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• 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.

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 stateindicates 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.

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

ACID is a acronym which is commonly used to define the properties of a relational database system, it stand for following terms:

Atomicity - This property guarantees that if one part of the transaction fails, the entire transaction will fail, and the database STATE will be left unchanged.

Consistency - This property ensures that any transaction will bring the database from one valid state to ANOTHER.

ISOLATION - This property ensures that the concurrent execution of transactions results in a system state that would be obtained if transactions were executed serially.

Durable - means that once a transaction has been COMMITTED, it will remain so, even in the event of POWER loss.

ACID is a acronym which is commonly used to define the properties of a relational database system, it stand for following terms:

Atomicity - This property guarantees that if one part of the transaction fails, the entire transaction will fail, and the database state will be left unchanged.

Consistency - This property ensures that any transaction will bring the database from one valid state to another.

Isolation - This property ensures that the concurrent execution of transactions results in a system state that would be obtained if transactions were executed serially.

Durable - means that once a transaction has been committed, it will remain so, even in the event of power loss.

• 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,
• 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.

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.

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:

• 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.

• 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.

In 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 />

In 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 />

• 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.

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.

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.

• 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.

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.

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.

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.

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.

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.

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.

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.

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.

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. 

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

• Round robin
• SESSION affinity or sticky session
• IP Address affinity.

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. 

The Purpose of load balancing is to

 Most common load balancing techniques in web based applications are

Middle tier clustering is just a cluster that is used for service the middle tier in a application. This is popular since many clients may be using middle tier and a lot of HEAVY load may also be served by middle tier that requires it be to highly available.

FAILURE of middle tier can CAUSE multiple clients and systems to fail, therefore its one of the approaches to do clustering at the middle tier of a application.

In java world, it is really common to have EJB SERVER clusters that are used by many clients. In general any application that has a business logic that can be shared across multiple client can USE a middle tier cluster for high availability.

Middle tier clustering is just a cluster that is used for service the middle tier in a application. This is popular since many clients may be using middle tier and a lot of heavy load may also be served by middle tier that requires it be to highly available.

Failure of middle tier can cause multiple clients and systems to fail, therefore its one of the approaches to do clustering at the middle tier of a application.

In java world, it is really common to have EJB server clusters that are used by many clients. In general any application that has a business logic that can be shared across multiple client can use a middle tier cluster for high availability.

• 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. 
• 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.
• 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.
• 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.

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

This INVOLVES adding more resources to the existing NODES. For example, adding more RAM, Storage or processing power.

Scaling Out

• 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.

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

This involves adding more resources to the existing nodes. For example, adding more RAM, Storage or processing power.

Scaling Out

• Having better service capacity with high availability and low latency is mission critical for ALMOST all businesses.
• Availability MEANS the ability of the application user to access the system, If a user cannot access the application, it is assumed unavailable. High Availability means the application will be available, without interruption.
• Achieving high availability for a application is not always a easy task. Using redundant server nodes with clustering is a common way to achieve HIGHER LEVEL of availability in web applications. 
• Availability is commonly expressed as a percentage of UPTIME in a given year.