Cassandra data modeling timestamps - cassandra

Question

I have a fairly simple data model. I am tracking events for users based on timestamps. I'm converting a JSON object which has this scema:
userID:{
event: [
{ timestamp: data },
{ timestamp: data }
]
}
I have come up with two Cassandra schemas.
The first:
CREATE TABLE users ( guid uuid, date timestamp, events varchar, PRIMARY KEY(guid, date) );
The second:
CREATE TABLE users ( guid uuid PRIMARY KEY, date timestamp, events map<text, text> );
Either one would work, requiring the data to be a stringified JSON object. My query will be returning all data from a user in a given time range. Which model makes more sense, or is there a better way to go about this?

Answer 1

The second approach won't allow you to do queries by time range since you don't have date as a clustering column. So you might want to do this:
CREATE TABLE users (
guid uuid,
date timestamp,
events map<text, text>,
PRIMARY KEY(guid, date) );
How you want to define the events field depends on what's in there and how you need to access it. If you access small parts of it often, you might want to break events in the map out into separate rows by making the event key another clustering column like this:
CREATE TABLE users (
guid uuid,
date timestamp,
event_type text,
event_value text,
PRIMARY KEY(guid, date, event_type) );
It's hard to give more specific advice since you didn't describe your use case in terms of what queries you want to run and the volume of data, number of users, etc.

Answer 2

As Jim was saying the second schema does not allow query on the timestamp since it is not contained in the primary key.
He suggested a valid solution but I would also suggest that you use not a uuid and timestamp but a TimeUUID (which provide both an id and a timestamp at the same time) if you can. However if you need to get the users by id only sometimes then the solution of Jim is probably the best :
PRIMARY KEY(guid, date, event_type)