What is graph Database?
A graph database is also often told as a collection of nodes and edges where each node represents an entity and each edge represents a connection or relationship between two nodes.
Advantages of Graph Database vs Traditional RDBMS
- Necessary for developers and application to structure the data being used in the applications.
- References to other rows and tables are indicated by referring to primary key attributes via foreign key columns
- Joins are computed at query time by matching primary and foreign keys of all rows in the connected tables.
- Lesser Dynamic Schema.
- Relationships are of equal importance in the graph data model to the data itself. This means we are not required to infer connections between entities using special properties like foreign keys or out-of-band processing like map-reduce.
- The data stays remarkably similar to its form in the real world – small, normalized, yet richly connected entities. This allows you to query and view your data from any imaginable point of interest, supporting many different use cases.
- Whenever you run the equivalent of a JOIN operation, the graph database uses this list, directly accessing the connected nodes and eliminating the need for expensive search and match computations.
- From the word itself “graph”, it is a clear model of the domain, It focused on the use cases you want to efficiently support.
Signs that you might consider switching to Graph database and some issues that is common using Traditional RDBMS
- A Large Number of JOINs
Utilizing queries that join many different tables, there’s a big possibility of explosion of complexity and computing resource consumption occurrence. This results in a corresponding increase in query response times.
- Large number Self JOINs or Recursive JOINs
Self-JOIN statements are common for hierarchy and tree representations of data, but traversing relationships by repeatedly joining tables to themselves is inefficient. Most common SQL queries in development industry involves self join statements.
- Recurring Schema Changes
Requests for changes are more often for businesses than not put off by DBAs because the structure of relational databases isn’t designed for constant changes. Common schema changes indicate that the data or requirements are rapidly evolving, that requires a more flexible model.
- Slow-Running Queries
Developers are trying to find ways on how to speed up query time but many SQL queries still aren’t fast enough to support application’s needs. In addition, denormalizing data models for performance can have a negative impact on data quality and update behavior.
- Computing Your Results in Advance
Most of applications pre-compute their results using past data because of slow running queries. However, this is effectively using previous data for queries that should be handled in real time today. Furthermore, your system usually must pre-compute 100% of your data, even if only 1-2% of it will be accessed at any given time.
- A Large Number of JOINs
Additional Advantage of Graph Database
- Better Performance
Better performance for querying related data, big or small. Since a graph is essentially an index data structure, tt never needs to load or deal with non related data for a given query. They’re an excellent solution for real-time big data analytical queries.
- Real-Time on Update Data and Simultaneous Query Support
Graph databases can perform real-time updates on big data while supporting queries at the same time. The assumption there was that any query will touch the majority of a file, while graph databases only touch relevant data, so a sequential scan is not an optimization assumption.
- Flexible Online Schema Environment
Graph databases offer a flexible schema evolution on serving query. You can constantly add and remove new vertex or edge types or their attributes to extend or reduce your data model. It’s so convenient to manage explosive and constantly changing object types. The relational database just cannot easily adapt to this requirement, which is commonplace in the modern data management era.
- Better Problem-Solving (if not best)
Graph databases solve problems that are both impractical and practical for relational queries. These include iterative algorithms such as PageRank-ing other data mining and machine learning algorithms. Research has proved that you can write any algorithm on some graph query languages.
- Better Performance
Top Graph Databases
- Graph Engine
- HyperGraph DB
Graph DB we used and why we decide to use it instead of sticking to Traditional RDBMS.
We used Neo4J for our e commerce project that involves membership to promote products. The challenge we have before is how to fetch data faster considering that number of membership grows as time goes by. On our system, we implemented a binary tree and team tree as a way of connecting members to other members. As we all know, binary tree is a data structure in which each node has at most two children, which are referred to as the left child and the right child.
The said project should support limitless membership which means it will have a growing number of many-to-many relationships occur in the model as the tree keeps growing or membership records grows along the way which requires JOINing table that will later on increase join operation costs to just somehow maintain the speed of the system.Based on study Neo4j is 60% faster than MySQL (refer from Neo4j doc) and with the help of it’s clear model of data presented in the graph, we can easily identify relationship types in each node.
What is Neo4J?
Neo4j is an open-source, NoSQL, native graph database that provides an ACID(Atomicity, Consistency, Isolation, Durability) -compliant transactional backend for your applications. Initial development began in 2003, but it has been publicly available since 2007. The source code, written in Java and Scala, is available for free on GitHub or as a user-friendly desktop application download.
Neo4j is referred to as a native graph database because it efficiently implements the property graph model down to the storage level. This means that the data is stored exactly as you whiteboard it, and the database uses pointers to navigate and traverse the graph.
What makes Neo4J very popular?
- Cypher, a declarative query language similar to SQL, but optimized for graphs. Now used by other databases like SAP HANA Graph and Redis graph via the openCypher project.
- Is built on the basic concepts and clauses of SQL but has a lot of additional graph-specific functionality to make it easy to work with your graph model.
- Constant time traversals in big graphs for both depth and breadth due to efficient representation of nodes and relationships. Enables scale-up to billions of nodes on moderate hardware.
- Flexible property graph schema that can adapt over time, making it possible to materialize and add new relationships later to shortcut and speed up the domain data when the business needs change.
Graph databases is the future. With it’s excellent infrastructure to link diverse data, we expect we will be seeing Graph database more often as technology progresses. We are living in a machine learning growing era where we try to solve issues to lessen manpower and rely on technologies or machines to do the job for us and Graph database can be the key to improve finding machine-based insights as data sources continue to rapidly expand.