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Search-Enable Your Application with Lucene
Search-Enable Your Application with Lucene

The e-commerce Web site that I work on has seen several incarnations of its search feature. We started with plain vanilla SQL using "like" clauses, but this didn't perform well and left a lot to be desired in language features such as stemming (e.g., "paint" = "painter" = "painting") and synonym matching (e.g., "cat" = "feline"). Next we tried an off-the-shelf solution. This addressed our efficiency and language demands, but it was ridden with strange quirks and we were limited in how much we could customize its behavior.

Then we discovered Lucene. Lucene is an open-source search framework from Apache's Jakarta project. As a framework, Lucene provides you with the building blocks you need to build a search engine that meets your specific searching requirements. Lucene is flexible, fully customizable, and amazingly fast.

In this article I show you how to use Lucene to build a search solution for your application. Although my examples will be geared toward an e-commerce application, Lucene is flexible enough to be used on any application whether it's Web, desktop, or CD-ROM based.

I used version 1.2 of Lucene to develop the examples in this article. It can be downloaded from http://jakarta.apache.org/lucene. Lucene is self-contained, so you'll need only a JVM (v1.1.8 or higher) to use it. Place lucene-1.2.jar into your classpath and you're ready to start.

Indexing Documents
To build a Lucene index, first you'll need an instance of IndexWriter. The following lines of code create an IndexWriter for an index located at c:\myindex.

Analyzer analyzer = new StopAnalyzer();
writer = new IndexWriter("c:/myindex", analyzer, true);

The first argument to the constructor is the path where the index will be written. If the path doesn't already exist, Lucene will create it for you. The second argument is the Analyzer you want IndexWriter to use when tokenizing text. Here I used StopAnalyzer to remove stop words ("and," "or," "the," etc.) from the token stream. The last argument tells IndexWriter whether to create a new index or to add documents to an existing one. Passing true to the constructor will create the index from scratch; passing false will append to an existing index.

Now that you have an IndexWriter, you're ready to start adding documents to the index. The following code creates a simple document that represents a Web page and uses IndexWriter to add it to the index.

String url = "http://jakarta.apache.org/lucene";
String content = indexer.retrieveWebPageContent(url);
String keywords = indexer.extractKeywords(content);

Document doc = new Document();
doc.add(Field.UnIndexed("url", url));
doc.add(Field.UnStored("keywords", keywords));
doc.add(Field.Text("content", content));
writer.addDocument(doc);

In this example, the document contains the URL metadata for Lucene's homepage, a keywords field that contains search terms to match against in a search, and a "content" field that contains the full content of the Web page.

Once all documents have been added, all that remains is to close the index.

writer.close();

Although this example adds only a single (hard-coded) document to an index, it serves well as a "Hello World" example of how to create indexes using Lucene. The complete source code for this example is in Listing 1. (Listings 1-10 can be downloaded from www.sys-con.com/java/sourcec.cfm.)

For a more interesting example, suppose you're indexing a product catalog to be searched on an e-commerce Web site. A product is made up of a SKU, a name, a price, and some keywords to be searched on (see Listing 2). ProductIndexer (see Listing 3) is a convenience class used to add products to a Lucene index.

The constructor for ProductIndexer takes a string that's the path where the Lucene index will be built and a Boolean parameter that specifies whether a new index will be created or an existing index appended. ProductIndexer uses StopAnalyzer for tokenizing text.

The addProduct() method creates an instance of Document and translates the attributes of the Product into document fields. As in the simple example earlier, the "keywords" field is created as unstored so it can be searched upon but is unavailable for retrieval. The other fields are created as unindexed because these fields will be retrieved only after a successful search, not searched upon themselves.

The close() method closes the IndexWriter, making it available for searching. Before closing, however, a call is made to the IndexWriter's optimize() method to have Lucene optimize the index. Although it's entirely optional, it's generally a good idea to call optimize() if the indexing is finished for the time being and no further documents will be added to the index for a while.

ProductDBIndexer (see Listing 4) reads products from a "catalog" table in a relational database (see Table 1 for the products that I used) and uses ProductIndexer to add the products to Lucene's index. ProductDBIndexer takes two command-line arguments: the path in which to build the index and an optional "create" flag to indicate that the index should be built from scratch.

Lucene Index Structure
Lucene indexes are file based. If you look in the directory where you created the index, you'll find several files that define the Lucene index. Depending on how large your index is, you'll see several groups of files where each file in a group has the same name but a different extension. Each of these groups is known as a "segment." Although this article won't delve into the details of how Lucene segments work, it may be interesting to note that IndexWriter's optimize() method optimizes Lucene's index by consolidating all segments into a single segment for more efficient searching.

While IndexWriter is writing indexes, a file called "write.lock" is created. This file prevents other instances of IndexWriter from writing to the index concurrently. Calling IndexWriter's close() method removes this file and makes the index available for writing by another IndexWriter.

Lucene keeps track of each segment in the index using a file called "segments". During indexing, it occasionally becomes necessary for Lucene to update the segments file to keep it synchronized with the segments in the index. While this synchronization is going on, Lucene creates a "commit.lock" file to prevent concurrent updates of the segments file. Once the segments file is in sync, the commit.lock file is removed.

What would happen if you were to write to an index while it's being searched on? You may write to the index (either by adding new documents or re-creating the index from scratch) while it's being searched, but doing so may have undesirable effects on the search results. The worst side effect that I've seen is a document appearing out of order in the Hits collection. Depending on how important the ordering is to you, it may be best to create your indexes off-line (i.e., in another directory) and then rename the directory to become the current index.

Searching
Now that you've built an index, it's time to perform search queries against it. ProductSearcher (see Listing 5) shows how to do this.

To search a Lucene index you need an instance of org.apache.lucene.search.Searcher. Two subclasses of Searcher come with Lucene. IndexSearcher is for searching a single Lucene index while MultiSearcher is used to search multiple indexes at once. Only the product catalog index will be searched, so IndexSearcher is the best choice for this example. It's constructed given the path to the index.

Searcher searcher = new IndexSearcher(indexPath);

Next you must construct a Query object. The best way to do this is to use the parse() method of org.apache.lucene.queryParser.QueryParser. Create an instance of QueryParser, passing the name of the default field (the field that's searched upon by default) and an analyzer to the constructor. Then call parse() on the QueryParser instance passing the query string. An instance of org.apache .lucene.search.Query will be returned.

QueryParser queryParser = new QueryParser("keywords", new StopAnalyzer());
Query query = queryParser.parse("cat food");

Note: QueryParser is not thread-safe. A new instance of QueryParser should be created for each thread.

For this example the choice of query string is hard coded as "cat food". This query will result in all documents containing either "cat" or "food", but not necessarily both. It's possible to require that a document's keyword field contain "cat" and "food" when searching. Simply place a plus (+) sign in front of each word so that the search string will be "+cat +food" to require resulting documents to contain both "cat" and "food" in their keyword field. More advanced search options will be discussed later.

Next make a call to the Searcher's search() method, passing in the Query object.

Hits hits = searcher.search(query);

The search() method returns an instance of org.apache.lucene.search.Hits. The Hits class represents a collection of documents matching the search criteria, along with each document's relevancy score. These scores range from 0.0 to 1.0 where 1.0 is considered highly relevant and 0.0 is considered completely irrelevant (and not included in the Hits collection).

Finally, cycle through each Document returned in the Hits object displaying the SKU and name of the product along with its relevancy score.

for (int i = 0; i < hits.length(); i++) {
Document document = hits.doc(i);
float score = hits.score(i);
System.out.println(document.get("sku") + " :: " +
document.get("name") + " :: " + score);
}

Advanced Queries
Up until now, the queries have been relatively simple ones such as "cat food" and "+cat +food". QueryParser has a powerful selection of query operators to facilitate more complex searches. Table 2 lists all of QueryParser's operators.

Wildcard queries are fairly straightforward. The "*" operator can be replaced by zero or more characters to match a word. The "?" operator is replaced by exactly one character when matching. For example, "ca*" will match "cat", "car", "cap", or "candle", while "ca?" will match "cat", "car", and "cap", but not "candle". This is consistent with the behavior of "*" and "?" on a DOS or Unix command line.

The tilde (~) character, when used alone, performs a fuzzy search, matching words that are spelled similarly. For example, "cat~" will match "cat", but it will also match "car" and "rat" because these words are similarly spelled.

Surrounding two or more words with quotes (" ") produces a phrase. When two or more words are part of a phrase, those words must appear together in order to be considered a match. For example, ""dog food"" will match documents where "dog" is immediately followed by "food".

If a tilde and a number follow a phrase, then a proximity search is performed. For example, ""dog food"~10" will produce results where "dog" and "food" are found within 10 words of each other, but not necessarily adjacent to each other.

The carat (^) is a term booster. What this means is that any word followed by a carat is considered to have higher relevance than words not followed by a carat. For example, "dog^ kennel" will match where the document contains "dog" or "kennel", but will give a higher relevance to documents containing "dog".

The Boolean operators, AND, OR, and NOT behave as you would expect them to. For example, "(cat AND food) OR bird" returns all documents containing "cat" and "food" along with all documents that contain "bird". "cat NOT food" returns all documents containing "cat", but not containing "food". As you have seen before in the simple "cat food" example, OR is the default conjunction operator.

As shown in the previous example, parentheses can be used to group terms into subqueries.

As discussed, the plus sign (+) requires that a word or phrase exist in a field. Conversely, the minus sign (-) prohibits a word from appearing in the results and is roughly equivalent to NOT. For example, "dog -food" returns all documents containing "dog" but not containing "food".

Finally, there are times when you may want to search multiple fields. When constructing a QueryParser, you must specify a default field to be searched upon. Unless you specify otherwise, any words in your query will be looked for in the default field. In the examples, "keywords" is the default field. You can search on nondefault fields (assuming that they're indexed) by using a colon (:). For example, had the name field been tokenized and indexed, the query string "+cat +name:nummies" would return all documents in which the keywords field contains "cat" and the name field contains "nummies".

Customizing Lucene
While Lucene comes with an impressive set of functionality, you may still find that you want it to do something more or different than is available out of the box. As a search framework, Lucene provides several hooks for you to extend and/or modify its behavior.

In the previous examples, the analyzer chosen was StopAnalyzer. Underneath the covers, Stop-Analyzer uses LetterTokenizer to tokenize text into individual words. LetterTokenizer treats any nonalphabetic character as a delimiter. This is fine in most cases, but what if you want to tokenize text that contains numeric characters ("0" - "9") as well as alphabetic characters? This would be desirable if the keyword text contains part numbers or model numbers. LetterTokenizer wouldn't help in this case.

Listing 6 defines AlphanumericTokenizer, a tokenizer that works like LetterTokenizer except for one small difference: it treats numeric characters as token characters along with alphabetic characters. It does this by subclassing LetterTokenizer and overriding the isTokenChar() method to return the results of LetterTokenizer's isTokenChar() implementation OR'd with a call to Character.isDigit().

AlphanumStopAnalyzer (see Listing 7) is an analyzer that uses AlphanumericTokenizer. The stop-word behavior of StopAnalyzer is still desired, so AlphanumericTokenizer is wrapped with a StopFilter. To normalize the text to lowercase, StopFilter is then wrapped with LowercaseFilter. AlphanumStopAnalyzer is functionally equivalent to StopAnalyzer, except, since it uses AlphanumericTokenizer, it does not treat numeric characters as delimiters. To try out AlphanumStopAnalyzer, use it in place of StopAnalyzer in both ProductIndexer and ProductSearcher. Be sure to reindex with ProductIndexer before searching the index with the new analyzer.

Suppose that synonym-matching capability is required so that "cat" will match "kitten", "kitty", or "feline". AliasFilter (see Listing 8) is a subclass of TokenFilter that does this. AliasFilter retrieves its synonym list from entries in AliasFilter.properties. For example:

cat=feline kitten kitty
dog=canine puppy mutt
food=feed chow
parrot=bird

With each invocation of next(), AliasFilter first checks to see if there are any synonyms in the alias stack. If there are, it pops the next alias off the stack and returns it. Otherwise, AliasFilter retrieves the next token from the input TokenStream, adds any aliases that may exist to the alias stack, and then returns the next token.

AliasAnalyzer (see Listing 9) constructs a TokenStream that does everything the TokenStream from Alphanum-StopAnalyzer does, but it also uses AliasFilter to add synonyms to the TokenStream. To try AliasAnalyzer, use it as your analyzer instead of StopAnalyzer in both ProductIndexer and ProductSearch. Again, be sure to reindex before searching.

When trying AliasFilter you may discover some strange, albeit desirable, behavior. Search for "feline". Even though there are no aliases for feline, all cat-related products appear in the search results. Why? When you use AliasAnalyzer to search for "feline", the token stream does not expand beyond "feline". So why do "cat" products appear? The reason is, you also used AliasAnalyzer to index the products. When you indexed a product containing "cat", AliasAnalyzer expanded the token stream to include "kitten", "kitty", and "feline" in the index. When searching for "feline" it will be found in products whose token stream was expanded to include "feline". In effect, you get an automatic two-way aliasing between "cat" and "feline", even though it appears to be only one way in AliasFilter.properties.

Another common problem in searching is paging the results. A search query could return anywhere from zero results to a seemingly infinite number of result documents. Good usability practices suggest that you page the results, showing the user only a handful at a time. This can be accomplished in Lucene using result filters.

To create a result filter, you must subclass org.apache.lucene.search.Filter. The only required method is the bits() method. It will return a java.util.BitSet where each bit represents a document in the result set. If the bit is true, the document will be returned in Hits, otherwise it won't be returned.

PageFilter (see Listing 10) is an example of a Filter that's used to paginate search results. Given a page number and a page size, PageFilter will pare down Lucene's result set to a specific page's subset of documents. It does this by creating a BitSet big enough to hold the maximum number of result bits and then looping through the bits that need to be turned on. To use PageFilter, change ProductSearcher's call to search() to look like this:

Hits hits = searcher.search(query,new PageFilter(1,20));

This new call to search() will result in showing only the second set of 20 results.

Conclusion
Building a full-featured search engine can be a daunting task. But, thanks to Lucene, much of the complicated details are abstracted behind an easy-to-use API. We've seen how easy it can be to create an index for searching practically any type of information. We've also seen how Lucene is flexible and can be extended to satisfy custom indexing and searching requirements.

Resources

  • Jakarta Lucene: http://jakarta.apache.org/lucene
  • NLucene, the .NET implementation of Lucene at SourceForge: http://sourceforge.net/projects/nlucene
  • JGuru FAQ on Lucene: www.jguru.com/faq/Lucene
  • About Lucene's creator, Doug Cutting: http://lucene.sourceforge.net/background.html

    SIDEBAR
    Index Components
    A Lucene index is a collection of documents organized in a way that allows quick retrieval of information when arbitrarily queried upon.

    Each document (implemented by org.apache.lucene.document.Document) in a Lucene index is made up of one or more fields that are name-value pairs, much like entries in a HashMap. A document can contain as much or as little information as is required to be searched upon. For example, a Lucene document could contain the complete contents of a Web page, text file, e-mail, etc. On the other hand, a Lucene document may contain only a minimal set of metadata, such as keywords, along with a URL, a product SKU, or some other identifying information used to reference a full information source stored outside of Lucene (such as in a file system or a relational database).

    Each field in a document can be defined as being any combination of stored, indexed, and tokenized. If a field is stored, its contents are fully retrievable upon a successful search. If a field is indexed, its content may be referenced in a query and searched upon. If a field is tokenized, its content is broken into one or more tokens (or words) prior to being indexed.

    Fields can be created using org.apache.lucene.document.Field. The Field class has several static factory methods that make short work of creating field entries. Table 3 illustrates these static methods and the types of Fields that they create.

    Why would you want to index a field, but not store it? Consider a field that contains keywords for your document: chances are you'll never display or perform any processing of this field, but you still want to be able to search upon it. By indexing it you're making the field searchable, but by not storing it, you're saving space because the text is not written verbatim to the index. On the other hand, you may want to store some data so that it can be retrieved later but not actually be able to search upon it. In that case, you'd choose a field that's stored but not indexed. When defining your fields, be mindful of what those fields will be used for, and for efficiency's sake choose an appropriate field definition.

    SIDEBAR
    Search Components
    A Searcher (org.apache.lucene.search.Searcher) is used to access a Lucene index and query its contents. There are two subclasses of Searcher: IndexSearcher that searches a single index and MultiSearcher that searches one or more indexes and collects all the results in a single result set.

    Searches are performed by calling one of Searcher's search() methods and passing it a query (org.apache.lucene.search.Query). The search method returns an instance of org.apache.lucene.search.Hits. The Hits class is an array-like collection of documents that matches your query. The documents are ordered in Hits by a relevancy score.

    A Query object can be constructed using org.apache.lucene.query-Parser.QueryParser. QueryParser's parse() method parses a query string that's written in its query language and builds an appropriate Query object for that query string. QueryParser also uses an Analyzer in performing the parsing of the query string. It's not required, but it is strongly recommended that you use the same Analyzer for parsing queries that you used when indexing your documents.

    SIDEBAR
    Text Analysis Components
    When a field is tokenized, its content is broken into one or more tokens or words. Facilitating this tokenization process is the notion of an analyzer (see Figure 1). An analyzer is any subclass of org.apache.lucene.analysis.Analyzer that defines the rules for tokenization.

    A token stream is an iterator that returns the next token with each call to its next() method or returns a null when there are no more tokens in the stream. Two important subclasses of TokenStream are Tokenizer and TokenFilter. Both of these classes are abstract and must be subclassed to define the specific rules on how to tokenize content.

    At the core of the tokenization process is a Tokenizer. A Tokenizer wraps an instance of java.io.Reader and performs the actual work of breaking a stream into individual tokens (not unlike the notion of a StringTokenizer).

    TokenFilters act as decorators of other TokenStreams. Token filters can be used to add, replace, or remove tokens from a TokenStream. For example, org.apache.lucene.analysis.PorterStemFilter is a TokenFilter that replaces each word in a TokenStream with its word stem (e.g., "painting" becomes "paint").

    Analyzers rely on token streams (subclasses of org.apache.lucene.analysis.TokenStream) in defining the tokenization rules. In fact, an analyzer is nothing more than a factory for creating instances of TokenStream.

    To see how the text analysis components are used together, consider some of the TokenStream and Analyzer implementations packaged with Lucene. StopAnalyzer is an analyzer whose job is to remove stop words (e.g., "and", "or", "the", etc.) from a tokenized stream. At the core of StopAnalyzer is an instance of LowerCaseTokenizer. It tokenizes the stream into individual words, normalizing them to lowercase as it goes, where any nonalphabetic character is considered a delimiter. An instance of StopFilter decorates LowerCaseTokenizer, removing stop words from the stream as they're found. StopAnalyzer's tokenStream() method is merely a factory method that returns the decorator chain made up of LowerCaseTokenizer and StopFilter.

    About Craig Walls
    Craig Walls is the manager of Internet development for a Dallas, Texas-based retailer. He has eight years of experience in software development, six in Java. Craig is a Sun Certified Java programmer and a Sun Certified architect for the Java platform. He holds a BS in computer science from New Mexico State University.

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    The Microservices architectural pattern promises increased DevOps agility and can help enable continuous delivery of software. This session is for developers who are transforming existing applications to cloud-native applications, or creating new microservices style applications. In his session at 17th Cloud Expo, Jim Bugwadia, CEO of Nirmata, introduced best practices, patterns, challenges, and solutions for the development and operations of microservices style applications. He discussed how a...
    There are so many tools and techniques for data analytics that even for a data scientist the choices, possible systems, and even the types of data can be daunting. In his session at @ThingsExpo, Chris Harrold, Global CTO for Big Data Solutions for EMC Corporation, showed how to perform a simple, but meaningful analysis of social sentiment data using freely available tools that take only minutes to download and install. Participants received the download information, scripts, and complete end-t...
    For manufacturers, the Internet of Things (IoT) represents a jumping-off point for innovation, jobs, and revenue creation. But to adequately seize the opportunity, manufacturers must design devices that are interconnected, can continually sense their environment and process huge amounts of data. As a first step, manufacturers must embrace a new product development ecosystem in order to support these products.
    Everyone talks about continuous integration and continuous delivery but those are just two ends of the pipeline. In the middle of DevOps is continuous testing (CT), and many organizations are struggling to implement continuous testing effectively. After all, without continuous testing there is no delivery. And Lab-As-A-Service (LaaS) enhances the CT with dynamic on-demand self-serve test topologies. CT together with LAAS make a powerful combination that perfectly serves complex software developm...
    Clearly the way forward is to move to cloud be it bare metal, VMs or containers. One aspect of the current public clouds that is slowing this cloud migration is cloud lock-in. Every cloud vendor is trying to make it very difficult to move out once a customer has chosen their cloud. In his session at 17th Cloud Expo, Naveen Nimmu, CEO of Clouber, Inc., advocated that making the inter-cloud migration as simple as changing airlines would help the entire industry to quickly adopt the cloud without ...
    The traditional, on-premise computing model has established processes, accreditations, certifications, governance and compliance rules - FISMA, NERC CIP, HIPAA, PCI-DSS, IRS 1075. While the security industry is aggressively addressing the technical security gaps in cloud-driven services, many organizations using cloud services are struggling to implement and adapt strategic processes, procedures, and controls for cloud governance and due diligence.
    Manufacturing connected IoT versions of traditional products requires more than multiple deep technology skills. It also requires a shift in mindset, to realize that connected, sensor-enabled “things” act more like services than what we usually think of as products. In his session at @ThingsExpo, David Friedman, CEO and co-founder of Ayla Networks, discussed how when sensors start generating detailed real-world data about products and how they’re being used, smart manufacturers can use the dat...
    With containerization using Docker, the orchestration of containers using Kubernetes, the self-service model for provisioning your projects and applications and the workflows we built in OpenShift is the best in class Platform as a Service that enables introducing DevOps into your organization with ease. In his session at DevOps Summit, Veer Muchandi, PaaS evangelist with RedHat, provided a deep dive overview of OpenShift v3 and demonstrated how it helps with DevOps.
    When it comes to IoT in the enterprise, namely the commercial building and hospitality markets, a benefit not getting the attention it deserves is energy efficiency, and IoT’s direct impact on a cleaner, greener environment when installed in smart buildings. Until now clean technology was offered piecemeal and led with point solutions that require significant systems integration to orchestrate and deploy. There didn't exist a 'top down' approach that can manage and monitor the way a Smart Buildi...
    Overgrown applications have given way to modular applications, driven by the need to break larger problems into smaller problems. Similarly large monolithic development processes have been forced to be broken into smaller agile development cycles. Looking at trends in software development, microservices architectures meet the same demands. Additional benefits of microservices architectures are compartmentalization and a limited impact of service failure versus a complete software malfunction. Th...
    Contextual Analytics of various threat data provides a deeper understanding of a given threat and enables identification of unknown threat vectors. In his session at @ThingsExpo, David Dufour, Head of Security Architecture, IoT, Webroot, Inc., discussed how through the use of Big Data analytics and deep data correlation across different threat types, it is possible to gain a better understanding of where, how and to what level of danger a malicious actor poses to an organization, and to determ...
    In their session at 17th Cloud Expo, Hal Schwartz, CEO of Secure Infrastructure & Services (SIAS), and Chuck Paolillo, CTO of Secure Infrastructure & Services (SIAS), provided a study of cloud adoption trends and the power and flexibility of IBM Power and Pureflex cloud solutions. In his role as CEO of Secure Infrastructure & Services (SIAS), Hal Schwartz provides leadership and direction for the company.
    @CloudExpo Stories
    Cognitive Computing is becoming the foundation for a new generation of solutions that have the potential to transform business. Unlike traditional approaches to building solutions, a cognitive computing approach allows the data to help determine the way applications are designed. This contrasts with conventional software development that begins with defining logic based on the current way a business operates. In her session at 18th Cloud Expo, Judith S. Hurwitz, President and CEO of Hurwitz & ...
    As someone who has been dedicated to automation and Application Release Automation (ARA) technology for almost six years now, one of the most common questions I get asked regards Platform-as-a-Service (PaaS). Specifically, people want to know whether release automation is still needed when a PaaS is in place, and why. Isn't that what a PaaS provides? A solution to the deployment and runtime challenges of an application? Why would anyone using a PaaS then need an automation engine with workflow ...
    SYS-CON Events announced today that Catchpoint Systems, Inc., a provider of innovative web and infrastructure monitoring solutions, has been named “Silver Sponsor” of SYS-CON's DevOps Summit at 18th Cloud Expo New York, which will take place June 7-9, 2016, at the Javits Center in New York City, NY. Catchpoint is a leading Digital Performance Analytics company that provides unparalleled insight into customer-critical services to help consistently deliver an amazing customer experience. Designed...
    The cloud competition for database hosts is fierce. How do you evaluate a cloud provider for your database platform? In his session at 18th Cloud Expo, Chris Presley, a Solutions Architect at Pythian, will give users a checklist of considerations when choosing a provider. Chris Presley is a Solutions Architect at Pythian. He loves order – making him a premier Microsoft SQL Server expert. Not only has he programmed and administered SQL Server, but he has also shared his expertise and passion w...
    With the proliferation of both SQL and NoSQL databases, organizations can now target specific fit-for-purpose database tools for their different application needs regarding scalability, ease of use, ACID support, etc. Platform as a Service offerings make this even easier now, enabling developers to roll out their own database infrastructure in minutes with minimal management overhead. However, this same amount of flexibility also comes with the challenges of picking the right tool, on the right ...
    SYS-CON Events announced today that FalconStor Software® Inc., a 15-year innovator of software-defined storage solutions, will exhibit at SYS-CON's 18th International Cloud Expo®, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. FalconStor Software®, Inc. (NASDAQ: FALC) is a leading software-defined storage company offering a converged, hardware-agnostic, software-defined storage and data services platform. Its flagship solution FreeStor®, utilizes a horizonta...
    SYS-CON Events announced today that Interoute, owner-operator of one of Europe's largest networks and a global cloud services platform, has been named “Bronze Sponsor” of SYS-CON's 18th Cloud Expo, which will take place on June 7-9, 2015 at the Javits Center in New York, New York. Interoute is the owner-operator of one of Europe's largest networks and a global cloud services platform which encompasses 12 data centers, 14 virtual data centers and 31 colocation centers, with connections to 195 ad...
    SYS-CON Events announced today that (ISC)²® (“ISC-squared”) will exhibit at SYS-CON's 18th International Cloud Expo®, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. Two leading non-profits focused on cloud and information security, (ISC)² and Cloud Security Alliance (CSA), developed the Certified Cloud Security Professional (CCSP) certification to address the increased demand for cloud security expertise due to rapid growth in cloud. Recently named “The Next...
    SYS-CON Events announced today that Alert Logic, Inc., the leading provider of Security-as-a-Service solutions for the cloud, will exhibit at SYS-CON's 18th International Cloud Expo®, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. Alert Logic, Inc., provides Security-as-a-Service for on-premises, cloud, and hybrid infrastructures, delivering deep security insight and continuous protection for customers at a lower cost than traditional security solutions. Ful...
    The Quantified Economy represents the total global addressable market (TAM) for IoT that, according to a recent IDC report, will grow to an unprecedented $1.3 trillion by 2019. With this the third wave of the Internet-global proliferation of connected devices, appliances and sensors is poised to take off in 2016. In his session at @ThingsExpo, David McLauchlan, CEO and co-founder of Buddy Platform, will discuss how the ability to access and analyze the massive volume of streaming data from mil...
    Join us at Cloud Expo | @ThingsExpo 2016 – June 7-9 at the Javits Center in New York City and November 1-3 at the Santa Clara Convention Center in Santa Clara, CA – and deliver your unique message in a way that is striking and unforgettable by taking advantage of SYS-CON's unmatched high-impact, result-driven event / media packages.
    SYS-CON Events announced today that Commvault, a global leader in enterprise data protection and information management, has been named “Bronze Sponsor” of SYS-CON's 18th International Cloud Expo, which will take place on June 7–9, 2016, at the Javits Center in New York City, NY, and the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Commvault is a leading provider of data protection and information management...
    With an estimated 50 billion devices connected to the Internet by 2020, several industries will begin to expand their capabilities for retaining end point data at the edge to better utilize the range of data types and sheer volume of M2M data generated by the Internet of Things. In his session at @ThingsExpo, Don DeLoach, CEO and President of Infobright, will discuss the infrastructures businesses will need to implement to handle this explosion of data by providing specific use cases for filte...
    WebSocket is effectively a persistent and fat pipe that is compatible with a standard web infrastructure; a "TCP for the Web." If you think of WebSocket in this light, there are other more hugely interesting applications of WebSocket than just simply sending data to a browser. In his session at 18th Cloud Expo, Frank Greco, Director of Technology for Kaazing Corporation, will compare other modern web connectivity methods such as HTTP/2, HTTP Streaming, Server-Sent Events and new W3C event APIs ...
    SYS-CON Events announced today that Avere Systems, a leading provider of enterprise storage for the hybrid cloud, will exhibit at SYS-CON's 18th International Cloud Expo®, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. Avere delivers a more modern architectural approach to storage that doesn’t require the overprovisioning of storage capacity to achieve performance, overspending on expensive storage media for inactive data or the overbuilding of data centers ...
    SYS-CON Events announced today that Pythian, a global IT services company specializing in helping companies adopt disruptive technologies to optimize revenue-generating systems, has been named “Bronze Sponsor” of SYS-CON's 18th Cloud Expo, which will take place on June 7-9, 2015 at the Javits Center in New York, New York. Founded in 1997, Pythian is a global IT services company that helps companies compete by adopting disruptive technologies such as cloud, Big Data, advanced analytics, and DevO...
    In most cases, it is convenient to have some human interaction with a web (micro-)service, no matter how small it is. A traditional approach would be to create an HTTP interface, where user requests will be dispatched and HTML/CSS pages must be served. This approach is indeed very traditional for a web site, but not really convenient for a web service, which is not intended to be good looking, 24x7 up and running and UX-optimized. Instead, talking to a web service in a chat-bot mode would be muc...
    SYS-CON Events announced today that AppNeta, the leader in performance insight for business-critical web applications, will exhibit and present at SYS-CON's @DevOpsSummit at Cloud Expo New York, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. AppNeta is the only application performance monitoring (APM) company to provide solutions for all applications – applications you develop internally, business-critical SaaS applications you use and the networks that deli...
    Fortunately, meaningful and tangible business cases for IoT are plentiful in a broad array of industries and vertical markets. These range from simple warranty cost reduction for capital intensive assets, to minimizing downtime for vital business tools, to creating feedback loops improving product design, to improving and enhancing enterprise customer experiences. All of these business cases, which will be briefly explored in this session, hinge on cost effectively extracting relevant data from ...
    More and more companies are looking to microservices as an architectural pattern for breaking apart applications into more manageable pieces so that agile teams can deliver new features quicker and more effectively. What this pattern has done more than anything to date is spark organizational transformations, setting the foundation for future application development. In practice, however, there are a number of considerations to make that go beyond simply “build, ship, and run,” which changes ho...

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    @CloudExpo Blogs
    Microservices are a type of software architecture where large applications are made up of small, self-contained units working together through APIs that are not dependent on a specific language. Each service has a limited scope, concentrates on a specific task and is highly independent. This setup allows IT managers and developers to build systems in a modular way. In his book, “Building Microservices,” Sam Newman said microservices are small, focused components built to do a single thing very well.
    2016 will be the year of the data lake. But I expect that much of 2016 data lake efforts will be focused on activities and projects that save the company more money. That is okay from a foundation perspective, but IT and Business will both miss the bigger opportunity to leverage the data lake (and its associated analytics) to make the company more money. This blog examines an approach that allows organizations to quickly achieve some “save me more money” cost benefits from their data lake without losing sight of the bigger “make me more money” payoff – by coupling the data lake with data scie...
    Earlier this week it was reported that researchers at Boston-based security company, Rapid7, identified several security flaws in an app connected to a new toy from Mattel's Fisher-Price brand. The news of the security vulnerability caught our attention for a few reasons: The name of the toy - Smart Toy Bear - is strangely close to the name of our company SmartBear Software. More importantly, the story caught our attention because the security vulnerability brought up an important reminder about the important issue of security in today's connected world
    The Net Neutrality fight has been all over the news this year with the latest installment on Net Neutrality coming in from T-Mobile. Private and public companies alike are tuned into to this continual saga to see how the eventual outcome will affect business and ultimately their online lives. But whether the FCC’s recent ruling on Net Neutrality stands the test of time or not, there is a new technology that will provide customers fast, reliable Internet services at much lower costs than private business lines.
    Adding public cloud resources to an existing application can be a daunting process. The tools that you currently use to manage the software and hardware outside the cloud aren’t always the best tools to efficiently grow into the cloud. All of the major configuration management tools have cloud orchestration plugins that can be leveraged, but there are also cloud-native tools that can dramatically improve the efficiency of managing your application lifecycle. In his session at 18th Cloud Expo, Alex Lovell-Troy, Director of Solutions Engineering at Pythian, will present a roadmap that can be l...
    With the proliferation of both SQL and NoSQL databases, organizations can now target specific fit-for-purpose database tools for their different application needs regarding scalability, ease of use, ACID support, etc. Platform as a Service offerings make this even easier now, enabling developers to roll out their own database infrastructure in minutes with minimal management overhead. However, this same amount of flexibility also comes with the challenges of picking the right tool, on the right provider and with the proper expectations. In his session at 18th Cloud Expo, Warner Chaves, Princ...
    Enterprises across all industries are in the midst of difficult, bet-the-company changes we call digital transformation. And yet, there remains broad confusion about the scope of the word digital. While the rise of mobile technologies are the impetus for many such transformations, digital goes well beyond the choice of user device. In fact, digital transformation reflects the fact that customer preferences and behavior are driving enterprise technology decisions – and such technology decisions impact the entire organization, from innovative, customer-focused efforts all the way to the traditi...
    Here in the US, the Super Bowl is more than just a football game - it's a national holiday, with its own traditions, festivities, and food and drinks. One of the most important aspects of super bowl is the commercials. Every year, brands spend millions of dollars to get the attention of the more than 120 million Super Bowl viewers. The Wall Street Journal created this brilliant interactive chart of past ad spending. We are sure that the all industries outdid themselves in terms of spending again this year.
    I sat down with Michael Rösch, COO of POOL4TOOL, to chat about cloud computing. With a lot of buzz about the impact of the cloud on business, it was a chance to get a perspective, as well as a few hints and tips, from someone who has been at the coalface of procurement cloud services for the past 15 years. Michael has been at POOL4TOOL since 2000, becoming COO in 2012, and has worked on projects with German giants like Behr, Hansgrohe, Heidelberger Printing Presses, Carl Zeiss and ThyssenKrupp Presta in that time.
    Once again, the boardroom is in a bitter battle over what edict its members will now levy on their hapless IT organization. On one hand, hybrid cloud is all the rage. Adopting this option promises all the cost savings of public cloud with the security and comfort of private cloud. This environment would not only check the box for meeting the cloud computing mandate, but also position the organization as innovative and industry-leading. Why wouldn’t a forward-leaning management team go all in with cloud?
    The arrival of the Microsoft Azure Stack Technical Preview marks a turning point in the cloud-computing market and forms a leading indicator of how dramatically Microsoft has changed in the past two years. The cloud turning point comes because the path to hybrid-cloud capabilities and benefits has a powerful new usher, one with the enterprise, developer, and service-provider presence, R and D budget, and competitive imperative to succeed in a market still underserved and nebulous.
    This post is the second in a series based on a discussion with Marc Andreessen. The first was on: AI, Robotics, Jobs and Accelerating The Future Gourley: Can you provide more context on what kind of education and training? Andreessen: It is fair to say I am biased on that subject but my view is that the quantitative skills will be in greater demand: Math, Economics, Science, Engineering, Computer Science. The requirements for these are rising at much higher rates so their premium is rising. All of us should seek to self educate on these when we can and this is what we should encourage our yo...
    Microsoft pulls a fast one! Good showing Microsoft. Project Natick is Microsoft's R&D feasibility project to explore, manufacture and operate a underwater. Hey, you don't look for cooling water, if you can take the salt out. How is latency improved? drop the datacenter at the nearest ocean or lake. Energy efficiency is no brainer considering the environment the datacenter is in. Bring in 3D manufacturing and you can have a datacenter manufactured and deployed in no time at all, no need for expensive land acquisition, licenses, certificates etc.
    With some of the busiest shopping days of the year behind us, now is the time for retailers to pause for a moment to evaluate what worked, and to consider new online strategies and technologies for the 2016 shopping season. We don’t yet have numbers for 2015, but in 2014, holiday season retail e-commerce spending in the United States amounted to 53.3 billion U.S. dollars with the most money being spent online on Cyber Monday. Fujitsu is working with Grid Dynamics to deliver an Oracle Commerce Platform as a fully-managed cloud service. I had a chance to sit down with Victoria Livschitz, E...
    Teams that have embraced DevOps and begun using the practice of test driven development are familiar with the headaches that accompany testing legacy code. This is particularly true for companies that have applications out in production that have been working for years, but have no formal tests or testing regiment associated with its codebase. Most of the time the physical code for the applications is known; many times it is not. In fact, both the code and the developer who wrote the code might have left the building years ago.