Some example queries are:
give me all attributes for entity1 where entity1.attribute1 > 1000 and entity1.attribute15 = "someValue"
give me all attributes for entity1 where entity1.parentId = 1
give me all attributes for entity1 & parent_entity where entity1.attribute2 = "this"
Nothing too complicated.
Our application level checks more consist of validating the uploaded data against user defined rules. They are fairly basic.
Scale wise...
Nothing huge. In terms of a more traditional schema, where each entity was a separate table, the largest table might contain 1,000,000 rows, with the vast majority of them under 10,000. Another issue is that there might be 30 attributes for a given entity. However the distribution of data might look like 8 of 30 are 100% populated (each row has a value). 4 of 30 are 80% populated, 15 are 50% populate, and the rest are <25% populated.
On Fri, Apr 14, 2017 at 11:23 AM, Vincent Elschot <vinny@xxxxxxxxx> wrote:
Op 14/04/2017 om 19:03 schreef Rj Ewing:
What kind of queries are you talking about?We do know where we want to end up. We've had the application running for a while using a triple store db. We're looking to move away from the triple store due to performance issues. Our core concept is that each project can define a set of entities and their relations. Each entity has a set of attributes. We need to be able to efficiently run ad-hoc queries across multiple entities in a project, filtering via the attribute values
I think our business needs probably eliminate the possibility of data integrity at the db level. We currently do application level data validation.
Application level checks can only be done if you exclusively lock the database from before you start the check until the moment you nolonger need the certainty.
That's usually a no-go because it makes your data single-user for the duration of the check.
Performance always depends entirely on what you are doing with it, and on what scale. This is something that you should experiment with.
Regarding EAV, is there a good way to do this? Most everything I read online says that EAV is a terrible idea and performance is lack luster. However there are 6NF advocators who say that done right, it is highly performant. Are there any articles on the correct way to implement EAV?
Can't tell without more information about what you actually do with the data.
would jsonb or eav likely provide better query performance?
But dynamic schemas can be just fine, as long as your application is 100% in control over what can and cannot be done,
and as long as you use separate databases per customer/project/whatever. You will probably want to scale up at some point
and move customers to different servers, so you might aswell take that into account before you start.
On Wed, Apr 12, 2017 at 7:43 AM, Merlin Moncure <mmoncure@xxxxxxxxx> wrote:
On Tue, Apr 11, 2017 at 12:46 PM, Rj Ewing <ewing.rj@xxxxxxxxx> wrote:
> I'm looking for thoughts on the best way to handle dynamic schemas.
>
> The application I am developing revolves around user defined entities. Each
> entity is a tabular dataset with user defined columns and data types.
> Entities can also be related to each other through Parent-Child
> relationships. Some entities will be 100% user driven, while others (such as
> an entity representing a photo) will be partially user driven (all photo
> entities will have common fields + custom user additions).
>
> I was hoping to get opinions on whether postgresql would be a suitable
> backend. A couple of options I have thought of are:
>
> 1. Each entity is represented as a table in psql. The schema would be
> dynamically updated (with limits) when an entity mapping is updated. I
> believe that this would provide the best data constraints and allow the best
> data normalization. A concern I have is that there could be an enormous
> amount of tables generated and the performance impacts this might have in
> the future. I could then run elasticsearch as a denormalized cache for
> efficient querying and full-text-search.
>
> 2. Use a nosql database. This provides the "dynamic" schema aspect. A
> concern here is the lack of relation support, thus leading to a more
> denormalized data structure and the potential for the data to become
> corrupted.
>
> Any opinions on the use of psql for this case, or other options would be
> greatly appreciated!
Postgres can function as a nosql database -- you can use jsonb for
example to archive data in such a way that the data model can be
changed without making schema adjustments. Another way to do it is
EAV pattern as noted. These might be good strategies if you're not
sure where you want to end up.
It really comes down to this: how formal do you want your data model
to be? Adding formality leads to performance optimizations, exposes
your data to the fantastic SQL language, and allows rigorous
assumptions to made made from external dependencies and trusted.
Formality also brings a degree of inflexibility since your data has to
be forced into predetermined structures.
merlin
