As enterprises move to AWS, they have great choices for MySQL compatible databases. Knowing the best database for the specific job can save you time and money. In this webinar, Lokesh Khosla will discuss high-performance databases for AWS and share his findings based on a benchmark test that simulates the workload of a high-transaction AWS-based solution. If you work with high transactional workloads, and you need a relational database to keep track of economically valuable items like revenue, inventory and monetary transactions, you'll be interested in this discussion about the strengths and weaknesses of Aurora and other MySQL solutions for AWS.

1. © 2015 CLUSTRIX
The First Scale-out SQL Database Engineered for Today’s Cloud
Beyond Aurora.
Scale-out SQL databases for AWS

2. Magnus Data
SCALE OUT RDBMS
3/4/2016
MagnusData

3. Agenda
 Market Landscape of DB market
 Options to Scale DB
 Scale-Out Architecture
 Comparisons of solutions for high transaction relational databases
3/4/2016

4. Generalized and Specialized
3/4/2016
High Concurrency/Write heavy /Real Time Analytics Historical Analytics Exploratory
Transactional Analytics
Traditional Databases
No
SQL
DW/Analytical
DBMS
Operational
System/OLTP (New
SQL)
Hadoop

5. Scale-Up vs. Scale-Out
3/4/2016
Scale-Out databases
Transactions Per Second
LatencyHigh
High
Scale-Up Databases
(like Aurora
and MySQL)

6. RDBMS Scaling Techniques
 Scale-Up
 Master Slave
 Master Master
 MySQL Clustering Technologies
 Sharding
 Scale-Out
3/4/2016

7. Options to Scale DBMS
3/4/2016
DBMS
Scale Out
e.g., MongoDB
No transactions
May have weak consistency
(CAP)
Application involves DB
Coding
e.g. ClustrixDB
ACID
Proven Scalability
(Reads and Writes)
Shared Nothing
Scale Up
e.g., Aurora
Reads Scale
limited scalability on writes
Not Shared nothing scale
out

8. Scaling-Up
 Keep increasing the size of the (single) database server
 Pros
 Simple, no application changes needed
 Cons
 Expensive. At some point, you’re paying 5x for 2x the performance
 ‘Exotic’ hardware (128 cores and above) become price prohibitive
 Eventually you ‘hit the wall’, and you literally cannot scale-up anymore
8

9. Scaling Reads: Master/Slave
 Add a ‘Slave’ read-server(s) to your ‘Master’ database server
 Pros
 Reasonably simple to implement.
 Read/write fan-out can be done at the proxy level
 Cons
 Only adds Read performance
 Data consistency issues can occur, especially if the application isn’t coded to
ensure reads from the slave are consistent with reads from the master
9

10. Scaling Writes: Master/Master
10
 Add additional ‘Master’(s) to your ‘Master’ database server
 Pros
 Adds Write scaling without needing to shard
 Cons
 Adds write scaling at the cost of read-slaves
 Adding read-slaves would add even more latency
 Application changes are required to ensure data consistency / conflict resolution

11. Scaling Reads & Writes: Sharding
11
SHARDO1 SHARDO2 SHARDO3 SHARDO4
 Partitioning tables across separate database servers
 Pros
 Adds both write and read scaling
 Cons
 Loses the ability of an RDBMS to manage transactionality, referential integrity and ACID
 ACID compliance & transactionality must be managed at the application level
 Consistent backups across all the shards are very hard to manage
 Read and Writes can be skewed / unbalanced
 Application changes can be significant
A - K L - O P - S T - Z

12. Scaling Reads & Writes: MySQL Cluster
 Provides shared-nothing clustering and auto-sharding for MySQL. (designed for Telco deployments: minimal cross-node
transactions, HA emphasis)
 Pros
 Distributed, multi-master model
 Provides high availability and high throughput
 Cons
 Only supports read-committed isolation
 Long-running transactions can block a node restart
 SBR replication not supported
 Range scans are expensive and lower performance than MySQL
 Unclear how it scales with many nodes
12

13. Application Workload Partitioning
13
 Partition entire application + RDBMS stack across several “pods”
 Pros
 Adds both write and read scaling
 Flexible: can keep scaling with addition of pods
 Cons
 No data consistency across pods (only suited for cases where it is not
needed)
 High overhead in DBMS maintenance and upgrade
 Queries / Reports across all pods can be very complex
 Complex environment to setup and support
APP
APP
APP
APP
APP
APP

14. DBMS Capacity, Elasticity and Resiliency
14
Scale-up
Master – Slave
Master – Master
MySQL Cluster
Sharding
Scale-Out
DBMS Scaling
Many cores – very expensive
Reads Only
Read / Write
Read / Write
Unbalanced Read/Writes
Read / Write
Capacity
Single Point Failure
Fail-over
Yes
Yes
Multiple points of failure
Yes
ResiliencyElasticity
No
No
No
No
No
Yes
None
Yes – for read scale
High – update conflict
None (or minor)
Very High
None
Application Impact

15. DBMS Architecture-Scale out
3/4/2016
Shared Nothing Architecture
Compiler Map
Engine Data
Compiler Map
Engine Data
Compiler Map
Engine Data
Each Node Contains:
 Query Parser/Planner: distribute partial query
fragments to the nodes.
 Data Map: all nodes metadata about data
across the cluster
 Database Engine: all nodes can perform all
database operations (no leader, aggregator,
leaf, data-only, etc nodes)
 Data: Table Distributed: All table auto-
redistributed

16. BillionsofRows
Database
Tables
S1 S2
S2
S3
S3
S4
S4
S5
S5
Intelligent Data Distribution
16
S1
ClustrixDB
 Tables Auto Distributed across nodes
 Tunable amount of redundancy of data across nodes
 Tables are auto distributed, auto-protected

17. Query
Distributed Query Processing
17
ClustrixDB
Load
Balancer
TRXTRXTRX
 Queries are fielded by any peer node
 Routed to node holding the data
 Complex queries are split into steps and processed in parallel
 Automatically distributed for optimized performance
 All nodes handle writes and reads
 Result is aggregated and returned to the user

18. DBMS Capacity, Elasticity and Resiliency
18
Features ClustrixDB Aurora
Write Scalability Writes scales by adding nodes Cannot add write nodes
High Concurrency Latency Low with High concurrency Latency climbs quickly with high
concurrency
ACID Yes Yes
On-Demand Write Scale Yes No
Automatically Distributed
queries
Yes: No Application changes No: Read/Write fanout needed.
Write contention on Master
Cloud/On Premises Yes No, only AWS Cloud
Shared Nothing Storage Yes: Parallel data access No: Contention at high write
concurrency

19. Benchmark Results
3/4/2016
0
10
20
30
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000
AverageLatency(ms)
Throughput (tps)
Sysbench OLTP 90:10 Mix
0
10
20
30
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000
AverageLatency(ms)
Throughput (tps)
Sysbench OLTP 90:10 Mix
Clustrix 4 Node Aurora Mysql RDS

20. Scalability Test
3/4/2016
0
10
20
30
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000
AverageLatency(ms)
Throughput (tps)
Sysbench OLTP 90:10 Mix

21. 21
Thank you.
Q&A