Google's Bigtable vs. A Relational Database

Question

Duplicates

• Why should I use document based database instead of relational database?
• Pros/Cons of document based database vs relational database

I don't know much about Google's Bigtable but am wondering what the difference between Google's Bigtable and relational databases like MySQL is. What are the limitations of both?

Solution 1

Bigtable is Google's invention to deal with the massive amounts of information that the company regularly deals in. A Bigtable dataset can grow to immense size (many petabytes) with storage distributed across a large number of servers. The systems using Bigtable include projects like Google's web index and Google Earth.

According to Google whitepaper on the subject:

A Bigtable is a sparse, distributed, persistent multidimensional sorted map. The map is indexed by a row key, column key, and a timestamp; each value in the map is an uninterpreted array of bytes.

The internal mechanics of Bigtable versus, say, MySQL are so dissimilar as to make comparison difficult, and the intended goals don't overlap much either. But you can think of Bigtable a bit like a single-table database. Imagine, for example, the difficulties you would run into if you tried to implement Google's entire web search system with a MySQL database -- Bigtable was built around solving those problems.

Bigtable datasets can be queried from services like AppEngine using a language called GQL ("gee-kwal") which is a based on a subset of SQL. Conspicuously missing from GQL is any sort of JOIN command. Because of the distributed nature of a Bigtable database, performing a join between two tables would be terribly inefficient. Instead, the programmer has to implement such logic in his application, or design his application so as to not need it.

Solution 2

Google's BigTable and other similar projects (ex: CouchDB, HBase) are database systems that are oriented so that data is mostly denormalized (ie, duplicated and grouped).

The main advantages are: - Join operations are less costly because of the denormalization - Replication/distribution of data is less costly because of data independence (ie, if you want to distribute data across two nodes, you probably won't have the problem of having an entity in one node and other related entity in another node because similar data is grouped)

This kind of systems are indicated for applications that need to achieve optimal scale (ie, you add more nodes to the system and performance increases proportionally). In an RDBMS like MySQL or Oracle, when you start adding more nodes if you join two tables that are not in the same node, the join cost is higher. This becomes important when you are dealing with high volumes.

RDBMS' are nice because of the richness of the storage model (tables, joins, fks). Distributed databases are nice because of the ease of scale.