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The Vertica Approach

Vertica is built from the Ground Up on the 4C's


Pic: Vertica.com   

 Column Store:
  • Vertica store table data as sections of columns rather than as rows.
  • Column store is ideal for read-intensive workloads as it can dramatically reduce disk I/O.

Pic: Vertica.com
 Compression:
  •  Vertica employs aggressive compression of data on disk, as well as a query execution
  • Store more data, provides more views, and uses less hardware, which allows keeping much more historical data in physical storage.


Pic: Vertica.com
  •  When similar data is grouped, we have even more compression options. The above figure shows few of the compression algorithms - RLE, Delta Encoding and Float Compression
  • Vertica applies over 12 compression techniques.
    • Dependent on data.
    • Vertica system choses which to apply.
    • NULLs have virtually no space.
  • Typically we can see, 50% - 90% compression in Vertica
  • Vertica queries data in encoded form.
Clustering:
  • Lets you scale out the database cluster easily by adding more hardware.
  • Columns are duplicated so if one machine goes down, you still have a copy.
    • Data warehouse log based recovery is impractical.
    • Instead, store enough projection for K-safely. 


Pic: Vertica.com  

Continuous Performance:

  •  Queries and laod data 24x7 with vertually no database admin.
  • Continuous loading and querying means that we can get real-time views and eliminate nightly load-windows.
  • On the fly schema changes means that we can add columns and projections without any database downtime.
  • Automatic database replication, failover and recovery provides for active-reduntancy, which increased performance. Nodes recover automatically by quering the system.

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