Enterprise search – Page 2

Building for Enterprise Search: A Systems View, Part 2

“It’s important to incorporate expected behaviors into modeling and monitoring on both applications and systems sides and how they interact with one another.”

When we left off, Michael Klatsky, VP of Systems Administration was telling me how important communication between the systems side and search side of is to developing an enterprise search solution. The process of building, testing, monitoring, adjusting, more testing, and more monitoring ensures systems function that way they are intended to function. Let’s resume our conversation where Michael discusses the tools he uses to ensure the system he’s building works the way the client wants it to. This is the second portion of a two part blog post.
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Tools for BDD: Part 2

Karen: It’s sounding like the Search Team and Sys Admin Team need to have a good relationship and communicate often to ensure the system will accommodate the work the search team does.

Michael: Yes, search sometimes has to construct their scripts to conforms to systems. Testing is run on both sides, but small changes can affect others down the line, so it’s important to incorporate expected behaviors into modeling and monitoring on both applications and systems sides and how they interact with one another.

Karen: How do you make sure that happens?

Michael: We’re exploring some tools to help us make sure the machine will act just as we expect it to, like cucumber and cucumber nagios We’re using certain tools to facilitate the systems behaves in the way that we expect it to. We’re exploring cucumber for basic modeling and for testing. Cucumber is cool for testing because it returns values to you in colors. Red, meaning it failed, yellow meaning there’s problem, and green meaning its good. According to their docs, they instruct you to “keep running it until it’s a cucumber.”

Karen: Ah, I get it.

Michael: Right. And what cucumber nagios does is it takes cucumber and allows you to create a nagios monitoring check script. So if you pass, great, if you god red, nagios will throw an alert to the systems administrator so we have an opportunity to fix it before more is built.

Karen: Sounds like it’s an attentive way to build a system.

Michael: The only way to scale is to have machines do things for themselves. That’s the way to do it.

Karen: To automate.

Michael: Yes. Automation. Not to just set things up to automatically do configuration management beforehand, but to test afterwards to determine that your machine is behaving just as you (and your client) envisioned it.

For more information on how you can plan your enterprise search in cooperation with your systems administration team, contact us for a free consultation.

Building for Enterprise Search: A Systems View, Part 1

We need to determine what right looks like, and have the system behave that way.

I sat down with our VP of Systems Administration, Michael Klatsky to discuss some of his thoughts on how Systems Administration needs to work in concert with the Search Team to implement search technologies for clients. This is the first portion of a two part blog post.

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Karen: You wanted to discuss how your approaching the systems side of search, and using a Behavior Driven Development (BDD) approach. Tell me about that.

Michael: Well, one of the problems we run into when systems brings up machines for enterprise search clusters is the search software (FAST ESP for example) is very particular about it’s environment- more so than many of the more common applications such as the Apache webserver. Properly configured DNS, specific environment variables, specific library versions have to be present. There are ownership and permissions that need to be in place, and performance metrics that must adhere to a given baseline. There can be slow disks can affect performance. There has to be the right amount of memory, and different classifications of systems roles. Currently, we have homegrown scripts that bring up systems, then we have other scripts we run to detect issues. These scripts will tell us if the system is ready for what we need it to do. We also monitor the systems for standard items such as diskspace, memory usage, as well as basic search functionality. For example we’ll run a quick search on say paper clips, and if comes back with results we know it’s running.

That’s what we’ve done historically. But now, we need to bring up larger numbers of machines,and have confidence that they will perform exactly as we expect. Additionally, we have a set of functional tasks that must be available without fail As we bring up clusters of larger numbers of machines, and as we need to be more nimble, how can we ensure that it will respond the way we expect it to?

Karen: This is where Behavior Driven Development comes in, right?

Michael: Right. There is a lot of discussion out there on Behavior Driven Development which would include behavior driven modelling, behavior driven monitoring, behavior driven architecture and infrastructure. So not only does a machine come up and is listening on these ports, but I can bring a machine up, I can go to that machine and I’m able to log in, install certain software, and peform tasks. I can go to another machine and perform a task. So, the question is, how do you model that? How do we ensure the system will behaves as it should?

Karen: So you’re looking at replicating the behavior of these systems so that every time we deploy something it will be the same way.

Michael: Right. And if a change is made, even a small change, we’ll see it right away because a system or service will fail and be able to fix it. Sometimes a service will fail silently. But we test and monitor constantly to ensure the system will do exactly what we expect it to do. It’s all a part of the build process.

Karen: Sounds like a smart approach.

Michael: Yes. And if we make a change, we’ll find out how that change will affect the rest of the system. For instance, we run tests and if something is wrong it should give you an error. For example if you change the location of your SSH keys. You may still be able to get into the machine by SSH, but one little change could make it impossible to SSH from one machine to another in the cluster. So rather than find that out after you begin your manual work on that, we make it part of the build process by constantly monitoring and testing the system as we build it.

Karen: It sounds like building a house and then realizing you have bricks out of place after it’s built.

Michael: Worse, it’s like building a house and realizing you forgot to build a door! At the very least while you are building, you can test, and let me know, “Hey! I don’t have a door to my house!” So that I can fix it before you move in.

There are certain things the search team needs to do to ensure their work will function in the system, like SSHing around the machines in the cluster–they need to be able to do that. There are certain ports that system need to be listening on, there are certain services that need to return a normal range of results. We need to define what a proper operation looks like. We can’t necessarily say that if we search for gold plated paperclips for example, that the search result should show 1000 results every time, that may or may not be the case–we don’t necessarily know if this is a proper result every time, but we should determine if the result returned is within a proper range of normal.

We’re defining what a proper operation looks like and ensure it functions that way. Part of the behavior driven model which is what I’m really interested in, we can set up a natural language looking config file. This config file should describe the actions or behaviors I expect. For example, when I go to ABC.com website and search for gold plated paperclips, I expect to see results. One result should be X. There should be more than Y results. When I return that result, I should be able to click on one result and go to that products feature list. Basically I’m describing how the customer will interact with the search, what I expect the customer to do, and design the system to respond with the customer’s actions in mind.

Karen: So your engineering it with the customer’s behaviors in mind.

Michael: That’s exactly what we’re doing. Then that if I look for a certain item, I get that result, describe the behavior of what the customer should do and make the system behave in cooperation with the customer behavior. We need to determine what right looks like, and have the system behave that way.

Karen: And what right looks like is really different for each client.

Micheal: Yes. You can write in somewhat natural English what that looks like. It’s not magic, but you still have to come up with specification of what right looks like. But you can do a lot of sophisticated things in this manner because you will know you’ll have a website that’s going to perform the way it’s suppose to perform. The bottom line is: Define what your systems should “look” like, deploy those systems using those definitions, and after deployment, test to ensure that those systems “look” like your definition.

For more information on how you can plan your enterprise search in cooperation with your systems administration team, contact us for a free consultation.

Open Source Search: Isn’t It Expensive?

You’ve heard the debate on open source search vs. proprietary search. One question that constantly comes up for prospective clients is “What’s all this going to cost me?”

In these times, it’s a good question. Because proprietary has neatly packaged, practically shrink wrapped plans, it’s much easier to discern how much you will spend on a solution. But how much will it cost? That’s an entirely different question.

I see you cocking your head sideways.

Proprietary search has hidden costs. What if the software doesn’t perform the way you need it to? Does the software understand the nuances of your business? How adaptable is it? How much will it cost to adapt that software to get it to perform the way my business needs it to? Questions like this need to be asked, and answered. Eventually you will ask yourself….why am I paying for all of this? And your developer will ask, “why can’t I access the source code?”

What I’m getting at is this: It is a reassuring feeling for a customer to see what a package costs, to understand what services you will get with a solution, and to anticipate what the licensing fee will cost on an annual basis. If it’s your job to research a solution and present findings to your executive team to make a decision, then proprietary search, on the surface, seems a more secure choice. But rarely, if ever, are these solutions a perfect fit for the customer. It’s like buying a Ferrari, with all the brand recognition and polish a Ferrari offers, and not ever driving it past second gear, or cutting the wheel more than 15 degrees, or getting a chance to have your trusted mechanic look under the hood. This is why open source is such a good solution for businesses who want their IT to move quickly.

We’re hearing more buzz about companies waking up to the agility of an open source solution. Most recently, with the acquisition of Autonomy by HP, the industry is telling stories of ex Autonomy customers migrating to Solr (open source search) with only the annual licencing budget to finance the migration. Without an annual expenditure of cash for licensing, and the freedom of not being under a licensing agreement, companies quickly recoup the initial expenditure of a migration.

What kind of car does your company drive?

If you are examining the different choices for implementing search technology in your organization, contact us. We’re happy to talk to you about the best solution for your business.

Migration Still Looms Large on the Horizon for FAST ESP Customers

“Designing a non-trivial search solution to fully meet your needs from scratch is hard enough on its own. If you are migrating an existing solution, it is very unlikely that you will find a one to one mapping of all of the features in a new search engine that you have come to depend upon with your existing implementation.” –Michael McIntosh, VP of Search Technologies, TNR Global, LLC

Microsoft acquired FAST all the way back in 2008 and then in early 2010 disclosed it’s plans to stop updating the FAST product on a Linux operating system after 2010, making FAST ESP 5.3 the latest and greatest, and very last update Linux users will see involving any improvements to the proprietary search platform. It was clear to anyone on Linux that a migration would need to occur, and as content grows, depending upon the size of your organization, that migration should probably happen sooner than later.

Buzz about migration ensued–an inevitable certainty for many companies, especially ones with huge amounts of data. But how many companies have jumped in with both feet? I had the opportunity to speak with an open source search engine expert who, along with the industry, believed that the move from Microsoft was a windfall for anyone in the business of enterprise search design and implementation. However, she admitted “we haven’t seen as large a response as we expected.”

This isn’t exactly surprising to everyone. “It’s coming” says our VP of Search Technologies, Michael McIntosh. “Corporations have an enormous investment in FAST ESP and it makes sense that they would be reluctant to move to something new until they absolutely have to.” That means, when their licenses expire.

“They will likely weigh the performance and support, or lack thereof, for the FAST ESP technical team with the timing of renewing a license and wait until they absolutely have to change to something else,” says McIntosh.

The purchase of Autonomy and the shift of HP from hardware to software could signal a recognition from Goliath HP the kind of growth opportunity enterprise search software offers, and that the “great shift” from FAST ESP to another search platform is very much on the horizon.

But as the clock continues to tick, companies using FAST ESP should be strategizing for migration now. “It’s an enormous undertaking to migrate an entire search solution from FAST to another platform. Designing a non-trivial search solution to fully meet your needs from scratch is hard enough on its own. If you are migrating an existing solution, it is very unlikely that you will find a one to one mapping of all of the features in a new search engine that you have come to depend upon with your existing implementation. Solving challenging issues like that requires both creativity and expertise to address your needs.” says McIntosh. If a need for migration is eminent, there will be a real need for expertise in the field of enterprise search on both proprietary and open source platforms, depending upon several factors like size, in house talent, and growth expectations.

How is your company preparing for the discontinuation of support of FAST ESP? Need guidance? Contact us for pointers, analysis, or architecture for a full migration.

Open Source Search Engines vs. Proprietary Search Engines

“If you are a cutting edge company, you will be severely limited by a proprietary search engine as a solution. The more open the technology, the more able we are to refine it to meet our client’s needs.” –Michael McIntosh, TNR Global

There are plenty of articles about the pros and cons of open source software vs. proprietary software. I sat down with our VP of Search Technologies Michael McIntosh to discuss the benefits of each in terms of search engines.

Karen: You’ve been working with proprietary search engines for some time now. Tell me your thoughts on that. What’s the upside of proprietary?

Michael: I’ve worked with proprietary search engines for several years, specifically with FAST ESP since 2003 back when it was known as FAST Data Search (FDS). Proprietary software products often have better documentation, better support; more thought out design and are more aggressively tested. Because the product supports an entire company—it must succeed. They have nicer tools, nicer interfaces.

Karen: And the downsides?

Michael: “Over the years, we’ve run into a number of difficulties with proprietary search engines. One thing that comes up is that if a problem isn’t outlined in user troubleshooting documentation, it can become incredibly difficult to diagnosis and correct, and doing that is a frustrating, time intensive problem. The black box nature of the product is very limiting. If it’s not in documentation, it might as well not exist at all.”

Karen: There are gaps in the user manual?

Michael: “Yes. But in defense of FAST ESP, the documentation has improved by leaps and bounds over the years. However, one anomaly we find is that the clean, easy to read PDF form of user documentation (the original) for ESP is often not as up-to-date or helpful as the searchable online documentation—which is harder to read, but usually more current and correct. Sometimes even the online documentation is wrong—which is also frustrating. But it has become something we cope with regard to FAST”

Karen: Give me an example of what kind of problems you run into when integrating the proprietary search engine into a client’s website.

Michael: “The enterprise search platform uses Service Oriented Architecture (SOA). That is a bunch of different components that are able communicate with each other as services. With this type of architecture, it doesn’t matter which language you write something in as long as it’s a service another language can communicate with via RESTful interfaces, SOAP or something like XML-RPC. These services can all work together, despite the fact you don’t have a unified api—and that’s actually awesome.

Karen: Why?

Michael: “Indexers for one—you generally don’t want them written in an interpreted language for performance reasons. Indexing can be a CPU intensive operation, which can be a weak spot for interpreted languages such as Python or Ruby compared to languages like C/C++ or Java. It is both CPU and disk intensive process so a scripting language can be great if you’re writing an application that’s not CPU intensive because code speed doesn’t matter so much. The true slow-down is something outside the script. You can optimize the speed of the script to make it run as fast as humanly possible but you’re limited because the disk can only rotate so fast. Your indexing service can be written in a low level language like C vs. some of the other services in languages like Python or Ruby or Java and get good performance. BUT if you don’t have documentation for compiled programs that make up the search engine product you’re going to have a terrible time trying to figure out how to fix issues when they arise.

Karen: “So basically, because you have so many different languages being used that you lack the source code for, and documentation is spotty, it becomes a needle in a haystack trying to figure out where the problem occurs.”

Michael: “Yes. And this is not such a problem for anyone using a search engine for really basic applications. The place where you run into problems is if you push the search engine technology to its limits or you are using it in ways outside its typical usage, which is almost everything we do. We are always trying to get the best possible performance out of the search engine. We’re trying to get the search engine to deal with features we need but it doesn’t natively support.

Karen: “What is it you’re pushing specifically for the search engine to do?”

Michael: “One thing we want is for FAST ESP to have is a feature to deal with creating a faceted search for arbitrary fields. The way ESP works is that it has an index profile which is a statically defined set of fields that it indexes. Inside its index profile you can mark certain fields to have navigators. One of our customers deals with product verticals. They have a whole bunch of products that aren’t unified—all with completely different attributes. We’ve managed to work around these roadblocks in ESP to create faceted navigation on arbitrary fields.

Karen: “So you get creative to make it better.”

Michael: “Yes we constantly get creative to make it better to use its strengths and find ways to work around its limitations.”

“Another issue we have with ESP is we have a number of websites we need crawled and each website has metadata associated with it. Unfortunately the way the ESP crawler works, there are not many straightforward ways to preserve metadata associated with the seed URL which we use to crawl a website and pass the meta information along to any associated links. We can’t do this easily inside the ESP crawler. Since its proprietary and black box, we can’t look at the source code to the crawler, and can’t modify the source code to the crawler. When it does something mysterious, we can have no idea why it might be behaving in an unwanted way. We had one instance when we had a number of websites the crawler was temporarily blacklisting for some reason. When the ESP crawler automatically blacklists a site, it stops crawling the site for 30 minutes and then begins again after 30 minutes. We learned one thing that triggers blacklisting is if a website has HTTP 503 errors. If a site has more than 20 or so of those errors, the crawler temporarily blacklists the site. The problem was that the documentation is too sparse on details for that topic. When we ran into that problem—it was really difficult to know what was going on so we could properly explain the issue to the client and address the problem. Conversely, if we had an open source search engine, I could have just searched the source code and speed up the diagnosis of the problem.”

Karen: So from a business prospective—using open source allows you to invest in a technology that gives you the power to modify the code to better meet your business needs.

Michael: “It certainly can. It can accelerate development time and speed of diagnosing problems when issues pop up. And issues always do pop up. If something is not working very well, we can look at the problem which a much higher degree of granularity.

If it’s a simple problem, we never contact support. We only contact support when we’re stumped. And we’re not easily stumped. Usually, they can’t answer they question immediately because if we’re asking for help, the problem is complex. Our ticket is escalated, and eventually we talk to someone who can help us. But it does take time. Even if a support staff is top notch, there is the time is costs to deal with that, and that costs us and our clients’ time. We have a highly customized ESP installation for one of our clients it always take an enormous amount of time to explain over and over how we have our systems set up, the different parts work, and it’s a big pain to go through that every time I run across a problem. If it were open source, I can simply look at the source code and solve the problem.”

Karen: Let’s talk in more detail about open source search engines. Upsides?

Michael: “If you choose a popular open source search engine solution like Lucene Solr, you have an active, passionate community behind that solution. There are several developers looking at that engine, working on it, and actively posting in publicly available forums. You can often get your questions answered there by top notch experts in search technology. You can potentially talk to the original coders and creators of the product—and they are often happy to help you. I’ve seen people post a Solr question on their twitter feed and within 7 seconds, the creator has responded with a link to a forum explaining the solution.

Karen: Wow, that’s amazing. Other advantages?

Michael: “It’s free. That’s attractive to most companies. The downside is the formal documentation isn’t usually as good as the proprietary, and there isn’t a dedicated support team for the product. But if you have some savvy software developers on your team, the open source community is robust and willing to share information about the product. And having access to the source code is extremely valuable.”

Karen: So in your opinion, what’s the bottom line on Open Source Search Engines vs. Proprietary Search Engines?

Michael: “If you are a cutting edge company, you will be severely limited by a proprietary search engine as a solution. The more open the technology, the more able we are to refine it to meet our client’s needs.”

If you’d like more information on the pros and cons of Open Source Search Engines vs. Proprietary Search Engines that are specific to your business or organization’s needs, feel free to contact us for a free consultation.

TNR Global to present at Apache Lucene Eurocon 2011 in Barcelona

We are happy to announce that TNR Global’s own Michael McIntosh will be presenting at the Apache Lucene Eurocon 2011 in Barcelona this October. Michael’s talk is titled “Enterprise Search: FAST ESP to Lucene Solr.” His presentation will discuss migration from the FAST ESP platform to a Lucene Solr search platform. There are many reasons an IT department with a large scale search installation would want to move from a proprietary platform to Lucene Solr. In the case of FAST Search, the company’s purchase by Microsoft and discontinuation of the Linux platform has created an urgency for FAST users. Illustrated through actual case studies, the presentation will include challenges and concerns, present solutions and work-arounds to overcome migration issues.

Michael has more than 16 years of experience in large scale systems design and operation, online consumer product development, high volume transaction processing and engineering management. He has extensive experience developing, integrating and maintaining search technology solutions for companies such as FAST Search and Lycos.

We’re excited that Michael will be presenting in Barcelona this fall. Please introduce yourself if you’re able to go!

Chris Miles Joins TNR Global as Senior Software Engineer

HADLEY, MA–July 18, 2011—TNR Global is pleased to welcome Chris Miles in the role of Senior Software Engineer on TNR’s Search Team. Miles will be responsible for designing and implementing custom enterprise search software for TNR’s clients. Among other projects, Chris will also develop solutions for one of TNR’s largest clients, a publisher of manufacturing parts and vendors. He is proficient in software languages Java, C++, C, Ruby, PHP, and CSS.

Prior to joining TNR Global, Miles was a Senior Systems Analyst at Cooley Dickinson Hospital. He has held consulting roles for CarePaths Inc., and was a Senior Developer for Miller Samuel Inc.

“We’re excited about the addition of Chris to TNR. He brings a wealth software development expertise to our team” said Michael McIntosh, VP of Search Technologies of TNR Global, LLC.

Join us as we extend Chris a warm welcome to the TNR team!

Continuous Integration for Large Search Solutions

Managing large projects takes a smart approach and some intuitive thinking. One project we are currently engaged in is with large publisher of manufacturing parts. This has been an extraordinary project due to its scale and ever changing scope. I spoke with our VP of Enterprise Search Technologies, Michael McIntosh about how TNR Global handles complex projects.

Karen: This project is a big one. Tell me more about the site’s function. What is the focus?

Michael: Product search is the focus. The site contains tens of millions of documents, both structured and unstructured content. They also have a huge amount of data provided by the advertisers and the companies themselves on products that they sell. One of the advantages we have over a search engine like Google is access to a vast amount of propriety data provided by the vendors themselves.

Karen: Tell me about how you are managing the project. What are some of the variables you work with?

Michael: With this particular project, we are dealing with many different data feeds. There are many different intermediary metadata stages we have to generate to support the final searchable content. The client also changes their business logic frequently enough that if it takes a month or more between data builds its likely something has changed. For instance, they might have changed an XML format or added an attribute to an element in the data feed that will break something else down the line. The problem is there are so many moving parts, it’s almost impossible to do it manually and always do it correctly.

Karen: What other kinds of business logic changes are you dealing with in top of the massive amounts of raw data?

Michael: Most of the business logic changes are when they need to modify how something behaves based on new data that’s available, or when they need to start treating the data in a different way. Sometimes there is a change in the way they want the overall system to behave. They sometimes have some classification rules for content they like to tweak occasionally.

Another thing we consider is the client’s relevancy scoring and query pre-processing rules. So you need to consider if you issue a query and it fails, what happens then? What kind of fallback query do you use? All these things are part of the business logic that is independent of the raw data. In summary, we have the raw data and we can do a number of things with it. They often want us to change exactly what we’re doing with it, how we’re conditioning it, and how we’re transforming it. We either tweak what exists or take advantage of new data that they’ve started including in their data feeds. The challenge is all these elements can change frequently.

Karen: This site is more of a portal than strictly an enterprise search project, isn’t it?

Michael: Yes. Enterprise search usually refers to searching for documents within an organization. This client is a public facing search engine that allows the public to perform product search across a very large number of vendors and service providers.

Changes come from their advertisers and data they provide. Advertisers come and go. People pay for placement within certain industrial categories. It’s not like we get a static list of sites to crawl and that’s that. It changes weekly, sometimes daily. This list of sites we crawl is on a weekly or daily basis. Also things need to be purged from the index. Say an advertiser’s contract ends and suddenly we need to stop crawling a site with thousands documents; that data needs to be purged from the index promptly. Not only do we have to crawl new sites but purge old ones as well. This is a project that is so massive that it’s not cut and dried. A lot of software development projects focus on a clear cut problem, come up with plan, tackle it, release it, and then maintain it. We’re constantly getting new information and learn new things about people hitting the site.

Karen: So this sounds like this project is always in a state of ongoing development.

Michael: We are building something that’s never been built before. One of the goals is to make this site remarkable. And we’re very excited to be a part of that. The scale of the project is quite big though, which is why we started using Continuous Integration.

The way our cycles work is we perform big data updates, but by using CI, we can continuously update and integrate new data. We’re moving to a place, by using the practice of CI, we can perform a daily builds which gives us the time we need to fix problems before we absolutely need it to be live.

Karen: How do you implement CI into your day to day management of the project?

Michael: There are some pretty great open source tools that we’re using to implement CI. We use Jenkins to help us do Continuous Integration for frequent data builds, which is an intensive process for this particular client.

We field questions from the client about the status of different data builds. We hope to use Jenkins in conjunction with other tools to automatically build data and have event-based data builds. We’re looking at a way to have it triggered by some other event and have Jenkins automatically generate reports as the data is being built. Each time we run a build script, if the output differs from the previous build, Jenkins makes it easy for you to see that something is different. There is a way to modify your output that Jenkins can understand. One of the cool things about Jenkins is they have graphs that illustrate differences to help us identify issues that could pose a potential problem and let us fix it before we need to go live with the data.

Karen: Any other tools?

Michael: For multi-node search clusters, we’re using a tool called fabric3 that uses SSH to copy data and execute scripts across multiple nodes of a cluster based upon roles. We have a clever set up where we’re able to inform fabric3 what services are running on each node in our cluster and have actions or commands linked to certain tasks, like building metadata. By linking them, they automatically know which nodes to deploy data to.

Using open source tools like Jenkins and fabric3 make it a lot more manageable considering the large number of moving parts. It’s allowed us to be successful in building this incredible site and making the search function relevant, accurate and up to date.

Dynamic Fields in Apache Solr

So, you’ve installed a fresh copy of <a taget=”_blank” href=”http://lucene.apache.org/solr/”>Apache Solr</a>. You have tested it out running the examples from the <a href=”http://lucene.apache.org/solr/tutorial.html”>Solr tutorial</a>. And now you are ready to start indexing some of your own data. Just one problem. The fields for your own data are not recognized by your Solr instance. You notice in the schema.xml file that the default fields have names like cat, weight, subject, includes, author, title, payloads, popularity, price, etc. These fields are all defined for the purpose of being used with the sample data provided with Solr. Most of their names are likely not relevant to your search project, and even if you are willing to put up with misnamed fields at least for experimenting with your instance, you also face the problem that their set properties may not be what you would expect them to be.

Of course you can modify the schema.xml file and apply strong data-typing to each field that you plan to use to fit the exact needs of your project, reload Solr, and then start to index your data. But if you are just getting started with Solr, or starting a new project and experimenting with adding your dataset, you may not know exactly what fields you need to define or what properties to define for them. Or you might be interested updating an existing index with some additional fields, but do not want to add to explicitly add them to the schema.

Fortunately, Solr gives the option to define dynamic fields – fields that are defined in the schema with a glob-like pattern that is either at the beginning or end of the name. Further, there are pre-defined dynamic fields for most of the common data-types that you may use, in the default schema. Here are the some of the dynamic fields that are defined in the default schema.xml:

<dynamicField name="*_i"  type="int"    indexed="true"  stored="true"/>
<dynamicField name="*_s"  type="string"  indexed="true"  stored="true"/>
<dynamicField name="*_l"  type="long"   indexed="true"  stored="true"/>
<dynamicField name="*_t"  type="text"    indexed="true"  stored="true"/>
<dynamicField name="*_b"  type="boolean" indexed="true"  stored="true"/>
<dynamicField name="*_f"  type="float"  indexed="true"  stored="true"/>
<dynamicField name="*_d"  type="double" indexed="true"  stored="true"/>
<dynamicField name="*_dt" type="date"    indexed="true"  stored="true"/>

The field names are defined with a glob-like pattern that is either at the beginning or end of the name. With the above dynamic fields, you can index data with field names that begin with any valid string and end in one of the suffixes in the name attributes (i.e. article_title_s, article_content_t, posted_date_dt, etc.) and Solr will dynamically create any dynamic field of the particular type with the name that you give it. After you’ve indexed some data, you can actually view this dynamically created field in the schema viewer for your instance, located at http;//YOUR-INSTANCE/admin/schema.jsp

<add>
<doc>
<field name="article_title_s">My Article</field>
<field name="article_content_t">Lorem Ipsum...</field>
<field name="posted_date_dt">1995-12-31T23:59:59Z</field>
</doc>
</add>

Cloud Platforms: The Promise vs. The Reality

Recently our VP of Search, Michael McIntosh sat down and talked to me about his thoughts on cloud computing and what businesses should be aware of when investing in the cloud.

Karen: So, how does enterprise search and cloud computing fit together? What’s good about it for companies?

Michael: The advent of cloud computing makes it a lot easier for companies to get into search without investing a huge sum of money up front. Some of the pay-as-you-go computing approaches make it possible to do things that in the past wouldn’t have been financially viable such as natural language processing on content. Something that could have taken days, weeks, or even months can now take much less time by throwing more hardware at a problem for a shorter time span.

For example, you could throw 20 machines at a problem for 12 hours and do a bunch of computations in a massively parallel way, and then stop it as soon as it’s done….versus the old model where you have to buy all the hardware, or rent it, and make sure it’s not underutilized so you make your investment back.

But if you need a lot of processing power for a short amount of time, it’s really quite amazing what we can do now with an approach like this.

Karen: Is this a new technology for TNR?

Michael: TNR has been using cloud computing platforms for several years now—3 or 4 years. Cloud computing in itself is sort of a buzz word, because distributed processing and hosting has been around for a while, but the pay-as-you-go computing model is relatively new. So we have a great deal of experience with the reality of cloud computing platforms vs. the promise of cloud computing platforms.

Karen: So, what is the difference between the “promise” and the “reality” of cloud computing platforms?

Michael: Well, A lot of people think of cloud computing as this magical thing; all their problems will be solved and it will be super dependable because there are very large businesses like Amazon running the underlying infrastructure and you don’t have to worry about it.

But, as the physical infrastructure becomes easier to deploy, other critical factors come into play. You won’t have to worry about the physical logistics of getting hardware in place. But, you will have to manage multiple instances, you have to make sure that when you provision temporary processing resources, you have to remember to retire it when it’s no longer needed. Otherwise you’ll be paying more than you need to. Since virtualization uses physical hardware you do not control or maintain—there are fewer warning signs to a potential systemic failure. Now Amazon, which is the one we use the most, does a good job of backing up instances and making things available to you even when there are failures. But we’ve had problems where we’ve lost entire zones. Even if we’ve had multiple machines configured with fault tolerance, Amazon has experienced outages that have taken entire regions offline despite every conservative effort to ensure continuous up time. So we’ve had our entire service clusters go down because of problems Amazon was having. It becomes critically important for companies to develop and maintain a disaster and recovery plan. Companies need to make sure things that are critical are backed up in multiple locations. Now historically, this has been hard to do because companies typically buy enough equipment for production needs, but not enough equipment for development and staging environments.

Karen: That sounds like a costly mistake.

Michael: It can be very costly because people often develop disaster recovery plans without ongoing testing to confirm the approach continues to work. If the approach is flawed, when you do suffer an outage, you can be offline for hours, days or weeks. Even worse, you may not be able to recover your data at all.

Karen: That sounds extremely costly.

Michael: Yes, it’s no fun at all.

There are upsides though. Some pluses are that cloud computing forces you to be more formal about how you manage your technical infrastructure. For example, for training purposes; with a new developer, we can just give them a copy of a production system, and have them go to town on it, make modifications, whatever without risking the actual production servers. And if they make a mistake, which is human (you have to factor in human error), you can reprovision a brand new one, and retire the one that is fouled up. Instead of having to spend hours and hours trying to fix the problem on the machine they were working on.

Karen: This sounds like it’s a lot more flexible and time efficient, with a layer of safety built in.

Michael: Yes. Cloud computing also comes in handy if you ever have a security breach. If a hacker gets into the system and the system is compromised–if this happens, system administrators can go in and try to correct the problem. But hackers can often install backdoors to get in and out. So a cloud platform with a good disaster contingency and backup can allow system administrators to bring a whole instance down and do the patch on a whole new machine without the security breaches and patches in place. This is pretty easy to do with a cloud platform.

Karen: So TNR can help their clients do all these things?

Michael: Yes, we’ve worked with large customers over many years and we’ve seen a wide variety of things that can possibly go wrong, and we’ve been through several physical service outages both with Amazon Web Services and with Rackspace.

Cloud computing in itself is no panacea, but if you have the technical and organization proficiency to effectively leverage the platform, it can be a powerful tool used to accelerate your company’s rate of innovation.

If you are assessing the cloud as a solution in your business, contact us. There are a variety of options for hosting that can save your company money and minimize outages. Let us show you the option that is the best fit for your organization.