MySQL Limitations in Big Data Analytics

MySQL is a popular open-source relational database management system (RDBMS) that has certain limitations when it comes to Big Data analytics. In this context, this post will delve into five key MySQL limitations that can be addressed by taking a modern approach to the data architecture.

1. Delivering Hot Data

In large Big Data analytics applications, the data cache stored in RAM can grow very large and be subjected to numerous requests per second, leading to bottlenecks and performance issues. The first MySQL limitation is that it lacks a strong memory-focused search engine, which compounds the issue.

Legacy Approach: In the past, companies turned to external solutions like Memcached or Redis for caches and hot data needs. However, this approach risks reading outdated data. Another option was internal scalability improvements such as the thread pool, available either as a MySQL enterprise feature or as an open-source implementation. Unfortunately, such features only enable many queries to run concurrently without improving the performance of any given query.

Modern Approach: The Apache Ignite in-memory computing platform offers two components: an in-memory data grid and an in-memory SQL grid that enables users to create an in-memory MySQL data store. In contrast to Redis and Memcached, which are primarily in-memory key-value stores, Apache Ignite, Redis, and Memcached all partition and cache data in memory. However, there are several differences in how caching, transactions, persistence, and data querying are supported. Apache Ignite includes many additional features that are often valuable for companies transitioning to in-memory computing, such as ANSI-99 SQL, distributed ACID-compliant transactions, advanced computing and streaming capabilities, and more.

2. Dealing with Highly Volatile Data in Big Data Analytics

When dealing with highly volatile data – for example, thousands of updates per second being applied to a single database row (such as flash online sales for high-demand concert tickets) – maintaining exact values at every instance is crucial. The second MySQL limitation is that it is designed with full transactional semantics and support for long transactions, and is not suitable for use with highly volatile data.

Legacy Approach: Good data design within MySQL can address this issue to some extent. Splitting the counter over several rows and optimal configuration of the MySQL installation can yield up to ten times better performance than a stock MySQL configuration. MySQL 5.7 has addressed the historical problem of parallel replication, but it can also be addressed through solutions such as Percona XtraDB Cluster. In some cases, users move frequently-updated data to Memcached or Redis and then periodically synchronize it to the RDBMS.

Modern Approach: Apache Ignite offers a better solution by automatically distributing data across all nodes in a cluster. Replication between nodes and clusters is configurable and automatic, and you can configure Apache Ignite to provide the necessary level of consistency. After the data is in Apache Ignite, you can leverage the ANSI-99 distributed SQL engine and ACID compliant transactions to work with your data the same way you work with it in MySQL. In-memory computing features in Apache Ignite are often highly valuable for companies moving to in-memory computing and are lacking in Percona XtraDB Cluster, Redis, and Memcached.

3. MySQL Scalability Limitations

Intended as a single-node system, MySQL was not designed with modern data center concepts in mind. Today's largest MySQL installations require sharding to scale, which involves splitting a data set over multiple nodes or instances. The third MySQL limitation is that most sharding solutions in MySQL are manual and add complexity to the application code. Additionally, any performance gains are negated when queries must access data across multiple shards.

Legacy Approach: To address these issues, many turn to tools like Vitess or ProxySQL to implement sharding for MySQL for their Big Data analytics applications. Other front-end solutions include Memcached, Redis, MongoDB, and Cassandra.

Legacy Approach: However, a better solution is Apache Ignite, which was purpose-built from the ground up as a highly scalable and high-performance distributed in-memory computing platform. Nodes are automatically load balanced, and there are no limitations to the amount of CPU and memory that can be used by any node. Data is automatically distributed across all nodes in a cluster, eliminating the need for manual sharding. In addition to these features, Apache Ignite also offers many additional capabilities beyond those provided by point solutions like Vitess, Redis, Memcached, MongoDB, and Cassandra.

4. Not Designed for Advanced Analytics

MySQL was not intended for processing complex queries on massive data sets that require analyzing a large amount of data on a vast scale. The MySQL optimizer is restricted to executing only one query at a time through a single thread. The fourth MySQL limitation is that queries cannot be scaled among multiple CPU cores in a single system or executed across multiple nodes.

Legacy Approach: External solutions such as Hadoop® and Apache® Spark™ have been utilized to manage large-scale data processing tasks since MySQL does not offer a comprehensive solution. However, analytic solutions like Vertica and ClickHouse have also emerged as potential options.

Modern Approach: Apache Ignite easily integrates with Hadoop and Spark, using in-memory technology to complement these technologies and achieve significantly better performance and scale. The in-memory SQL grid provided by Apache Ignite is highly optimized and can be easily tuned to execute high-performance SQL queries. Additionally, the in-memory SQL grid offers JDBC/ODBC connections to any analytics solution.

5. Inability to Handle Full Text Searches at Scale

MySQL is capable of conducting basic full text searches for Big Data analytics. The fifth MySQL limitation is, as the amount of data increases, its inability to handle parallel processing leads to a lack of scalability in the search function.

Legacy Approach:InnoDB, which was introduced with MySQL 5.6, can be used for small-scale search applications. However, for searches that exceed a few gigabytes, it is advisable to use advanced search solutions such as Elasticsearch, Apache Solr,, or Sphinx Search.

Modern Approach: Apache Ignite uses Lucene indexing to facilitate text-based queries. Text searches are distributed across the cluster for parallel processing against data stored in memory, resulting in highly scalable and high-performance full text searches.MySQL can handle basic full text searches. However, because of its inability to manage parallel processing, searches do not scale well as data volumes increase.

When Two Trends Converge: The Impact on Large-Scale Data Processing

MySQL has been an excellent database choice for many applications. However, the landscape of large-scale data processing for Big Data analytics is changing due to two major trends that have converged. They are forcing a wholesale change in the database world.

  • As memory prices continue to plummet, it has become much more affordable for large enterprises to demand real-time data processing and analytics of data sets of massive scale. For example, today, a clustered terabyte of RAM can range between $5,000 and $15,000, depending on the type of RAM, location, and provider.
  • Memory prices have dropped to the point where, for example, a clustered terabyte of RAM costs about $20,000, making it affordable for large enterprises that demand real-time data processing and analytics of data sets of that scale.

The good news is that as these trends converge, in-memory platforms are ready to deliver as the most sophisticated distributed systems in existence because they are efficient and well-architected. The largest implementations of Apache Ignite rival the largest installations of Cassandra and Hadoop with several thousand nodes and clusters – while providing greater performance and scalability. All of this means in-memory computing platforms can overcome MySQL limitations by providing the scalability you need for your Big Data analytics applications.