Introduction to SQL and NoSQL Databases
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Introduction to SQL and NoSQL Databases

SQL (Structured Query Language) and NoSQL (commonly defined as “Not only Structured Query Language”) are the 2 most-popular querying languages in database management systems. They are not the only query languages however, with others including: XPath, XBase++ and RDF query language (such as SPARQL – SPARQL Protocol and RDF Query Language). A list of query languages is provided on Wikipedia.

A database management system can be based on different data models, with the most common being:

  • Relational
  • Key-value
  • Column-oriented/tabular
  • Document-oriented
  • Graph-based

Others include: multi-model, object models, XML, multidimensional, multivalue, and time series. These are mostly NoSQL models and will be mentioned in the NoSQL section.

SQL Relational Databases

SQL is based on Relational Database Management Systems (RDBMS), hence consists of relational data models. In relational databases, a table (called relation) follows a schema, i.e. a predefined structure. Relations have a set of attributes (represented by columns) and tuples (represented by rows), therefore each tuple has a set of (equal number of) attributes. Popular SQL databases include: MySQL, PostgreSQL, and SQLite.



Data in relational databases should be in the first normal form (1NF), where every tuple contains exactly one value for each attribute. This is often confused and used interchangeably with the term atomic form which implies indivisible data. For example, the date attribute (2014-01-01) is not in the atomic form because it can be subdivided into year, month and day, but it is considered normalized because each ‘sub-attribute’ does not provide the same information. A denormalized attribute would be one which contains multiple values (dates in this case) for an attribute for a given tuple. Address is another example of normalized but non-atomic. While ‘date’ attribute can be stored in its atomic form, database management systems can manipulate non-atomic (but normalized) values to return part of the date, the whole date or even perform simple arithmetic (add/subtract intervals). Thus, the non-atomic form is not necessary for such attribute.

While normalization is required for SQL databases, it is not necessary for NoSQL. Normalization leads to faster queries, however updating information will be significantly slower since multiple records need to be modified.

NoSQL databases

Unlike SQL models, NoSQL databases do not require the pre-determined fixing of data types for each attributes (i.e. schema), and thus are more flexible. However, this may cause consistency issues. Popular NoSQL databases include: MongoDB, CouchDB, Apache Cassandra, Redis, Couchbase, Elastic, and DynamoDB.

There are several NoSQL data models:

  • Column-oriented
  • Document-oriented
  • Key Value / Tuple store
  • Graph databases
  • Multi-model databases
  • Object Databases (soft NoSQL systems)
  • Grid & Cloud Database Solutions
  • XML Databases
  • Multidimensional databases
  • Multivalue databases
  • Event Sourcing
  • Time Series / Streaming Databases
  • Other NoSQL-related
  • Scientific and specialized DBs

For each data model, several solutions are currently available. A non-comprehensive list of these is below (compiled from

Column-oriented Document-oriented Key-Value Graph models Multimodel
Hadoop/HBase Elastic DynamoDB Neo4J ArangoDB
MapR ArangoDB Azure Table Storage ArangoDB OrientDB
Cassandra OrientDB Riak OrientDB Datomic
Scylla gunDB Redis gunDB gunDB
Hypertable MongoDB Aerospike Infinite Graph CortexDB
Accumulo Cloud Datastore LevelDB Sparksee AlchemyDB
Amazon SimpleDB Azure DocumentDB RocksDB TITAN WonderDB
Cloudata RethinkDB Berkeley DB InfoGrid RockallDB
MonetDB Couchbase Oracle NoSQL HyperGraphDB  
HPCC CouchDB GenieDB Trinity  
Apache Flink ToroDB BangDB AllegroGraph  
IBM Informix SequoiaDB Chordless BrightstarDB  
Splice Machine RavenDB Scalaris Bigdata  
eXtremeDB MarkLogic Tyrant Meronymy  
ConcourseDB Clusterpoint Scalien WhiteDB  
Druid JSON ODM Voldemort Onyx Database  
KUDU NeDB Dynomite OpenLink Virtuoso  
Elassandra Terrastore KAI VertexDB  
  AmisaDB memcacheDB FlockDB  
  JasDB Faircom C-Tree Weaver  
  RaptorDB LSM BrightstarDB  
  Djondb KitaroDB Execom IOG  
  EJDB Upscaledb Fallen 8  
  Densodb STSdb    
  SisoDB Tarantool/Box    
  SDB Chronicle Map    
  NoSQL embedded db Maxtable    
  ThruDB Quasaradb    
  iBoxDB Pincaster    
  BergDB RaptorDB    
  IBM Cloudant TIBO Active spaces    
    Symas LMDB    

I will not go into each database model, but document-oriented are worth noting given their relatively unique structure and increasing popularity. Elastic is one solution that utilizes JSON files as documents, each containing inner objects. Each document can have objects with different fields each time and hence unlike in relational databases, the same attributes are not required let alone a fixed schema. For reference, below is an example taken from the ElasticSearch documentation:

  "title": "Nest eggs",
  "body":  "Making your money work...",
  "tags":  [ "cash", "shares" ],
  "comments": [ 
      "name":    "John Smith",
      "comment": "Great article",
      "age":     28,
      "stars":   4,
      "date":    "2014-09-01"
      "name":    "Alice White",
      "comment": "More like this please",
      "age":     31,
      "stars":   5,
      "date":    "2014-10-22"

SQL vs. NoSQL –Which to choose?

While both SQL and NoSQL database models serve the same function, i.e. that of storing and retrieving/querying data, this is achieved in different approaches and is optimized for different scenarios. SQL models such as MySQL enables fast and complex querying, but do not scale as well as NoSQL models, such as Apache Cassandra. Related to size, the InnoDB engine for MySQL (commonly used engine) imposes a maximum 1017 column-limit. NoSQL databases often have larger limits, with Cassandra having a limit of 2 billion cells (rows x columns).

If the data is unstructured/less structured and document-based, than document-oriented NoSQL models, such as elastic, may be a no-brainer. The problem requires further thought however when the data structure can fit (justifiably and appropriately) in both a relational database and a column-oriented database. The CAP triangle has been (over-)used as a guide to determine which database model to choose. It is based on 3 database properties:

  • C: consistency = when using multiple copies, each client has same view of the data. This means that you need to make it appear as though there is only a single copy of the data even though there may be copies (replicas, caches) of the data in multiple places. The term “consistency” has been argued to be a misnomer. Linearizability is more appropriate.
  • A: availability = all clients can read and write. All nodes provide a non-error response.
  • P: partition tolerance = system works well across physical network partitions. Nowadays, this has been recognized as impossible to achieve due to internet/asynchronous networking that may drop or delay messages.

However, several disagree with the usage of the CAP theorem. A blog post by Martin Kleppmann goes into detail about this. This leave us without a “rule-of-thumb” (or multiple thumbs in this case) when deciding what’s the ‘best solution’, making such task certainly not a straightforward one. Even ‘worse’ when the differences between SQL and NoSQL are becoming more blurred with the adoption of features from one another.

Nonetheless, some simple questions to start asking are: what’s the size and structure the data (Is a relational table or less structured documents appropriate)? How often will the records be updated (Should the data be normalized or not)? Will complex queries be required?

Dieter Galea

Dieter Galea
Originally a biologist and chemist, now a PhD student at Imperial College London working on computational methods for biomedical data; analysis, visualizations, setting up data repositories, developing online tools, and mostly: fixing bugs. On my right is Kaiser - my Siberian husky and best friend.

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