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  • Situations to prefer Apache Lucene over Solr?

    - by Karussell
    There are several advantages to use Solr (out-of-the-box facetting search, grouping, replication, http administration vs. luke, ...). Even if I embed a search-functionality in my Java application I could use SolrJ to avoid the HTTP trade-off when using Solr. So, when would you recommend to use "pure-Lucene"? Does it have a better performance or requires less RAM? Is it better unit-testable? PS: I am aware of this question.

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  • Have boost effect on lucene/compass field search.

    - by PeterP
    Hi there, In our compass mapping, we're boosting "better" documents to push them up in the list of search results. Something like this: <boost name="boostFactor" default="1.0"/> <property name="name"><meta-data>name</meta-data></property> While this works fine for fulltext search, it does not when doing a field search, e.g. the boost is ignored when searching something like name:Peter Is there any way to enable boosting for field searches? Thanks for your help and sorry if this is a dumb question - I am new to Lucene/Compass. Best regards, Peter

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  • Where can I find different versions of Lucene.Net Analyzer

    - by Vinay Pandey
    Hi All, I know its silly question but I am struggling in allowing japanese/other such languages search for my web application using lucene.net. I know that different analyzers can be used for all different languages and can be implemented but I could not find any dll for analyzers or example for the same. the question is:- Will using different analyzers be a good option for web application, as search text can be in any form. Where can I find dll and sample application for implementing search for all different sets of language I have spend whole day but no luck :(.

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  • StructureMap 'conditional singleton' for Lucene.Net IndexReader

    - by Gareth D
    I have a threadsafe object that is expensive to create and needs to be available through my application (a Lucene.Net IndexReader). The object can become invalid, at which point I need to recreate it (IndexReader.IsCurrent is false, need a new instance using IndexReader.Reopen). I'd like to able to use an IoC container (StructureMap) to manage the creation of the object, but I can't work out if this scenario is possible. It feels like some kind of "conditional singleton" lifecycle. Does StructureMap provide such a feature? Any alternative suggestions?

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  • How to get total number of potential results in Lucene

    - by Slace
    I'm using lucene on a site of mine and I want to show the total result count from a query, for example: Showing results x to y of z But I can't find any method which will return me the total number of potential results. I can only seem to find methods which you have to specify the number of results you want, and since I only want 10 per page it seems logical to pass in 10 as the number of results. Or am I doing this wrong, should I be passing in say 1000 and then just taking the 10 in the range that I require?

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  • Createing a new Index in SQL when current records don't meet that index

    - by Jonathan
    Hey all- I'd like to add an index to a table that already contains data. I know that there a few records currently in the table that are not unique with this new index. Clearly, MySQL won't let me add the index until all of them are. I need a query to identify the rows which currently have the same index. I can then delete or modify these rows as necessary. The new index contains 6 fields. Thanks- Jonathan

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  • Lucene.NET 2.9 and BitArray/DocIdSet

    - by Paul Knopf
    I found a great example on grabbing facet counts on a base query. It stores the bitarray of the base query to improve the performance each time the a facet gets counted. var genreQuery = new TermQuery(new Term("genre", genre)); var genreQueryFilter = new QueryFilter(genreQuery); BitArray genreBitArray = genreQueryFilter.Bits(searcher.GetIndexReader()); Console.WriteLine("There are " + GetCardinality(genreBitArray) + " document with the genre " + genre); // Next perform a regular search and get its BitArray result Query searchQuery = MultiFieldQueryParser.Parse(term, new[] {"title", "description"}, new[] {BooleanClause.Occur.SHOULD, BooleanClause.Occur.SHOULD}, new StandardAnalyzer()); var searchQueryFilter = new QueryFilter(searchQuery); BitArray searchBitArray = searchQueryFilter.Bits(searcher.GetIndexReader()); Console.WriteLine("There are " + GetCardinality(searchBitArray) + " document containing the term " + term); The only problem is that I am using a newer version of Lucene.NET (2.9) and Filter.Bits is obsolete. We are told to use DocIdSet instead (rather than BitArray). I cannot found out how to do the bitArray.And(bitArray) with a docIdSet. I looked in reflector and found OpenIdSet which has And operations. Not sure if OpenIdSet is the route to go, I'm just stating. Thanks in advance!

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  • How to create more complex Lucene query strings?

    - by boris callens
    This question is a spin-off from this question. My inquiry is two-fold, but because both are related I think it is a good idea to put them together. How to programmatically create queries. I know I could start creating strings and get that string parsed with the query parser. But as I gather bits and pieces of information from other resources, there is a programattical way to do this. What are the syntax rules for the Lucene queries? --EDIT-- I'll give a requirement example for a query I would like to make: Say I have 5 fields: First Name Last Name Age Address Everything All fields are optional, the last field should search over all the other fields. I go over every field and see if it's IsNullOrEmpty(). If it's not, I would like to append a part of my query so it adds the relevant search part. First name and last name should be exact matches and have more weight then the other fields. Age is a string and should exact match. Address can varry in order. Everything can also varry in order. How should I go about this?

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  • Best cross-language analyzer to use with lucene index

    - by Halirob
    Hello, I'm looking for feedback on which analyzer to use with an index that has documents from multiple languages. Currently I am using the simpleanalyzer, as it seems to handle the broadest amount of languages. Most of the documents to be indexed will be english, but there will be the occasional double-byte language indexed as well. Are there any other suggestions or should I just stick with the simpleanalyzer. Thanks

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  • Loading index in MemoryIndex instance

    - by Javi
    Hello, Is there any way to load an existing index into an instance of MemoryIndex?. I have an application which uses Hibernate Search so I can use index() in FullTextEntityManager instance to index an object. I'd like to recover back the created index and insert it into a MemoryIndex instance to execute several queries over it. Is it possible? Thanks.

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  • Loading index in MamoryIndex instance

    - by Javi
    Hello, Is there any way to load an existing index into an instance of MemoryIndex?. I have an application which uses Hibernate Search so I can use index() in FullTextEntityManager instance to index an object. I'd like to recover back the created index and insert it into a MemoryIndex instance to execute several queries over it. Is it possible? Thanks.

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  • Which non-clustered index should I use?

    - by Junior Mayhé
    Here I am studying nonclustered indexes on SQL Server Management Studio. I've created a table with more than 1 million records. This table has a primary key. CREATE TABLE [dbo].[Customers]( [CustomerId] [int] IDENTITY(1,1) NOT NULL, [CustomerName] [varchar](100) NOT NULL, [Deleted] [bit] NOT NULL, [Active] [bit] NOT NULL, CONSTRAINT [PK_Customers] PRIMARY KEY CLUSTERED ( [CustomerId] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] This is the query I'll be using to see what execution plan is showing: SELECT CustomerName FROM Customers Well, executing this command with no additional non-clustered index, it leads the execution plan to show me: I/O cost = 3.45646 Operator cost = 4.57715 Now I'm trying to see if it's possible to improve performance, so I've created a non-clustered index for this table: 1) First non-clustered index CREATE NONCLUSTERED INDEX [IX_CustomerID_CustomerName] ON [dbo].[Customers] ( [CustomerId] ASC, [CustomerName] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, SORT_IN_TEMPDB = OFF, IGNORE_DUP_KEY = OFF, DROP_EXISTING = OFF, ONLINE = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] GO Executing again the select against Customers table, the execution plan shows me: I/O cost = 2.79942 Operator cost = 3.92001 It seems better. Now I've deleted this just created non-clustered index, in order to create a new one: 2) First non-clustered index CREATE NONCLUSTERED INDEX [IX_CustomerIDIncludeCustomerName] ON [dbo].[Customers] ( [CustomerId] ASC ) INCLUDE ( [CustomerName]) WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, SORT_IN_TEMPDB = OFF, IGNORE_DUP_KEY = OFF, DROP_EXISTING = OFF, ONLINE = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] GO With this new non-clustered index, I've executed the select statement again and the execution plan shows me the same result: I/O cost = 2.79942 Operator cost = 3.92001 So, which non-clustered index should I use? Why the costs are the same on execution plan for I/O and Operator? Am I doing something wrong or this is expected? thank you

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  • Create an index only on certain rows in mysql

    - by dhruvbird
    So, I have this funny requirement of creating an index on a table only on a certain set of rows. This is what my table looks like: USER: userid, friendid, created, blah0, blah1, ..., blahN Now, I'd like to create an index on: (userid, friendid, created) but only on those rows where userid = friendid. The reason being that this index is only going to be used to satisfy queries where the WHERE clause contains "userid = friendid". There will be many rows where this is NOT the case, and I really don't want to waste all that extra space on the index. Another option would be to create a table (query table) which is populated on insert/update of this table and create a trigger to do so, but again I am guessing an index on that table would mean that the data would be stored twice. How does mysql store Primary Keys? I mean is the table ordered on the Primary Key or is it ordered by insert order and the PK is like a normal unique index? I checked up on clustered indexes (http://dev.mysql.com/doc/refman/5.0/en/innodb-index-types.html), but it seems only InnoDB supports them. I am using MyISAM (I mention this because then I could have created a clustered index on these 3 fields in the query table). I am basically looking for something like this: ALTER TABLE USERS ADD INDEX (userid, friendid, created) WHERE userid=friendid

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  • java AbstractMethodError

    - by Akhil
    How to handle this error in lucene: java.lang.AbstractMethodError: org.apache.lucene.store.Directory.listAll()[Ljava/lang/String; at org.apache.lucene.index.SegmentInfos$FindSegmentsFile.run(SegmentInfos.java:568) at org.apache.lucene.index.DirectoryReader.open(DirectoryReader.java:69) at org.apache.lucene.index.IndexReader.open(IndexReader.java:316) at org.apache.lucene.index.IndexReader.open(IndexReader.java:188) I am making a lucene function call but unfortunately it itself calls an abstract method of some class, as is evident from the error above. What is the work around for this? Thanks, --Akhil

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  • Html index page and files in that directory

    - by Frank
    On my web site, there is an index page, but if I take out that index page, users will see the files in that directory, for instance my site is : XYZ.com and I have a directory called "My_Dir", so when a user typed in "XYZ.com/My_Dir" he will see the index.html if there is one, but if it's not there, he will see a list of all my files inside "My_Dir", so is it safe to assume that with an index page in any of my sub directories, I can hide all the files in those directories from users, in other words if I have "123.txt, abc.html and index.html" in "My_Dir", users won't be able to see "123.txt, abc.html" because of the existence of "index.html" [ unless of course I mention those two files inside index.html ] ? Frank

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  • lucene.net get starting and end index of a highlighted fragment in a searched field

    - by user339995
    "My search returns a highlighted fragment from a field. I want to know that in that field of particular searched document, where does that fragment starts and ends ?" for instance. consider i am searching "highlighted fragment" in above lines (consider the above para as single document). I am setting my fragmenter as : SimpleFragmenter fragmenter = new SimpleFragmenter(30); now the output of GetBestFragment is somewhat like : "returns a highlighted fragment from" Is it possible to get the starting and ending index of this fragment in the text above (say starting is 10 and ending is 45)

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  • Lucene.Net memory consumption and slow search when too many clauses used

    - by Umer
    I have a DB having text file attributes and text file primary key IDs and indexed around 1 million text files along with their IDs (primary keys in DB). Now, I am searching at two levels. First is straight forward DB search, where i get primary keys as result (roughly 2 or 3 million IDs) Then i make a Boolean query for instance as following +Text:"test*" +(pkID:1 pkID:4 pkID:100 pkID:115 pkID:1041 .... ) and search it in my Index file. The problem is that such query (having 2 million clauses) takes toooooo much time to give result and consumes reallly too much memory.... Is there any optimization solution for this problem ?

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  • SQL Server Search Proper Names Full Text Index vs LIKE + SOUNDEX

    - by Matthew Talbert
    I have a database of names of people that has (currently) 35 million rows. I need to know what is the best method for quickly searching these names. The current system (not designed by me), simply has the first and last name columns indexed and uses "LIKE" queries with the additional option of using SOUNDEX (though I'm not sure this is actually used much). Performance has always been a problem with this system, and so currently the searches are limited to 200 results (which still takes too long to run). So, I have a few questions: Does full text index work well for proper names? If so, what is the best way to query proper names? (CONTAINS, FREETEXT, etc) Is there some other system (like Lucene.net) that would be better? Just for reference, I'm using Fluent NHibernate for data access, so methods that work will with that will be preferred. I'm using SQL Server 2008 currently. EDIT I want to add that I'm very interested in solutions that will deal with things like commonly misspelled names, eg 'smythe', 'smith', as well as first names, eg 'tomas', 'thomas'. Query Plan |--Parallelism(Gather Streams) |--Nested Loops(Inner Join, OUTER REFERENCES:([testdb].[dbo].[Test].[Id], [Expr1004]) OPTIMIZED WITH UNORDERED PREFETCH) |--Hash Match(Inner Join, HASH:([testdb].[dbo].[Test].[Id])=([testdb].[dbo].[Test].[Id])) | |--Bitmap(HASH:([testdb].[dbo].[Test].[Id]), DEFINE:([Bitmap1003])) | | |--Parallelism(Repartition Streams, Hash Partitioning, PARTITION COLUMNS:([testdb].[dbo].[Test].[Id])) | | |--Index Seek(OBJECT:([testdb].[dbo].[Test].[IX_Test_LastName]), SEEK:([testdb].[dbo].[Test].[LastName] >= 'WHITDþ' AND [testdb].[dbo].[Test].[LastName] < 'WHITF'), WHERE:([testdb].[dbo].[Test].[LastName] like 'WHITE%') ORDERED FORWARD) | |--Parallelism(Repartition Streams, Hash Partitioning, PARTITION COLUMNS:([testdb].[dbo].[Test].[Id])) | |--Index Seek(OBJECT:([testdb].[dbo].[Test].[IX_Test_FirstName]), SEEK:([testdb].[dbo].[Test].[FirstName] >= 'THOMARþ' AND [testdb].[dbo].[Test].[FirstName] < 'THOMAT'), WHERE:([testdb].[dbo].[Test].[FirstName] like 'THOMAS%' AND PROBE([Bitmap1003],[testdb].[dbo].[Test].[Id],N'[IN ROW]')) ORDERED FORWARD) |--Clustered Index Seek(OBJECT:([testdb].[dbo].[Test].[PK__TEST__3214EC073B95D2F1]), SEEK:([testdb].[dbo].[Test].[Id]=[testdb].[dbo].[Test].[Id]) LOOKUP ORDERED FORWARD) SQL for above: SELECT * FROM testdb.dbo.Test WHERE LastName LIKE 'WHITE%' AND FirstName LIKE 'THOMAS%' Based on advice from Mitch, I created an index like this: CREATE INDEX IX_Test_Name_DOB ON Test (LastName ASC, FirstName ASC, BirthDate ASC) INCLUDE (and here I list the other columns) My searches are now incredibly fast for my typical search (last, first, and birth date).

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  • Different analyzers for each field

    - by user72185
    Hi, How can I enable different analyzers for each field in a document I'm indexing with Lucene? Example: RAMDirectory dir = new RAMDirectory(); IndexWriter iw = new IndexWriter(dir, new StandardAnalyzer(Lucene.Net.Util.Version.LUCENE_CURRENT), true, IndexWriter.MaxFieldLength.UNLIMITED); Document doc = new Document(); Field field1 = new Field("field1", someText1, Field.Store.YES, Field.Index.ANALYZED, Field.TermVector.WITH_POSITIONS_OFFSETS); Field field2 = new Field("field2", someText2, Field.Store.YES, Field.Index.ANALYZED, Field.TermVector.WITH_POSITIONS_OFFSETS); doc.Add(field1); doc.Add(field2); iw.AddDocument(doc); iw.Commit(); The analyzer is an argument to the IndexWriter, but I want to use StandardAnalyzer for field1 and SimpleAnalyzer for field2, how can I do that? The same applies when searching, of course. The correct analyzer must be applied for each field.

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  • Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • SQL Server Index cost

    - by yellowstar
    I have read that one of the tradeoffs for adding table indexes in SQL Server is the increased cost of insert/update/delete queries to benefit the performance of select queries. I can conceptually understand what happens in the case of an insert because SQL Server has to write entries into each index matching the new rows, but update and delete are a little more murky to me because I can't quite wrap my head around what the database engine has to do. Let's take DELETE as an example and assume I have the following schema (pardon the pseudo-SQL) TABLE Foo col1 int ,col2 int ,col3 int ,col4 int PRIMARY KEY (col1,col2) INDEX IX_1 col3 INCLUDE col4 Now, if I issue the statement DELETE FROM Foo WHERE col1=12 AND col2 > 34 I understand what the engine must do to update the table (or clustered index if you prefer). The index is set up to make it easy to find the range of rows to be removed and do so. However, at this point it also needs to update IX_1 and the query that I gave it gives no obvious efficient way for the database engine to find the rows to update. Is it forced to do a full index scan at this point? Does the engine read the rows from the clustered index first and generate a smarter internal delete against the index? It might help me to wrap my head around this if I understood better what is going on under the hood, but I guess my real question is this. I have a database that is spending a significant amount of time in delete and I'm trying to figure out what I can do about it. When I display the execution plan for the deletion, it just shows an entry for "Clustered Index Delete" on table Foo which lists in the details section the other indices that need to be updated but I don't get any indication of the relative cost of these other indices. Are they all equal in this case? Is there some way that I can estimate the impact of removing one or more of these indices without having to actually try it?

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  • Lucene.NET faceted search.

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    I found a great tutorial on performing a faceted search. http://www.devatwork.nl/articles/lucenenet/faceted-search-and-drill-down-lucenenet/ This article does not explain how to retrieve the narrowed available attributes to filter from (for further drill down). Lets say I am looking for planners that are red. When I perform the faceted search, I want to return all available attributes to filter from that are red. Then when I add a "weekly format" filter, I want the attribute list to get even smaller, containing only filters available for the segmented group.

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  • Solr/Lucene Scorer

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    We are currently working on a proof-of-concept for a client using Solr and have been able to configure all the features they want except the scoring. Problem is that they want scores that make results fall in buckets: Bucket 1: exact match on category (score = 4) Bucket 2: exact match on name (score = 3) Bucket 3: partial match on category (score = 2) Bucket 4: partial match on name (score = 1) First thing we did was develop a custom similarity class that would return the correct score depending on the field and an exact or partial match. The only problem now is that when a document matches on both the category and name the scores are added together. Example: searching for "restaurant" returns documents in the category restaurant that also have the word restaurant in their name and thus get a score of 5 (4+1) but they should only get 4. I assume for this to work we would need to develop a custom Scorer class but we have no clue on how to incorporate this in Solr. Another option is to create a custom SortField implementation similar to the RandomSortField already present in Solr. Maybe there is even a simpler solution that we don't know about. All suggestions welcome!

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  • Which can handle a huge surge of queries: SQL Server 2008 Fulltext or Lucene

    - by Luke101
    I am creating a widget that will be installed on several websites and blogs. The widget will analyse the remote webpage title and content, then it will return relevent articles/links on my website. The amount of traffic we expect will be very huge roughly 500K queries a day and up from there. I need the queries to be returned very quickly, so I need the candidate to be high performance, similar to google adsense. The remote title can be from 5 to 50 words and the description we will use no more then 3000 words. Which of these two do you think can handle the load.

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