The Limitation of MySQL Database in a Typical Big Data Environment
MySQL is the pioneering name in enterprise database management systems and is widely used as an open-source RDBMS (Relational Database Management System). Many of the enterprise applications and CMS web development platforms rely on MySQL database as an excellent solution in web-scale business applications. However, when it comes to the changing big data environment, it seems that MySQL architecture; which was developed with the needs of that time in mind, has some limitations too.
MySQL limitations in big data
On having a closer look into the strengths and weaknesses of the MySQL DBMS, we can see that even though it is powerful, you may need some further assistance to get benefited from it. To start with, here we will discuss the major limitations of MySQL in the area of big data.
1. Delivery of hot data
Considering the need for the larger application, we can see that the cache data in the RAM storage may grow huge over time, given that there are hundreds of thousands of requests generated every second. One disadvantage of MySQL here is that it is not a memory-centered search engine. IT is not designed with a higher concurrency in mind so that users may experience bottlenecks in terms of performance. MySQL may saddle in such situations with high overhead and also may not be able to deliver good speed.
Solutions for this problem is to use any applications like Redis or Memcached as external solutions for hot data needs to eliminate any overhead in terms of SQL parsing. Caching is somewhat difficult, and you are at risk of reading the data which is not current. You may also use the scope of internal scalability in order to enhance MySQL-like thread pools as like an enterprise feature of MySQL. Such add-on features will help run multiple queries concurrently.
2. Capacity to deal with volatile data
Simultaneously handling many updates in a single row of database like a flash sale of a new model cell phone etc., it is important to maintain the exact values at each second. MySQL DB is designed around the transactional semantics to support the tasks with disk-based log durability. So, MySQL is suited for the tasks with highly volatile data but has some limitations in it.
An ideal solution for this issue is that, up to an extent, to do proper data design in MySQL. Splitting the counter into multiple rows will help to attain optimal configuration during MySQL installation, and it will ensure better performance than the primary configuration of stock MySQL. Another big problem with MySQL is parallel replication, which is however addressed in the MySQL 5.7 version. If you still face it, try solutions like the Percona XtraDB Cluster. Many users may tend to move their data to Redis or Memcached and then synchronize periodically to RDBMS.
3. Handling huge data volume
MySQL is a single-node database system, which is not compliant with the modern cloud data center concepts. Even the biggest MySQL installations today cannot scale up to that level with MySQL alone and may have to rely on further applications to ensure sharding, data split across multiple nodes, etc. However, most of the sharding solutions are manual, and it makes coding of application very complex. Any advantage in terms of performance gain will be lost when the queries access the data across multiple shards.
The solution for this problem as suggested by RemoteDBA.com is the tools like Vitess, which is a framework released by YouTube for MySQL sharding. ProxySQL is also used to implement better sharding. Redis and Memcached are other front-end solutions for this issue, and MongoDB and Cassandra are the alternatives to consider for MySQL.
4. Analytical capabilities
MySQL is not designed to handle complicated queries in massive data volumes which require to crunch through large scale data loads. MySQL optimizer has limitations in executing one single query at a time with the use of a single threat. The MySQL queries cannot scale across multiple machine cores in a system and also cannot execute the distributed queries at multiple nodes.
As a solution, even though MySQL is offering no comprehensive solution for any robust data processing solution at large-scale DB management environment, you can try many third-party solutions like Hadoop or Apache Spark, etc. Click House and Vertica etc. have also emerged lately as analytical solutions to be used over MySQL. Apache Ignite can also integrate with Spark and Hadoop to use the in-memory technology which completes well to these technologies and ensure better performance at scale.
5. Enabling full-text search
Even though MySQL can handle the basic text searches, with its inability in parallel processing, searches a scale will not be handled properly when the data volume multiplies. One solution to try out for small-scale searches is InnoDB, which was made available with the version MySQL 5.6. When a particular go beyond a few GB, you have to use better search solutions like Apache Solr, Elasticsearch, Sphinx Search, or Crate.io to manage it better.
The convergence of database trends
Even though MySQL is deemed to be an excellent choice for database, the top trends which converge now to change the data processing landscape now are:
- The in-memory computing and cache concept has been there around for long, but it has lately come to the forefront, and the need for real-time data processing has now become a standard procedure in all sizes of enterprises. In-memory computing remains as the primary pointer to speed, and the advancement in memory technology will lead to a better world for in-memory storage, making disks just historical backups.
- The memory prices are largely dropping over time. You may get a terabyte RSM at the cost of nearly $20,000, which makes storage more affordable to all size of enterprises to meet their real-time data management and analytical needs at a scale.
This further change the large-scale data processing landscape. Above trends come together to force a big change in the database world and database management systems approach.
The reassuring thing is that these as these trends converge; the existing in-memory platforms will deliver more sophisticated distributed systems which are more efficient and architected to provide greater scalability and higher performance.
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