This troll job by MongoDB made me wonder if anyone is really hardcore in favor of NoSQL for general use-cases.
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This troll job by MongoDB made me wonder if anyone is really hardcore in favor of NoSQL for general use-cases.
For further actions, you may consider blocking this person and/or reporting abuse
Fahmi Noor Fiqri -
Sohail Jafri -
Boopathi -
ppaanngggg -
Top comments (53)
Sometimes NoSQL is really important... but I really believe most apps should use a relational model for main stuff.
It's not white and black, it always depends.
Example from real life:
github.com/coretabs-academy/websit...
In our academy system, we have a track which consists of many workshops which has many modules, each module has many lessons.
Okay, so:
Track => Workshop => Module => lesson
Sounds like a document model right?
So our academy library is begging for NoSQL, but we use django... and django hates mongo :(
And here we improvised and used relational model, guess what we end up with?
We got 4 joins between the four tables... so each time the user opens the academy to see the lessons, we need to perform 3 sub queries (let alone the ugly long query for calculating the shown lesson percentage).
Hmm, okay... how would the documentDB solution look like: it's really simple, one query (get track document) !
Yeah, we will rewrite it in dynamodb with lambda soon. Anytime we might get our server loaded.
Ok you have 3 joins. What bad about this? I guess response is still 20ms. Did you research how to create local development environment with DynamoDB? Last time I checked AWS wasn't friendly for that case.
I haven't put my hands dirty with dynamo, but I'm pretty sure the response time won't be 20ms in the relational model cuz from a scalability point of view, we do this fat query:
github.com/coretabs-academy/websit...
This is the with_is_shown function:
github.com/coretabs-academy/websit...
You see here that we store is_shown values of all users all in one table, and this will get slow in time the user base gets into 100,000 users where each user watched 100 lessons watched = 10,000,000 records to get the shown lessons !
I really think the models are shouting: "Please bring me the DOCUMENT model !" :D
You might mention sharding, but you see the problem isn't with the data growing bigger, the problem is within the model itself.
It's a shame I'm not that good with Django. If it would be ActiveRecord it would be much easier for me to understand what is behind. I will try to read it but no guarantees.
Can you get output of explain queries from the production db for those queries?
It would take me some time to get done right now, cuz I need to:
I will do once we do the first 3 steps these days
I hope you will post a blog about how the transition to new DB has gone and what decision process was. Without seeing actual DB (and hardly able to read Django) it is hard to judge, maybe you really have a good case for DocumentDB.
Sure... will do ;)
It is pretty simple to create a local environment with DynamoDB. I found a docker image a time ago with it, where you can use the javascript shell playground to learn and test some queries. It also have a jar from AWS if I'm not wrong.
I found the image that I have used (dwmkerr/dynamodb:latest), that was my docker-compose.yml
dynamodb:
image: dwmkerr/dynamodb:latest
ports:
- 8000:8000
command: -sharedDb
Thanks... this will help us a lot :)
What about caching?
We do memcaching... but what's the point of caching is_shown for the lessons?
The user will have bad experience and say (I watched this lesson, why isn't shown till now)
Thanks for your reply.
I wasn't asking specifically on the is_shown part, but rather about the performance issues you've talked about. you said "so each time the user opens the academy to see the lessons. we need to perform 3 sub queries" why can't you cache that?
even on the is_shown part - why can't you expire the cache when you need to?
github.com/coretabs-academy/websit...
(we really get lots of responses as I watched the lesson why isn't it there, and that's just because of the frontend caching layer... cuz everyone wants the completion certificate :D ). That's why we accept the cruel query for this part.
Aside from all that, do you think optimizing with caching is really enough with all that mess... especially with the m2m ugly relationships :(
Use neo4j! A NoSQL graph database. 😉😉😉
Technically, NoSQL reffers mostly to non-relational databases, and a Graph DB is all about relations, so I would say a Graph is more SQL than a standard RDBMS is :))
Also Neo4J doesn't scale (main advantage of the NoSQL), some new graph databases does like DGraph and Neptune.
Neo4j and Amazon Neptune are slightly different breeds. They're technically triple store databases. But yeah. Other than that I agree with you.
Is it not quite graph database use-case? I thought you would need graph DB when you need to traverse graph, like give me all friends of all friends of A (wherein relational DB you would join table on table N times so eventually you will run out of RAM), but graph DB literally traverse graph, so there is no penalty in memory.
That's true. It really depends what kind of queries someone wants to run. Even in current example, you could end up joining same table multiple times to get a desired result and graphs would do better than a relational database.
Actually the document model fits more cuz we don't actually need to traverse but to compose everything into one UI.
As in the pic, we show all the track workshops on the right side, and we calculate the percentage of the shown lessons of each workshop, so we need to get everything of each workshop at once.
But for the profile we have a similar case, each profile has dozens of tasks, quizze, and projects... and we will traverse them on demand (lazy-loaded).
Hmmm, I read about the graph DBs... but how does it solve our problem?
I see the problem as an aggregate root of Track (de-normalized all in one model) which is what the document model solves.
How would the graph model look like?
Neo4j allows you to have entities, quite similar to what a row in a table is. The key difference, subjectively, is flexibility to declare relationships between these entities in an easier manner than in a relational database. Aggregates can be easily created using their query language, Cypher, which isn't too hard and too different from SQL.
Yet again, if read speeds are critical and you can live without immediate consistency, then a key value or a document database would do the job perfectly.
Thanks for the elaboration, very appreciated !
Surely, we will discuss that with the team to see how things go... guess we are probably gonna use Neo (or any other suitable graphdb) with the profile model as well.
When all you have is a hammer ...
This is funny in so much as it keeps repeating in all aspects of our industry. "My tool is the best there ever has been!"
Boring.
Use the correct tool for the job. Sometimes that means an RDBMS (please show me how you'd build a sophisticated transactional system like accounting records or banking actions with NoSQL), sometimes that means NoSQL (Solr / ES for full text search -- RDBMSs are just not good at full text search as efficiently as these are hands down).
But the right tool is more than your database choice. Be open to different languages, frameworks, libraries, methodologies, etc. To pigeon-hole yourself into only solving things with C#/Angular/React/Oracle/Python/OOP is to limit your ability to provide actual solutions; but kudos to you for ticking off another box to say "Yep, I 'fixed' the issue with my standard kit!".
The aggregation pipeline in MongoDB and Lookup mean that you can do meaningful queries using it now. There does appear to be a memory limit however as it merges the datasets in memory.
A wide developer once said "horses for courses", meaning you use the right tool for the job. For more than 20 years I worked with SQL of various flavours. However, a key discovery for me has been a different way of thinking about development; primarily the separation of the domain from the code and database schema.
Schamaless databases allow me to define data structures at runtime with ease. There is still a schema, but it is defined in data, not code. This means a huge degree of flexibility. If you are writing standard web applications that are bound to the domain model as you have been taught a SQL database will work just fine unless it is huge. The reason I adopted MongoDB wasn't about size, it was about flexibility.
My applications are more like spreadsheets in that the user defines the data structures and relationships. They do this at runtime and the data structures are stored as data, but used when data is submitted. We have introduced referential links between entities and it is possible to create views which traverse the references. We have implemented GraphQL to be able to get data, which is also able to traverse between documents using references.
In relation to maintaining referential integrity because there is no coupling to the domain there really is only one area of the code that needs to worry about this. We reap other benefits from this approach, including a elegant security model which means we have fine grained access controls over what fields and documents are visible to users based on an access control policy.
Trying to author your own aggregations is folly. In our application we have been able to do complex data transformations easily by having easy to configure transforms which generate the aggregations. Doing it by hand would be a living nightmare.
Is MongoDB the best solution for everything? Nah. For highly structured data like telco call records SQL is the way. For apps that are tightly coupled to the domain, which is typically how things have been done, is fine. But... and this is a big but... the way we tightly couple applications to the data model is making our applications less flexible than they need to be.
Schemaless systems are opening the door. Ten years ago I was where you are now; SQL was the light and the truth. Today my view is broader and I have been given good reason to question the accepted orthodoxy. That said we can't be blind to the downsides.
This pisses me off so much. Relational theory is the foundation to the majority of software in existence today that persists anything. Relational databases are rock solid, and offer SO many things directly out-of-the-box for FREE. NoSQL has it's place in real-time and schema-less data, but even relational databases can be coerced to perform there.
Just because is popular doesnt mean is a good thing, we should evolve and learn from our mistakes.
And nothing is free, SQL has many downsides but we are tough to live with them and we think that "is natural".
One example where SQL looses is Graph databases, but (MonogoDB sucks here too
¯\_(ツ)_/¯). Otherwise PostgreSQL rules, with current hardware you can easily fit all database in RAM. If PostgreSQL not enough, there is also CockroachDB and Spanner.I can miss some other use cases for other DBs, like BigTable and DynamoDB etc.
PostgreSQL supports document database (XML, JSON). PostgreSQL supports key/value stores.
PostgreSQL, it's Not Only SQL :p
Am using CouchDB for a project and, franquly, I like it. Have used MongoDB in the past too. But am a long time SQL user, but since I have worked with NoSQL and when I have the choice, I’ve ended up always choosing NoSQL.
I'm an Oracle Database user here, I just enjoyed reading the vitriol on Twitter this morning.
Looking at examples between what a MongoDB query looks like to one of my Oracle ones made it look messy as hell.
It's a great model for document-like structures. This includes actual documents, but things like user records as well. If your data has a lot of single-entity structure to it, then I think the noSQL type databases are a better fit.
They also make development easier as they aren't as rigid. Playing with the "schema" is easier.
For mass record-like data I'd still use a relational DB.
Ideally, a good DB-engine would just provide both types of data and stop pretending one is "better" than the other. It's like trying to argue that functional is better than imperative when both combined is preferred.
Yes, me, but I will rest my case by doing the reverse, saying that SQL should have less believers :D
As I study databases more and more, the reasons to use a RDBMS/standard SQL are getting fewer and fewer. From the only hammer I knew how to use (a few years ago), I became an anti-SQL basically.
A few examples:
If you have a (fast iteration product) prototype/startup/small project there is no reason to waste precious time to handle a schema and applying migrations every day, so NoSQL is the smart choice.
If you have a huge project you would need a scalable DB, if you shard you will lose the benefits of relationships. You can insist on using SQL at this scale, but you will have to write/use something like Vitess.
*I don't include Spanner/CosmosDB/Cassandra in this topic, I'm referring to "regular SQLs" like mysql, oracle, sql server, postgres.
If you have (too) many relationships (2+ degrees of connections) you would want to move them into a Graph, and whats left probably could fit into a NoSQL.
If you have a financial product you would be crazy if you don't use an event-sourcing architecture (which can be later aggregated into SQLs, thats true).
If you want to store text for search, you would use something like ElasticSearch.
Let's not forget about TimeSeries (logs, analytics) and GeoData data, which none are best fit with SQL engines.
Like I said, fewer and fewer use cases the *SQL products have nowdays.