[This article was written by Andreea Borca, developer of patient-empowering solutions for the healthcare industry, co-host of Farstuff: The IoT Podcast, and featured author in DZone's 2014 Guide to Internet of Things]. The creation of the Internet was a significant shift in the way people acquire information, interact with each other, and make decisions. Now, the Internet is expanding its reach to a range of devices that can gather and analyze physical data and react to that data in a variety of applications that we’ve never seen before. This “Internet of Things” marks another dynamic shift in the history of technology. This new stage in the Internet’s evolution is changing it from a tool that we actively need to engage with—deliberately using a browser to access it—to one that passively endows the world around us with a “mind” of its own. We are developing a world where things interact intelligently and cooperate to achieve goals without explicit guidance from human operators. Defining the Internet of Things First, we need to define the Internet of Things (also called “The Internet of Everything” by Cisco). A system falls under the Internet of Things definition if it meets the following criteria, known as the “3 Cs”: 1. It must Connect – to the physical world around itself collecting information, to other things in order to interact with them effectively, to the internet or a network, etc. 2. It must Compute – by processing the inputs it receives in some way and making them meaningful to other systems. 3. It must Communicate – with the network, with other things, and with the user if necessary (more often than not, as you’ll see, communicating to the user may be an unnecessary burden). Challenges for the Internet of Things Efficiency Devices within the Internet of Things (IoT) only need to do the bare minimum necessary to effectively work within the existing ecosystem. Many of the newest products rely heavily on the power of your smartphone to connect to the Internet and orchestrate devices, but there is also extensive pressure to reduce the size, energy consumption, and cost of the processing entities within IoT devices. In order to reduce power consumption and manage node outages, there is a concept of daisy-chaining across a network of devices into a more powerful central hub. This is known as mesh networking, and it’s becoming quite popular for IoT systems. Security, Privacy & the need to Share A core requirement of a well-functioning IoT device is to collect, transfer, and store data from a wide variety of sources. As more sensors arrive in cities and healthcare institutions, that increasingly connected information will unavoidably lead to more concern about security and privacy. The debate is still raging over balancing the clear benefits of new discoveries from processing Big Data with the strong personal fear of losing privacy. With IoT now in the picture, there is concern about devices that continuously and passively collect information on users. One recent clash over always-on sensors came with the release of Microsoft’s Xbox One Kinect console, which has a camera that is constantly pointed at your living room. Although the camera itself is not always on, the backlash over that possibility was fierce [1]. Finding this balance will quickly become a requirement for continued progress. Furthermore, the very nature of IoT and the connectivity network necessary for its success does make it particularly vulnerable in certain instances. Devices are especially vulnerable when connected over WiFi, because low tech sensor nodes with minimal computing power tend to be less secure, making them the ideal point of entry for infiltrators. Standards As with all new technologies, the battle over standards is always a struggle. Nest, the company that developed the most popular smart home thermostat, and its new owner, Google, are now making significant strides trying to establish the Nest platform as the foundation for all consumer-based IoT devices and their software counterparts [2]. Cisco, Qualcomm, IBM, Microsoft, and most other major players have a similar strategy for creating standard models for approaching the Internet of Things. The pressure to standardize is especially clear when new devices are appearing weekly. ZigBee already has extensive reach as an established standard for many household IoT devices. However, as a preferred codebase has yet to emerge as the standard of choice, it is recommended to connect with major standardization organizations like the IEEE, IETF, and the ZigBee Alliance [3]. Currently, the most common sensor networks use protocols such as Bluetooth Low Energy (BLE), RFID tags, ZigBee, and Wi-Fi. There are also iBeacons, which allow devices like smartphones to better identify their location and potential needs with NFC-powered micro-location and GPS technology. Opportunities for the Internet of Things There are numerous prospects to consider when looking to develop IoT products. Given the multi-trillion dollar projections for the future IoT economy, we should take a look at these emerging markets for IoT tech [4]. Consumers The consumer IoT space has bred a small but growing segment of followers that have invested early into “smart” tech. At this year’s CES, we saw everything from the Babolat Tennis Racket that becomes your personal tennis coach to the Kolibree Toothbrush that monitors your gum health while you brush. The fastest growing consumer IoT segment seems to be in smart home technology, with products such as self-managing refrigerators and resident-sensing door locks. Commercial Retailers have already proven adept at collecting a consumer’s shopping history. With the functionality of NFC-powered beacons, these retailers are eager to personalize your shopping experience in a whole new way. Essentially, each physical shopping trip can now be as littered with targeted ads as any typical online search, much like a scene from the 2002 sci-fi film Minority Report. Walk into a store and instantly the advertising screens on the wall change to address your particular demographic, income level, and shopping preferences. If you’ve connected your Google calendar to certain applications, these screens would show outfits targeting your next big event. Signs on clothing racks sense you coming near and change prices, fully leveraging a custom pricing model that would have economists drooling. And as you try on outfits, the smart mirror in the dressing room recommends accessories or comments on alternatives that might be a better fit for your body type. After all of these IoT events have helped you with your purchase, there’s no need to checkout. You’ve registered with the store and there’s a beacon at the exit that registers what you picked up and charges your card automatically as you leave. Healthcare With the recent U.S. mandate that all health records must be digital, there has been an explosion in the marketplace of new, patient-centered, smart health devices. The excitement of a healthcare revolution among top innovative companies, incubators, and startups predicts that this trend is not likely to taper off anytime soon. The key areas of focus so far have been: monitoring technologies like wearables (especially passive monitoring), function improving technologies, education, and notification technologies. Wearables are generally the first consumer touch point in the IoT health sphere. With the popularity of Fitbit pedometers and Withings scales, the market is starting to experiment with internal monitoring and potentially replacing some organs completely in the near future. A study at Boston University has had incredibly positive results creating an artificial pancreas for Type 1 diabetics by inserting an insulin and glucagon pump that responds when an attached glucometer goes below a certain level, just like an actual pancreas. Proteus, a promising startup out of San Francisco, has created an all-natural microchip in a pill that the patient swallows in order to monitor whether they are remembering to take their medication. The pill sends data to an armband that the user is wearing, which then can send notifications to family members regarding the patient’s status. The most impressive feature is the fact that these chips are powered by the energy in the patient’s digestive system. Cities, Infrastructure, and Industry The long-term vision of the future includes technology such as self-driving cars and city lights that alert police when there’s been an accident. In this stage of development, the majority of value is coming from technologies that monitor and collect data in urban settings. From an evolutionary perspective, the IoT city as a whole is still in what many would consider a learning phase. The main objective is to collect as much data as possible, make it available via open APIs, and encourage motivated data analysts to find opportunities for improvement in utility usage, environmental impacts, and service management for larger populations. This is one area where being an industrial country like the U.S. may actually impede the ability to progress as quickly as our less established counterparts. Third world countries that haven’t yet built a solid infrastructure allow for the creativity and flexibility to implement sophisticated solutions unfettered by generations of previous development. Silicon Valley powerhouses like Facebook and Google are actively engaged in projects to create a free global Wi-Fi network, and key locations in Africa have allowed them to experiment with these projects. Being an Early Mover In the very near future, as more and more things connect to the internet, internet connectivity from IoT devices will dwarf the amount of traditional web browsing. The core standards and assumptions that will drive this next revolution in computing technology are still being established and, as a result, building anything that can add value to this exploding industry (software, hardware, devices, sensors, beacons etc.) is a remarkable opportunity for the right developer. Right now is the time to start contributing to the development of these technologies if you want to be an early mover in IoT. 2014 Guide to Internet of Things The 2014 Guide to Internet of Things covers 39 different IoT SDKs, developer programs, and hardware options, plus: Key findings from our survey of over 2,000 developers "How to IoT Your Life: The Complete Shopping List" "The Scale of IoT" Infographic Glossary of common IoT terms Four in-depth articles from industry experts DOWNLOAD NOW

In this blog, we will discuss one particular data grid platform from Redhat namely JBoss Data Grid (JDG). We will firstly cover how to access and install this data grid platform and then we will demonstrate how to develop and deploy a simple remote client/server data grid application which utilises the HotRod protocol. We will be using the latest release JDG 6.2 from Redhat in this article. Installation Overview To start using JDG, firstly log on to the redhat site https://access.redhat.com/home and download the software from the Downloads section of the site. We wish to download JDG 6.2 server by clicking on the appropriate links in the Downloads section. For future reference, it is also useful to download the quickstart and maven repository zip files. To install JDG, we simply unzip the JDG server package into an appropriate directory in your environment. JDG Overview In this section, we will provide a brief overview of the contents of the JDG installation package and the most notable configuration options available to users. Out of the box, users are provided with two runtime options either to run JDG in standalone or clustered mode. We can start JDG in either mode by invoking the stanadalone or clustered start up scripts in the / bin directory. To configure the JDG in either mode we need to configure the files standalone.xml and clustered.xml. In our case we will creating a distributed cache which will run on 3 node JDG cluster so we will be utilizing the clustered startup script. In order to set up and add new cache instances to JDG, we modify the infinispan subsystems in the appropriate xml configuration file above. We should also note the principal difference between the standalone and clustered configuration file is that in the clustered configuration file there is a JGroups subsystem configured element which allows for communication and messaging between configured cache instances running in a JDG cluster. Development Environment Setup and Configuration In this section, we will detail how to develop and configure a simple datagrid application which will be deployed to a 3 node JDG cluster. We will demonstrate how to configure and deploy a distributed cache in JDG and also show how to develop a HotRod Java client application which will be used to insert, update and display entries in the distributed cache. We will firstly discuss setting a new distributed cache on a 3 node JDG cluster. In this example, we will run our JDG cluster on a single machine by running each JDG instance on different ports. Firstly, we will create 3 instances of JDG by creating 3 directories (server1, server2, server3) on our host machine and unzipping each JDG installation into each directory. We will now configure each node in our cluster by copying and renaming the clustered.xml configuration file in the \server1\jboss-datagrid-6.2.0-server\standalone\configuration directory. We will name each of the cluster configuration files as "clustered1.xml", "clustered2.xml" and "clustered3.xml" for the JDG instances denoted by "server1", "server2" and "server3" respectively. We will now set up a new distributed cache on our JDG cluster by modifying the infinispan subsystem element in each clustered.xml file. We will demonstrate this for the node denoted "server1" here by modifying the file "clustered1.xml". The cache configuration shown here will be the same across all 3 nodes. To setup a new distributed cache named "directory-dist-cache", we configure the following elements in the file named "clustered1.xml" ......... ...... .............. ...... ...... /socket-binding-group> We will discuss the key elements and attributes relating to the configuration above. In the infinispan endpoint subsystem, we will configure hotrod clients to connect to the JDG server instance on socket 11222. The name of the cache container to host each of the cache instances will be held in the container named "clusteredcache". We have configured the infinispan core subsystem to the default cache container named "clusteredcacahe" whereby we will allow for jmx statistics to be collected relating the configured cache entries i.e statistics="true" We have created a new distributed cache named "directory-dist-cache" whereby there will be two copies of each cache entry held on two of the 3 cluster nodes. We have also set up an eviction policy whereby should there be more than 20 entries in our cache then cache entries will be removed using the LRU algorithm We should have configured nodes "server2" and "server3" to start up with a port offset of 100 and 200 respectively by configuring the socketing binding group element appropriately. Please view the socket bindings noted below. To set the socket binding element with a port offset of 100 on "server2", we configure "clustered2.xml" with the following entry: ...... ...... /socket-binding-group> To set the socket binding element with a port offset of 200 on "server3", we configure "clustered3.xml" with the following entry: ...... ...... /socket-binding-group> Before discussing the setup and configuration of our Hotrod client which will be used to interact with our JDG clustered HotRod server, we will start up each server instance to ensure our newly configured JDG distributed cache starts up correctly. Open up 3 Windows or Linux consoles and execute the following start up commands: Console 1: 1) Navigate to \server1\jboss-datagrid-6.2.0-server\bin 2) Execute this command to start the first instance of our JDG cluster denoted "server1": clustered -c=clustered1.xml -Djboss.node.name=server1 Console 2: 1) Navigate to \server2\jboss-datagrid-6.2.0-server\bin 2) Execute this command to start the second instance of our JDG cluster denoted "server2": clustered -c=clustered2.xml -Djboss.node.name=server2 Console 3: 1) Navigate to \server3\jboss-datagrid-6.2.0-server\bin 2) Execute this command to start the third instance of our JDG cluster denoted "server3": clustered -c=clustered3.xml -Djboss.node.name=server3 Providing all 3 JDG instances have started up correctly, you should see output in the console window whereby we can see there are 3 JDG instances in the JGroups view: HotRod Client Development Setup Now that the Hotrod server is up and running, we need to develop a Hotrod Java client which will interact with the clustered server application. The development environment consists of the following tools. 1) JDK Hotspot 1.7.0_45 2) IDE - Eclipse Kepler Build id: 20130919-0819 The HotRod client application is a simple application consisting of two Java classes. The application allows users to retrieve a reference to the distributed cache from the JDG server and then perform these actions: a) add new cinema objects. b) add and remove shows to each cinema object. c) print the list of all cinemas and shows stored in our distributed cache. The source code can be downloaded from github @ https://github.com/davewinters/JDG. We could use maven here to build and execute our application by configuring the maven settings.xml to point to the maven repository files we downloaded earlier and set up a maven project file (pom.xml) to build and execute the client application. In this article we will build our application using the Eclipse IDE and run the client application on the command line. To create a HotRod client application and execute the sample application, one should complete the following steps: 1) Create a new Java Project in Eclipse 2) Create a new package named uk.co.c2b2.jdg.hotrod and import the source code that has been downloaded from Github mentioned previously. 3) Now we need to configure the build path in Eclipse to contain the appropriate JDG client jar files which are required to compile the application. You should include all the client jar files in the project build path. These jar files are contained in the JDG installation zip file. For example on my machine these jar files are located in the directory: \server1\jboss-datagrid-6.2.0-server\client\hotrod\java 4. Providing the Eclipse build path has been configured appropriately, the application source should compile without issue. 5. We will need to execute the Hotrod application by opening the console window and executing the following command. Note the path specified here will differ depending on where the JDG client jar files and application class files are located in your environment: java -classpath ".;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\commons-pool-1.6-redhat-4.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\infinispan-client-hotrod-6.0.1.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\infinispan-commons-6.0.1.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\infinispan-query-dsl-6.0.1.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\infinispan-remote-query-client-6.0.1.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\jboss-logging-3.1.2.GA-redhat-1.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\jboss-marshalling-1.4.2.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\jboss-marshalling-river-1.4.2.Final-redhat-2.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\protobuf-java-2.5.0.jar;C:\Users\David\Installs\jbossdatagrids62\server1\jboss-datagrid-6.2.0-server\client\hotrod\java\protostream-1.0.0.CR1-redhat-1.jar" uk/co/c2b2/jdg/hotrod/CinemaDirectory 6. The Hotrod client at runtime provides the end user with a number of different options to interact with the distributed cache as we can view from the console window below. Client Application Principal API Details We will not provide a detailed overview of the Hotrod application code however we will describe the principal API and code details briefly. In order to interact with the distributed cache on the JDG cluster using the Hotrod protocol, we will use the RemoteCacheManager Object which will allow us to retrieve a remote reference to the distributed cache. We have initialised a Properties object with the list of JDG instances and the associated with HotRod server port on each instance. We can add Cinema objects into the distributed cache using the RemoteCache.put() method. private RemoteCacheManager cacheManager; private RemoteCache cache; ..... Properties properties = new Properties(); properties.setProperty(ConfigurationProperties.SERVER_LIST, "127.0.0.1:11222;127.0.0.1:11322;127.0.0.1:11422"); cacheManager = new RemoteCacheManager(properties); cache = cacheManager.getCache("directory-dist-cache"); ..... cache.put(cinemaKey, cinemalist); In the webinar below, I describe in further detail how to set up a JDG cluster and how to develop and run the JDG application discussed above. For further details on JDG please visit: http://www.redhat.com/products/jbossenterprisemiddleware/data-grid/ Webinar: Introduction to JBoss Data Grid -- Installation, Configuration and Development In this webinar we will look at the basics of setting up JBoss Data Grid covering installation, configuration and development. We will look at practical examples of storing data, viewing the data in the cache and removing it. We will also take a look at the different clustered modes and what effect these have on the storage of your data:

In this post we explore 5 reasons to use a Java Data Grid for caching Java objects in-memory in your applications. In a later post we will explore some of the other data grid capabilities, beyond data storage, that can revolutionize your Java architectures, like on-grid computation and events. Memory is Fast Java Data Grids store Java objects in memory. Memory access is fast with low latency. So if access to data storage either disk or database is the primary bottleneck in your application then using a data grid as an in-memory cache in front of your storage tier will give you a performance boost. Scale out your Application Shared State If you need to share state across JVMs to scale out your application then using a Java Data Grid rather than a database will increase your scalability. A typical shared state architecture is shown below, the application server tier stores shared Java objects in the data grid and these objects are available to all application server nodes in your architecture. Separating the data grid tier from the application server tier has a number of advantages; Applications can be redeployed and restarted without losing the shared state Data Grid JVMs and Application JVMs can be tuned separately State can be shared across multiple different applications. Each tier can be scaled horizontally separately depending on work load Typical use cases for shared state include; PCI compliant storage of card security codes; In-game state in online games; web session data; prices and catalogues in ecommerce. Anything that needs low latency access can be stored in the shared data grid. High Availability for In-Memory Data As well as low latency access and scaling out shared state. Java Data Grids also provide high availability for your in-memory data. When storing Java objects in a data grid a primary object is stored in one of the Data Grid JVMs and secondary back up copies of the object are stored in different Data Grid JVM node, ensuring that if you lose a node then you don't lose any data. Clients of the data grid do not need to know where data is to access it so high availability is transparent to your application. Scale Out In-Memory Data Volumes Java objects, in data grids, aren't fully replicated across all Data Grid JVMs but are stored as a primary object and a secondary object. This means the more Data Grid JVM nodes we add the more JVM heap we have for storing Java objects in-memory (and remember memory is fast). For example if we build a Data Grid with 20 JVMs each with 4Gb free heap (after per JVM overhead) we could theoretically store 80Gb (4 times 20) of shared Java objects. If we assume we have 1 duplicate for high availability this cuts our storage in half so we can store 40Gb (.5 time 4 times 20 ) of Java Objects in memory. Native Integration with JPA Java Data Grids have native integration with JPA frameworks like TopLink and Hibernate whereby the Data Grid can act as a second level cache between JPA and the database. This can give a large performance boost to your database driven application if latency associated with database access is a key performance bottleneck.

Originally written by Scott Jarr for VoltDB. In post one of this series, we introduced the ideas that a Corporate Data Architecture was taking shape and that working with Fast Data is different from working with Big Data. In the second post we looked at examples of Fast Data and what is required of applications that interact with Fast Data. In this post, I will illustrate how I envision the corporate architecture that will enable companies to achieve the data dream that integrates Fast and Big. The following diagram depicts a basic view of how the “Big” side of the picture is starting to fill out. At the center is a Data Lake, or pool or reservoir or…. there is no shortage of clever names and debate over what to call it. What is clear is this is the spot in which the enterprise will dump ALL of its data. This component is not necessarily unique because of its design or functionality, but because it is an enormously cost effective system to store everything. Essentially, it is a distributed file system on cheap commodity machines. There may or may not be a single winning technology here. It may be HDFS or some other store (maybe S3 if you’re on Amazon), but the point is, this is where all data will go. This platform will: 1. Store data that will be sent to other data management products, and 2. Support frameworks for executing jobs directly against the data in the file system. Moving around the outside of our Data Lake are the complementary pieces of technology that allow people to gain insight and value from the data stored in the Data Lake. Starting at 12 o’clock in the diagram above and moving clockwise: BI – Reporting: Data warehouses do an excellent job of reporting, and will continue to offer this capability. Some data will be exported to those systems and temporarily stored there, while other data will be accessed directly from the Data Lake in a hybrid fashion. These data warehouse systems were specifically designed to run complex report analytics, and do this well. SQL on Hadoop: There is a lot of innovation here. The goal of many of these products is to displace the data warehouse. Advances have been made with the likes of Hawq and Impala. But make no mistake, there is a long way to go for these systems to get near the speed and efficiency of the data warehouses, especially those with columnar designs. SQL-on-Hadoop systems exist for a couple of important reasons: 1) SQL is still the best way to get at data, and 2) Processing can occur without moving big chunks of data around. Exploratory Analytics: This is the realm of the data scientist. These tools offer the ability to “find” things in data – patterns, obscure relationships, statistical rules, etc. Mahout and R are popular tools in this category. MapReduce: This is a lazily-named group of all the job scheduling and management tasks that often occur on Hadoop (I really should come up with something more accurate). Many Hadoop use cases today involve pre-processing or cleaning data prior to the use of the analytics tools described above. These are the tools and interfaces that allow that to happen. ETL of Enterprise Apps: Last at 6 o’clock is the ETL process that will help get all the legacy data from our trusty enterprise applications into our data lake that stores everything. These applications will slowly migrate to full-fledged Fast+Big Data apps in time, which I will discuss in a future post. But suffice it to say: once I add sensors to a manufacturing line, I have a Fast+Big Data problem. OK, we now have analytics … so what? Why do we do analytics in the first place? Simple. We want: Better decisions Better personalization Better detection Better …. Interaction. Interaction is what the application is responsible for, and the most valuable improvements come when you can do these interactions accurately and in real-time. This brings us to the second half of the architecture where we deal with Fast Data to make better, faster real-time applications, depicted in the diagram below. The first thing to notice is that there is a tight coupling of Fast and Big, although they are separate systems. They have to be, at least at scale. The database system designed to work with millions of event decisions per second is wholly different from the system designed to hold Petabytes of data and generate extensive reports. The nature of Fast Data produces a number of critical requirements to get the most out of it. These include the ability to: Ingest / interact with the data feed Make decisions on each event in the feed Provide visibility into fast-moving data with real-time analytics Seamlessly integrate into the systems designed to store Big Data Ability to serve analytic results and knowledge from the Big Data systems quickly to users and applications, closing the data loop. There is no better technology to meet these requirements than an operational database. The challenge we have faced is that there hasn’t been an operational database that can manage this kind of throughput. As a result, there have been a number of Band-Aids people have used to attempt to meet their needs, often giving up capabilities and always adding complexity. In a next post, I will detail the capabilities I see customers looking for to support their Fast Data applications. Then we will take a look at the results of attempting this solution with a popular alternative, stream processing. Originally written by Scott Jarr for VoltDB.

scalability has become one of those core concept slash buzzwords of big data. it’s all about scaling out, web scale, and so on. in principle, the idea is to be able to take one piece of code and then throw any number of computers at it to make it fast. the terms “scalable” and “large scale” have been used in machine learning circles long before there was big data. there had always been certain problems which lead to a large amount of data, for example in bioinformatics, or when dealing with large number of text documents. so finding learning algorithms, or more generally data analysis algorithms which can deal with a very large set of data was always a relevant question. interestingly, this issue of scalability were seldom solved using actual scaling in in machine learning, at least not in the big data kind of sense. part of the reason is certainly that multicore processors didn’t yet exist at the scale they do today and that the idea of “just scaling out” wasn’t as pervasive as it is today. instead, “scalable” machine learning is almost always based on finding more efficient algorithms, and most often, approximations to the original algorithm which can be computed much more efficiently. to illustrate this, let’s search for nips papers (the annual advances in neural information processing systems, short nips, conference is one of the big ml community meetings) for papers which have the term “scalable” in the title. here are some examples: scalable inference for logistic-normal topic models … this paper presents a partially collapsed gibbs sampling algorithm that approaches the provably correct distribution by exploring the ideas of data augmentation … partially collapsed gibbs sampling is a kind of estimation algorithm for certain graphical models. a scalable approach to probabilistic latent space inference of large-scale networks … with […] an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices […] on a single machine in a matter of hours … stochastic variational inference algorithm is both an approximation and an estimation algorithm. scalable kernels for graphs with continuous attributes … in this paper, we present a class of path kernels with computational complexity $o(n^2(m + \delta^2 ))$ … and this algorithm has squared runtime in the number of data points, so wouldn’t even scale out well even if you could. usually, even if there is potential for scalability, it usually something that is “embarassingly parallel” (yep, that’s a technical term), meaning that it’s something like a summation which can be parallelized very easily. still, the actual “scalability” comes from the algorithmic side. so how do scalable ml algorithms look like? a typical example are the stochastic gradient descent (sgd) class of algorithms. these algorithms can be used, for example, to train classifiers like linear svms or logistic regression. one data point is considered at each iteration. the prediction error on that point is computed and then the gradient is taken with respect to the model parameters, giving information about how to adapt these parameters slightly to make the error smaller. vowpal wabbit is one program based on this approach and it has a nice definition of what it considers to mean scalable in machine learning: there are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. this project is about approach (b), and it’s reached a state where it may be useful to others as a platform for research and experimentation. so “scalable” means having a learning algorithm which can deal with any amount of data, without consuming ever growing amounts of resources like memory. for sgd type algorithms this is the case, because all you need to store are the model parameters, usually a few ten to hundred thousand double precision floating point value, so maybe a few megabytes in total. the main problem to speed this kind of computation up is how to stream the data by fast enough. to put it differently, not only does this kind of scalability not rely on scaling out, it’s actually not even necessary or possible to scale the computation out because the main state of the computation easily fits into main memory and computations on it cannot be distributed easily. i know that gradient descent is often taken as an example for map reduce and other approaches like in this paper on the architecture of spark , but that paper discusses a version of gradient descent where you are not taking one point at a time, but aggregate the gradient information for the whole data set before making the update to the model parameters. while this can be easily parallelized, it does not perform well in practice because the gradient information tends to average out when computed over the whole data set. if you want to know more, this large scale learning challenge sören sonnneburg organized in 2008 still has valuable information on how to deal with massive data sets. of course, there are things which can be easily scaled well using hadoop or spark, in particular any kind of data preprocessing or feature extraction where you need to apply the same operation to each data point in your data set. another area where parallelization is easy and useful is when you are using cross validation to do model selection where you usually have to train a large number of models for different parameter sets to find the combination which performs best. again, even here there is more potential for even speeding up such computations using better algorithms like in this paper of mine . i’ve just scratched the surface of this, but i hope you got the idea that scalability can mean quite different things. in big data (meaning the infrastructure side of it) what you want to compute is pretty well defined, for example some kind of aggregate over your data set, so you’re left with the question of how to parallelize that computation well. in machine learning, you have much more freedom because data is noisy and there’s always some freedom in how you model your data, so you can often get away with computing some variation of what you originally wanted to do and still perform well. often, this allows you to speed up your computations significantly by decoupling computations. parallelization is important, too, but alone it won’t get you very far. luckily, there are projects like spark and stratosphere/flink which work on providing more useful abstractions beyond map and reduce to make the last part easier for data scientists, but you won’t get rid of the algorithmic design part any time soon.

So you already know what Hadoop is? Why it is used? What problems you can solve with it?

to converge internet of thing devices with corporate it solutions, teams require a reference architecture for the internet of things (iot). the reference architecture must include devices, server-side capabilities, and cloud architecture required to interact with and manage the devices. a reference architecture should provide architects and developers of iot projects with an effective starting point that addresses major iot project and system requirements. a high-level iot reference architecture may include the following layers (see figure 1): external communications - web/portal, dashboard, apis event processing and analytics (including data storage) aggregation / bus layer – esb and message broker device communications devices cross-‐cutting layers include: device and application management identity and access management a more detailed architecture component description can be found in the iot reference architecture white paper .

Explore different message brokers, and discover how these important web technologies impact a customer's backlog of messages, and cluster/data performance.

In the last week I was working on a key project to migrate a BI platform from MySQL to MongoDB. We chose that database due to its support and scalability.

it's very important to learn hadoop by practice. one of the learning curves is how to write the first map reduce app and debug in favorite ide, eclipse. do we need any eclipse plugins? no, we do not. we can do hadoop development without map reduce plugins this tutorial will show you how to set up eclipse and run your map reduce project and mapreduce job right from your ide. before you read further, you should have setup hadoop single node cluster and your machine. you can download the eclipse project from github . use case: we will explore the weather data to find maximum temperature from tom white’s book hadoop: definitive guide (3rd edition) chapter 2 and run it using toolrunner i am using linux mint 15 on virtualbox vm instance. in addition, you should have hadoop (mrv1 am using 1.2.1) single node cluster installed and running, if you have not done so, would strongly recommend you do it from here download eclipse ide, as of writing this, latest version of eclipse is kepler 1. create new java project 2. add dependencies jars right click on project properties and select java build path add all jars from $hadoop_home/lib and $hadoop_home (where hadoop core and tools jar lives) 3. create mapper package com.letsdobigdata; import java.io.ioexception; import org.apache.hadoop.io.intwritable; import org.apache.hadoop.io.longwritable; import org.apache.hadoop.io.text; import org.apache.hadoop.mapreduce.mapper; public class maxtemperaturemapper extends mapper { private static final int missing = 9999; @override public void map(longwritable key, text value, context context) throws ioexception, interruptedexception { string line = value.tostring(); string year = line.substring(15, 19); int airtemperature; if (line.charat(87) == '+') { // parseint doesn't like leading plus // signs airtemperature = integer.parseint(line.substring(88, 92)); } else { airtemperature = integer.parseint(line.substring(87, 92)); } string quality = line.substring(92, 93); if (airtemperature != missing && quality.matches("[01459]")) { context.write(new text(year), new intwritable(airtemperature)); } } } 4. create reducer package com.letsdobigdata; import java.io.ioexception; import org.apache.hadoop.io.intwritable; import org.apache.hadoop.io.text; import org.apache.hadoop.mapreduce.reducer; public class maxtemperaturereducer extends reducer { @override public void reduce(text key, iterable values, context context) throws ioexception, interruptedexception { int maxvalue = integer.min_value; for (intwritable value : values) { maxvalue = math.max(maxvalue, value.get()); } context.write(key, new intwritable(maxvalue)); } } 5. create driver for mapreduce job map reduce job is executed by useful hadoop utility class toolrunner package com.letsdobigdata; import org.apache.hadoop.conf.configured; import org.apache.hadoop.fs.path; import org.apache.hadoop.io.intwritable; import org.apache.hadoop.io.text; import org.apache.hadoop.mapreduce.job; import org.apache.hadoop.mapreduce.lib.input.fileinputformat; import org.apache.hadoop.mapreduce.lib.output.fileoutputformat; import org.apache.hadoop.util.tool; import org.apache.hadoop.util.toolrunner; /*this class is responsible for running map reduce job*/ public class maxtemperaturedriver extends configured implements tool{ public int run(string[] args) throws exception { if(args.length !=2) { system.err.println("usage: maxtemperaturedriver "); system.exit(-1); } job job = new job(); job.setjarbyclass(maxtemperaturedriver.class); job.setjobname("max temperature"); fileinputformat.addinputpath(job, new path(args[0])); fileoutputformat.setoutputpath(job,new path(args[1])); job.setmapperclass(maxtemperaturemapper.class); job.setreducerclass(maxtemperaturereducer.class); job.setoutputkeyclass(text.class); job.setoutputvalueclass(intwritable.class); system.exit(job.waitforcompletion(true) ? 0:1); boolean success = job.waitforcompletion(true); return success ? 0 : 1; } public static void main(string[] args) throws exception { maxtemperaturedriver driver = new maxtemperaturedriver(); int exitcode = toolrunner.run(driver, args); system.exit(exitcode); } } 6. supply input and output we need to supply input file that will be used during map phase and the final output will be generated in output directory by reduct task. edit run configuration and supply command line arguments. sample.txt reside in the project root. your project explorer should contain following ] 7. map reduce job execution 8. final output if you managed to come this far, once the job is complete, it will create output directory with _success and part_nnnnn , double click to view it in eclipse editor and you will see we have supplied 5 rows of weather data (downloaded from ncdc weather) and we wanted to find out the maximum temperature in a given year from input file and the output will contain 2 rows with max temperature in (centigrade) for each supplied year 1949 111 (11.1 c) 1950 22 (2.2 c) make sure you delete the output directory next time running your application else you will get an error from hadoop saying directory already exists. happy hadooping!

As it turns out, doing work on big data sets is quite hard. To start with, you need to get the data, and it is… well, big. So that takes a while. Instead, I decided to test my theory on the following scenario. Given 4 GB of random numbers, let us find how many times we have the number 1. Because I wanted to ensure a consistent answer, I wrote: public static IEnumerable RandomNumbers() { const long count = 1024 * 1024 * 1024L * 1; var random = new MyRand(); for (long i = 0; i < count; i++) { if (i % 1024 == 0) { yield return 1; continue; } var result = random.NextUInt(); while (result == 1) { result = random.NextUInt(); } yield return result; } } /// /// Based on Marsaglia, George. (2003). Xorshift RNGs. /// http://www.jstatsoft.org/v08/i14/paper /// public class MyRand { const uint Y = 842502087, Z = 3579807591, W = 273326509; uint _x, _y, _z, _w; public MyRand() { _y = Y; _z = Z; _w = W; _x = 1337; } public uint NextUInt() { uint t = _x ^ (_x << 11); _x = _y; _y = _z; _z = _w; return _w = (_w ^ (_w >> 19)) ^ (t ^ (t >> 8)); } } I am using a custom Rand function because it is significantly faster than System.Random. This generate 4GB of random numbers, at also ensure that we get exactly 1,048,576 instances of 1. Generating this in an empty loop takes about 30 seconds on my machine. For fun, I run the external sort routine in 32 bits mode, with a buffer of 256MB. It is currently processing things, but I expect it to take a while. Because the buffer is 256 in size, we flush it every 128 MB (while we still have half the buffer free to do more work). The interesting thing is that even though we generate random number, sorting then compressing the values resulted in about 60% compression rate. The problem is that for this particular case, I am not sure if that is a good thing. Because the values are random, we need to select a pretty high degree of compression just to get a good compression rate. And because of that, a significant amount of time is spent just compressing the data. I am pretty sure that for real world scenario, it would be better, but that is something that we’ll probably need to test. Not compressing the data in the random test is a huge help. Next, external sort is pretty dependent on the performance of… sort, of course. And sort isn’t that fast. In this scenario, we are sorting arrays of about 26 million items. And that takes time. Implementing parallel sort cut this down to less than a minute per batch of 26 million. That let us complete the entire process, but then it halts with the merge. The reason for that is that we push all the values into a heap, and there are 1 billion of them. Now, the heap never exceed 40 items, but those are still 1 billion * O(log 40) or about 5.4 billion comparisons that we have to do, and we do this sequentially, which takes time. I tried thinking about ways to parallel, but I am not sure how that can be done. We have 40 sorted files, and we want to merge all of them. Obviously we can sort each 10 files set in parallel, then sort the resulting 4, but the cost we have now is the actual sorting cost, not I/O. I am not sure how to approach this. For what is it worth, you can find the code for this here.

I mentioned several times that the entire point of the exercise was to just see how this works, not to actually do anything production worthy. But it is interesting to see how we could do better here. In no particular order, I think that there are at least several things that we could do to significantly improve the time it takes to sort. Right now we defined 2 indexes on top of a 1GB file, and it took under 1 minute to complete. That gives us a runtime of about 10 days over a 15TB file. Well, one of the reason for this performance is that we execute this in a serial fashion, that is, one after another. But we have to completely isolated indexes, there is no reason why we can’t parallelize the work between them. For that matter, we are buffering in memory up to a certain point, then we sort, then we buffer some more, etc. That is pretty inefficient. We can push the actual sorting to a different thread, and continue parsing and adding to a buffer while we are adding to the buffer. We wrote to intermediary files, but we wrote to those using plain file I/O. But it is usually a lot more costly to write to disk than to compress and then write to disk. We are writing sorted data, so it is probably going to compress pretty well. Those are the things that pop to mind. Can you think of additional options?

I have completed my own exercise, and while I wanted to try it with “few allocations” rule, it is interesting to see just how far out there the code is. This isn’t something that you can really use for anything except as a basis to see how badly you are doing. Let us start with the index format. It is just a CSV file with the value and the position in the original file. That means that any search we want to do on the file is actually a binary search, as discussed in the previous post. But doing a binary search like that is an absolute killer for performance. Let us consider our 15TB data set. In my tests, a 1GB file with 4.2 million rows produced roughly 80MB index. Assuming the same is true for the larger file, that gives us a 1.2 TB file. In my small index, we have to do 24 seeks to get to the right position in the file. And as you should know, disk seeks are expensive. They are in the order of 10ms or so. So the cost of actually searching the index is close to quarter of a second. Now, to be fair, there is going to be a lot of caching opportunities here, but probably not that many if we have a lot of queries to deal with ere. Of course, the fun thing about this is that even with a 1.2 TB file, we are still talking about less than 40 seeks (the beauty of O(logN) in action), but that is still pretty expensive. Even worse, this is what happens when we are running on a single query at a time. What do you think will happen if we are actually running this with multiple threads generating queries. Now we will have a lot of seeks (effective random) that would generate a big performance sink. This is especially true if we consider that any storage solution big enough to store the data is going to be composed of an aggregate of HDD disks. Sure, we get multiple spindles, so we get better performance overall, but still… Obviously, there are multiple solutions for this issue. B+Trees solve the problem by packing multiple keys into a single page, so instead of doing a O(log2N), you are usually doing O(log36N) or O(log100N). Consider those fan outs, we will have 6 – 8 seeks to do to get to our data. Much better than the 40 seeks required using plain binary search. It would actually be better than that in the common case, since the first few levels of the trees are likely to reside in memory (and probably in L1, if we are speaking about that). However, given that we are storing sorted strings here, one must give some attention to Sorted Strings Tables. The way those work, you have the sorted strings in the file, and the footer contains two important bits of information. The first is the bloom filter, which allows you to quickly rule out missing values, but the more important factor is that it also contains the positions of (by default) every 16th entry to the file. This means that in our 15 TB data file (with 64.5 billion entries), we will use about 15GB just to store pointers to the different locations in the index file (which will be about 1.2 TB). Note that the numbers actually are probably worse. Because SST (note that when talking about SST I am talking specifically about the leveldb implementation) utilize many forms of compression, it is actually that the file size will be smaller (although, since the “value” we use is just a byte position in the data file, we won’t benefit from compression there). Key compression is probably a lot more important here. However, note that this is a pretty poor way of doing things. Sure, the actual data format is better, in the sense that we don’t store as much, but in terms of the number of operations required? Not so much. We still need to do a binary search over the entire file. In particular, the leveldb implementation utilizes memory mapped files. What this ends up doing is rely on the OS to keep the midway points in the file in RAM, so we don’t have to do so much seeking. Without that, the cost of actually seeking every time would make SSTs impractical. In fact, you would pretty much have to introduce another layer on top of this, but at that point, you are basically doing trees, and a binary tree is a better friend here. This leads to an interesting question. SST is probably so popular inside Google because they deal with a lot of data, and the file format is very friendly to compression of various kinds. It is also a pretty simple format. That make it much nicer to work with. On the other hand, a B+Tree implementation is a lot more complex, and it would probably several orders of magnitude more complex if it had to try to do the same compression tricks that SSTs do. Another factor that is probably as important is that as I understand it, a lot of the time, SSTs are usually used for actual sequential access (map/reduce stuff) and not necessarily for the random reads that are done in leveldb. It is interesting to think about this in this fashion, at least, even if I don’t know what I’ll be doing with it.

Learn how to set up a four node Hadoop cluster using AWS EC2, PuTTy(gen), and WinSCP.

The index I created for the exercise is just a text file, sorted by the indexed key. When doing a search by a human, that makes it very easy to work with. Much easier than trying to work with a binary file, it also helps debugging. However, it does make it running a binary search on the data a bit harder. Mostly because there isn’t a nice way to say “give me the #th line”. Instead, I wrote the following: public void SetPositionToLineAt(long position) { // now we need to go back until we either get to the start of the file // or find a \n character const int bufferSize = 128; _buffer.Capacity = Math.Max(bufferSize, _buffer.Capacity); var charCount = _encoding.GetMaxCharCount(bufferSize); if (charCount > _charBuf.Length) _charBuf = new char[Utils.NearestPowerOfTwo(charCount)]; while (true) { _input.Position = position - (position < bufferSize ? 0 : bufferSize); var read = ReadToBuffer(bufferSize); var buffer = _buffer.GetBuffer(); var chars = _encoding.GetChars(buffer, 0, read, _charBuf, 0); for (int i = chars - 1; i >= 0; i--) { if (_charBuf[i] == '\n') { _input.Position = position - (bufferSize - i) + 1; return; } } position -= bufferSize; if (position < 0) { _input.Position = 0; return; } } } This code starts at an arbitrary byte position, and go backward until it find the new line character ‘\n’. This give me the ability to go to a rough location and get the line oriented input. Once I have that, the rest is pretty easy. Here is the binary search: while (lo <= hi) { position = (lo + hi) / 2; _reader.SetPositionToLineAt(position); bool? result; do { result = _reader.ReadOneLine(); } while (result == null); // skip empty lines if (result == false) yield break; // couldn't find anything var entry = _reader.Current.Values[0]; match = Utils.CompareArraySegments(expectedIndexEntry, entry); if (match == 0) { break; } if (match > 0) lo = position + _reader.Current.Values.Sum(x => x.Count) + 1; else hi = position - 1; } if (match != 0) { // no match yield break; } The idea is that this positions us on the location of the index that has an entry with a value that is equal to what we are searched on. We then write the following to actually get the data from the actual data file: // we have a match, now we need to return all the matches _reader.SetPositionToLineAt(position); while(true) { bool? result; do { result = _reader.ReadOneLine(); } while (result == null); // skip empty lines if(result == false) yield break; // end of file var entry = _reader.Current.Values[0]; match = Utils.CompareArraySegments(expectedIndexEntry, entry); if (match != 0) yield break; // out of the valid range we need _buffer.SetLength(0); _data.Position = Utils.ToInt64(_reader.Current.Values[1]); while (true) { var b = _data.ReadByte(); if (b == -1) break; if (b == '\n') { break; } _buffer.WriteByte((byte)b); } yield return _encoding.GetString(_buffer.GetBuffer(), 0, (int)_buffer.Length); } As you can see, we are moving forward in the index file, reading one line at a time. Then we take the second value, the position of the relevant line in the data file, and read that. We continue to do so as long as the indexed value is the same. Pretty simple, all told. But it comes with its own set of problems. I’ll discuss that in my next post.

the interesting thing about this problem is that i was very careful in how i phrased things. i said what i wanted to happen, but didn’t specify what needs to be done. that was quite intentional. for that matter, the fact that i am posting about what is going to be our acceptance criteria is also intentional. the idea is to have a non trivial task, but something that should be very well understood and easy to research. it also means that the candidate needs to be able to write some non trivial code. and i can tell a lot about a dev from such a project. at the same time, this is a very self contained scenario. the idea is that this is something that you can do in a short amount of time. the reason that this is an interesting exercise is that this is actually at least two totally different but related problems. first, in a 15tb file, we obviously cannot rely on just scanning the entire file. that means that we have to have an index. and that means we have to build it. interestingly enough, an index being a sorted structure, that means that we have to solve the problem of sorting more data than can fit in main memory. the second problem is probably easier, since it is just an implementation of external sort, and there are plenty of algorithms around to handle that. note that i am not really interested in actual efficiencies for this particular scenario. i care about being able to see the code. see that it works, etc. my solution, for example, is a single threaded system that make no attempt at parallelism or i/o optimizations. it clocks at over 1 gb / minute and the memory consumption is at under 150mb. queries for a unique value return the result in 0.0004 seconds. queries that returned 153k results completed in about 2 seconds. when increasing the used memory to about 650mb, there isn’t really any difference in performance, which surprised me a bit. then again, the entire code is probably highly inefficient. but that is good enough for now. the process is kicked off with indexing: 1: var options = new directoryexternalstorageoptions("/path/to/index/files"); 2: var input = file.openread(@"/path/to/data/crimes_-_2001_to_present.csv"); 3: var sorter = new externalsorter(input, options, new int[] 4: { 5: 1,// case number 6: 4, // ichr 7: 8: }); 9: 10: sorter.sort(); i am actually using the chicago crime data for this. this is a 1gb file that i downloaded from the chicago city portal in csv format. this is what the data looks like: the externalsorter will read and parse the file, and start reading it into a buffer. when it gets to a certain size (about 64mb of source data, usually), it will sort the values in memory and output them into temporary files. those file looks like this: initially, i tried to do that with binary data, but it turns out that that was too complex to be easy, and writing this in a human readable format made it much easier to work with. the format is pretty simple, you have the value of the left, and on the right you have start position of the row for this value. we generate about 17 such temporary files for the 1gb file. one temporary file per each 64 mb of the original file. this lets us keep our actual memory consumption very low, but for larger data sets, we’ll probably want to actually do the sort every 1 gb or maybe more. our test machine has 16 gb of ram, so doing a sort and outputting a temporary file every 8 gb can be a good way to handle things. but that is beside the point. the end result is that we have multiple sorted files, but they aren’t sequential. in other words, in file #1 we have values 1,4,6,8 and in file #2 we have 1,2,6,7. we need to merge all of them together. luckily, this is easy enough to do. we basically have a heap that we feed entries from the files into. and that pretty much takes care of this. see merge sort if you want more details about this. the end result of merging all of those files is… another file, just like them, that contains all of the data sorted. then it is time to actually handle the other issue, actually searching the data. we can do that using simple binary search, with the caveat that because this is a text file, and there is no fixed size records or pages, it is actually a big hard to figure out where to start reading. in effect, what i am doing is to select an arbitrary byte position, then walk backward until i find a ‘\n’. once i found the new line character, i can read the full line, check the value, and decide where i need to look next. assuming that i actually found my value, i can now go to the byte position of the value in the original file and read the original line, giving it to the user. assuming an indexing rate of 1 gb / minute a 15 tb file would take about 10 days to index. but there are ways around that as well, but i’ll touch on them in my next post. what all of this did was bring home just how much we usually don’t have to worry about such things. but i consider this research well spent, we’ll be using this in the future.

I’ll review Google’s most important Big Data publications and discuss where they are (as far as they’ve disclosed).

Your mission-critical projects need complex event processing, realtime management and monitoring. Talend 5.4 (released in December 2013, https://www.talend.com) offers a great new feature: Talend Event Logging. It allows logging, processing and monitoring of all technical events and business data. In this article, I will focus on how to process, filter and monitor business data. You can find more details about monitoring technical events and logs (e.g. OSGi events of the ESB container) in Talend’s documentation (www.help.talend.com). This new feature is very powerful, but also extendable to fit custom requirements. You can solve and monitor much more complex scenarios than the one I describe here. Talend Event Logging with Logstash, ElasticSearch and Kibana First, let’s take a look at the components / projects which are integrated and extended into Talend’s products for implementing this new feature. logstash (http://logstash.net) is a tool for managing events and logs. You can use it to collect different (distributed) logs, parse them, and store them for later use. Speaking of searching, logstash comes with a simple, but fine web interface for searching and drilling into all of your logs. It uses ElasticSearch under the hood. So, you can easily query through all your logs for specific errors or business analytics (e.g. searching for all lines matching an unique order id). Additional to the pure collection of events, the Event Logging feature supports custom processing (e.g. custom filtering, customer data enrichment/reduction), aggregation, signing and also server side custom pre- and post-processing of events - e.g. to send them to an intrusion detection system or to any other kind of potential higher level log processing /management system. Kibana (http://www.elasticsearch.org/overview/kibana) is a browser based analytics and search interface to logstash and other timestamped data sets stored in ElasticSearch. Kibana strives to be easy to get started with, while also being flexible and powerful, just like logstash and ElasticSearch. Main difference to logstash is a much more powerful HTML5 based web interface. You can • use multiple concurrent search inputs • highlight to drill down bar charts • create line charts, stacked, unstacked, filled or unfilled, with or without points • create Pie and donut charts that compare top terms or the results of multiple queries • create custom dashboards with multiple charts • and much more… Therefore, logstash, Elasticsearch and Kibana are a perfect combination. You can use Kibana to analyze and monitor your data as you do with logstash, however, Kibana’s web interface is much more powerful and comfortable than logstash. There is a great book about logstash: “The logstash book” – for just 9.99 USD. I can really recommend this book for getting started: http://www.logstashbook.com/. For Elasticsearch, you can find several books on Amazon. Unfortunately, Kibana has no good and extensive documentation yet. I heard from its developers that this topic is addressed for Q1 2014. Integration of Event Logging and Monitoring into Talend’s Unified Platform Talend 5.4 has integrated logstash and Kibana into its Unified Platform. Talend Administration Center (TAC) is Talend’s central web application for management and monitoring. It got a new logging view: Here, you can use very flexible and powerful realtime search capabilities of Elasticsearch. Some dashboards are available by default. You can also create your own custom dashboards easily within this site thanks to Kibana. Many panel types are available, such as pie, histogram, table, hits or trends. You can analyze every technical event or business data down to the message level: By default, you see fields such as message, source, timestamp, type, and others. Of course, you can also add custom fields suitable for your business case. This way, you have a central monitoring capability which allows analyzing data on distributed clusters easily. Under the hood, this data comes from logstash. Many alternative inputs are available for logstash, such as log4j input or tcp input. For business data, I often use file input (http://logstash.net/docs/1.2.2/inputs/file) to analyze files such as CSV. Adding new inputs is very simple. You just have to add an input to your logstash configuration file of your logstash server. As I mentioned already, all this is integrated into the Talend’s Unified Platform: In this example, there are three log4j inputs and one file input. I use the file input to analyze text files in a specific directory. In this case, output is an embedded Elasticsearch instance. In production, you should use an external Elasticsearch cluster, of course. Processing such as filtering can also be configured in this file. Thus, you can process, analyze and monitor all your different inputs within one central monitoring application thanks to Kibana. Building Talend Integration Jobs, Routes and Web Services As mentioned before, you can analyze almost all data with logstash, Elasticsearch and Kibana. It does not matter if your input is technical events from a container (e.g. OSGi events) or any business data such as CSV files, log4j logs, or something else. Talend implicitly supports technical events which are created by the ESB container, by MDM, etc. However, you can also add your custom business data from your Talend jobs (integration perspective), SOAP / REST Web Services (integration perspective) or Talend routes (mediation perspective), easily: This is just one example (part of Talend’s DI demos which are included in every DI installation). The job generates some random data and stores it to a CSV file. You just have to add the configured file or directory of tFileOutputDelimited to your logstash configuration using file input (with wild cards for more complex scenarios). That’s it. You can now monitor and analyze the business data in realtime. This example showed a Talend DI Job (i.e. ETL job). However, you can also monitor your business data from SOAP / REST Web Services or Talend Routes the same way. Conclusion Your mission-critical projects need management and monitoring. Today, this is not just possible with complex and expensive tools of large vendors, but also with Talend’s Unified Platform products such as Talend ESB or Talend MDM. Under the hood, Talend integrates and extends widely used open source products: logstash, Elasticsearch and Kibana. Have fun with Talend 5.4’s new event logging and monitoring features… Best regards, Kai Wähner (@KaiWaehner) CONTENT FROM MY BLOG: http://www.kai-waehner.de/blog/2013/12/17/realtime-event-logging-complex-event-processing-cep-and-monitoring-with-talends-unified-platform-5-4-di-esb-dq-mdm-bpm-using-elasticsearch-logstash-and-kibana/

Editor's note: Be sure to check out part 2 as well. This is the fourth of an introductory series of blogs on Apache HBase. In the third part, we saw a high level view of HBase architecture . In this part, we'll use the HBase Java API to create tables, insert new data, and retrieve data by row key. We'll also see how to setup a basic table scan which restricts the columns retrieved and also uses a filter to page the results. Having just learned about HBase high-level architecture, now let's look at the Java client API since it is the way your applications interact with HBase. As mentioned earlier you can also interact with HBase via several flavors of RPC technologies like Apache Thrift plus a REST gateway, but we're going to concentrate on the native Java API. The client APIs provide both DDL (data definition language) and DML (data manipulation language) semantics very much like what you find in SQL for relational databases. Suppose we are going to store information about people in HBase, and we want to start by creating a new table. The following listing shows how to create a new table using the HBaseAdmin class. Configuration conf = HBaseConfiguration.create(); HBaseAdmin admin = new HBaseAdmin(conf); HTableDescriptor tableDescriptor = new HTableDescriptor(TableName.valueOf("people")); tableDescriptor.addFamily(new HColumnDescriptor("personal")); tableDescriptor.addFamily(new HColumnDescriptor("contactinfo")); tableDescriptor.addFamily(new HColumnDescriptor("creditcard")); admin.createTable(tableDescriptor); The people table defined in preceding listing contains three column families: personal, contactinfo, and creditcard. To create a table you create an HTableDescriptor and add one or more column families by adding HColumnDescriptor objects. You then call createTable to create the table. Now we have a table, so let's add some data. The next listing shows how to use the Put class to insert data on John Doe, specifically his name and email address (omitting proper error handling for brevity). Configuration conf = HBaseConfiguration.create(); HTable table = new HTable(conf, "people"); Put put = new Put(Bytes.toBytes("doe-john-m-12345")); put.add(Bytes.toBytes("personal"), Bytes.toBytes("givenName"), Bytes.toBytes("John")); put.add(Bytes.toBytes("personal"), Bytes.toBytes("mi"), Bytes.toBytes("M")); put.add(Bytes.toBytes("personal"), Bytes.toBytes("surame"), Bytes.toBytes("Doe")); put.add(Bytes.toBytes("contactinfo"), Bytes.toBytes("email"), Bytes.toBytes("john.m.doe@gmail.com")); table.put(put); table.flushCommits(); table.close(); In the above listing we instantiate a Put providing the unique row key to the constructor. We then add values, which must include the column family, column qualifier, and the value all as byte arrays. As you probably noticed, the HBase API's utility Bytes class is used a lot; it provides methods to convert to and from byte[] for primitive types and strings. (Adding a static import for the toBytes() method would cut out a lot of boilerplate code.) We then put the data into the table, flush the commits to ensure locally buffered changes take effect, and finally close the table. Updating data is also done via the Put class in exactly the same manner as just shown in the prior listing. Unlike relational databases in which updates must update entire rows even if only one column changed, if you only need to update a single column then that's all you specify in the Put and HBase will only update that column. There is also a checkAndPut operation which is essentially a form of optimistic concurrency control - the operation will only put the new data if the current values are what the client says they should be. Retrieving the row we just created is accomplished using the Get class, as shown in the next listing. (From this point forward, listings will omit the boilerplate code to create a configuration, instantiate the HTable, and the flush and close calls.) Get get = new Get(Bytes.toBytes("doe-john-m-12345")); get.addFamily(Bytes.toBytes("personal")); get.setMaxVersions(3); Result result = table.get(get); The code in the previous listing instantiates a Get instance supplying the row key we want to find. Next we use addFamily to instruct HBase that we only need data from the personal column family, which also cuts down the amount of work HBase must do when reading information from disk. We also specify that we'd like up to three versions of each column in our result, perhaps so we can list historical values of each column. Finally, calling get returns a Result instance which can then be used to inspect all the column values returned. In many cases you need to find more than one row. HBase lets you do this by scanning rows, as shown in the second part which showed using a scan in the HBase shell session. The corresponding class is the Scan class. You can specify various options, such as the start and ending row key to scan, which columns and column families to include and the maximum versions to retrieve. You can also add filters, which allow you to implement custom filtering logic to further restrict which rows and columns are returned. A common use case for filters is pagination. For example, we might want to scan through all people whose last name is Smith one page (e.g. 25 people) at a time. The next listing shows how to perform a basic scan. Scan scan = new Scan(Bytes.toBytes("smith-")); scan.addColumn(Bytes.toBytes("personal"), Bytes.toBytes("givenName")); scan.addColumn(Bytes.toBytes("contactinfo"), Bytes.toBytes("email")); scan.setFilter(new PageFilter(25)); ResultScanner scanner = table.getScanner(scan); for (Result result : scanner) { // ... } In the above listing we create a new Scan that starts from the row key smith- and we then use addColumn to restrict the columns returned (thus reducing the amount of disk transfer HBase must perform) to personal:givenName and contactinfo:email. A PageFilter is set on the scan to limit the number of rows scanned to 25. (An alternative to using the page filter would be to specify a stop row key when constructing the Scan.) We then get a ResultScanner for the Scan just created, and loop through the results performing whatever actions are necessary. Since the only method in HBase to retrieve multiple rows of data is scanning by sorted row keys, how you design the row key values is very important. We'll come back to this topic later. You can also delete data in HBase using the Delete class, analogous to the Put class to delete all columns in a row (thus deleting the row itself), delete column families, delete columns, or some combination of those. Connection Handling In the above examples not much attention was paid to connection handling and RPCs (remote procedure calls). HBase provides the HConnection class which provides functionality similar to connection pool classes to share connections, for example you use the getTable() method to get a reference to an HTable instance. There is also an HConnectionManager class which is how you get instances of HConnection. Similar to avoiding network round trips in web applications, effectively managing the number of RPCs and amount of data returned when using HBase is important, and something to consider when writing HBase applications. Conclusion to Part 4 In this part we used the HBase Java API to create a people table, insert a new person, and find the newly inserted person information. We also used the Scan class to scan the people table for people with last name "Smith" and showed how to restrict the data retrieved and finally how to use a filter to limit the number of results. In the next part, we'll learn how to deal with the absence of SQL and relations when modeling schemas in HBase. References HBase web site, http://hbase.apache.org/ HBase wiki, http://wiki.apache.org/hadoop/Hbase HBase Reference Guide http://hbase.apache.org/book/book.html HBase: The Definitive Guide, http://bit.ly/hbase-definitive-guide Google Bigtable Paper, http://labs.google.com/papers/bigtable.html Hadoop web site, http://hadoop.apache.org/ Hadoop: The Definitive Guide, http://bit.ly/hadoop-definitive-guide Fallacies of Distributed Computing, http://en.wikipedia.org/wiki/Fallacies_of_Distributed_Computing HBase lightning talk slides, http://www.slideshare.net/scottleber/hbase-lightningtalk Sample code, https://github.com/sleberknight/basic-hbase-examples

One thing that Big Data certainly made happen is that it brought the database/infrastructure community and the data analysis/statistics/machine learning communities closer together. As always, each community had it’s own set of models, methods, and ideas about how to structure and interpret the world. You can still tell these differences when looking at current Big Data projects, and I think it’s important to be aware of the distinctions in order to better understand the relationships between different projects. Because, let’s face it, every project claims to re-invent Big Data. Hadoop and MapReduce being something like the founding fathers of Big Data, other’s projects have since appeared. Most notably, there are stream processing projects like Twitter’s Storm who move from batch-oriented processing to event-based processing which is more suited for real-time, low-latency processing. Spark is yet something different, a bit like Hadoop, but puts greater emphasis on iterative algorithms, and in-memory processing to achieve that landmark “100x faster than Hadoop” every current project seems to need to sport. Twitter’s summingbird project tries to bridge the gap between MapReduce and stream processing by providing us with a high-level set of operators which can then either run on MapReduce or Storm. However, both Spark or summingbird leave me sort of flat because you can see that they come from a database background, which means that there will still be a considerable gap to serious machine learning. So, what exactly is the difference? In the end, it’s the difference between relational and linear algebra. In the database world, you model relationships between objects, which you encode in tables, and foreign keys to link up entries between different tables. Probably the most important insight of the database world was to develop a query language, a declarative description of what you want to extract from your database, leaving the optimization of the query and the exact details of how to perform them efficiently to the database guys. The machine learning community, on the other hand, has its roots in linear algebra and probability theory. Objects are usually encoded as a feature vector, that is, a list of numbers describing different properties of an object. Data is often collected in matrices where each row corresponds to an object, and each column to a feature, not much unlike a table in a database. However, the operations you perform in order to do data analysis are quite different from the data base world. Take something as basic as linear regression: your try to learn a linear function f(x)=di=1wixi in a d-dimensional space (that is, where your objects are described by a d-dimensional vector) given n examples Xi, and Yi, where Xi are the features describing your objects and Yi is the real number you attach to Xi. One way to “learn” w is to tune it such that the quadratic error on the training examples is minimal. The solution can be written in closed form as w=(XXT)−1XY where X is the matrix built from the Xi (putting the Xi in the columns of X), and Y is the vector of outputs Yi. In order to solve this, you need to solve the linear equation (XXT)w=XY which can be done by one of a large number of algorithms, starting with Gaussian elimination, which you’ve probably learned in your undergrad studies, or the conjugate gradient algorithm, or by first computing a Cholesky decomposition. All of these algorithms have in common that they are iterative. They go through a number of operations, for example O(d3) for the Gaussian elimination case. They also need to store intermediate results. Gaussian elimination and Cholesky decomposition have rather elementary operations acting on individual entries, while the conjugate gradient algorithm performs a matrix-vector multiplication in each iteration. Most importantly, these algorithms can only be expressed very badly in SQL! It’s certainly not impossible, but you’d need to store your data in much different ways than you would in idiomatic database usage. So, it’s not about whether or not your framework can support iterative algorithms without significant latency, it’s about understanding that joins, group bys, and count() won’t get you far, but you need scalar products, matrix-vector and matrix-matrix multiplications. You don’t need indices for most ML algorithms, maybe except for being able to quickly find the k-nearest neighbors, because most algorithms tend to either take in the whole data set in each iteration or otherwise stream the whole set by some model which is iteratively updated like in stochastic gradient descent. I’m not sure projects like Spark or Stratosphere have fully grasped the significance of this yet. Database infrastructure-inspired Big Data has it’s place when it comes to extracting and preprocessing data, but eventually, you move from database land to machine learning land, which invariably means linear algebra land (or probability theory land, which often also reduces to linear algebra like computations). What often happens today is that you either painstakingly have to break down your linear algebra into MapReduce jobs, or you actively look for algorithms which fit the database view better. I think we’re still at the beginning of what is possible. Or, to be a bit more aggressive, claims that existing (infrastructure, database, parallelism inspired) frameworks provide you with sophistic data analytics are widely exaggerated. They take care of a very important problem by giving you a reliable infrastructure to scale your data analysis code, but there’s still a lot of work that needs to be done on your side. High-level DSLs like Apache Hive or Pig are a first step in this direction but still too much rooted in the database world IMHO. In summary, one should be aware of the difference between a framework which mostly is concerned with scaling and a tool which actually provides some piece of data analysis. And even if it comes with basic database-like analytics mechanisms, there is still a long way to go to do some serious data science. That’s why we’re also thinking that streamdrill occupies an interesting spot, because it is a bit of infrastructure, allowing you to process a serious amount of event data, but it also provides valuable analysis based on algorithms you wouldn’t want to implement yourself, even if you had some Big Data framework like Hadoop at hand. That’s an interesting direction I also would like to see more of in the future. Note: Just saw that Spark has a logistic regression example on their landing page. Well, doing matrix operations explicitly via map() on collections doesn’t count in my view ;)