Overview presentation of TokuDB in slide format (PDF - Tokutek

Introduction to TokuDB v6.6

B-Tree Indexes Can’t Keep Up
B-trees, over 40 years old and used in virtually all
databases, are adequate for sequential workloads, but
fail for more information rich (random) workloads.
• HDD I/O Reduced: Disk head
spends majority of time on seeks
• SSD Life Limited: Small blocks
written repeatedly
• Limited Compression: Algorithms
ineffective on small block sizes
• Database Performance Drops: Hi
insertion workloads can’t keep up

The Performance Gap
Today’s database categories - OLAP, OLTP are compromises to
get around dated, rigid indexing technology
Usage
B-tree
Challenge
OLAP OLTP
Columnar DBs Hadoop
MongoDB
Read-intensive Write-intensive

Usage
Addressing the Gap
Real Time
OLAP
Log analysis
Ad analytics
Copyright
Surveillance
What everybody wants
Cloud storage
metadata
Analytic
OLTP
Read-intensive Write-intensive

Usage
B-tree indexes
Addressing the Gap
Real Time
OLAP
Log analysis
Ad analytics
Copyright
Surveillance
Fractal Trees
Eliminate Death Valley
Cloud storage
metadata
Analytic
OLTP
Read-intensive Write-intensive
Fractal Tree ® Indexing

Smarter Software Algorithms…
Fractal Tree ® indexes are a new data structure that
perform up to two orders of magnitude faster than
B-trees, with optimal read/write characteristics
Faster Queries
Optimal
Curve
Faster Insertions
Fractal Tree indexes Are At Optimal Point
Enjoy the write performance of LSMs while maintaining
the read performance of B-trees
• Intelligently aggregates and
rebalances data at tree nodes
• Scales Performances for high
insertion workloads
• Yields Higher Compression
achieved over larger blocks
• Extends Flash Life with larger, less
frequent I/O

…Keep Pace with New Hardware Trends
New Technology for today’s denser drives and faster flash technology
Application
MySQL Database
SQL Processing, Query
Optimization…
File System
Speed
Early Drives
Flash
Addresses Space and
Wear-life constraints
Capacity
Multi TB HDD
Addresses I/O
Constraints

The Benefits of Better Indexes
• TokuDB ® Fractal Tree ® indexes change the game with huge
insertion rates and richer indexed queries in scalable systems
Indexed insertions
Insert 1 billion rows into a table
maintaining three multi-column
secondary indexes.
Terminal rate (last 10MM rows):
InnoDB ® : 876 inserts/sec
TokuDB: 16,507 inserts/sec
(19x faster!)
More on how FT indexes
work: http://goo.gl/Smu6H

Compression Reduces Effective Storage Cost
• InnoDB: 2-4x compression
– Small block sizes used
– Fixed on-disk block size causes split /
recompress operations that further
reduce performance
• TokuDB: 5x – 25x compression
– Larger block sizes allow better compression
• TokuDB compression doesn’t
sacrifice performance
– InnoDB performance suffers when
compression is enabled
– TokuDB compression is always enabled

Agility at Scale with Hot Schema Changes
TokuDB ® - The only MySQL Storage Engine with Hot Schema Support!
• Hot Column Addition/Deletion/Rename
– Provides capability to add/drop/rename a
column to a database as a fast operation
– Enables database administrators to rapidly
define and add new fields
– Allows for much larger tables to be created
given simplified maintenance
• Hot Indexing
– Allows for concurrent operation on the
database and index
– Enables ad hoc queries to run fast with realtime,
optimized index support
– Brings familiar Enterprise Database online
operations to to MySQL
In Test: InnoDB and TokuDB
rate limited to 10k insertions/sec

Improved Life and Utilization for SSDs
• Reduced Write Wear
– B-trees write small blocks, resulting in
more writes and increased wear
– Fractal Tree indexes write large blocks,
reducing wear
• Better Utilization
– B-trees allow only 75% of capacity to be
used: their smaller / more numerous
writes make the Flash Translation Layer
(FTL) a bottleneck
– Fractal Tree indexes allow 90%
utilization because they generate
larger / fewer writes
In Test:
InnoDB avg block size 23k
TokuDB avg block size 215k

TokuDB ® Performance, Compression, Agility
• Insertion Speed: 5-20x faster index inserts
• Query Performance: Fast ad-hoc and rich
queries
• Scalability: Predictable performance at 10+
billion rows
• Compression: Up to 10x
• Hot Schema Changes: Hot indexing, hot
column addition and rename
• Better Life for SSDs: Reduces write wear
• Continuous Operation: Avoid downtime for
index de-fragmentation
• Replication: Ensure slaves can keep up
• Support: MySQL and MariaDB

TokuDB Serves a Broad Target Market
• Social Networks
– Evidenzia (P2P Network Monitoring)
– Profile Technology (Advanced Search for
Facebook)
– Marketyou (Professional Network)
– Swaylo (Social Media Monitoring)
• Cloud Enablement
– FictionPress (Millions of Pieces of Fiction)
– Frequency.com (Video Services)
– Limelight Networks (Agile Storage Offering)
• eCommerce Solutions
– Intent Media (Online Advertising)
– Paybox (Electornic Payment Solutions)
• Data Processing
– Jawa (Mobile Gaming)
– Mozilla (Data Visualization)
– SwRI (Machine Data for NASA)

GigaOM: Twitter open sources its MySQL
secret sauce
Silicon Angle: Scaling MySQL for the Era
of Big Data
Forbes: Tokutek Makes Big Data Dance
O’Reilly: 2012 Strata Startup Finalist
VentureWire: “BIG DATA: Amid Deluge,
Database Start-Ups Find Traction With New
Tools”
Xconomy: It was a big week for “Big Data”
Recent Coverage
Computerworld: Tokutek Boosts MySQL
Scalability for Big Data Applications
CTO Edge: Driving MySQL Performance
Network World: Selected for Product of the
Week
IT News: MySQL Gets Live Schema Updating
with Tokutek Engine
DBTA: Tokutek Enables Big Data Scalability on
MySQL with Support for Hot Schema Changes
ReadWriteCloud: Tokutek Updates Its
MySQL-based Big Database