An Associative Array is one of the most basic and useful data structures where each value is identified by a key, usually a string. In contrast, values in a normal array are identified by indices. Associative Array maps keys to values. There is one-to-one relationship between keys and values, such that each key can only be mapped to a single value only. This concept is used by many languages: PHP calls it Associative Array, Dictionary in Python, HashMap in Java.

How HashMap or Dictionary stores data

In the figure above, if you access the key ‘firstName’, the return value will be ‘Bugs’. In Python 3, I can create this as follows:

mydictionary = { 'firstName' : 'Bugs', 'lastName': 'Bunny', 'location': 'Earth'} #create a dictionary print(mydictionary['firstName']); #get value associated with key 'fistName'

The Output is:

$ python3 list.py Bugs

Cassandra follows the same concept as Associative Maps or Dictionaries, but with a slight twist: The value in Cassandra has another embedded Associative Array with its own keys and values. Let me explain. Like an Associative Array, Cassandra has keys which point to values. These top-level keys are called ‘Row Keys‘. The value itself contains sub-keys, called ‘Column Names‘ associated to values. For example, we can store all movies by director in Cassandra sorted by year. To get movie directed by Quentin Tarantin and James Cameron in 1994 and 2009 respectively, we can:

[qtarantino][1994] == 'Pulp Fiction' //tarantino is the Row Key and 1994 is the sub-key, aka Column Name), or,

[jcameron][2009] == 'Avatar'

Another way of looking at it:

A key in Cassandra can have multiple values. Each of these values have to be assigned another identifier for Cassandra to retrieve that particular value. Cassandra lets you name individual values of a key so you can retrieve that value with pin-point accuracy without obtaining all values. This name or sub-key is called “Column Name” in Cassandra. A Column Name is nothing but a key identifying a unique value (one-to-one relation) inside a bigger key, called “Row Key“.

[figure]



Cassandra Data Model

The above picture reminds me of the movie Inception, how it had dream within a dream. I see a Dictionary inside another Dictionary, if you think of ‘Column Name 1’ as a sub key with an associated value. I call this the “Inception Concept” and its present everywhere in the computing world, not just Cassandra. (think Recursion)

Column:

A column in Cassandra is very much like a Key-Value pair: It has a key, called Column Name which has an associated value. A column in Cassandra has an additional field called timestamp.



To understand the timestamp field, let’s recall that Cassandra is a distributed databases running on multiple nodes. timestamp is provided by the client application and Cassandra uses this value to determine which node has the most up-to-date value. Let us say 2 nodes in Cassandra respond to our queue and return a column. Cassandra will examine the timestamp field of both columns and the one that is the most recent will be returned to the client. Cassandra will also update the node that returned the older value by doing what is called a ‘Read Repair’.

An important point to remember is that the timestamp value is provided by the application: Cassandra doesn’t automatically update this value on write or update. Most applications ignore timestamp values which is fine, however if you are using Cassandra as a real-time data store, the timestamp values become very important.

Cassandra allows null or empty values.

Column Family:

Very, very loosely speaking, a column family in Cassandra is like table in RDBMS database like MySQL: it is a container for row keys and their values (Column Names). But the comparison stops there: In RDBMS you define table schema and each row must adhere to that schema. In other words you specify the table columns, their names, data types and whether they can be null or not. In Cassandra, you have the freedom to choose whether you want to specify schema or not. Cassandra supports two types of Column Families:

1. Static Column Family: You can specify schema such as Column Names, their Data Types (more on Types later) and indexes (more on this later). At this point, you may be thinking this is like RDBMS. You are right, it is. However, one difference I can see is that in an RDBMS a table must strictly adhere to the schema and each row must reserve space for each column defined in the schema, even though the column may be empty or null for some rows. Cassandra rows are not required to reserve storage for every column defined in the schema and can be sparsed. Space is only used for the columns that are present in the row.

The client application is not required to provide all Columns

2. Dynamic Column Family: There is no schema. The application is free to store whatever columns it want and their data types at run-time.



Note: An application can still insert arbitrary column in static column family. However, it must meet the contract for a column name that is defined in the schema, e.g. the Data Type of the value.

Keyspace:

A keyspace in Cassandra is a container for column families. Just like a database in RDBMS is a container of tables. Most application typically have one keyspace. For example, a voting application has a keyspace called “voting”. Inside that keyspace, there are several column families: users, stats, campaigns, etc.

So far:

The picture looks like the following:



Super Columns: Another Inception level

Super Columns are yet another nesting inside row key. It groups Column Names together. Back to our inception analogy, starting from the inner most level: Column Families are dictionaries nested inside Super Columns which is another dictionary nested inside the top most dictionary called Row Key. Suppose your row key is the UserID. You can have a Super Column family called Name which contains the Columns FirstName and LastName. When you retrive the Name super column you get all the column names within it, in this case Fistname and LastName.



RowKey => Super Column => Column Names

UserID => Name => Firstname, LastName



Counter Columns:

If you have used Redis before, you must love the increment feature which lets you increment and retreive a value at the same time. E.g. incr key_name. Cassandra has something similar: Counter Column. A Counter Column stores a number which can be incremented or decremented as you would a variable in Java: i++ or i–. Possible use cases of Counter Columns are to store the number of times a web page has been viewed, limits, etc.

Counter columns do not require timestamp. I would imagine Cassandra tracks this internally. In fact, when you update a Counter Column (increment or decrement), Cassandra internally performs a read from other nodes to make sure it is updating the most recent value. A consistency level of ONE should be used with Counter Columns.

Summary:

Ok, we have covered a lot of ground here. Let’s summarize:

Keyspace: Top level container for Column Families.

Column Family: A container for Row Keys and Column Families

Row Key: The unique identifier for data stored within a Column Family

Column: Name-Value pair with an additional field: timestamp

Super Column: A Dictionary of Columns identified by Row Key.

Here’s how we will get a value: [Keyspace][ColumnFamily][RowKey][Column] == Column’s Value

Or for a Super Column: [Keyspace][ColumnFamily][RowKey][SuperColumn][Column] == Column’s Value