DataCluster

DATABASE SCALE OUT Optimal approach to insure high level control and

performance of information system

DATA CLUSTER

OBJECTIVES OF THE INNOVATION DEVELOPMENT

IS scale option?

Scale Up – increases servers characteristics, such as memory, number of cores, drive speed and etc.

Scale Out – designs database nodes cluster by the way of the addition of new nodes and load balancing

Increase of IS* load

The number of users grows

Intensive growth of IS

Prerequisites of IS scale out

Variants of IS scale out

What to do?

*-here is and after “IS” means “information system”

IS Scaling options

Scale Up

Simplicity, scale out speed;

Early or later the scale out achieves the technical limit in terms of cores numbers, memory, disks subsystem and then does NOT give a valid performance growth.

Scale Out

Valid effect of load balancing. The number of nodes in cluster is not limited;

Setting and adaptation difficulties for a particular application. As a rule, a change in IS architecture and application code are required . That is complex and non-trivial task with significant many-sided expenses: finances, time and technology, including the application support services.

Cluster Solutions for MS SQL SERVER

SCALE OUT

Variant #1. Common model of IT- system with DBMS cluster

Common Case Users are working with data base through

single server MS SQL IS;

Systems of Back-Up, mirroring, replicating are realized for the security purpose;

Failover Cluster is created to provide fault tolerance.

NEEDS To effectively distribute IS load through existing

hardware;

To increase combined IS performance by prompt server scale out;

To optimally leverage back-ups and fault tolerance.

Users

Terminal Servers

Servers Applications

Cluster DBMS controller

DATA BASE

Node #1 Node #2

Switching option in case of dropout

Variant #2. AlwaysOn technology in SQL Server cluster

What did change? Actual copy DB is kept on each

additional node, replicating with main node;

It is possible promptly to transfer a work to another DBMS sever in case of dropout.

What is worth to work on? To use all the hardware resources

Cluster DBMS controller Cluster work control panel

DATA BASE #1

Node #1 Node #2

Switching option in case of dropout

DATA BASE #2Data replication

Fact Only master-node is working

while others are «off-line».

Innovative Solution for MS SQL SERVER 2014/2012

DATA CLUSTER

Scale Out

To balance load between cluster master –node and secondary-nodes;

To increase IS fault tolerance in case of software/hardware dropout or overload on cluster node without any decrease in IS performance;

To provide constant 24x7 availability of database for prompt users work, as well as for overloaded by-the-book procedure with distribution between DB severs in DBMS cluster;

To increase data processing rate.

MAIN TASKS OF DATA CLUSTER SOLUTION:

DATA CLUSTER

DATA CLUSTER ARCHITECTURE

AlwaysOn was added, allowing to make analysis of DB requests and distribute them between cluster nodes in depends on their load

USERS USERSAPPLICATION/WEB APPLICATION

ASYNCHRONOUS DATA BASE EXCHANGE (ALWAYSON)

ASYNCHRONOUS DATA BASE EXCHANGE (ALWAYSON)

FILE STORE BD1FILE STORE BD1FILE STORE BD1

MS SQL 2012/2014NODE 1 (MASTER)

MS SQL 2012/2014NODE 2 (SLAVE)

MS SQL 2012/2014NODE 3 (SLAVE)

DATA CLUSTER DATA CLUSTER DATA CLUSTERCONSOLE

DATA CLUSTER ARCHITECTURE

It analyses current load of hardware and makes decision regarding request balancing on data reading between master- and secondary-nodes;

It is tracking DB servers unsynchronization time and making decision regarding requests balancing on data reading between master- and secondary-servers cluster;

It directs all the queries only on master-node DB; In case of IS dropout it promptly switches to secondary-node and it becomes

master-node.

PRINCIPLES OF WORK PERFORMANCE

Can be adapted on any application on MS SQL base, without any changes in the application code;

It is easy to learn («coach hints» goes from application code depends on server choice for query performance) to increase the data processing effectivity.

ARCHITECTURE PRINCIPLES

DATA CLUSTER. CONTROL CONSOLE.

INTERESTING FACTS

DATA CLUSTER

SCALE OUT

DATA CLUSTER. LOAD TESTING IN MICROSOFT TECHNOLOGY CENTER

IS: 1С 8.2.16

DB: > 1 TB

Testing scenario:

~90% - data reading

~10% - data changes

Queries SQL Intensity:-to 25000 requests/second

Testing scenario:

For 125 sessions

For 250 sessions

For 250 sessions with increased intensity

LOAD SERVERS

Virtual data base servers

Load server #1 Load server #3

License server 1C

Application Server 1CGYSTELL 1 coordinator

DB SERVER

SHELVES OFDB SYSTEM

GYSTELL coordinator 1

GYSTELL coordinator 2

GYSTELL coordinator 3

Main ServerSQL

Additional Server SQL

Additional Server SQL

DATA CLUSTER. LOAD TESTING IN MICROSOFT TECHNOLOGY CENTER

Facts:

Real performance growth, in case of one or two additional nodes, composes 90-95% and 180-185%, correspondingly. While the balanced load distribution occurs between physical servers/cluster nodes and lineal time performance decrease of the main operations (proportionally to the number of additional nodes in cluster).

High Effective Load balance according to analytical operations between server nodes

in cluster, flexible system of setting up of load distribution rules

IS fault tolerancein peak moments with load distribution

IS reliabilitywith reserve base data in servers cluster,

having minimum deviation from main database

Average operation performance time(in comparison with testing data on one node)

more than 250 users250 users150 users

1 node 2 node (AlwaysOn + SPDC) 3 node (AlwaysOn + SPDC)

DATA CLUSTER. Implementation in “Enter - Sviaznoy”

Business description:- It stays in the TOP 10 of e-commerce companies;- It has more than 100 branches.

Information system description:- More than 1000 information system users;- Data Base server MS SQL 2012 with AlwaysOn technology ;- Data Base capacity is more than 1 TB;- Transactions number to 40-50 per second;- Number of servers DBMS cluster nodes – 3 (1 – main, 2 – secondary).

Effect of DATA CLUSTER implementation – high IS availability in the seasonal sales period:- More 50% of the composed load is redirected to the additional server DBMS

cluster;- In the moments of overloads (pre-holiday days and retail discounts) system

performance quality and response were improved in several times;- The possibility of cluster SDC command usage is provided in the application

code, so the client got the possibility to make an additional increase of cluster performance independently.

Thank you!