I need to build a SAS application, part is MySQL (containing customer information and data etc.) the other part is on MongoDB as it uses great ammounts of quite raw unrelated data, which is subject to MapReduce and Aggregation. It would be highly inefficient to use just one of the two, so I have to use them both. The scenario summary would be like this, I have a table in the MySQL db with customer accounts, under which I have actual users. The relevant one is the customer_id. Now, each customer has particular formatted data in the MongoDB side so I would require collection sets for each of the customers, example 31filters,31data,31logs where 31 would be the customer id, all these collections are to reside in the same mongodb database. Is this an acceptable approach or it would be better to actually have separate mongodbdatabases for each customer? What would be the best approach in terms of scalability?
Thank you
Related
I'm new to NoSQL and I'm trying to figure out the best way to model my database. I'll be using ArangoDB in the project but I think this question also stands if using MongoDB.
The database will store 12 categories of products. Each category is expected to hold hundreds or thousands of products. Products will also be added / removed constantly.
There will be a number of common fields across all products, but each category will also have unique fields / different restrictions to data.
Keep in mind that there are instances where I'd need to query all the categories at the same time, for example to search a product across all categories, and other instances where I'll only need to query one category.
Should I create one single collection "Product" and use a field to indicate the category, or create a seperate collection for each category?
I've read many questions related to this idea (1 collection vs many) but I haven't been able to reach a conclusion, other than "it dependes".
So my question is: In this specific use case which option would be most optimal, multiple collections vs single collection + sharding, in terms of performance and speed ?
Any help would be appreciated.
As you mentioned, you need to play with your data and use-case. You will have better picture.
Some decisions required as below.
Decide the number of documents you will have in near future. If you will have 1m documents in an year, then try with at least 3m data
Decide the number of indices required.
Decide the number of writes, reads per second.
Decide the size of documents per category.
Decide the query pattern.
Some inputs based on the requirements
If you have more writes with more indices, then single monolithic collection will be slower as multiple indices needs to be updated.
As you have different set of fields per category, you could try with multiple collections.
There is $unionWith to combine data from multiple collections. But do check the performance it purely depends on the above decisions. Note this open issue also.
If you decide to go with monolithic collection, defer the sharding. Implement this once you found that queries are slower.
If you have more writes on the same document, writes will be executed sequentially. It will slow down your read also.
Think of reclaiming the disk space when more data is cleared from the collections. Multiple collections do good here.
The point which forces me to suggest monolithic collections is that I'd need to query all the categories at the same time. You may need to add more categories, but combining all of them in single response would not be better in terms of performance.
As you don't really have a join use case like in RDBMS, you can go with single monolithic collection from model point of view. I doubt you could have a join key.
If any of my points are incorrect, please let me know.
To SQL or to NoSQL?
I think that before you implement this in NoSQL, you should ask yourself why you are doing that. I quite like NoSQL but some data is definitely a better fit to that model than others.
The data you are describing is a classic case for a relational SQL DB. That's fine if it's a hobby project and you want to try NoSQL, but if this is for a production environment or client, you are likely making the situation more difficult for them.
Relational or non-relational?
You mention common fields across all products. If you wish to update these fields and have those updates reflected in all products, then you have relational data.
Background
It may be worth reading Sarah Mei 2013 article about this. Skip to the section "How MongoDB Stores Data" and read from there. Warning: the article is called "Why You Should Never Use MongoDB" and is (perhaps intentionally) somewhat biased against Mongo, so it's important to read this through the correct lens. The message you should get from this article is that MongoDB is not a good fit for every data type.
Two strategies for handling relational data in Mongo:
every time you update one of these common fields, update every product's document with the new common field data. This is generally only ok if you have few updates or few documents, but not both.
use references and do joins.
In Mongo, joins typically happen code-side (multiple db calls)
In Arango (and in other graph dbs, as well as some key-value stores), the joins happen db-side (single db call)
Decisions
These are important factors to consider when deciding which DB to use and how to model your data
I've used MongoDB, ArangoDB and Neo4j.
Mongo definitely has the best tooling and it's easy to find help, but I don't believe it's good fit in this case
Arango is quite pleasant to work with, but doesn't yet have the adoption that it deserves
I wouldn't recommend Neo4j to anyone looking for a NoSQL solution, as its nodes and relations only support flat properties (no nesting, so not real documents)
It may also be worth considering MariaDB or Postgres
I do my first steps in NoSQL databases, thus I would like to hear the best practices about implementing the following requirement.
Let suppose I have a messages database, which is powered by MongoDB engine. This DB contains a collection of documents, where each document has the following fields:
time stamp;
message author/source;
message content.
Now, I want to build a list of authors/sources in order to add some metadata about each source. In the case of the classical RDBMS, I would define a table tblSources where I would store the names of the message sources and all additional meta-data (or links to the relevant tables) for each author.
What is the right approach to such task in NoSQL/MongoDB world?
It really depends on how you want to use the data. NoSQL dbs are generally not designed with fast joins in mind but they are still capable of doing joins and storing foreign keys.
Your options here are really
duplicate data aka store the author metadata in every document. This might be better in the case where you are really trying to optimize lookups and use Mongo as a key value store
Join on foreign key - this is pretty similar to how you would use a RDBMS
I'm developing a cms using MongoDb and am trying to get some modelling advice. It's multi-tenant and each tenant can create their own schema and choose what custom fields they want searchable/indexed. The only thing I'm waffling on is how to model my collections. It seems to me like it would be ideal for each tenant to have their own collection due to indexing, but I am not very experienced with MongoDb and would love to hear if that's even a valid statement or not.
I'm thinking about separating each tenant's schema definitions from their data - perhaps a customSchema and customData collection for each tenant. Maybe something like customSchema_5543e1191a85d8946f0ee6fc and customData_5543e1191a85d8946f0ee6fc? The major question here being how many collections are feasible in MongoDb. I'm not clear if there's a cap with the new WiredTiger or not. If not, would such a large number of collections have any downsides?
Or, is it better to have just two collections with all tenant's data in them, along with all of their individual indexes? What are the pros and cons of this approach?
Any thoughts or suggestions are welcome, particularly if anyone has had experience doing something like this before.
Update:
My use case is a cms where tenants can specify their own data, like in Sharepoint or Expression Engine, or most other content apis, like contentful or CloudCMS. A user can say, "I want to store Products, and each product has a Name, Description, Quantity, and a price". Another user could say, "I want to store bands, and each band has a Name, a HomeCity, and a whatever." The users would then want to retrieve and display that data on their pages however they like. It's a basic cms scenario where tenants can create their own schema, then create, edit, and retrieve entries of those schemas. Tenants would need to be able to denote which fields they can search on, so this highly customizable indexing per tenant is the primary area of focus and concern in the modelling strategy.
I'm waffling between two big collections to store schemas and data, shared by all tenants, and a pair of those collections for every tenant. I just don't know the pros and cons of each of those solutions in MongoDb. I'm also open to any ideas I haven't thought of yet :)
I am new to MongoDB and I have difficulties implementing a solution in it.
Consider a case where I have two collections: a client and sales collection with such designs
Client
==========
id
full name
mobile
gender
region
emp_status
occupation
religion
Sales
===========
id
client_id //this would be a DBRef
trans_date //date time value
products //an array of collections of product sold in the form {product_code, description, units, unit price, amount}
total sales
Now there is a requirement to develop another collection for analytical queries where the following questions can be answered
What are the distribution of sales by gender, region and emp_status?
What are the mostly purchase products for clients in a particular region?
I considered implementing a very denormalized collection to create a flat and wide collection of the properties of the sales and client collection so that I can use map-reduce to further answer the questions.
In RDBMS, an aggregation back by a join would answer these question but I am at loss to how to make Map-Reduce or Agregation help out.
Questions:
How do I implement Map-Reduce to map across 2 collections?
Is it possible to chain MapReduce operations?
Regards.
MongoDB does not do JOINs - period!
MapReduce always runs on a single collection. You can not have a single MapReduce job which selects from more than one collection. The same applies to aggregation.
When you want to do some data-mining (not MongoDBs strongest suit), you could create a denormalized collection of all Sales with the corresponding Client object embedded. You will have to write a little program or script which iterates over all clients and
finds all Sales documents for the clinet
merges the relevant fields from Client into each document
inserts the resulting document into the new collection
When your Client document is small and doesn't change often, you might consider to always embed it into each Sales. This means that you will have redundant data, which looks very evil from the viewpoint of a seasoned RDB veteran. But remember that MongoDB is not a relational database, so you should not apply all RDBMS dogmas unreflected. The "no redundancy" rule of database normalization is only practicable when JOINs are relatively inexpensive and painless, which isn't the case with MongoDB. Besides, sometimes you might want redundancy to ensure data persistence. When you want to know your historical development of sales by region, you want to know the region where the customer resided when they bought the product, not where they reside now. When each Sale only references the current Client document, that information is lost. Sure, you can solve this with separate Address documents which have date-ranges, but that would make it even more complicated.
Another option would be to embed an array of Sales in each Client. However, MongoDB doesn't like documents which grow over time, so when your clients tend to return often, this might result in sub-par write-performance.
I have the following objects Company, User and Order (contains orderlines). User's place orders with 1 or more orderlines and these relate to a Company. The time period for which orders can be placed for this Company is only a week.
What I'm not sure on is where to place the orders array, should it be a collection of it's own containing a link to the User and a link to the Company or should it sit under the Company or finally should the orders be sat under the User.
Numbers wise I need to plan for 50k+ in orders.
Queries wise, I'll probably be looking at Orders by Company mainly but I would need to find an Order by Company based for a specific user.
1) For folks coming from the SQL world (such as myself) one of the hardest learn about MongoDB is the new style of schema design. In the SQL world, everything goes into third normal form. Folks come to think that there is a single right way to design their schema, because there typically is one.
In the MongoDB world, there is no one best schema design. More accurately, in MongoDB schema design depends on how the application is going to access the data.
2) Here are the key questions that you need to have answered in order to design a good schema for MongoDB:
How much data do you have?
What are your most common operations? Will you be mostly inserting new data, updating existing data, or doing queries?
What are your most common queries?
How many I/O operations do you expect per second?
What you're talking about here is modeling Many-to-One relationships:
Company -> User
User -> Order
Order -> Order Lines
Company -> Order
Using SQL you would create a pair of master/detail tables with a primary key/foreign key relationship. In MongoDB, you have a number of choices: you can embed the data, you can create a linked relationship, you can duplicate and denormalize the data, or you can use a hybrid approach.
The correct approach would depend on a lot of details about the use case of your application, many of which you haven't provided.
3) This is my best guess - and it's only a guess - as to a good schema for you.
a) Have separate collections for Users, Companies, and Orders
If you're looking at 50k+ orders, there are too many to embed in a single document. Having them as a separate collection will allow you to reference them from both the Company and the User documents.
b) Have an array of references to the Order documents in both the Company and the User documents. This makes the query "Find all Orders for this Company" a single-document query
c) If your query pattern supports it, you might also have a duplicate link from Orders back to the owning Company and/or User.
d) Assuming that the order lines are unique to the individual Order, you would embed the Order Lines in an array within the Order documents.
e) If your order lines refer back to individual Products, you might want to have a separate Product collection, and include a reference to the Product document in the order line sub-document
4) Here are some good general references on MongoDB schema design.
MongoDB presentations:
http://www.10gen.com/presentations/mongosf2011/schemabasics
http://www.10gen.com/presentations/mongosv-2011/schema-design-by-example
http://www.10gen.com/presentations/mongosf2011/schemascale
Here are a couple of books about MongoDB schema design that I think you would find useful:
http://www.manning.com/banker/ (MongoDB in Action)
http://shop.oreilly.com/product/0636920018391.do
Here are some sample schema designs:
http://docs.mongodb.org/manual/use-cases/
Note that the "MongoDB in Action" book includes a sample schema for an e-commerce application, which is very similar to what you're trying to build -- I recommend you check it out.