Why many refer to Cassandra as a Column oriented database?

Reading several papers and documents on internet, I found many contradictory information about the Cassandra data model. There are many which identify it as a column oriented database, other as a row-oriented and then who define it as a hybrid way of both.

According to what I know about how Cassandra stores file, it uses the *-Index.db file to access at the right position of the *-Data.db file where it is stored the bloom filter, column index and then the columns of the required row.

In my opinion, this is strictly row-oriented. Is there something I'm missing?

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Yes, the "column-oriented" terminology is a bit confusing.

The model in Cassandra is that rows contain columns. To access the smallest unit of data (a column) you have to specify first the row name (key), then the column name.

So in a columnfamily called Fruit you could have a structure like the following example (with 2 rows), where the fruit types are the row keys, and the columns each have a name and value.

apple -> colour  weight  price variety
"red"   100     40    "Cox"


orange -> colour    weight  price  origin
"orange"  120     50     "Spain"

One difference from a table-based relational database is that one can omit columns (orange has no variety), or add arbitrary columns (orange has origin) at any time. You can still imagine the data above as a table, albeit a sparse one where many values might be empty.

However, a "column-oriented" model can also be used for lists and time series, where every column name is unique (and here we have just one row, but we could have thousands or millions of columns):

temperature ->  2012-09-01  2012-09-02  2012-09-03 ...
40          41          39         ...

which is quite different from a relational model, where one would have to model the entries of a time series as rows not columns. This type of usage is often referred to as "wide rows".

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You both make good points and it can be confusing. In the example where

apple -> colour  weight  price variety
"red"   100     40    "Cox"

apple is the key value and the column is the data, which contains all 4 data items. From what was described it sounds like all 4 data items are stored together as a single object then parsed by the application to pull just the value required. Therefore from an IO perspective I need to read the entire object. IMHO this is inherently row (or object) based not column based.

Column based storage became popular for warehousing, because it offers extreme compression and reduced IO for full table scans (DW) but at the cost of increased IO for OLTP when you needed to pull every column (select *). Most queries don't need every column and due to compression the IO can be greatly reduced for full table scans for just a few columns. Let me provide an example

apple -> colour  weight  price variety
"red"   100     40    "Cox"


grape -> colour  weight  price variety
"red"   100     40    "Cox"

We have two different fruits, but both have a colour = red. If we store colour in a separate disk page (block) from weight, price and variety so the only thing stored is colour, then when we compress the page we can achieve extreme compression due to a lot of de-duplication. Instead of storing 100 rows (hypothetically) in a page, we can store 10,000 colour's. Now to read everything with the colour red it might be 1 IO instead of thousands of IO's which is really good for warehousing and analytics, but bad for OLTP if I need to update the entire row since the row might have hundreds of columns and a single update (or insert) could require hundreds of IO's.

Unless I'm missing something I wouldn't call this columnar based, I'd call it object based. It's still not clear on how objects are arranged on disk. Are multiple objects placed into the same disk page? Is there any way of ensuring objects with the same meta data go together? To the point that one fruit might contain different data than another fruit since its just meta data or xml or whatever you want to store in the object itself, is there a way to ensure certain matching fruit types are stored together to increase efficiency?

Larry

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最佳答案

Cassandra is a partitioned row store. Rows are organized into tables with a required primary key.

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster.

Row store means that like relational databases, Cassandra organizes data by rows and columns.

  • Column oriented or columnar databases are stored on disk column wise.

    e.g: Table Bonuses table

      ID         Last    First   Bonus
1          Doe     John    8000
2          Smith   Jane    4000
3          Beck    Sam     1000

  • In a row-oriented database management system, the data would be stored like this: 1,Doe,John,8000;2,Smith,Jane,4000;3,Beck,Sam,1000;

  • In a column-oriented database management system, the data would be stored like this:
    1,2,3;Doe,Smith,Beck;John,Jane,Sam;8000,4000,1000;

  • Cassandra is basically a column-family store

  • Cassandra would store the above data as,

     "Bonuses" : {
row1 : { "ID":1, "Last":"Doe", "First":"John", "Bonus":8000},
row2 : { "ID":2, "Last":"Smith", "First":"Jane", "Bonus":4000}
...
}

  • Also, the number of columns in each row doesn't have to be the same. One row can have 100 columns and the next row can have only 1 column.

  • Read this for more details.

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Column Family does not mean it is column-oriented. Cassandra is column family but not column-oriented. It stores the row with all its column families together.

Hbase is column family as well as stores column families in column-oriented fashion. Different column families are stored separately in a node or they can even reside in different node.

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The most unambiguous term I have come across is wide-column store.

It is a kind of two-dimensional key-value store, where you use a row key and a column key to access data.

The main difference between this model and the relational ones (both row-oriented and column-oriented) is that the column information is part of the data.

This implies data can be sparse. That means different rows don't need to share the same column names nor number of columns. This enables semi-structured data or schema free tables.

You can think of wide-column stores as tables that can hold an unlimited number of columns, and thus are wide.

Here's a couple of links to back this up:

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IMO that's the wrong term used for Cassandra. Instead, it is more appropriate to call it row-partition store. Let me provide you some details on it:

Primary Key, Partitioning Key, Clustering Columns, and Data Columns:

Every table must have a primary key with unique constraint.

Primary Key = Partition key + Clustering Columns


# Example
Primary Key: ((col1, col2), col3, col4)     # primary key uniquely identifies a row
# we need to choose its components partition key
# and clustering columns so that each row can be
# uniquely identified
Partition Key: (col1, col2)                 # decides on which node to store the data
# partitioning key is mandatory, and it
# can be made up of one column or multiple
Clustering Columns: col3, col4              # decides arrangement within a partition
# clustering columns are optional

Partition key is the first component of Primary key. Its hashed value is used to determine the node to store the data. The partition key can be a compound key consisting of multiple columns. We want almost equal spreads of data, and we keep this in mind while choosing primary key.

Any fields listed after the Partition Key in Primary Key are called Clustering Columns. These store data in ascending order within the partition. The clustering column component also helps in making sure the primary key of each row is unique.

You can use as many clustering columns as you would like. You cannot use the clustering columns out of order in the SELECT statement. You may choose to omit using a clustering column in you SELECT statement. That's OK. Just remember to sue them in order when you are using the SELECT statement. But note that, in your CQL query, you can not try to access a column or a clustering column if you have not used the other defined clustering columns. For example, if primary key is (year, artist_name, album_name) and you want to use city column in your query's WHERE clause, then you can use it only if your WHERE clause makes use of all of the columns which are part of primary key.

Tokens:

Cassandra uses tokens to determine which node holds what data. A token is a 64-bit integer, and Cassandra assigns ranges of these tokens to nodes so that each possible token is owned by a node. Adding more nodes to the cluster or removing old ones leads to redistributing these token among nodes.

A row's partition key is used to calculate a token using a given partitioner (a hash function for computing the token of a partition key) to determine which node owns that row.

Cassandra is Row-partition store:

Row is the smallest unit that stores related data in Cassandra.

Don't think of Cassandra's column family (that is, table) as a RDBMS table, but think of it as a dict of a dict (here dict is data structure similar to Python's OrderedDict):

  • the outer dict is keyed by a row key (primary key): this determines which partition and which row in partition
  • the inner dict is keyed by a column key (data columns): this is data in dict with column names as keys
  • both dict are ordered (by key) and are sorted: the outer dict is sorted by primary key

This model allows you to omit columns or add arbitrary columns at any time, as it allows you to have different data columns for different rows.

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Cassandra has a concept of column families(table), which originally comes from BigTable. Though, it is really misleading to call them column-oriented as you mentioned. Within each column family, they store all columns from a row together, along with a row key, and they do not use column compression. Thus, the Bigtable model is still mostly row-oriented.


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