Why MySQL FULLTEXT is Slow

MySQL FULLTEXT search becomes painfully slow as your data grows. Here's why, and how MygramDB solves it.

The Problem

Disk I/O Bottleneck

MySQL FULLTEXT stores indexes on disk using B-tree pages. Every search query requires:

1. Reading B-tree pages from disk
2. Loading posting lists into memory
3. Sorting results (often with external sort)

Even with SSDs, this I/O overhead adds 100-3000ms per query.

Uncompressed Posting Lists

MySQL stores posting lists without compression. For common terms matching millions of rows, this means:

• Reading megabytes of data per query
• Memory pressure on buffer pool
• Cache thrashing under concurrent load

Cache Dependency

Performance varies 2-3x between cold and warm cache:

State	Query Time
Cold cache	2,980ms
Warm cache	908ms

In production, cache is often cold after restarts, deployments, or buffer pool contention.

Concurrency Collapse

Under heavy concurrent load, MySQL FULLTEXT fails catastrophically:

Concurrent Queries	Success Rate	Avg Response
1	100%	908ms
10	10%	4,641ms
100	0%	timeout

90% of queries fail at just 10 concurrent connections.

The Solution: MygramDB

MygramDB solves these problems with a fundamentally different architecture:

In-Memory Index

All data lives in RAM. Zero disk I/O during queries.

Compressed Posting Lists

Delta encoding + Roaring bitmaps reduce memory usage by 60-80% while enabling faster intersections.

SIMD-Accelerated Operations

Bitmap intersections use CPU SIMD instructions for maximum throughput.

Consistent Performance

No cache warmup needed. Same sub-80ms response time, always.

Concurrent Queries	Success Rate	Avg Response
1	100%	32ms
10	100%	35ms
100	100%	190ms

Real-time MySQL Sync

GTID-based binlog replication keeps MygramDB in sync with MySQL. No ETL pipelines, no data staleness.

When to Use

MygramDB is ideal for:

• High-traffic web services — E-commerce, news sites, Q&A platforms with many concurrent users
• Real-time search requirements — When users expect instant results
• FULLTEXT queries >100ms — Slow searches hurting user experience
• Search fails under load — Timeouts during traffic spikes

Get Started

docker run -d --name mygramdb \
  -p 11016:11016 \
  -e MYSQL_HOST=your-mysql-host \
  -e MYSQL_USER=repl_user \
  -e MYSQL_PASSWORD=your_password \
  -e MYSQL_DATABASE=mydb \
  -e TABLE_NAME=articles \
  ghcr.io/libraz/mygram-db:latest

See GitHub for full documentation.