Innovation in Customer Experience Starts with a Shift in Perspective

Who should own the experience? And what skills will be important in the future?
The best companies in CX take a different perspective regarding this question. They start with acknowledging that the person who owns the customer experience is…wait for it…the customer.
Think about that for a second.
They absolutely own their experience. Yet, here we are debating, who should own it. It seems that companies do everything, but understand their behaviors, expectations, preferences et al.
I define CX this way: it’s the sum of all engagements a customer has with your brand in every touchpoint, in each moment of truth, throughout the customer lifecycle. The question to ask is then, what is the experience they have? What experiences do they expect or desire? What experiences they’re receiving from other companies? More so, how are their favorite apps – for example Uber or Tinder – changing their expectations and how should you rethink the customer journey to be native, frictionless, and delightful based on outside innovation? As such, the question who owns CX, is something that should be answered in a future state and work toward that goal now.
Companies excelling here are looking at ideal customer experiences and building inside and outside for them. New cross-functional groups lead collaboration to remove friction, optimize effective touchpoints and invest innovation based on new areas of opportunity. An empathetic customer-centric approach to CX improves retention, acquisition and relationships. Great Customer Experience is all the work, that you do so your customers don’t have to…

What is the importance of CX as part of the digital transformation?
Digital transformation means something different to everyone. Just like customer experience. It is something, that is started independently in each group with different objectives. But like CX, everything is on a collision course towards convergence. Everything has to work together, otherwise you compete against yourself.
I define digital transformation this way: The re-alignment of, or new investment in, technology and business models to more effectively engage digital consumers (and employees), create new value and deliver delightful and relevant experiences at every touchpoint in the customer and employee journey.
In my research (see below), I’ve found that a common catalyst for rapid and ultimately holistic digital transformation is indeed CX. More so, by zooming in on the Digital Customer Experience (DCX) and asking what would my digital customer do and how is it affecting traditional behavior, companies can beeline towards fast innovation.

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Marketing tech and the evolution of hybrid roles

Marketing now a growth-driving engine

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Traditionally, the marketing function was seen as the creative function focused on developing, building and executing campaigns to build brand awareness. Increasingly, thanks to direct feedback from customers, marketing campaigns can be fine-tuned and adapted, virtually in real time.
For the first time, we can start to really understand the impact that marketing has on the business, rather than using fuzzy metrics like reach and frequency.

Thanks to technology, marketing performance can be measured and benchmarked to capture the impact on top and bottom line growth. Increasingly, the marketing function, underpinned by technology, has become an engine to drive growth and tangible return on investment.
This shift in emphasis is creating new roles and responsibilities within the marketing function, which have a wider impact on established organisation structures.

Technology, often referred to as 'digital', is no longer a vertical function controlled by IT. Rather, it is a horizontal function which impacts on every function within an organisation.

As a result, the marketing function has become a hybrid model of creativity and technology with new roles being created to exploit the opportunities afforded by technology. These include:

• Strategy – a move away from tactical deployment of a sales strategy to a central strategic function has seen a wave of new job titles including chief customer officer, chief digital officer, chief customer experience officer
• Analysis – from the rich sources of data available, there has been an increase in the need and demand for data scientists and statisticians to analyse and derive insights to inform and drive marketing strategy. Titles associated include chief data scientist, consumer insights director, eCRM director
• Technology – marketing now owns and operates its own “technology stack” to identify the right platforms and software products to build a coherent architecture to best serve the customer. New titles include chief marketing technologist and, in start-ups, growth hacker

As in any organisation or function undergoing change, there are tensions and striving to keep a balance between left and right brain thinking is not easy.

The integration of technology and creativity is finely nuanced. Relying too much on data, which is historic and helps make sense of the past, is dangerous and can impede on creative thinking and sometimes plain common sense.

Given the speed of change in terms of new channels, customer behaviour and technologies, historical data can quickly become outdated. Agile marketing teams comprising creative and technical people with complementary skills and experience with a unifying customer-first mind-set will be successful.
In summary, combining creativity and marketing is the way we do marketing now. The bottom line is that the guiding principle of marketing in the 21st century is a combination of art and creativity augmented by code and data with 100% focus on the customer.

What Skills are Most Relevant for the Digital Age?
a
check out the Capgemini report here

Digital demand and supply: The psychology of self-development for digital careers

 

The Agile Marketing Organization

Marketers know that digital channels are critical to engaging today’s consumer. Global spending on digital advertising will reach $178 billion in 2016, almost 30 percent of total ad spending, according to eMarketer. Digital advertising already represents a third of all ad spending in the U.S. today, and many forecasts see digital reaching parity with TV in a few years’ time. In the UK, almost 60 percent of consumers use social media each week for an average of 52 minutes per day. In Germany, about 13 percent of all commerce is now transacted online or via mobile devices; online sales are growing at more than 20 percent a year. Worldwide, a quarter of consumers use smartphones, a percentage that will rise to a third, or some 2.5 billion people, by 2018. In developed economies, of course, the percentages are much higher.

Marketing organizations are feeling the pressure created by these shifts. And while still important, traditional skills such as creativity and brand building no longer suffice in a digital-first reality. Marketing has become much more of a science requiring technical, data-crunching abilities. With new digital channels and tools constantly emerging, marketing organizations must become more agile—to borrow a term from the world of software development—iterating much more quickly in order to adapt to rapidly changing conditions.

To increase their agility, chief marketing officers (CMOs) require very different capabilities and structures than were needed in the past. Capabilities include new talent in agile development, big data for consumer understanding, programmatic buying, and branded content, as well as redefined roles for existing talent in areas such as marketing effectiveness analytics, marketing innovation, and agency management. At the same time, organizations must consciously build structures that align with their key business objectives. Organizations that aren’t able to build these capabilities and structures over the next year will, we believe, fall behind their competitors.

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Digital Marketing - Online Sales Campaign Tracking

Campaign tracking is one of the most fundamental strategies to increase conversion for your company.  Campaign tracking is a huge topic. 

Read more from 1 | 2 | 3 | ...

Terminology 
VISTA
SAINT

Responsive web design (RWD) - Step by Step

Responsive web design (RWD) is a web design approach aimed at crafting sites to provide an optimal viewing experience—easy reading and navigation with a minimum of resizing, panning, and scrolling—across a wide range of devices including mobile phones, tablets, gaming devices, televisions, and an ever-growing array of Internet-connected devices.

Getting started with responsive web design - start from here

Responsive design techniques - you should know some, if not, check it out.

Building responsive web designs with Adobe Edge Reflow - Must check it out

Responsive Web Design: 50 Examples and Best Practices - Check out the screenshots on different device screen and choose one suite you best from here 

More Responsive Design Framework - If you are not overwhelm with so much info and like to see what else, more here.   

Demographic Profiling vs. Behavioral Modeling

1) Demographic  - This is a set of characteristics like gender, age, marital status, geographic location, socio-economic status, etc. It provides enough information to create a mental picture of the typical member of the hypothetical group. For example, a marketer might speak of the "single, female, middle-class, age 18 to 24, college educated demographic."

2) Behavioral - This type of profiling is more concerned with what the customer is actually doing. It cares only about customer activity. This is infinitely more important than demographical information.

The first is pretty typical and any crm can store information like age, address, and even supply custom fields for information like socio-economic status (perhaps in the form of a "score" or comparative rating). When it comes to behavioral data, some web tagging system makes this achievable.

What are customers doing? When's the last time they've engaged with my website? How long have they gone without purchasing? How long have they gone without clicking on a link? Will they visit again? Will they buy again? These are the questions answered by collecting and analyzing behavioral data.

Simply put - customer behavior is a much stronger predictor of your future relationship with a customer than demographic information will ever be.

Granted, demographic profiling is useful if your business relies on selling advertising. But even then, the behavioral profile trumps it because if the customer stops visiting your site or interacting with you, you're not going to have eyeballs to serve ads to, no matter how personalized or customized to the visitor's demographic profile the ads may be.

Customer modeling is probably a better word for the latter profile because it is action-oriented. Models aren't about "set-in-stone" characteristics like "my customer is a female between the age of 18 and 24." Models are about actions over time like, "If a customer does not make a purchase in the next 30 days, they are unlikely to come back and make any further purchases." That's a model.

Modeling seeks to look at customers who are engaging in a certain behavior and tries to find a commonality in them.

The real power comes in combining both demographic and behavioral data to produce an even crystal clearer picture of the customer. Just remember that if you must choose, always go with behavior. If you've got finite dollars and one company offers to analyze and compile the demographics of your customer base versus another analyzing behavioral trends and actions, go with the latter.

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Data Multi-tenancy

Multi-tenancy

The term multi-tenancy in general is applied to software development to indicate an architecture in which a single running instance of an application simultaneously serves multiple clients (tenants). This is highly common in SaaS solutions.

Data Multi-tenancy

Isolating information (data, customizations, etc) pertaining to the various tenants is a particular challenge in these systems. This includes the data owned by each tenant stored in the database. It is this last piece, sometimes called multi-tenant data, on which we will focus.

Multi-tenant data approaches

There are 3 main approaches to isolating information in these multi-tenant systems which goes hand-in-hand with different database schema definitions and JDBC setups. Each approach has pros and cons as well as specific techniques and considerations. Multi-TenantData Architecture does a great job of covering these topics.

Separate database

Each tenant's data is kept in a physically separate database instance. JDBC Connections would point specifically to each database, so any pooling would be per-tenant. A general application approach here would be to define a JDBC Connection pool per-tenant and to select the pool to use based on the“tenant identifier” associated with the currently logged in user.

Separate schema

Each tenant's data is kept in a distinct database schema on a single database instance. There are 2 different ways to define JDBC Connections here:

• Connections could point specifically to each schema, as we saw with the Separate databaseapproach. This is an option provided that the driver supports naming the default schema in the connection URL or if the pooling mechanism supports naming a schema to use for its Connections. Using this approach, we would have a distinct JDBC Connection pool per-tenant where the pool to use would be selected based on the “tenant identifier” associated with the currently logged in user.
• Connections could point to the database itself (using some default schema) but the Connections would be altered using the SQL SET SCHEMA (or similar) command. Using this approach, we would have a single JDBC Connection pool for use to service all tenants, but before using the Connection it would be altered to reference the schema named by the “tenant identifier” associated with the currently logged in user.

Partitioned (discriminator) data

All data is kept in a single database schema. The data for each tenant is partitioned by the use of partition value or discriminator. The complexity of this discriminator might range from a simple column value to a complex SQL formula. Again, this approach would use a single Connection pool to service all tenants. However, in this approach the application needs to alter each and every SQL statement sent to the database to reference the “tenant identifier” discriminator.

Data Multi-tenancy in Hibernate

Using Hibernate with multi-tenant data comes down to both an API and then integration piece(s). As usual Hibernate strives to keep the API simple and isolated from any underlying integration complexities. The API is really just defined by passing the tenant identifier as part of opening any session.

Example 16.1. Specifying tenant identifier from SessionFactory

Session session = sessionFactory.withOptions()

        .tenantIdentifier( yourTenantIdentifier )

        ...

        .openSession();

Additionally, when specifying configuration, a org.hibernate.MultiTenancyStrategy should be named using the hibernate.multiTenancy setting. Hibernate will perform validations based on the type of strategy you specify. The strategy here correlates to the isolation approach discussed above.

NONE

(the default) No multi-tenancy is expected. In fact, it is considered an error if a tenant identifier is specified when opening a session using this strategy

            

 SCHEMA

Correlates to the separate schema approach. It is an error to attempt to open a session without a tenant identifier using this strategy. Additionally, aorg.hibernate.service.jdbc.connections.spi.MultiTenantConnectionProvider must be specified.

DATABASE

Correlates to the separate database approach. It is an error to attempt to open a session without a tenant identifier using this strategy. Additionally, aorg.hibernate.service.jdbc.connections.spi.MultiTenantConnectionProvider must be specified.

DISCRIMINATOR

Correlates to the partitioned (discriminator) approach. It is an error to attempt to open a session without a tenant identifier using this strategy. This strategy is not yet implemented in Hibernate as of 4.0 and 4.1. Its support is planned for 5.0.

 Google doc link

Spring JDBCTemplate Vs Hibernate Performance + JPA & Caching

Spring JDBCTemplate Vs Hibernate Performance

If you do all you can to make Spring JDBCTemplate / Hibernate implementations very fast, the JDBC template will probably be a bit faster, because it doesn't have the overhead that Hibernate has, but it will probably take much more time and lines of code to implement.

Hibernate has its learning curve, and you have to understand what happens behind the scenes, when to use projections instead of returning entities, etc. But if you master it, you'll gain much time and have cleaner and simpler code than with a JDBC-based solution.

In 95% of the cases, Hibernate is fast enough, or even faster than non-optimized JDBC code. For the 5% left, nothing forbids you to use something else, like Spring-JDBC for example. Both solutions are not mutually exclusive.

Hibernate

Hibernate is not intended for batch-jobs, it is an anti patterns of O/R Mapper usage.  Even the Hibernate documentation warns to not do that. Batch processing often can be much more easily done by a single clever SQL statement instead via O/R mappers which need a bunch of statements to do the same. Not because they are bad, but because they have not been built for this use case. Data is always by default loaded lazyly in Hibernate.

One of the common problems of people that start using Hibernate is performance, if you don’t have much experience in Hibernate you will find how quickly your application becomes slow. If you enable sql traces, you would see how many queries are sent to database that can be avoided with little Hibernate knowledge. Good use of Hibernate Cache to avoid amount of traffic between your application and database.

JPA + Spring

If you are using spring and JPA, it is very likely that you utilize ehcache (or another cache provider). And you do that in two separate scenarios: JPA 2nd level cache and spring method caching.

When you configure your application, you normally set the 2nd level cache provider of your JPA provider (hibernate, in my case) and you also configure spring with the “cache” namespace. Everything looks OK and you continue with the project. But there’s a caveat. If you follow the most straightforward way, you get two separate cache managers which load the same cache configuration file. This is not bad per-se, but it is something to think about – do you really need two cache manager and the problems that may arise from this

JPA & Caching

Caching is the most important performance optimization technique. There are many things that can be cached in persistence, objects, data, database connections, database statements, query results, meta-data, relationships, to name a few. Caching in object persistence normally refers to the caching of objects or their data.

Caching also influences object identity, that is that if you read an object, then read the same object again you should get the identical object back (same reference).

JPA 1.0 does not define a shared object cache, JPA providers can support a shared object cache or not, however most do.

Caching in JPA is required with-in a transaction or within an extended persistence context to preserve object identity, but JPA does not require that caching be supported across transactions or persistence contexts.

JPA 2.0 defines the concept of a shared cache. The @Cacheable annotation or cacheable XML attribute can be used to enable or disable caching on a class.

Hibernate cache

Cache Types : Hibernate uses different types of caches. Each type of cache is used for different purposes. Let us first have a look at this cache types.

The first cache type is the session cache. The session cache caches object within the current session.
The second cache type is the query Cache. The query cache is responsible for caching queries and their results.
The third cache type is the second level cache. The second level cache is responsible for caching objects across sessions.

The Session Cache
session cache caches values within the current session.  This cache is enabled by default.

The Query Cache
The query cache can be really usefull to optimize the performance of your data access layer. However there are a number of pitfalls as well.  This blog post describes a serious problem regarding memory consumption of the Hibernate query cache when using objects as parameters. It could consumpt ton of memories and hit
OutOfMemoryError (How to fix it)

The Second Level Cache
The key characteristic of the second-level cache is that is is used across sessions, which also differentiates it from the session cache, which only – as the name says – has session scope. Hibernate provides a flexible concept to exchange cache providers for the second-level cache. By default Ehcache is used as caching provider.

However more sophisticated caching implementation can be used like the distributed JBoss Cache or Oracle Coherence.

Additional readings:

Understanding caching in hibernate (1 | 2 | 3) , tutorial and tuning

Top 10 Performance Problems taken from Zappos, Monster, Thomson and Co

52 weeks of Application Performance – The dynaTrace Almanac

High Availability (HA), Disaster Recovery (DR) & HADR Solution

High availability (HA), answers the question 'what do I do in case a single machine fails?' -  means a machine that can immediately take over in case of a problem with the main machine with little down time, and no loss of data.

HA is the measurement of a system’s ability to remain accessible in the event of a system component failure. Generally, HA is implemented by building in multiple levels of fault tolerance and/or load balancing capabilities into a system

Disaster Recovery (DR), on the other hand, answers 'what do I do in case a disaster happens (fire, floods, war, ISP goes bankrupt, whatever) to the whole data center?'. it is something intended to take over in the event of a  disaster at the main site.

DR is the process by which a system is restored to a previous acceptable state, after a natural or man-made disaster.

While both increase overall availability, a notable difference is that with HA there is, generally, no loss of service. HA refers to the retaining of the service and DR to the retaining of the data. Whereas, with DR there is usually a slight loss of service while the DR plan is executed and the system is restored. HA and DR strategies should strive to address any non-functional requirements, such as performance, system availability, fault tolerance, data retention, business continuity, and user experience. It is imperative that selection of the appropriate HA and DR strategy be driven by business requirements. For HA, determine any service level agreements expected of your system. For DR, use measurable characteristics, such as Recovery Time Objective (RTO) and Recovery Point Objective (RPO), to drive your DR plan.

The following requirements are the most common IT considerations for establishing an HADR solution:

Recovery time objective (RTO)

The time as measured from the time of application unavailability to the time of recovery (resuming business operations).

Recovery point objective (RPO)

The last data point to which production is recovered upon a failure. Ideally, customers want the RPO to be zero lost data. Practically speaking, we tend to accept a recovery point associated with a particular application state.

A comprehensive end to end HADR solution has the following basic components:

- Application data resiliency

Data resiliency is the base or foundational element for a high availability and disaster recovery solution deployment. Methods and characteristics : 

Storage-based resiliency: Storage replication is the most commonly used technique for deploying cluster-wide data resiliency. There are two general categories for storage-based resiliency: shared-disk topology and shared-everything topology.

Log-based replication: Log-based replication is a form of resiliency primarily associated with databases. Typically, database logs are used to monitor changes that are then replicated to a second system where those changes are applied. 

- Application infrastructure resiliency

Infrastructure resiliency provides the overall environment that is required to resume full production at a standby node. This environment includes the entire list of resources that the application requires upon failover for the operations to resume automatically. Methods and characteristics:

Application infrastructure resiliency has two aspects. First, it provides the application with all the resources that it requires to resume operations at an alternate node in the cluster. Second, it provides for cluster integrity by using monitoring and verification. These resources include items such as dependent hardware, middleware, IP connectivity, configuration files, attached devices (printers), security profiles, application specific custom resources (crypto card) and the application data itself.

- Application state resiliency

Application state resiliency is characterized by the application recovery point as described when the production environment resumes on a secondary node in the cluster. Characteristic of the application to resume varies by application design and customer requirements.

Where will the application recovery point be with respect to the last application transaction? If your application is designed with commit boundaries and the outage is an unplanned failover, then the recovery point in the application will be to that last commit boundary. If you are conducting a planned outage role swap, then the application is quiesced so that memory can be flushed to the shared-disk resource and the data and application are subsequently varied on to the secondary node

A complete end-to-end solution incorporates all three elements into one integrated environment that addresses one or all of the outage.

A solution to a customer depends upon the inclusion and incorporation of these basic elements into the clustering configuration. For example, you can have a solution based purely upon data resiliency and leave the application resiliency aspects of the final recovery process to IT operational procedures. Alternatively you can incorporate the data resiliency into the overall clustering topology enabling automated recovery processing.

References:

http://technet.microsoft.com/en-us/library/hh393522.aspx

http://www.redbooks.ibm.com/redpapers/pdfs/redp4669.pdf

http://www.drj.com/articles/online-exclusive/understanding-high-availability-and-disaster-recovery-in-your-overall-recovery-strategy.html

DB HADR google Doc

IBM HADR google Doc

Virtualization Computing

There are several kinds of virtualization techniques which provide similar features but differ in the degree of abstraction and the methods used for virtualization.

Full virtualazation (VMs)

In computer science, full virtualization is a virtualization technique used to provide a certain kind of virtual machine environment, namely, one that is a complete simulation of the underlying hardware. Full virtualization requires that every salient feature of the hardware be reflected into one of several virtual machines – including the full instruction set, input/output operations, interrupts, memory access, and whatever other elements are used by the software that runs on the bare machine, and that is intended to run in a virtual machine.

Virtual machines emulate some real or fictional hardware, which in turn requires real resources from the host (the machine running the VMs). This approach, used by most system emulators, allows the emulator to run an arbitrary guest operating system without modifications because guest OS is not aware that it is not running on real hardware. The main issue with this approach is that some CPU instructions require additional privileges and may not be executed in user space thus requiring a virtual machines monitor (VMM), also called a hypervisor, to analyze executed code and make it safe on-the-fly. Hardware emulation approach is used by VMware products, VirtualBox, QEMU, Parallels and Microsoft Virtual Server.

Hypervisor
In computing, a hypervisor, also called virtual machine manager (VMM), is one of many hardware virtualization techniques allowing multiple operating systems, termed guests, to run concurrently on a host computer. It is so named because it is conceptually one level higher than a supervisory program. The hypervisor presents to the guest operating systems a virtual operating platform and manages the execution of the guest operating systems. Multiple instances of a variety of operating systems may share the virtualized hardware resources. Hypervisors are very commonly installed on server hardware, with the function of running guest operating systems, that themselves act as servers.

- Type 1 (or native, bare metal) hypervisors run directly on the host's hardware to control the hardware and to manage guest operating systems. A guest operating system thus runs on another level above the hypervisor.
This model represents the classic implementation of virtual machine architectures; the original hypervisors were the test tool, SIMMON, and CP/CMS, both developed at IBM in the 1960s. CP/CMS was the ancestor of IBM's z/VM. Modern equivalents of this are Oracle VM Server for SPARC, the Citrix XenServer, KVM, VMware ESX/ESXi, and Microsoft Hyper-V hypervisor.
- Type 2 (or hosted) hypervisors run within a conventional operating system environment. With the hypervisor layer as a distinct second software level, guest operating systems run at the third level above the hardware. VMware Workstation and VirtualBox are examples of Type 2 hypervisors.

In other words, Type 1 hypervisor runs directly on the hardware; a Type 2 hypervisor runs on another operating system, such as FreeBSD,[4] Linux[5] or Windows.[5] (http://en.wikipedia.org/wiki/Hypervisor)

Paravirtualization
This technique also requires a VMM, but most of its work is performed in the guest OS code, which in turn is modified to support this VMM and avoid unnecessary use of privileged instructions. The paravirtualization technique also enables running different OSs on a single server, but requires them to be ported, i.e. they should «know» they are running under the hypervisor. The paravirtualization approach is used by products such as Xen and UML.

Virtualization on the OS level, a.k.a. containers virtualization
Most applications running on a server can easily share a machine with others, if they could be isolated and secured. Further, in most situations, different operating systems are not required on the same server, merely multiple instances of a single operating system. OS-level virtualization systems have been designed to provide the required isolation and security to run multiple applications or copies of the same OS (but different distributions of the OS) on the same server. OpenVZ, Virtuozzo, Linux-VServer, Solaris Zones and FreeBSD Jails are examples of OS-level virtualization.

The three techniques differ in complexity of implementation, breadth of OS support, performance in comparison with standalone server, and level of access to common resources. For example, VMs have wider scope of usage, but poorer performance. Para-VMs have better performance, but can support fewer OSs because one has to modify the original OS.

Virtualization on the OS level provides the best performance and scalability compared to other approaches. Performance difference of such systems can be as low as 1…3%, comparing with that of a standalone server. Virtual Environments are usually also much simpler to administer as all of them can be accessed and administered from the host system. Generally, such systems are the best choice for server consolidation of same OS workloads.

HP CloudSystem

HP CloudSystem is a cloud infrastructure from Hewlett-Packard (HP) that combines storage, servers, networking and software for organizations to build complete private, public and hybrid Cloud computing environments.

HP CloudSystem is an integrated infrastructure used to create, manage and consume cloud-based services. The cloud types supported are private, public and hybrid. HP has claimed that user organizations can build and deploy new cloud services using HP CloudSystem in minutes.

Comparison of platform virtual machines

VMware ESX

VMware ESX is an enterprise-level computer virtualization product offered by VMware, Inc. ESX is a component of VMware's larger offering, VMware Infrastructure, and adds management and reliability services to the core server product. VMware is replacing the original ESX with ESXi.

VMware ESX and VMware ESXi are bare metal embedded hypervisors that are VMware's enterprise software hypervisors for guest virtual servers that run directly on host server hardware without requiring an additional underlying operating system.

How to Analyze Java Thread / Heap Dumps

The content of this article was originally written by Tae Jin Gu on the Cubrid blog.

When there is an obstacle, or when a Java based Web application is running much slower than expected, we need to use thread dumps. If thread dumps feel like very complicated to you, this article may help you very much. Here I will explain what threads are in Java, their types, how they are created, how to manage them, how you can dump threads from a running application, and finally how you can analyze them and determine the bottleneck or blocking threads. This article is a result of long experience in Java application debugging.

Java and Thread

A web server uses tens to hundreds of threads to process a large number of concurrent users. If two or more threads utilize the same resources, a contention between the threads is inevitable, and sometimes deadlock occurs.

Thread contention is a status in which one thread is waiting for a lock, held by another thread, to be lifted. Different threads frequently access shared resources on a web application. For example, to record a log, the thread trying to record the log must obtain a lock and access the shared resources.

Deadlock is a special type of thread contention, in which two or more threads are waiting for the other threads to complete their tasks in order to complete their own tasks.

Different issues can arise from thread contention. To analyze such issues, you need to use the thread dump. A thread dump will give you the information on the exact status of each thread.

Background Information for Java Threads

Thread Synchronization

A thread can be processed with other threads at the same time. In order to ensure compatibility when multiple threads are trying to use shared resources, one thread at a time should be allowed to access the shared resources by using thread synchronization.
Thread synchronization on Java can be done using monitor. Every Java object has a single monitor. The monitor can be owned by only one thread. For a thread to own a monitor that is owned by a different thread, it needs to wait in the wait queue until the other thread releases its monitor.

Thread Status

In order to analyze a thread dump, you need to know the status of threads. The statuses of threads are stated on java.lang.Thread.State.
 
Figure 1: Thread Status.

• NEW: The thread is created but has not been processed yet.
• RUNNABLE: The thread is occupying the CPU and processing a task. (It may be in WAITING status due to the OS's resource distribution.)
• BLOCKED: The thread is waiting for a different thread to release its lock in order to get the monitor lock.
• WAITING: The thread is waiting by using a wait, join or park method.
• TIMED_WAITING: The thread is waiting by using a sleep, wait, join or park method. (The difference from WAITING is that the maximum waiting time is specified by the method parameter, and WAITING can be relieved by time as well as external changes.) 

Thread Types

Java threads can be divided into two:

1. daemon threads;
2. and non-daemon threads.

Daemon threads stop working when there are no other non-daemon threads. Even if you do not create any threads, the Java application will create several threads by default. Most of them are daemon threads, mainly for processing tasks such as garbage collection or JMX.
A thread running the 'static void main(String[] args)’ method is created as a non-daemon thread, and when this thread stops working, all other daemon threads will stop as well. (The thread running this main method is called the VM thread in HotSpot VM.)

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Commands to create thread dump on solaris:

[user-account@localhost]$ ps -ef | grep java
To list all running java pid 
[user-account@localhost]$ pargs pid
To determine which pid is for dump 
[user-account@localhost]$ cd %JAVA_HOME%/bin
Go to java binary folder which having jstack command
[user-account@localhost]$ ./jstack -l pid /var/tmp/thread-dump-pid.out
Create the running thread dump

Solaris CMD QRC From Internet

My Google link

Know more about Java Heap Dump Analysis

How to analyze heap dumps

Heap Dump Analysis with Memory Analyzer (1 | 2) 

Eclipse Memory Analyzer

Big (unstructure) Data, Grid computing, Hadoop, HDFS & BigTable

Big Data => It is really very big data, imaging u have perabyte data to be processed

You have app server and db. If ur app server need to process perabyte data, it needs to retrieve and copy the data from db to app server first, then process. This is traditional computing, move the data close to processing logic.

if it is small data, it is ok and right way to do that. But if the data volume is big, moving data from db to app server could take days. Just imaging how long it take to copy perabyte data files from one server to another server.

In this case, grid computing comes in and move the logic close to data rather than moving data to processing logic.

If we move logic to data, first of all, you save the time of moving data around. Then, people may argue, then u need to move result back, sure, but most of time, we need to process more data to produce one result, otherwise there is no point to process it, but copy it only.

Doing work by one man is less productive than doing work by more man. The best stratigy is allocate some time to teach others to do the same work and then get others to teach others recursively and do the work. The effect will be 2*2*2*..... faster than doing it alone.

So, we divide & distribute the data to different machines, teach & monitor (Hadoop and map & reduce model - It enables applications to work with thousands of computational independent computers and petabytes of data) them process currently, this is where distributed file system comes into picture (HDFS).

Instead of distributed file system, it could be nicer if we can put them into dictributed db as it could be easier to manage them.

So, What DB we can use? Big Data means unstructured data and it could include text strings, documents of all types, audio and video files, metadata, web pages, email messages, social media feeds, form data, and so on.  Big Data may well have different data types within the same set that do not contain the same structure. The consistent trait of these varied data types is that the data schema isn’t known or defined when the data is captured and stored. Rather, a data model is often applied at the time the data is used.

Then BigTable (casandra / HBase / MongoDB) come into picture, it is more of name-value pair + timestamp (NOSQL) DB, each value may have different fields like json string and it can be a nested name-value pair and flexiable enough to cater for any data format.

{
    "_id": ObjectId("4efa8d2b7d284dad101e4bc9"),
    "Last Name": "DUMONT",
    "First Name": "Jean",
    "Date of Birth": "01-22-1963"
},
{
    "_id": ObjectId("4efa8d2b7d284dad101e4bc7"),
    "Last Name": "PELLERIN",
    "First Name": "Franck",
    "Date of Birth": "09-19-1983",
    "Address": "1 chemin des Loges",
    "City": "VERSAILLES"
}

Fortunately, Google's BigTable Paper clearly explains what BigTable actually is. Here is the first sentence of the "Data Model" section:

A Bigtable is a sparse, distributed, persistent multidimensional sorted map. It explains that:

The map is indexed by a row key, column key, and a timestamp; each value in the map is an uninterpreted array of bytes.

HBase

HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning that the database isn't an RDBMS which supports SQL as its primary access language, but there are many types of NoSQL databases: BerkeleyDB is an example of a local NoSQL database, whereas HBase is very much a distributed database. Technically speaking, HBase is really more a "Data Store" than "Data Base" because it lacks many of the features you find in an RDBMS, such as typed columns, secondary indexes, triggers, and advanced query languages, etc.

When Should Use HBase?

HBase isn't suitable for every problem.

First, make sure you have enough data. If you have hundreds of millions or billions of rows, then HBase is a good candidate. If you only have a few thousand/million rows, then using a traditional RDBMS might be a better choice due to the fact that all of your data might wind up on a single node (or two) and the rest of the cluster may be sitting idle.

Second, make sure you can live without all the extra features that an RDBMS provides (e.g., typed columns, secondary indexes, transactions, advanced query languages, etc.) An application built against an RDBMS cannot be "ported" to HBase by simply changing a JDBC driver, for example. Consider moving from an RDBMS to HBase as a complete redesign as opposed to a port.

Third, make sure you have enough hardware. Even HDFS doesn't do well with anything less than 5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode.

HBase can run quite well stand-alone on a laptop - but this should be considered a development configuration only.

What Is The Difference Between HBase and Hadoop/HDFS?

HDFS is a distributed file system that is well suited for the storage of large files. It's documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files. HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables. This can sometimes be a point of conceptual confusion. HBase internally puts your data in indexed "StoreFiles" that exist on HDFS for high-speed lookups.

What are the real life BIG DATA use cases we can use Hadoop or similar technologies and where to start?

Check out Cloudera and use cases from their recommendation:

 E-tailing

• Recommendation engines — increase average order size by recommending complementary products based on predictive analysis for cross-selling.
• Cross-channel analytics — sales attribution, average order value, lifetime value (e.g., how many in-store purchases resulted from a particular recommendation, advertisement or promotion).
• Event analytics — what series of steps (golden path) led to a desired outcome (e.g., purchase, registration).

Financial Services

• Compliance and regulatory reporting.
• Risk analysis and management.
• Fraud detection and security analytics.
• CRM and customer loyalty programs.
• Credit scoring and analysis.
• Trade surveillance.

Government

• Fraud detection and cybersecurity.
• Compliance and regulatory analysis.
• Energy consumption and carbon footprint management.

Health & Life Sciences

• Campaign and sales program optimization.
• Brand management.
• Patient care quality and program analysis.
• Supply-chain management.
• Drug discovery and development analysis.

Retail/CPG

• Merchandizing and market basket analysis.
• Campaign management and customer loyalty programs.
• Supply-chain management and analytics.
• Event- and behavior-based targeting.
• Market and consumer segmentations.

Telecommunications

• Revenue assurance and price optimization.
• Customer churn prevention.
• Campaign management and customer loyalty.
• Call Detail Record (CDR) analysis.
• Network performance and optimization.

Web & Digital Media Services

• Large-scale clickstream analytics.
• Ad targeting, analysis, forecasting and optimization.
• Abuse and click-fraud prevention.
• Social graph analysis and profile segmentation.
• Campaign management and loyalty programs.

AppDynamics ? What, Why & How

If you ask me what is "AppDymamics", in my own words, it is real-time web application profiler and monitoring tool, reason i called it in such way is the features i used most in the free version. 

Actually, AppDynamics call itself Application Performance Management (APM) product and has more features then i blogged here. This post is just a jump-start and personal experience of AppDymamics Lite (vs Commercial version) in my development experience.

Two most important reasons made me to try it out: 

- Easy to install

My team is so busy in development and no time to setup individual application profiler on their IDEs. 

With few minutes' effort, i setup one from our DEV server and allow all my developers to use with no wait. 

- Easy to use 

Very intuitive GUI and allow to drill down all the way to JDBC and SQL query with detail call stack (call graph) and time spending.

No training required, just follow the color code and double-click and click, click until I found the root cause in minutes.

So, When should use application Profiler instead of AppDynamics? Here is a short answer:    

WHEN TO USE A PROFILER	WHEN TO USE APPDYNAMICS LITE
You need to troubleshoot high CPU usage and high memory usage	You need to troubleshoot slow response times, slow SQL, high error rates, and stalls
Your environment is Development or QA	Your environment is Production or performance load-test
15-20% overhead is okay	You can't afford more than 2% overhead

Now, let me walk you through how to start & use the AppDynamics


- Download the appdynamics and install it in 4 steps. 

- Open web browser and browse through the slow web pages or the processes you intend to monitor and profile

- Open web browser and login AppDynamics (Be patient, if you don't see any data. AppDynamics need a short while to build and load up the analytic info) 

^Landing page will show list of recent transactions (web request) and highlight the response time in different color code depending on the settings (slow, very slow and stalling) which you can change at your own will. 

- Double-click the page name (URL) in red color and until you see the full call graph & timing of each spending.


    * First, it will drill down to all sampling of the same transaction (web request) 

   * Chose one of them and double-click to drill down to call graph

    ^you can filter the graph by method timing spending and class type (servlet, pojo, spring bean, ejb and etc)  

    

   *Click and click ... until you see something you are familiar 

    ^Take note of color code, don't confuse the total time spending (ORANGE) with method self time spending (BLUE)

As recent transactions are not good enough, we need to check out long transaction in past 30 minutes or 1 hours. Never mind, go and visit SnapShot page.

   * Click on SnapShot Tab

   ^ Too much info in the page, never mind, enable filter to view less and more concerned transactions.

    * Double-click the long transaction to pull out the call graph

   ^ There is JDBC call in this transaction

    * Right click on slow method to further expend the graph

   * Click on JDBC to check out list of executed SQL & time spending 

    * Scroll Up & Down and check what are the SQL and how much they spend in the total timing. 

    

   * Save the call graph for offline or offsite study by other developers  

    * Save to local disc and sent it to developers for further study. 

    ^ This file also includes list of executed SQL and timing of each.   

That is all, it is sweet & easy experience. In fact, i have put it up behind an Apach Server so that our offsite team can access and check out root cause of long transaction.