Infrastructure Concept

What Is Scalability? Scalability is the ability of a system, network, or process to handle a growing amount of load by adding more resources. The adding of resource can be done in two ways
Scaling Up (Vertical Scaling)
This involves adding more resources to the existing nodes. For example, adding more RAM, Storage or processing power.
Scaling Out (Horizontal Scaling)
This involves adding more nodes to support more users.Any of the approaches can be used for scaling up/out a application, however the cost of adding resources (per user) may change as the volume increases. If we add resources to the system It should increase the ability of application to take more load in a proportional manner of added resources.

An ideal application should be able to serve high level of load in less resources. However, in practical, linearly scalable system may be the best option achievable.

Poorly designed applications may have really high cost on scaling up/out since it will require more resources/user as the load increases.

What Is A Cluster? A cluster is group of computer machines that can individually run a software. Clusters are typically utilized to achieve high availability for a server software.

Clustering is used in many types of servers for high availability.

App Server Cluster
An app server cluster is group of machines that can run a application server that can be reliably utilized with a minimum of down-time.

Database Server Cluster
An database server cluster is group of machines that can run a database server that can be reliably utilized with a minimum of down-time.

Why Do You Need Clustering? 1. Clustering is needed for achieving high availability for a server software. The main purpose of clustering is to achieve 100% availability or a zero down time in service.
2. A typical server software can be running on one computer machine and it can serve as long as there is no hardware failure or some other failure.
3. By creating a cluster of more than one machine, we can reduce the chances of our service going un-available in case one of the machine fails.
4. Doing clustering does not always guarantee that service will be 100% available since there can still be a chance that all the machine in a cluster fail at the same time. However it in not very likely in case you have many machines and they are located at different location or supported by their own resources.
What Is Load Balancing? Load balancing is simple technique for distributing workloads across multiple machines or clusters.
The most common and simple load balancing algorithm is Round Robin. In this type of load balancing the request is divided in circular order ensuring all machines get equal number of requests and no single machine is overloaded or underloaded.
Purpose of load balancing: The Purpose of load balancing is to
Optimize resource usage (Avoid overload and under-load of any machines.)
Achieve Maximum Throughput
Minimize response time
Most common load balancing techniques in web based applications are Most common load balancing techniques in web based applications are
Round robin
Session affinity or sticky session
IP Address affinity
What Is Sticky Session (session Affinity) Load Balancing? What Do You Mean By ‘session Affinity’? Sticky session or a session affinity technique another popular load balancing technique that requires a user session to be always served by a allocated machine.
Why Sticky Session? In a load balanced server application where user information is stored in session it will be required to keep the session data available to all machines. This can be avoided by always serving a particular user session request from one machine.
How It Is Done? The machine is associated with a session as soon as the session is created. All the requests in a particular session are always redirected to the associated machine. This ensures the user data is only at one machine and load is also shared.

In Java world, this is typically done by using jsessionid cookie. The cookie is sent to the client for the first request and every subsequent request by client must be containing that same cookie to identify the session.

What Are The Issues With Sticky Session? There are few issues that you may face with this approach
The client browser may not support cookies, and your load balancer will not be able to identify if a request belongs to a session. This may cause strange behavior for the users who use no cookie based browsers.
In case one of the machine fails or goes down, the user information (served by that machine) will be lost and there will be no way to recover user session.
What Is IP Address Affinity Technique For Load Balancing? IP address affinity is another popular way to do load balancing. In this approach, the client IP address is associated with a server node. All requests from a client IP address are served by one server node.

This approach can be really easy to implement since IP address is always available in a HTTP request header and no additional settings need to be performed.

This type of load balancing can be useful if you clients are likely to have disabled cookies.

However there is a down side of this approach. If many of your users are behind a NATed IP address then all of them will end up using the same server node. This may cause uneven load on your server nodes.

NATed IP address is really common, in fact anytime you are browsing from a office network its likely that you and all your coworkers are using same NATed IP address.

What Is Fail Over? Fail over means switching to another machine when one of the machine fails.

Fail over is a important technique in achieving high availability. Typically a load balancer is configured to fail over to another machine when the main machie fails.

To achieve least down time, most load balancer support a feature of heart beat check. This ensures that target machine is responding. As soon as a hear beat signal fails, load balancer stops sending request to that machine and redirects to other machines or cluster

What Is Session Replication? Session replication is used in application server clusters to achieve session failover.
A user session is replicated to other machines of a cluster, every time the session data changes.
If a machine fails, the load balancer can simply send incoming requests to another server in the cluster.
The user can be sent to any server in the cluster since all machines in a cluster have copy of the session.Session replication may allow your application to have session failover but it may require you to have extra cost in terms of memory and network bandwidth.
What Does Distributable Tag Means In Web.xml ? n Java world, JEE applications use the concept of distributable web applications to provide session-failover and enable load balancing.

You can set a JEE application to support session replication by adding distributable tag in web.xml file.

<distributable />

What Are The Requirements For Making A Java EE Application Session Replication Enabled? Setting distributable tag in web.xml just enables the application to support session replication, however it does not guarantee that your application will work fine in a session replicated environment.

JEE Application developer needs to make sure following things are taken care during web application development.
All attributes/objects that are saved in HTTP Session are serializable. This means all your custom objects and child objects of that should be serializable.
Making changes to any session attribute should be done using session.setAttribute() method. If you have reference to a java object that was previously set in session, you must call session.setAttribute() method every time you make any change to the object.

What Are Different Mechanism Of Session Replication? Session replication between multiple cluster nodes can be done in many ways. The best approach may depend on the type of application. However there are few common methods used by application server vendors.

Using session persistence, and saving the session to a shared file system (PersistenceManager + FileStore) . This will allow all machines in a cluster to be able to access the persisted session from the shared file system.
Using session persistence, and saving the session to a shared database (PersistenceManager + JDBCStore) – This will allow all machines in a cluster to be able to access the persisted session from the shared database system.
Using in-memory-replication, This will create a in memory copy of session in all the cluster nodes.

What Is CAP Theorem? The CAP Theorem for distributed computing was published by Eric Brewer, This states that it is not possible for a distributed computer system to simultaneously provide all three of the following guarantees:
Consistency (all nodes see the same data even at the same time with concurrent updates )
Availability (a guarantee that every request receives a response about whether it was successful or failed)
Partition tolerance (the system continues to operate despite arbitrary message loss or failure of part of the system)The CAP acronym corresponds to these 3 guarantees. This theorem has created the base for modern distributed computing approaches.

Worlds most high volume traffic companies (e.g. Amazon, Google, Facebook) use this as basis for deciding their application architecture.

Its important to understand that only two of these three conditions can be guaranteed to be met by a system.

What Is Sharding? Sharding is a architectural approach that distributes a single logical database system into a cluster of machines.

Sharding is Horizontal partitioning design scheme. In this database design rows of a database table are stored separately, instead of splitting into columns (like in normalization and vertical partitioning). Each partition is called as a shard, which can be independently located on a separate database server or physical location.

Sharding makes a database system highly scalable. The total number of rows in each table in each database is reduced since the tables are divided and distributed into multiple servers. This reduces the index size, which generally means improved search performance.

The most common approach for creating shards is by the use of consistent hashing of a unique id in application (e.g. user id).

The downsides of sharding are The downsides of sharding are,
It requires application to be aware of the data location.
Any addition or deletion of nodes from system will require some rebalance to be done in the system.
If you require lot of cross node join queries then your performance will be really bad. Therefore, knowing how the data will be used for querying becomes really important.
A wrong sharding logic may result in worse performance. Therefore make sure you shard based on the application need.
What Is BASE Property Of A System? BASE properties are the common properties of recently evolved NOSQL databases. According to CAP theorem, a BASE system does not guarantee consistency. This is a contrived acronym that is mapped to following property of a system in terms of the CAP theorem

Basically available indicates that the system is guaranteed to be available
Soft state indicates that the state of the system may change over time, even without input. This is mainly due to the eventually consistent model.
Eventual consistency indicates that the system will become consistent over time, given that the system doesn’t receive input during that time.

What Do You Mean By Eventual Consistency? What Does Eventually Consistent Mean? Unlike relational database property of Strict consistency, eventual consistency property of a system ensures that any transaction will eventually (not immediately) bring the database from one valid state to another.

This means there can be intermediate states that are not consistent between multiple nodes.

Eventually consistent systems are useful at scenarios where absolute consistency is not critical. For example in case of Twitter status update, if some users of the system do not see the latest status from a particular user its may not be very devastating for system.

Eventually consistent systems can not be used for use cases where absolute/strict consistency is required. For example a banking transactions system can not be using eventual consistency since it must consistently have the state of a transaction at any point of time. Your account balance should not show different amount if accessed from different ATM machines.

What Is Shared Nothing Architecture? How Does It Scale? A shared nothing architecture (SN) is a distributed computing approach in which each node is independent and self-sufficient, and there is no single point of contention required across the system.

This means no resources are shared between nodes (No shared memory, No shared file storage)
The nodes are able to work independently without depending on each other for any work.
Failure on one node affects only the users of that node, however other nodes continue to work without any disruption.
This approach is highly scalable since it avoid the existence of single bottleneck in the system. Shared nothing is recently become popular for web development due to its linear scalability. Google has been using it for long time.

In theory, A shared nothing system can scale almost infinitely simply by adding nodes in the form of inexpensive machines.

How Do You Update A Live Heavy Traffic Site With Minimum Or Zero Down Time? Deploying a newer version of a live website can be a challenging task specially when a website has high traffic. Any downtime is going to affect the users. There are a few best practices that we can follow

Before deploying on Production

Thoroughly test the new changes and ensure it working in a test environment which is almost identical to production system.
If possible do automation of test cases as much as possible. We use selenium for a lot of functional testing.
Create a automated sanity testing script (also called as smoke test) that can be run on production (without affecting real data). These are typically readonly type of test cases. However depending on your application needs you can add more cases to this. Make sure it can be run quickly by keeping it short.
Create scripts for all manual tasks(if possible), avoiding any hand typing mistakes during day of deployment.
Test the script to make sure they work on a non-production environment.
Keep the build artifacts ready. e.g application deployment files, database scripts, config files etc.
Create a checklist of things to do on day of deployment.
Rehearse. Deploy in a non-prod environment is almost identical to production. Try this with production data volumes(if possible). Make a note of time required for your tasks so you can plan accordingly.

 Can you name 3 kinds of SLA?
What is a Operational Level Agreement?
A service-level agreement (SLA) is a part of a service contract where the level of service is formally defined.

SLAs are also defined at different levels:

Customer-based SLA: An agreement with an individual customer group, covering all the services they use. For example, an SLA between a supplier (IT service provider) and the finance department of a large organization for the services such as finance system, payroll system, billing system, procurement/purchase system, etc.
Service-based SLA: An agreement for all customers using the services being delivered by the service provider. For example:
A car service station offers a routine service to all the customers and offers certain maintenance as a part of offer with the universal charging.
A mobile service provider offers a routine service to all the customers and offers certain maintenance as a part of offer with the universal charging
An email system for the entire organization. There are chances of difficulties arising in this type of SLA as level of the services being offered may vary for different customers (for example, head office staff may use high-speed LAN connections while local offices may have to use a lower speed leased line).
Multilevel SLA: The SLA is split into the different levels, each addressing different set of customers for the same services, in the same SLA.
Corporate-level SLA: Covering all the generic service level management (often abbreviated as SLM) issues appropriate to every customer throughout the organization. These issues are likely to be less volatile and so updates (SLA reviews) are less frequently required.
Customer-level SLA: covering all SLM issues relevant to the particular customer group, regardless of the services being used.
Service-level SLA: covering all SLM issue relevant to the specific services, in relation to this specific customer group.


 What are the key activities associated with Capacity Management? Capacity Management is a process used to manage information technology (IT). Its primary goal is to ensure that IT capacity meets current and future business requirements in a cost-effective manner. One common interpretation of Capacity Management is described in the ITIL framework
These activities are intended to optimize performance and efficiency, and to plan for and justify financial investments. Capacity management is concerned with:
1. Monitoring the performance and throughput or load on a server, server farm, or property
2. Performance analysis of measurement data, including analysis of the impact of new releases on capacity
3. Performance tuning of activities to ensure the most efficient use of existing infrastructure
4. Understanding the demands on the Service and future plans for workload growth (or shrinkage)
5. Influences on demand for computing resources
6. Capacity planning – developing a plan for the Service