Use tcpdump to capture any UDP packets on port 8125.

In my office laptop, I have installed two versions of JDK. For the office work, I need JDK6 because the internal framework needs it. I’m using JDK7 for my personal projects and exploring the latest and greatest in Java. I have two versions of Eclipse too (one for office work and one is the latest Juno). But, the tricky thing is to manage these multiple JDKs and IDEs. It’s a piece of cake if I just use Eclipse for compiling my code, because the IDE allows me to configure multiple versions of Java runtime. Unfortunately (or fortunately), I have to use the command line/shell to build my code. So, it is important that I have the right version of JDK present in the PATH and other related environment variables (such as JAVA_HOME). Manually modifying the environment variables every time I want to switch between JDKs, isn’t a happy task. But, thanks to Windows Powershell, I’m able to write a scriplet that can do the heavy-lifting for me. Basically, what I want to achieve is to set PATH variable to add Java bin folder and set the JAVA_HOME environment variable and then launch the correct Eclipse IDE. And, I want to do this with a single command. Let’s do it. Open a Windows Powershell. I prefer writing custom Windows scripts in my profile file so that it is available to run when ever I open the shell. To edit the profile, run this command: notepad.exe $profile - the $profile is a special variable that points to your profile file. Write the below script in the profile file and save it. function myIDE{ $env:Path += "C:\vraa\java\jdk7\bin;" $env:JAVA_HOME = "C:\vraa\java\jdk7" C:\vraa\ide\eclipse\eclipse set-location C:\vraa\workspace\myproject play } function officeIDE{ $env:Path += "C:\vraa\java\jdk6\bin;" $env:JAVA_HOME = "C:\vraa\java\jdk6" C:\office\eclipse\eclipse } Close and restart the Powershell. Now you can issue the command myIDE which will set the proper PATH and environment variables and then launch the eclipse IDE. As you can see, there are two functions with different configurations. Just call the function name that you want to launch from the Powershell command line (myIDE or officeIDE).

We recently adopted the use of Spring Data. Spring Data provides a nice pattern/API that you can layer on top of JPA to eliminate boiler-plate code. With that adoption, we started looking at the DAO layer we use against Cassandra for some of our operations. Some of the data we store in Cassandra is simple. It does *not* leverage the flexible nature of NoSQL. In other words, we know all the table names, the column names ahead of time, and we don't anticipate them changing all that often. We could have stored this data in an RDBMs, using hibernate to access it, but standing up another persistence mechanism seemed like overkill. For simplicity's sake, we preferred storing this data in Cassandra. That said, we want the flexibility to move this to an RDBMs if we need to. Enter JPA. JPA would provide us a nice layer of abstraction away from the underlying storage mechanism. Wouldn't it be great if we could annotate the objects with JPA annotations, and persist them to Cassandra? Enter Kundera. Kundera is a JPA implementation that supports Cassandra (among other storage mechanisms). OK -- so JPA is great, and would get us what we want, but we had just adopted the use of Spring Data. Could we use both? The answer is "sort of". I forked off SpringSource's spring-data-cassandra: https://github.com/boneill42/spring-data-cassandra And I started hacking on it. I managed to get an implementation of the PagingAndSortingRepository for which I wrote unit tests that worked, but I was duplicating a lot of what should have come for free in the SimpleJpaRepository. When I tried to substitute my CassandraJpaRepository for the SimpleJpaRepository, I ran into some trouble w/ Kundera. Specifically, the MetaModel implementation appeared to be incomplete. MetaModelImpl was returning null for all managedTypes(). SimpleJpa wasn't too happy with this. Instead of wrangling with Kundera, we punted. We can achieve enough of the value leveraging JPA directly. Perhaps more importantly, there is still an impedance mismatch between JPA and NoSQL. In our case, it would have been nice to get at Cassandra through Spring Data using JPA for a few cases in our app, but for the vast majority of the application, a straight up ORM layer whereby we know the tables, rows and column names ahead of time is insufficient. For those cases where we don't know the schema ahead of time, we're going to need to leverage the converters pattern in Spring Data. So, I started hacking on a proper Spring Data layer using Astyanax as the client. Follow along here: https://github.com/boneill42/spring-data-cassandra More to come on that....

Don’t be afraid – Some clarification to common misunderstandings Since version 3.1, Apache Lucene and Solr use MMapDirectory by default on 64bit Windows and Solaris systems; since version 3.3 also for 64bit Linux systems. This change lead to some confusion among Lucene and Solr users, because suddenly their systems started to behave differently than in previous versions. On the Lucene and Solr mailing lists a lot of posts arrived from users asking why their Java installation is suddenly consuming three times their physical memory or system administrators complaining about heavy resource usage. Also consultants were starting to tell people that they should not use MMapDirectory and change their solrconfig.xml to work instead with slow SimpleFSDirectory or NIOFSDirectory (which is much slower on Windows, caused by a JVM bug #6265734). From the point of view of the Lucene committers, who carefully decided that using MMapDirectory is the best for those platforms, this is rather annoying, because they know, that Lucene/Solr can work with much better performance than before. Common misinformation about the background of this change causes suboptimal installations of this great search engine everywhere. In this blog post, I will try to explain the basic operating system facts regarding virtual memory handling in the kernel and how this can be used to largely improve performance of Lucene (“VIRTUAL MEMORY for DUMMIES”). It will also clarify why the blog and mailing list posts done by various people are wrong and contradict the purpose of MMapDirectory. In the second part I will show you some configuration details and settings you should take care of to prevent errors like “mmap failed” and suboptimal performance because of stupid Java heap allocation. Virtual Memory[1] Let’s start with your operating system’s kernel: The naive approach to do I/O in software is the way, you have done this since the 1970s – the pattern is simple: whenever you have to work with data on disk, you execute a syscall to your operating system kernel, passing a pointer to some buffer (e.g. a byte[] array in Java) and transfer some bytes from/to disk. After that you parse the buffer contents and do your program logic. If you don’t want to do too many syscalls (because those may cost a lot processing power), you generally use large buffers in your software, so synchronizing the data in the buffer with your disk needs to be done less often. This is one reason, why some people suggest to load the whole Lucene index into Java heap memory (e.g., by using RAMDirectory). But all modern operating systems like Linux, Windows (NT+), MacOS X, or Solaris provide a much better approach to do this 1970s style of code by using their sophisticated file system caches and memory management features. A feature called “virtual memory” is a good alternative to handle very large and space intensive data structures like a Lucene index. Virtual memory is an integral part of a computer architecture; implementations require hardware support, typically in the form of a memory management unit (MMU) built into the CPU. The way how it works is very simple: Every process gets his own virtual address space where all libraries, heap and stack space is mapped into. This address space in most cases also start at offset zero, which simplifies loading the program code because no relocation of address pointers needs to be done. Every process sees a large unfragmented linear address space it can work on. It is called “virtual memory” because this address space has nothing to do with physical memory, it just looks like so to the process. Software can then access this large address space as if it were real memory without knowing that there are other processes also consuming memory and having their own virtual address space. The underlying operating system works together with the MMU (memory management unit) in the CPU to map those virtual addresses to real memory once they are accessed for the first time. This is done using so called page tables, which are backed by TLBs located in the MMU hardware (translation lookaside buffers, they cache frequently accessed pages). By this, the operating system is able to distribute all running processes’ memory requirements to the real available memory, completely transparent to the running programs. Schematic drawing of virtual memory (image from Wikipedia [1], http://en.wikipedia.org/wiki/File:Virtual_memory.svg, licensed by CC BY-SA 3.0) By using this virtualization, there is one more thing, the operating system can do: If there is not enough physical memory, it can decide to “swap out” pages no longer used by the processes, freeing physical memory for other processes or caching more important file system operations. Once a process tries to access a virtual address, which was paged out, it is reloaded to main memory and made available to the process. The process does not have to do anything, it is completely transparent. This is a good thing to applications because they don’t need to know anything about the amount of memory available; but also leads to problems for very memory intensive applications like Lucene. Lucene & Virtual Memory Let’s take the example of loading the whole index or large parts of it into “memory” (we already know, it is only virtual memory). If we allocate a RAMDirectory and load all index files into it, we are working against the operating system: The operating system tries to optimize disk accesses, so it caches already all disk I/O in physical memory. We copy all these cache contents into our own virtual address space, consuming horrible amounts of physical memory (and we must wait for the copy operation to take place!). As physical memory is limited, the operating system may, of course, decide to swap out our large RAMDirectory and where does it land? – On disk again (in the OS swap file)! In fact, we are fighting against our O/S kernel who pages out all stuff we loaded from disk [2]. So RAMDirectory is not a good idea to optimize index loading times! Additionally, RAMDirectory has also more problems related to garbage collection and concurrency. Because the data residing in swap space, Java’s garbage collector has a hard job to free the memory in its own heap management. This leads to high disk I/O, slow index access times, and minute-long latency in your searching code caused by the garbage collector driving crazy. On the other hand, if we don’t use RAMDirectory to buffer our index and use NIOFSDirectory or SimpleFSDirectory, we have to pay another price: Our code has to do a lot of syscalls to the O/S kernel to copy blocks of data between the disk or filesystem cache and our buffers residing in Java heap. This needs to be done on every search request, over and over again. Memory Mapping Files The solution to the above issues is MMapDirectory, which uses virtual memory and a kernel feature called “mmap” [3] to access the disk files. In our previous approaches, we were relying on using a syscall to copy the data between the file system cache and our local Java heap. How about directly accessing the file system cache? This is what mmap does! Basically mmap does the same like handling the Lucene index as a swap file. The mmap() syscall tells the O/S kernel to virtually map our whole index files into the previously described virtual address space, and make them look like RAM available to our Lucene process. We can then access our index file on disk just like it would be a large byte[] array (in Java this is encapsulated by a ByteBuffer interface to make it safe for use by Java code). If we access this virtual address space from the Lucene code we don’t need to do any syscalls, the processor’s MMU and TLB handles all the mapping for us. If the data is only on disk, the MMU will cause an interrupt and the O/S kernel will load the data into file system cache. If it is already in cache, MMU/TLB map it directly to the physical memory in file system cache. It is now just a native memory access, nothing more! We don’t have to take care of paging in/out of buffers, all this is managed by the O/S kernel. Furthermore, we have no concurrency issue, the only overhead over a standard byte[] array is some wrapping caused by Java’s ByteBuffer interface (it is still slower than a real byte[] array, but that is the only way to use mmap from Java and is much faster than all other directory implementations shipped with Lucene). We also waste no physical memory, as we operate directly on the O/S cache, avoiding all Java GC issues described before. What does this all mean to our Lucene/Solr application? We should not work against the operating system anymore, so allocate as less as possible heap space (-Xmx Java option). Remember, our index accesses rely on passed directly to O/S cache! This is also very friendly to the Java garbage collector. Free as much as possible physical memory to be available for the O/S kernel as file system cache. Remember, our Lucene code works directly on it, so reducing the number of paging/swapping between disk and memory. Allocating too much heap to our Lucene application hurts performance! Lucene does not require it with MMapDirectory. Why does this only work as expected on operating systems and Java virtual machines with 64bit? One limitation of 32bit platforms is the size of pointers, they can refer to any address within 0 and 232-1, which is 4 Gigabytes. Most operating systems limit that address space to 3 Gigabytes because the remaining address space is reserved for use by device hardware and similar things. This means the overall linear address space provided to any process is limited to 3 Gigabytes, so you cannot map any file larger than that into this “small” address space to be available as big byte[] array. And when you mapped that one large file, there is no virtual space (address like “house number”) available anymore. As physical memory sizes in current systems already have gone beyond that size, there is no address space available to make use for mapping files without wasting resources (in our case “address space”, not physical memory!). On 64bit platforms this is different: 264-1 is a very large number, a number in excess of 18 quintillion bytes, so there is no real limit in address space. Unfortunately, most hardware (the MMU, CPU’s bus system) and operating systems are limiting this address space to 47 bits for user mode applications (Windows: 43 bits) [4]. But there is still much of addressing space available to map terabytes of data. Common misunderstandings If you have read carefully what I have told you about virtual memory, you can easily verify that the following is true: MMapDirectory does not consume additional memory and the size of mapped index files is not limited by the physical memory available on your server. By mmap() files, we only reserve address space not memory! Remember, address space on 64bit platforms is for free! MMapDirectory will not load the whole index into physical memory. Why should it do this? We just ask the operating system to map the file into address space for easy access, by no means we are requesting more. Java and the O/S optionally provide the option to try loading the whole file into RAM (if enough is available), but Lucene does not use that option (we may add this possibility in a later version). MMapDirectory does not overload the server when “top” reports horrible amounts of memory. “top” (on Linux) has three columns related to memory: “VIRT”, “RES”, and “SHR”. The first one (VIRT, virtual) is reporting allocated virtual address space (and that one is for free on 64 bit platforms!). This number can be multiple times of your index size or physical memory when merges are running in IndexWriter. If you have only one IndexReader open it should be approximately equal to allocated heap space (-Xmx) plus index size. It does not show physical memory used by the process. The second column (RES, resident) memory shows how much (physical) memory the process allocated for operating and should be in the size of your Java heap space. The last column (SHR, shared) shows how much of the allocated virtual address space is shared with other processes. If you have several Java applications using MMapDirectory to access the same index, you will see this number going up. Generally, you will see the space needed by shared system libraries, JAR files, and the process executable itself (which are also mmapped). How to configure my operating system and Java VM to make optimal use of MMapDirectory? First of all, default settings in Linux distributions and Solaris/Windows are perfectly fine. But there are some paranoid system administrators around, that want to control everything (with lack of understanding). Those limit the maximum amount of virtual address space that can be allocated by applications. So please check that “ulimit -v” and “ulimit -m” both report “unlimited”, otherwise it may happen that MMapDirectory reports “mmap failed” while opening your index. If this error still happens on systems with lot’s of very large indexes, each of those with many segments, you may need to tune your kernel parameters in /etc/sysctl.conf: The default value of vm.max_map_count is 65530, you may need to raise it. I think, for Windows and Solaris systems there are similar settings available, but it is up to the reader to find out how to use them. For configuring your Java VM, you should rethink your memory requirements: Give only the really needed amount of heap space and leave as much as possible to the O/S. As a rule of thumb: Don’t use more than ¼ of your physical memory as heap space for Java running Lucene/Solr, keep the remaining memory free for the operating system cache. If you have more applications running on your server, adjust accordingly. As usual the more physical memory the better, but you don’t need as much physical memory as your index size. The kernel does a good job in paging in frequently used pages from your index. A good possibility to check that you have configured your system optimally is by looking at both "top" (and correctly interpreting it, see above) and the similar command "iotop" (can be installed, e.g., on Ubuntu Linux by "apt-get install iotop"). If your system does lots of swap in/swap out for the Lucene process, reduce heap size, you possibly used too much. If you see lot's of disk I/O, buy more RUM (Simon Willnauer) so mmapped files don't need to be paged in/out all the time, and finally: buy SSDs. Happy mmapping! Bibliography [1] http://en.wikipedia.org/wiki/Virtual_memory [2] https://www.varnish-cache.org/trac/wiki/ArchitectNotes [3] http://en.wikipedia.org/wiki/Memory-mapped_file [4] http://en.wikipedia.org/wiki/X86-64#Virtual_address_space_details

Here is a quick example of how to programmatically access Camel MBeans to monitor and manipulate routes... first, get a connection to a JMX server (assumes localhost, port 1099, no auth) note, always cache the connection for subsequent requests (can cause memory utilization issues otherwise) JMXServiceURL url = new JMXServiceURL("service:jmx:rmi:///jndi/rmi://localhost:1099/jmxrmi"); JMXConnector jmxc = JMXConnectorFactory.connect(url); MBeanServerConnection server = jmxc.getMBeanServerConnection(); use the following to iterate over all routes and retrieve statistics (state, exchanges, etc)... ObjectName objName = new ObjectName("org.apache.camel:type=routes,*"); List cacheList = new LinkedList(server.queryNames(objName, null)); for (Iterator iter = cacheList.iterator(); iter.hasNext();) { objName = iter.next(); String keyProps = objName.getCanonicalKeyPropertyListString(); ObjectName objectInfoName = new ObjectName("org.apache.camel:" + keyProps); String routeId = (String) server.getAttribute(objectInfoName, "RouteId"); String description = (String) server.getAttribute(objectInfoName, "Description"); String state = (String) server.getAttribute(objectInfoName, "State"); ... } use the following to execute operations against a Camel route (stop,start, etc) ObjectName objName = new ObjectName("org.apache.camel:type=routes,*"); List cacheList = new LinkedList(server.queryNames(objName, null)); for (Iterator iter = cacheList.iterator(); iter.hasNext();) { objName = iter.next(); String keyProps = objName.getCanonicalKeyPropertyListString(); if(keyProps.contains(routeID)) { ObjectName objectRouteName = new ObjectName("org.apache.camel:" + keyProps); Object[] params = {}; String[] sig = {}; server.invoke(objectRouteName, operationName, params, sig); return; } } summary These APIs can easily be used to build a web or command line based tool to support remote Camel management features. All of these features are available via the JMX console and Camel does provide a web console to support some management/monitoring tasks. See these pages for more information... http://camel.apache.org/camel-jmx.html http://camel.apache.org/web-console.html

Riak is one databases that uses vector clocks for conflict resolution. I came across these two blog posts on Basho.com, company which develops Riak, and these posts are great at explaining the basics of Vector Clocks - definitely a must read if you're into distributed systems: Why vector clocks are easy? Why vector clocks are hard? Voldermort DB (by LinkedIn) is another DB that uses Vector Clocks, as explained below. Not surprisingly, it also takes the idea from Amazon's Dynamo (like Riak): The redundancy of storage makes the system more resilient to server failure. Since each value is stored N times, you can tolerate as many as N – 1 machine failures without data loss. This causes other problems, though. Since each value is stored in multiple places it is possible that one of these servers will not get updated (say because it is crashed when the update occurs). To help solve this problem Voldemort uses a data versioning mechanism called Vector Clocks that are common in distributed programming. This is an idea we took from Amazon’s Dynamo system. This data versioning allows the servers to detect stale data when it is read and repair it. Voldermort's code in Java can be on code.google.com. Finally, before I end this post, you may be asking "why complicate so much?" (if I could get a penny every time I heard that when discussing distributed systems... :-). But in this case, it's a good and typical question: can't we just use timestamp and last one wins? The problem, though, is that it requires times to be perfectly synchronized - which is very difficult and oftentimes impossible. By using vector clocks, you don't have this requirement on the system.

I’ve been playing with Hive recently and liking what I’ve found. In theory at least it provides a very nice, simple way of getting into analysing large data sets. To make it even easier to show other people what you’re up to Hive has a nascent web interface with a little documentation on the wiki On the one hand it’s rather simple at this point, but that should be easily enought to prettify given a bit of time. The bigger problem was getting it working in the first place. What follows worked for me using the latest cloudera packages on debian testing. I’m assuming you already have Hive and Hadoop installed, the basic packages worked fine for me here. Next up you’ll need the JDK (not just the JRE) as their is some compilation that will go on the first time you run the web interface. apt-get install ant sun-java6-jdk Next up I had to modify the installed /etc/hive/conf/hive-site.xml file as follows: I changed this: hive.metastore.uris file:///var/lib/hivevar/metastore/metadb/ Comma separated list of URIs of metastore servers. The first server that can be connected to will be used. To this. Note the hivevar path doesn’t exist so I’m not sure if this was a typo in the source. hive.metastore.uris file:///var/lib/hive/var/metastore/metadb/ Comma separated list of URIs of metastore servers. The first server that can be connected to will be used. I also change the following section regarding the metastore name: javax.jdo.option.ConnectionURL jdbc:derby:;databaseName=/var/lib/hive/metastore/${user.name}_db;create=true JDBC connect string for a JDBC metastore To this, with a fixed name. When using the above confirguration the file was actually called ${user.name} rather than my username being subsituted in. Elsewhere this seems to work fine. javax.jdo.option.ConnectionURL jdbc:derby:;databaseName=/var/lib/hive/metastore/metastore_db;create=true JDBC connect string for a JDBC metastore I’m not convinced the above two changes are needed but have left them here just in case. The main tricky part is making sure a load of environment variables are correctly set. The following worked for me: export ANT_LIB=/usr/share/ant/lib export HIVE_HOME=/usr/lib/hive export HADOOP_HOME=/usr/lib/hadoop export PATH=$PATH:$HADOOP_HOME/bin export JAVA_HOME=/usr/lib/jvm/java-6-sun All being well that should allow you to run the hive command with the web interface like so: hive --service hwi That should bring up a webserver on port 9999 where you should see something similar to the screenshot above.

Konrad recently shared on our company’s technical room an interesting article on how caching is done is a big polish social network, nk.pl. One of the central concepts in the algorithm is generational caching (see here or here). The basic idea is that for cache keys you use some entity-specific string + version number. The version number increases whenever data changes, thus invalidating any old cache entries, and preventing stale data reads. This makes the assumption that the cache has some garbage collection, e.g. it may simply be a LRU cache. Of course on each request we must know the version number – that’s why it must be stored in a global cache (but depending on our consistency requirements, it also may be distributed across the cluster asynchronously). However the data itself can be stored in local caches. So if our system is read-most, the only “expensive” operation that we will have to do per request is retrieve the version numbers for the entities we are interested in. And this is usually very simple information, which can be kept entirely in-memory. Depending on the type of data and the usage patterns, you can cache individual entities (e.g. for a Person entity, the cache key could be person-9128-123, 9128 being the id, 123 the version number), or the whole lot (e.g. for a Countries entity, the cache key could be countries-8, 8 being the version number). Moreover in the global cache you can keep the latest version number per-id or per-entity; meaning that when the version changes, you invalidate a specific entity or all of them. Having written most of Envers, it quite naturally occurred to me that you may use the entity revision numbers as the cache versions. Subsequent Envers revisions are monotonically increasing numbers, for each transaction you get the next one. So whenever a cached entity changes, you would have to populate the global cache with the latest revision number. Envers provides several ways to get the revision numbers. During the transaction, you can call AuditReader.getCurrentRevision() method, which will give you the revision metadata, including the revision number. If you want more fine-grained control, you may implement your own listener (EntityTrackingRevisionListener), see the docs), and get notified whenever an entity is changed, and update the global cache in there. You can also register an after-transaction-completed callback, and update the cache outside of the transaction boundaries. Or, if you know the entity ids, you may lookup the maximum revision number using either AuditReader.getRevisions or an AuditQueryCreator. As you can obtain the current revision number during a transaction, you may even update the version/revision in the global cache atomically, if you use a transactional cache such as Infinispan. All of that of course in addition to auditing, which is still the main purpose of Envers :)

A document-oriented database is a designed for storing, retrieving, and managing document-oriented, or semi structured data. Document-oriented databases are one of the main categories of NoSQL databases. The central concept of a document-oriented database is the notion of a Document. While each document-oriented database implementation differs on the details of this definition, in general, they all assume documents encapsulate and encode data (or information) in some standard format(s) (or encoding(s)). Encodings in use include XML, YAML, JSON and BSON, as well as binary forms like PDF and Microsoft Office documents (MS Word, Excel, and so on). MongoDB: MongoDB is a collection-oriented, schema-free document database. Data is grouped into sets that are called ‘collections’. Each collection has a unique name in the database, and can contain an unlimited number of documents. Collections are analogous to tables in a RDBMS, except that they don’t have any defined schema. It store data (which is in BASON – “Binary Serialized dOcument Notation” format) that is a structured collection of key-value pairs, where keys are strings, and values are any of a rich set of data types, including arrays and documents. Home: http://www.mongodb.org/ Quick Start: http://www.mongodb.org/display/DOCS/Quickstart Download: http://www.mongodb.org/downloads CouchDB: CouchDB is a document database server, accessible via a RESTful JSON API. It is Ad-hoc and schema-free with a flat address space. Its Query-able and index-able, featuring a table oriented reporting engine that uses JavaScript as a query language. A CouchDB document is an object that consists of named fields. Field values may be strings, numbers, dates, or even ordered lists and associative maps. Home: http://couchdb.apache.org/ Quick Start: http://couchdb.apache.org/docs/intro.html Download: http://couchdb.apache.org/downloads.html Terrastore: Terrastore is a modern document store which provides advanced scalability and elasticity features without sacrificing consistency. It is based on Terracotta, so it relies on an industry-proven, fast clustering technology. Home: http://code.google.com/p/terrastore/ Quick Start: http://code.google.com/p/terrastore/wiki/Documentation Download: http://code.google.com/p/terrastore/downloads/list RavenDB: Raven is a .NET Linq enabled Document Database, focused on providing high performance, schema-less, flexible and scalable NoSQL data store for the .NET and Windows platforms. Raven store any JSON document inside the database. It is schema-less database where you can define indexes using C#’s Linq syntax. Home: http://ravendb.net/ Quick Start: http://ravendb.net/tutorials Download: http://ravendb.net/download OrientDB: OrientDB is an open source NoSQL database management system written in Java. Even if it is a document-based database, the relationships are managed as in graph databases with direct connections between records. It supports schema-less, schema-full and schema-mixed modes. It has a strong security profiling system based on users and roles and supports SQL as a query languages. Home: http://www.orientechnologies.com/ Quick Start: http://code.google.com/p/orient/wiki/Tutorials Download: http://code.google.com/p/orient/wiki/Download ThruDB: Thrudb is a set of simple services built on top of the Apache Thrift framework that provides indexing and document storage services for building and scaling websites. Its purpose is to offer web developers flexible, fast and easy-to-use services that can enhance or replace traditional data storage and access layers. It supports multiple storage backends such as BerkeleyDB, Disk, MySQL and also having Memcache and Spread integration. Home: http://code.google.com/p/thrudb/ Quick Start: http://thrudb.googlecode.com/svn/trunk/doc/Thrudb.pdf Download: http://code.google.com/p/thrudb/source/checkout SisoDB: SisoDb is a document-oriented db-provider for Sql-Server written in C#. It lets you store object graphs of POCOs (plain old clr objects) without having to configure any mappings. Each entity is treated as an aggregate root and will get separate tables created on the fly. Home: http://www.sisodb.com Quick Start: http://www.sisodb.com/Wiki Download: https://github.com/danielwertheim/SisoDb-Provider/ RaptorDB: RaptorDB is a extremely small size and fast embedded, noSql, persisted dictionary database using b+tree or MurMur hash indexing. It was primarily designed to store JSON data (see my fastJSON implementation), but can store any type of data that you give it. Home: http://www.codeproject.com/KB/database/RaptorDB.aspx Quick Start: http://www.codeproject.com/KB/database/RaptorDB.aspx Download: http://www.codeproject.com/KB/database/RaptorDB.aspx CloudKit: CloudKit provides schema-free, auto-versioned, RESTful JSON storage with optional OpenID and OAuth support, including OAuth Discovery. Home: http://getcloudkit.com/ Quick Start: http://getcloudkit.com/api/ Download: https://github.com/jcrosby/cloudkit Perservere: Persevere is an open source set of tools for persistence and distributed computing using an intuitive standards-based JSON interfaces of HTTP REST, JSON-RPC, JSONPath, and REST Channels. The core of the Persevere project is the Persevere Server. The Persevere server includes a Persevere JavaScript client, but the standards-based interface is intended to be used with any framework or client. Home: http://code.google.com/p/persevere-framework/ Quick Start: http://code.google.com/p/persevere-framework/w/list Download: http://code.google.com/p/persevere-framework/downloads/list Jackrabbit: The Apache Jackrabbit™ content repository is a fully conforming implementation of the Content Repository for Java Technology API (JCR, specified in JSR 170 and 283). A content repository is a hierarchical content store with support for structured and unstructured content, full text search, versioning, transactions, observation, and more. Home: http://jackrabbit.apache.org Quick Start: http://jackrabbit.apache.org/getting-started-with-apache-jackrabbit.html Download: http://jackrabbit.apache.org/downloads.html Conclusion: Document databases store and retrieve documents and basic atomic stored unit is a document. As always your requirement leads into the decision. You need to think about your data-access patterns / use-cases to create a smart document-model. When your domain model can be split and partitioned across some documents, a document-database will be a suitable one for you. For example for a blog-software, a CMS or a wiki-software a document-db works extremely well. But at the same time a non-relational database is not better than a relational one in some cases where your database have a lot of relations and normalization. Just check the following link from stackoverflow also to cover the pros/cons of Relational Vs Document based databases. http://stackoverflow.com/questions/337344/pros-cons-of-document-based-databases-vs-relational-databases

srgolla - SQL Server Scheduling Flowchart This is a basic flowchart explaining SQL Server Scheduling at a very high level. This will appeal to a limited audience, but is still something I thought very informative and not something I see flowcharted every day (week/month/year).

Introduction When the web was founded, it was designed as a system to distribute and update data. If you look at the HTTP protocol you can clearly see these origins. It has commands to GET, UPDATE, POST or DELETE data, but it is always the client (and if we ignore REST, this means in most cases the browser) who takes the initiative. However, the web is changing. It has evolved from a pure data distribution system to an application distribution system. Today, the magic word of IT vendors for this is the ‘cloud’, but in fact this shift to an application distribution system has already started some years ago. In order to start massively replacing traditional desktop applications, the web needs a new trick: two way communication between browser and server. The back-end must be able to update parts of a web page without user initiative. A definitive technical solution is underway in the form of web sockets, but do we really need to wait until this technology is broadly supported by the browsers of most users? In my opinion, the answer should be a clear NO. Where SignalR comes in When there are limitations, people become creative and are able to circumvent the issues blocking them from developing great products. Many popular web applications/sits are already capable of updating their content dynamically, yet they do not rely on web sockets. How is this possible? Because they rely on patterns such as ‘long polling’. With long polling, the browser sends a request for information to the web server with a huge timeout. The web server does not immediately sends data back to the browser, but waits until it has data to send back. When the client receives back data from the server, it will immediately resend a new request to the server. This long polling pattern and similar patterns give the user the illusion that a persistent two way connection exists between the browser and the web server, but it causes some unnecessary hard work for applicative developers and this is where SignalR comes in for .NET developers. It makes an abstraction of the long polling pattern and gives applicative developers the same illusion as their end users: a persistent two way connection between browser and web server. SignalR takes care of all the details and allows developers to focus on their most important task: building a great application for users. Because SignalR abstracts the underlying communication protocol, it can both support WebSockets and patterns, such as long polling. This makes an upgrade to WebSockets fairly easy when your organization adopts Windows Server 2012 and your users have moved to modern browsers such as Internet Explorer 10 or recent versions of Firefox and Google Chrome. Conclusion As I already wanted to emphasize in the title, comparing WebSockets with SignalR is pointless. Yes, WebSockets is technically superior and will probably give you some extra performance on your server side. But SignalR makes it possible to start developing today the web applications of tomorrow. When WebSockets become broadly available, SignalR will make it possible for you to move away from long polling without a lot of impact on your code.

While writing list navigation and search features in websites today there is a constant need to find/replace and play with query string elements, so that you can easily manipulate these mystical items while you’re carrying them around in your website’s URLs. I have a few little methods I’ve used over the years and carry with me project to project, and this post is putting them on the record for easy access later. I have a secret. This post is actually more aimed at an audience of 'myself', and my ability to have an easy bit of source code to call upon when I’m on the go looking for a quick solution to cut and paste – as most of my blog posts are. But you, dear reader, you get to share in this benefit with me by pulling from the awesomeness within this post as well. Solution: .Net c# When doing this with c# you have a few pretty cool features up your sleeve. One of these is HttpUtility.ParseQueryString(urlPath) framework method. This static method allows you to extract a NameValueCollection that is editable from a given query string. Why is this cool? Because it allows you to very easily play with the query string collection like it is any other NameValueCollection – with Add() and Remove() methods. This makes it incredibly powerful. Quick & Dirty code beware! The code I’m pasting below is far from being the most elegant solution, i seem to have misplaced my nicer piece of code and am in too much of a rush to find it right now (sorry). Until i find my nicer solution, the method below will get you by – whether you have a hatred for ternary’s or not. public static string ReplaceQueryStringParam(string currentPageUrl, string paramToReplace, string newValue) { string urlWithoutQuery = currentPageUrl.IndexOf('?') >= 0 ? currentPageUrl.Substring(0, currentPageUrl.IndexOf('?')) : currentPageUrl; string queryString = currentPageUrl.IndexOf('?') >= 0 ? currentPageUrl.Substring(currentPageUrl.IndexOf('?')) : null; var queryParamList = queryString != null ? HttpUtility.ParseQueryString(queryString) : HttpUtility.ParseQueryString(string.Empty); if (queryParamList[paramToReplace] != null) { queryParamList[paramToReplace] = newValue; } else { queryParamList.Add(paramToReplace, newValue); } return String.Format("{0}?{1}", urlWithoutQuery, queryParamList); } To call this, you can do the following: // var currentUrl = HttpContext.Current.Request.Url; var currentUrl = "http://www.mysite.com/mypage?category=cool-products&sort=price&page=3"; // change the my sort-by param named"sort" to "name" var newUrlWithChangedSort = ReplaceQueryStringParam(currentUrl, "sort", "name"); Solution: JavaScript The second part of this post includes a JavaScript solution, as you never know when you have to do this on the client side. function replaceQueryString(url, param, value) { if (url.lastIndexOf('?') <= 0) url = url + "?"; var re = new RegExp("([?|&])" + param + "=.*?(&|$)", "i"); if (url.match(re)) return url.replace(re, '$1' + param + "=" + value + '$2'); else return url.substring(url.length - 1) == '?' ? url + param + "=" + value : url + '&' + param + "=" + value; } And to use the above code in your client-side javascript simply write something along the lines of: //var currentUrl = self.location; var currentUrl = "http://www.mysite.com/mypage?category=cool-products&sort=price&page=3"; // change the my sort-by param named"sort" to "name" var newUrlWithChangedSort = replaceQueryString(currentUrl, "sort", "name"); Easy – now next time you need to knock something together, instead of writing it yourself, you can simply cut & paste mine!

Autoscaling your webserver tier is typically straightforward. Image your apache server with source code or without, then sync down files from S3 upon spinup. Roll that image into the autoscale configuration and you’re all set. With the database tier though, things can be a bit tricky. The typical configuration we see is to have a single master database where your application writes. But scaling out or horizontally on Amazon EC2 should be as easy as adding more slaves, right? Why not automate that process? Below we’ve set out to answer some of the questions you’re likely to face when setting up slaves against your master. We’ve included instructions on building an AMI that automatically spins up as a slave. Fancy! How can I autoscale my database tier? Build an auto-starting MySQL slave against your master. Configure those to spinup. Amazon’s autoscaling loadbalancer is one option, another is to use a roll-your-own solution, monitoring thresholds on servers, and spinning up or dropping off slaves as necessary. Does an AWS snapshot capture subvolume data or just the SIZE of the attached volume? In fact, if you have an attached EBS volume and you create an new AMI off of that, you will capture the entire root volume, plus your attached volume data. In fact we find this a great way to create an auto-building slave in the cloud. How do I freeze MySQL during AWS snapshot? mysql> flush tables with read lock;mysql> system xfs_freeze -f /data At this point you can use the Amazon web console, ylastic, or ec2-create-image API call to do so from the command line. When the server you are imaging off of above restarts – as it will do by default – it will start with /data partition unfrozen and mysql’s tables unlocked again. Voila! If you’re not using xfs for your /data filesystem, you should be. It’s fast! The xfsprogs docs seem to indicate this may also work with foreign filesystems. Check the docs for details. How do I build an AMI mysql slave that autoconnects to master? Install mysql_serverid script below. Configure mysql to use your /data EBS mount. Set all your my.cnf settings including server_id Configure the instance as a slave in the normal way. When using GRANT to create the ‘rep’ user on master, specify the host with a subnet wildcard. For example ’10.20.%’. That will subsequently allow any 10.20.x.y servers to connect and replicate. Point the slave at the master. When all is running properly, edit the my.cnf file and remove server_id. Don’t restart mysql. Freeze the filesystem as described above. Use the Amazon console, ylastic or API call to create your new image. Test it of course, to make sure it spins up, sets server_id and connects to master. Make a change in the test schema, and verify that it propagates to all slaves. How do I set server_id uniquely? As you hopefully already know, in MySQL replication environment each node requires a unique server_id setting. In my Amazon Machine Images, I want the server to startup and if it doesn’t find the server_id in the /etc/my.cnf file, to add it there, correctly! Is that so much to ask? Here’s what I did. Fire up your editor of choice and drop in this bit of code: #!/bin/shif grep -q “server_id” /etc/my.cnf then : # do nothing – it’s already set else # extract numeric component from hostname – should be internet IP in Amazon environment export server_id=`echo $HOSTNAME | sed ‘s/[^0-9]*//g’` echo “server_id=$server_id” >> /etc/my.cnf # restart mysql /etc/init.d/mysql restart fi Save that snippet at /root/mysql_serverid. Also be sure to make it executable: $ chmod +x /root/mysql_serverid Then just append it to your /etc/rc.local file with an editor or echo: $ echo "/root/mysql_serverid" >> /etc/rc.local Assuming your my.cnf file does *NOT* contain the server_id setting when you re-image, then it’ll set this automagically each time you spinup a new server off of that AMI. Nice! Can you easily slave off of a slave? How? It’s not terribly different from slaving off of a normal master. A. First enable slave updates. The setting is not dynamic, so if you don’t already have it set, you’ll have to restart your slave. log_slave_updates=true B. Get an initial snapshot of your slave data. You can do that the locking way: mysql> flush tables with read lock;mysql> show master status\G; mysql> system mysqldump -A > full_slave_dump.mysql mysql> unlock tables; You may also choose to use Percona’s excellent xtrabackup utility to create hotbackups without locking any tables. We are very lucky to have an open-source tool like this at our disposal. MySQL Enterprise Backup from Oracle Corp can also do this. C. On the slave, seed the database with your dump created above. $ mysql < full_slave_dump.mysql D. Now point your slave to the original slave. mysql> change master to master_user='rep', master_password='rep', master_host='192.168.0.1', master_log_file='server-bin-log.000004', master_log_pos=399;mysql> start slave; mysql> show slave status\G; Slave master is set as an IP address. Is there another way? It’s possible to use hostnames in MySQL replication, however it’s not recommended. Why? Because of the wacky world of DNS. Suffice it to say MySQL has to do a lot of work to resolve those names into IP addresses. A hickup in DNS can interrupt all MySQL services potentially as sessions will fail to authenticate. To avoid this problem do two things: A. Set this parameter in my.cnf skip_name_resolve = true Remove entries in mysql.user table where hostname is not an IP address. Those entries will be invalid for authentication after setting the above parameter. Doesn’t RDS take care of all of this for me? RDS is Amazon’s Relational Database Service which is built on MySQL. Amazon’s RDS solution presents MySQL as a service which brings certain benefits to administrators and startups: Simpler administration. Nuts and bolts are handled for you. Push-button replication. No more struggling with the nuances and issues of MySQL’s replication management. Simplicity of administration of course has it’s downsides. Depending on your environment, these may or may not be dealbreakers. No access to the slow query log. This is huge. The single best tool for troubleshooting slow database response is this log file. Queries are a large part of keeping a relational database server healthy and happy, and without this facility, you are severely limited. Locked in downtime window When you signup for RDS, you must define a thirty minute maintenance window. This is a weekly window during which your instance *COULD* be unavailable. When you host yourself, you may not require as much downtime at all, especially if you’re using master-master mysql and zero-downtime configuration. Can’t use Percona Server to host your MySQL data. You won’t be able to do this in RDS. Percona server is a high performance distribution of MySQL which typically rolls in serious performance tweaks and updates before they make it to community addition. Well worth the effort to consider it. No access to filesystem, server metrics & command line. Again for troubleshooting problems, these are crucial. Gathering data about what’s really happening on the server is how you begin to diagnose and troubleshoot a server stall or pileup. You are beholden to Amazon’s support services if things go awry. That’s because you won’t have access to the raw iron to diagnose and troubleshoot things yourself. Want to call in an outside consultant to help you debug or troubleshoot? You’ll have your hands tied without access to the underlying server. You can’t replicate to a non-RDS database. Have your own datacenter connected to Amazon via VPC? Want to replication to a cloud server? RDS won’t fit the bill. You’ll have to roll your own – as we’ve described above. And if you want to replicate to an alternate cloud provider, again RDS won’t work for you. Related posts: Deploying MySQL on Amazon EC2 – 8 Best Practices Review: Host Your Web Site In The Cloud, Amazon Web Services Made Easy 5 Ways to Boost MySQL Scalability Top MySQL DBA interview questions (Part 2) MySQL Cluster In The Cloud – Managers Guide

Spring for Apache Hadoop is a Spring project to support writing applications that can benefit of the integration of Spring Framework and Hadoop. This post describes how to use Spring Data Apache Hadoop in an Amazon EC2 environment using the “Hello World” equivalent of Hadoop programming – a Wordcount application. 1./ Launch an Amazon Web Services EC2 instance. - Navigate to AWS EC2 Console (“https://console.aws.amazon.com/ec2/home”): - Select Launch Instance then Classic Wizzard and click on Continue. My test environment was a “Basic Amazon Linux AMI 2011.09″ 32-bit., Instant type: Micro (t1.micro , 613 MB), Security group quick-start-1 that enables ssh to be used for login. Select your existing key pair (or create a new one). Obviously you can select another AMI and instance types depending on your favourite flavour. (Should you vote for Windows 2008 based instance, you also need to have cygwin installed as an additional Hadoop prerequisite beside Java JDK and ssh, see “Install Apache Hadoop” section) 2./ Download Apache Hadoop - as of writing this article, 1.0.0 is the latest stable version of Apache Hadoop, that is what was used for testing purposes. I downloaded hadoop-1.0.0.tar.gz and copied it into /home/ec2-user directory using pscp command from my PC running Windows: c:\downloads>pscp -i mykey.ppk hadoop-1.0.0.tar.gz [email protected]:/home/ec2-user (the computer name above – ec2-ipaddress-region-compute.amazonaws.com – can be found on AWS EC2 console, Instance Description, public DNS field) 3./ Install Apache Hadoop: As prerequisites, you need to have Java JDK 1.6 and ssh installed, see Apache Single-Node Setup Guide. (ssh is automatically installed with Basic Amazon AMI). Then install hadoop itself: $ cd ~ # change directory to ec2-user home (/home/ec2-user) $ tar xvzf hadoop-1.0.0.tar.gz $ ln -s hadoop-1.0.0 hadoop $ cd hadoop/conf $ vi hadoop-env.sh # edit as below export JAVA_HOME=/opt/jdk1.6.0_29 $ vi core-site.xml # edit as below – this defines the namenode to be running on localhost and listeing to port 9000. fs.default.name hdfs://localhost:9000 $ vi hdsf-site.xml # edit as below this defines that file system replicate is 1 (in production environment it is supposed to be 3 by default) dfs.replication 1 $ vi mapred-site.xml # edit as below – this defines the jobtracker to be running on localhost and listeing to port 9001. mapred.job.tracker localhost:9001 $ cd ~/hadoop $ bin/hadoop namenode -format $ bin/start-all.sh At this stage all hadoop jobs are running in pseudo distributed mode, you can verify it by running: $ ps -ef | grep java You should see 5 java processes: namenode, secondarynamenode, datanode, jobtracker and tasktracker. 4./ Install Spring Data Hadoop Download Spring Data Hadoop package from SpringSource community download site. As of writing this article, the latest stable version is spring-data-hadoop-1.0.0.M1.zip. $ cd ~ $ tar xzvf spring-data-hadoop-1.0.0.M1.zip $ ln -s spring-data-hadoop-1.0.0.M1 spring-data-hadoop 5./ Build and Run Spring Data Hadoop Wordcount example $ cd spring-data-hadoop/spring-data-hadoop-1.0.0.M1/samples/wordcount Spring Data Hadoop is using gradle as build tool. Check build.grandle build file. The original version packaged in the tar.gz file does not compile, it complains about thrift, version 0.2.0 and jdo2-api, version2.3-ec. Add datanucleus.org maven repository to the build.gradle file to support jdo2-api (http://www.datanucleus.org/downloads/maven2/) . Unfortunatelly, there seems to be no maven repo for thrift 0.2.0 . You should download thrift 0.2.0.jar and thrift.0.2.0.pom file e.g. from this repo: “http://people.apache.org/~rawson/repo“ and then add it to local maven repo. $ mvn install:install-file -DgroupId=org.apache.thrift -DartifactId=thrift -Dversion=0.2.0 -Dfile=thrift-0.2.0.jar -Dpackaging=jar $ vi build.grandle # modify the build file to refer to datanucleus maven repo for jdo2-api and the local repo for thrift repositories { // Public Spring artefacts mavenCentral() maven { url “http://repo.springsource.org/libs-release” } maven { url “http://repo.springsource.org/libs-milestone” } maven { url “http://repo.springsource.org/libs-snapshot” } maven { url “http://www.datanucleus.org/downloads/maven2/” } maven { url “file:///home/ec2-user/.m2/repository” } } I also modified the META-INF/spring/context.xml file in order to run hadoop file system commands manually: $ cd /home/ec2-user/spring-data-hadoop/spring-data-hadoop-1.0.0.M1/samples/wordcount/src/main/resources $vi META-INF/spring/context.xml # remove clean-script and also the dependency on it for JobRunner. xmlns=”http://www.springframework.org/schema/beans” xmlns:xsi=”http://www.w3.org/2001/XMLSchema-instance” xmlns:context=”http://www.springframework.org/schema/context” xmlns:hdp=”http://www.springframework.org/schema/hadoop” xmlns:p=”http://www.springframework.org/schema/p” xsi:schemaLocation=”http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd http://www.springframework.org/schema/hadoop http://www.springframework.org/schema/hadoop/spring-hadoop.xsd”> fs.default.name=${hd.fs} Copy the sample file – nietzsche-chapter-1.txt – to Hadoop file system (/user/ec2-user-/input directory) $ cd src/main/resources/data $ hadoop fs -mkdir /user/ec2-user/input $ hadoop fs -put nietzsche-chapter-1.txt /user/ec2-user/input/data $ cd ../../../.. # go back to samples/wordcount directory $ ../gradlew Verify the result: $ hadoop fs -cat /user/ec2-user/output/part-r-00000 | more “AWAY 1 “BY 1 “Beyond 1 “By 2 “Cheers 1 “DE 1 “Everywhere 1 “FROM” 1 “Flatterers 1 “Freedom 1

I had a relational database, that I wanted to migrate to cassandra. Cassandra's sstableloader provides option to load the existing data from flat files to a cassandra ring. Hence this can be used as a way to migrate data in relational databases to cassandra, as most relational databases let us export the data into flat files. sqoop gives the option to do this effectively. Interestingly, DataStax Enterprise provides everything we want in the big data space as a package. This includes, cassandra, hadoop, hive, pig, sqoop, and mahout, which comes handy in this case. Under the resources directory, you may find the cassandra, dse, hadoop, hive, log4j-appender, mahout, pig, solr, sqoop, and tomcat specific configurations. For example, from resources/hadoop/bin, you may format the hadoop name node using ./hadoop namenode -format as usual. * Download and extract DataStax Enterprise binary archive (dse-2.1-bin.tar.gz). * Follow the documentation, which is also available as a PDF. * Migrating a relational database to cassandra is documented and is also blogged. * Before starting DataStax, make sure that the JAVA_HOME is set. This also can be set directly on conf/hadoop-env.sh. * Include the connector to the relational database into a location reachable by sqoop. I put mysql-connector-java-5.1.12-bin.jar under resources/sqoop. * Set the environment $ bin/dse-env.sh * Start DataStax Enterprise, as an Analytics node. $ sudo bin/dse cassandra -t where cassandra starts the Cassandra process plus CassandraFS and the -t option starts the Hadoop JobTracker and TaskTracker processes. if you start without the -t flag, the below exception will be thrown during the further operations that are discussed below. No jobtracker found Unable to run : jobtracker not found Hence do not miss the -t flag. * Start cassandra cli to view the cassandra keyrings and you will be able to view the data in cassandra, once you migrate using sqoop as given below. $ bin/cassandra-cli -host localhost -port 9160 Confirm that it is connected to the test cluster that is created on the port 9160, by the below from the CLI. [default@unknown] describe cluster; Cluster Information: Snitch: com.datastax.bdp.snitch.DseDelegateSnitch Partitioner: org.apache.cassandra.dht.RandomPartitioner Schema versions: f5a19a50-b616-11e1-0000-45b29245ddff: [127.0.1.1] If you have missed mentioning the host/port (starting the cli by just bin/cassandra-cli) or given it wrong, you will get the response as "Not connected to a cassandra instance." $ bin/dse sqoop import --connect jdbc:mysql://127.0.0.1:3306/shopping_cart_db --username root --password root --table Category --split-by categoryName --cassandra-keyspace shopping_cart_db --cassandra-column-family Category_cf --cassandra-row-key categoryName --cassandra-thrift-host localhost --cassandra-create-schema Above command will now migrate the table "Category" in the shopping_cart_db with the primary key categoryName, into a cassandra keyspace named shopping_cart, with the cassandra row key categoryName. You may use the --direct mysql specific option, which is faster. In my above command, I have everything runs on localhost. +--------------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------------+-------------+------+-----+---------+-------+ | categoryName | varchar(50) | NO | PRI | NULL | | | description | text | YES | | NULL | | | image | blob | YES | | NULL | | +--------------+-------------+------+-----+---------+-------+ This also creates the respective java class (Category.java), inside the working directory. To import all the tables in the database, instead of a single table. $ bin/dse sqoop import-all-tables -m 1 --connect jdbc:mysql://127.0.0.1:3306/shopping_cart_db --username root --password root --cassandra-thrift-host localhost --cassandra-create-schema --direct Here "-m 1" tag ensures a sequential import. If not specified, the below exception will be thrown. ERROR tool.ImportAllTablesTool: Error during import: No primary key could be found for table Category. Please specify one with --split-by or perform a sequential import with '-m 1'. To check whether the keyspace is created, [default@unknown] show keyspaces; ................ Keyspace: shopping_cart_db: Replication Strategy: org.apache.cassandra.locator.SimpleStrategy Durable Writes: true Options: [replication_factor:1] Column Families: ColumnFamily: Category_cf Key Validation Class: org.apache.cassandra.db.marshal.UTF8Type Default column value validator: org.apache.cassandra.db.marshal.UTF8Type Columns sorted by: org.apache.cassandra.db.marshal.UTF8Type Row cache size / save period in seconds / keys to save : 0.0/0/all Row Cache Provider: org.apache.cassandra.cache.SerializingCacheProvider Key cache size / save period in seconds: 200000.0/14400 GC grace seconds: 864000 Compaction min/max thresholds: 4/32 Read repair chance: 1.0 Replicate on write: true Bloom Filter FP chance: default Built indexes: [] Compaction Strategy: org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy ............. [default@unknown] describe shopping_cart_db; Keyspace: shopping_cart_db: Replication Strategy: org.apache.cassandra.locator.SimpleStrategy Durable Writes: true Options: [replication_factor:1] Column Families: ColumnFamily: Category_cf Key Validation Class: org.apache.cassandra.db.marshal.UTF8Type Default column value validator: org.apache.cassandra.db.marshal.UTF8Type Columns sorted by: org.apache.cassandra.db.marshal.UTF8Type Row cache size / save period in seconds / keys to save : 0.0/0/all Row Cache Provider: org.apache.cassandra.cache.SerializingCacheProvider Key cache size / save period in seconds: 200000.0/14400 GC grace seconds: 864000 Compaction min/max thresholds: 4/32 Read repair chance: 1.0 Replicate on write: true Bloom Filter FP chance: default Built indexes: [] Compaction Strategy: org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy You may also use hive to view the databases created in cassandra, in an sql-like manner. * Start Hive $ bin/dse hive hive> show databases; OK default shopping_cart_db When the entire database is imported as above, separate java classes will be created for each of the tables. $ bin/dse sqoop import-all-tables -m 1 --connect jdbc:mysql://127.0.0.1:3306/shopping_cart_db --username root --password root --cassandra-thrift-host localhost --cassandra-create-schema --direct 12/06/15 15:42:11 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead. 12/06/15 15:42:11 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset. 12/06/15 15:42:11 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:11 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `Category` AS t LIMIT 1 12/06/15 15:42:11 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Category.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:13 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Category.jar 12/06/15 15:42:13 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:13 INFO mapreduce.ImportJobBase: Beginning import of Category 12/06/15 15:42:14 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 12/06/15 15:42:15 INFO mapred.JobClient: Running job: job_201206151241_0007 12/06/15 15:42:16 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:42:25 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:42:25 INFO mapred.JobClient: Job complete: job_201206151241_0007 12/06/15 15:42:25 INFO mapred.JobClient: Counters: 18 12/06/15 15:42:25 INFO mapred.JobClient: Job Counters 12/06/15 15:42:25 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=6480 12/06/15 15:42:25 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:42:25 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:42:25 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:42:25 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:42:25 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:42:25 INFO mapred.JobClient: Bytes Written=2848 12/06/15 15:42:25 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:42:25 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21419 12/06/15 15:42:25 INFO mapred.JobClient: CFS_BYTES_WRITTEN=2848 12/06/15 15:42:25 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:42:25 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:42:25 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:42:25 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:42:25 INFO mapred.JobClient: Map input records=1 12/06/15 15:42:25 INFO mapred.JobClient: Physical memory (bytes) snapshot=119435264 12/06/15 15:42:25 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:42:25 INFO mapred.JobClient: CPU time spent (ms)=630 12/06/15 15:42:25 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:42:25 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2085318656 12/06/15 15:42:25 INFO mapred.JobClient: Map output records=36 12/06/15 15:42:25 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:42:25 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 11.4492 seconds (0 bytes/sec) 12/06/15 15:42:25 INFO mapreduce.ImportJobBase: Retrieved 36 records. 12/06/15 15:42:25 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:25 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `Customer` AS t LIMIT 1 12/06/15 15:42:25 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Customer.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:25 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Customer.jar 12/06/15 15:42:26 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:26 INFO mapreduce.ImportJobBase: Beginning import of Customer 12/06/15 15:42:26 INFO mapred.JobClient: Running job: job_201206151241_0008 12/06/15 15:42:27 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:42:35 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:42:35 INFO mapred.JobClient: Job complete: job_201206151241_0008 12/06/15 15:42:35 INFO mapred.JobClient: Counters: 17 12/06/15 15:42:35 INFO mapred.JobClient: Job Counters 12/06/15 15:42:35 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=6009 12/06/15 15:42:35 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:42:35 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:42:35 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:42:35 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:42:35 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:42:35 INFO mapred.JobClient: Bytes Written=0 12/06/15 15:42:35 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:42:35 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21489 12/06/15 15:42:35 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:42:35 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:42:35 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:42:35 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:42:35 INFO mapred.JobClient: Map input records=1 12/06/15 15:42:35 INFO mapred.JobClient: Physical memory (bytes) snapshot=164855808 12/06/15 15:42:35 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:42:35 INFO mapred.JobClient: CPU time spent (ms)=510 12/06/15 15:42:35 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:42:35 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2082869248 12/06/15 15:42:35 INFO mapred.JobClient: Map output records=0 12/06/15 15:42:35 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:42:35 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.3143 seconds (0 bytes/sec) 12/06/15 15:42:35 INFO mapreduce.ImportJobBase: Retrieved 0 records. 12/06/15 15:42:35 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:35 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `OrderEntry` AS t LIMIT 1 12/06/15 15:42:35 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/OrderEntry.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:35 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/OrderEntry.jar 12/06/15 15:42:36 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:36 INFO mapreduce.ImportJobBase: Beginning import of OrderEntry 12/06/15 15:42:36 INFO mapred.JobClient: Running job: job_201206151241_0009 12/06/15 15:42:37 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:42:45 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:42:45 INFO mapred.JobClient: Job complete: job_201206151241_0009 12/06/15 15:42:45 INFO mapred.JobClient: Counters: 17 12/06/15 15:42:45 INFO mapred.JobClient: Job Counters 12/06/15 15:42:45 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=6381 12/06/15 15:42:45 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:42:45 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:42:45 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:42:45 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:42:45 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:42:45 INFO mapred.JobClient: Bytes Written=0 12/06/15 15:42:45 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:42:45 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21569 12/06/15 15:42:45 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:42:45 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:42:45 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:42:45 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:42:45 INFO mapred.JobClient: Map input records=1 12/06/15 15:42:45 INFO mapred.JobClient: Physical memory (bytes) snapshot=137252864 12/06/15 15:42:45 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:42:45 INFO mapred.JobClient: CPU time spent (ms)=520 12/06/15 15:42:45 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:42:45 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2014703616 12/06/15 15:42:45 INFO mapred.JobClient: Map output records=0 12/06/15 15:42:45 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:42:45 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.2859 seconds (0 bytes/sec) 12/06/15 15:42:45 INFO mapreduce.ImportJobBase: Retrieved 0 records. 12/06/15 15:42:45 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:45 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `OrderItem` AS t LIMIT 1 12/06/15 15:42:45 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/OrderItem.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:45 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/OrderItem.jar 12/06/15 15:42:46 WARN manager.CatalogQueryManager: The table OrderItem contains a multi-column primary key. Sqoop will default to the column orderNumber only for this job. 12/06/15 15:42:46 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:46 INFO mapreduce.ImportJobBase: Beginning import of OrderItem 12/06/15 15:42:46 INFO mapred.JobClient: Running job: job_201206151241_0010 12/06/15 15:42:47 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:42:55 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:42:55 INFO mapred.JobClient: Job complete: job_201206151241_0010 12/06/15 15:42:55 INFO mapred.JobClient: Counters: 17 12/06/15 15:42:55 INFO mapred.JobClient: Job Counters 12/06/15 15:42:55 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=5949 12/06/15 15:42:55 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:42:55 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:42:55 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:42:55 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:42:55 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:42:55 INFO mapred.JobClient: Bytes Written=0 12/06/15 15:42:55 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:42:55 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21524 12/06/15 15:42:55 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:42:55 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:42:55 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:42:55 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:42:55 INFO mapred.JobClient: Map input records=1 12/06/15 15:42:55 INFO mapred.JobClient: Physical memory (bytes) snapshot=116674560 12/06/15 15:42:55 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:42:55 INFO mapred.JobClient: CPU time spent (ms)=590 12/06/15 15:42:55 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:42:55 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2014703616 12/06/15 15:42:55 INFO mapred.JobClient: Map output records=0 12/06/15 15:42:55 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:42:55 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.2539 seconds (0 bytes/sec) 12/06/15 15:42:55 INFO mapreduce.ImportJobBase: Retrieved 0 records. 12/06/15 15:42:55 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:42:55 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `Payment` AS t LIMIT 1 12/06/15 15:42:55 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Payment.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:42:55 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Payment.jar 12/06/15 15:42:56 WARN manager.CatalogQueryManager: The table Payment contains a multi-column primary key. Sqoop will default to the column orderNumber only for this job. 12/06/15 15:42:56 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:42:56 INFO mapreduce.ImportJobBase: Beginning import of Payment 12/06/15 15:42:56 INFO mapred.JobClient: Running job: job_201206151241_0011 12/06/15 15:42:57 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:43:05 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:43:05 INFO mapred.JobClient: Job complete: job_201206151241_0011 12/06/15 15:43:05 INFO mapred.JobClient: Counters: 17 12/06/15 15:43:05 INFO mapred.JobClient: Job Counters 12/06/15 15:43:05 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=5914 12/06/15 15:43:05 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:43:05 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:43:05 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:43:05 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:43:05 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:43:05 INFO mapred.JobClient: Bytes Written=0 12/06/15 15:43:05 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:43:05 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21518 12/06/15 15:43:05 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:43:05 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:43:05 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:43:05 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:43:05 INFO mapred.JobClient: Map input records=1 12/06/15 15:43:05 INFO mapred.JobClient: Physical memory (bytes) snapshot=137998336 12/06/15 15:43:05 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:43:05 INFO mapred.JobClient: CPU time spent (ms)=520 12/06/15 15:43:05 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:43:05 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2082865152 12/06/15 15:43:05 INFO mapred.JobClient: Map output records=0 12/06/15 15:43:05 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:43:05 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.2642 seconds (0 bytes/sec) 12/06/15 15:43:05 INFO mapreduce.ImportJobBase: Retrieved 0 records. 12/06/15 15:43:05 INFO tool.CodeGenTool: Beginning code generation 12/06/15 15:43:05 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `Product` AS t LIMIT 1 12/06/15 15:43:06 INFO orm.CompilationManager: HADOOP_HOME is /home/pradeeban/programs/dse-2.1/resources/hadoop/bin/.. Note: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Product.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 12/06/15 15:43:06 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-pradeeban/compile/926ddf787c73be06c4e2ad1f8fc522f1/Product.jar 12/06/15 15:43:06 INFO manager.DirectMySQLManager: Beginning mysqldump fast path import 12/06/15 15:43:06 INFO mapreduce.ImportJobBase: Beginning import of Product 12/06/15 15:43:07 INFO mapred.JobClient: Running job: job_201206151241_0012 12/06/15 15:43:08 INFO mapred.JobClient: map 0% reduce 0% 12/06/15 15:43:16 INFO mapred.JobClient: map 100% reduce 0% 12/06/15 15:43:16 INFO mapred.JobClient: Job complete: job_201206151241_0012 12/06/15 15:43:16 INFO mapred.JobClient: Counters: 18 12/06/15 15:43:16 INFO mapred.JobClient: Job Counters 12/06/15 15:43:16 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=5961 12/06/15 15:43:16 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 12/06/15 15:43:16 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 12/06/15 15:43:16 INFO mapred.JobClient: Launched map tasks=1 12/06/15 15:43:16 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0 12/06/15 15:43:16 INFO mapred.JobClient: File Output Format Counters 12/06/15 15:43:16 INFO mapred.JobClient: Bytes Written=248262 12/06/15 15:43:16 INFO mapred.JobClient: FileSystemCounters 12/06/15 15:43:16 INFO mapred.JobClient: FILE_BYTES_WRITTEN=21527 12/06/15 15:43:16 INFO mapred.JobClient: CFS_BYTES_WRITTEN=248262 12/06/15 15:43:16 INFO mapred.JobClient: CFS_BYTES_READ=87 12/06/15 15:43:16 INFO mapred.JobClient: File Input Format Counters 12/06/15 15:43:16 INFO mapred.JobClient: Bytes Read=0 12/06/15 15:43:16 INFO mapred.JobClient: Map-Reduce Framework 12/06/15 15:43:16 INFO mapred.JobClient: Map input records=1 12/06/15 15:43:16 INFO mapred.JobClient: Physical memory (bytes) snapshot=144871424 12/06/15 15:43:16 INFO mapred.JobClient: Spilled Records=0 12/06/15 15:43:16 INFO mapred.JobClient: CPU time spent (ms)=1030 12/06/15 15:43:16 INFO mapred.JobClient: Total committed heap usage (bytes)=121241600 12/06/15 15:43:16 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2085318656 12/06/15 15:43:16 INFO mapred.JobClient: Map output records=300 12/06/15 15:43:16 INFO mapred.JobClient: SPLIT_RAW_BYTES=87 12/06/15 15:43:16 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 9.2613 seconds (0 bytes/sec) 12/06/15 15:43:16 INFO mapreduce.ImportJobBase: Retrieved 300 records. I found DataStax an interesting project to explore more. I have blogged on the issues that I faced on this as a learner, and how easily can they be fixed - Issues that you may encounter during the migration to Cassandra using DataStax/Sqoop and the fixes.

The last couple of days I've been playing around with three.js and geo information. I wanted to be able to render map/geo data (e.g. in geojson format) inside the three.js scene. That way I have another dimension I could use to show a specific metric instead of just using the color in a 2D map. In this article I'll show you how you can do this. The example we'll create shows a 3D map of the Netherlands, rendered in Three.js, that uses a color to indicate the population density per municipality and the height of each municipality represents the actual number of residents. Or if you can look at a working example. This information is based on open data available from the Dutch government. If you look at the source from the example, you can see the json we use for this. For more information on geojson and how to parse it see the other articles I did on this subject: Using d3.js to visualize GIS Election site part 1: Basics with Knockout.js, Bootstrap and d3.js To get this working we'll take the following steps: Load the input geo data Setup a three.js scene Convert the input data to a Three.js path using d3.js Set the color and height of the Three.js object Render everything Just a reminder to see everything working, just look at the example. Load the input geo data D3.js has support to load json and directly transform it to an SVG path. Though this is a convenient way, I only needed the path data, not the complete SVG elements. So to load json I just used jquery's json support. // get the data jQuery.getJSON('data/cities.json', function(data, textStatus, jqXHR) { .. }); This will load the data and pass it in the data object to the supplied function. Setup a three.js scene Before we do anything with the data lets first setup a basic Three.js scene. // Set up the three.js scene. This is the most basic setup without // any special stuff function initScene() { // set the scene size var WIDTH = 600, HEIGHT = 600; // set some camera attributes var VIEW_ANGLE = 45, ASPECT = WIDTH / HEIGHT, NEAR = 0.1, FAR = 10000; // create a WebGL renderer, camera, and a scene renderer = new THREE.WebGLRenderer({antialias:true}); camera = new THREE.PerspectiveCamera(VIEW_ANGLE, ASPECT, NEAR, FAR); scene = new THREE.Scene(); // add and position the camera at a fixed position scene.add(camera); camera.position.z = 550; camera.position.x = 0; camera.position.y = 550; camera.lookAt( scene.position ); // start the renderer, and black background renderer.setSize(WIDTH, HEIGHT); renderer.setClearColor(0x000); // add the render target to the page $("#chart").append(renderer.domElement); // add a light at a specific position var pointLight = new THREE.PointLight(0xFFFFFF); scene.add(pointLight); pointLight.position.x = 800; pointLight.position.y = 800; pointLight.position.z = 800; // add a base plane on which we'll render our map var planeGeo = new THREE.PlaneGeometry(10000, 10000, 10, 10); var planeMat = new THREE.MeshLambertMaterial({color: 0x666699}); var plane = new THREE.Mesh(planeGeo, planeMat); // rotate it to correct position plane.rotation.x = -Math.PI/2; scene.add(plane); } Nothing to special, the comments inline should nicely explain what we're doing here. Next it gets more interesting. Convert the input data to a Three.js path using d3.js What we need to do next is convert our geojson input format to a THREE.Path that we can use in our scene. Three.js itself doesn't support geojson or SVG for that matter. Luckily though someone already started work on integrating d3.js with three.js. This project is called "d3-threeD" (sources can be found on github here). With this extension you can automagically render SVG elements in 3D directly from D3.js. Cool stuff, but it didn't allow me any control over how the elements were rendered. It does however contain a function we can use for our scenario. If you look through the source code of this project you'll find a method called "transformSVGPath". This method converts an SVG path string to a Three.Shape element. Unfortunately this method isn't exposed, but that's quickly solved by adding this to the d3-threeD.js file: // at the top var transformSVGPathExposed; ... // within the d3threeD(exports) function transformSVGPathExposed = transformSVGPath; This way we can call this method separately. Now that we have a way to transform an SVG path to a Three.js shape, we only need to convert the geojson to an SVG string and pass it to this function. We can use the geo functionaly from D3.js for this: geons.geoConfig = function() { this.TRANSLATE_0 = appConstants.TRANSLATE_0; this.TRANSLATE_1 = appConstants.TRANSLATE_1; this.SCALE = appConstants.SCALE; this.mercator = d3.geo.mercator(); this.path = d3.geo.path().projection(this.mercator); this.setupGeo = function() { var translate = this.mercator.translate(); translate[0] = this.TRANSLATE_0; translate[1] = this.TRANSLATE_1; this.mercator.translate(translate); this.mercator.scale(this.SCALE); } } The path variable from the previous piece of code can now be used like this: var feature = geo.path(geoFeature); To convert a geojson element to an SVG path. So how does this look combined? // add the loaded gis object (in geojson format) to the map function addGeoObject() { // keep track of rendered objects var meshes = []; ... // convert to mesh and calculate values for (var i = 0 ; i < data.features.length ; i++) { var geoFeature = data.features[i] var feature = geo.path(geoFeature); // we only need to convert it to a three.js path var mesh = transformSVGPathExposed(feature); // add to array meshes.push(mesh); ... } As you can see we iterate over the data.features list (this contains all the geojson representations of the municipalities). Each municipality is converted to an svg string, and each svg string is converted to a mesh. This mesh is a Three.js object that we can render on the scene. Set the color and height of the Three.js object Now we just need to set the height and the color of the Three.js shape and add it to the scene. The extended addGeoObject method now looks like this: // add the loaded gis object (in geojson format) to the map function addGeoObject() { // keep track of rendered objects var meshes = []; var averageValues = []; var totalValues = []; // keep track of min and max, used to color the objects var maxValueAverage = 0; var minValueAverage = -1; // keep track of max and min of total value var maxValueTotal = 0; var minValueTotal = -1; // convert to mesh and calculate values for (var i = 0 ; i < data.features.length ; i++) { var geoFeature = data.features[i] var feature = geo.path(geoFeature); // we only need to convert it to a three.js path var mesh = transformSVGPathExposed(feature); // add to array meshes.push(mesh); // we get a property from the json object and use it // to determine the color later on var value = parseInt(geoFeature.properties.bev_dichth); if (value > maxValueAverage) maxValueAverage = value; if (value < minValueAverage || minValueAverage == -1) minValueAverage = value; averageValues.push(value); // and we get the max values to determine height later on. value = parseInt(geoFeature.properties.aant_inw); if (value > maxValueTotal) maxValueTotal = value; if (value < minValueTotal || minValueTotal == -1) minValueTotal = value; totalValues.push(value); } // we've got our paths now extrude them to a height and add a color for (var i = 0 ; i < averageValues.length ; i++) { // create material color based on average var scale = ((averageValues[i] - minValueAverage) / (maxValueAverage - minValueAverage)) * 255; var mathColor = gradient(Math.round(scale),255); var material = new THREE.MeshLambertMaterial({ color: mathColor }); // create extrude based on total var extrude = ((totalValues[i] - minValueTotal) / (maxValueTotal - minValueTotal)) * 100; var shape3d = meshes[i].extrude({amount: Math.round(extrude), bevelEnabled: false}); // create a mesh based on material and extruded shape var toAdd = new THREE.Mesh(shape3d, material); // rotate and position the elements nicely in the center toAdd.rotation.x = Math.PI/2; toAdd.translateX(-490); toAdd.translateZ(50); toAdd.translateY(extrude/2); // add to scene scene.add(toAdd); } } // simple gradient function function gradient(length, maxLength) { var i = (length * 255 / maxLength); var r = i; var g = 255-(i); var b = 0; var rgb = b | (g << 8) | (r << 16); return rgb; } A big piece of code, but not that complex. What we do here is we keep track of two values for each municipality: the population density and the total population. These values are used to respectively calculate the color (using the gradient function) and the height. The height is used in the Three.js extrude function which converts our 2D Three.Js path to a 3D shape. The color is used to define a material. This shape and material is used to create the Mesh that we add to the scene. Render everything All that is left is to render everything. For this example we're not interested in animations or anything so we can make a single call to the renderer: renderer.render( scene, camera ); And the result is as you saw in the beginning. The following image shows a different example. This time we once again show the population density, but now the height represents the land area of the municipality. I'm currently creating a new set of geojson data, but this time for the whole of Europe. So in the next couple of weeks expect some articles using maps of Europe.

If you have already used the ASP.NET ListView and DataPager controls, you know how easily you can display your data using custom templates and provide pagination functionality. You can do that in only few minutes. The ListView and DataPager controls work perfectly fine in combination with DataSource controls (SqlDataSource, LinqDataSource, ObjectDataSource etc.), however if you use them with custom data collection, without using Data Source controls, you may find some unexpected behavior and have to add some little more code to make it work. Note: I saw question related to this issue asked multiple times in different asp.net forums, so I thought it would be nice to document it here. Lets create a working demo together… 1. Create sample ASP.NET Web Application project, add new ASPX page. 2. Add ListView control and modify the markup so that you will end up having this: ( ) No data The ListView control has three templates defined: LayoutTemplate – where we define the way we want to represent our data. We have PlaceHolder where data from ItemTemplate will be placed. ListView recognizes this automatically. ItemTemplate – where the ListView control will show the items from the data source. It makes automatically iterating over each item in the collection (same as any other data source control) EmptyDataTemplate – If the collection has no data, this template will be displayed. 3. Add DataPager control and modify the markup in the following way: We add the DataPager control, associate it with the ListView1 control and add the PageSize property. After that, we need to define where we put the field type and button type. If we were binding from Data Source Control (SqlDataSource, LinqDataSource or any other…) this would be it and the ListView and DataPager would work perfectly fine. However, if we bind custom data collection to the ListView without using Data Source controls, we will have problems with the pagination. Lets add custom data in our C# code and bind it to the ListView. 4. C# code adding custom data collection - Define Product class public class Product { public string Name { get; set; } public decimal Price { get; set; } public string Currency { get; set; } } - Define method that will create List of products (sample data) List SampleData() { List p = new List(); p.Add(new Product() { Name = "Microsoft Windows 7", Price = 70, Currency = "USD" }); p.Add(new Product() { Name = "HP ProBook", Price = 320, Currency = "USD" }); p.Add(new Product() { Name = "Microsoft Office Home", Price = 60, Currency = "USD" }); p.Add(new Product() { Name = "NOKIA N900", Price = 350, Currency = "USD" }); p.Add(new Product() { Name = "BlackBerry Storm", Price = 100, Currency = "USD" }); p.Add(new Product() { Name = "Apple iPhone", Price = 400, Currency = "USD" }); p.Add(new Product() { Name = "HTC myTouch", Price = 200, Currency = "USD" }); return p; } This method should be part of the ASPX page class (e.g. inside _Default page class if your page is Default.aspx) - Bind the sample data to ListView on Page_Load protected void Page_Load(object sender, EventArgs e) { if (!IsPostBack) { BindListView(); } } void BindListView() { ListView1.DataSource = SampleData(); ListView1.DataBind(); } Now, run the project and you should see the data displayed where only the first five items will be shown. The data pager should have two pages (1 2) and you will be able to click the second page to navigate to the last two items in the `collection. Now, if you click on page 2, you will see it won’t display the last two items automatically, instead you will have to click again on page 2 to see the last two items. After, if you click on page 1, you will encounter another problem where the five items are displayed, but the data for the last two items in the ListView are shown (see print screen bellow) If you notice in the previous two pictures, the behavior doesn’t seem to work properly. The problem here is that DataPager doesn’t know about current ListView page changing property. Therefore, we should explicitly set the DataPager Page Properties in the ListView’s PagePropertiesChanging event. Here is what we need to do: 1. Add OnPagePropertiesChanging event to ListView control 2. Implement the ListView1_PagePropertiesChanging method protected void ListView1_PagePropertiesChanging(object sender, PagePropertiesChangingEventArgs e) { //set current page startindex, max rows and rebind to false lvDataPager1.SetPageProperties(e.StartRowIndex, e.MaximumRows, false); //rebind List View BindListView(); } Now, if you test the functionality, it should work properly. Hope this was helpful. Regards, Hajan

Java Message Service is a mechanism for integrating applications in a loosely coupled, flexible manner and delivers data asynchronously across applications.

i switch between programming languages quite a bit; i often wondered what happens when having to deal with the different syntaxes, does the syntax allow you to be more expressive or faster at coding in one language or another. i don't really know about that; but what i do know what keys are pressed when writing with different programming languages. this might be something interesting for people who are deciding to select a programming language might look into, here is a post on the answer to the aged question of: which programming language should i learn? as far as i can tell languages with a wider focused spread across the keyboard are usually syntaxes we usually associate with ugly languages (ugly to read and code). ex. shell and perl. you might argue that the variables names being used will alter the results, but as most languages programming have conventions for naming but we can assume a decent spread for variable names. i don’t offer conclusions, just poorly layout the facts. although the heat map does miss out on things like shift and caps. ex. in perl with the dollar sign. ($) whitespace hasn’t been taken into consideration (tabs and spaces) which would have been a cool thing to see. the data that was used to gather this information was spread amongst various popular github projects. javascript shell java c c++ ruby python php perl objc lisp lisp code here was written by paul graham. references heatmap.js http://www.patrick-wied.at/projects/heatmap-keyboard/

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