Hi! I am a newbie to Cassandra and seeking some advice regarding the data model I should use to best address my needs. For simplicity, what I want to accomplish is: I have a system that has users (potentially ~10,000 per day) and they perform actions in the system (total of ~50,000 a day). Each User’s action is taking place in a certain point in time, and is also classified into categories (1 to 5) and tagged by 1-30 tags. Each action’s Categories and Tags has a score associated with it, the score is between 0 to 1 (let’s assume precision of 0.0001). I want to be able to identify similar actions in the system (performed usually by more than one user). Similarity of actions is calculated based on their common Categories and Tags taking scores into account. I need the system to store:
The list of my users with attributes like name, age etc For each action – the categories and tags associated with it and their score, the time of the action, and the user who performed it. Groups of similar actions (ActionGroups) – the id’s of actions in the group, the categories and tags describing the group, with their scores. Those are calculated using an algorithm that takes into account the categories and tags of the actions in the group. When a user performs a new action in the system, I want to add it to a fitting ActionGroups (with similar categories and tags). For this I need to be able to perform the following: Find all the recent ActionGroups (those who were updated with actions performed during the last T minutes), who has at list one of the new action’s categories AND at list one of the new action’s tags. I thought of two ways to address the issue and I would appreciate your insights. First one using secondary indexes Column Family:Users Key: userId Compare with Bytes Type Columns: name: <>, age: <> etc… Column Family:Actions Key: actionId Compare with Bytes Type Columns: Category1 : <Score> …. CategoriN: <Score>, Tag1 : <Score>, …. TagK:<Score> Time: timestamp user: userId Column Family:ActionGroups Key: actionGroupId Compare with Bytes Type Columns: Category1 : <Score> …. CategoriN: <Score>, Tag1 : <Score> …. TagK:<Score> lastUpdateTime: timestamp actionId1: null, … , actionIdM: null I will then define secondary index on each tag columns, category columns, and the update time column. Let’s assume the new action I want to add to ActionGroup has NewActionCategory1 - NewActionCategoryK, and has NewActionTag1 – NewActionTagN. I will perform the following query: Select * From ActionGroups where (NewActionCategory1 > 0 … or NewActionCategoryK > 0) and (NewActionTag1 > 0 … or NewActionTagN > 0) and lastUpdateTime > T; Second solution Have the same CF as in the first solutionwithout the secondaryindex, and have two additional CF-ies: Column Family:CategoriesToActionGroupId Key: categoryId Compare with ByteType Columns: {Timestamp, ActionGroupsId1 } : null {Timestamp, ActionGroupsId2} : null ... *timestamp is the update time for the ActionGroup A similar CF will be defined for tags. I will then be able to run several queries on CategoriesToActionGroupId (one for each of the new story Categories), with column slice for the right update time of the ActionGroup. I will do the same for the TagsToActionGroupId. I will then use my client code to remove duplicates (ActionGroups who are associated with more than one Tag or Category). My questions are: Are the two solutions viable? If yes, which is better Is there any better way of doing this? Can I use jdbc and CQL with both method, or do I have to use Hector (I am using Java). Thanks Tamar