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I am making a web app using node and dynamodb. For sql database there is Sequelize and for mongodb there is mongoose. But for dynamodb, what should I use? Should I use dynamodb data mapper? Are there any other alternative for dyanmodb?
Why not using the one AWS has made ? DynamoDB Data mapper. Its a typescript project so it comes with types as well. I dont know the difference with this and the one you picked. Speaking for mine, it's a good choice for many applications. If your attributes are of types string,int,datetime... then it would help you. But I couldn't use it with nested types. If you a 'map' attribute and it has some mutable nested attributes the mapper cannot handle that. There is only just update item functionality.
In short,
If you are going with a schemeless model unlike SQL where you attributes vary for each entry
If you are doing an attribute specific operations like only updating a field of map instead of the entire map attribute (because UpdateItem updates all the nested fields)
Then I wouldn't suggest using mappers. Instead, like me, for such projects you can create small dynamodb modules (like clients) that handles all the DocumentClient operations. Document client becomes so natural and easy to use a while after you get used to it.
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I'm new to the MERN Stack and I'm building my first project but I'm stuck on finding a way to filter products by category
I was thinking of adding a "Category" field for each item in MongoDB but I had no idea how to show only this specific category
If you could give me a general idea, maybe an example or even a resource to learn from... because on YouTube I've seen A LOT of videos where they were filtering Static Products but in my case, I have a database, and I couldn't find anything that helped...
Yes, you can add a category field and then just make a query for documents with some particular category.
const cursor = db.collection('inventory').find({ category: 'red' });
Here you are querying your collection "inventory" for the documents which have value "red" in their category field.
Here is MongoDB documentation about queries. Or your might want to use an ORM system, like prisma - it makes things little less complex.
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I'm working on a Rust project that collects daily statistics for a web-site (number of requests, number of unique users, average latency etc.). I'd like to store this data in a compact key-value store where the key is a date (or a date string) and the value is an object that contain the statistics. I also need this data to be persisted to a file.
I don't have any special performance or storage requirements. That's why I don't want to use major DBs like Redis, MongoDB or Cassandra that require a separate installation and significant resources to run. I'd like something much simpler and lightweight.
The ideal solution for me would be a library that can read and write key-value data and persist it into a file. The data size I'm aiming for is around 1000-2000 records.
Can you recommend a library I can use?
I can recommend PickleDB-rs. I think it answers most of your requirements. PickleDB-rs is a Rust version of Python's PickleDB. It's intended for small DBs (I think 1000-2000 records should be ok) and the performance isn't guaranteed to be as great as large scale DBs, but for the purpose of dumping daily web-site stats into a file it should be sufficient.
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I'm new to both Node.js and MongoDB. I'm using Mongoose to do the query and aggregation. Mongoose's syntax is a bit different to native MongoDB. I'm looking for kind of tools, GUI tools is better, to test my Mongoose query.
You could give mongobooster a try. Mongobooster support mongoose-like fluent query builder which enables you to build up a query using chaining syntax, rather than specifying a JSON object.
// instead of writing:
db.user.find({age:{:18,:65}},{name,1,age:1,_id:-1}).sort({age:-1, name:1});
// we can write:
db.user.where('age').gte(18).lte(65).select('name age -_id').sort("-age name");
// passing query conditions is permitted too
db.collection.find().where({ name: 'mongobooster' })
// chaining
db.collection
.where('age').gte(18).lte(65)
.where({ 'name': /^mongobooster/i })
.where('friends').slice(10)
// aggregation
db.companies.aggregate(qb.where('founded_year').gte(2000).lte(2010)) //qb:querybuilder
.group({_id:"$category_code",count:{$sum:1}})
.sort('-count')
.limit(100)
http://www.mongobooster.com
It's not very much different though..
I have used RoboMongo. But I think it supports only Mongodb not Mongoose.
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What would be a good or recommended way to model SVG DOM tree in Google's Realtime API? Specifically, stringify the SVG DOM tree and choose a collaborative string model or is there a better way? Thanks.
It depends on what you want to do with it. If all you want to do is to display something, without it being editable, then I would just store it is a blob. E.g., maybe just a static string.
If you want to be able to edit it, a collaborative string is problematic, as its hard to guarantee that the results of merging different collaborator's actions will result in well-formed XML.
Instead you could use custom objects to model the various nodes in the tree. You could do this either with a generic dom-like model where nodes have arbitrary attributes, or with specific classes different element types. I think the last would be the most powerful way to deal with it, and the nicest to work with, but also the most work to setup.
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I have several "main" XSD documents, which import "supporting" XSD documents. These schema documents happen to be from the HR-XML 3.0 specification, in particular related to Background Screening.
For the purposes of code-generation and convenient single-file schema to reference in my WSDL, I am trying to "flatten" the schema. The imports should be resolved; and there should be no duplication between common (formerly imported) content.
I tried the Oxygen XML editor, which has a function specifically for that task; but this appears to support only one "main" document at a time - it is not possible to flatten/merge several "main" documents. I've also looked into XMLSpy, but it seems to be missing that functionality altogether.
Can anyone recommend specific tools, or offer general theory, behind this schema "flattening" process?
Thanks very much!