Need to figure out if Endeca can support Federated Search. Note that this search isn't across mulitple Endeca indices but also against various vendor search. e.g. Microsoft FAST, SOLR etc. Does Endeca have support to create a plugin or something which can go against outside Endeca indices and combine the results etc?
Endeca does not support Federated Search (at least not at this point in time). Endeca does support loading huge amounts of data into it's index and then allow for semi-realtime updates to that data.
So basically to perform the same task as a federated search you would do a few things:
Have large imports of data from the various data sources at predetermined intervals (weekly, daily, etc.) into an Endeca Agraph.
Have smaller, semi-realtime, data imports into the Endeca Agraph to populate any changes that might have occurred in that small timeframe. This should happen fairly often. (every 5 min, half hour, etc.).
With that setup you are able to return information from multiple systems, but those systems aren't queried real-time when a search is performed.
EDIT: This is not true as of July 2012. A main part of Endeca release at this point was the so called Endeca Assempler or Assembler API, which lets you define external systems, that are being queried at run time and returned in one request. For a programmer it is very transparent. You send a request to the Endeca engine and it will return an object containing results from all federated engines as well. It can return the data in various formats, Java, POJO, XML, JSON and some more.
Related
I have a database comprising of the following schema depicting the linkage between individuals who connect with multiple Advisors and these Advisors have affiliations with multiple organizations
Individuals--> Advisors (m:n relationship)
Advisors --> Enterprises (m:n relationship)
The business need is to enable search on all these concepts and organize results around AdvisorIds. As an example, display of a search result could be as follows
a) Advisor1-> connected to Individuals A,B,C; and linked to Enterprises X,Y
b) Advisor2-> connected to Individuals A, E; and linked to Enterprises M,X,Z
Towards this, we created a flattened table on these concepts and the relationship between them. Hence the same AdvisorId would appear in multiple rows
When I search for a string, I want to ensure that ALL records around an AdvisorId to be returned together irrespective of search score of the individual records.
One approach could be
a) first run an Azure Search and get a result of AdvisorId, ordered by search score of each record. This will repeat Advisor Ids
b) take a distinct set of AdvisorIds (across pages) via standard SQL
c) for each AdvisorId, pick all the related records via standard SQL
2 questions
Here a lot of processing in (b) and (c) will be done outside Azure leading to delays. Also, if I were to use pagination for (a), I am never sure of number of AdvisorId's, I end up with after the distinct operation
I wanted to check if there is a way to get the nested search implemented in Azure to do (a), (b) and (c) as a single API call
If I were to use facets for handling (a) and (b) together, how do I ensure that the ordering is based on the best search-score document within a facet
There isn't a way to achieve what you want in a single request unless you model your data differently. Instead of denormalizing the Individuals-Advisors and Advisors-Enterprises relationships, it may be possible instead to have one document per Advisor and use collections to store information about the related Individuals and Enterprises. This may or may not work for you depending on whether you need to supported correlated filtering on Individuals and Enterprises that are related to an Advisor. There is a whitepaper here that should help you evaluate whether this approach would work for you.
Another option might be to model Individuals, Advisors, and Enterprises as separate indexes, issue three queries, and do a client-side join. However, this is limited by the number of Advisor IDs you'd need to send in the queries on Individuals and Enterprises. Azure Search has limits on the size of filters that can make this impractical unless your queries have low recall.
We are working on making Azure Search better for scenarios like yours. For example, we're currently working on adding support for complex types. Please vote on User Voice and feel free to suggest other features that would help.
We are interested in using DocumentDb as a data store for a number of data sources and as such we are running a quick POC to establish whether it meets the criteria we are looking for.
One of the areas we are keen to provide is look ahead search capabilities for certain fields. These are traditionally provided using the SQL LIKE syntax which does not appear to be supported at present.
Searching online I have seen people talking about integrating Azure search but this appears to be a very costly mechanism for such a simple use case.
I have also seen people mention the use of UDF's but this appears to require an entire collection scan which is not practical from a performance perspective.
Does anyone have any alternative suggestions? One thing I considered was simply using a SQL table and initiating an update each time a document was inserted\updated\deleted?
DocumentDB supports STARTSWITH and range indexes to support prefix/look ahead searching.
You can progressively make queries like the following based on what your user types in a text box:
SELECT TOP 10 * FROM hotel H WHERE STARTSWITH(H.name, "H")
SELECT TOP 10 * FROM hotel H WHERE STARTSWITH(H.name, "Hi")
SELECT TOP 10 * FROM hotel H WHERE STARTSWITH(H.name, "Hil")
SELECT TOP 10 * FROM hotel H WHERE STARTSWITH(H.name, "Hilton")
Note that you must configure the collection, or the path/property you're using for these queries with a range index. You can extend this approach to handle additional cases as well:
To query in a case-insensitive manner, you must store the lower case form of the search property, and use that for querying.
I faced a similar situation, where a fast lookup was required, as a user typed search terms.
My scenario was that potentially thousands of simultaneous users would be performing such lookups; when testing this under load, to avoid saturation and throttling, we found we would have to increase the DocumentDB Request Unit (RU) throughput amount to a point that was not financially viable for us, in our specific circumstances.
We decided that DocumentDB was best used as the persistent store, and 'full' data retrieval - and this role it performs exceptionally well - while a small ElasticSearch cluster performed the role it was designed for - text search, faceted search, weighted search, stemming, and most relevant to your question, autocomplete analyzersand completion suggesters.
The subject of type ahead queries, creation of indexes, autocomplete analyzer and query time 'search as you type' in ElasticSearch can be found here, here and here
The fact that you plan to have several data sources would also potentially make the ElasticSearch cluster approach more attractive, to aggregate search data.
I used the Bitnami template available in the Azure market place to create relatively small instances, and most importantly, this allowed me to place the cluster on the same Virtual Network as my other components, which greatly increased performance.
Cost was lower than Azure Search (which uses ElasticSearch under the hood).
I am developing an Azure based website and I want to provide search capabilities using Lucene. (structured json objects would be indexed and stored in Lucene and other content such as Word documents, etc. would be indexed in lucene but stored in blob storage) I want the search to be secure, such that one user would never see a document belonging to another user. I want to allow ad-hoc searches as typed by the user. Lastly, I want to query programmatically to return predefined sets of data, such as "all notes for user X". I think I understand how to add properties to each document to achieve these 3 objectives. (I am listing them here so if anyone is kind enough to answer, they will have better idea of what I am trying to do)
My questions revolve around performance and security.
Can I improve document security by having a separate index for each user, or is including the user's ID as a parameter in each search sufficient?
Can I improve indexing speed and total throughput of the system by having a separate index for each user? My thinking is that having separate indexes would allow me to scale the system by having multiple index writers (perhaps even on different server instances) working at the same time, each on their own index.
Any insight would be greatly appreciated.
Regards,
Nate
Of course, one index.
You can do even better than what you suggested by using ManifoldCF (Apache product that knows how to handle Solr) to manage security.
And one off topic, uninformed suggestion: I'd rather use CloudBees or Heroku (or Amazon) instead of Azure.
Until you will use several machines for indexing I guess it's more convenient to use single index. Lucene community done a lot of work to make indexing process as efficient as it can. So unless you intentionally want to implement distributed indexing I doesn't recommend you to split indexes.
However there are several reasons why you would want to split indexes:
if your machine have several IO devices which could be utilized in parallel. In this case, if you are IO bound, splitting indexes is good idea.
splitting document fields between indexes (this is what ParallelReader is supposed for). This is more exotic form of splitting, but it may be a good idea if search is performed using different groups of fields. Suppose, we have two search query types: the first is using field name and type, and the second is using fields price and discount. If those fields are updated at different rate (I guess, name updates are far more rarely than price updates), updating only part of index would require less IO resources. This will give more overall throughput to the system.
I am currently architecting a large SharePoint deployment.
This deployment has the potential to grow to petabytes in size over the course of several years.
One of the current issues we are discussing is the option of storing our data in SharePoint using InfoPath Forms. Some of these forms contain hundres of fields and require a lot of mapping to content types for persistence and search. Our search requirement is primarily a singular identifier and NOT the contents of the forms, although I am told I should preempt the "want" to search in the future.
We require our information to be utilised for secondary purposes (such as reporting etc). The information MUST be accessible instantly after persisting to the system.
My core questions therefore are:
What are the benefit/risks of this approach compared to storing
our data in a singular relational store using web-service
persistence?
If we decided on this approach what would be the
impact of changing the forms, content-types over time?
What happens when our farm grows beyond a single web-application / site collection how accessible will the information be?
Will I know where it is and how portable will the information be overtime?
1.)
Benefit:
Form templates can be created & deployed (relatively) easy
You can easily configure Field Validation
Probably no code involved
Risks:
Hitting SharePoint 2010 Limits (not so uncommon as you might
think)
Needs careful form design/planning (correct XML structure)
Information only accessible via SharePoint Object model or
WebService's (very slow)
2.) Well this is a tough one. Changing the form template and re-deploying is easy and only takes a few minutes. However changing the structure (underlying XML) of the template can get you in trouble very easily, because older (filled out) forms will be invalid - there is an option to "upgrade" older forms out-of-the-box, but in my experience it never worked as it supposed to.
Content Types behave very similar, say you want to delete a column from a content type because it's no longer needed - you'll have to remove all references to it, which means removing all items so you can delete the column.
3.) Well portability is definitly an issue with InfoPath, because it heavily relies on the corresponding URL structure. You absolutely can add more site collections, but this means you have to deploy your form template to each site collection. Information (filled out forms) can't easily be shared between site collection's because each form contains the SourceURL (where it came from) and the Namespace of the template (which changes constantly once you deploy).
Considering your requirements, i would strongly recommend a relational store instead of InfoPath - simply because it is not designed to be a data storage.
I would use a SQL database to store the data and a custom UI (WebPart or Application Page) to perform CRUD operations. This means that the information is not actually stored in SharePoint - just displayed (which also means that it can't be searched with the builtin SharePoint Search). There is also the possibilty to use the Business Connectivity Services (which basically does all of the above without you needing to create a custom UI - however very slow with large amount of data).
If you do need the information just in SharePoint, why not just make all this happen with Lists only?
This is going to be a long one and may not have an answer just because there's no silver bullet for what you're looking for. It's mostly insight and ultimately the choice is up to you.
the option of storing our data in SharePoint using InfoPath Forms
This statement throws me a little. SharePoint data is stored in SharePoint (well, SQL technically) but InfoPath is just a UI layer for accessing any part of that data.
Some of these forms contain 100s of fields and require alot of mapping to content types for persistence and search
From this I assume there are multiple forms which would mean different types of data being accessed (and probably different purposes). Hundreds of fields is no problem and it really boils down to managing the form and view design.
From the form side you should check out cxpartners form design crib sheet. This gives you a nice standard to follow to manage all those fields. Another thing would be to look at breaking the form up in tabs or views itself (in InfoPath) based on what the user needs to fill out. Basically it breaks down to not creating a form with 100s of fields on one massively scrolling screen the user will just freak out over.
Same with the views on the form or document library you're storing the form data in. InfoPath forms are just xml stored in a library (so regardless of how many fields you have, the footprint is pretty minimal). You don't want to map and surface every field in the form nor do you want to have a view with 100 columns on it. You should look at breaking down the views as they're fit for purpose, with only a few hundred items in each view with a few columns. It's a balancing act too as you don't want to create 100s of views either so you need to find out what's right. A good B.A. or Information Architect will help with this with the SharePoint/InfoPath guru and business user helping out.
We require our information to be utilised for secondary purposes (such as reporting etc). The information MUST be accessible instantly
This is another requirement that's going to be a little difficult to meet exactly. If the library has thousands of items (or 10s of thousands) and a view has dozens of fields then expect the view to come to crawl (especially if the user is insistent on "seeing everything" and wants the limits of each view to be set to 1000 items, like anyone could process that much information at once). Instant access is difficult if you're keeping everything online for a long time (like for reporting). There's the operational side where users are filling out forms, finding forms, editing them, etc. and for that you only want a few hundred items to be live at any given moment (up to a few thousand but you need to be careful on the views). If you have a list with 100,000 items in it and users are using this for daily activities and trying to run reports for trending or long term operations against it, you're going to lose the performance battle. Look at doing reporting offline, potentially shipping the data that's reportable to a second source like SQL and using SSRS against it. Performance Point is an option but adds a layer of complexity to the architecture. The question will ultimately fall to what reporting looks like and how important is it in relation to daily operations.
To try to answer your questions directly:
The benefits to using SharePoint over a database are that the data can be easily viewed and sliced and diced up. Creating a view is child's play and can quickly show you useful information like # of sales in a month or customer feedback grouped by call centre person. SharePoint makes it easy to view this information and even setup dashboards, hook in KPIs, etc. without having to get some developer to craft custom web pages. As far as risks go, you need to be careful with letting things grow organically and out of control. Don't let the users design views of data, they generally want something but not sure and will ask for all columns to be available which they just export to Excel to slice and dice. Make sure there's a good design around the views and lists and they're fit for purpose and meet what needs the user is trying to get out of the data. Ask the question of what they're looking for and why, that will help shape what to expose.
Any change needs to be thought out and planned and tested. It's no different in SharePoint if you add a column to a list as you would by adding a column to a SQL database. Form updates should be considered and while you won't get it 100% right the first time, you should try to get as much as possible without going overboard and putting in crazy things like 100 "blank" fields that are players to be named later. Strike a balance by understanding the needs of the users and company and where things are going. Hopefully someone will have a vision of what this thing might be when it grows up and that'll go a long way to understanding the impact of change.
Data is just xml and as long as you're not doing stupid stuff in the form like hard coding absolute paths to services (use data connection libraries) the impact of growth will be minimal. Growing beyond a web application into multiple ones is a pretty big change and not something to be taken lightly. Even splitting site collections out is big and there needs to be a really good reason for this. Site collections can handle thousands of sites and millions of documents without issue. Web applications are really there for dividing up areas of interest or separation of purpose (like team sites on one web app and a publishing portal on another) and not really meant for splitting data due to growth concerns.
Like I said, there's no silver bullet here and what you're asking for is an architecture for a solution that nobody here has all the requirements for. Hope this helps.
I am building a tool that searches people based on a number of attributes. The values for these attributes are scattered across several systems.
As an example, dateOfBirth is stored in a SQL Server database as part of system ABC. That person's sales region assignment is stored in some horrible legacy database. Other attributes are stored in a system only accessible over an XML web service.
To make matters worse, the the legacy database and the web service can be really slow.
What strategies and tips should I consider for implementing a search across all these systems?
Note: Although I posted an answer, I'm not confident its a great answer. I don't intend to accept my own answer unless no one else gives better insight.
You could consider using an indexing mechanism to retrieve and locally index the data across all the systems, and then perform your searches against the index. Searches would be an awful lot faster and more reliable.
Of course, this just shifts the problem from one part of your system to another - now your indexing mechanism has to handle failures and heterogeneous systems, but that may be an easier problem to solve.
Another factor is how often the data changes. If you have to query data in real-time that goes stale very quickly, then indexing may not be practical.
If you can get away with a restrictive search, start by returning a list based on the search criteria corresponding to the fastest data source. Then join up those records with the other systems and remove records which don't match the search criteria.
If you have to implement OR logic, this approach is not going to work.
While not an actual answer, this might at least get you partway to a workable solution. We had a similar situation at a previous employer - lots of data sources, different ways of accessing those data sources, different access permissions, military/government/civilian sources, etc. We used Mule, which is built around the Enterprise Service Bus concept, to connect these data sources to our application. My details are a bit sketchy, as I wasn't the actual implementor, just an integrator, but what we did was define a channel in Mule. Then you write a simple integration piece to go between the channel and the data source, and the application and the channel. The integration piece does the work of making the actual query, and formatting the results, so we had a generic SQL integration piece for accessing a database, and for things like web services, we had some base classes that implemented common functionality, so the actual customization of the integration piecess was a lot less work than it sounds like. The application could then query the channel, which would handle accessing the various data sources, transforming them into a normalized bit of XML, and return the results to the application.
This had a lot of advantages for our situation. We could include new data sources for existing queries by simply connecting them to the channel - the application didn't have to know or care what data sources where there, as it only looked at the data from the channel. Since data can be pushed or pulled from the channel, we could have a data source update the application when, for example, it was updated.
It took a while to get it configured and working, but once we got it going, we were pretty successful with it. In our demo setup, we ended up with 4 or 5 applications acting as both producers and consumers of data, and connecting to maybe 10 data sources.
Have you thought of moving the data into a separate structure?
For example, Lucene stores data to be searched in a schema-less inverted indexed. You could have a separate program that retrieves data from all your different sources and puts them in a Lucene index. Your search could work against this index and the search results could contain a unique identifier and the system it came from.
http://lucene.apache.org/java/docs/
(There are implementations in other languages as well)
Have you taken a look at YQL? It may not be the perfect solution but I might give you starting point to work from.
Well, for starters I'd parallelize the queries to the different systems. That way we can minimize the query time.
You might also want to think about caching and aggregating the search attributes for subsequent queries in order to speed things up.
You have the option of creating an aggregation service or middleware that aggregates all the different systems so that you can provide a single interface for querying. If you do that, this is where I'd do the previously mentioned cache and parallize optimizations.
However, with all of that it you will need weighing up the development time/deployment time /long term benefits of the effort against migrating the old legacy database to a faster more modern one. You haven't said how tied into other systems those databases are so it may not be a very viable option in the short term.
EDIT: in response to data going out of date. You can consider caching if your data if you don't need the data to always match the database in real time. Also, if some data doesn't change very often (e.g. dates of birth) then you should cache them. If you employ caching then you could make your system configurable as to what tables/columns to include or exclude from the cache and you could give each table/column a personalizable cache timeout with an overall default.
Use Pentaho/Kettle to copy all of the data fields that you can search on and display into a local MySQL database
http://www.pentaho.com/products/data_integration/
Create a batch script to run nightly and update your local copy. Maybe even every hour. Then, write your query against your local MySQL database and display the results.