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A table for status updates

In the MyStatus application, we'll begin by creating a timeline of status updates for each user. Users can view their friends' status updates by accessing the timeline of the friend in question.

The user timeline requires a new level of organization that we didn't see in the users table that we created in the previous chapter. Specifically, we have two requirements:

  • Rows (individual status updates) should be logically grouped by a certain property (the user who created the update)
  • Rows should be accessible in sorted order (in this case, by creation date)

Fortunately, compound primary keys provide exactly these qualities.

Creating a table with a compound primary key

The syntax for creating tables with compound primary keys is a bit different from the single-column primary key syntax we saw in the previous chapter. We create a user_status_updates table with a compound primary key, as follows:

CREATE TABLE "user_status_updates" (
  "username" text,
  "id" timeuuid,
  "body" text,
  PRIMARY KEY ("username", "id")
);

In particular, instead of appending a single column definition with a PRIMARY KEY modifier, we make a separate PRIMARY KEY declaration at the end of the list of columns, which itself specifies the two columns that make up the compound primary key. While this is the only way to declare a compound primary key, it's also a perfectly valid way to declare a single-column primary key. So, our users table from the previous chapter could have been declared like this:

CREATE TABLE "users" (
  "username" text,
  "email" text,
  "encrypted_password" blob,
  PRIMARY KEY ("username")
);

Here, we simply move the PRIMARY KEY declaration to the end of the column list. It's a little less concise, but it has the exact same effect as a single column definition with a PRIMARY KEY modifier.

The structure of the status updates table

The most notable aspect of user_status_updates is that it has two columns for a primary key. This means that each row is identified uniquely by the combination of its username and id columns. It also means that every row must have a value in both of these columns.

In addition to this, our user_status_updates table is the first time we've seen a timeuuid column in the wild. As you can recollect from the previous chapter, a UUID is essentially a very large number that is generated using an algorithm that guarantees that the identifier is unique across time and space.

You will also recollect that Cassandra does not have the ability to generate auto-incrementing sequences for use in primary keys, as this would require an unacceptably high level of coordination between nodes in the cluster. In the users table, we used a natural key; we want each user to have a unique username, so the username column makes a perfectly good unique identifier for rows.

In the case of a status update, however, there is no obvious natural key. The only user-generated data associated with a status update is the body, but a free text field doesn't make a very good primary key, and anyway there's no guarantee that status update bodies will be unique. This is where UUIDs come in handy. Since they're guaranteed to be unique, we can use them as a surrogate key—a unique identifier that isn't derived from the data in the row. Auto-incrementing primary keys in relational databases are also surrogate keys.

UUIDs and timestamps

While there are several algorithms that can be used to generate UUIDs, the Version 1 UUID has an additional useful property: part of the UUID encodes the timestamp at which the UUID is generated. This timestamp can be extracted from the full UUID, meaning that it's possible to know exactly when any Version 1 UUID was generated.

Cassandra's timeuuid type lets us capitalize on that property. Cassandra is aware of the structure of a timeuuid, and is able to both convert timestamps into UUIDs and to extract the creation timestamp from a UUID. As we'll soon see, Cassandra can also sort our rows by their creation time using the timestamps encoded in the UUIDs.

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