Introduction to ClustrixDB

Flexible transactional scale for the connected world.
ClustrixDB Technical Overview

o What is ClustrixDB?
o Why is ClustrixDB used?
o Deployment Patterns
o ClustrixDB Architecture
– Core Components
– Data Distribution
– Query Execution Concept
– Online Schema Change
o Performance Benchmarks
o Backup, Replication & Administration
o ClustrixDB 8.0 New Features
Agenda – ClustrixDB Technical Overview
PROPRIETARY & CONFIDENTIAL

What does ClustrixDB do?
ApplicationsApplications
When MySQL Applications
(or just SQL) have out-grown MySQL
And you don’t want to
modify the application
to use sharding or replication
ClustrixDB combines multiple servers
into a single, powerful database server
That applications continue to see
as a single MySQL server
No sharding or replication necessary
Too many TPS
Too many connections
Too much CPU
ClustrixDB

What does ClustrixDB do?
Scale Beyond MySQL without Replication or Sharding
Applications
 More Transactions
 More Connections
 More Reads
 More Writes

ClustrixDB: Scale-Out, Fault-tolerant, MySQL-Compatible
ClustrixDB
ACID Compliant
Transactions & Joins
Optimized for OLTP
Built-In Fault Tolerance
Flex-Up and Flex-Down
Minimal DB Admin
Also runs great in
the Data Center
Built to run
in the Cloud

Common Approaches to Scaling MySQL
Bigger Server
 Easy to do
 No app changes
X Very limited gains
X No HA
Reader Slaves
Writes
Reads
Reads
 Scales reads
 Fairly easy to setup
X No write scaling
X Adds slave lag
app needs to be aware
X App changes
needs to split reads & writes
X No HA
adds DR but needs failover
X Fragile
High burden to maintain
Synchronous Replication Clusters
Galera | Percona XtraDB Cluster | MariaDB Galera Cluster
 Adds HA
 Scales reads
X No write scaling
Writes are multiplied
X Slows down writes
sync rep adds write latency
X No storage scaling
data fully replicated on each node
Database Sharding
 Scales reads
 Scales writes
 Scales storage
X Significant app changes
or entire redesign
X Does not add HA
multiplies the failure points
X DB management overhead
i.e. consistent backup of data set

Scaling with ClustrixDB
Scale Beyond MySQL without Replication or Sharding
Load Balancer
Scales reads
Scales writes
Scales storage
No App Re-design
Adds HA
Easy to deploy
ClustrixDB

Flex Licensing = Business Agility
o ClustrixDB has:
 Technical capability to grow & shrink cluster while online
 Licensing model that aligns with a seasonal business
or temporary workloads
o Flex Licensing
– Annual license covers base cluster size
• e.g. 3-nodes year round
– Monthly Flex license covers seasonal demand
• e.g. 2 extra nodes for Nov & Dec
• And drop to 1 extra node in Jan
ClustrixDB

Typical Cloud Deployment
8/16
cores
8/16
cores
8/16
cores
8/16
cores
…
3 nodes
minimum
+N nodes
App Servers
Load
Balancer
ELB or Linux or
Traffic Manager
or HAProxy
• AMI Available
• C3 (General) or I2 (High IO)
• SSD w/ Enhanced Net
• 2xlarge (8-core)
• 4xlarge (16-core)
• LB: Elastic Load Balancing
• Standard w/ SSD
• D13: 8 cores
• D14: 16 cores
• High Performance w/ SSD
• G3: 8 cores
• G4 16 cores
• LB: Azure Traffic Manager
• Standard w/ SSD
• IO1-30: 8 cores
• IO1-60: 16 cores
• LB: Cloud Load Balancer
ClustrixDB
• n1-standard-8: 8 cores
• n1-standard-16: 16 cores
• LB: GCE Load Balancing

Typical On-Premises Deployment
3 nodes minimum
App Servers
Hardware or SW
e.g. Linux w/ HAProxy
ClustrixDB
PrivateNetwork
+N nodes
Load
Balancer
…
ClustrixDB Server Specs
• Cores:
• 8 to 16 physical cores
• Disable HT (recommended)
• RAM:
• 64 GB+
• Disks:
• SSD 300 GB+ (6 or more)
• RAID-0 (HW recommended)
• Optionally 1+ HDD for OS &
scratch
• Network:
• Public: 1 GbE (or 10 GbE)
• Private: 10 GbE
(highly recommended)
PublicNetwork

Typical Full Lifecycle Environment
Production Standby
DR + Big Query Reporting
Real-time Replication
Staging
Scripted
Refresh
Upgrades, Perf Tuning,
Pre-Prod Validation
Dev
Patches, New Code
Dev/QA, Shared

How It Works

ClustrixDB Overview
Fully Distributed & Consistent Cluster
o Fully Consistent, and ACID-compliant database
– Supports transactions
– Support joins
– Optimized for OLTP
– But also supports reporting SQL
o All nodes are equal (no “special” node)
o All servers are read/write
o All servers accept client connections
o Tables & Indexes distributed across all nodes
– Fully automatic distribution, re-balancing
& re-protection
PrivateNetwork
ClustrixDB on commodity/cloud servers
HW or SW Load
Balancer
SQL-based
Applications
High Concurrency
Custom:
PHP, Java, Ruby, etc
Packaged:
Magento, etc

ClustrixDB Design
Shared Nothing Architecture
Each Node Contains:
o Query Compiler: distribute compiled partial
query fragments to the node containing the
ranking replica
o Data Map: all nodes know where all replicas
are, and current ranking replicas
o Database Engine: all nodes can perform all
database operations (no leader, aggregator,
leaf, data-only, etc nodes)
o Data: Table Slices: All table slices (default:
replicas=2) auto-redistributed by the
Rebalancer
ClustrixDB
Compiler Map
Engine Data
Compiler Map
Engine Data
Compiler Map
Engine Data

Data Distribution

Intelligent Data Distribution
S
1
S
2
S
2 S
3
S
3 S
4
S
4 S
5
S
5
o Tables auto-split into slices
o Every slice has a replica on another node
– Slices are auto distributed, auto-protected
S
1
ClustrixDB
BillionsofRows
Database
Tables

Adding Nodes – Flex Up
S
1
S
2
S
3
S
3
S
4
S
4
S
5
o Easy and simple Flex Up & Flex Down
– Single minimal interruption of service
o Data is automatically rebalanced across
the cluster
– Tables are online for reads & writes
o All servers handle writes + reads
– Workload is spread across more servers
after Flex Up
S
1
ClustrixDB
S
2
S
5

Loss of a Node – Automatic Fault Tolerance
S
2
S
5
S
1
S
2
S
3
S
3
S
4
S
4
S
5
o ClustrixDB detects the loss of a node
– System automatically re-protects
– Data is automatically redistributed
o Slices lost on the failed node are
rapidly re-protected
– Re-protection occurs while tables are
available for reads & writes
o Automated self-healing
– After re-protect the cluster is fully
protected and operational S
1
ClustrixDB
S
2
S
5
S
2
S
5

ClustrixDB Rebalancer: Making the Complex, Simple
Q: How do you ensure data stays well distributed in a clustered environment?
A: You let the Rebalancer handle it!
The Rebalancer automatically:
• Initial Data: Distributes the data into even slices across nodes
• Data Growth: Splits large slices into smaller slices
• Flex-Up/Flex-Down: Moves slices to leverage new nodes and/or evacuate nodes
• Failed Nodes: Re-protects slices to ensure proper replicas exist
• Skewed Data: Re-distributes the data to even out across nodes
• Hot Slice Balancing: Finds hot slices and balances then across nodes
…while the DB stays open for reads & writes
Patent 8,543,538
Patent 8,554,726
Patent 9,348,883

Query Processing Model
 Parse
 Plan
 Compile
Session
SQL-based
Applications
UPDATE users
SET online = 1
WHERE id = 8797;
HW or SW Load
Balancer
ID: 8797 | … | ONLINE:0
Fragment
o Load balancer spreads DB connections
across all nodes
o A session is established on any node
o Session controls query(s) execution
– Parse SQL
– Generate the execution plan
– Compile into binary fragments
– Lookup record(s) location
– Send fragments
– Complete the transaction

Query Processing Model
ID: 8797 | … | ONLINE:0
Session
SQL-based
Applications
UPDATE users
SET online = 1
WHERE id = 8797;
HW or SW Load
Balancer
ID: 8797 | … | ONLINE:1
Ack
o Load balancer spreads DB connections
across all nodes
o A session is established on any node
o Session controls query(s) execution
– Parse SQL
– Generate the execution plan
– Compile into binary fragments
– Lookup record(s) location
– Send fragments
– Complete the transaction
Ack

Online Schema Change

Table
o Allows reads & writes during
ALTER TABLE operations
– Add or remove column
– Rename column
– Change data type
– CREATE INDEX
o Process:
– Queue created to track changes
– Copy of table created
– Queues replayed until synchronized
– Atomic flip of table between transactions
Table
QueueQueueQueue
MYTABLE __building_MYTABLE
Atomic Flip
Reads & Writes
ALTER TABLE mytable ADD (foo int);

Table
o Allows reads & writes during ALTER
TABLE operations
– Add or remove column
– Rename column
– Change data type
– CREATE INDEX
o Process:
– Queue created to track changes
– Copy of table created
– Queues replayed until synchronized
– Atomic flip of table between transactions
Table
MYTABLE__building_MYTABLE
Atomic Flip
Reads & Writes
ALTER TABLE mytable ADD (foo int);


Sysbench OLTP 90:10 Mix
PROPRIETARY AND CONFIDENTIAL
o 90% Reads + 10% Writes
– Very typical workload mix
o 1 TPS = 10 SQL
– 9 SELECT + 1 UDATE
– a.k.a 10 operations/sec
o Shows scaling TPS by
adding servers:
– Oak4 = 4 (8-core) servers
To scale TPS while maintaining
Performance (i.e. 20 ms response)
just add servers to ClustrixDB
800,000 SQL/sec
@ 20 ms

Sysbench OLTP 100% Reads
o 100% Reads
– Max throughput test
o 1 TPS = 10 SQL
– 10 SELECT
adding servers:
>1 Million SQL/sec
@ 20 ms

Yahoo! Cloud Service Benchmark (YCSB) 95:05 Mix
– 1 Ops/sec = 1 SQL
o Designed to compare NoSQL
engines
– ClustrixDB runs equally fast
adding servers:
– 4 nodes = 4 (8-core) servers
> 500,000 SQL/sec
@ 3 ms

Yahoo! Cloud Service Benchmark (YCSB) 95:05 Mix
– 1 Ops/sec = 1 SQL
o Over 1 Million SQL/sec
– With <3 ms query response
– And mixed (95:05) workload
– Using 30 ClustrixDB servers
• 16-core AWS instances
Over 1 Million SQL/sec @ 3 ms
on a mixed (95:05) workload
> 1,000,000 SQL/sec
@ 3 ms

MySQL
(largest)
Aurora
(largest)
ClustrixDB
ClustrixDB vs. Aurora vs. MySQL RDS
– Very typical workload mix
o 1 TPS = 10 SQL
– 9 SELECT + 1 UDATE
o ClustrixDB shows scaling TPS
by adding servers:
– Aws4 = 4 (8-core) servers
ClustrixDB scales TPS past Aurora’s
largest instance (db.r3.8xlarge)

Backup, Replication & Administration

Backup, Replication & Disaster Recovery
Asynchronous multi-point Replication
ClustrixDB
Parallel Backup
(up to 10x faster)
Replicate to any cloud, any datacenter, anywhere

ClustrixGUI: Performance Monitoring, Diagnostics & Flex
Cluster Health Dashboard
Visually Compare Workloads

ClustrixGUI:
CPU: Dashboard
1.27 Million TPS
On 32 nodes

ClustrixGUI: Rich Monitoring Metrics
Historical Graphs
of various ClustrixDB
performance metrics
Groups:
 Performance
 Diagnostic
 Queries and Rows
 Sessions and Transactions
 Replication
 Disk

o ClustrixDB 8.0 builds upon the significant performance
improvements in ClustrixDB 7.5
– More reliable
– Easier to use
– Many enhancements requested by our customers
o Additionally, ClustrixDB 8.0 adds these 3 main new features:
– In-Memory Tables
– On-Disk Encryption
– Simplified Cluster Provisioning
ClustrixDB 8.0 – New Features

o Special table container designed for high concurrency writes
– Supports same data types as regular tables
– Supports joins with regular tables
– Read consistent (i.e. MVCC)
– Survives node failure without data loss
o Ideal for scenarios that have:
– High concurrency, write-intensive transactions
– …which are then filtered or aggregated
– …and finally persisted into regular tables
– Examples:
• Sensor data collection
• Log collection
• Write-intensive batch processing
ClustrixDB 8.0 – In-Memory Tables

o Adds encryption for “Data-at-Rest”
o Transparent to applications
– Applications are unaware that the tables are encrypted
– ClustrixDB handles encryption/decryption and the keys
o Uses AES 256-bit encryption
o All tables and all databases are stored as encrypted
ClustrixDB 8.0 – On-Disk Encryption

o Web GUI-driven
o Container-based
o Use for simplified cluster install, and Flex-up/Flex-down
o Designed for:
– AWS environments
– On-premises environments (i.e. bare metal)
ClustrixDB 8.0 – Simplified Cluster Provisioning

Questions & Answers Time

ClustrixDBFlexible Transactional Scale for the Connected World

Introduction to ClustrixDB

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