The NoSQL databases differ from the conventional SQL databases in the model of data they use and how they store it. There is a mismatch between the data models of NoSQL databases, which the users can make or break based on your database requirements in hand.
On considering the scalable NoSQL databases, the critical question is how big an application will grow and how much scaling is needed to support this need. Some of the NoSQL databases are mostly memory-based and do not scale across many machines, whereas many other NoSQL DBs like Cassandralinearly scale across many applications.
Choosing the right data model for NoSQL
The primary requirement is to check what data type you have to store to choose the apt data model. Based on the nature of the data, we can easily choose a data storage model. For example:
- If your data needs to be represented in pictorial form as a graph, then a graph database may be the best choice.
- If we plan to store data in the form of key-value pairs, then look for the top key-value databases or even document-database DBs. We have discussed these database variants in another article in detail.
There are various data models used to address multiple types of problems. Say, for example, if we are solving a graph problem with the data from a relational database, it is better to resolve the issue using a graph database.
Understanding CAP Theorem
CPA theorem refers to the three essential qualities of a database as consistency(C), availability (A), and partition tolerance (P). These are the desired essentials in terms of a distributed database system. However, in an actual scenario, the CAP theorem demonstrates that all these three qualities cannot be achieved simultaneously in distributed systems.
CAP Theorem in light of NoSQL
As in the case of NoSQL, the data is stored at multiple nodes in the network; all these nodes must see the same data, which means that when the data gets updated at any one data than the same update needed to be reflected on the other nodes too which store the same data. For a sample instance, if we perform a read operation, it may return the value of the latest writing operation from all nodes.
A system can be consistent if the transaction starts in the system’s constant state, and it ends with the system being in the same consistent state. In this model, there are chances that a system may go into an inconsistent state between a transaction, but in this case, the entire transaction may roll back if there is an error at any stage of the process.
To ensure high availability, the given system must always remain operational. So, we can query for and get a response at any time of the day. According to this need, whenever a user makes a request/query, regardless of the system’s state at the given point in time, the user should get an accurate response. For remote administration services for NoSQL implementation and maintenance, you may contact RemoteDBA experts.
- Partition Tolerance
Based on this need, a system must work appropriately despite any partition failure or message loss scenarios. A system can be called partition-tolerant if it can sustain any network failure level. A partition tolerance system can effectively maintain various types of failure scenarios and make sure that failure does not end up in complete network failure.
The common databases storage system which falls under the CP (consistency and partition tolerance) category are:
- AppFabric caching
- Redis, and
- Memcached DB etc.
Those databases which come under tolerance are the ones that store data in multiple nodes. As in relational database models, it is required in non-relational databases to follow the ACID properties of atomicity, consistency, isolation, and durability. However, it may not be possible with the NoSQL databases or the data storage structures to follow all C, A, and P by default.
The data storage models in NoSQL databases may usually follow any of the following modes:
CA(by ensuring consistency and availability)
AP(by providing availability and partition tolerance)
CP(with consistency and partition tolerance)
In the case of NoSQL, the column store databases can be categorized into two as:
- Group A: Databases like HBase, Bigtable, Hypertable, and Cassandra, etc. This is not meant to be a complete list.
- Group B: Databases like C-Store, Sybase IQ, VectorWise, Vertica, ParAcce, MonetDB, Infobright, etc.
The differences between the column-based store databases divided into the above categories are based on the following parameters.
- Data Model
Group A databases use a multi-dimensional map model. It can be a column name, raw name, and timestamp, sufficient to map the database values. Group B does not use the relational DBMS model. This is the reason why there is a common assumption that column-store databases are nonrelational.
- Independent of columns
Group A stores tend to part the data entity and row in different column-families. They also have the ability to ability to access the column families separately. There may be many columns in column-family databases, and columns within the column-families may not be accessed independently. In Group B, the columns are stored separately and so can be accessed separately.
Challenges in NoSQL include
- Data Model Differences
The companies may struggle with the need for a mental switch from the conventional relational DBs to the NoSQL model of database administration. The projects needed to be broken based on whether the team had modeled the data correctly for NoSQL databases to maximize its capabilities.
- Distribution Model
Some of the NoSQL databases may work with the master-slave architecture, which may be only scale-read operations compared to the peer-to-peer database architecture, which can scale out both in terms of read and write.
- Lack of expertise
As we can see, NoSQL databases are still in their infancy stage, so many of the developers working on it are just learning it now, but we can expect that challenge will be curbed over time as there are more and more experts skilled in handling NoSQL suite.
So, we have discussed various specialties and limitations of NoSQL databases here. It is essential to do a thorough evaluation of your needs in hand and its cost if you are planning for a database migration project to shift your DB to NoSQL.