting the integrity of your data?
Please lord, take him in his sleep.
But if you do this, god forbid you go beyond where indices can fit in RAM of a single machine. And you will do so, with probability one given your product doesn't shut down. So you're running a gauntlet against a redesign.
At least OrientDB has had both schema+schema-free and SQL + NoSQL querying interfaces.
That is, you can optionally supply a schema for your documents. IIRC you could choose either schemaless, schema or mixed (where mixed allows fields not in the schema to exist as schemaless fields).
The default query language was SQL with "enhancements" (to allow for graph traversal), but you could also query with Gremlin. Not sure if this is still the case or not as I don't use OrientDB.
The above was true in 2012 and possibly a lot earlier. I see ToroDB's first Github commit was in 2014.
Thanks!
So use an ORM that understands Postgres' JSON columns. Don't need to write a single SQL statement, automagic migrations, no explicit schema (unless you make one), no constraints (unless you add them).
It works great, we did a rather large project last year using Django's ORM and postgres where we didn't know the final data schema until months after launch.
Use the right tool for the job, right? Admittedly something like MongoDB could be the right tool for the job (examples around here include RethinkDB and CouchDB). MongoDB, however, is like a hammer with no head.
Mongo is a dumb, dead-end platform, but they know how important ease-of-use is.
By “ease of use”, do you mean “ease of making something that seems to work” or “ease of making something that actually works”? I've never used a schema-free database, and ended up thinking to myself “I'm completely sure this database can't possibly contain garbage data”. Or do programmers simply not care about data integrity anymore?
How many times have you promised to fix something later and then later comes and...
Prototyping is not an excuse for laziness. It does feel like some programmers don't care.
Why not just prototype with Sqlite? You don't even need a server.
All that I can say is congrats, man!
In NoSQL you could be reinventing the wheel, or storing data that you can't query efficiently because you can't index it well etc.
All the excuses of not using some document stores beyond ACID really sound like people won't know what the heck they're doing.
For me, it means, under no circumstance, no interleaving of transactions or scheduling of commands, nothing, nichts, nada, can the database be in a state where a business rule is violated. If I need to worry what silly intermediate transaction state can be observed from another transaction, or if I need to worry whether a master record can be deleted without cascade-deleting everything that references it, then the DBMS has failed me.
> not normalising when you should or the other way around.
I've never seen a situation where anything less than 3NF (actually, ideally, at least EKNF) is acceptable.
Postgres/mysql/sqlserver/etc are nowhere near as easy to install, as fast to get started with or as portable to move around.
Also the fact that managed services help so much only speaks to the fact of how difficult these relational databases typically are to work with operationally.
if you're on a mac you can download postgresql.app[0] which produces a small icon in the top right status bar. You don't have to install users or permissions or anything it's super easy to set up. Getting it on prod can come later but for the first five minutes it works.
(granted, this neglects contrib extensions like hstore)
But installing and configuring Postgres "properly" on a server is still something of a challenge. Do I need to modify random_page_cost on a SSD or not? What are good memory limits on modern big servers? What exactly needs to go into pg_hba.conf?
None of these seem too difficult after reading a few tutorials and wikis, but it would be nice if the server set itself up with reasonable defaults based on the machine its running on.
Yes, it will help you to cover cases like where the server phyically explodes, but that's basically irrelevant, most problems where you need a DBA are caused either by data corruption caused by application code or developer, or performance issues caused by DB structure - in those cases the cloud platform won't do anything for you, they just host the server. They can restore backups, do monitoring and tune the server, not your particular app/db structure - but all the big problems are there.
Is running Mongo going to solve any of those problems? Without a rigidly enforced schema I would guess those problems are going to be amplified rather than solved.
While I no longer use PostgreSQL much, every time I need to touch it seems rather developer unfriendly, just last month I found MySQL, heck even SQLite supports triggers with code inlined into the trigger body but PostgreSQL mandates writing a separate function for the trigger. And, of course, it needs to be in plpgsql because reasons. The most trivial "let's calculate another column" becomes a complicated nightmare.
So then if you don't want to use PostgreSQL what then? The answer now is MySQL, again, because 5.7 has JSON.
And mind you, I have grown to dislike MongoDB slowly over the years as new types of queries have appeared and it's a complete mess by now. There was an excellent article on this posted on Linkedin of all places this March https://www.linkedin.com/pulse/mongodb-frankenstein-monster-...
It's really interesting how MySQL is the most usable and most supported database by now...
I think if you re-read it, you might see that at no point did the post that you're replying to imply that Postgres was preferred because it had a longer list of features. They're speaking entirely about the strong guarantees that an ACID system gets you.
Document stores are only mentioned because this is one of the (incorrectly) perceived advantages that Mongo has over Postgres and other databases.
The right response, as a postgres developer, is to agree that you describe a useful feature, and perhaps implement it to help other users.
But my advice to you is to be willing to put up with some short-term annoyances. Sometimes the best choices are a little annoying, and if you refuse to consider them, it will cost you (or your employer) much more later.
Similar to the top comment, all the real problems I've ever encountered with postgres (heck, all major RDBMS's for that matter) come from certain areas, mainly triggers.
I found something similar (and in the last month too) – insofar as we're talking missing popular features – but with MySQL's and Postgres's positions reversed.
`ALTER TABLE ... ADD CONSTRAINT CHECK ...` runs on MySQL without an issue, and so does any INSERT or UPDATE violating that CHECK constraint. A bug was filed in 2004.
Its a phenomenal product.
Yes, and that's why I'm using it.
> but you're going to pay for all of that later. [...] When things get busier you're in for a world of pain.
Will never happen.
You're dealing with a torrent of incoming semi-unstructured data, where losing a good chunk of it is minor nuisance because you only need a decent sample, from which you extract data.
In those kind of scenarios, making it easy to work on the code can often be far more important than reliability.
I have a project like that now. I'd love to use Postgres, and probably will eventually once things "settle down" and we know what data we need to store. But or now MongoDB is the "quick and dirty" solution. We define a schema client side for everything we nail down, so as we nail down more aspects of what data to process, it gets easier to transition to a proper database.
As ORMs get better support for Postgres' JSON capabilities, it will likely get less and less appealing to use MongoDB for stuff like this too.
I have no intention of defending MongoDB because what do I know, never worked with it in real life - but just out of curiosity I took the free courses they offer (https://university.mongodb.com/) and I find that a sizable share of the complaints about MongoDB come from people who don't seem to have learned much about the product they are using. It's like people complaining their new truck behaves badly in water.
A lot of critics seem to have chosen MongoDB when they needed a SQL DB from day one. If you need full flexibility to (re)combine data you need SQL, for example. A document store isn't "schema-less" at all - much of the schema is built-in and very inflexible after that.
The official Java driver is the easiest way to waste otherwise useful CPU time due to its blocking nature and wasteful threading model.
If you need denormalized/distributed caching, Redis does a good job. If you need to store some unstructured json blobs, postgres and now sql server 2016 can do that. If you need reliable syncing for offline capable apps, you probably want CouchDB. If you need real time, use Rethink Obviously, relational data belongs in a relational database.
I think the problem is that all of these databases do one or two things really well. Mongo tries to do all of these things, and does so very poorly.
Unless you are coinbase or an organization that deals with money/bitcoins/etc and you need ACID compliant transactions so that "debits/credits" don't just magically disappear.
When the bitcoin craze was going crazy, coinbase had all kinds of problems due to their mongodb backend.
I'm kind of curious as to where this hype is. I've almost never heard anybody say anything positive about mongodb. All I ever see is people saying it's terrible / hilarious for various reasons.
Not saying those are impossible goals but so far no database has managed to deliver that. Building a large-scale endlessly-scalable database is still very hard and very detailed and easy to screw up.
>The Standard for Modern Applications
>MongoDB 3.0 features performance and scalability enhancements that place MongoDB at the forefront of the database market as the standard DBMS for modern applications.
>Also included in the release is our new and highly flexible storage architecture, which dramatically expands the set of mission-critical applications that you can run on MongoDB.
>These enhancements and more allow you to use MongoDB 3.0 to build applications never before possible at efficiency levels never before attainable.
Hang out on the freenode room for mongo and you will see that most people, working on production software, make inquiries that reflect a complete lack of knowledge, common sense and leave you facepalming with no hope in humanity at a large.
I wish people would stop using acronyms and realize Express/Angular/Node is just as good with something other than Mongo.
great incentive to dive into React further and ditch Mongo
Take it with a grain of salt...
Just today CNBC announced its list of "the 2016 CNBC Disruptor 50 companies"[1]
MongoDB is #19. It apparently is:
Big data's thought leader
Because of its excellent integration with Spark/Hadoop and the schemaless nature it's very useful in the analytics space.
There are some things MongoDB does fairly well:
* MongoDB is really easy to use
* Document databases can be great and flexible solutions for some kinds of projects
* Documentation is fairly good so learning the basics isn't too hard even if you know nothing about it
* scales fairly well at the initial stages
* arguably quicker to get a project off of the ground with than traditions RDBMs, which might be the most important consideration for any startup even if a complete rewrite would eventually need to take place
That being said, I've used MongoDB significantly before and it wouldn't be my first choice for most types of new project: PostgreSQL probably would be
I doubt they meet in the middle, but both have great use cases. scaffolding out a quick isomorphic js app is great for mongodb for example. pg is faster and more robust.
its just tech, depends what you are optimizing for
* Mongo is only easy to learn. Beyond simple demos, it gets harder and harder to use as projects evolve i.e. you have to do a lot of work yourself imo this is a common problem with nosql datastores that isn't exclusive to Mongo
* "Document databases can be great and flexible solutions for some kinds of projects": Postgresql has been able to work directly with JSON for some time now. There are also other document datastores that are more reliable than Mongo
* Scaling with Mongo is difficult, specifically the crazy setup. Even if you set it up properly, the results don't tend to match the marketing https://aphyr.com/posts/322-jepsen-mongodb-stale-reads
* "arguably quicker to get a project off of the ground with than traditions RDBMs" unless you're using Meteor, I'm also going to disagree here. Most frameworks target a relational database by default. Developing by convention tends to get you off the ground much faster than using something more specialized and niche
Not sure what that means, but scalability is the worst thing about mongo (though my experience with mongo is all from ~2.5 ish years ago). As soon as your working set of indices gets bigger than memory, performance falls off a cliff and your entire app grinds to a halt. In my experience, mysql and postgres have a more gradual decline in performance so you have some time with only mildly degraded performance to figure out a solution (plus they have more options for tuning which can buy you more time).
MongoDB is the fastest database I've ever used. The easiest to get running, stable and scaled out. The best documentation by far and has excellent integration e.g. Spark, Hadoop.
If you are doing Big Data it's a great tool in the arsenal.
Perhaps you should have been using a relational database from the get go.
Sounds more like your issue, not mongo's.
If you have a lot of relations in your data don't
use mongo
I know Mongo has issues but it's never going to beat an RDBMS on relational queries.
Denormalizing on day 1 (Mongo) has you making guesses about your data access patterns at the worst possible time instead of just thinking about the data itself.
This has nothing to do with database choice. This is just shitty development. It's a strawman at best.
Normalization is just a method of organization to minimize repetition of data. It has nothing to do with efficiency of operation. This is perfectly valid code:
person = {
_id: "person123",
username: "lloyd-christmas"
}
comment = {
_id: "comment123",
person: "person123",
text: "This is how I start",
}
person = {
_id: "person123",
username: "lloyd-christmas"
}
comment = {
_id: "comment456",
person: {
_id: "person123",
username: "lloyd-christmas"
},
text: "This is also valid"
};
Obviously this is just an MCVE. My original point was that I find this to be a drastically more flexible process than starting off relational.
To nit-pick, I think normalisation improves the efficiency of updates, as you only have to modify the one place where the piece of data lies.
I'm just curious how that's an upside to using a relational database from the beginning when your plan is to migrate to a relational database anyways.
Switch out "Mongo" for "Postgres" in your bulk paragraph and you have the same scenario but with less work on your part and more features to help establish your data model.
One upside I can see if it you're more familiar with Mongo where using a relational database slows you down.
The bottom level of our application layer is a query builder which is almost a drag and drop replacement between mongo and postgres. By the time that layer is built out, we know what our database needs to look like. I find that adding/dropping fields and models in mongo to be drastically faster than moving models around in postgres. The above example would obviously end up in the same structure, whether we started with relational or not. It was nothing more than demonstrating an iterative process where you don't need to START denormalized just because "that's why you use nosql".
We try to be as incremental as possible when building our apps, and have found that using nosql allows for 20 small refactors that often end up being 2 larger refactors with a relational db. We've just found that it ends up being a faster production process, and we end up with a much more application-specific database instead of just "This is a Person, this is an Address, this is a Comment". Sure, we know beforehand that the application will contain all those components. We don't necessarily know how they'll be used in a request-by-request basis, and whether or not they will actually end up being one-to-one, one-to-many, or many-to-many.
Quite the opposite. We feel that going in with the assumption that you know where the application is going to end up is hard-headed. However, acknowledging that the situation will definitely change doesn't absolve you of planning it out properly given the information currently available to you.
> Or is it just the kind of projects you work on?
We build mainly internal facing or b2b apps in the medtech space. Given that we need to integrate with larger players that don't really care much for small businesses, we can receive slow response times for data/api requests from any external sources we deal with.
e.g., Recently we built an application for a home-town pharmacy where we were forced to use two databases; one under our control and one which was controlled by a pharmacy management system. We needed to update certain models in their database while reading from other ones. They promised to build out a few stored procedures that we needed. They flat out lied on a couple, and then quoted us a 8 month turn around on the other ones. We'd be stuck with them regardless, the expectation of a curveball like that allows us to rapidly adapt.
Obviously, the pharmacy's business model doesn't change very rapidly. Iterating over the app through a few of their business cycles tends to give you enough knowledge of what you can build them, as well as what they really want. Regardless of how much time we spend planning with them, they'll always leave us with some form of an XY problem that we'll only understand after they use the application for an extended period.
We don't deal with web-scale, so the problems generally encountered in the mongo complaint arena tend to be irrelevant to us. Given our use case, I think the decision is pretty reasonable.
> Do you find there's less rewriting over time as you become more experienced?
We've factored that into our development strategy, which is why I mentioned the query wrapper. That query wrapper paired with a DAO level that's reasonably database agnostic makes our transition fairly simple and quick.
https://wiki.postgresql.org/wiki/What%27s_new_in_PostgreSQL_...
JSONB values are indexable and queryable, so there aren't very many downsides.
So... you are not against MongoDB but against NoSQL in general? I've used MongoDB and I've never ended up with lots of joins in my code. But I guess it all depends on the use case and how you've structured your data. Document databases are not a silver bullet.
It's quite possibly the best document store out right now. Many others in this thread have said good things about it, but give it a try and you'll see.
Here's a technical comparison of RethinkDB and Mongo: https://rethinkdb.com/docs/comparison-tables/
Here's the aphyr review of RethinkDB (based on 2.2.3): https://aphyr.com/posts/330-jepsen-rethinkdb-2-2-3-reconfigu...
I'm not an expert on couchbase (and neither on RethinkDB, to be frank, though I am a huge fan), but here's what RethinkDB has going for it:
- Changefeeds - easily open a persistent connection to the server and get updates when the results of an almost arbitrary query changes.
- Joins
- Expressive query language that is pretty functionally minded, really shines in their clojure/haskell drivers
- Excellent client libraries, well maintained
- Geospatial queries/objects
- Amazing admin interface (it has been amazing for a long time, too, not a recent change)
- First class consideration of replication & sharding (it is not a bolt-on in any way shape or form)
- API-driven cluster configuration
- API driven permissions management (this is relatively new)
- Excellent, easy to follow documentation
There are more things, but this is just what I can think of off the top of my head.
The team at RethinkDB is also just great -- I've met them in person and gotten help from them and they're straight shooters.
They've also got this great project coming up called Horizon: https://www.youtube.com/watch?v=Sb1lH5mvYmU
Also they have a video up with a member of the team building a realtime game with React Native: https://www.youtube.com/watch?v=xRK0SYSgVF0
Maybe someone who is very familiar with couchbase can help make a list... I'll start it off:
- Custom query language
- First class consideration for scale -- replication and sharding
Firstly, regarding the marketing, it may not have been to many people's tastes - but it definitely worked, and achieved a lot of what was set out in terms of raising the awareness of what was a decent product that wasn't as well known as its competitors. There may be cases where people avoid it because they don't like the marketing, but the reality, having seen its effect, is they are in the minority, and would probably serve themselves better by assessing products based on technology rather than spiel.
Now, on the actual technology!
What Couchbase has historically been good at is highly scalable Key-Value access, at very high performance and low latency. Performance is comparable to Redis, but CB much more mature sharding, clustering and HA. e.g. fully online growing/shrinking of cluster, protection from node failures, rack/zone failures and data center failures. Redis may be a good fit for single-machine caching situations, and also has its own advantages in terms its datastructures support, etc.
Quality of SDK's is pretty subjective, but I'd say the 2.x re-write of Couchbase SDK's makes them very solid. The Java SDK in particular is extremely good both in performance and by providing native RxJava interfaces.
In terms of query interface, there's geospatial and a new freetext capability on the way.
Couchbase chose down to go down the route of a SQL based interface as their main query language. This seems to be a bit love/hate with developers with some delighted and some perplexed. Maybe for devs it should really be about higher level interfaces like Spring are increasingly important anyway?
The native interface being SQL based is usually very popular with the BI / Reporting side of things.
Changefeeds (continuous query?) is a feature not in Couchbase which I would very much like to see in the future. One thing I would say is that it's something you have to be very careful in the design of to ensure scalability and performance. Consistency is something which would obviously need thought as well.
Correct me if I'm wrong, but it's a completely different database.
Remember, not all software needs to scale to millions of users so something affordable and easy to install, use, and maintain makes a lot of sense. Long story short, use the best tool for the job.
The behavior is well documented here https://jira.mongodb.org/browse/SERVER-14766
and in the linked issues. Seasoned users of mongodb know to structure their queries to avoid depending on a cursor if the collection may be concurrently updated by another process.
The usual pattern is to re-query the db in cases where your cursor may have gone stale. This tends to be habit due to the 10-minute cursor timeout default.
MongoDB may not be perfect, but like any tool, if you know its limitations it can be extremely useful, and it certainly is way more approachable for programmers who do not have the luxury of learning all the voodoo and lore that surrounds SQL-based relational DB's.
Look for some rational discussion at the bottom of this mongo hatefest!
While I agree that it's good to know the limitations of the tools you chose those limitations should be clearly spelled out in the documentation.
I don't think most programmers have the luxury of learning all the voodoo and lore that surrounds MongoDB from JIRA tickets and blog posts.
That's how I learned everything I know about most FOSS products I have encountered - through the code pages and social media surrounding the project.
Pretty much everything about the mongodb hate derives from their marketing and sales. The truth is, they've obviously stumbled onto something the market wants, otherwise they would never have become so successful.
For me, as a long-time programmer with no database experience, the mental mapping of JSON constructs as both data and query language was far easier for me to absorb than the relational model, which didn't fit the paradigms that I was used to.
At my present gig, we've used Mongo DB for two years, scaling up to quite a large production setup. Like any technology it has strengths and weaknesses, but it has not been the utter failure that readers of Hacker News would be led to expect. We adopted it knowing quite a bit about its history, and it has turned out to be an excellent choice that has held up over time.
Periodically we've considered switching to postgres, and we may do so for part of our stack. But for the core jobs of data collection and batch processing data with fluid schema, I'm pretty sure we will stick with mongodb for the duration.
It's just a tool, folks.
You mean, every time you query the DB? Do you also need to re-query the re-queries?
Yes, with enough extremely careful coding knowing exactly all the internals of the database, you can probably avoid these giant gotchas. But that's a hell of a lot of work for a DB that's supposed to be easy.
And then you're still dealing with a database without ACID guarantees. No transactions, very little atomicity.... and good luck if your server crashes... we've had multiple customers have their DB corrupted that way.
MongoDB is only good for storing data you don't really care about... in which case, why are you bothering to store it at all?
What most people end up storing in Mongo: strongly schema'ed, relational data that is critical to their application. This is exactly what mongo is not for.
Have you tried ToroDB (https://github.com/torodb/torodb)? It still has a lot of room for improvement, but it basically gives you what MongoDB does (even the same API at the wire level) while transforming data into a relational form. Completely automatically, no need to design the schema. It uses Postgres, but it is far better than JSONB alone, as it maps data to relational tables and offers a MongoDB-compatible API.
Needless to say, queries and cursors run under REPEATABLE READ isolation mode, which means that the problem stated by OP will never happen here. Problem solved.
Please give it a try and contribute to its development, even just with providing feedback.
P.S. ToroDB developer here :)
At a later release, we also plan to natively support MongoDB's sharding protocol.
Its marketing was pretty silly about all the various things it would do, when it didn't even have a reliable storage engine.
Its defaults at launch would consider a write stored when it was buffered for send on the client, which is nuts. There's lots of ways to solve the problems that people use MongoDB for, without all of the issues it brings.
> reliable storage engine
By "reliable" I assume you mean "consistent?" While MongoDB claims that it's CP (which it's not, as per the article) there's nothing wrong with inconsistent databases (AP, e.g. CouchDB). Mathematically there is no reason for MongoDB to behave like this. It's fundamentally broken; it's neither AP nor CP.
They did that so they could look good in benchmarks, and it's why they recommended so strongly that your memory completely fit in RAM or else things would fall apart (pro-tip, any system that recommends that has a poorly designed storage engine).
They also screwed up the consistent side of things as well.
I run Cassandra in production and I love its operational simplicity, scale-out design, and write performance. But I think its support for time series is perhaps over-hyped. To me, it seems the only queries you can run in Cassandra is a key lookup (partition key row get) and a column slice (partition key row get filtered by an ordered range of columns). This allows for a certain time series use case e.g. where each row represents exactly one series, and where the only thing you want to do with a series is to get its raw values. But it doesn't allow for many of the things I personally think of when I think about "time series queries", e.g. resampling, aggregates, rollups, and the like.
Cassandra, as you know, forces a certain amount of "low level awareness" requirement on the programmer because to tap into its uniqueness, you need to know how you will query stuff, so that Cassandra will ensure that the most common range queries are contiguously stored in rows. All other databases hide the on-disk storage order from you in an abstraction, and you can find atomisation causing inefficiency. Cassandra forces you to think about it, and in return, guarantees contiguous storage order on disk along one of your keys so that along that key, retrieval is lightning fast as it requires only one pass.
Basically, both spinning disks but also SSDs, are in essence, 1d media (ie, a lot in common with tape) in the sense that along one dimension you can read stuff massively fast, but as soon as you need to seek (ie start using dimension 2), even on an SSD, your performance dramatically declines. Cassandra forces you to think about your queries so that they will be "aligned" along the most efficient direction on disk.
Now agreed that if your queries cannot be aligned along said direction, then Cassandra drops to being no better than all the others, and penalises you with some complexity. That includes some examples of aggregrates, resampling etc (though I would argue that the order of magnitude contiguous read still helps these). Some of this can be mitigated with denormalisation ie: storing stuff more than once, in transposed or sub-sampled orders, something that relational DB purists will hate, with some justification (potential for inconsistency).
FWIW Riak TS sounds promising with automatic "blob" style storage etc and resampling capabilities which might take Cassandra on quite explicity and in a higher level, more convenient way. I am about to evaluate it because I agree with you that the resampling capability in particular could be better supported in Cassandra, though ultimately, both databases will still be limited by the underlying D1 v D2 "contiguous v seek" capabilities of the storage so I'm not expecting miracles from Riak.
By the way, I'm not even touching on Cassandra's scale-out ease. More perf needed? Literally just add boxes though it would be unfair not to comment on the cost of this, which is Cassandra's node-level consistency tradeoffs for very recently added data, and which is, if I recall correctly, why Facebook went to Hbase. You can force consistency at the query level, but performance can suffer.
I've also worked with Cassandra and have nothing but good to say about it, did what we asked it right out-of-the-box. Datastax was really helpful as well.
--
And I have no affiliation with either Basho nor Datastax, just really happy with one product and completely blown away with the poor performance of the other.
They seem to still have this outstanding marketing team.
All these people saying Mongo is garbage are all likely neckbeards sysadmins. Unless you're hiring database admin and sysadmins, Postgres (unless managed - then you have a different set of scaling problems) or any other tradition SQL store is not a viable alternative. This author uses Bigtable as a point of comparison. Stay tuned for his next blog post comparing IIS to Cloudflare.
Almost every blog post titled "why we're moving from Mongo to X" or "Top 10 reason to avoid Mongo" could have been prevented with a little bit of research. People have spent their entire life working with the SQL world so throw something new at them and they reject it like the plague. Postgres is only good now because they had to do some of the features in order to compete with Mongo. Postgres been around since 1996 and you're only now using it? Tell me more about how awesome it is.
No such thing can happen in a sane RDBMS, no matter the transaction isolation level.
Under read-committed isolation, within a single operation, you must not be able to see inconsistent data. So if you do "SELECT <star>" on a table while rows are being updated, you're guaranteed to always see either the old value or the new value. But if you do two separate statements, "SELECT <star> WHERE value='new'" and "SELECT <star> WHERE value='old'" in the same transaction, you may not see the row because its value could have changed. Serializable isolation prevents this case, typically by holding locks until the transaction commits.
It gets messy because the ANSI SQL isolation levels are of course defined in terms of SQL statements, which don't map perfectly to the operations that a MongoDB client can do. Mongo apparently treats an "index scan" as a sequence of many individual operations, not as a single read. So you could argue that it technically obeys read-committed isolation, but it definitely violates the spirit.
> Specifically, if a document is updated while the query is running, MongoDB may not return it from the query — even if it matches both before and after the update!
How's that compatible with READ COMMITTED isolation level?
I recommend you check out the Apollo Meteor Starter Kit: https://github.com/apollostack/meteor-starter-kit
Fixed that for ya.
Yes yes I know, HN is not Reddit. But there's still something fishy about declarations of what is/isn't a "proper" RDBMS.
Another reason why I never bothered with Meteor.
I have personally used MongoDB in production two times for fairly busy and loaded projects, and both times I ended up to be the person that encouraged migrating away from MongoDB to a SQL based storage solution. Even at my current job there's still evidence that MongoDB was used for our product, but eventually got migrated to PostgreSQL.
Most of the times I've thought that I chose the wrong tool for the right job, which may be true, but still leaves a lot of thought about the correct application. Right now I have a MongoDB anxiety - as soon as I start thinking about maybe using it(with an emphasis on maybe), I remember all the troubles I went through and just forget it.
It is certainly not a bad product, but it's a niche product in my opinion. Maybe I just haven't found the niche.
Does this guy have so many containers running that the status info can't be kept in RAM? I have a status table in MySQL that's kept by the MEMORY engine; it's thus in RAM. It doesn't have to survive reboots.
Unfortunately, it didn't work out as well as one might hope because the people who implemented the idea didn't seem to be able to resist using the DB the way they would use a relational db. Instead of maintaining the concept of a write only ledger, they started to use it as a data store for things that were ephemeral. Also, instead of replicating the db, using a view to create a new db that was optimal for certain queries, they wrote a huge number of views in the main db. Finally they organised the views by relation rather than by use, so you would have 60-80 views in the same design document that would have to be reindexed if one of them changed.
The result was something with very poor performance and where the storage for the indexes was more than an order of magnitude more than the storage for the documents themselves.
CouchDB is also not super speedy at the best of times. There is a lot of latency involved in serializing the documents and farming them out to view servers, etc. So it takes a good 10 minutes to process a million documents, but you will find that your CPU is chugging along at 30-40% utilisation.
Having said all that, one of the things I want to try (but have only done some preliminary trials with) is to keep the concept of the write only ledger, but to replicate the db into several views of the data (some with severely restricted content). Then instead of building something like a rails application to farm out the data, make "couch applications" where you serve the HTML and JS directly from attachments on documents in the DB. In fact, I've written a React application to allow users to interact with portions of the data and it was quite simple. Then you can write a really small coordinating application to allow users to navigate to the parts of the system (really single page apps) that they want to use.
Again, the nice thing about this is that you have a write only data store with versioning and the ability to audit history. You have views that allow you to interact with a small subset of the overall data. You can easily write single page applications where deployment is as easy as pushing a document to the DB. Replication is relatively cheap and you can move expensive view creation to restricted versions of the DB. You can stick the whole thing behind a load balancer and scale it as cheaply as setting up a new replication (again just another document in your DB).
But, I will warn you. Don't use it like you would a relational DB, or else you will be in for a world of hurt. Especially you will see comments in this thread about migrations. If you are migrating your data, by definition you do not have a write-only-with-versioning application. Your application will have to deal with multiple versions of data or else you will not have the ability to audit history. If you do not care about this, then possibly there are better solutions than this.
one of the things I want to try (but have only done some
preliminary trials with) is to keep the concept of the
write only ledger, but to replicate the db into several
views of the data (some with severely restricted content).
An example of this might be that you have a large number of daily reports. They all need different aspects of the data, but you end up writing views that sort by date and then collate the result in the server. So you end up maintaining an index for data that you will never query again and you are doing lots of extra processing merging the data after the query. Much better to replicate one day's worth of data to a new db every evening (possibly setting up a continuous replication to keep it up to date) and then add views on that db to do what you want. Like I said a full replication of a million documents takes about 10 minutes, so it's a reasonable thing to do.
I like what you suggested very much, because what CouchDB can currently do with views is very limited to what a powerful views implementation would, and the one you've suggested is a good suggestion of how to do it better.
Thank you. I'll give this a lot of thinking when I restart writing my https://github.com/fiatjaf/summadb/
It is in some ways magic, and I literally don't know how we'd achieve what we do without it.
But.
The big pain for us is filtered replication: we have a bunch of cell phones (Pouch) that should only see some of the documents from the server (Couch). No mater how you slice it pretty much, you need filtered replication in there somewhere.
And it's sllllooooooowwwwwwwww. It basically canes a CPU core while it's running per user, which means you can have CORE user's replicating at any given time, which is terrible. There isn't really much a solution for it, except "don't use filtered replication".
I recently posted to the mailing list about it, you can read more here: https://mail-archives.apache.org/mod_mbox/couchdb-user/20160... (I'm the OP in that thread).
To be fair, I've never been much of a fan of the whole NoSQL solution, so I may be biased, but what real benefits do you gain from using NoSQL over anything else?
When you're developing MVPs, work with ever changing designs and features, ability to cut off this whole migration part comes around really handy. I would however recommend to anybody to keep migration plan for the moment the product stabilizes. If you don't, you end up in the world of pain.
We had some aches and pains with anti-entropy ops back in the day (7TB + on too small a ring) but it's my favorite storage system :)
I'm not sure that I'd choose Mongo over alternatives these days, if I'm on AWS and can use RDS, I'd go PostgreSQL mostly, but RethinkDB, ElasticSearch, C*, and others also have their places.
Mongo reminds me a wee bit of JS...
I use that extensively to build custom replication cluster topoligies (overlapping rings, star, hierarchy) etc.
Has HTTP interface so easy to build clients for.
Transactions are per document only. So have to design your application to accomodate it. Raw single document write speed is not as fast as Mongo or Postgres. But I noticed in a concurrent environment, multiple connections writing it scaled pretty well.
Moreover, CouchDB 2.0 will have built-in clustering from code donated by Cloudant. And it will also have a similar query language like MongoDB (instead of having to use Javascript / Python / Other map-reduce functions).
This being said, I would feel embarrassed to post this on behalf of the engineering department of a company.
This post is just a very illustrated way of saying "we have no idea about what we are doing and our services are completely unreliable".
This is so bad that is more of an HR problem than it is an engineering problem.
Zookeeper is fine for things like service discovery that deal with a bounded amount of data. You don't want to use it for something where the amount of data depends on, say, how many containers your customers decide to start. Every ZK server keeps all of its data on the Java heap, so if your data gets too big, pow. How big is too big? Don't worry, you'll find out the hard way sooner or later!
Plus, there's no sharding -- every write operation has to be acknowledged by a majority of nodes in your cluster. So for write-heavy workloads (which is what I would expect a service status dashboard to experience) your cluster actually gets slower if you try to add more machines.
But that doesn't limit the amount of independent clusters you can have.
The reason I suggested Zookeeper is because it offers you ephemeral nodes, which is convenient to mark stuff as unavailable.
I searched the comments for "percona" and found nothing...
Figures.
Meanwhile, https://github.com/percona/percona-server-mongodb/pull/17
[1] https://setandbma.wordpress.com/2012/05/28/technology-adopti...
We've migrated our largest collections to Postgres tables and our happiness with that decision increases by the day.
I am someone just getting into Meteor Js and it seems like moving from MongoDB would make it Meteor trickier to learn.
Is it difficult to switch to an alternative? Thanks
If I was writing this 2 years ago, I would say horizontal scaling is much easier. Add a node to your replica, watch it catch up, and continue.
Have data stored in an array 4 levels deep? Mongo will find it for you. It's only difficult to switch to an alternative to the extent that you've convolved your schema in an unfriendly way. Most migration entails normalising your data into different SQL table and exporting it. Not rocket science as people make it seem to be.
I use SQL at work, Oracle, SAS excuse, a bit of MySQL and sometimes Postgres - I'm a consultant. I have tried some NoSQL DBs but always come back to Mongo, for personal projects.
I've done a few prototypes for clients using Mongo, but those are almost always for geospatial support.
I have two VM instances in google cloud platform. One is a web app and the other is a MongoDB instance. They are in the same network. The connection I use is their internal IP.
Can other people eaves drop between my two instances?
Man, it's just taking too damn long to run this update. What should we do?
mongo developer 2
Uh....remove all the locks?
mongo developer 1
Oh, yeah, that makes sense, let's do that.
mongo developer 3
Hey, what are you guys up to?
mongo developer 2
Making it web-scale!
mongo developer 3
Good job, keep it up!
Why all the Mongo hate? I'm sure this would happen with other databases.
The thing about relational databases is that they do everything for you. You just lay the schema out (with ancient E-R tools maybe) load your relational data, write the queries, indexes, that's it.
The problem was scalability, or any tough performance situation really. That's when you realized RDBMSs were huge lock-ins, in the sense that they would require an enormous amount of time to figure out how to optimize queries and db parameters so that they could do that magic outer join for you. I remember queries that would take 10x more time to finish just by changing the order of tables in a FROM. I recall spending days trying different Oracle hints just to see if that would make any difference. And the SQL-way, with PK constraints and things like triggers, just made matters worse by claiming the database was actually responsible for maintaining data consistency. SQL, with its naturalish language syntax, was designed so that businessman could inquire the database directly about their business, but somehow that became a programming interface, and finally things like ORMs where invented that actually translated code into English so that a query compiler could translate that back into code. Insane!
Mongo, like most NoSQL, forces you to denormalize and do data consistency in your code, moving data logic into solid models that are tested and versioned from day one. That's the way it's supposed to be done, it sorta screams take control over your data goddammit. So, yes, there's a long way to go with Mongo or any generalistic NoSQL database really, but RDBMS seems a step back even if your data is purely relational.
Why not? We do exactly that. We prototype in mongo and then migrate to postgres when we're comfortable with where the app is headed.
I...just...I can't.
Your database should ALWAYS be responsible for maintaining consistency. Your application will likely be dead in a couple years, and if it isn't then you are going to end up having something else interfacing with the database at some point, guaranteed. The only sane place to guarantee consistency is in the database itself, otherwise every time you change constraints you are going to be updating every integration you have and hoping you didn't miss something.
My lord, what has come over developers?
So your consistency check code is more reliable that that which has been built and tested over many years, people and projects?
And your code is guaranteed to be deployed into any project that need to access that database later?
Postgres can enforce both, in a way that's not subject to race conditions. 1) Is a unique constraint and 2) is an exclusion constraint.
As far as I'm aware, the only way for application code to do this would be for it to 1) lock an entire table/collection of data 2) do a query to see what data is there 3) check the new data against the existing 4) write new data 5) unlock the database.
Postgres probably has to do the same thing conceptually, but by using indexes and running the checks in C on the same machine where the data lives, it can do it very quickly.
Are you saying you have a better solution?
> How does MongoDB ensure consistency?
> Applications can optionally read from secondary replicas, where data is eventually consistent by default. Reads from secondaries can be useful in scenarios where it is acceptable for data to be slightly out of date, such as some reporting applications.