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#SGvFabric

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vSphere 5

vFabric: What’s in it?

Application Services vFabric

Frameworks & Tools Rich Web

Social and Mobile

Data Access

Integration Patterns

Batch Framework

Spring Tool Suite

Perf, Mgmt Hyperic / Insight

Application Srv tc Server

Web Runtime ERS

Elastic Data Grid Gemfire / SQLFire

Messaging RabbitMQ

EM4J Data Director

vCops/APM

DBaaS vPostgres

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Cloud-scale challenge…

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Challenge

Managing on-line applications on a cloud-scale is hard. As number of users grows, database becomes the bottleneck.

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DB Bottleneck

Scales…

Scales…

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Cause

Traditional databases were never designed to support thousands of concurrent users.

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Traditional DB Characteristics

§ Designed against no longer relevant constraints • Network unreliable/slow • RAM prices prohibitive

§ One size fits all • Designed for everything, optimized for nothing • Often incompatible with modern workloads

§ Centralized in nature • Data change capture an

afterthought • Lacks data partitioning

facilities

§ Obsessed with ACID • Constant contention for

resources cause locks

§ Monolithic design §  Requires lots of hardware to

scale

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Traditional DB Loves IO

First write to LOG

Second write to Data files

Buffers primarily tuned for IO

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12%

8%

21%

19%

10%

30% Data Btrees keys Logging Locking Latching Buffer management

Source: Research by MIT and Brown: “OLTP Under the Looking Glass” by S. Harizopoulos, D. J. Abadi, S. Madden, M. Stonebraker, SIGMOD 2008.

Percentage of Computer cycles based on 3.5M

sample

Transaction in Traditional DB

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Cloud-scale solution…

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Apparent Choices

Build expensive database clustering solution or lengthy re-write for “big data”?

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Next generation option

SQLFire is different; it’s build for speed and scale.

Scale much?

Hablo SQL?

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New Approach

Elastic, in-memory database designed specifically for speed and low latency accessible through a familiar SQL interface.

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SQLFire Characteristics

§ Highly concurrent data structures resident in and optimized for main memory

§ Rethink ACID transactions; all state resides in distributed memory to avoid any single points of contention

§ Partition-aware DB design spreads workloads across both data set and physical nodes

§ Shared nothing logs on disk; application writes are never exposed to the disk seek latencies

§ Parallelize data access and application behavior; dynamically “shard SQL”

§ Dynamic rebalancing of data as cluster size grows/shrinks. Most efficient way of managing resources/data.

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SQLFire speed…

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SQLFire v Traditional Databases

SQLFire response times are faster and more consistent under increased database load.

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Sample Comparison

§  Spring Travel Application §  Similar hardware (8 vCPU, 4GB) § Out-of-the-box configuration

SQLF R/T (ms) SQLF CPU % MySQL R/T (ms) MYSQL CPU %

14 9 25 1

8 32 23 19

5 61 172 76

6 77 fail fail

984 98 fail fail

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Response Time

0

20

40

60

80

100

120

140

160

180

200

0 500 1000 1500 2000

MySQL increased with load

SQLFire near constant much lower

R/T

Threads

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Number of Threads

0  

200  

400  

600  

800  

1000  

1200  

0   1000   2000   3000   4000   5000   6000   7000   8000  

MySQL reaches saturation

at 1850 threads

SQLFire scales to 7200 threads

with 1 second R/T

R/T

Threads

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Distributed data…

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Why Scale Horizontally?

Sub-divide system into independent data sets, eliminate distributed transactions to achieve elasticity, linear scalability and predictable latency.

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Horizontal Scalability – Throughput

0

200

400

600

800

1000

1200

1400

0

100000

200000

300000

400000

500000

600000

700000

800000

2 4 6 8 10

Clie

nt th

read

s

Que

ries

per s

econ

d

Number of servers

queriesPerSecond

client threads

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Horizontal Scalability – Consistency/HA

§  Resiliency through replication, synchronous but in parallel

§  Row updates are always atomic; no need for transactions

§  Shared nothing architecture, including storage §  Instant failover at protocol level § Apps retain their connections

§ Data remains available

SQLFire

APP

SQLFire SQLFire

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Data management strategies…

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Data strategies – Partitioning

§  Balances data across SQLFire cluster

§  Delivers redundancy for high availability

SQLFire

APP

SQLFire SQLFire

Write operation (with 2 redundant copies)

Read operation

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SQLFire Hash Partitioning

§  Partition by column or primary key • Can specify multiple columns

• Uses hashCode() for single column or primary key • Uses serialized bytes for multiple columns

• Creates uniform distribution of data across the cluster

// Partition by column CREATE TABLE MY_TABLE ( . . . ) PARTITION BY COLUMN ( COLUMN_A) // Partition by primary key CREATE TABLE MY_TABLE ( . . . ) PARTITION BY PRIMARY KEY

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SQLFire Range Partitioning

§  Partition by range of column values • Can specify multiple ranges

• Colocates data in specified ranges • Used to ensure locality of data in a partition for range queries or cross table

joins

// Partition by range CREATE TABLE MY_TABLE ( . . . ) PARTITION BY RANGE ( COLUMN_A) ( VALUES BETWEEN 1 AND 10, VALUES BETWEEN 50 AND 60 )

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SQLFire List Partitioning

§  Partition by a set of column values • Can specify column value sets

• Colocates data with specified column values • Used to ensure locality of data in a partition for sets of values or cross table

joins

// Partition by list CREATE TABLE MY_TABLE ( . . . ) PARTITION BY LIST ( COLUMN_A) ( VALUES (‘VALUE_A’, ‘VALUE_B’), VALUES (‘VALUE_Y’, ‘VALUE_Z’) )

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SQLFire Expression Partitioning

§  Partition by a column expression •  Expression must be valid SQL function

• Must reference only columns in the table • Hash partition with value determined by the expression

// Partition by expression CREATE TABLE MY_TABLE ( . . . ) PARTITION BY ( MONTH ( MY_DATE ) )

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SQLFire Default Partitioning

§  Default hash partitioning strategy •  Start server with table-default-partitioned property set to true!

•  First foreign key whose referenced primary key is also a partition column •  Primary key •  First unique key •  SQLFire-generated row id

// No PARTITION BY clauses CREATE TABLE MY_TABLE (COLUMN_A INT NOT NULL CONSTRAINT A_PK PRIMARY KEY, . . .) CREATE TABLE MY_OTHER_TABLE (COLUMN_B INT NOT NULL CONSTRAINT B_PK PRIMARY KEY, COLUMN_C INT CONSTRAINT A_FK REFERENCES MY_TABLE (COLUMN_A), . . .)

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Data strategies – Replication

§  Copies all data across SQLFire cluster

§  Appropriate for reference data

SQLFire

APP

SQLFire SQLFire

Write operation (with replicated copies)

Read operation

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SQLFire Replicated Tables

§  Created by default with no PARTITION BY clause

§  Created with REPLICATE clause

§  Reference data or fact tables are good candidates

§  Replicates data across all peers in server group

§  Replication is parallel and synchronous

§  Automatic replication failure detection

// Replication example CREATE TABLE MY_TABLE ( . . . ) REPLICATE

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Topology strategies…

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Topology

Client-server JVM JVM

JVM JVM

APP APP

JVM

SQLFire SQLFire SQLFire

SQLFire Locator

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Topology

Embedded Peer-to-peer

JVM JVM JVM

SQLFire

APP

SQLFire

APP

SQLFire

APP

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Synchronization strategies…

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Synchronous strategy

In data-center or over private network

JVM JVM

JVM JVM JVM

APP APP

SQLFire SQLFire SQLFire

SQLFire Locator

JVM JVM

JVM JVM JVM

APP APP

SQLFire SQLFire SQLFire

SQLFire Locator

Site 1 Site 2 Redundancy Zone A Redundancy Zone B

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Asynchronous strategy

Multi-site over the Cloud

JVM JVM

JVM JVM JVM

APP APP

SQLFire SQLFire SQLFire

SQLFire Locator

JVM JVM

JVM JVM JVM

APP APP

SQLFire SQLFire SQLFire

SQLFire Locator

Site 1 Site 2

WAN Gateway

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Data strategies – Server Groups

Group 1

Group 2

SQLFire Cluster

JVM

SQLFire

JVM

SQLFire

JVM

SQLFire

JVM

SQLFire

JVM

SQLFire

JVM

SQLFire

Group 3

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data demo…

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Summary…

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Why SQLFire?

In-memory, delivers maximum speed and minimum latency Horizontally scalable, easily adopts to changing workloads, usage patterns Familiar SQL interface, accessible from Java and .NET

Speed

Scale

SQL

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If you forgot everything else…

SQLFire is better in supporting on-line applications than traditional databases.

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Sample Apps §  Side-by-side comparison of SQLFire v MySQL

performance - https://github.com/vFabric/sqlf-demo

§  Demo call-center application, SQLFire configuration scripts https://github.com/vFabric/sqlf-cloud

Demo Video §  Real-life performance comparison (YouTube, 3 min.)

http://youtu.be/HV-broQHJlk

SQLFire Artifacts

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http://vmware.com/go/sqlfire

@vFabricSQLFire, @_cmc

The end