ClickHouse for Analytics Alongside MySQL

By Kokil Thapa | Last reviewed: September 2026

MySQL remains the right choice for transactional web workloads in 2026. Laravel apps, WooCommerce stores, and client portals depend on row-level consistency and familiar tooling. Heavy analytics on the same instance is a different problem. Those queries scan millions of rows and fight checkout writes. A practical fix is ClickHouse for analytics alongside MySQL: keep MySQL authoritative for OLTP, and send reporting to a columnar store built for aggregations. See our MySQL query optimization for slow queries guide for what happens when you skip that split. I've used this pattern on production eCommerce and booking systems where dashboards outgrew nightly CSV exports. Below is the architecture, sync options, schema choices, and PHP integration path.

Why run ClickHouse for analytics alongside MySQL instead of querying MySQL directly?

MySQL 9.7 and the 8.4 LTS line excel at indexed lookups, foreign keys, and short transactional writes. Analytics workloads behave differently. They read wide date ranges, aggregate across many rows, and often need sub-second response on billion-row datasets. Running those queries on your primary MySQL server steals buffer pool space from live traffic.

Read replicas help, but they replicate row-oriented storage. A dashboard that sums revenue by city across three years still scans enormous data. Partitioning and indexing help until they do not. Our MySQL partitioning for large tables article shows the ceiling many teams hit.

ClickHouse stores data by column. Compression is aggressive. Aggregations use vectorised execution. A query that stresses MySQL for minutes often completes in seconds on ClickHouse. You trade flexible row updates for append-friendly analytics speed. That trade is intentional.

The split also simplifies mental models for your team. Application developers treat MySQL as the only write target. Data or backend engineers own ClickHouse pipelines and materialised views. Product analytics and marketing analytics diverge cleanly, similar to the framing in our product analytics vs marketing analytics comparison.

For most Laravel and WooCommerce stacks, the verdict is clear. Keep MySQL. Add ClickHouse when reporting queries appear in slow-query logs or when stakeholders want real-time dashboards over event streams.

How do you sync data from MySQL into ClickHouse?

Sync strategy determines whether your analytics lag by seconds or hours. There is no single correct answer. Pick based on freshness needs, team skills, and tolerance for operational complexity.

Option 1: Scheduled batch ETL

The simplest path exports changed rows on a cron schedule. Laravel scheduled tasks or a standalone worker queries MySQL for rows updated since the last watermark. It bulk-inserts into ClickHouse. Latency is typically five to sixty minutes. That is acceptable for daily revenue reports on many client projects.

# Example: export yesterday's orders (run from ETL server)
mysql -u etl_user -p app_db -e "
  SELECT id, user_id, total, status, created_at, updated_at
  FROM orders
  WHERE updated_at >= CURDATE() - INTERVAL 1 DAY
" --batch --raw | clickhouse-client --query="
  INSERT INTO analytics.orders FORMAT TabSeparated
"

Batch ETL is boring and reliable. It fits teams already running cron on Ubuntu servers. Our Linux system administration work often includes exactly these scheduled pipelines.

Option 2: Change data capture from MySQL binary logs

For near-real-time analytics, read MySQL binary logs. Tools such as PeerDB, Debezium, or ClickHouse's native MySQL database engine can stream inserts and updates. MySQL must run with log_bin=ON and binlog_format=ROW, as documented in the official MySQL binary log reference.

CDC preserves freshness without polling wide tables. It adds moving parts. Monitor replication lag and schema migrations carefully. A new nullable column in MySQL needs a matching change in ClickHouse before events arrive.

Option 3: Application-level event publishing

Your Laravel app publishes domain events to a queue after each write. A consumer batches them into ClickHouse. This pattern works well when analytics needs custom fields not stored in MySQL, such as experiment buckets or session metadata.

1. Enable row-based binary logging on MySQL if you choose CDC.
2. Create matching ClickHouse tables with compatible types before the first load.
3. Backfill historical data once via batch export, then switch to incremental sync.
4. Monitor row counts and checksum samples between both systems weekly.
5. Document which system owns each metric definition to prevent dashboard drift.

On sister sites sharing a Deployer pipeline, I keep ETL scripts in the repo and run them from a dedicated worker—not from web-facing PHP-FPM pools. That isolation prevents a heavy backfill from starving request threads. Related reading: MySQL binary logs for replication and backup and ClickHouse for analytics workloads.

What ClickHouse table engines and schemas work best for analytics?

ClickHouse schema design differs from normalised MySQL. Denormalise aggressively. Pre-compute dimensions you join repeatedly in MySQL. Use LowCardinality for status enums and country codes. Pick the right table engine upfront—migrations on billion-row tables are painful.

MergeTree family for fact tables

MergeTree is the default choice for event and order fact tables. Partition by month on your primary timestamp. Order by columns you filter most often, typically (created_date, tenant_id, product_id) for multi-tenant eCommerce.

CREATE TABLE analytics.orders_fact (
    order_id       UInt64,
    user_id        UInt64,
    status         LowCardinality(String),
    total_npr      Decimal(12, 2),
    payment_method LowCardinality(String),
    created_at     DateTime,
    order_date     Date
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(order_date)
ORDER BY (order_date, status, payment_method)
SETTINGS index_granularity = 8192;

Official engine behaviour is documented in the ClickHouse MergeTree guide. Read it before choosing variants like ReplacingMergeTree for upsert-like semantics.

Materialised views for rollups

Instead of scanning raw events for every dashboard load, aggregate on insert. A materialised view writes hourly revenue summaries as new facts land. Dashboard queries hit the summary table and return instantly.

CREATE TABLE analytics.revenue_hourly (
    hour_start DateTime,
    region     LowCardinality(String),
    revenue    Decimal(14, 2),
    orders     UInt64
) ENGINE = SummingMergeTree()
ORDER BY (hour_start, region);

CREATE MATERIALIZED VIEW analytics.revenue_hourly_mv
TO analytics.revenue_hourly AS
SELECT
    toStartOfHour(created_at) AS hour_start,
    region,
    sum(total_npr) AS revenue,
    count() AS orders
FROM analytics.orders_fact
GROUP BY hour_start, region;

Map MySQL types carefully. DATETIME becomes DateTime. DECIMAL widths should match NPR amounts to avoid rounding drift in financial reports. Validate JSON exports with our JSON formatter before loading malformed batches.

For eCommerce KPIs—conversion rate, average order value, repeat purchase rate—define metrics once in ClickHouse SQL or dbt-style models. Our eCommerce analytics KPIs you should track post lists the business definitions; this stack makes them cheap to compute at scale.

How do you query ClickHouse from Laravel or PHP applications?

Never route user-facing CRUD through ClickHouse. Use it for admin dashboards, internal APIs, and exported reports. PHP 8.3+ or 8.5 Laravel 12/13 apps can query ClickHouse via HTTP or native drivers.

HTTP interface from Laravel

The simplest integration uses ClickHouse's HTTP API. Store connection settings in .env. Wrap queries in a dedicated service class—not Eloquent models.

# .env
CLICKHOUSE_HOST=127.0.0.1
CLICKHOUSE_PORT=8123
CLICKHOUSE_DATABASE=analytics
CLICKHOUSE_USER=readonly
CLICKHOUSE_PASSWORD=secret

<?php
// app/Services/ClickHouseAnalytics.php
namespace App\Services;

use Illuminate\Support\Facades\Http;

class ClickHouseAnalytics
{
    public function revenueByDay(string $from, string $to): array
    {
        $sql = "
            SELECT order_date, sum(total_npr) AS revenue
            FROM analytics.orders_fact
            WHERE order_date BETWEEN '{$from}' AND '{$to}'
            GROUP BY order_date
            ORDER BY order_date
        ";

        $response = Http::withBasicAuth(
            config('clickhouse.user'),
            config('clickhouse.password')
        )->post(
            'http://' . config('clickhouse.host') . ':8123/',
            ['query' => $sql . ' FORMAT JSON']
        );

        return $response->json('data') ?? [];
    }
}

Use parameterised query builders or allow-list filters for any user-supplied dates. Do not concatenate raw request input into SQL. For complex reporting APIs, pair this with our API development patterns: pagination on pre-aggregated tables, rate limits, and read-only credentials.

Read replicas vs ClickHouse for admin panels

Teams sometimes point Blade admin charts at a MySQL replica first. That works until marketing asks for cross-session funnels. At that point, replica lag and row scans hurt again. ClickHouse becomes the read target for analytics endpoints only. Primary-key admin screens stay on MySQL.

On a booking platform like Adventure Third Pole Trek, operational staff need live seat availability from MySQL. Leadership needs seasonal revenue trends from ClickHouse. Splitting those paths keeps both groups happy.

Cache dashboard JSON responses in Redis 8.10 for thirty to sixty seconds if charts refresh frequently. Invalidate caches by pipeline completion, not by arbitrary TTL alone. Deeper MySQL tuning remains relevant for the OLTP side—see MySQL performance tuning for web applications and how to optimize MySQL queries for high traffic applications.

When should you not add ClickHouse to your stack?

ClickHouse is not free complexity. Skip it if Google Analytics 4 or your existing BI warehouse already answers every stakeholder question. Our Google Analytics 4 setup guide for Nepal covers many SMB marketing needs without a second database.

Also skip ClickHouse when:

• Your MySQL dataset is under a few million analytics-relevant rows and indexed queries stay under 200 ms.
• You need mutable row updates inside the analytics store itself—MySQL or PostgreSQL 18 is a better fit.
• No one owns pipeline monitoring, backfills, or schema drift.
• Compliance requires all reporting data to stay inside the same audited MySQL instance.
• Budget covers only a single Rs 1,500/mo (~USD 11) shared host with no room for another service.

Incremental improvements often suffice first. Add composite indexes per MySQL index design deep dive. Archive cold rows. Offload search to a dedicated engine as described in full-text search comparisons. Add ClickHouse when the bottleneck is aggregate scan volume, not missing indexes.

For greenfield enterprise builds with predictable analytics growth, plan the split early. Enterprise application development engagements benefit when OLTP and OLAP boundaries are drawn before the first million orders land in one monolithic table.

Compare alternatives in PostgreSQL vs MySQL for production if you are still choosing OLTP. ClickHouse sits beside either engine—not instead of a transactional database.

Key Takeaways

• Keep MySQL as the single write source for orders, users, and payments; never dual-write business facts.
• Start with scheduled batch ETL; move to CDC only when dashboards need sub-minute freshness.
• Denormalise in ClickHouse, partition by month, and pre-aggregate with materialised views.
• Query ClickHouse from dedicated PHP service classes with read-only credentials—not Eloquent on production writes.
• Monitor row-count drift between MySQL and ClickHouse weekly; pipeline silence is how bad KPIs ship.
• Add ClickHouse when aggregate queries dominate slow logs—not when a missing index is the real problem.

People Also Ask

Can ClickHouse replace MySQL for a Laravel application?

No. ClickHouse is not an ACID OLTP replacement for Laravel's transactional models. Use it only for analytics reads alongside MySQL. Checkout, authentication, and inventory updates must stay on MySQL or another row-oriented database.

How much data lag is normal between MySQL and ClickHouse?

Batch ETL typically lags fifteen to sixty minutes. CDC pipelines often achieve one to thirty seconds. Pick the path that matches your slowest acceptable dashboard staleness, then monitor lag explicitly.

Does ClickHouse work on a small VPS next to MySQL?

Yes for moderate volumes on a 4 GB+ Ubuntu server, but isolate services. ClickHouse merges can spike CPU and disk I/O. For production, separate VMs or containers so analytics load never stalls PHP-FPM workers handling HTTP.

Is the MySQL Table Engine in ClickHouse enough for sync?

The MySQL table engine is useful for ad hoc federated queries and prototypes. Production pipelines usually prefer explicit ETL or CDC into MergeTree tables for predictable performance, retention policies, and materialised views.

Ship analytics without slowing MySQL

ClickHouse for analytics alongside MySQL is a proven way to grow reporting on Laravel, WooCommerce, and custom PHP systems without turning every dashboard into a production incident. Start small: one fact table, one hourly rollup, one internal chart. Prove freshness and accuracy before you migrate every legacy report.

If your MySQL slow-query log is mostly BI traffic, or stakeholders need same-day funnels your replica cannot serve, the split is worth the ops cost. For architecture review, pipeline setup, or a full custom software development engagement, contact us. You can also browse the portfolio for production Laravel and eCommerce systems built with long-term maintainability in mind.