So Im designing currently three tables, an organization, organization_relationships, members.
Organization
OrgID PK
Metdata..
Org_Relationships
ParentOrgID PK
ChildOrgID Range/GSI
Member
OrgID PK
MemberID Range/GSI
One way that I need to access data, is by determining whether two members share a parent organization. With the way this is right now, I would basically have to do a weird search on the tables, that requires multiple calls to the table to determine whether two members belong to the same parent organization. With that being said is there a more efficient way of designing the table to do this without requiring multiple calls to the table.
The reason you're having to perform multiple queries is because you've modeled the relationship across several tables. This is a common approach when using traditional relational databases, but could be considered an anti-pattern with NoSQL databases.
Keep in mind that DynamoDB does not have a join operation like SQL databases. Therefore, it is a best practice to store related data in the same DynamoDB table. This can be counter-intuitive if you're used to working with relational DBs.
There are several ways to model your data in DynamoDB. The approach you choose depends on your access patterns. In other words, you store your data in a way that makes it easier to get the data your application needs.
For example, here's one way to model Users and Organizations:
The primary key is made up of a user id (e.g. USER#) and a sort key of META. This record (called an "item") in DynamoDB is where I'll define various user attributes. In this example, I've provided a name and an org attribute.
For illustrative purposes, I've also created a global secondary index (GSI) that swaps the partition key/sort key pattern in your base table. Your GSI will look like this:
This lets you fetch all users by organization.
If I wanted to check if two users are in the same organization, I can either query the GSI, or fetch both user records and compare the org fields.
This is just an example meant to give you a starting point with NoSQL design. The key takeaways here are:
NoSQL (or non-relational) data modeling is different than SQL (relational) data modeling.
You want to store related data in the same table.
How you store your data depends entirely on how you plan to use the data.
Official recommendation from the team is, to my knowledge, to put all datatypes into single collection that have something like type=someType field on documents to distinguish types.
Now, if we assume large databases with partitioning where different object types can be:
Completely different fields (so no common field for partitioning)
Related (through reference)
How to organize things so that things that should go together end up in same partition?
For example, lets say we have:
User
BlogPost
BlogPostComment
If we store them as separate types with type=user|blogPost|blogPostComment, in same collection, how do we ensure that user, his blogposts and all the corresponding comments end up in same partition?
Is there some best practice for this?
[UPDATE]
Can you ever avoid cross-partition queries completely? Should that be a goal? Or you just try to minimize them?
For example, you can partition your data perfectly for 99% of cases/queries but then you need some dashboard to show aggregates from all-the-data. Is that something you just accept as inevitable and try to minimize or is it possible to avoid it completely?
I've written about this somewhat extensively in other similar questions regarding Cosmos.
Basically, when dealing with many different logical entity types in a single Cosmos collection the easiest option is to put a generic (or abstract, as you refer to it) partition key on all your documents. At this point it's the concern of the application to make sure that at runtime the appropriate value is chosen. I usually name this document property either partitionKey, routingKey or something similar.
This is extremely important when designing for optimal query efficiency as your choice of partition keys can have a huge impact on query and throughput performance. A generic key like this lets you design the optimal storage of your data as it benefits whatever application you're building.
Even something like tenant does not make sense as different tenants might have wildly different data size and access patterns. Instead you could include the tenantId at runtime as part of your partition key as a kind of composite.
UPDATE:
For certain query patterns it might be possible to serve them entirely out of a single partition. It's definitely not the end of the world if things end up going cross partition though. The system is still quick. If possible, limiting the amount of partitions that need to be touched for a given query is ideal but you're never going to get away from it 100% of the time.
A partition should hold data related to a group that is expected to grow, for instance a Tenant which will group many documents (which can be of different types as you have mentioned) So the Partition Key in this instance should be the TenantId. The partitioning is more about the data relating to a group than the type of data. If the data is related to a User then you could use the UserId, however many users may comment on the same posts so it doesn't seem like a good candidate for a partition key unless there is some de-normalization of the user info so it doest have to relate back to the other users directly.. if that makes sense?
I design my firebase structure and I'm not sure if that the right way.
Little information:
Each college has some departments.
Each departments has many courses.Courses can belong to several departments.
Each Course has some lessons. lesson can be belong to one course.
I have node of courses with all key courses and information
I have node of departments with all departments and informations.
I have node Course_Departments and Departments_courses
in addition I have courses_lectures and courses_lectures
for display each course learn by some lecturers and each lecturers teach some courses.
so my questions are:
1.How I connect the lesson to these table for example? I want to find all the lessons of the course_key1 that lecture_key2 teach?
2. using with many tables in this way can be take many time to get the data?
for UI I don't want to users to wait much for the data.
Looking at your design, I think, you are on the right track.
I'll give some possible hints of what you can take care of. When designing the structure of a Firebase database design rules of non-relational databases should be kept in mind.
One of them is denormalization. Keep the hierarchy flat! That's an important performance factor for data change listeners since all subnodes are involved. That's what you've done so far.
Relationship of entities can be achied by using the keys. Exactly as you did it in the Courses_Department node. The built-in creation of keys should be used. They should be universally unique.
Here's a good explanation when coming from relational databases.
I have an interesting situation. I am allowing users to provide their own data sources to be imported into neo4j. The data sources could be the same across different users, but I would like cypher queries to only query nodes and relations specified by a particular user's sources.
I can think of several ways to do this:
Separate neo4j instances for each user
Tag nodes and relationships by user
Currently node duplicates are prevented by indexes so I would have to alter that approach since nodes which already exist simply cause a new relationship to that node. Number of relationships to a node are used in my analysis so separating relationships by user are important.
I will have to update an existing graph database to account for these new attributes. I'm thinking that tagging relationships might be the way to go. Any thoughts pro/con against this approach? This way I can include the user tag as a relationship parameter.
Thoughts?
Henry
You can tag all your users with labels and use these even to tag the source:
http://docs.neo4j.org/chunked/preview/query-match.html#match-get-all-nodes-with-a-label
I am planning to create a website using ASP.NET and SQL Server. However, my plan for the database design leaves me wondering if there is a better way.
The website will serve as a repository of information for various users. I figure I would have two databases, a Membership and Profile database.
The profile database would contain user data for all users, where each user may have ~20 tables. I would create the tables when the user account is created and generate a key used to name the tables. The tables are not directly related.
For Example a set of tables for two different users could look like:
User1 Tables - TransactionTable_Key1, AssetTable_Key1, ResearchTable_Key1 ....;
User2 Tables - TransactionTable_Key2, AssetTable_Key2, ResearchTable_Key2 ....;
The Key1, Key2 etc.. values would be retrieved based on the MembershipID data when the account was created. This could result in a very large number of tables over time. I'm not sure if this will limit scalability by setting up the database in this way. Any recommendations?
Edit: I should mention that some of these tables would contain 20k+ rows.
Realistically it sounds like you only really need one database for this.
From the way you worded your question, it sounds like you're trying to dynamically create tables for users as they create accounts. I wouldn't recommend this method.
What you want to do is create a master table that contains a primary key for each individual user. I'm assuming this is the Membership table. Then create the ~20 tables that you need for the profiles of these members. Every record, no matter the number of users that you have, will go into these tables. These 20 tables would need to have a foreign key pointing to the unique identifier of the Membership table.
When you want to query a Member for their user information, just select from the tables where the membership table's primary Id matches the foreign key in the profile tables.
This would result in only a few tables in the end and is easily maintainable and follows better database design.
Your ORM layer (EF, LINQ, DAL code) will hate having to deal with one set of tables per tenant. It is much better to have either one set of tables for all tenant in a single database, or a separate database per tenant. The later is only better if schema upgrade has to be vetted by tenant (like Salesforce.com has). If you can afford to upgrade all tenant to a new schema at once then there is no reason for database per tenant.
When you design a schema that hold multiple tenant the important things to remember are
don't use heaps, all tables must be clustered index
add the tenant ID as the leftmost key to every clustered
add the tenant ID as the leftmost key to every non-clustered index too
add the Left.tenantID = right.tenantID predicate to every join
add the table.TenantID = #currentTenantID to every query
These are fairly simple rules and if you obey them (with no exceptions) you will get a perfect partitioning per tenant of every query (no query will ever ever scan rows in a range of a different tenant) so you eliminate contention between tenants. To be more through, you can disable lock escalation to make sure no tenant escalates to block every other tenant.
This design also lends itself to table partitioning and to sharing the database for scale-out.
You definitely don't want to create a set of tables for each user, and you would want these only in one database. Even with SQL Server 2008's large capacity for tables (note really total objects in database), it would quickly become unmanageable. Your best bet is to use 20 tables, and separate them via a column into user areas. You might consider partitioning the tables by this user value, but that should be tested for performance reasons too.
Yes, since the tables only contain id, key, and value, why not make one single table?
Have the columns:
id, user ID, key, value
Put an Index on the user ID field.
A key idea behind a relational database is that the table structure does not change. You create a solid set of tables, and these are the "bones" of your application.
Cheers,
Daniel
Neal,
The solution really depends on your requirement. If security and data access are concern and you have only a handful of users, you can set up a different db for each user with access for him set to only his/her database.
Other wise, what Daniel Williams suggested is a good alternative where you have one DB and tables laid out with a indexed column partitioning the users data rows.
It's hard to tell from the summary, but it looks like you are designing for dynamic attribution by user. This design approach is called EAV (Entity-Attribute-Value) and consists of a simple base collection key (UserID, SiteID, ProductID...) and then rows consisting of name/value pairs. In a more complex version, categories are sometimes added as "super columns" to the tuple/row and provide sub-groupings for a set of name/value pairs.
Designing in this way moves responsibility for data type integrity, relational integrity and tuple integrity to the application layer.
The risk with doing this in a relational system involves the breaking of the tuple or row into a set of rows. Updates, deletes, missing values and the definition of a tuple are no longer easily accessible through human interaction. As your application evolves and the definition of a tuple changes, it becomes almost impossible to tell if a name/value pair is missing because it's part of an earlier-version tuple or because it was unintentionally deleted. Ad-hoc research as well becomes harder to manage as business analysts must keep an understanding of the virtual structure either in their heads or in documentation provided.
If you are looking to implement an EAV model, I would suggest you look at a non-relational solution (nosql) like MongoDB or CouchDB. These stores allow a developer to save and retrieve "documents" or json-formatted messages that are essentially made up of a collection of name/value pairs and can look very much like a serialized object. The advantage here is that you can store dynamic attribution without breaking your tuple. You always know that you have a complete tuple because you can store and retrieve it as a single "blob" of information that can be serialized and deserialized at-will. You can also update single attributes within the tuple, if that's a concern.
MongoDB also provides some database-like features such as multiple-attribute indexes, a query engine that is robust in comparison to other similar non-relational offerings and a sharding solution that is much less trouble than trying to do it with MySQL.
I hope this helps.