A Complete Guide to Hybrid Database With The List Of The Best Hybrid Database In The Market:
A Hybrid Database is a balanced Database Management System offering high-performance data processing in main memory along with huge storage capacities of the physical disk.
This tutorial will give you a detailed explanation of the Meaning, Benefits, Architecture, and Implementation of Hybrid Database in simple terms. A list of the most popular Hybrid Databases that are used worldwide has also been included here for your reference.
What You Will Learn:
- What Is A Hybrid Database?
- Difference Between Relational Databases, NoSQL Databases, And Hybrid Databases
- How Are Relational And NoSQL Databases Different From A Hybrid Database?
- Benefits Of Hybrid Database
- Hybrid Database Architecture
- How Do Hybrid Databases Work?
- Hybrid DB Use Cases
- Best Practices For Implementing A Hybrid Database System
- Top Hybrid Databases To Watch Out
What Is A Hybrid Database?
A Hybrid Database offers the characteristics of both an in-memory database and an on-disk database in a single integrated engine. Hence, data can be stored and operated either only in the main memory only, or in the on-disk, or in the combination of both.
The best example of a Hybrid Database is Altibase.
The unified arrangement of both the kinds of databases permit extraordinary flexibility and vigorous functionalities. Furthermore, these days, the definition of Hybrid Databases is not just restricted to this data storage sense, but a hybrid database of the present-day does a lot more than that.
Illustration of a Hybrid Database:
As most of the organizations are now moving to cloud, hence hybrid databases need to be hybrid in the architectural sense as well as combine the use of public and private clouds. At times, a hybrid database is also defined as the integration of Relational and NoSQL databases.
A good Hybrid Database should be fully distributed and must possess high-availability, reliability, and scalability.
Difference Between Relational Databases, NoSQL Databases, And Hybrid Databases
In Relational databases, data exist in the form of relations (set tables) and can be fetched by SQL or other structured language commands.
On the other hand, a NoSQL database does not make use of tables for the storage of data. It stores data in another number of ways including key-value stores, document stores, graphs, object store methods, etc. This makes it simpler for complex and distributed systems to access the database information.
However, some NoSQL databases may lack immediate data consistency. As you understand that a Hybrid Database is a combination of Relational and NoSQL databases, it’s important for us that we deeply go through the differences between relational and NoSQL databases.
Let us differentiate them on the following parameters:
#1) Scalability and Performance:
Relational or SQL databases use vertical scalability.
It means that when the amount of data is being increased, it requires expansion in the storage capacity and processing power of the existing node. For instance, the capacity of CPU, the RAM and the stage storage device (SSD) of the DB server. This vertical scalability is very costly because of the underlying hardware cost.
In contrast, the NoSQL databases use horizontal scalability i.e. when the amount of data gets increased, the system is expanded by adding mode nodes for data storage and computing power, E.g, adding servers to NoSQL DB infrastructure. This is a cheaper solution when compared to vertical scalability.
Generally, NoSQL databases also have auto-sharding features that distribute data on different servers in order to increase performance.
The main aim of Relational databases is to strictly meet the ACID (Atomicity, Consistency, Isolation, and Durability) properties which is a kind of infeasible task for NoSQL databases. Hence, the integrity and reliability of Relational databases are definitely more than NoSQL databases.
For NoSQL databases, maintaining ACID properties is difficult as they use horizontal scaling. They rely on BASE (Basically Available, Soft state, Eventually consistent) principles and thus are more flexible than the Relational databases.
SQL databases have static and pre-defined schema whereas NoSQL databases have a dynamic schema that is not required to be pre-defined. Modification of schema in SQL databases is complicated and failure-prone whereas it’s easy to accommodate changes in the data structure in case of NoSQL databases.
This is the reason why NoSQL databases are preferred in agile and scalable environments. Also, SQL databases handle structured data only, whereas NoSQL databases can handle structured, unstructured and semi-structured data.
#3) Query Language:
Relational databases use SQL(Structured Query Language). SQL is a powerful query language and can manage complex queries via a standardized interface.
On the contrary, NoSQL databases do not have any standardized language for querying data. They use their own query language provided by the respective vendor. They generally lack in handling complex queries like aggregations, joins, etc.
Thus, SQL is definitely an advantage to Relational databases, whereas for NoSQL databases, there is a need to develop a standardized query language.
Relational databases are very secure in their architecture. But, in the case of NoSQL databases, as they provide the sharding feature and data is distributed, so managing confidentiality, privacy, and security is a challenging part.
In NoSQL databases, the authorization, authentication, and auditing are required to be performed through external methods depending upon which NoSQL DB is being used.
#5) Data Management – Storage and Access:
SQL databases store highly normalized and very clean data. Data redundancy is avoided by normalization and slicing of data in relations(logical tables). Thus, the usage of storage happens in a reasonable way.
On the contrary, NoSQL DBs store data in collections which logical relationships and involve a lower degree of normalization. Hence, they contain data redundancy. The replication helps in improving data availability in NoSQL databases and also ensures data loss.
This was all about the major differences between SQL and NoSQL databases.
How Are Relational And NoSQL Databases Different From A Hybrid Database?
A Hybrid Database is one that employs both Relational and NoSQL database methods in a single DB instance. A Hybrid Database enjoys the benefits of both Relational and NoSQL databases and eliminates their limitations.
There can be instances where the software apps can take even more advantage by employing different solutions within the application for specific tasks.
For the applications that require high-speed transactions and quick response, or that execute complex queries on data in real-time, it is more suitable to combine various database technologies for particular processing needs.
The combination of both Relational and NoSQL database technology (i.e. a hybrid database) generates a better system with higher availability, scalability, and performance.
Benefits Of Hybrid Database
A Hybrid Database offers considerable advantages over in-memory as well as on-disk DBs. The Hybrid Database consumes physical disk for storing and retrieving data, but still, it makes use of memory for the data which is in active use to boost the performance.
As a Hybrid Database supports both types of databases, one of the obvious benefits of a hybrid DB is its flexibility. Using a hybrid DB, you can maintain a balance between performance, cost, and persistence.
To fully understand the benefits of a hybrid database, let us first explore the benefits and limitations of the in-memory database and on-disk database individually.
An in-memory database will always be considerably faster than an on-disk database. As data exists directly in RAM, the response time is very quick, and latency is extremely low (microsecond scale). Conversely, the limitation is that the RAM is very costly than a traditional hard disk and it possesses a very little storage capacity.
On the other hand, on-disk databases have huge storage capacity and the storage is quite cheap. But, the on-disk databases tend to have poor performance as the disk I/O operation is very expensive, and the design of the disk-resident database frequently spends a lot of CPU resources in order to optimize the disk access patterns.
This is the reason for which a Hybrid Database is so attractive. It retains all the advantages of both in-memory DB & on-disk DB and eliminates their disadvantages in a single solution. You can use memory tables if you require high performance. If you require lots of storage, then you can use disk tables.
Advantages Of A Hybrid Database Include:
- Performance: Sorting, storing and retrieving frequently accessed data entirely happens in-memory rather than from disk. This indeed makes the Hybrid Database perform fast. Also, Hybrid Databases make use of optimizers to automatically choose the best execution plan on the basis of statistics and the available indexes in order to improve the overall performance irrespective of the data’s location.
- Cost: Hard disk is cheaper than RAM. Thus, the money saved can be utilized to add more memory in order to increase performance.
- Persistence: As the RAM chips can’t get close to the storage density of a physical storage disk, the hard drives are still employed to store the data required for later usage. This assures that the data is not lost in case of power failure.
- Flexibility: Hybrid databases gives you the capability to execute transactional (OLTP) and analytical (OLAP) workloads parallelly. This is called as HTAP (Hybrid Transactional and Analytical Processing). HTAP provides better flexibility to the developers while updating the existing software or building new software. This makes hybrid databases highly suitable for real-time, data-driven apps.
- Rows and Columns: Hybrid Database allows for both row-based and column-based storage. This helps in optimizing both transactional and analytical queries, thereby resulting in faster searching and reporting. A hybrid storage plan in a unified database gives a highly efficient platform, with all data stored in a manner that optimizes the task at hand.
- Deployment: Hybrid Database allows for both cloud-based deployments and on-premise deployments. Cloud-based deployment removes the necessity for continuous management of database and technology by internal IT resources. In the meantime, on-premise deployment gives better control when required. This indeed helps the businesses to use their resources and staff in a more efficient way.
Hybrid Database Architecture
We will understand the architecture of the Hybrid Database through the example of a hybrid database system designed for the storage and management of big data.
Let us consider a hybrid system made up of MySQL database (relational) and MongoDB (NoSQL). Data is classified into a structured and unstructured categories.
Structured data is sent to MongoDB, while the selection of database for unstructured data relies on the mode in which the application gets executed. In hybrid mode, data is sent to MongoDB and in SQL mode, data is sent to the MySQL database.
As you can see in the above architectural diagram, the system is composed of two main components i.e. SQL component and MongoDB component.
#1) SQL Component: This component has a storage engine that manages data storage in MySQL DB. The storage engine is composed of a transactional log file and data filegroups that can be orderly divided into data files, tables, indexes, extent, and page.
The transaction log file is utilized to attain data integrity and data recovery. The beginning and end of each operation and all modifications done are recorded in the transaction log file.
#2) MongoDB Component: This component is responsible for ensuring redundancy and consistency. It makes use of replication for the same. The inflow of data from various locations and various formats are divided and equally circulated to a group of non-static extensible terminals known as shards.
Metadata is saved in the configuration servers. To ensure redundancy, each of the servers possesses a replica of all metadata. In the event of a client request, it starts one of the routing processes to examine the configuration servers to see the position of the request.
Overview Of The DB Hybrid Interface.
This system integrates the flavors of both the DBs (relational and non-relational) in one single instance. It can be utilized for the management and storage of big data, by eliminating the weaknesses of both databases.
How Do Hybrid Databases Work?
For resource-constrained and high-performance systems, a hybrid database is produced by the fusion of two systems i.e. in-memory database and on-disk database. It lets the developer join both the database models i.e. in-memory and on-disk in a single DB instance.
Denoting one set of data as transitory (managed in memory), while selecting on-disk storage for the rest of the record types, needs a simple database schema declaration. The resulting database preserves in-memory potencies (speed, small database footprint, intuitive native API, etc.), whilst possibly controlling the cost savings and built-in stability of an on-disk database.
The underlying working of the hybrid databases is based upon the HTAP (Hybrid Transactional and Analytical Processing) functionality. For data storage, both the medium-types i.e. in-memory and on-disk are available inside a single application. This permits customers to manage trade-offs between latency, cost, and storage preservation options.
For customers and applications, the variance in operation crosswise storage types will be negligible, as data manipulation will be consistent across all tables, however, cost savings can be significant.
Hybrid databases employ optimizers to automatically choose the most suitable execution plan based on the statistics and existing indexes to improve the overall performance irrespective of the data’s location.
The Hybrid Database optimizes the transactional and analytical queries by supporting both Row-based (for transactional queries) and Column-based storage (for analytical queries) with a single DB instance. All the data is kept in a manner that optimizes the current operation.
Hybrid DB Use Cases
There are certain business scenarios where it is not advisable to use either NoSQL database alone or Relational database alone. In such scenarios, the use of hybrid DB comes into a picture where a NoSQL database is added to an existing Relational database or visa versa.
Let us discuss some of the use cases of a hybrid DB.
#1) Use Case: Document Database
Enterprise Resource Planning(ERP) software is traditionally a stranglehold for Relational databases, however, they are missing the flexibility to let their users customize the entry forms, without any modification to the database schema.
If we add a NoSQL document database to this existing ERP solution, then users can create and edit the forms quickly, as required. The data will be saved as documents and it will be future-sealed for any form parameter changes pushing ahead.
Some Relational database providers have perceived the requirement for such a mixed arrangement and have actualized something like a document database within their relational database. For instance, Microsoft SQL Server 2016, provides support for storing JSON documents inside cells, which in turn facilitates up some workflow, yet muddles updating that data contrasted with updating data in a normal table.
Document databases keep everything in the form of a “document,” normally a JSON object. As they don’t need any structure, you can add various fields to every JSON object, while remembering that it’s dependent upon you to make that data meaningful while fetching it. Famous document databases include MongoDB and Couchbase.
#2) Use Case: In-Memory Database And Graph Database
The achievement of e-commerce websites depends intensely on their capacity to prescribe something that might interest you specifically. How would they do this? They investigate your past buys, and track the things you’ve watched, however, didn’t buy.
They’ll do likewise for your companions, for different clients in your region, and associate this information with what is in vogue. The challenge is that this data analysis should occur rapidly for each page opening and every customer, it’s an infeasible act if you are required to query your relational database and combine numerous tables so as to get results.
A possible way could be to have an in-memory database ahead of your relational database to cache all the required data to execute queries in memory, rather than heading off to the disk each time. An improved solution would be to add a graph database as well to maintain a record of all your relationships as a customer with respect to your choices, who your companions are, their likes and dislikes.
In-memory databases are generally key-value store that gets executed in your RAM, yet some of them can persevere information to the hard drive, plus offer replication support, snapshots, and transaction logging. The most famous in-memory databases include Memcached and Redis.
Graph databases keep their data graph structures and they’re streamlined for quick querying and lookups. This is achieved by adding a pointer to each entry to their connected entries. For Graph databases, you can explore Neo4j and InfiniteGraph.
#3) Use Case: Fraud Detection
Regardless of whether you’re running an online shop or a physical retail store, it’s critical to constantly be vigilant for fraud endeavors. To do that you have to quickly log a great deal of data, from different pieces of your framework.
Obviously, as the data is originating from a wide range of spots, you need to think about your web servers, your file servers, or payment gateways and it’s not organized in the same manner for each of these, it would be hard to create a relational database for this purpose.
Likewise, it’s quite possible that after some time you’ll begin or quit logging a few parameters somewhere in the system, and you require a database that can deal with that. Column databases were designed in light of this reason and they give you quick writes, and yet you have to be careful while designing one to ensure that it meets your requirements.
Best Practices For Implementing A Hybrid Database System
- Select the right hybrid model based upon your data, cost, performance and management requirements. Ensure a scalable database solution that meets your business needs i.e. all while sustaining security, accessibility, flexibility, and interoperability within your present infrastructure. Try to keep your data movement minimum and maintain a simple architecture.
- Prepare for hybrid implementation. Define the workflows well.
- Review data placement i.e. where to keep the data and how to fetch it.
- Alter your security approach. Check for any security issues in data transfer between on-premise and cloud resources.
- Try to maintain the following three competencies:
- Integration Competency: The capability to connect distinct streams of data across the organization in an agile, efficient and progressive way.
- Information Competency: The capability to handle meaning and context and thus the business value of data.
- Transformation Competency: The capability to do complex cross-functional changes in the business as demanded by market conditions, technology advances and business opportunities not just once, but as a continuing process.
In some situations, switching from one or more RDBMS to NoSQL database might not be beneficial. In these circumstances, it might be a better choice to create a hybrid system.
Top Hybrid Databases To Watch Out
Let us take a look at some of the best Hybrid Databases that are available in the industry.
#1) Altibase Enterprise Hybrid Database [BEST Overall]
Altibase is a Hybrid Database that simultaneously supports in-memory and disk storage into a single database solution. The architecture of Altibase permits the use of memory tables for high performance and disk tables for cost-effective storage.
It provisions synchronous and asynchronous replication and also proffers real-time ACID compliance. It is compatible with AIX, HP-UX, Linux, and Windows Operating systems.
Its main features include support for the entire SQL standard, Multiversion concurrency control (MVCC), Fuzzy and Ping-Pong checkpointing for periodic data Backups, Replication, and Database link functionality. In fact, Altibase was the first database vendor in the world to develop and commercialize a hybrid database back in 2005.
#2) DataStax Hybrid Cloud Database
DataStax Enterprise is a distributed hybrid cloud database developed on Apache Cassandra. This database is created for a hybrid cloud. It provides a single platform for all kinds of applications anywhere, on any cloud. It supports all models i.e. key-value, JSON, graph, tabular.
Another exciting feature of this database is its deployment-ready advanced workloads. Within a single security model, it provides a wholly integrated and optimized database, graph, analytics, in-memory, search, and Apache Kafka.
#3) Orient DB
OrientDB is one of its kind multi-model open source NoSQL DBMS which brings all together with the capability of graphs with document, key/value, reactive, object-oriented, and geospatial models into a unified scalable, high-performance operational database.
It works a lot faster on graph operations. It supports atomic operations as well as ACID transactions with a transactional DBMS. While using OrientDB, you do not need to learn another proprietary language as it simply works with a database built on SQL.
LeanXcale is easy to work with a database designed for transactional and analytical workloads. This ACID-compliant database allows quick insertion and aggregation over real-time data.
With LeanXcale, you can execute operations and analytics within the same database manager at any scale. You can linearly scale-out from 1 to 100s nodes.
In this tutorial, we explored the concept of the Hybrid Database, along with its underlying architecture and working.
We learned the benefits of using a Hybrid Database, how it can join the advantages of Relational and Non-Relational DBs, in-memory and on-disk storage in a single DB instance and eliminate their shortcomings and how it can handle big data so well. We also had a look at some of the top Hybrid databases that are available in the market.
Hope you enjoyed this informative tutorial on Hybrid Databases!!