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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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    Culture is the most important ingredient of DevOps. The challenge for most organizations is defining and communicating a vision of beneficial DevOps culture for their organizations, and then facilitating the changes needed to achieve that. Often this comes down to an ability to provide true leadership. As a CIO, are your direct reports IT managers or are they IT leaders? The hard truth is that many IT managers have risen through the ranks based on their technical skills, not their leadership ab...
    DevOps is about increasing efficiency, but nothing is more inefficient than building the same application twice. However, this is a routine occurrence with enterprise applications that need both a rich desktop web interface and strong mobile support. With recent technological advances from Isomorphic Software and others, rich desktop and tuned mobile experiences can now be created with a single codebase – without compromising functionality, performance or usability. In his session at DevOps Su...
    WebRTC: together these advances have created a perfect storm of technologies that are disrupting and transforming classic communications models and ecosystems. In his session at WebRTC Summit, Cary Bran, VP of Innovation and New Ventures at Plantronics and PLT Labs, provided an overview of this technological shift, including associated business and consumer communications impacts, and opportunities it may enable, complement or entirely transform.
    WebRTC sits at the intersection between VoIP and the Web. As such, it poses some interesting challenges for those developing services on top of it, but also for those who need to test and monitor these services. In his session at WebRTC Summit, Tsahi Levent-Levi, co-founder of testRTC, reviewed the various challenges posed by WebRTC when it comes to testing and monitoring and on ways to overcome them.
    WebRTC has had a real tough three or four years, and so have those working with it. Only a few short years ago, the development world were excited about WebRTC and proclaiming how awesome it was. You might have played with the technology a couple of years ago, only to find the extra infrastructure requirements were painful to implement and poorly documented. This probably left a bitter taste in your mouth, especially when things went wrong.
    WebRTC is about the data channel as much as about video and audio conferencing. However, basically all commercial WebRTC applications have been built with a focus on audio and video. The handling of “data” has been limited to text chat and file download – all other data sharing seems to end with screensharing. What is holding back a more intensive use of peer-to-peer data? In her session at @ThingsExpo, Dr Silvia Pfeiffer, WebRTC Applications Team Lead at National ICT Australia, looked at diffe...
    With microservices, SOA and distributed architectures becoming more popular, it is becoming increasingly harder to keep track of where time is spent in a distributed application when trying to diagnose performance problems. Distributed tracing systems attempt to address this problem by following application requests across service boundaries, persisting metadata along the way that provide context for fine-grained performance monitoring.
    At first adopted by enterprises to consolidate physical servers, virtualization is now widely used in cloud computing to offer elasticity and scalability. On the other hand, Docker has developed a new way to handle Linux containers, inspired by version control software such as Git, which allows you to keep all development versions. In his session at 17th Cloud Expo, Dominique Rodrigues, the co-founder and CTO of Nanocloud Software, discussed how in order to also handle QEMU / KVM virtual machin...
    With the Apple Watch making its way onto wrists all over the world, it’s only a matter of time before it becomes a staple in the workplace. In fact, Forrester reported that 68 percent of technology and business decision-makers characterize wearables as a top priority for 2015. Recognizing their business value early on, FinancialForce.com was the first to bring ERP to wearables, helping streamline communication across front and back office functions. In his session at @ThingsExpo, Kevin Roberts...
    DevOps has often been described in terms of CAMS: Culture, Automation, Measuring, Sharing. While we’ve seen a lot of focus on the “A” and even on the “M”, there are very few examples of why the “C" is equally important in the DevOps equation. In her session at @DevOps Summit, Lori MacVittie, of F5 Networks, explored HTTP/1 and HTTP/2 along with Microservices to illustrate why a collaborative culture between Dev, Ops, and the Network is critical to ensuring success.
    The Federal Government’s “Cloud First” policy mandates that agencies take full advantage of cloud computing benefits to maximize capacity utilization, improve IT flexibility and responsiveness, and minimize cost. The Federal Risk and Authorization Management Program (FedRAMP) is a mandatory government-wide program that provides a standardized approach to security assessment, authorization, and continuous monitoring for cloud products and services. Advantages for business include being able to ma...
    Father business cycles and digital consumers are forcing enterprises to respond faster to customer needs and competitive demands. Successful integration of DevOps and Agile development will be key for business success in today’s digital economy. In his session at DevOps Summit, Pradeep Prabhu, Co-Founder & CEO of Cloudmunch, covered the critical practices that enterprises should consider to seamlessly integrate Agile and DevOps processes, barriers to implementing this in the enterprise, and pr...
    Sensors and effectors of IoT are solving problems in new ways, but small businesses have been slow to join the quantified world. They’ll need information from IoT using applications as varied as the businesses themselves. In his session at @ThingsExpo, Roger Meike, Distinguished Engineer, Director of Technology Innovation at Intuit, showed how IoT manufacturers can use open standards, public APIs and custom apps to enable the Quantified Small Business. He used a Raspberry Pi to connect sensors...
    The principles behind DevOps are not new - for decades people have been automating system administration and decreasing the time to deploy apps and perform other management tasks. However, only recently did we see the tools and the will necessary to share the benefits and power of automation with a wider circle of people. In his session at DevOps Summit, Bernard Sanders, Chief Technology Officer at CloudBolt Software, explored the latest tools including Puppet, Chef, Docker, and CMPs needed to...
    @CloudExpo Stories
    University of Colorado Athletics has selected FORTRUST, Colorado’s only Tier III Gold certified data center, as their official data center and colocation services provider, FORTRUST announced today. A nationally recognized and prominent collegiate athletics program, CU provides a high quality and comprehensive student-athlete experience. The program sponsors 17 varsity teams and in their history, the Colorado Buffaloes have collected an impressive 28 national championships. Maintaining uptime...
    Basho Technologies has announced the latest release of Basho Riak TS, version 1.3. Riak TS is an enterprise-grade NoSQL database optimized for Internet of Things (IoT). The open source version enables developers to download the software for free and use it in production as well as make contributions to the code and develop applications around Riak TS. Enhancements to Riak TS make it quick, easy and cost-effective to spin up an instance to test new ideas and build IoT applications. In addition to...
    As organizations shift towards IT-as-a-service models, the need for managing and protecting data residing across physical, virtual, and now cloud environments grows with it. Commvault can ensure protection, access and E-Discovery of your data – whether in a private cloud, a Service Provider delivered public cloud, or a hybrid cloud environment – across the heterogeneous enterprise. In his general session at 18th Cloud Expo, Randy De Meno, Chief Technologist - Windows Products and Microsoft Part...
    IoT offers a value of almost $4 trillion to the manufacturing industry through platforms that can improve margins, optimize operations & drive high performance work teams. By using IoT technologies as a foundation, manufacturing customers are integrating worker safety with manufacturing systems, driving deep collaboration and utilizing analytics to exponentially increased per-unit margins. However, as Benoit Lheureux, the VP for Research at Gartner points out, “IoT project implementers often ...
    Presidio has received the 2015 EMC Partner Services Quality Award from EMC Corporation for achieving outstanding service excellence and customer satisfaction as measured by the EMC Partner Services Quality (PSQ) program. Presidio was also honored as the 2015 EMC Americas Marketing Excellence Partner of the Year and 2015 Mid-Market East Partner of the Year. The EMC PSQ program is a project-specific survey program designed for partners with Service Partner designations to solicit customer feedbac...
    Edge Hosting has announced a partnership with and the availability of CloudFlare, a web application firewall, CDN and DDoS mitigation service. “This partnership enhances Edge Hosting’s world class, perimeter layer, application (layer 7) defensive mechanism,” said Mark Houpt, Edge Hosting CISO. “The goal was to enable a new layer of customer controlled defense and compliance through the application of DDoS filters and mitigations, the web application firewall (WAF) feature and the added benefit ...
    Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to imp...
    In his general session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed cloud as a ‘better data center’ and how it adds new capacity (faster) and improves application availability (redundancy). The cloud is a ‘Dynamic Tool for Dynamic Apps’ and resource allocation is an integral part of your application architecture, so use only the resources you need and allocate /de-allocate resources on the fly.
    It is one thing to build single industrial IoT applications, but what will it take to build the Smart Cities and truly society changing applications of the future? The technology won’t be the problem, it will be the number of parties that need to work together and be aligned in their motivation to succeed. In his Day 2 Keynote at @ThingsExpo, Henrik Kenani Dahlgren, Portfolio Marketing Manager at Ericsson, discussed how to plan to cooperate, partner, and form lasting all-star teams to change t...
    Digital Initiatives create new ways of conducting business, which drive the need for increasingly advanced security and regulatory compliance challenges with exponentially more damaging consequences. In the BMC and Forbes Insights Survey in 2016, 97% of executives said they expect a rise in data breach attempts in the next 12 months. Sixty percent said operations and security teams have only a general understanding of each other’s requirements, resulting in a “SecOps gap” leaving organizations u...
    In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination – the Blockchain. Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life sett...
    There are several IoTs: the Industrial Internet, Consumer Wearables, Wearables and Healthcare, Supply Chains, and the movement toward Smart Grids, Cities, Regions, and Nations. There are competing communications standards every step of the way, a bewildering array of sensors and devices, and an entire world of competing data analytics platforms. To some this appears to be chaos. In this power panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, Bradley Holt, Developer Advocate a...
    You are moving to the Cloud. The question is not if, it’s when. Now that your competitors are in the cloud and lapping you, your “when” better hurry up and get here. But saying and doing are two different things. In his session at @DevOpsSummit at 18th Cloud Expo, Robert Reeves, CTO of Datical, explained how DevOps can be your onramp to the cloud. By adopting simple, platform independent DevOps strategies, you can accelerate your move to the cloud. Spoiler Alert: He also makes sure you don’t...
    Creating replica copies to tolerate a certain number of failures is easy, but very expensive at cloud-scale. Conventional RAID has lower overhead, but it is limited in the number of failures it can tolerate. And the management is like herding cats (overseeing capacity, rebuilds, migrations, and degraded performance). Download Slide Deck: ▸ Here In his general session at 18th Cloud Expo, Scott Cleland, Senior Director of Product Marketing for the HGST Cloud Infrastructure Business Unit, discusse...
    Connected devices and the industrial internet are growing exponentially every year with Cisco expecting 50 billion devices to be in operation by 2020. In this period of growth, location-based insights are becoming invaluable to many businesses as they adopt new connected technologies. Knowing when and where these devices connect from is critical for a number of scenarios in supply chain management, disaster management, emergency response, M2M, location marketing and more. In his session at @Th...
    The cloud market growth today is largely in public clouds. While there is a lot of spend in IT departments in virtualization, these aren’t yet translating into a true “cloud” experience within the enterprise. What is stopping the growth of the “private cloud” market? In his general session at 18th Cloud Expo, Nara Rajagopalan, CEO of Accelerite, explored the challenges in deploying, managing, and getting adoption for a private cloud within an enterprise. What are the key differences between wh...
    "A lot of times people will come to us and have a very diverse set of requirements or very customized need and we'll help them to implement it in a fashion that you can't just buy off of the shelf," explained Nick Rose, CTO of Enzu, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
    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...
    The pace of innovation, vendor lock-in, production sustainability, cost-effectiveness, and managing risk… In his session at 18th Cloud Expo, Dan Choquette, Founder of RackN, discussed how CIOs are challenged finding the balance of finding the right tools, technology and operational model that serves the business the best. He also discussed how clouds, open source software and infrastructure solutions have benefits but also drawbacks and how workload and operational portability between vendors ...
    The initial debate is over: Any enterprise with a serious commitment to IT is migrating to the cloud. But things are not so simple. There is a complex mix of on-premises, colocated, and public-cloud deployments. In this power panel at 18th Cloud Expo, moderated by Conference Chair Roger Strukhoff, Randy De Meno, Chief Technologist - Windows Products and Microsoft Partnerships at Commvault; Dave Landa, Chief Operating Officer at kintone; William Morrish, General Manager Product Sales at Interou...

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    Sponsorship opportunities are now open for Cloud Expo 2016 Silicon Valley, Nov. 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA, and for Cloud Expo 2017 New York, June 6-8, 2017, at the Javits Center in New York City, New York.. For sponsorship, exhibit opportunities and show prospectus, please contact Carmen Gonzalez, carmen (at) sys-con.com.
    Cloud Expo New York All-Star Speakers Include

    DE MENO
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    REEVES
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    MANN
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    YOO
    Fuze

    COTY
    Alert Logic

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    Kintone

    CHOQUETTE
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    HURWITZ
    Hurwitz & Assc.

    Cloud Expo Silicon Valley All-Star Speakers Include

    KOWALL
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    Red Hat Inc

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    VERVAET
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    Cloud Expo New York All-Star Speakers Included

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    CHOU
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    KIM
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    SICULAR
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    Liaison

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    Liaison

    KHAN
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    Coalfire

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    RMS

    LYNN
    CodeFutures

    HEABERLIN
    Windstream

    RAMA
    MURTHY

    Virtusa

    BOSTOCK
    IndependenceIT

    DE MENO
    CommVault

    GRILLI
    Adobe

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    Rancher Labs

    CRISWELL
    Alert Logic

    COTY
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    JACOBS
    SingleHop

    MARAVEI
    Cisco

    JACKSON
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    SINGH
    IBM

    HAZARD
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    Softlayer

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    Emcien

    LEVESQUE
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    Soha

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    IBM Watson

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    Microsoft

    BEARFIELD
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    SIASMSP

    HOLT
    IBM Cloudant

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    CTS

    PICCININNI
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    Modulus

    PAIGE
    CenturyLink

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    MILLS
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    SLOPER
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    Systems Services Inc.

    COLLISON
    Apcera

    PRABHU
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    LYNN
    CodeFutures

    SWARTZ
    Ericsson

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    BERMINGHAM
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    @CloudExpo Blogs
    Big Data automation can mean writing dozens of scripts to process different input sources and aligning them in order to consolidate all this data and produce the required output. Why exactly do you need Big Data for your enterprise projects? Many industry observers have been noting that although a lot of enterprises like to claim that their big data projects are aimed at "deriving insights" that replace human intuition with data-driven alternatives, in reality though, the objective appears to be automation. They point out that the role of data scientists at a lot of organizations has got litt...
    Security is one of the most controversial topics in the software industry. How do you measure security? Is your favorite software fundamentally insecure? Are Docker containers secure? Dan Walsh, SELinux architect, wrote: "Some people make the mistake of thinking of containers as a better and faster way of running virtual machines. From a security point of view, containers are much weaker." Meanwhile, James Bottomley, Linux Maintainer and former Parallels CTO, wrote: "There's contentions all over the place that containers are not actually as secure as hypervisors. This is not really true. Para...
    Karen Field, Penton Communications' IoT Institute director, in her article "Start Small to Gain Big," postulated an oil drilling platform with 30,000 sensors would generate about 1 Terabyte of data per day. She also stressed that only 1% of that data would likely be used. From a systems engineering point of view this data flow is multiplied by the trillions of other IoT sensors in the cloud, introducing unprecedented data processing and data transport stress. Industries and competing companies within those industries will also be forced to weigh the economic impact of paying for this transport...
    Advances in thin clients, desktop virtualization (VDI), cloud management services, and mobile delivery networks are allowing both increased security and edge applications performance gains. The next BriefingsDirect thought leadership panel discussion focuses on the heightened role of security in the age of global cloud and mobile delivery of apps and data. As enterprises and small to medium-sized businesses (SMBs) alike weigh the balance of apps and convenience with security -- a new dynamic is emerging. Security concerns increasingly dwarf other architecture considerations. Yet advances ...
    xMatters for the Enterprise DevOps toolchain stitches together the disparate operational tools to help orchestrate hand-offs between the tools and team members. Customers like us because we understand the need for DevOps or NoOps automation at enterprise scale and recognize that humans are still involved when something goes wrong.
    Fog Computing is being touted as the data communication solution our Internet of Things (IoT) devices are asking for by bringing the power of cloud computing closer to the end user. The fact is, the number of connected devices is going to continue to grow exponentionally. In fact, Gartner predicts that by 2020 IoT will include 26 billion connected things. Consider the impact that amount of data collected and processed will have.
    Information security has become a critical priority for many businesses over the past decade, and for good reason. It seems like a new breach is exposed on nearly a daily basis, impacting another organization and its patrons. However, some companies believe that they're safe because they're either too small or too big to be affected by any of these cyberattacks. The truth is that groups of all sizes from Target to your local dentist are being hacked or having their data compromised, and it's causing a major upheaval in the security community.
    One of the greatest challenges facing business and government organizations is how to store and move sensitive data around the world while maintaining security. Storing and securing critical data held within data centers (whether physical or virtual) is top of mind from the boardroom to the remote data center. According to the International Data Corporation (IDC) Worldwide Quarterly Cloud IT Infrastructure Tracker, spending on private cloud IT infrastructure in 2015 will grow by 15.8 percent year over year to $12.1 billion, while spending on public cloud IT infrastructure will increase by 29.6...
    Too many myths and hypes create the confusion around digital transformation. Digital transformation isn't a technology change matter, it's primarily about transforming business models: infrastructure and operating model. Digital transformation when tackled with proven methodologies and approaches isn't as complex as it seems, they provide the step-by-step guide, the logic, and the tools to make it easy.
    Cloud Expo 2016 New York at the Javits Center New York was characterized by increased attendance and a new focus on operations. These were both encouraging signs for all involved in Cloud Computing and all that it touches. As Conference Chair, I work with the Cloud Expo team to structure three keynotes, numerous general sessions, and more than 150 breakout sessions along 10 tracks. Our job is to balance the state of enterprise IT today with the trends that will be commonplace tomorrow. Mobile Computing, Big Data and Analytics, the Internet of Things, Platforms and Microservices and Containe...
    Organizations are experiencing a new emphasis when it comes to cybersecurity. They are moving from securing the perimeter to securing the data within it, which is the result of the proliferation of connected devices in organizations today: smartphones, tablets and the IoT. Organizations used to focus their efforts on keeping attackers outside the perimeter, because just a few years ago, the network perimeter was much more static and limited. Today, the perimeter is everywhere – and constantly moving.
    Consider the following integration scenarios: Moving medical records between EMR systems; financial information between banking systems; HR information between ERP systems; and software development information between SDLC tools. At first glance the approaches required for these integrations may seem the same. But if you look slightly deeper you will realize that this can’t be the case because of impedance mismatch. I’m defining impedance mismatch as the friction that occurs when trying to align two things or concepts that don’t naturally/actually match. Because many of the hardest impedance ...
    Although we often write about and discuss digital transformation, we often fail to identify the end goal we are really trying to achieve. We talk at great length about data, analytics, speed, information logistics systems and personalized user experiences, but none of these are the end goal. Ultimately we must digitally transform so we can remove the “fog of war,” and have clear visibility and insights into our businesses and the needs of our customers. The end goal of digital transformation, however, is the ability to rapidly act and react to changing data, competitive conditions and strat...
    The "Internet of Things" depends on the "Cloud" for the processing and storage of data. The Cloud's backbone, however, is the network. This logic train reveals the importance of professional rigor and solid System Engineering (SE) of the network. Imagine a sea of sensors, put out in the field by multiple independent vendors. Complying with specifications only in an informal sense, these sensors are sending Terabytes of data to the cloud. The availability of this data to anyone globally is impressive enough. But even more amazing is the fact that anyone in the world can also develop an appli...
    Social gaming company Playtika uses big-data analytics to deliver captivating user experiences and engagement. We'll learn how feedback from massive user action streams can be analyzed in bulk rapidly to improve the features and attractions of online games and so help Playtika react well in an agile market. To learn more about leveraging big data in the social casino industry, we're pleased to welcome Jack Gudenkauf, Vice President of Big Data at Playtika in Santa Monica, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.