The Challenges of MySQL, and Why It Might Be Time to Migrate to SingleStore

Databases are the backbone of any application, crucial for storing and processing data efficiently at scale.

As businesses grow, the limitations of single-node databases like MySQL become apparent — particularly for larger datasets and real-time analytics. While reliable, MySQL struggles with slow ingestion and analytical performance. SingleStore offers high throughput parallelized data ingestion and retrieval, ensuring that applications run smoothly and deliver data-driven insights in real time, meeting the demands of modern businesses and enhancing user experiences.

Why and where MySQL fails

MySQL has long been a popular choice for powering web applications, and is still a sought-after tool for startups. Yet as intelligent applications become increasingly complex and distributed,, the rigid structure and scaling challenges of MySQL make it less suitable compared to newer database technologies.

While MySQL remains a viable choice for simpler applications with modest scalability needs, it may not be the optimal fit for the ever-evolving landscape of modern and AI/ML application development:

Poor query performance. This occurs when the database is not optimized, leading to slow query execution and timeouts. Common causes include lack of indexing, inefficient queries, hardware limitations or high concurrent access leading to bottlenecks.
Poor application experience. This might be due to inefficient handling of large datasets or high-volume traffic from new or major customers. Poorly optimized queries or inadequate database scaling can result in slower response times and degraded user experience for these customers.
User-facing analytics reflect stale. Stale data results from delays in updates or synchronization issues. This often occurs in systems where real-time data processing is not implemented, leading to users seeing outdated information.
ETL takes hours. ETL (Extract, Transform, Load) processes can be time consuming if not optimized. Large volumes of data, complex transformations and inadequate infrastructure often lead to long ETL processing times.
Maintaining multiple other databases. Managing multiple databases can be complex and labor intensive. It often involves handling different data models, synchronization issues and increased administrative overhead, leading to higher chances of errors and inefficiencies.

These issues highlight common challenges with managing MySQL databases, particularly in large-scale or high-traffic environments. Optimizing database performance, ensuring timely data updates and streamlining ETL processes are essential to mitigating these headaches.

MySQL simply isn’t designed for scale or analytical performance — and it lacks cost efficiency

You know why you shouldn’t use MySQL if you are serious about managing your data efficiently. Let’s look at further challenges associated with this database.

MySQL isn't distributed

MySQL primarily uses a single-node architecture, which means it relies on a single server to handle all database operations. This can lead to scalability issues when dealing with large datasets or high traffic. To handle larger volumes of data, complex sharding techniques are often required, which can be difficult to manage and maintain. MySQL struggles with handling data at terabyte (TB) and petabyte (PB) scale.

MySQL isn't meant for OLAP

MySQL is optimized for Online Transaction Processing (OLTP) rather than Online Analytical Processing (OLAP). It uses a rowstore format, which is less efficient for read-heavy analytical queries compared to a columnstore format. MySQL lacks advanced OLAP features including column-store indexes, materialized views (MVs) and vectorized query execution, which are essential for high-performance analytical processing. As a result, MySQL performs poorly on queries with high cardinality (many distinct values).

MySQL costs much more than you'd expect

MySQL can incur higher costs due to several factors. It does not support columnar compression, which is a method used to reduce storage costs and improve query performance by compressing data stored in columns. Additionally, MySQL does not provide a clear separation of storage and compute resources (S+C), leading to inefficient resource utilization. Managing large-scale applications often requires integrating MySQL with other specialized databases, increasing the overall cost and complexity of the system.

These structural issues highlight the limitations of MySQL in handling large-scale, complex and analytical workloads, which can lead to increased costs and management challenges.

Application architecture with MySQL and other databases

In a typical enterprise setup, you might have a user-facing analytical application powered by MySQL or PostgreSQL, a Point of Sale (POS) application supported by Oracle or SAP and BI dashboards running on Snowflake or Redshift. Each of these specialized databases is chosen for its specific strengths, but this approach significantly increases the complexity of your data architecture. Managing multiple databases means dealing with different technologies, synchronization issues, increased maintenance costs and potential latency.

By centralizing all these functions into a single database like SingleStore, you can streamline operations, reduce the overhead associated with maintaining multiple systems and improve performance through a unified platform that supports both transactional and analytical workloads. This consolidation simplifies the data infrastructure, enhances real-time data processing capabilities and ultimately leads to a more efficient, responsive system — improving the overall user experience and operational efficiency.

When companies are small, using MySQL for all their data needs often seems sufficient and straightforward. However, as businesses grow, and the volume and complexity of data increases, the limitations of MySQL become apparent. To handle high-traffic transactional workloads, businesses might introduce specialized databases optimized for these tasks. Similarly, for analytical workloads requiring complex queries and real-time insights, companies may adopt data warehouses or OLAP databases.

For vector storage and search capabilities, databases like Pinecone are used. Over time, as different databases are integrated to address specific pain points — like operational, batch processing, real-time analytics and AI/ML — managing these disparate systems becomes increasingly complex. This multi-database architecture can lead to increased latency, synchronization issues and higher maintenance costs, ultimately affecting customer experience due to slower performance and potential data inconsistencies.

Migrate from MySQL to SingleStore today

We’ve shared plenty of data points highlighting why it might be time to migrate your MySQL setup. But move out to where? Of course, SingleStore.

SingleStore surpasses MySQL in all aspects whether it is data ingestion speed, vector store capabilities, retrieval performance, real-time analytics or scalability for large datasets. When you already know the shortcomings affecting your applications, why not make the switch to a better alternative?

SingleStore's distributed architecture supports both transactional and analytical workloads seamlessly, eliminating the need for multiple databases and reducing complexity while ensuring superior performance and faster data processing.

You might think it's a headache to move data from one place to another, involving a ton of work and complexity, right? a lot of work and complexity involved, right? Wrong. We’re going to break down how you can migrate from MySQL to SingleStore in five easy steps.

The preceding diagram provides a visual comparison between the two databases, emphasizing their differences in storage engine, replication, indexing and partitioning partitioning, as well as additional features unique to SingleStore like query compilation and data ingestion speed.

Migrating from MySQL to SingleStore

There are three methods to migrate from MySQL to SingleStore:

SingleStore CDC
MySQL dump
ETL

In this tutorial, we will demonstrate how you can migrate from MySQL to SingleStore using SingleStore’s native Change Data Capture (CDC).

Prerequisites

For this scenario, imagine data resides somewhere on the internet inside MySQL. This can be either:

Free SingleStore Helios account
MySQL instance

If you haven’t already, start free with SingleStore Helios® cloud and create a workspace.

Now, let’s navigate to SingleStore’s Notebook feature to get working with the migration flow. Go to the ‘Develop’ tab where you will see the Notebook option.

Create a new Notebook and start working.

First, create a database.

%%sql
DROP DATABASE IF EXISTS mysql_replicate_cdc;
CREATE DATABASE IF NOT EXISTS mysql_replicate_cdc;

Once you run the preceding command, select the database you just created from the Notebook dashboard.

You can see that our database has no tables or data (yet).

%%sql
-- Start with an empty database
SHOW TABLES;

Enter the hostname or address of the database server securely using the getpass module

from getpass import getpass
database_hostname = getpass("Please enter address: ")

Create a link and let SingleStore know it’s from the MySQL system. The following is the SQL command to create a replication link between a source MySQL database, and target SingleStore database. Here are the key details:

The command is CREATE LINK to set up a replication link
The source database is a MySQL database
The target database is a SingleStore database
The link is configured with various parameters like the hostnames, ports, SSL mode, included/excluded tables and schemas
Database credentials like username and password are provided to establish the link

sql_query = f"""
CREATE LINK mysql_replicate_link AS MYSQL
CONFIG '{{"database.hostname": "{database_hostname}",
         "database.exclude.list": "mysql.performance_schema",
         "table.include.list": "s2mysqlcdctest.supplier",
         "database.port": 3306,
        "database.ssl.mode": "required"}}'
CREDENTIALS '{{"database.password": "Password@123",
              "database.user": "repl_user"}}';
"""

Let's create a database connection.

from sqlalchemy import *

db_connection = create_engine(connection_url)

Here’s the query to import data into our database.

with db_connection.connect() as connection:
    connection.execute(text(sql_query))

Next, ask SingleStore to show us the links.

%%sql
SHOW LINKS;

Create a table named "supplier" in SingleStore using the "CREATE TABLE" statement.

Use the "INFER PIPELINE" option, which automatically infers the schema of the table based on the data being loaded
The data is being loaded from a MySQL replication link named "mysql_replicate_link"
The specific table being loaded is "s2mysqlcdctest.supplier", where "s2mysqlcdctest" is the source MySQL database name and "supplier" is the table name
The "FORMAT AVRO" clause specifies the incoming data format from the MySQL replication link is in Apache Avro format

%%sql
CREATE TABLE supplier AS 
INFER PIPELINE AS 
LOAD DATA LINK mysql_replicate_link 
    "s2mysqlcdctest.supplier" 
FORMAT AVRO;

Show the tables that have been imported from MySQL.

%%sql
SHOW TABLES;

Ask SingleStore to describe the supplier that’s been imported from MySQL.

%%sql
DESCRIBE supplier;

Start the pipeline to ingest the data.

%%sql
START ALL PIPELINES;

Show the pipeline.

%%sql
SHOW PIPELINES;

Let’s count the table created on the SingleStore side.

%%sql
SELECT COUNT(*) FROM supplier;

You can see the first five rows ingested.

%%sql
SELECT * FROM supplier LIMIT 5;

Now, let’s copy the SQL code shown here, and enter some new values/data into our MySQL database.

INSERT INTO supplier (s_suppkey, s_name, s_address, s_nationkey, s_phone, s_acctbal, s_comment, 
new_col)
VALUES
(101, 'Supplier1', '1234 Elm St', 10, '555-1234', 1000.50, 'Supplier comment 1', 'Optional1'),
(102, 'Supplier2', '5678 Oak St', 20, '555-5678', 2000.75, 'Supplier comment 2', 'Optional2'),
(103, 'Supplier3', '9101 Pine St', 30, '555-9101', 3000.25, 'Supplier comment 3', NULL), 
(104, 'Supplier4', '1213 Maple St', 40, '555-1213', 4000.10, 'Supplier comment 4', NULL),
(105, 'Supplier5', '1415 Cedar St', 50, '555-1415', 5000.55, 'Supplier comment 5', 'Optional5'),
(106, 'Supplier6', '1617 Birch St', 60, '555-1617', 6000.65, 'Supplier comment 6', 'Optional6'),
(107, 'Supplier7', '1819 Walnut St', 70, '555-1819', 7000.75, 'Supplier comment 7', 'Optional7'),
(108, 'Supplier8', '2021 Spruce St', 80, '555-2021', 8000.85, 'Supplier comment 8', NULL),
(109, 'Supplier9', '2223 Ash St', 90, '555-2223', 9000.95, 'Supplier comment 9', NULL),
(110, 'Supplier10', '2425 Chestnut St', 100, '555-2425', 10000.00, 'Supplier comment 10', 
'Optional10');

Connect to the MySQL system through MySQL CLI.

Now, let’s add the copied data. When you see the supplier table now, you can see an increase in the number since we just inserted that data.

If you go back and rerun the following command to see if there is any incremental changes in the data from SingleStore, you should see an increase in the number here as well:

%%sql
SELECT COUNT(*) FROM supplier;

As you see, the new data lands in the MySQL database and after the connection was established from the SingleStore side, you can see the data arriving in milliseconds using SingleStore CDC.

Want to know more about migrating from MySQL to SingleStore? Check out the following resources:

What are you waiting for? Don't let sluggish performance hold your business back. Migrate from MySQL to SingleStore now and experience the power of real-time analytics and effortless scalability.

Start free today.