Is your MYSQL Analytics Slow? Here’s Why and How to Fix It

This is part of blog Series “Database to Analytics Ready Data“

If you are reading this, chances are you’re battling slow queries on MySQL every time you try to run complex analytics. It can be deeply frustrating to watch a query spin indefinitely, or to see your BI dashboards fail to load insights in a timely manner.

As your data grows—maybe you now have gigabytes or even hundreds of gigabytes—the problem only gets worse.

In this blog, we’ll break down why MySQL, a very popular relational database, often struggles with advanced analytical workloads.

1. MySQL analytical queries taking hours to run.

2. Aggregations, multiple table lookups take forever.

3. Issues with real time querying of MySQL data. [Real time MySQL data analysis]

4. Your manager sent you this article to read.

If any of the above mentioned issues ring a bell, you are at the right place.

We’ll explore what the market currently does to solve this issue—like connecting MySQL directly to BI tools, using query virtualization layers, or performing manual ETL into a warehouse.

Finally, we’ll show you a better way with Datazip’s OneStack, powered by ClickHouse. With this approach, the same query that took over a minute on MySQL can complete in just a few seconds, and your BI dashboards load almost instantly.

Let’s dive in.

Why Is MySQL Challenging for Analytics?

MySQL was originally designed as a fast and stable OLTP (Online Transaction Processing) database. It excels at tasks like handling simple, repetitive queries, supporting user-facing applications, and processing a high volume of small transactions.

It’s superb for CRUD operations (Create, Read, Update, Delete) on structured data with well-defined schemas.

However, advanced analytics is a completely different beast. Here’s why MySQL often falls short when you try to use it as your analytical engine:

1. Architectural Bias Toward OLTP, Not Big Data

MySQL’s underlying design is geared toward Online Transaction Processing (OLTP) rather than large-scale analytics. The default InnoDB engine stores data in a row-based format and optimizes for fast inserts and small point-lookups.

This structure works well when your data volumes are in the tens or low hundreds of gigabytes, but once you approach terabyte-level datasets (e.g., 1–5 TB and beyond), queries that require full table scans, multi-way joins, and aggregations often slow to a crawl.
Why?

• Row-Oriented Storage: Suited for transactional reads but inefficient for wide analytical scans that require only a few columns from billions of rows.

• Lack of Distributed Execution: MySQL typically runs on a single node, so you can’t just add more machines and expect linear performance gains. Sharding is manual and complex.
  Result: Running a multi-join aggregation query on a dataset of ~500 million rows might take minutes to hours in MySQL, whereas a columnar, distributed system could return similar results in seconds.

2. Performance Degradation with Scaling Data Volumes

As data grows into the hundreds of millions or billions of rows, MySQL frequently requires heavy indexing to maintain performance. But indexes alone aren’t enough once you reach large data scales.

Each new index adds overhead on writes, and still doesn’t guarantee sub-second queries on massive data. Even with well-tuned indexes and server resources (e.g., 64GB RAM, NVMe SSDs), complex analytical queries can degrade from sub-second on small datasets (<10GB) to tens of seconds or minutes at scale (>100GB).

Columnar databases (like ClickHouse) can skip irrelevant columns, compress data more effectively (like deduplication engines), and leverage vectorized execution to speed up complex calculations. MySQL does not provide such capabilities natively, making it slow for queries that only need a subset of columns from massive tables.

Common Bottleneck: Sequential scans on large tables (200GB+) become I/O heavy, ballooning response times, as MySQL must load far more data into memory than a columnar engine would.

3. Manual Sharding and Horizontal Scaling Complexity

MySQL was conceived in the 1990s for single-server use. Scaling horizontally across multiple nodes to handle billions of rows (e.g., when data surpasses 1–2TB) is not a built-in feature.

Users must implement sharding manually or rely on tools like Vitess, ProxySQL, or custom application logic. This adds operational complexity and potential downtime when redistributing data.

4. Limited Advanced SQL and Analytics Features

Compared to engines like PostgreSQL, Vertica, or ClickHouse, MySQL’s advanced SQL feature set is narrower. Historically, MySQL lagged behind in functionalities such as window functions (only fully supported in recent versions), complex stored procedures, and advanced analytical functions.

Analytics teams might need workarounds or ETL pipelines to more capable engines. For instance, companies often move data into warehouses like Snowflake, BigQuery, or ClickHouse to run complex BI queries that MySQL either can’t handle efficiently or executes too slowly.

5. Concurrency and Locking Issues Under Heavy Load

While InnoDB introduced row-level locking and improved concurrency over MyISAM’s table-level locks, MySQL can still struggle at very high concurrency levels (e.g., thousands of writes/reads per second against large tables). If this is what your infra looks like, its time to move out of MySQL for good..

Lock contention, transaction deadlocks, and I/O bottlenecks become more pronounced as load and dataset size increase.

6. Challenges in Real-Time Analytics and Streaming Data

MySQL’s architecture does not cater well to streaming analytics, where data arrives at high velocity (e.g., tens of thousands of events per second) and insights are needed within milliseconds to seconds.

Maintaining a real-time dashboard against a rapidly updating MySQL table often results in slow queries and expensive indexing strategies.

Companies integrate in-memory caches like Redis or Memcached, or use Apache Kafka and Spark for streaming analytics. This patchwork adds complexity and eventual consistency issues, as MySQL alone isn’t designed for low-latency analytics on fast-arriving data.

7. Inadequate Big Data Search and Aggregations

Full-text search and complex filtering queries don’t scale well as you move beyond a few gigabytes of text. Unlike search engines (e.g., Elasticsearch) or distributed systems with parallel search capabilities, MySQL runs full-text searches on a single server and doesn’t distribute the query.

When dealing with billions of text entries, these searches can take tens of seconds or longer. Aggregations like SUM, COUNT, AVG over billions of rows also become I/O heavy and CPU bound due to the lack of columnar compression and vectorized execution.

Example: A sum aggregation on a 200GB table might take ~30–60 seconds to several minutes on MySQL, whereas a columnar database can often do it in a few seconds.

8. BI Dashboards Not Loading:

Tools like Metabase or Tableau, when pointed directly at MySQL, often show spinners or timeouts.

Now that we have seen the most common problems with MySQL, lets take a look at how current industry tries to solve it (more like workarounds)

Current Market Approaches to Solve MySQL Issues

How do companies try to solve these challenges today?

1. Query Directly on MySQL (Hope & Pray Method):
   Users connect MySQL to tools like DBeaver or DataGrip and try running queries. The result: slow execution times and complex query tuning. Eventually, frustration sets in.

2. Use a Data Virtualization / Query Layer:
   Some tools promise a SQL virtualization layer that can talk to MySQL and provide a single SQL interface. While convenient, this does not fundamentally fix MySQL’s performance limits. The tool translates queries into MySQL SQL, and performance still suffers.

3. Export Data and Use a Warehouse (ETL):
   A common solution: periodically export your MySQL data into a dedicated analytical warehouse (like BigQuery, Snowflake, or ClickHouse). Then run your analytics there. This approach often works well, but building and maintaining ETL pipelines is time-consuming. Also, your data freshness might lag by hours or a day.

4. Use a Data Lake or Lakehouse:
   Some organizations dump MySQL data into a data lake (like S3 + a query engine like Trino/Presto or Lakehouses like Iceberg, Hudi or Delta). This can work for large-scale analytics, but it often introduces complexity: schema evolution, data governance, and performance tuning are non-trivial.

Introducing Datazip’s OneStack: Speed up Your MySQL Data Analytics

We built Datazip’s OneStack to solve these problems simply. Instead of forcing you to manually set up pipelines or struggle with slow queries, we provide a unified platform that:

1. Connects Seamlessly to Your MySQL:
   With over 150 source connectors, hooking up your MySQL instance is straightforward. You configure a connector, and we do a full load of your data into our platform.

2. Continuous Data Replication (CDC):
   After the initial full load of tables, we apply Change Data Capture (CDC) to keep your data in sync. As inserts and updates happen in MySQL, we reflect those changes in near real-time within our analytical engine.

3. ClickHouse Under the Hood:
   We use ClickHouse as our underlying storage and query engine. ClickHouse is a columnar OLAP database known for its speed and efficiency. It can run complex aggregations over billions of rows in seconds.

4. SQL Queries with Familiar Tools:
   Once your data is in Datazip’s OneStack, you can run the same SQL queries but get results far faster. No more 1-minute run times—often queries return in under a few seconds. You can also connect Metabase, Tableau, or other BI tools directly to our platform and see dashboards load almost instantly. You can also query the data from languages of choice like Java, Python or Golang.

5. Table additions, Schema evolution & auto-data type conversion:
   Onestack also handles new column or table additions and takes care of non-breaking data type conversions.

Real-World Comparison - Simple Benchmarking

We took TPCH data and generated data using this.

We ran a complex join across multiple tables (e.g., orders joined other tables, filtered by region and date, grouped by month, and aggregated by revenue and count of orders).

• Directly on MySQL:
  The query might take 60+ seconds for most queries. You might see CPU spiking on the MySQL server (the server crashed in some queries). Your BI dashboards might even time out or show partial results.

• On Datazip’s OneStack (ClickHouse):
  With the same logical query, we often see results in seconds. By leveraging columnar storage, vectorized execution, and MPP capabilities, ClickHouse reduces the query time drastically. Now your analysts can actually explore data interactively, drill down into details, and run ad-hoc queries without dreading slow response times.

Query1: Top 5 Customers by Revenue Contribution Over a Year

1. Direct run on MySQL

2. Run on Datazip’s OneStack

RESULT: MySQL failed to execute the query, it kept on running for 30+ minutes until I terminated. An Unoptimised clickhouse query for the same took 1minute to execute but optimised ClickHouse query (shown above) did that in 13.6seconds.

With better query writing, you can get blazing fast analytics with ClickHouse.

Query2: Top 5 Parts by Average Extended Price

1. Direct run on MySQL

2. Run on Datazip’s OneStack

RESULT: 33x faster results in ClickHouse with one INNER JOIN, GROUP BY, ORDER BY.

Query3: Total Revenue per Region for Orders in 1995

1. Direct run on MySQL

2. Run on Datazip’s OneStack

RESULT: 12x faster results in ClickHouse when involved multiple JOINS, GROUP BY’s, various filtering.

Query4: Aggregation with Nested Subquery Filters

1. Direct run on MySQL

2. Run on Datazip’s OneStack

RESULT: MySQL took more than 10 minutes to run, whereas ClickHouse took 2 seconds, 100x + performance edge.

We also did look into ClickBench: a Benchmark For Analytical Databases. Take a look.

Source - ClickBench (Q0, Q1, Q2, etc are standard queries, hovering on them will give you the exact query that was being tested)

So, you did execute your queries and got the intended result, now what? Yeah getting the data to a BI so you can visualise the data.

Assuming you cleaned up the data, applied some transformations, got the data from 🥉Bronze -> 🥈Silver -> 🥇Gold following the medallion architecture, now it's time you make sense of this data.

Connecting BI Tools (Metabase / Tableau / Superset / PowerBI / Redash) and Seeing Immediate Improvements in chart rendering times

Refer to our guide on “Connecting your BI tool directly on MySQL vs with OneStack Data for faster chart loading times” here.

Running Queries with SaaS data source and multiple Database data (MongoDB + Postgres + Google Analytics)

Modern analytics often requires combining data from multiple sources—maybe you want to join mongoDB data with a SQL database or with a SaaS application’s API data.

Since Datazip supports 150+ source connectors, you can pull data from multiple DBs and SaaS sources into a unified analytical layer. Suddenly, joining across sources and building holistic dashboards becomes trivial and fast.

If your final query requires you to aggregate data from multiple database sources, a SaaS source, you can perform those queries here as well.

1. Sync Database1 with OneStack

2. Sync Database2 with OneStack

Add database details to connect and sync data instantly.

3. Sync SaaS source data with OneStack (click here to see all the SaaS source connectors we support [oh, we support many])

4. Decide the joining key (assuming your data has some common key to make a JOIN)

5. Run the query.

6. Yeah that’s basically it.

Refer the below picture

The query:

SELECT *
FROM
(
    SELECT *
    FROM
    (
        -- Data from MongoDB
        SELECT
            _id,
            _timestamp AS timestamp,
            delete,
            entities
        FROM ch_db.otterdb_stream_3
    ) t1
    JOIN
    (
        -- Data from Postgres DB
        SELECT *
        FROM ch_db.ankittable_dz_stag_plan_raw
    ) t2
        ON DATE(t1.timestamp) = DATE(t2._timestamp)
) t3  -- Data after joining different tables from two databases
JOIN
(
    -- Google Analytics data
    SELECT *
    FROM ch_db.daily_active_users_raw
) t4
    ON DATE(t3.timestamp) = DATE(t4._timestamp);

Here, we used the Data from MongoDB + Postgres and Google Analytics to show even joining multiple Databases together and on top of that joining another SaaS source and running queries under 1 second is also possible with OneStack Data. So, what are you waiting for?

When Is a Database Better? When Is a Warehouse Better?

• Database (MySQL) Strengths:
  Ideal for transactional workloads—fast inserts, updates, and immediate reads of single records. Perfect for powering your web application, user signups, order entries, or inventory lookups that wont grow up into 100s of GB of data.

• Data Warehouse (ClickHouse) Strengths:
  Ideal for analytical workloads—massive scans, heavy aggregations, historical analysis, time-series queries, and building dashboards on very large datasets (think terabytes, daily operation on millions of rows). Perfect for monthly trend analysis, year-over-year comparisons, complex joins across billions of rows.

In short, MySQL is great for OLTP, while a warehouse like ClickHouse is great for OLAP. Most modern architectures keep both, using MySQL for production and a warehouse for analytics. Datazip’s OneStack helps you achieve this without manual ETL or complex data pipelines.

Read more here: Why Move from a Database to a Data Warehouse for Analytics?

Summary MySQL vs. OneStack Data

(These are indicative numbers. Actual performance may vary based on your schema, hardware, and concurrency.)

Conclusion: Move Beyond Slow MySQL Analytics

If you’ve struggled to run analytical queries directly on MySQL and faced timeouts, slow dashboards, and unhappy analysts, you’re not alone. MySQL’s architecture, built for OLTP, simply isn’t suited for large-scale analytics.

While there are many partial fixes—data virtualization layers, ETL pipelines, or data lakes—the simplest, most effective solution is to pair your MySQL with a dedicated analytical engine.

Datazip’s OneStack offers a seamless path. Connect your MySQL instance, replicate data with full load and CDC, and start querying in ClickHouse—all without complex overhead. Enjoy the best of both worlds: MySQL for day-to-day operations and ClickHouse (through Datazip) for lightning-fast analytics. With this approach, queries that took minutes can now return in seconds, and your BI dashboards will finally feel truly interactive.

No more frustration, no more waiting. Turn your MySQL data into actionable insights—fast and effortlessly.

Ready to Try It?

• Connect your MySQL database to Datazip’s OneStack.

• Run the same queries and watch results appear in seconds.

• Connect your favorite BI tool and see the difference instantly.

This is how you transform slow MySQL queries into a powerful analytics engine, enabling real-time insights and better decision-making.

If you have any questions or want to learn more, drop us a line at hello@datazip.io or book a quick demo meeting with us.

Disclaimer: Actual query performance depends on data size, cluster configuration, and query complexity. The above examples illustrate common scenarios and the general benefits of using a columnar data warehouse like ClickHouse for analytical queries.