1. IKT437 Knowledge Engineering and Representation
NoSQL
Terje Gjøsæter, Ph.D.
UiA, Grimstad – 16. November 2015

2. Overview Introduction and Motivation History of NoSQL
Categories of NoSQL
Examples of NoSQL systems
Encodings
Querying
Examples
Summary

3. Introduction
NoSQL has become increasingly popular and important lately.
NoSQL – No SQL, or Not Only SQL?
Many different variants, covering many different needs and use cases.
So what is NoSQL? Every data store that is not SQL-based RDBMS?
Q: Opinions?

4. Typical characteristics
Non-relational
Flexible schema
Less structured data
Supports big data
Other or additional query languages than SQL
Distributed – horizontal scaling
Eventual consistency – tradeoff due to CAP theorem
Q: Are you all familiar with the CAP theorem and consistency models?

5. CAP Theorem
It is impossible for a distributed system to provide all three of the following at the same time:
Consistency (all nodes see the same data at the same time)
Availability (a guarantee that every request receives a)
Partition tolerance (the system continues despite partitioning due to network failures)

6. Consistency Models (from distributed computing)
Eventual Consistency
A weak consistency model in a system with lack of simultaneous updates.
If no update takes very long time, all replicas eventually will become consistent.
Strict consistency
The strongest consistency model.
Requires that if a process reads any memory location, the value returned by the read operation is the value written by the most recent write operation to that location.

7. Motivation Why NoSQL? Less structured databases needed.
Not all data fit into relational table-based structure.
Social Media and Big Data are the big drivers for new database types.
Data tends to be less structured and too big for traditional RDBMS.
Let’s briefly introduce data storage needs of Social Media and Big Data.

8. Social Media and Web 2.0 – Example of Big Data
Google, Facebook, Twitter, Instagram, Amazon and Yahoo among others need to store and handle enormous amounts of data
These data tend to have different characteristics and requirements compared to «typical» structured database data.
Less strict structure in the data.
Need for a way to distribute of data across clusters that is easy to manage and use.
Different requirements for consistency (see CAP-theorem)
Example: sometimes we see a post on facebook disappearing and then showing up again later

9. Big Data – Early Definitions
Large data sets, taxing the capacities of main memory, local disk, and even remote disk (1997)
data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges
datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze
McKinsey

10. HANDLING AND STORAGE OF «TOO BIG» DATA
Source: Georgia Tech Library (

11. Meanwhile: Big Data – Opportunity-Enablers
Data Warehousing
Central repository of integrated (and highly structured) data for reporting and analysis
Business Intelligence
Analysing data to make informed business decisions.
Data Mining
Searching for interesting trends and patterns in data

12. Defining Big Data as an Opportunity
Mayer-Schönberger & Cukier 2013: “The ability of society to harness information in novel ways to produce useful insights…” and “…things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value.”
License: CC0 Public Domain

13. Big Data can be…….. SECURITY AND PRIVACY
First mention 1997 – NASA: “data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of big data. When data sets do not fit in main memory (in core), or when they do not fit even on local disk, the most common solution is to acquire more resources.”
Oxford English Dictionary: “data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.”
McKinsey: “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze,”
All these initial definitions tended to focus on challenges of handling and storage: «Too big» data.
SECURITY AND PRIVACY
Source: Wikimedia common, Camelia Bobanlicensed under the Creative Commons Attribution-Share Alike 3.0 Unported

14. Big Data Aspects and Life Cycle Overview
Selection, Harvesting, Data Integration
Store all? Include external data from different sources?
Structuring and Storage
Structured or unstructured?
With meta- information?
SQL or NoSQL?
Analysis, Visualisation
Machine learning; graphs, maps
Protection and Usage Policy
Security, privacy
Sharing policy

15. NoSQL to the Rescue!
NoSQL is able to cover the needs of Social Media and Big Data
But different variants of NoSQL also support
Small data
Simple data
Awkwardly shaped data
Funny data
Odd data

16. Typical Features of NoSQL
Running well on clusters
Mostly open-source
Schema-less
Not having to convert your data to and from a relational data model but can use the data model of your software directly.

17. Overview Introduction and Motivation History of NoSQL
Categories of NoSQL
Examples of NoSQL systems
Encodings
Querying
Examples
Summary

18. History of NoSQL
Q: When did people first start talking about NoSQL?

19. History of NoSQL Q: When did people first start talking about NoSQL?
The term NoSQL was used by Carlo Strozzi in 1998 to name his lightweight, Strozzi NoSQL open-source relational database that did not expose the standard SQL interface, but was still relational.
Johan Oskarsson of Last.fm reintroduced the term NoSQL in early 2009 when he organised an event to discuss "open source distributed, non relational databases".
Most early NoSQL systems did not support ACID and Joins. This is changing lately…
Q: Are you all familiar with the ACID requirements for databases?

20. ACID
Atomicity means that database modifications must follow an all or nothing rule. Each transaction is said to be atomic. If one part of the transaction fails, the entire transaction fails.
Consistency means that only valid data will be written to the database.
Isolation requires that multiple transactions occurring at the same time not impact each other’s execution.
Durability ensures that any transaction committed to the database will not be lost.

21. Overview Introduction and Motivation History of NoSQL
Categories of NoSQL
Examples of NoSQL systems
Encodings
Querying
Examples
Summary

22. Categories of NoSQL – Key-value-based
Supports  a dictionary or map of key-value pairs.
Value may be simple or (un/semi/-)structured blob of data.
Often used as basis for more complex data models.
Wide Column Store
A type of key-value database. It uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table.

23. Categories of NoSQL – Document-oriented
Supports storing, retrieving, and managing document-oriented information.
 Documents encapsulate and encode data in some standard formats or encodings.
XML
subclass of document-oriented databases that are optimized to extract their metadata from XML documents.
Object store
Object includes data itself, variable amount of metadata, and globally unique identifier.
 Storing photos on Facebook, songs on Spotify, or files in online collaboration service such as Dropbox.

24. Categories of NoSQL – Graph-based
uses graph structures for semantic queries with nodes, edges and properties to represent and store data.
Triplestore RDF
Variant of graph-based.
Stores triples: subject-predicate-object
Alice knows Bob; Bob has Cat; Cat catches Mouse; Alice fears Mouse
Adding a name to the triple makes a "quad store" or named graph.

25. Categories of NoSQL – Hybrids
Multi-model
Support multiple data models against a single, integrated backend.
May also contain relational elements
MultiValue
Differs from RDBMS in that it support and encourage the use of attributes which can take a list of values, rather than all attributes being single-valued.

26. Key-Value Databases
Key-value systems treat the data as a single opaque collection which may have different fields for every record.
This offers considerable flexibility and more closely follows modern concepts like object-oriented programming.
Because optional values are not represented by placeholders as in most RDBs, key-value stores often use far less memory to store the same database, which can lead to large performance gains in certain workloads.
Examples:
CouchDB, 
Oracle NoSQL Database, 
Dynamo, 
MemcacheDB, 
Redis 

27. Column-oriented Databases
Wide Column-store
The names and format of the columns can vary from row to row in the same table.
A column has three elements:
Unique name: Used to reference the column.
Value: The content of the column. Simple type.
Timestamp: The system timestamp used to determine the valid content.
The timestamp is used to differentiate the valid content from stale ones.
Examples:
Accumulo, 
Cassandra, 
Druid, 
HBase, 
Vertica

28. Document-oriented Databases
Databases has Collections that has Documents that has semi-structured data
In a key-value store the data is considered to be opaque to the database.
Document-oriented system relies on internal structure in the document to extract metadata that the database engine uses for optimization.
Designed to offer a richer experience with modern programming techniques.
XML databases are a specific subclass of document-oriented databases that are optimised to extract their metadata from XML documents.
Examples:
Clusterpoint,
Apache CouchDB,
Couchbase,
Lotus Notes,
MongoDB

29. Graph-based Databases
Graph databases are based on graph theory.
Nodes, properties, and edges.
Nodes represent entities such as people, businesses, accounts, etc.
Properties are information that relate to nodes.
Edges are the lines that connect nodes to nodes or nodes to properties
Most of the important information is really stored in the edges.
Meaningful patterns emerge when one examines the connections and interconnections of nodes, properties, and edges
Examples:
Allegro, Neo4J, InfiniteGraph, OrientDB,
Virtuoso,
Stardog

30. How do we Choose a NoSQL Database for our Project?
Key-value databases are generally useful for storing session information, user profiles, preferences, shopping cart data. We would avoid using Key-value databases when we need to query by data, have relationships between the data being stored or we need to operate on multiple keys at the same time.
Column oriented databases are generally useful for content management systems, blogging platforms, maintaining counters, expiring usage, heavy write volume such as log aggregation. We would avoid using column family databases for systems that are in early development, changing query patterns.
Document databases are generally useful for content management systems, blogging platforms, web analytics, real-time analytics, ecommerce-applications. We would avoid using document databases for systems that need complex transactions spanning multiple operations or queries against varying aggregate structures.
Graph databases are very well suited to problem spaces where we have connected data, such as social networks, spatial data, routing information for goods and money, recommendation engines.

31. Performance Data Model Performance Scalability Flexibility Complexity
Functionality
Key–Value Store
high
none
variable (none)
Column-Oriented Store
moderate
low
minimal
Document-Oriented Store
variable (high)
variable (low)
Graph Database
variable
graph theory
Relational Database
relational algebra
Source: Ben Scofield

32. Overview Introduction and Motivation History of NoSQL
Categories of NoSQL
Examples of NoSQL systems
Encodings
Querying
Examples
Summary

33. Examples of NoSQL Systems
Examples of real world popular NoSQL database systems:
MongoDB
CouchDB
BaseX
Apache Cassandra
Amazon DynamoDB
Redis
Neo4J

34. MongoDB Document-oriented
Search by field, range queries, regular expression searches. Queries can return specific fields of documents and also include user-defined JavaScript functions.
Any field in a document can be indexed
Replication
MongoDB provides high availability with replica sets.
Load balancing
MongoDB scales horisontally. The data is split into ranges and distributed across multiple servers.
MapReduce can be used for batch processing of data and aggregation operations.
JavaScript can be used in queries and aggregation functions (e.g. MapReduce).

35. Apache CouchDB Document-oriented
“A database that completely embraces the web"
Uses JSON to store data
JavaScript as query language using MapReduce
HTTP for API

36. BaseX XML-database XPath query language XQuery 3.1
Client-Server architecture with user and transaction management and logging facilities
APIs: RESTful API, WebDAV, XML:DB, Java, C#, Perl, PHP, Python and others
Supported data formats: XML, HTML, JSON, CSV, Text, binary data
GUI including several visualisations: Treemap, table view, tree view, scatter plot

37. Apache Cassandra Hybrid between key-value and wide-column-oriented
Decentralized: Every node in the cluster has the same role - no single point of failure.
Data is distributed across the cluster but every node can service any request.
Replication strategies are configurable.
Scalable: Read and write throughput increase linearly as new machines are added.
Data is automatically replicated to multiple nodes for fault-tolerance.
Tunable consistency
MapReduce support, Hadoop integration
Query language: CQL

38. Amazon DynamoDB Key-value db
fully managed proprietary NoSQL database service offered by Amazon.com.
"built on the principles of Dynamo" (used initially for their own website).
Language bindings for Java, Node.js, .NET, Perl, PHP, Python, Ruby, and Erlang.

39. Redis Key-value db Redis maps keys to types of values.
Redis typically holds the whole dataset in memory.
By default, Redis syncs data to the disk at least every 2 seconds, with more or less robust options available if needed. In the case of a complete system failure on default settings, only a few seconds of data would be lost.
Language bindings include ActionScript, C, C++, C#, Clojure, Common Lisp, D, Dart, Erlang, Go, Haskell, Haxe, Io, Java, JavaScript (Node.js), Julia, Lua, Objective-C, Perl, PHP, Pure Data, Python, R, Racket, Ruby, Rust, Scala, Smalltalk and Tcl.

40. Neo4J
Graph-oriented
Implemented in Java and accessible from software written in other languages using the Cypher query language through a transactional HTTP endpoint.
ACID-compliant transactional database with native graph storage and processing.
The most popular graph database.
Everything is stored as an edge, a node or an attribute.
Each node and edge can have any number of attributes.
Both the nodes and edges can be labelled.
Labels can be used to narrow searches.

41. Overview Introduction and Motivation History of NoSQL
Categories of NoSQL
Examples of NoSQL systems
Encodings
Querying
Examples
Summary

42. Encodings XML Includes structure and meta-data JSON
JavaScript Object Notation
Simpler and less formal than XML.
YAML
a human-readable data serialization format, inspired by XML and JSON
BSON
Binary variant of JSON used by MongoDB
RDF – Many variants!
Q: Mention some RDF encodings?

43. Encodings RDF – Many different serialisation formats for RDF graphs!
Turtle a compact, human-friendly format.
N-Triples a very simple, easy-to-parse, line-based format that is not as compact as Turtle.
N-Quads a superset of N-Triples, for serializing multiple RDF graphs.
JSON-LD a JSON-based serialization.
N3 or Notation3, a non-standard serialization that is very similar to Turtle, but has some additional features, such as the ability to define inference rules.
RDF/XML an XML-based syntax that was the first standard format for serializing RDF.

44. Overview Introduction and Motivation History of NoSQL
Categories of NoSQL
Examples of NoSQL systems
Encodings
Querying
Examples
Summary

45. Querying How to Query a NoSQL database?
SPARQL (SPARQL Protocol and RDF Query Language)
HTTP REST API (with JSON)
Specialised query language, e.g. CQL (Cassandra Query Language)
Specialised API and/or client app (e.g. mongo client for MongoDB)
Java or various other general purpose programming languages…

46. Querying How to Query a NoSQL database?
SPARQL (SPARQL Protocol and RDF Query Language)
HTTP REST API (with JSON)
Specialised query language, e.g. CQL (Cassandra Query Language)
Specialised API and/or client app (e.g. mongo client for MongoDB)
Java or various other general purpose programming languages…
Q: And?

47. Querying How to Query a NoSQL database?
SPARQL (SPARQL Protocol and RDF Query Language)
HTTP REST API (with JSON)
Specialised query language, e.g. CQL (Cassandra Query Language)
Specialised API and/or client app (e.g. mongo client for MongoDB)
Java or various other general purpose programming languages…
Q: And?
SQL – NoSQL = «not only SQL»
But SQL is still not very common.

48. Querying Lack of join means that we will often either:
Do multiple queries.
Create and store complex documents with all the application needs inside – e.g. a blogpost with all comments included.
Un-normalise the database by duplicating information that is needed in multiple locations.

49. Overview Introduction and Motivation History of NoSQL
Categories of NoSQL
Examples of NoSQL systems
Encodings
Querying
Examples
Summary

50. Example – Semistructured Sensor Data
Case: Storing sensor readings from mobile phone (CIEM: SmartRescue Project)
MongoDB for storing readings in semi-structured documents containing available measurements at a given time.
Transforming data to JSON document
Querying, analysis and visualisation

51. Example – Native XML Storage
Case: Storing anonymised IDS alarms in XML database.
Alarms are formatted as IDMEF messages (an XML-based format)
To be stored in BaseX XML database.

52. Example – Emergency information integration
Case: Collecting information about emergencies from multiple sources (CIEM)
Storage: Redis database.

53. Overview Introduction and Motivation History of NoSQL
Categories of NoSQL
Examples of NoSQL systems
Encodings
Querying
Examples
Summary

54. Summary
Scalable distributable databases for un/semi-structured (big) data.
Very flexible concerning datamodels.
Not always ACID.
CAP-theorem -> may be less emphasis on consistency.
Not only SQL means SQL still allowed!
Relational databases are still alive and have their uses; choose wisely!

55. The End