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  • Object reference not set to an instance of an object- Linked List Example

    - by Zoro Roronoa
    I am seeing following errors : Object reference not set to an instance of an object! Check to determinate if the object is null before calling the method! I'am new with C#,and I made a program for Sorted Linked Lists. Here is the code where the error comes! public void Insert(double data) { Link newLink = new Link(data); Link current = first; Link previous = null; if (first == null) { first = newLink; } else { while (data > current.DData && current != null) { previous = current; current = current.Next; } previous.Next = newLink; newLink.Next = current; } } It says that the current referenc is null while (data current.DData && current != null), but I assigned it current = first; Please Help ! The rest is the complete code of the Program! class Link { double dData; Link next=null; public Link Next { get { return next; } set { next = value; } } public double DData { get { return dData; } set { dData = value; } } public Link(double dData) { this.dData = dData; } public void DisplayLink() { Console.WriteLine("Link : "+ dData); } } class SortedList { Link first; public SortedList() { first = null; } public bool IsEmpty() { return (this.first == null); } public void Insert(double data) { Link newLink = new Link(data); Link current = first; Link previous = null; if (first == null) { first = newLink; } else { while (data > current.DData && current != null) { previous = current; current = current.Next; } previous.Next = newLink; newLink.Next = current; } } public Link Remove() { Link temp = first; first = first.Next; return temp; } public void DisplayList() { Link current; current = first; Console.WriteLine("Display the List!"); while (current != null) { current.DisplayLink(); current = current.Next; } } } class SortedListApp { public void TestSortedList() { SortedList newList = new SortedList(); newList.Insert(20); newList.Insert(22); newList.Insert(100); newList.Insert(1000); newList.Insert(15); newList.Insert(11); newList.DisplayList(); newList.Remove(); newList.DisplayList(); } }

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  • SQL SERVER – Importing CSV File Into Database – SQL in Sixty Seconds #018 – Video

    - by pinaldave
    Importing data into database is one of the most important tasks. I often receive questions regarding what is the quickest way to insert CSV data or how to import CSV Data into SQL Server Table. Honestly the process is very simple and the script is even simpler. In today’s SQL in Sixty Seconds Video we will learn how quickly we can insert CSV data into SQL Server. The steps to import CSV are very simple. Create Table Use Bulk Insert to import the data Verify the data Done! Absolutely it is that simple. More on Importing CSV Data: SQL SERVER – Import CSV File Into SQL Server Using Bulk Insert – Load Comma Delimited File Into SQL Server SQL SERVER – Import CSV File into Database Table Using SSIS SQL SERVER – Create a Comma Delimited List Using SELECT Clause From Table Column SQL SERVER – Comma Separated Values (CSV) from Table Column SQL SERVER – Comma Separated Values (CSV) from Table Column – Part 2 I encourage you to submit your ideas for SQL in Sixty Seconds. We will try to accommodate as many as we can. If we like your idea we promise to share with you educational material. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Query, SQL Scripts, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology, Video

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  • Using a "white list" for extracting terms for Text Mining, Part 2

    - by [email protected]
    In my last post, we set the groundwork for extracting specific tokens from a white list using a CTXRULE index. In this post, we will populate a table with the extracted tokens and produce a case table suitable for clustering with Oracle Data Mining. Our corpus of documents will be stored in a database table that is defined as create table documents(id NUMBER, text VARCHAR2(4000)); However, any suitable Oracle Text-accepted data type can be used for the text. We then create a table to contain the extracted tokens. The id column contains the unique identifier (or case id) of the document. The token column contains the extracted token. Note that a given document many have many tokens, so there will be one row per token for a given document. create table extracted_tokens (id NUMBER, token VARCHAR2(4000)); The next step is to iterate over the documents and extract the matching tokens using the index and insert them into our token table. We use the MATCHES function for matching the query_string from my_thesaurus_rules with the text. DECLARE     cursor c2 is       select id, text       from documents; BEGIN     for r_c2 in c2 loop        insert into extracted_tokens          select r_c2.id id, main_term token          from my_thesaurus_rules          where matches(query_string,                        r_c2.text)>0;     end loop; END; Now that we have the tokens, we can compute the term frequency - inverse document frequency (TF-IDF) for each token of each document. create table extracted_tokens_tfidf as   with num_docs as (select count(distinct id) doc_cnt                     from extracted_tokens),        tf       as (select a.id, a.token,                            a.token_cnt/b.num_tokens token_freq                     from                        (select id, token, count(*) token_cnt                        from extracted_tokens                        group by id, token) a,                       (select id, count(*) num_tokens                        from extracted_tokens                        group by id) b                     where a.id=b.id),        doc_freq as (select token, count(*) overall_token_cnt                     from extracted_tokens                     group by token)   select tf.id, tf.token,          token_freq * ln(doc_cnt/df.overall_token_cnt) tf_idf   from num_docs,        tf,        doc_freq df   where df.token=tf.token; From the WITH clause, the num_docs query simply counts the number of documents in the corpus. The tf query computes the term (token) frequency by computing the number of times each token appears in a document and divides that by the number of tokens found in the document. The doc_req query counts the number of times the token appears overall in the corpus. In the SELECT clause, we compute the tf_idf. Next, we create the nested table required to produce one record per case, where a case corresponds to an individual document. Here, we COLLECT all the tokens for a given document into the nested column extracted_tokens_tfidf_1. CREATE TABLE extracted_tokens_tfidf_nt              NESTED TABLE extracted_tokens_tfidf_1                  STORE AS extracted_tokens_tfidf_tab AS              select id,                     cast(collect(DM_NESTED_NUMERICAL(token,tf_idf)) as DM_NESTED_NUMERICALS) extracted_tokens_tfidf_1              from extracted_tokens_tfidf              group by id;   To build the clustering model, we create a settings table and then insert the various settings. Most notable are the number of clusters (20), using cosine distance which is better for text, turning off auto data preparation since the values are ready for mining, the number of iterations (20) to get a better model, and the split criterion of size for clusters that are roughly balanced in number of cases assigned. CREATE TABLE km_settings (setting_name  VARCHAR2(30), setting_value VARCHAR2(30)); BEGIN  INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.clus_num_clusters, 20);  INSERT INTO km_settings (setting_name, setting_value)     VALUES (dbms_data_mining.kmns_distance, dbms_data_mining.kmns_cosine);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_off);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.kmns_iterations,20);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.kmns_split_criterion,dbms_data_mining.kmns_size);   COMMIT; END; With this in place, we can now build the clustering model. BEGIN     DBMS_DATA_MINING.CREATE_MODEL(     model_name          => 'TEXT_CLUSTERING_MODEL',     mining_function     => dbms_data_mining.clustering,     data_table_name     => 'extracted_tokens_tfidf_nt',     case_id_column_name => 'id',     settings_table_name => 'km_settings'); END;To generate cluster names from this model, check out my earlier post on that topic.

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  • Hello Operator, My Switch Is Bored

    - by Paul White
    This is a post for T-SQL Tuesday #43 hosted by my good friend Rob Farley. The topic this month is Plan Operators. I haven’t taken part in T-SQL Tuesday before, but I do like to write about execution plans, so this seemed like a good time to start. This post is in two parts. The first part is primarily an excuse to use a pretty bad play on words in the title of this blog post (if you’re too young to know what a telephone operator or a switchboard is, I hate you). The second part of the post looks at an invisible query plan operator (so to speak). 1. My Switch Is Bored Allow me to present the rare and interesting execution plan operator, Switch: Books Online has this to say about Switch: Following that description, I had a go at producing a Fast Forward Cursor plan that used the TOP operator, but had no luck. That may be due to my lack of skill with cursors, I’m not too sure. The only application of Switch in SQL Server 2012 that I am familiar with requires a local partitioned view: CREATE TABLE dbo.T1 (c1 int NOT NULL CHECK (c1 BETWEEN 00 AND 24)); CREATE TABLE dbo.T2 (c1 int NOT NULL CHECK (c1 BETWEEN 25 AND 49)); CREATE TABLE dbo.T3 (c1 int NOT NULL CHECK (c1 BETWEEN 50 AND 74)); CREATE TABLE dbo.T4 (c1 int NOT NULL CHECK (c1 BETWEEN 75 AND 99)); GO CREATE VIEW V1 AS SELECT c1 FROM dbo.T1 UNION ALL SELECT c1 FROM dbo.T2 UNION ALL SELECT c1 FROM dbo.T3 UNION ALL SELECT c1 FROM dbo.T4; Not only that, but it needs an updatable local partitioned view. We’ll need some primary keys to meet that requirement: ALTER TABLE dbo.T1 ADD CONSTRAINT PK_T1 PRIMARY KEY (c1);   ALTER TABLE dbo.T2 ADD CONSTRAINT PK_T2 PRIMARY KEY (c1);   ALTER TABLE dbo.T3 ADD CONSTRAINT PK_T3 PRIMARY KEY (c1);   ALTER TABLE dbo.T4 ADD CONSTRAINT PK_T4 PRIMARY KEY (c1); We also need an INSERT statement that references the view. Even more specifically, to see a Switch operator, we need to perform a single-row insert (multi-row inserts use a different plan shape): INSERT dbo.V1 (c1) VALUES (1); And now…the execution plan: The Constant Scan manufactures a single row with no columns. The Compute Scalar works out which partition of the view the new value should go in. The Assert checks that the computed partition number is not null (if it is, an error is returned). The Nested Loops Join executes exactly once, with the partition id as an outer reference (correlated parameter). The Switch operator checks the value of the parameter and executes the corresponding input only. If the partition id is 0, the uppermost Clustered Index Insert is executed, adding a row to table T1. If the partition id is 1, the next lower Clustered Index Insert is executed, adding a row to table T2…and so on. In case you were wondering, here’s a query and execution plan for a multi-row insert to the view: INSERT dbo.V1 (c1) VALUES (1), (2); Yuck! An Eager Table Spool and four Filters! I prefer the Switch plan. My guess is that almost all the old strategies that used a Switch operator have been replaced over time, using things like a regular Concatenation Union All combined with Start-Up Filters on its inputs. Other new (relative to the Switch operator) features like table partitioning have specific execution plan support that doesn’t need the Switch operator either. This feels like a bit of a shame, but perhaps it is just nostalgia on my part, it’s hard to know. Please do let me know if you encounter a query that can still use the Switch operator in 2012 – it must be very bored if this is the only possible modern usage! 2. Invisible Plan Operators The second part of this post uses an example based on a question Dave Ballantyne asked using the SQL Sentry Plan Explorer plan upload facility. If you haven’t tried that yet, make sure you’re on the latest version of the (free) Plan Explorer software, and then click the Post to SQLPerformance.com button. That will create a site question with the query plan attached (which can be anonymized if the plan contains sensitive information). Aaron Bertrand and I keep a close eye on questions there, so if you have ever wanted to ask a query plan question of either of us, that’s a good way to do it. The problem The issue I want to talk about revolves around a query issued against a calendar table. The script below creates a simplified version and adds 100 years of per-day information to it: USE tempdb; GO CREATE TABLE dbo.Calendar ( dt date NOT NULL, isWeekday bit NOT NULL, theYear smallint NOT NULL,   CONSTRAINT PK__dbo_Calendar_dt PRIMARY KEY CLUSTERED (dt) ); GO -- Monday is the first day of the week for me SET DATEFIRST 1;   -- Add 100 years of data INSERT dbo.Calendar WITH (TABLOCKX) (dt, isWeekday, theYear) SELECT CA.dt, isWeekday = CASE WHEN DATEPART(WEEKDAY, CA.dt) IN (6, 7) THEN 0 ELSE 1 END, theYear = YEAR(CA.dt) FROM Sandpit.dbo.Numbers AS N CROSS APPLY ( VALUES (DATEADD(DAY, N.n - 1, CONVERT(date, '01 Jan 2000', 113))) ) AS CA (dt) WHERE N.n BETWEEN 1 AND 36525; The following query counts the number of weekend days in 2013: SELECT Days = COUNT_BIG(*) FROM dbo.Calendar AS C WHERE theYear = 2013 AND isWeekday = 0; It returns the correct result (104) using the following execution plan: The query optimizer has managed to estimate the number of rows returned from the table exactly, based purely on the default statistics created separately on the two columns referenced in the query’s WHERE clause. (Well, almost exactly, the unrounded estimate is 104.289 rows.) There is already an invisible operator in this query plan – a Filter operator used to apply the WHERE clause predicates. We can see it by re-running the query with the enormously useful (but undocumented) trace flag 9130 enabled: Now we can see the full picture. The whole table is scanned, returning all 36,525 rows, before the Filter narrows that down to just the 104 we want. Without the trace flag, the Filter is incorporated in the Clustered Index Scan as a residual predicate. It is a little bit more efficient than using a separate operator, but residual predicates are still something you will want to avoid where possible. The estimates are still spot on though: Anyway, looking to improve the performance of this query, Dave added the following filtered index to the Calendar table: CREATE NONCLUSTERED INDEX Weekends ON dbo.Calendar(theYear) WHERE isWeekday = 0; The original query now produces a much more efficient plan: Unfortunately, the estimated number of rows produced by the seek is now wrong (365 instead of 104): What’s going on? The estimate was spot on before we added the index! Explanation You might want to grab a coffee for this bit. Using another trace flag or two (8606 and 8612) we can see that the cardinality estimates were exactly right initially: The highlighted information shows the initial cardinality estimates for the base table (36,525 rows), the result of applying the two relational selects in our WHERE clause (104 rows), and after performing the COUNT_BIG(*) group by aggregate (1 row). All of these are correct, but that was before cost-based optimization got involved :) Cost-based optimization When cost-based optimization starts up, the logical tree above is copied into a structure (the ‘memo’) that has one group per logical operation (roughly speaking). The logical read of the base table (LogOp_Get) ends up in group 7; the two predicates (LogOp_Select) end up in group 8 (with the details of the selections in subgroups 0-6). These two groups still have the correct cardinalities as trace flag 8608 output (initial memo contents) shows: During cost-based optimization, a rule called SelToIdxStrategy runs on group 8. It’s job is to match logical selections to indexable expressions (SARGs). It successfully matches the selections (theYear = 2013, is Weekday = 0) to the filtered index, and writes a new alternative into the memo structure. The new alternative is entered into group 8 as option 1 (option 0 was the original LogOp_Select): The new alternative is to do nothing (PhyOp_NOP = no operation), but to instead follow the new logical instructions listed below the NOP. The LogOp_GetIdx (full read of an index) goes into group 21, and the LogOp_SelectIdx (selection on an index) is placed in group 22, operating on the result of group 21. The definition of the comparison ‘the Year = 2013’ (ScaOp_Comp downwards) was already present in the memo starting at group 2, so no new memo groups are created for that. New Cardinality Estimates The new memo groups require two new cardinality estimates to be derived. First, LogOp_Idx (full read of the index) gets a predicted cardinality of 10,436. This number comes from the filtered index statistics: DBCC SHOW_STATISTICS (Calendar, Weekends) WITH STAT_HEADER; The second new cardinality derivation is for the LogOp_SelectIdx applying the predicate (theYear = 2013). To get a number for this, the cardinality estimator uses statistics for the column ‘theYear’, producing an estimate of 365 rows (there are 365 days in 2013!): DBCC SHOW_STATISTICS (Calendar, theYear) WITH HISTOGRAM; This is where the mistake happens. Cardinality estimation should have used the filtered index statistics here, to get an estimate of 104 rows: DBCC SHOW_STATISTICS (Calendar, Weekends) WITH HISTOGRAM; Unfortunately, the logic has lost sight of the link between the read of the filtered index (LogOp_GetIdx) in group 22, and the selection on that index (LogOp_SelectIdx) that it is deriving a cardinality estimate for, in group 21. The correct cardinality estimate (104 rows) is still present in the memo, attached to group 8, but that group now has a PhyOp_NOP implementation. Skipping over the rest of cost-based optimization (in a belated attempt at brevity) we can see the optimizer’s final output using trace flag 8607: This output shows the (incorrect, but understandable) 365 row estimate for the index range operation, and the correct 104 estimate still attached to its PhyOp_NOP. This tree still has to go through a few post-optimizer rewrites and ‘copy out’ from the memo structure into a tree suitable for the execution engine. One step in this process removes PhyOp_NOP, discarding its 104-row cardinality estimate as it does so. To finish this section on a more positive note, consider what happens if we add an OVER clause to the query aggregate. This isn’t intended to be a ‘fix’ of any sort, I just want to show you that the 104 estimate can survive and be used if later cardinality estimation needs it: SELECT Days = COUNT_BIG(*) OVER () FROM dbo.Calendar AS C WHERE theYear = 2013 AND isWeekday = 0; The estimated execution plan is: Note the 365 estimate at the Index Seek, but the 104 lives again at the Segment! We can imagine the lost predicate ‘isWeekday = 0’ as sitting between the seek and the segment in an invisible Filter operator that drops the estimate from 365 to 104. Even though the NOP group is removed after optimization (so we don’t see it in the execution plan) bear in mind that all cost-based choices were made with the 104-row memo group present, so although things look a bit odd, it shouldn’t affect the optimizer’s plan selection. I should also mention that we can work around the estimation issue by including the index’s filtering columns in the index key: CREATE NONCLUSTERED INDEX Weekends ON dbo.Calendar(theYear, isWeekday) WHERE isWeekday = 0 WITH (DROP_EXISTING = ON); There are some downsides to doing this, including that changes to the isWeekday column may now require Halloween Protection, but that is unlikely to be a big problem for a static calendar table ;)  With the updated index in place, the original query produces an execution plan with the correct cardinality estimation showing at the Index Seek: That’s all for today, remember to let me know about any Switch plans you come across on a modern instance of SQL Server! Finally, here are some other posts of mine that cover other plan operators: Segment and Sequence Project Common Subexpression Spools Why Plan Operators Run Backwards Row Goals and the Top Operator Hash Match Flow Distinct Top N Sort Index Spools and Page Splits Singleton and Range Seeks Bitmaps Hash Join Performance Compute Scalar © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • How to Omit the Page Number From the First Page of a Word 2013 Document Without Using Sections

    - by Lori Kaufman
    Normally, the first page, or cover page, of a document does not have a page number or other header or footer text. You can avoid putting a page number on the first page using sections, but there is an easier way to do this. If you don’t plan to use sections in any other part of your document, you may want to avoid using them completely. We will show you how to easily take the page number off the cover page and start the page numbering at one on the second page of your document by simply using a footer (or a header) and changing one setting. Click the Page Layout tab. In the Page Setup section of the Page Layout tab, click the Page Setup dialog box launcher icon in the lower, right corner of the section. On the Page Setup dialog box, click the Layout tab and select the Different first page check box in the Headers and footers section so there is a check mark in the box. Click OK. You’ll notice there is no page number on the first page of your document now. However, you might want the second page to be page one of your document, only to find it is currently page two. To change the page number on the second page to one, click the Insert tab. In the Header & Footer section of the Insert tab, click Page Number and select Format Page Numbers from the drop-down menu. On the Page Number Format dialog box, select Start at in the Page numbering section. Enter 0 in the edit box and click OK. This allows the second page of your document to be labeled as page one. You can use the drop-down menu on the Format Page Numbers button in the Header & Footer section of the Insert tab to add page numbers to your document as well. Easily insert formatted page numbers at the top or bottom of the page or in the page margins. Use the same menu to remove page numbers from your document.     

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  • UTF-8 encoding problem with flash mysql and php

    - by alibhp
    Hi, As you may know, I am programming an on-line game using FLASH. I am connecting my FLASH 8 movie with MySQL database through PHP. I am doing very good in that, and I have everything working fine. The problems come when I am trying to insert (Using the INSERT SQL func) data to the database that are non-english. In other words, UTF-8 data. I red a lot of articls about that stuff and found and apply the fallowing: 1. In PHP4, you need to tell the PHP to use UTF-8 when using the xml_parser_crater() func, however, in PHP5 that is done automatically. Even though I told PHP5 to use the UTF-8 when calling the func. Adding the header to the XML sent to PHP from flash. Force the FLASH to use UTF-8 encoding in the preference options. Set the encoding in MySQL to UTF-8 (utf8_unicode_ci with InnoDB engine). I can read and insert the other language data correctly in the phpadmin as well. I did all that in my coding, and still I can't insert such data. one more strange thing is that, when I use the same link, that the FLASH using, with the XML, that the FLASH creating, on the browser (google chrome), I got the data inserted right in the database!!!!! I am about to get crazy about that stuff, What am I missing? what cause the problem? Thank you in advance.

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  • how to troubleshoot sql server issues

    - by joe
    i have an ASP .net application with sql server database, and i am wondering if you can give your ideas on how to troubleshoot the following issue: i can insert / update / delete from any table, but i have one page that uses transactions to insert into different tables. the c# code is correct and very simple, but it fails. i used the sql profiler to see how my app interacts with the DB, especially that the app is using stored procedures, i can catch the exec procedure statement and run it manually from SSMS and it works fine, but the same stored procedure fails from the application!!! which lead me to think that issue is coming from the user account and settings, i am no expert in sql server and wondering if anyone can explain how to verify the required settings for user account. thanks EDIT: in web.config here is how i reference my connection <connectionStrings> <add name="Conn" connectionString="Data Source=localhost;Initial Catalog=myDB;Persist Security Info=True;User ID=DbUser;Password=password1254_3" providerName="System.Data.SqlClient"> </connectionstring> EDIT: i will try to describe the process here: 1- i begin a transaction 2- i call a stored proc to insert (which succeeds) and return the scope identity ( that will be used in the next step) 3- i call another stored procedure to insert some more info + scope identity from step 2, which is a foreign key here 4- i get error, foreign key violation 5- transaction rolled back, now tables empty again... thanks

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  • New features in SQL Prompt 6.4

    - by Tom Crossman
    We’re pleased to announce a new beta version of SQL Prompt. We’ve been trying out a few new core technologies, and used them to add features and bug fixes suggested by users on the SQL Prompt forum and suggestions forum. You can download the SQL Prompt 6.4 beta here (zip file). Let us know what you think! New features Execute current statement In a query window, you can now execute the SQL statement under your cursor by pressing Shift + F5. For example, if you have a query containing two statements and your cursor is placed on the second statement: When you press Shift + F5, only the second statement is executed:   Insert semicolons You can now use SQL Prompt to automatically insert missing semicolons after each statement in a query. To insert semicolons, go to the SQL Prompt menu and click Insert Semicolons. Alternatively, hold Ctrl and press B then C. BEGIN…END block highlighting When you place your cursor over a BEGIN or END keyword, SQL Prompt now automatically highlights the matching keyword: Rename variables and aliases You can now use SQL Prompt to rename all occurrences of a variable or alias in a query. To rename a variable or alias, place your cursor over an instance of the variable or alias you want to rename and press F2: Improved loading dialog box The database loading dialog box now shows actual progress, and you can cancel loading databases:   Single suggestion improvement SQL Prompt no longer suggests keywords if the keyword has been typed and no other suggestions exist. Performance improvement SQL Prompt now has less impact on Management Studio start up time. What do you think? We want to hear your feedback about the beta. If you have any suggestions, or bugs to report, tell us on the SQL Prompt forum or our suggestions forum.

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  • 12c - Invisible Columns...

    - by noreply(at)blogger.com (Thomas Kyte)
    Remember when 11g first came out and we had "invisible indexes"?  It seemed like a confusing feature - indexes that would be maintained by modifications (hence slowing them down), but would not be used by queries (hence never speeding them up).  But - after you looked at them a while, you could see how they can be useful.  For example - to add an index in a running production system, an index used by the next version of the code to be introduced later that week - but not tested against the queries in version one of the application in place now.  We all know that when you add an index - one of three things can happen - a given query will go much faster, it won't affect a given query at all, or... It will make some untested query go much much slower than it used to.  So - invisible indexes allowed us to modify the schema in a 'safe' manner - hiding the change until we were ready for it.Invisible columns accomplish the same thing - the ability to introduce a change while minimizing any negative side effects of that change.  Normally when you add a column to a table - any program with a SELECT * would start seeing that column, and programs with an INSERT INTO T VALUES (...) would pretty much immediately break (an INSERT without a list of columns in it).  Now we can add a column to a table in an invisible fashion, the column will not show up in a DESCRIBE command in SQL*Plus, it will not be returned with a SELECT *, it will not be considered in an INSERT INTO T VALUES statement.  It can be accessed by any query that asks for it, it can be populated by an INSERT statement that references it, but you won't see it otherwise.For example, let's start with a simple two column table:ops$tkyte%ORA12CR1> create table t  2  ( x int,  3    y int  4  )  5  /Table created.ops$tkyte%ORA12CR1> insert into t values ( 1, 2 );1 row created.Now, we will add an invisible column to it:ops$tkyte%ORA12CR1> alter table t add                     ( z int INVISIBLE );Table altered.Notice that a DESCRIBE will not show us this column:ops$tkyte%ORA12CR1> desc t Name              Null?    Type ----------------- -------- ------------ X                          NUMBER(38) Y                          NUMBER(38)and existing inserts are unaffected by it:ops$tkyte%ORA12CR1> insert into t values ( 3, 4 );1 row created.A SELECT * won't see it either:ops$tkyte%ORA12CR1> select * from t;         X          Y---------- ----------         1          2         3          4But we have full access to it (in well written programs! The ones that use a column list in the insert and select - never relying on "defaults":ops$tkyte%ORA12CR1> insert into t (x,y,z)                         values ( 5,6,7 );1 row created.ops$tkyte%ORA12CR1> select x, y, z from t;         X          Y          Z---------- ---------- ----------         1          2         3          4         5          6          7and when we are sure that we are ready to go with this column, we can just modify it:ops$tkyte%ORA12CR1> alter table t modify z visible;Table altered.ops$tkyte%ORA12CR1> select * from t;         X          Y          Z---------- ---------- ----------         1          2         3          4         5          6          7I will say that a better approach to this - one that is available in 11gR2 and above - would be to use editioning views (part of Edition Based Redefinition - EBR ).  I would rather use EBR over this approach, but in an environment where EBR is not being used, or the editioning views are not in place, this will achieve much the same.Read these for information on EBR:http://www.oracle.com/technetwork/issue-archive/2010/10-jan/o10asktom-172777.htmlhttp://www.oracle.com/technetwork/issue-archive/2010/10-mar/o20asktom-098897.htmlhttp://www.oracle.com/technetwork/issue-archive/2010/10-may/o30asktom-082672.html

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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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  • SQL SERVER – 2008 – Introduction to Snapshot Database – Restore From Snapshot

    - by pinaldave
    Snapshot database is one of the most interesting concepts that I have used at some places recently. Here is a quick definition of the subject from Book On Line: A Database Snapshot is a read-only, static view of a database (the source database). Multiple snapshots can exist on a source database and can always reside on the same server instance as the database. Each database snapshot is consistent, in terms of transactions, with the source database as of the moment of the snapshot’s creation. A snapshot persists until it is explicitly dropped by the database owner. If you do not know how Snapshot database work, here is a quick note on the subject. However, please refer to the official description on Book-on-Line for accuracy. Snapshot database is a read-only database created from an original database called the “source database”. This database operates at page level. When Snapshot database is created, it is produced on sparse files; in fact, it does not occupy any space (or occupies very little space) in the Operating System. When any data page is modified in the source database, that data page is copied to Snapshot database, making the sparse file size increases. When an unmodified data page is read in the Snapshot database, it actually reads the pages of the original database. In other words, the changes that happen in the source database are reflected in the Snapshot database. Let us see a simple example of Snapshot. In the following exercise, we will do a few operations. Please note that this script is for demo purposes only- there are a few considerations of CPU, DISK I/O and memory, which will be discussed in the future posts. Create Snapshot Delete Data from Original DB Restore Data from Snapshot First, let us create the first Snapshot database and observe the sparse file details. USE master GO -- Create Regular Database CREATE DATABASE RegularDB GO USE RegularDB GO -- Populate Regular Database with Sample Table CREATE TABLE FirstTable (ID INT, Value VARCHAR(10)) INSERT INTO FirstTable VALUES(1, 'First'); INSERT INTO FirstTable VALUES(2, 'Second'); INSERT INTO FirstTable VALUES(3, 'Third'); GO -- Create Snapshot Database CREATE DATABASE SnapshotDB ON (Name ='RegularDB', FileName='c:\SSDB.ss1') AS SNAPSHOT OF RegularDB; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO Now let us see the resultset for the same. Now let us do delete something from the Original DB and check the same details we checked before. -- Delete from Regular Database DELETE FROM RegularDB.dbo.FirstTable; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO When we check the details of sparse file created by Snapshot database, we will find some interesting details. The details of Regular DB remain the same. It clearly shows that when we delete data from Regular/Source DB, it copies the data pages to Snapshot database. This is the reason why the size of the snapshot DB is increased. Now let us take this small exercise to  the next level and restore our deleted data from Snapshot DB to Original Source DB. -- Restore Data from Snapshot Database USE master GO RESTORE DATABASE RegularDB FROM DATABASE_SNAPSHOT = 'SnapshotDB'; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO -- Clean up DROP DATABASE [SnapshotDB]; DROP DATABASE [RegularDB]; GO Now let us check the details of the select statement and we can see that we are successful able to restore the database from Snapshot Database. We can clearly see that this is a very useful feature in case you would encounter a good business that needs it. I would like to request the readers to suggest more details if they are using this feature in their business. Also, let me know if you think it can be potentially used to achieve any tasks. Complete Script of the afore- mentioned operation for easy reference is as follows: USE master GO -- Create Regular Database CREATE DATABASE RegularDB GO USE RegularDB GO -- Populate Regular Database with Sample Table CREATE TABLE FirstTable (ID INT, Value VARCHAR(10)) INSERT INTO FirstTable VALUES(1, 'First'); INSERT INTO FirstTable VALUES(2, 'Second'); INSERT INTO FirstTable VALUES(3, 'Third'); GO -- Create Snapshot Database CREATE DATABASE SnapshotDB ON (Name ='RegularDB', FileName='c:\SSDB.ss1') AS SNAPSHOT OF RegularDB; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO -- Delete from Regular Database DELETE FROM RegularDB.dbo.FirstTable; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO -- Restore Data from Snapshot Database USE master GO RESTORE DATABASE RegularDB FROM DATABASE_SNAPSHOT = 'SnapshotDB'; GO -- Select from Regular and Snapshot Database SELECT * FROM RegularDB.dbo.FirstTable; SELECT * FROM SnapshotDB.dbo.FirstTable; GO -- Clean up DROP DATABASE [SnapshotDB]; DROP DATABASE [RegularDB]; GO Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: SQL, SQL Authority, SQL Backup and Restore, SQL Data Storage, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • SQL SERVER – Enumerations in Relational Database – Best Practice

    - by pinaldave
    Marko Parkkola This article has been submitted by Marko Parkkola, Data systems designer at Saarionen Oy, Finland. Marko is excellent developer and always thinking at next level. You can read his earlier comment which created very interesting discussion here: SQL SERVER- IF EXISTS(Select null from table) vs IF EXISTS(Select 1 from table). I must express my special thanks to Marko for sending this best practice for Enumerations in Relational Database. He has really wrote excellent piece here and welcome comments here. Enumerations in Relational Database This is a subject which is very basic thing in relational databases but often not very well understood and sometimes badly implemented. There are of course many ways to do this but I concentrate only two cases, one which is “the right way” and one which is definitely wrong way. The concept Let’s say we have table Person in our database. Person has properties/fields like Firstname, Lastname, Birthday and so on. Then there’s a field that tells person’s marital status and let’s name it the same way; MaritalStatus. Now MaritalStatus is an enumeration. In C# I would definitely make it an enumeration with values likes Single, InRelationship, Married, Divorced. Now here comes the problem, SQL doesn’t have enumerations. The wrong way This is, in my opinion, absolutely the wrong way to do this. It has one upside though; you’ll see the enumeration’s description instantly when you do simple SELECT query and you don’t have to deal with mysterious values. There’s plenty of downsides too and one would be database fragmentation. Consider this (I’ve left all indexes and constraints out of the query on purpose). CREATE TABLE [dbo].[Person] ( [Firstname] NVARCHAR(100), [Lastname] NVARCHAR(100), [Birthday] datetime, [MaritalStatus] NVARCHAR(10) ) You have nvarchar(20) field in the table that tells the marital status. Obvious problem with this is that what if you create a new value which doesn’t fit into 20 characters? You’ll have to come and alter the table. There are other problems also but I’ll leave those for the reader to think about. The correct way Here’s how I’ve done this in many projects. This model still has one problem but it can be alleviated in the application layer or with CHECK constraints if you like. First I will create a namespace table which tells the name of the enumeration. I will add one row to it too. I’ll write all the indexes and constraints here too. CREATE TABLE [CodeNamespace] ( [Id] INT IDENTITY(1, 1), [Name] NVARCHAR(100) NOT NULL, CONSTRAINT [PK_CodeNamespace] PRIMARY KEY ([Id]), CONSTRAINT [IXQ_CodeNamespace_Name] UNIQUE NONCLUSTERED ([Name]) ) GO INSERT INTO [CodeNamespace] SELECT 'MaritalStatus' GO Then I create a table that holds the actual values and which reference to namespace table in order to group the values under different namespaces. I’ll add couple of rows here too. CREATE TABLE [CodeValue] ( [CodeNamespaceId] INT NOT NULL, [Value] INT NOT NULL, [Description] NVARCHAR(100) NOT NULL, [OrderBy] INT, CONSTRAINT [PK_CodeValue] PRIMARY KEY CLUSTERED ([CodeNamespaceId], [Value]), CONSTRAINT [FK_CodeValue_CodeNamespace] FOREIGN KEY ([CodeNamespaceId]) REFERENCES [CodeNamespace] ([Id]) ) GO -- 1 is the 'MaritalStatus' namespace INSERT INTO [CodeValue] SELECT 1, 1, 'Single', 1 INSERT INTO [CodeValue] SELECT 1, 2, 'In relationship', 2 INSERT INTO [CodeValue] SELECT 1, 3, 'Married', 3 INSERT INTO [CodeValue] SELECT 1, 4, 'Divorced', 4 GO Now there’s four columns in CodeValue table. CodeNamespaceId tells under which namespace values belongs to. Value tells the enumeration value which is used in Person table (I’ll show how this is done below). Description tells what the value means. You can use this, for example, column in UI’s combo box. OrderBy tells if the values needs to be ordered in some way when displayed in the UI. And here’s the Person table again now with correct columns. I’ll add one row here to show how enumerations are to be used. CREATE TABLE [dbo].[Person] ( [Firstname] NVARCHAR(100), [Lastname] NVARCHAR(100), [Birthday] datetime, [MaritalStatus] INT ) GO INSERT INTO [Person] SELECT 'Marko', 'Parkkola', '1977-03-04', 3 GO Now I said earlier that there is one problem with this. MaritalStatus column doesn’t have any database enforced relationship to the CodeValue table so you can enter any value you like into this field. I’ve solved this problem in the application layer by selecting all the values from the CodeValue table and put them into a combobox / dropdownlist (with Value field as value and Description as text) so the end user can’t enter any illegal values; and of course I’ll check the entered value in data access layer also. I said in the “The wrong way” section that there is one benefit to it. In fact, you can have the same benefit here by using a simple view, which I schema bound so you can even index it if you like. CREATE VIEW [dbo].[Person_v] WITH SCHEMABINDING AS SELECT p.[Firstname], p.[Lastname], p.[BirthDay], c.[Description] MaritalStatus FROM [dbo].[Person] p JOIN [dbo].[CodeValue] c ON p.[MaritalStatus] = c.[Value] JOIN [dbo].[CodeNamespace] n ON n.[Id] = c.[CodeNamespaceId] AND n.[Name] = 'MaritalStatus' GO -- Select from View SELECT * FROM [dbo].[Person_v] GO This is excellent write up byMarko Parkkola. Do you have this kind of design setup at your organization? Let us know your opinion. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Database, DBA, Readers Contribution, Software Development, SQL, SQL Authority, SQL Documentation, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • A way for an Upstart event to be sent whenever ecryptfs homedir mounted/unmounted?

    - by David Olivier
    I have an encrypted homedir (ecryptfs) and I'm wanting to run a private mysql daemon with the database files in my homedir. The daemon should be started whenever the homedir is mounted, and stopped before the homedir is unmounted. It seems I have to write an Upstart script, which doesn't seem too hard; the problem is triggering it. Is there already any Upstart event that is sent on these occasions? Or must I insert an "initctl emit" somewhere? Where? It seems the encrypted homedir is mounted whenever I either open my GUI session or ssh to my account. Is there a common place in these two processes where I might insert code? (I don't want to patch and compile any C code, just insert maybe a few lines somewere.) David

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  • C#/.NET Little Wonders: The ConcurrentDictionary

    - by James Michael Hare
    Once again we consider some of the lesser known classes and keywords of C#.  In this series of posts, we will discuss how the concurrent collections have been developed to help alleviate these multi-threading concerns.  Last week’s post began with a general introduction and discussed the ConcurrentStack<T> and ConcurrentQueue<T>.  Today's post discusses the ConcurrentDictionary<T> (originally I had intended to discuss ConcurrentBag this week as well, but ConcurrentDictionary had enough information to create a very full post on its own!).  Finally next week, we shall close with a discussion of the ConcurrentBag<T> and BlockingCollection<T>. For more of the "Little Wonders" posts, see the index here. Recap As you'll recall from the previous post, the original collections were object-based containers that accomplished synchronization through a Synchronized member.  While these were convenient because you didn't have to worry about writing your own synchronization logic, they were a bit too finely grained and if you needed to perform multiple operations under one lock, the automatic synchronization didn't buy much. With the advent of .NET 2.0, the original collections were succeeded by the generic collections which are fully type-safe, but eschew automatic synchronization.  This cuts both ways in that you have a lot more control as a developer over when and how fine-grained you want to synchronize, but on the other hand if you just want simple synchronization it creates more work. With .NET 4.0, we get the best of both worlds in generic collections.  A new breed of collections was born called the concurrent collections in the System.Collections.Concurrent namespace.  These amazing collections are fine-tuned to have best overall performance for situations requiring concurrent access.  They are not meant to replace the generic collections, but to simply be an alternative to creating your own locking mechanisms. Among those concurrent collections were the ConcurrentStack<T> and ConcurrentQueue<T> which provide classic LIFO and FIFO collections with a concurrent twist.  As we saw, some of the traditional methods that required calls to be made in a certain order (like checking for not IsEmpty before calling Pop()) were replaced in favor of an umbrella operation that combined both under one lock (like TryPop()). Now, let's take a look at the next in our series of concurrent collections!For some excellent information on the performance of the concurrent collections and how they perform compared to a traditional brute-force locking strategy, see this wonderful whitepaper by the Microsoft Parallel Computing Platform team here. ConcurrentDictionary – the fully thread-safe dictionary The ConcurrentDictionary<TKey,TValue> is the thread-safe counterpart to the generic Dictionary<TKey, TValue> collection.  Obviously, both are designed for quick – O(1) – lookups of data based on a key.  If you think of algorithms where you need lightning fast lookups of data and don’t care whether the data is maintained in any particular ordering or not, the unsorted dictionaries are generally the best way to go. Note: as a side note, there are sorted implementations of IDictionary, namely SortedDictionary and SortedList which are stored as an ordered tree and a ordered list respectively.  While these are not as fast as the non-sorted dictionaries – they are O(log2 n) – they are a great combination of both speed and ordering -- and still greatly outperform a linear search. Now, once again keep in mind that if all you need to do is load a collection once and then allow multi-threaded reading you do not need any locking.  Examples of this tend to be situations where you load a lookup or translation table once at program start, then keep it in memory for read-only reference.  In such cases locking is completely non-productive. However, most of the time when we need a concurrent dictionary we are interleaving both reads and updates.  This is where the ConcurrentDictionary really shines!  It achieves its thread-safety with no common lock to improve efficiency.  It actually uses a series of locks to provide concurrent updates, and has lockless reads!  This means that the ConcurrentDictionary gets even more efficient the higher the ratio of reads-to-writes you have. ConcurrentDictionary and Dictionary differences For the most part, the ConcurrentDictionary<TKey,TValue> behaves like it’s Dictionary<TKey,TValue> counterpart with a few differences.  Some notable examples of which are: Add() does not exist in the concurrent dictionary. This means you must use TryAdd(), AddOrUpdate(), or GetOrAdd().  It also means that you can’t use a collection initializer with the concurrent dictionary. TryAdd() replaced Add() to attempt atomic, safe adds. Because Add() only succeeds if the item doesn’t already exist, we need an atomic operation to check if the item exists, and if not add it while still under an atomic lock. TryUpdate() was added to attempt atomic, safe updates. If we want to update an item, we must make sure it exists first and that the original value is what we expected it to be.  If all these are true, we can update the item under one atomic step. TryRemove() was added to attempt atomic, safe removes. To safely attempt to remove a value we need to see if the key exists first, this checks for existence and removes under an atomic lock. AddOrUpdate() was added to attempt an thread-safe “upsert”. There are many times where you want to insert into a dictionary if the key doesn’t exist, or update the value if it does.  This allows you to make a thread-safe add-or-update. GetOrAdd() was added to attempt an thread-safe query/insert. Sometimes, you want to query for whether an item exists in the cache, and if it doesn’t insert a starting value for it.  This allows you to get the value if it exists and insert if not. Count, Keys, Values properties take a snapshot of the dictionary. Accessing these properties may interfere with add and update performance and should be used with caution. ToArray() returns a static snapshot of the dictionary. That is, the dictionary is locked, and then copied to an array as a O(n) operation.  GetEnumerator() is thread-safe and efficient, but allows dirty reads. Because reads require no locking, you can safely iterate over the contents of the dictionary.  The only downside is that, depending on timing, you may get dirty reads. Dirty reads during iteration The last point on GetEnumerator() bears some explanation.  Picture a scenario in which you call GetEnumerator() (or iterate using a foreach, etc.) and then, during that iteration the dictionary gets updated.  This may not sound like a big deal, but it can lead to inconsistent results if used incorrectly.  The problem is that items you already iterated over that are updated a split second after don’t show the update, but items that you iterate over that were updated a split second before do show the update.  Thus you may get a combination of items that are “stale” because you iterated before the update, and “fresh” because they were updated after GetEnumerator() but before the iteration reached them. Let’s illustrate with an example, let’s say you load up a concurrent dictionary like this: 1: // load up a dictionary. 2: var dictionary = new ConcurrentDictionary<string, int>(); 3:  4: dictionary["A"] = 1; 5: dictionary["B"] = 2; 6: dictionary["C"] = 3; 7: dictionary["D"] = 4; 8: dictionary["E"] = 5; 9: dictionary["F"] = 6; Then you have one task (using the wonderful TPL!) to iterate using dirty reads: 1: // attempt iteration in a separate thread 2: var iterationTask = new Task(() => 3: { 4: // iterates using a dirty read 5: foreach (var pair in dictionary) 6: { 7: Console.WriteLine(pair.Key + ":" + pair.Value); 8: } 9: }); And one task to attempt updates in a separate thread (probably): 1: // attempt updates in a separate thread 2: var updateTask = new Task(() => 3: { 4: // iterates, and updates the value by one 5: foreach (var pair in dictionary) 6: { 7: dictionary[pair.Key] = pair.Value + 1; 8: } 9: }); Now that we’ve done this, we can fire up both tasks and wait for them to complete: 1: // start both tasks 2: updateTask.Start(); 3: iterationTask.Start(); 4:  5: // wait for both to complete. 6: Task.WaitAll(updateTask, iterationTask); Now, if I you didn’t know about the dirty reads, you may have expected to see the iteration before the updates (such as A:1, B:2, C:3, D:4, E:5, F:6).  However, because the reads are dirty, we will quite possibly get a combination of some updated, some original.  My own run netted this result: 1: F:6 2: E:6 3: D:5 4: C:4 5: B:3 6: A:2 Note that, of course, iteration is not in order because ConcurrentDictionary, like Dictionary, is unordered.  Also note that both E and F show the value 6.  This is because the output task reached F before the update, but the updates for the rest of the items occurred before their output (probably because console output is very slow, comparatively). If we want to always guarantee that we will get a consistent snapshot to iterate over (that is, at the point we ask for it we see precisely what is in the dictionary and no subsequent updates during iteration), we should iterate over a call to ToArray() instead: 1: // attempt iteration in a separate thread 2: var iterationTask = new Task(() => 3: { 4: // iterates using a dirty read 5: foreach (var pair in dictionary.ToArray()) 6: { 7: Console.WriteLine(pair.Key + ":" + pair.Value); 8: } 9: }); The atomic Try…() methods As you can imagine TryAdd() and TryRemove() have few surprises.  Both first check the existence of the item to determine if it can be added or removed based on whether or not the key currently exists in the dictionary: 1: // try add attempts an add and returns false if it already exists 2: if (dictionary.TryAdd("G", 7)) 3: Console.WriteLine("G did not exist, now inserted with 7"); 4: else 5: Console.WriteLine("G already existed, insert failed."); TryRemove() also has the virtue of returning the value portion of the removed entry matching the given key: 1: // attempt to remove the value, if it exists it is removed and the original is returned 2: int removedValue; 3: if (dictionary.TryRemove("C", out removedValue)) 4: Console.WriteLine("Removed C and its value was " + removedValue); 5: else 6: Console.WriteLine("C did not exist, remove failed."); Now TryUpdate() is an interesting creature.  You might think from it’s name that TryUpdate() first checks for an item’s existence, and then updates if the item exists, otherwise it returns false.  Well, note quite... It turns out when you call TryUpdate() on a concurrent dictionary, you pass it not only the new value you want it to have, but also the value you expected it to have before the update.  If the item exists in the dictionary, and it has the value you expected, it will update it to the new value atomically and return true.  If the item is not in the dictionary or does not have the value you expected, it is not modified and false is returned. 1: // attempt to update the value, if it exists and if it has the expected original value 2: if (dictionary.TryUpdate("G", 42, 7)) 3: Console.WriteLine("G existed and was 7, now it's 42."); 4: else 5: Console.WriteLine("G either didn't exist, or wasn't 7."); The composite Add methods The ConcurrentDictionary also has composite add methods that can be used to perform updates and gets, with an add if the item is not existing at the time of the update or get. The first of these, AddOrUpdate(), allows you to add a new item to the dictionary if it doesn’t exist, or update the existing item if it does.  For example, let’s say you are creating a dictionary of counts of stock ticker symbols you’ve subscribed to from a market data feed: 1: public sealed class SubscriptionManager 2: { 3: private readonly ConcurrentDictionary<string, int> _subscriptions = new ConcurrentDictionary<string, int>(); 4:  5: // adds a new subscription, or increments the count of the existing one. 6: public void AddSubscription(string tickerKey) 7: { 8: // add a new subscription with count of 1, or update existing count by 1 if exists 9: var resultCount = _subscriptions.AddOrUpdate(tickerKey, 1, (symbol, count) => count + 1); 10:  11: // now check the result to see if we just incremented the count, or inserted first count 12: if (resultCount == 1) 13: { 14: // subscribe to symbol... 15: } 16: } 17: } Notice the update value factory Func delegate.  If the key does not exist in the dictionary, the add value is used (in this case 1 representing the first subscription for this symbol), but if the key already exists, it passes the key and current value to the update delegate which computes the new value to be stored in the dictionary.  The return result of this operation is the value used (in our case: 1 if added, existing value + 1 if updated). Likewise, the GetOrAdd() allows you to attempt to retrieve a value from the dictionary, and if the value does not currently exist in the dictionary it will insert a value.  This can be handy in cases where perhaps you wish to cache data, and thus you would query the cache to see if the item exists, and if it doesn’t you would put the item into the cache for the first time: 1: public sealed class PriceCache 2: { 3: private readonly ConcurrentDictionary<string, double> _cache = new ConcurrentDictionary<string, double>(); 4:  5: // adds a new subscription, or increments the count of the existing one. 6: public double QueryPrice(string tickerKey) 7: { 8: // check for the price in the cache, if it doesn't exist it will call the delegate to create value. 9: return _cache.GetOrAdd(tickerKey, symbol => GetCurrentPrice(symbol)); 10: } 11:  12: private double GetCurrentPrice(string tickerKey) 13: { 14: // do code to calculate actual true price. 15: } 16: } There are other variations of these two methods which vary whether a value is provided or a factory delegate, but otherwise they work much the same. Oddities with the composite Add methods The AddOrUpdate() and GetOrAdd() methods are totally thread-safe, on this you may rely, but they are not atomic.  It is important to note that the methods that use delegates execute those delegates outside of the lock.  This was done intentionally so that a user delegate (of which the ConcurrentDictionary has no control of course) does not take too long and lock out other threads. This is not necessarily an issue, per se, but it is something you must consider in your design.  The main thing to consider is that your delegate may get called to generate an item, but that item may not be the one returned!  Consider this scenario: A calls GetOrAdd and sees that the key does not currently exist, so it calls the delegate.  Now thread B also calls GetOrAdd and also sees that the key does not currently exist, and for whatever reason in this race condition it’s delegate completes first and it adds its new value to the dictionary.  Now A is done and goes to get the lock, and now sees that the item now exists.  In this case even though it called the delegate to create the item, it will pitch it because an item arrived between the time it attempted to create one and it attempted to add it. Let’s illustrate, assume this totally contrived example program which has a dictionary of char to int.  And in this dictionary we want to store a char and it’s ordinal (that is, A = 1, B = 2, etc).  So for our value generator, we will simply increment the previous value in a thread-safe way (perhaps using Interlocked): 1: public static class Program 2: { 3: private static int _nextNumber = 0; 4:  5: // the holder of the char to ordinal 6: private static ConcurrentDictionary<char, int> _dictionary 7: = new ConcurrentDictionary<char, int>(); 8:  9: // get the next id value 10: public static int NextId 11: { 12: get { return Interlocked.Increment(ref _nextNumber); } 13: } Then, we add a method that will perform our insert: 1: public static void Inserter() 2: { 3: for (int i = 0; i < 26; i++) 4: { 5: _dictionary.GetOrAdd((char)('A' + i), key => NextId); 6: } 7: } Finally, we run our test by starting two tasks to do this work and get the results… 1: public static void Main() 2: { 3: // 3 tasks attempting to get/insert 4: var tasks = new List<Task> 5: { 6: new Task(Inserter), 7: new Task(Inserter) 8: }; 9:  10: tasks.ForEach(t => t.Start()); 11: Task.WaitAll(tasks.ToArray()); 12:  13: foreach (var pair in _dictionary.OrderBy(p => p.Key)) 14: { 15: Console.WriteLine(pair.Key + ":" + pair.Value); 16: } 17: } If you run this with only one task, you get the expected A:1, B:2, ..., Z:26.  But running this in parallel you will get something a bit more complex.  My run netted these results: 1: A:1 2: B:3 3: C:4 4: D:5 5: E:6 6: F:7 7: G:8 8: H:9 9: I:10 10: J:11 11: K:12 12: L:13 13: M:14 14: N:15 15: O:16 16: P:17 17: Q:18 18: R:19 19: S:20 20: T:21 21: U:22 22: V:23 23: W:24 24: X:25 25: Y:26 26: Z:27 Notice that B is 3?  This is most likely because both threads attempted to call GetOrAdd() at roughly the same time and both saw that B did not exist, thus they both called the generator and one thread got back 2 and the other got back 3.  However, only one of those threads can get the lock at a time for the actual insert, and thus the one that generated the 3 won and the 3 was inserted and the 2 got discarded.  This is why on these methods your factory delegates should be careful not to have any logic that would be unsafe if the value they generate will be pitched in favor of another item generated at roughly the same time.  As such, it is probably a good idea to keep those generators as stateless as possible. Summary The ConcurrentDictionary is a very efficient and thread-safe version of the Dictionary generic collection.  It has all the benefits of type-safety that it’s generic collection counterpart does, and in addition is extremely efficient especially when there are more reads than writes concurrently. Tweet Technorati Tags: C#, .NET, Concurrent Collections, Collections, Little Wonders, Black Rabbit Coder,James Michael Hare

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  • Oracle Sequences

    - by jkrebsbach
    Reminder to myself - SQL Server has nice index columns directly tied to their tables. Oracle has sequences that are islands to themselves. select seq_name.currval from dual; select seq_name.nextval from dual; currval - return current number at top of sequence nextval - increment sequence by 1, return new number   therefore - to create functionality in oracle similar to an index column - OPTION A) - Create insert trigger: CREATE OR REPLACE TRIGGER dept_bir BEFORE INSERT ON departments FOR EACH ROW WHEN (new.id IS NULL) BEGIN SELECT dept_seq.NEXTVAL INTO :new.id FROM dual; END; This will handle creating a unique identity, but will not necessarily inform process flow of identity without additional logic. OPTION B) - Select indentity into temp variable, insert whole item into tab **** When attemptint to query currval, the below error was being thrown - SELECT seq_name.currval from dual; ERROR : TABLE OR VIEW DOES NOT EXIST *** Although Oracle sys tables may have access to the sequences, that isn't to say the Oracle user may have access to those sequences - verify permissions when the system can't see object that are being reported in the object explorer.

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  • A little SQL tip for C# developers

    - by MikeParks
    The other day at work I came across a handy little block of SQL code from Jeremiah Clark's blog. It's pretty simple logic but through the mind of a C# developer making some quick DB updates, seems to me that it's more likely to end up writing out the code in Solution 1 instead of Solution 2 below to solve the problem. Basically, I needed to check and see if a specific record existed in Table1. If it does exist, then update that record, otherwise insert a new record into Table1. Solution 1: IF EXISTS (SELECT * FROM Table1 WHERE Column1='SomeValue')     UPDATE Table1 SET (...) WHERE Column1='SomeValue' ELSE     INSERT INTO Table1 VALUES (...) Solution 2: UPDATE Table1 SET (...) WHERE Column1='SomeValue' IF @@ROWCOUNT=0     INSERT INTO Table1 VALUES (...)         As Jeremiah explains, they both accomplish the same thing but from a performance standpoint, Solution 2 is the better way to go (saved table/index scan). Just wanted to throw this small tip out there. Thanks! - Mike

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  • Sharing Bandwidth and Prioritizing Realtime Traffic via HTB, Which Scenario Works Better?

    - by Mecki
    I would like to add some kind of traffic management to our Internet line. After reading a lot of documentation, I think HFSC is too complicated for me (I don't understand all the curves stuff, I'm afraid I will never get it right), CBQ is not recommend, and basically HTB is the way to go for most people. Our internal network has three "segments" and I'd like to share bandwidth more or less equally between those (at least in the beginning). Further I must prioritize traffic according to at least three kinds of traffic (realtime traffic, standard traffic, and bulk traffic). The bandwidth sharing is not as important as the fact that realtime traffic should always be treated as premium traffic whenever possible, but of course no other traffic class may starve either. The question is, what makes more sense and also guarantees better realtime throughput: Creating one class per segment, each having the same rate (priority doesn't matter for classes that are no leaves according to HTB developer) and each of these classes has three sub-classes (leaves) for the 3 priority levels (with different priorities and different rates). Having one class per priority level on top, each having a different rate (again priority won't matter) and each having 3 sub-classes, one per segment, whereas all 3 in the realtime class have highest prio, lowest prio in the bulk class, and so on. I'll try to make this more clear with the following ASCII art image: Case 1: root --+--> Segment A | +--> High Prio | +--> Normal Prio | +--> Low Prio | +--> Segment B | +--> High Prio | +--> Normal Prio | +--> Low Prio | +--> Segment C +--> High Prio +--> Normal Prio +--> Low Prio Case 2: root --+--> High Prio | +--> Segment A | +--> Segment B | +--> Segment C | +--> Normal Prio | +--> Segment A | +--> Segment B | +--> Segment C | +--> Low Prio +--> Segment A +--> Segment B +--> Segment C Case 1 Seems like the way most people would do it, but unless I don't read the HTB implementation details correctly, Case 2 may offer better prioritizing. The HTB manual says, that if a class has hit its rate, it may borrow from its parent and when borrowing, classes with higher priority always get bandwidth offered first. However, it also says that classes having bandwidth available on a lower tree-level are always preferred to those on a higher tree level, regardless of priority. Let's assume the following situation: Segment C is not sending any traffic. Segment A is only sending realtime traffic, as fast as it can (enough to saturate the link alone) and Segment B is only sending bulk traffic, as fast as it can (again, enough to saturate the full link alone). What will happen? Case 1: Segment A-High Prio and Segment B-Low Prio both have packets to send, since A-High Prio has the higher priority, it will always be scheduled first, till it hits its rate. Now it tries to borrow from Segment A, but since Segment A is on a higher level and Segment B-Low Prio has not yet hit its rate, this class is now served first, till it also hits the rate and wants to borrow from Segment B. Once both have hit their rates, both are on the same level again and now Segment A-High Prio is going to win again, until it hits the rate of Segment A. Now it tries to borrow from root (which has plenty of traffic spare, as Segment C is not using any of its guaranteed traffic), but again, it has to wait for Segment B-Low Prio to also reach the root level. Once that happens, priority is taken into account again and this time Segment A-High Prio will get all the bandwidth left over from Segment C. Case 2: High Prio-Segment A and Low Prio-Segment B both have packets to send, again High Prio-Segment A is going to win as it has the higher priority. Once it hits its rate, it tries to borrow from High Prio, which has bandwidth spare, but being on a higher level, it has to wait for Low Prio-Segment B again to also hit its rate. Once both have hit their rate and both have to borrow, High Prio-Segment A will win again until it hits the rate of the High Prio class. Once that happens, it tries to borrow from root, which has again plenty of bandwidth left (all bandwidth of Normal Prio is unused at the moment), but it has to wait again until Low Prio-Segment B hits the rate limit of the Low Prio class and also tries to borrow from root. Finally both classes try to borrow from root, priority is taken into account, and High Prio-Segment A gets all bandwidth root has left over. Both cases seem sub-optimal, as either way realtime traffic sometimes has to wait for bulk traffic, even though there is plenty of bandwidth left it could borrow. However, in case 2 it seems like the realtime traffic has to wait less than in case 1, since it only has to wait till the bulk traffic rate is hit, which is most likely less than the rate of a whole segment (and in case 1 that is the rate it has to wait for). Or am I totally wrong here? I thought about even simpler setups, using a priority qdisc. But priority queues have the big problem that they cause starvation if they are not somehow limited. Starvation is not acceptable. Of course one can put a TBF (Token Bucket Filter) into each priority class to limit the rate and thus avoid starvation, but when doing so, a single priority class cannot saturate the link on its own any longer, even if all other priority classes are empty, the TBF will prevent that from happening. And this is also sub-optimal, since why wouldn't a class get 100% of the line's bandwidth if no other class needs any of it at the moment? Any comments or ideas regarding this setup? It seems so hard to do using standard tc qdiscs. As a programmer it was such an easy task if I could simply write my own scheduler (which I'm not allowed to do).

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  • SQL SERVER – Securing TRUNCATE Permissions in SQL Server

    - by pinaldave
    Download the Script of this article from here. On December 11, 2010, Vinod Kumar, a Databases & BI technology evangelist from Microsoft Corporation, graced Ahmedabad by spending some time with the Community during the Community Tech Days (CTD) event. As he was running through a few demos, Vinod asked the audience one of the most fundamental and common interview questions – “What is the difference between a DELETE and TRUNCATE?“ Ahmedabad SQL Server User Group Expert Nakul Vachhrajani has come up with excellent solutions of the same. I must congratulate Nakul for this excellent solution and as a encouragement to User Group member, I am publishing the same article over here. Nakul Vachhrajani is a Software Specialist and systems development professional with Patni Computer Systems Limited. He has functional experience spanning legacy code deprecation, system design, documentation, development, implementation, testing, maintenance and support of complex systems, providing business intelligence solutions, database administration, performance tuning, optimization, product management, release engineering, process definition and implementation. He has comprehensive grasp on Database Administration, Development and Implementation with MS SQL Server and C, C++, Visual C++/C#. He has about 6 years of total experience in information technology. Nakul is an member of the Ahmedabad and Gandhinagar SQL Server User Groups, and actively contributes to the community by actively participating in multiple forums and websites like SQLAuthority.com, BeyondRelational.com, SQLServerCentral.com and many others. Please note: The opinions expressed herein are Nakul own personal opinions and do not represent his employer’s view in anyway. All data from everywhere here on Earth go through a series of  four distinct operations, identified by the words: CREATE, READ, UPDATE and DELETE, or simply, CRUD. Putting in Microsoft SQL Server terms, is the process goes like this: INSERT, SELECT, UPDATE and DELETE/TRUNCATE. Quite a few interesting responses were received and evaluated live during the session. To summarize them, the most important similarity that came out was that both DELETE and TRUNCATE participate in transactions. The major differences (not all) that came out of the exercise were: DELETE: DELETE supports a WHERE clause DELETE removes rows from a table, row-by-row Because DELETE moves row-by-row, it acquires a row-level lock Depending upon the recovery model of the database, DELETE is a fully-logged operation. Because DELETE moves row-by-row, it can fire off triggers TRUNCATE: TRUNCATE does not support a WHERE clause TRUNCATE works by directly removing the individual data pages of a table TRUNCATE directly occupies a table-level lock. (Because a lock is acquired, and because TRUNCATE can also participate in a transaction, it has to be a logged operation) TRUNCATE is, therefore, a minimally-logged operation; again, this depends upon the recovery model of the database Triggers are not fired when TRUNCATE is used (because individual row deletions are not logged) Finally, Vinod popped the big homework question that must be critically analyzed: “We know that we can restrict a DELETE operation to a particular user, but how can we restrict the TRUNCATE operation to a particular user?” After returning home and having a nice cup of coffee, I noticed that my gray cells immediately started to work. Below was the result of my research. As what is always said, the devil is in the details. Upon looking at the Permissions section for the TRUNCATE statement in Books On Line, the following jumps right out: “The minimum permission required is ALTER on table_name. TRUNCATE TABLE permissions default to the table owner, members of the sysadmin fixed server role, and the db_owner and db_ddladmin fixed database roles, and are not transferable. However, you can incorporate the TRUNCATE TABLE statement within a module, such as a stored procedure, and grant appropriate permissions to the module using the EXECUTE AS clause.“ Now, what does this mean? Unlike DELETE, one cannot directly assign permissions to a user/set of users allowing or revoking TRUNCATE rights. However, there is a way to circumvent this. It is important to recall that in Microsoft SQL Server, database engine security surrounds the concept of a “securable”, which is any object like a table, stored procedure, trigger, etc. Rights are assigned to a principal on a securable. Refer to the image below (taken from the SQL Server Books On Line). urable”, which is any object like a table, stored procedure, trigger, etc. Rights are assigned to a principal on a securable. Refer to the image below (taken from the SQL Server Books On Line). SETTING UP THE ENVIRONMENT – (01A_Truncate Table Permissions.sql) Script Provided at the end of the article. By the end of this demo, one will be able to do all the CRUD operations, except the TRUNCATE, and the other will only be able to execute the TRUNCATE. All you will need for this test is any edition of SQL Server 2008. (With minor changes, these scripts can be made to work with SQL 2005.) We begin by creating the following: 1.       A test database 2.        Two database roles: associated logins and users 3.       Switch over to the test database and create a test table. Then, add some data into it. I am using row constructors, which is new to SQL 2008. Creating the modules that will be used to enforce permissions 1.       We have already created one of the modules that we will be assigning permissions to. That module is the table: TruncatePermissionsTest 2.       We will now create two stored procedures; one is for the DELETE operation and the other for the TRUNCATE operation. Please note that for all practical purposes, the end result is the same – all data from the table TruncatePermissionsTest is removed Assigning the permissions Now comes the most important part of the demonstration – assigning permissions. A permissions matrix can be worked out as under: To apply the security rights, we use the GRANT and DENY clauses, as under: That’s it! We are now ready for our big test! THE TEST (01B_Truncate Table Test Queries.sql) Script Provided at the end of the article. I will now need two separate SSMS connections, one with the login AllowedTruncate and the other with the login RestrictedTruncate. Running the test is simple; all that’s required is to run through the script – 01B_Truncate Table Test Queries.sql. What I will demonstrate here via screen-shots is the behavior of SQL Server when logged in as the AllowedTruncate user. There are a few other combinations than what are highlighted here. I will leave the reader the right to explore the behavior of the RestrictedTruncate user and these additional scenarios, as a form of self-study. 1.       Testing SELECT permissions 2.       Testing TRUNCATE permissions (Remember, “deny by default”?) 3.       Trying to circumvent security by trying to TRUNCATE the table using the stored procedure Hence, we have now proved that a user can indeed be assigned permissions to specifically assign TRUNCATE permissions. I also hope that the above has sparked curiosity towards putting some security around the probably “destructive” operations of DELETE and TRUNCATE. I would like to wish each and every one of the readers a very happy and secure time with Microsoft SQL Server. (Please find the scripts – 01A_Truncate Table Permissions.sql and 01B_Truncate Table Test Queries.sql that have been used in this demonstration. Please note that these scripts contain purely test-level code only. These scripts must not, at any cost, be used in the reader’s production environments). 01A_Truncate Table Permissions.sql /* ***************************************************************************************************************** Developed By          : Nakul Vachhrajani Functionality         : This demo is focused on how to allow only TRUNCATE permissions to a particular user How to Use            : 1. Run through, step-by-step through the sequence till Step 08 to create a test database 2. Switch over to the "Truncate Table Test Queries.sql" and execute it step-by-step in two different SSMS windows, one where you have logged in as 'RestrictedTruncate', and the other as 'AllowedTruncate' 3. Come back to "Truncate Table Permissions.sql" 4. Execute Step 10 to cleanup! Modifications         : December 13, 2010 - NAV - Updated to add a security matrix and improve code readability when applying security December 12, 2010 - NAV - Created ***************************************************************************************************************** */ -- Step 01: Create a new test database CREATE DATABASE TruncateTestDB GO USE TruncateTestDB GO -- Step 02: Add roles and users to demonstrate the security of the Truncate operation -- 2a. Create the new roles CREATE ROLE AllowedTruncateRole; GO CREATE ROLE RestrictedTruncateRole; GO -- 2b. Create new logins CREATE LOGIN AllowedTruncate WITH PASSWORD = 'truncate@2010', CHECK_POLICY = ON GO CREATE LOGIN RestrictedTruncate WITH PASSWORD = 'truncate@2010', CHECK_POLICY = ON GO -- 2c. Create new Users using the roles and logins created aboave CREATE USER TruncateUser FOR LOGIN AllowedTruncate WITH DEFAULT_SCHEMA = dbo GO CREATE USER NoTruncateUser FOR LOGIN RestrictedTruncate WITH DEFAULT_SCHEMA = dbo GO -- 2d. Add the newly created login to the newly created role sp_addrolemember 'AllowedTruncateRole','TruncateUser' GO sp_addrolemember 'RestrictedTruncateRole','NoTruncateUser' GO -- Step 03: Change over to the test database USE TruncateTestDB GO -- Step 04: Create a test table within the test databse CREATE TABLE TruncatePermissionsTest (Id INT IDENTITY(1,1), Name NVARCHAR(50)) GO -- Step 05: Populate the required data INSERT INTO TruncatePermissionsTest VALUES (N'Delhi'), (N'Mumbai'), (N'Ahmedabad') GO -- Step 06: Encapsulate the DELETE within another module CREATE PROCEDURE proc_DeleteMyTable WITH EXECUTE AS SELF AS DELETE FROM TruncateTestDB..TruncatePermissionsTest GO -- Step 07: Encapsulate the TRUNCATE within another module CREATE PROCEDURE proc_TruncateMyTable WITH EXECUTE AS SELF AS TRUNCATE TABLE TruncateTestDB..TruncatePermissionsTest GO -- Step 08: Apply Security /* *****************************SECURITY MATRIX*************************************** =================================================================================== Object                   | Permissions |                 Login |             | AllowedTruncate   |   RestrictedTruncate |             |User:NoTruncateUser|   User:TruncateUser =================================================================================== TruncatePermissionsTest  | SELECT,     |      GRANT        |      (Default) | INSERT,     |                   | | UPDATE,     |                   | | DELETE      |                   | -------------------------+-------------+-------------------+----------------------- TruncatePermissionsTest  | ALTER       |      DENY         |      (Default) -------------------------+-------------+----*/----------------+----------------------- proc_DeleteMyTable | EXECUTE | GRANT | DENY -------------------------+-------------+-------------------+----------------------- proc_TruncateMyTable | EXECUTE | DENY | GRANT -------------------------+-------------+-------------------+----------------------- *****************************SECURITY MATRIX*************************************** */ /* Table: TruncatePermissionsTest*/ GRANT SELECT, INSERT, UPDATE, DELETE ON TruncateTestDB..TruncatePermissionsTest TO NoTruncateUser GO DENY ALTER ON TruncateTestDB..TruncatePermissionsTest TO NoTruncateUser GO /* Procedure: proc_DeleteMyTable*/ GRANT EXECUTE ON TruncateTestDB..proc_DeleteMyTable TO NoTruncateUser GO DENY EXECUTE ON TruncateTestDB..proc_DeleteMyTable TO TruncateUser GO /* Procedure: proc_TruncateMyTable*/ DENY EXECUTE ON TruncateTestDB..proc_TruncateMyTable TO NoTruncateUser GO GRANT EXECUTE ON TruncateTestDB..proc_TruncateMyTable TO TruncateUser GO -- Step 09: Test --Switch over to the "Truncate Table Test Queries.sql" and execute it step-by-step in two different SSMS windows: --    1. one where you have logged in as 'RestrictedTruncate', and --    2. the other as 'AllowedTruncate' -- Step 10: Cleanup sp_droprolemember 'AllowedTruncateRole','TruncateUser' GO sp_droprolemember 'RestrictedTruncateRole','NoTruncateUser' GO DROP USER TruncateUser GO DROP USER NoTruncateUser GO DROP LOGIN AllowedTruncate GO DROP LOGIN RestrictedTruncate GO DROP ROLE AllowedTruncateRole GO DROP ROLE RestrictedTruncateRole GO USE MASTER GO DROP DATABASE TruncateTestDB GO 01B_Truncate Table Test Queries.sql /* ***************************************************************************************************************** Developed By          : Nakul Vachhrajani Functionality         : This demo is focused on how to allow only TRUNCATE permissions to a particular user How to Use            : 1. Switch over to this from "Truncate Table Permissions.sql", Step #09 2. Execute this step-by-step in two different SSMS windows a. One where you have logged in as 'RestrictedTruncate', and b. The other as 'AllowedTruncate' 3. Return back to "Truncate Table Permissions.sql" 4. Execute Step 10 to cleanup! Modifications         : December 12, 2010 - NAV - Created ***************************************************************************************************************** */ -- Step 09A: Switch to the test database USE TruncateTestDB GO -- Step 09B: Ensure that we have valid data SELECT * FROM TruncatePermissionsTest GO -- (Expected: Following error will occur if logged in as "AllowedTruncate") -- Msg 229, Level 14, State 5, Line 1 -- The SELECT permission was denied on the object 'TruncatePermissionsTest', database 'TruncateTestDB', schema 'dbo'. --Step 09C: Attempt to Truncate Data from the table without using the stored procedure TRUNCATE TABLE TruncatePermissionsTest GO -- (Expected: Following error will occur) --  Msg 1088, Level 16, State 7, Line 2 --  Cannot find the object "TruncatePermissionsTest" because it does not exist or you do not have permissions. -- Step 09D:Regenerate Test Data INSERT INTO TruncatePermissionsTest VALUES (N'London'), (N'Paris'), (N'Berlin') GO -- (Expected: Following error will occur if logged in as "AllowedTruncate") -- Msg 229, Level 14, State 5, Line 1 -- The INSERT permission was denied on the object 'TruncatePermissionsTest', database 'TruncateTestDB', schema 'dbo'. --Step 09E: Attempt to Truncate Data from the table using the stored procedure EXEC proc_TruncateMyTable GO -- (Expected: Will execute successfully with 'AllowedTruncate' user, will error out as under with 'RestrictedTruncate') -- Msg 229, Level 14, State 5, Procedure proc_TruncateMyTable, Line 1 -- The EXECUTE permission was denied on the object 'proc_TruncateMyTable', database 'TruncateTestDB', schema 'dbo'. -- Step 09F:Regenerate Test Data INSERT INTO TruncatePermissionsTest VALUES (N'Madrid'), (N'Rome'), (N'Athens') GO --Step 09G: Attempt to Delete Data from the table without using the stored procedure DELETE FROM TruncatePermissionsTest GO -- (Expected: Following error will occur if logged in as "AllowedTruncate") -- Msg 229, Level 14, State 5, Line 2 -- The DELETE permission was denied on the object 'TruncatePermissionsTest', database 'TruncateTestDB', schema 'dbo'. -- Step 09H:Regenerate Test Data INSERT INTO TruncatePermissionsTest VALUES (N'Spain'), (N'Italy'), (N'Greece') GO --Step 09I: Attempt to Delete Data from the table using the stored procedure EXEC proc_DeleteMyTable GO -- (Expected: Following error will occur if logged in as "AllowedTruncate") -- Msg 229, Level 14, State 5, Procedure proc_DeleteMyTable, Line 1 -- The EXECUTE permission was denied on the object 'proc_DeleteMyTable', database 'TruncateTestDB', schema 'dbo'. --Step 09J: Close this SSMS window and return back to "Truncate Table Permissions.sql" Thank you Nakul to take up the challenge and prove that Ahmedabad and Gandhinagar SQL Server User Group has talent to solve difficult problems. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Pinal Dave, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Security, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Coherence - How to develop a custom push replication publisher

    - by cosmin.tudor(at)oracle.com
    CoherencePushReplicationDB.zipIn the example bellow I'm describing a way of developing a custom push replication publisher that publishes data to a database via JDBC. This example can be easily changed to publish data to other receivers (JMS,...) by performing changes to step 2 and small changes to step 3, steps that are presented bellow. I've used Eclipse as the development tool. To develop a custom push replication publishers we will need to go through 6 steps: Step 1: Create a custom publisher scheme class Step 2: Create a custom publisher class that should define what the publisher is doing. Step 3: Create a class data is performing the actions (publish to JMS, DB, etc ) for the custom publisher. Step 4: Register the new publisher against a ContentHandler. Step 5: Add the new custom publisher in the cache configuration file. Step 6: Add the custom publisher scheme class to the POF configuration file. All these steps are detailed bellow. The coherence project is attached and conclusions are presented at the end. Step 1: In the Coherence Eclipse project create a class called CustomPublisherScheme that should implement com.oracle.coherence.patterns.pushreplication.publishers.AbstractPublisherScheme. In this class define the elements of the custom-publisher-scheme element. For instance for a CustomPublisherScheme that looks like that: <sync:publisher> <sync:publisher-name>Active2-JDBC-Publisher</sync:publisher-name> <sync:publisher-scheme> <sync:custom-publisher-scheme> <sync:jdbc-string>jdbc:oracle:thin:@machine-name:1521:XE</sync:jdbc-string> <sync:username>hr</sync:username> <sync:password>hr</sync:password> </sync:custom-publisher-scheme> </sync:publisher-scheme> </sync:publisher> the code is: package com.oracle.coherence; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import com.oracle.coherence.patterns.pushreplication.Publisher; import com.oracle.coherence.configuration.Configurable; import com.oracle.coherence.configuration.Mandatory; import com.oracle.coherence.configuration.Property; import com.oracle.coherence.configuration.parameters.ParameterScope; import com.oracle.coherence.environment.Environment; import com.tangosol.io.pof.PofReader; import com.tangosol.io.pof.PofWriter; import com.tangosol.util.ExternalizableHelper; @Configurable public class CustomPublisherScheme extends com.oracle.coherence.patterns.pushreplication.publishers.AbstractPublisherScheme { /** * */ private static final long serialVersionUID = 1L; private String jdbcString; private String username; private String password; public String getJdbcString() { return this.jdbcString; } @Property("jdbc-string") @Mandatory public void setJdbcString(String jdbcString) { this.jdbcString = jdbcString; } public String getUsername() { return username; } @Property("username") @Mandatory public void setUsername(String username) { this.username = username; } public String getPassword() { return password; } @Property("password") @Mandatory public void setPassword(String password) { this.password = password; } public Publisher realize(Environment environment, ClassLoader classLoader, ParameterScope parameterScope) { return new CustomPublisher(getJdbcString(), getUsername(), getPassword()); } public void readExternal(DataInput in) throws IOException { super.readExternal(in); this.jdbcString = ExternalizableHelper.readSafeUTF(in); this.username = ExternalizableHelper.readSafeUTF(in); this.password = ExternalizableHelper.readSafeUTF(in); } public void writeExternal(DataOutput out) throws IOException { super.writeExternal(out); ExternalizableHelper.writeSafeUTF(out, this.jdbcString); ExternalizableHelper.writeSafeUTF(out, this.username); ExternalizableHelper.writeSafeUTF(out, this.password); } public void readExternal(PofReader reader) throws IOException { super.readExternal(reader); this.jdbcString = reader.readString(100); this.username = reader.readString(101); this.password = reader.readString(102); } public void writeExternal(PofWriter writer) throws IOException { super.writeExternal(writer); writer.writeString(100, this.jdbcString); writer.writeString(101, this.username); writer.writeString(102, this.password); } } Step 2: Define what the CustomPublisher should basically do by creating a new java class called CustomPublisher that implements com.oracle.coherence.patterns.pushreplication.Publisher package com.oracle.coherence; import com.oracle.coherence.patterns.pushreplication.EntryOperation; import com.oracle.coherence.patterns.pushreplication.Publisher; import com.oracle.coherence.patterns.pushreplication.exceptions.PublisherNotReadyException; import java.io.BufferedWriter; import java.util.Iterator; public class CustomPublisher implements Publisher { private String jdbcString; private String username; private String password; private transient BufferedWriter bufferedWriter; public CustomPublisher() { } public CustomPublisher(String jdbcString, String username, String password) { this.jdbcString = jdbcString; this.username = username; this.password = password; this.bufferedWriter = null; } public String getJdbcString() { return this.jdbcString; } public String getUsername() { return username; } public String getPassword() { return password; } public void publishBatch(String cacheName, String publisherName, Iterator<EntryOperation> entryOperations) { DatabasePersistence databasePersistence = new DatabasePersistence( jdbcString, username, password); while (entryOperations.hasNext()) { EntryOperation entryOperation = (EntryOperation) entryOperations .next(); databasePersistence.databasePersist(entryOperation); } } public void start(String cacheName, String publisherName) throws PublisherNotReadyException { System.err .printf("Started: Custom JDBC Publisher for Cache %s with Publisher %s\n", new Object[] { cacheName, publisherName }); } public void stop(String cacheName, String publisherName) { System.err .printf("Stopped: Custom JDBC Publisher for Cache %s with Publisher %s\n", new Object[] { cacheName, publisherName }); } } In the publishBatch method from above we inform the publisher that he is supposed to persist data to a database: DatabasePersistence databasePersistence = new DatabasePersistence( jdbcString, username, password); while (entryOperations.hasNext()) { EntryOperation entryOperation = (EntryOperation) entryOperations .next(); databasePersistence.databasePersist(entryOperation); } Step 3: The class that deals with the persistence is a very basic one that uses JDBC to perform inserts/updates against a database. package com.oracle.coherence; import com.oracle.coherence.patterns.pushreplication.EntryOperation; import java.sql.*; import java.text.SimpleDateFormat; import com.oracle.coherence.Order; public class DatabasePersistence { public static String INSERT_OPERATION = "INSERT"; public static String UPDATE_OPERATION = "UPDATE"; public Connection dbConnection; public DatabasePersistence(String jdbcString, String username, String password) { this.dbConnection = createConnection(jdbcString, username, password); } public Connection createConnection(String jdbcString, String username, String password) { Connection connection = null; System.err.println("Connecting to: " + jdbcString + " Username: " + username + " Password: " + password); try { // Load the JDBC driver String driverName = "oracle.jdbc.driver.OracleDriver"; Class.forName(driverName); // Create a connection to the database connection = DriverManager.getConnection(jdbcString, username, password); System.err.println("Connected to:" + jdbcString + " Username: " + username + " Password: " + password); } catch (ClassNotFoundException e) { e.printStackTrace(); } // driver catch (SQLException e) { e.printStackTrace(); } return connection; } public void databasePersist(EntryOperation entryOperation) { if (entryOperation.getOperation().toString() .equalsIgnoreCase(INSERT_OPERATION)) { insert(((Order) entryOperation.getPublishableEntry().getValue())); } else if (entryOperation.getOperation().toString() .equalsIgnoreCase(UPDATE_OPERATION)) { update(((Order) entryOperation.getPublishableEntry().getValue())); } } public void update(Order order) { String update = "UPDATE Orders set QUANTITY= '" + order.getQuantity() + "', AMOUNT='" + order.getAmount() + "', ORD_DATE= '" + (new SimpleDateFormat("dd-MMM-yyyy")).format(order .getOrdDate()) + "' WHERE SYMBOL='" + order.getSymbol() + "'"; System.err.println("UPDATE = " + update); try { Statement stmt = getDbConnection().createStatement(); stmt.execute(update); stmt.close(); } catch (SQLException ex) { System.err.println("SQLException: " + ex.getMessage()); } } public void insert(Order order) { String insert = "insert into Orders values('" + order.getSymbol() + "'," + order.getQuantity() + "," + order.getAmount() + ",'" + (new SimpleDateFormat("dd-MMM-yyyy")).format(order .getOrdDate()) + "')"; System.err.println("INSERT = " + insert); try { Statement stmt = getDbConnection().createStatement(); stmt.execute(insert); stmt.close(); } catch (SQLException ex) { System.err.println("SQLException: " + ex.getMessage()); } } public Connection getDbConnection() { return dbConnection; } public void setDbConnection(Connection dbConnection) { this.dbConnection = dbConnection; } } Step 4: Now we need to register our publisher against a ContentHandler. In order to achieve that we need to create in our eclipse project a new class called CustomPushReplicationNamespaceContentHandler that should extend the com.oracle.coherence.patterns.pushreplication.configuration.PushReplicationNamespaceContentHandler. In the constructor of the new class we define a new handler for our custom publisher. package com.oracle.coherence; import com.oracle.coherence.configuration.Configurator; import com.oracle.coherence.environment.extensible.ConfigurationContext; import com.oracle.coherence.environment.extensible.ConfigurationException; import com.oracle.coherence.environment.extensible.ElementContentHandler; import com.oracle.coherence.patterns.pushreplication.PublisherScheme; import com.oracle.coherence.environment.extensible.QualifiedName; import com.oracle.coherence.patterns.pushreplication.configuration.PushReplicationNamespaceContentHandler; import com.tangosol.run.xml.XmlElement; public class CustomPushReplicationNamespaceContentHandler extends PushReplicationNamespaceContentHandler { public CustomPushReplicationNamespaceContentHandler() { super(); registerContentHandler("custom-publisher-scheme", new ElementContentHandler() { public Object onElement(ConfigurationContext context, QualifiedName qualifiedName, XmlElement xmlElement) throws ConfigurationException { PublisherScheme publisherScheme = new CustomPublisherScheme(); Configurator.configure(publisherScheme, context, qualifiedName, xmlElement); return publisherScheme; } }); } } Step 5: Now we should define our CustomPublisher in the cache configuration file according to the following documentation. <cache-config xmlns:sync="class:com.oracle.coherence.CustomPushReplicationNamespaceContentHandler" xmlns:cr="class:com.oracle.coherence.environment.extensible.namespaces.InstanceNamespaceContentHandler"> <caching-schemes> <sync:provider pof-enabled="false"> <sync:coherence-provider /> </sync:provider> <caching-scheme-mapping> <cache-mapping> <cache-name>publishing-cache</cache-name> <scheme-name>distributed-scheme-with-publishing-cachestore</scheme-name> <autostart>true</autostart> <sync:publisher> <sync:publisher-name>Active2 Publisher</sync:publisher-name> <sync:publisher-scheme> <sync:remote-cluster-publisher-scheme> <sync:remote-invocation-service-name>remote-site1</sync:remote-invocation-service-name> <sync:remote-publisher-scheme> <sync:local-cache-publisher-scheme> <sync:target-cache-name>publishing-cache</sync:target-cache-name> </sync:local-cache-publisher-scheme> </sync:remote-publisher-scheme> <sync:autostart>true</sync:autostart> </sync:remote-cluster-publisher-scheme> </sync:publisher-scheme> </sync:publisher> <sync:publisher> <sync:publisher-name>Active2-Output-Publisher</sync:publisher-name> <sync:publisher-scheme> <sync:stderr-publisher-scheme> <sync:autostart>true</sync:autostart> <sync:publish-original-value>true</sync:publish-original-value> </sync:stderr-publisher-scheme> </sync:publisher-scheme> </sync:publisher> <sync:publisher> <sync:publisher-name>Active2-JDBC-Publisher</sync:publisher-name> <sync:publisher-scheme> <sync:custom-publisher-scheme> <sync:jdbc-string>jdbc:oracle:thin:@machine_name:1521:XE</sync:jdbc-string> <sync:username>hr</sync:username> <sync:password>hr</sync:password> </sync:custom-publisher-scheme> </sync:publisher-scheme> </sync:publisher> </cache-mapping> </caching-scheme-mapping> <!-- The following scheme is required for each remote-site when using a RemoteInvocationPublisher --> <remote-invocation-scheme> <service-name>remote-site1</service-name> <initiator-config> <tcp-initiator> <remote-addresses> <socket-address> <address>localhost</address> <port>20001</port> </socket-address> </remote-addresses> <connect-timeout>2s</connect-timeout> </tcp-initiator> <outgoing-message-handler> <request-timeout>5s</request-timeout> </outgoing-message-handler> </initiator-config> </remote-invocation-scheme> <!-- END: com.oracle.coherence.patterns.pushreplication --> <proxy-scheme> <service-name>ExtendTcpProxyService</service-name> <acceptor-config> <tcp-acceptor> <local-address> <address>localhost</address> <port>20002</port> </local-address> </tcp-acceptor> </acceptor-config> <autostart>true</autostart> </proxy-scheme> </caching-schemes> </cache-config> As you can see in the red-marked text from above I've:       - set new Namespace Content Handler       - define the new custom publisher that should work together with other publishers like: stderr and remote publishers in our case. Step 6: Add the com.oracle.coherence.CustomPublisherScheme to your custom-pof-config file: <pof-config> <user-type-list> <!-- Built in types --> <include>coherence-pof-config.xml</include> <include>coherence-common-pof-config.xml</include> <include>coherence-messagingpattern-pof-config.xml</include> <include>coherence-pushreplicationpattern-pof-config.xml</include> <!-- Application types --> <user-type> <type-id>1901</type-id> <class-name>com.oracle.coherence.Order</class-name> <serializer> <class-name>com.oracle.coherence.OrderSerializer</class-name> </serializer> </user-type> <user-type> <type-id>1902</type-id> <class-name>com.oracle.coherence.CustomPublisherScheme</class-name> </user-type> </user-type-list> </pof-config> CONCLUSIONSThis approach allows for publishers to publish data to almost any other receiver (database, JMS, MQ, ...). The only thing that needs to be changed is the DatabasePersistence.java class that should be adapted to the chosen receiver. Only minor changes are needed for the rest of the code (to publishBatch method from CustomPublisher class).

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  • The case of the phantom ADF developer (and other yarns)

    - by Chris Muir
    A few years of ADF experience means I see common mistakes made by different developers, some I regularly make myself.  This post is designed to assist beginners to Oracle JDeveloper Application Development Framework (ADF) avoid a common ADF pitfall, the case of the phantom ADF developer [add Scooby-Doo music here]. ADF Business Components - triggers, default table values and instead of views. Oracle's JDeveloper tutorials help with the A-B-Cs of ADF development, typically built on the nice 'n safe demo schema provided by with the Oracle database such as the HR demo schema. However it's not too long until ADF beginners, having built up some confidence from learning with the tutorials and vanilla demo schemas, start building ADF Business Components based upon their own existing database schema objects.  This is where unexpected problems can sneak in. The crime Developers may encounter a surprising error at runtime when editing a record they just created or updated and committed to the database, based on their own existing tables, namely the error: JBO-25014: Another user has changed the row with primary key oracle.jbo.Key[x] ...where X is the primary key value of the row at hand.  In a production environment with multiple users this error may be legit, one of the other users has updated the row since you queried it.  Yet in a development environment this error is just plain confusing.  If developers are isolated in their own database, creating and editing records they know other users can't possibly be working with, or all the other developers have gone home for the day, how is this error possible? There are no other users?  It must be the phantom ADF developer! [insert dramatic music here] The following picture is what you'll see in the Business Component Browser, and you'll receive a similar error message via an ADF Faces page: A false conclusion What can possibly cause this issue if it isn't our phantom ADF developer?  Doesn't ADF BC implement record locking, locking database records when the row is modified in the ADF middle-tier by a user?  How can our phantom ADF developer even take out a lock if this is the case?  Maybe ADF has a bug, maybe ADF isn't implementing record locking at all?  Shouldn't we see the error "JBO-26030: Failed to lock the record, another user holds the lock" as we attempt to modify the record, why do we see JBO-25014? : Let's verify that ADF is in fact issuing the correct SQL LOCK-FOR-UPDATE statement to the database. First we need to verify ADF's locking strategy.  It is determined by the Application Module's jbo.locking.mode property.  The default (as of JDev 11.1.1.4.0 if memory serves me correct) and recommended value is optimistic, and the other valid value is pessimistic. Next we need a mechanism to check that ADF is issuing the LOCK statements to the database.  We could ask DBAs to monitor locks with OEM, but optimally we'd rather not involve overworked DBAs in this process, so instead we can use the ADF runtime setting –Djbo.debugoutput=console.  At runtime this options turns on instrumentation within the ADF BC layer, which among a lot of extra detail displayed in the log window, will show the actual SQL statement issued to the database, including the LOCK statement we're looking to confirm. Setting our locking mode to pessimistic, opening the Business Components Browser of a JSF page allowing us to edit a record, say the CHARGEABLE field within a BOOKINGS record where BOOKING_NO = 1206, upon editing the record see among others the following log entries: [421] Built select: 'SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings'[422] Executing LOCK...SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings WHERE BOOKING_NO=:1 FOR UPDATE NOWAIT[423] Where binding param 1: 1206  As can be seen on line 422, in fact a LOCK-FOR-UPDATE is indeed issued to the database.  Later when we commit the record we see: [441] OracleSQLBuilder: SAVEPOINT 'BO_SP'[442] OracleSQLBuilder Executing, Lock 1 DML on: BOOKINGS (Update)[443] UPDATE buf Bookings>#u SQLStmtBufLen: 210, actual=62[444] UPDATE BOOKINGS Bookings SET CHARGEABLE=:1 WHERE BOOKING_NO=:2[445] Update binding param 1: N[446] Where binding param 2: 1206[447] BookingsView1 notify COMMIT ... [448] _LOCAL_VIEW_USAGE_model_Bookings_ResourceTypesView1 notify COMMIT ... [449] EntityCache close prepared statement ....and as a result the changes are saved to the database, and the lock is released. Let's see what happens when we use the optimistic locking mode, this time to change the same BOOKINGS record CHARGEABLE column again.  As soon as we edit the record we see little activity in the logs, nothing to indicate any SQL statement, let alone a LOCK has been taken out on the row. However when we save our records by issuing a commit, the following is recorded in the logs: [509] OracleSQLBuilder: SAVEPOINT 'BO_SP'[510] OracleSQLBuilder Executing doEntitySelect on: BOOKINGS (true)[511] Built select: 'SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings'[512] Executing LOCK...SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings WHERE BOOKING_NO=:1 FOR UPDATE NOWAIT[513] Where binding param 1: 1205[514] OracleSQLBuilder Executing, Lock 2 DML on: BOOKINGS (Update)[515] UPDATE buf Bookings>#u SQLStmtBufLen: 210, actual=62[516] UPDATE BOOKINGS Bookings SET CHARGEABLE=:1 WHERE BOOKING_NO=:2[517] Update binding param 1: Y[518] Where binding param 2: 1205[519] BookingsView1 notify COMMIT ... [520] _LOCAL_VIEW_USAGE_model_Bookings_ResourceTypesView1 notify COMMIT ... [521] EntityCache close prepared statement Again even though we're seeing the midtier delay the LOCK statement until commit time, it is in fact occurring on line 412, and released as part of the commit issued on line 419.  Therefore with either optimistic or pessimistic locking a lock is indeed issued. Our conclusion at this point must be, unless there's the unlikely cause the LOCK statement is never really hitting the database, or the even less likely cause the database has a bug, then ADF does in fact take out a lock on the record before allowing the current user to update it.  So there's no way our phantom ADF developer could even modify the record if he tried without at least someone receiving a lock error. Hmm, we can only conclude the locking mode is a red herring and not the true cause of our problem.  Who is the phantom? At this point we'll need to conclude that the error message "JBO-25014: Another user has changed" is somehow legit, even though we don't understand yet what's causing it. This leads onto two further questions, how does ADF know another user has changed the row, and what's been changed anyway? To answer the first question, how does ADF know another user has changed the row, the Fusion Guide's section 4.10.11 How to Protect Against Losing Simultaneous Updated Data , that details the Entity Object Change-Indicator property, gives us the answer: At runtime the framework provides automatic "lost update" detection for entity objects to ensure that a user cannot unknowingly modify data that another user has updated and committed in the meantime. Typically, this check is performed by comparing the original values of each persistent entity attribute against the corresponding current column values in the database at the time the underlying row is locked. Before updating a row, the entity object verifies that the row to be updated is still consistent with the current state of the database.  The guide further suggests to make this solution more efficient: You can make the lost update detection more efficient by identifying any attributes of your entity whose values you know will be updated whenever the entity is modified. Typical candidates include a version number column or an updated date column in the row.....To detect whether the row has been modified since the user queried it in the most efficient way, select the Change Indicator option to compare only the change-indicator attribute values. We now know that ADF BC doesn't use the locking mechanism at all to protect the current user against updates, but rather it keeps a copy of the original record fetched, separate to the user changed version of the record, and it compares the original record against the one in the database when the lock is taken out.  If values don't match, be it the default compare-all-columns behaviour, or the more efficient Change Indicator mechanism, ADF BC will throw the JBO-25014 error. This leaves one last question.  Now we know the mechanism under which ADF identifies a changed row, what we don't know is what's changed and who changed it? The real culprit What's changed?  We know the record in the mid-tier has been changed by the user, however ADF doesn't use the changed record in the mid-tier to compare to the database record, but rather a copy of the original record before it was changed.  This leaves us to conclude the database record has changed, but how and by who? There are three potential causes: Database triggers The database trigger among other uses, can be configured to fire PLSQL code on a database table insert, update or delete.  In particular in an insert or update the trigger can override the value assigned to a particular column.  The trigger execution is actioned by the database on behalf of the user initiating the insert or update action. Why this causes the issue specific to our ADF use, is when we insert or update a record in the database via ADF, ADF keeps a copy of the record written to the database.  However the cached record is instantly out of date as the database triggers have modified the record that was actually written to the database.  Thus when we update the record we just inserted or updated for a second time to the database, ADF compares its original copy of the record to that in the database, and it detects the record has been changed – giving us JBO-25014. This is probably the most common cause of this problem. Default values A second reason this issue can occur is another database feature, default column values.  When creating a database table the schema designer can define default values for specific columns.  For example a CREATED_BY column could be set to SYSDATE, or a flag column to Y or N.  Default values are only used by the database when a user inserts a new record and the specific column is assigned NULL.  The database in this case will overwrite the column with the default value. As per the database trigger section, it then becomes apparent why ADF chokes on this feature, though it can only specifically occur in an insert-commit-update-commit scenario, not the update-commit-update-commit scenario. Instead of trigger views I must admit I haven't double checked this scenario but it seems plausible, that of the Oracle database's instead of trigger view (sometimes referred to as instead of views).  A view in the database is based on a query, and dependent on the queries complexity, may support insert, update and delete functionality to a limited degree.  In order to support fully insertable, updateable and deletable views, Oracle introduced the instead of view, that gives the view designer the ability to not only define the view query, but a set of programmatic PLSQL triggers where the developer can define their own logic for inserts, updates and deletes. While this provides the database programmer a very powerful feature, it can cause issues for our ADF application.  On inserting or updating a record in the instead of view, the record and it's data that goes in is not necessarily the data that comes out when ADF compares the records, as the view developer has the option to practically do anything with the incoming data, including throwing it away or pushing it to tables which aren't used by the view underlying query for fetching the data. Readers are at this point reminded that this article is specifically about how the JBO-25014 error occurs in the context of 1 developer on an isolated database.  The article is not considering how the error occurs in a production environment where there are multiple users who can cause this error in a legitimate fashion.  Assuming none of the above features are the cause of the problem, and optimistic locking is turned on (this error is not possible if pessimistic locking is the default mode *and* none of the previous causes are possible), JBO-25014 is quite feasible in a production ADF application if 2 users modify the same record. At this point under project timelines pressure, the obvious fix for developers is to drop both database triggers and default values from the underlying tables.  However we must be careful that these legacy constructs aren't used and assumed to be in place by other legacy systems.  Dropping the database triggers or default value that the existing Oracle Forms  applications assumes and requires to be in place could cause unexpected behaviour and bugs in the Forms application.  Proficient software engineers would recognize such a change may require a partial or full regression test of the existing legacy system, a potentially costly and timely exercise, not ideal. Solving the mystery once and for all Luckily ADF has built in functionality to deal with this issue, though it's not a surprise, as Oracle as the author of ADF also built the database, and are fully aware of the Oracle database's feature set.  At the Entity Object attribute level, the Refresh After Insert and Refresh After Update properties.  Simply selecting these instructs ADF BC after inserting or updating a record to the database, to expect the database to modify the said attributes, and read a copy of the changed attributes back into its cached mid-tier record.  Thus next time the developer modifies the current record, the comparison between the mid-tier record and the database record match, and JBO-25014: Another user has changed" is no longer an issue. [Post edit - as per the comment from Oracle's Steven Davelaar below, as he correctly points out the above solution will not work for instead-of-triggers views as it relies on SQL RETURNING clause which is incompatible with this type of view] Alternatively you can set the Change Indicator on one of the attributes.  This will work as long as the relating column for the attribute in the database itself isn't inadvertently updated.  In turn you're possibly just masking the issue rather than solving it, because if another developer turns the Change Indicator back on the original issue will return.

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  • High Server Load cannot figure out why

    - by Tim Bolton
    My server is currently running CentOS 5.2, with WHM 11.34. Currently, we're at 6.43 to 12 for a load average. The sites that we're hosting are taking a lot time to respond and resolve. top doesn't show anything out of the ordinary and iftop doesn't show a lot of traffic. We have many resellers, and some not so good at writing code, how can we find the culprit? vmstat output: vmstat procs -----------memory---------- ---swap-- -----io---- --system-- -----cpu------ r b swpd free buff cache si so bi bo in cs us sy id wa st 0 2 84 78684 154916 1021080 0 0 72 274 0 14 6 3 80 12 0 top output (ordered by %CPU) top - 21:44:43 up 5 days, 10:39, 3 users, load average: 3.36, 4.18, 4.73 Tasks: 222 total, 3 running, 219 sleeping, 0 stopped, 0 zombie Cpu(s): 5.8%us, 2.3%sy, 0.2%ni, 79.6%id, 11.8%wa, 0.0%hi, 0.2%si, 0.0%st Mem: 2074580k total, 1863044k used, 211536k free, 174828k buffers Swap: 2040212k total, 84k used, 2040128k free, 987604k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 15930 mysql 15 0 138m 46m 4380 S 4 2.3 1:45.87 mysqld 21772 igniteth 17 0 23200 7152 3932 R 4 0.3 0:00.02 php 1586 root 10 -5 0 0 0 S 2 0.0 11:45.19 kjournald 21759 root 15 0 2416 1024 732 R 2 0.0 0:00.01 top 1 root 15 0 2156 648 560 S 0 0.0 0:26.31 init 2 root RT 0 0 0 0 S 0 0.0 0:00.35 migration/0 3 root 34 19 0 0 0 S 0 0.0 0:00.32 ksoftirqd/0 4 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/0 5 root RT 0 0 0 0 S 0 0.0 0:02.00 migration/1 6 root 34 19 0 0 0 S 0 0.0 0:00.11 ksoftirqd/1 7 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/1 8 root RT 0 0 0 0 S 0 0.0 0:01.29 migration/2 9 root 34 19 0 0 0 S 0 0.0 0:00.26 ksoftirqd/2 10 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/2 11 root RT 0 0 0 0 S 0 0.0 0:00.90 migration/3 12 root 34 19 0 0 0 R 0 0.0 0:00.20 ksoftirqd/3 13 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/3 top output (ordered by CPU time) top - 21:46:12 up 5 days, 10:41, 3 users, load average: 2.88, 3.82, 4.55 Tasks: 217 total, 1 running, 216 sleeping, 0 stopped, 0 zombie Cpu(s): 3.7%us, 2.0%sy, 2.0%ni, 67.2%id, 25.0%wa, 0.0%hi, 0.1%si, 0.0%st Mem: 2074580k total, 1959516k used, 115064k free, 183116k buffers Swap: 2040212k total, 84k used, 2040128k free, 1090308k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ TIME COMMAND 32367 root 16 0 215m 212m 1548 S 0 10.5 62:03.63 62:03 tailwatchd 1586 root 10 -5 0 0 0 S 0 0.0 11:45.27 11:45 kjournald 1576 root 10 -5 0 0 0 S 0 0.0 2:37.86 2:37 kjournald 27722 root 16 0 2556 1184 800 S 0 0.1 1:48.94 1:48 top 15930 mysql 15 0 138m 46m 4380 S 4 2.3 1:48.63 1:48 mysqld 2932 root 34 19 0 0 0 S 0 0.0 1:41.05 1:41 kipmi0 226 root 10 -5 0 0 0 S 0 0.0 1:34.33 1:34 kswapd0 2671 named 25 0 74688 7400 2116 S 0 0.4 1:23.58 1:23 named 3229 root 15 0 10300 3348 2724 S 0 0.2 0:40.85 0:40 sshd 1580 root 10 -5 0 0 0 S 0 0.0 0:30.62 0:30 kjournald 1 root 17 0 2156 648 560 S 0 0.0 0:26.32 0:26 init 2616 root 15 0 1816 576 480 S 0 0.0 0:23.50 0:23 syslogd 1584 root 10 -5 0 0 0 S 0 0.0 0:18.67 0:18 kjournald 4342 root 34 19 27692 11m 2116 S 0 0.5 0:18.23 0:18 yum-updatesd 8044 bollingp 15 0 3456 2036 740 S 1 0.1 0:15.56 0:15 imapd 26 root 10 -5 0 0 0 S 0 0.0 0:14.18 0:14 kblockd/1 7989 gmailsit 16 0 3196 1748 736 S 0 0.1 0:10.43 0:10 imapd iostat -xtk 1 10 output [root@server1 tmp]# iostat -xtk 1 10 Linux 2.6.18-53.el5 12/18/2012 Time: 09:51:06 PM avg-cpu: %user %nice %system %iowait %steal %idle 5.83 0.19 2.53 11.85 0.00 79.60 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 1.37 118.83 18.70 54.27 131.47 692.72 22.59 4.90 67.19 3.10 22.59 sdb 0.35 39.33 20.33 61.43 158.79 403.22 13.75 5.23 63.93 3.77 30.80 Time: 09:51:07 PM avg-cpu: %user %nice %system %iowait %steal %idle 1.50 0.00 0.50 24.00 0.00 74.00 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 25.00 2.00 2.00 128.00 108.00 118.00 0.03 7.25 4.00 1.60 sdb 0.00 16.00 41.00 145.00 200.00 668.00 9.33 107.92 272.72 5.38 100.10 Time: 09:51:08 PM avg-cpu: %user %nice %system %iowait %steal %idle 2.00 0.00 1.50 29.50 0.00 67.00 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 95.00 3.00 33.00 12.00 480.00 27.33 0.07 1.72 1.31 4.70 sdb 0.00 14.00 1.00 228.00 4.00 960.00 8.42 143.49 568.01 4.37 100.10 Time: 09:51:09 PM avg-cpu: %user %nice %system %iowait %steal %idle 13.28 0.00 2.76 21.30 0.00 62.66 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 21.00 1.00 19.00 16.00 192.00 20.80 0.06 3.55 1.30 2.60 sdb 0.00 36.00 28.00 181.00 124.00 884.00 9.65 121.16 617.31 4.79 100.10 Time: 09:51:10 PM avg-cpu: %user %nice %system %iowait %steal %idle 4.74 0.00 1.50 25.19 0.00 68.58 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 20.00 3.00 15.00 12.00 136.00 16.44 0.17 7.11 3.11 5.60 sdb 0.00 0.00 103.00 60.00 544.00 248.00 9.72 52.35 545.23 6.14 100.10 Time: 09:51:11 PM avg-cpu: %user %nice %system %iowait %steal %idle 1.24 0.00 1.24 25.31 0.00 72.21 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 75.00 4.00 28.00 16.00 416.00 27.00 0.08 3.72 2.03 6.50 sdb 2.00 9.00 124.00 17.00 616.00 104.00 10.21 3.73 213.73 7.10 100.10 Time: 09:51:12 PM avg-cpu: %user %nice %system %iowait %steal %idle 1.00 0.00 0.75 24.31 0.00 73.93 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 24.00 1.00 9.00 4.00 132.00 27.20 0.01 1.20 1.10 1.10 sdb 4.00 40.00 103.00 48.00 528.00 212.00 9.80 105.21 104.32 6.64 100.20 Time: 09:51:13 PM avg-cpu: %user %nice %system %iowait %steal %idle 2.50 0.00 1.75 23.25 0.00 72.50 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 125.74 3.96 46.53 15.84 689.11 27.92 0.20 4.06 2.41 12.18 sdb 2.97 0.00 91.09 84.16 419.80 471.29 10.17 85.85 590.78 5.66 99.11 Time: 09:51:14 PM avg-cpu: %user %nice %system %iowait %steal %idle 0.75 0.00 0.50 24.94 0.00 73.82 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 88.00 1.00 7.00 4.00 380.00 96.00 0.04 4.38 3.00 2.40 sdb 3.00 7.00 111.00 44.00 540.00 208.00 9.65 18.58 581.79 6.46 100.10 Time: 09:51:15 PM avg-cpu: %user %nice %system %iowait %steal %idle 11.03 0.00 3.26 26.57 0.00 59.15 Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util sda 0.00 145.00 7.00 53.00 28.00 792.00 27.33 0.15 2.50 1.55 9.30 sdb 1.00 0.00 155.00 0.00 800.00 0.00 10.32 2.85 18.63 6.46 100.10 [root@server1 tmp]# MySQL Show Full Processlist mysql> show full processlist; +------+---------------+-----------+-----------------------+----------------+------+----------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Id | User | Host | db | Command | Time | State | Info | +------+---------------+-----------+-----------------------+----------------+------+----------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | 1 | DB_USER_ONE | localhost | DB_ONE | Query | 3 | waiting for handler insert | INSERT DELAYED INTO defers (mailtime,msgid,email,transport_method,message,host,ip,router,deliveryuser,deliverydomain) VALUES(FROM_UNIXTIME('1355879748'),'1TivwL-0003y8-8l','[email protected]','remote_smtp','SMTP error from remote mail server after initial connection: host mx1.mail.tw.yahoo.com [203.188.197.119]: 421 4.7.0 [TS01] Messages from 75.125.90.146 temporarily deferred due to user complaints - 4.16.55.1; see http://postmaster.yahoo.com/421-ts01.html','mx1.mail.tw.yahoo.com','203.188.197.119','lookuphost','','') | | 2 | DELAYED | localhost | DB_ONE | Delayed insert | 52 | insert | | | 3 | DELAYED | localhost | DB_ONE | Delayed insert | 68 | insert | | | 911 | DELAYED | localhost | DB_ONE | Delayed insert | 99 | Waiting for INSERT | | | 993 | DB_USER_TWO | localhost | DB_TWO | Sleep | 832 | | NULL | | 994 | DB_USER_ONE | localhost | DB_ONE | Query | 185 | Locked | delete from failures where FROM_UNIXTIME(UNIX_TIMESTAMP(NOW())-1296000) > mailtime | | 1102 | DB_USER_THREE | localhost | DB_THREE | Query | 29 | NULL | commit | | 1249 | DB_USER_FOUR | localhost | DB_FOUR | Query | 13 | NULL | commit | | 1263 | root | localhost | DB_FIVE | Query | 0 | NULL | show full processlist | | 1264 | DB_USER_SIX | localhost | DB_SIX | Query | 3 | NULL | commit | +------+---------------+-----------+-----------------------+----------------+------+----------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 10 rows in set (0.00 sec)

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  • How the number of indexes built on a table can impact performances?

    - by Davide Mauri
    We all know that putting too many indexes (I’m talking of non-clustered index only, of course) on table may produce performance problems due to the overhead that each index bring to all insert/update/delete operations on that table. But how much? I mean, we all agree – I think – that, generally speaking, having many indexes on a table is “bad”. But how bad it can be? How much the performance will degrade? And on a concurrent system how much this situation can also hurts SELECT performances? If SQL Server take more time to update a row on a table due to the amount of indexes it also has to update, this also means that locks will be held for more time, slowing down the perceived performance of all queries involved. I was quite curious to measure this, also because when teaching it’s by far more impressive and effective to show to attended a chart with the measured impact, so that they can really “feel” what it means! To do the tests, I’ve create a script that creates a table (that has a clustered index on the primary key which is an identity column) , loads 1000 rows into the table (inserting 1000 row using only one insert, instead of issuing 1000 insert of one row, in order to minimize the overhead needed to handle the transaction, that would have otherwise ), and measures the time taken to do it. The process is then repeated 16 times, each time adding a new index on the table, using columns from table in a round-robin fashion. Test are done against different row sizes, so that it’s possible to check if performance changes depending on row size. The result are interesting, although expected. This is the chart showing how much time it takes to insert 1000 on a table that has from 0 to 16 non-clustered indexes. Each test has been run 20 times in order to have an average value. The value has been cleaned from outliers value due to unpredictable performance fluctuations due to machine activity. The test shows that in a  table with a row size of 80 bytes, 1000 rows can be inserted in 9,05 msec if no indexes are present on the table, and the value grows up to 88 (!!!) msec when you have 16 indexes on it This means a impact on performance of 975%. That’s *huge*! Now, what happens if we have a bigger row size? Say that we have a table with a row size of 1520 byte. Here’s the data, from 0 to 16 indexes on that table: In this case we need near 22 msec to insert 1000 in a table with no indexes, but we need more that 500msec if the table has 16 active indexes! Now we’re talking of a 2410% impact on performance! Now we can have a tangible idea of what’s the impact of having (too?) many indexes on a table and also how the size of a row also impact performances. That’s why the golden rule of OLTP databases “few indexes, but good” is so true! (And in fact last week I saw a database with tables with 1700bytes row size and 23 (!!!) indexes on them!) This also means that a too heavy denormalization is really not a good idea (we’re always talking about OLTP systems, keep it in mind), since the performance get worse with the increase of the row size. So, be careful out there, and keep in mind the “equilibrium” is the key world of a database professional: equilibrium between read and write performance, between normalization and denormalization, between to few and too may indexes. PS Tests are done on a VMWare Workstation 7 VM with 2 CPU and 4 GB of Memory. Host machine is a Dell Precsioni M6500 with i7 Extreme X920 Quad-Core HT 2.0Ghz and 16Gb of RAM. Database is stored on a SSD Intel X-25E Drive, Simple Recovery Model, running on SQL Server 2008 R2. If you also want to to tests on your own, you can download the test script here: Open TestIndexPerformance.sql

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  • Cannot Mount USB 3.0 Hard Disk ?!!

    - by Tenken
    Hi, I have a USB 3.0 external hard disk which I am unable to mount. The entry appears in the "lsusb" command, but I do not exactly understand how to mount it. This is the output for my lsusb command. "ASMedia Technology Inc." is the USB 3.0 device. I would appreciate some help in mounting and accessing the hard disk. This the relevant output of my "lsusb -v" : Bus 009 Device 002: ID 174c:5106 ASMedia Technology Inc. Device Descriptor: bLength 18 bDescriptorType 1 bcdUSB 2.10 bDeviceClass 0 (Defined at Interface level) bDeviceSubClass 0 bDeviceProtocol 0 bMaxPacketSize0 64 idVendor 0x174c ASMedia Technology Inc. idProduct 0x5106 bcdDevice 0.01 iManufacturer 2 ASMedia iProduct 3 AS2105 iSerial 1 00000000000000000000 bNumConfigurations 1 Configuration Descriptor: bLength 9 bDescriptorType 2 wTotalLength 32 bNumInterfaces 1 bConfigurationValue 1 iConfiguration 0 bmAttributes 0xc0 Self Powered MaxPower 0mA Interface Descriptor: bLength 9 bDescriptorType 4 bInterfaceNumber 0 bAlternateSetting 0 bNumEndpoints 2 bInterfaceClass 8 Mass Storage bInterfaceSubClass 6 SCSI bInterfaceProtocol 80 Bulk (Zip) iInterface 0 Endpoint Descriptor: bLength 7 bDescriptorType 5 bEndpointAddress 0x81 EP 1 IN bmAttributes 2 Transfer Type Bulk Synch Type None Usage Type Data wMaxPacketSize 0x0200 1x 512 bytes bInterval 0 Endpoint Descriptor: bLength 7 bDescriptorType 5 bEndpointAddress 0x02 EP 2 OUT bmAttributes 2 Transfer Type Bulk Synch Type None Usage Type Data wMaxPacketSize 0x0200 1x 512 bytes bInterval 0 Device Qualifier (for other device speed): bLength 10 bDescriptorType 6 bcdUSB 2.10 bDeviceClass 0 (Defined at Interface level) bDeviceSubClass 0 bDeviceProtocol 0 bMaxPacketSize0 64 bNumConfigurations 1 Device Status: 0x0001 Self Powered Bus 009 Device 001: ID 1d6b:0003 Linux Foundation 3.0 root hub Device Descriptor: bLength 18 bDescriptorType 1 bcdUSB 3.00 bDeviceClass 9 Hub bDeviceSubClass 0 Unused bDeviceProtocol 3 bMaxPacketSize0 9 idVendor 0x1d6b Linux Foundation idProduct 0x0003 3.0 root hub bcdDevice 2.06 iManufacturer 3 Linux 2.6.35-28-generic xhci_hcd iProduct 2 xHCI Host Controller iSerial 1 0000:04:00.0 bNumConfigurations 1 Configuration Descriptor: bLength 9 bDescriptorType 2 wTotalLength 25 bNumInterfaces 1 bConfigurationValue 1 iConfiguration 0 bmAttributes 0xe0 Self Powered Remote Wakeup MaxPower 0mA Interface Descriptor: bLength 9 bDescriptorType 4 bInterfaceNumber 0 bAlternateSetting 0 bNumEndpoints 1 bInterfaceClass 9 Hub bInterfaceSubClass 0 Unused bInterfaceProtocol 0 Full speed (or root) hub iInterface 0 Endpoint Descriptor: bLength 7 bDescriptorType 5 bEndpointAddress 0x81 EP 1 IN bmAttributes 3 Transfer Type Interrupt Synch Type None Usage Type Data wMaxPacketSize 0x0004 1x 4 bytes bInterval 12 Hub Descriptor: bLength 9 bDescriptorType 41 nNbrPorts 4 wHubCharacteristic 0x0009 Per-port power switching Per-port overcurrent protection TT think time 8 FS bits bPwrOn2PwrGood 10 * 2 milli seconds bHubContrCurrent 0 milli Ampere DeviceRemovable 0x00 PortPwrCtrlMask 0xff Hub Port Status: Port 1: 0000.0100 power Port 2: 0000.0100 power Port 3: 0000.0503 highspeed power enable connect Port 4: 0000.0503 highspeed power enable connect Device Status: 0x0003 Self Powered Remote Wakeup Enabled This is the error given when I try to mount the hard drive: shinso@shinso-IdeaPad:~$ sudo mount /dev/sdb /mnt [sudo] password for shinso: mount: /dev/sdb: unknown device This the output of "dmesg|tail": [30062.774178] Either the lower file is not in a valid eCryptfs format, or the key could not be retrieved. Plaintext passthrough mode is not enabled; returning -EIO [30535.800977] usb 9-4: USB disconnect, address 3 [30659.237342] Valid eCryptfs headers not found in file header region or xattr region [30659.237351] Either the lower file is not in a valid eCryptfs format, or the key could not be retrieved. Plaintext passthrough mode is not enabled; returning -EIO [31259.268310] Valid eCryptfs headers not found in file header region or xattr region [31259.268313] Either the lower file is not in a valid eCryptfs format, or the key could not be retrieved. Plaintext passthrough mode is not enabled; returning -EIO [31860.059058] Valid eCryptfs headers not found in file header region or xattr region [31860.059062] Either the lower file is not in a valid eCryptfs format, or the key could not be retrieved. Plaintext passthrough mode is not enabled; returning -EIO [32465.220590] Valid eCryptfs headers not found in file header region or xattr region [32465.220593] Either the lower file is not in a valid eCryptfs format, or the key could not be retrieved. Plaintext passthrough mode is not enabled; returning -EIO I am using Ubuntu 10.10 (64 bit). Any help is appreciated.

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  • EF4 CTP5 Conflicting changes detected. This may happen when trying to insert multiple entities with the same key.

    - by user658332
    hi, I am new to EF4 CTP5. I am just hanging at one problem.. I am using CodeFirst without Database, so when i execute application, it generated DB for me. here is my scenario, i have following Class structure... public class KVCalculationWish { public KVCalculationWish() { } public int KVCalculationWishId { get; set; } public string KVCalculationWishName { get; set; } public int KVSingleOfferId { get; set; } public virtual KVSingleOffer SingleOffer { get; set; } public int KVCalculationsForPersonId { get; set; } public virtual KVCalculationsForPerson CaculationsForPerson { get; set; } } public class KVSingleOffer { public KVSingleOffer() { } public int KVSingleOfferId { get; set; } public string KVSingleOfferName { get; set; } public KVCalculationWish CalculationWish { get; set; } } public class KVCalculationsForPerson { public KVCalculationsForPerson() { } public int KVCalculationsForPersonId { get; set; } public string KVCalculationsForPersonName { get; set; } public KVCalculationWish CalculationWish { get; set; } } public class EntiyRelation : DbContext { public EntiyRelation() { } public DbSet<KVCalculationWish> CalculationWish { get; set; } public DbSet<KVSingleOffer> SingleOffer { get; set; } public DbSet<KVCalculationsForPerson> CalculationsForPerson { get; set; } protected override void OnModelCreating(System.Data.Entity.ModelConfiguration.ModelBuilder modelBuilder) { base.OnModelCreating(modelBuilder); modelBuilder.Entity<KVCalculationWish>().HasOptional(m => m.SingleOffer).WithRequired(p => p.CalculationWish); modelBuilder.Entity<KVCalculationWish>().HasOptional(m => m.CaculationsForPerson).WithRequired(p => p.CalculationWish); } } i want to use KVCalcuationWish object in KVCalcuationsForPerson and KVSingleOffer class. So when i am creating object of KVCalcuationForPerson and KVSingleOffer class i initialize both object with New KVCalcuationWish object. like this KVCalculationsForPerson calcPerson = new KVCalculationsForPerson(); KVCalculationWish wish = new KVCalculationWish() { CaculationsForPerson = calcPerson }; calcPerson.KVCalculationsForPersonName = "Person Name"; calcPerson.CalculationWish = wish; KVSingleOffer singleOffer = new KVSingleOffer(); KVCalculationWish wish1 = new KVCalculationWish() { SingleOffer = singleOffer }; singleOffer.KVSingleOfferName = "Offer Name"; singleOffer.CalculationWish = wish1; but my problem is when i save this records using following code try { db.CalculationsForPerson.Add(calcPerson); db.SingleOffer.Add(singleOffer); db.SaveChanges(); } catch (Exception ex) { } i can save successfully in DB, but in Table KVCalcuationWish i am not able to get the ID of SingleOffer and CalcuationsForPerson class object. Following is the data of KVCalcuationWish table. KVCalcuationWishID KVCalcuationWishName KVSingleOfferID KVCalcuationsForPersonID 1 NULL 0 0 Following is the data of KVSingleOFfer Table KVSingleOfferID KVSingleOfferName 1 Offer Name Follwing is the data of KVCalcuationsForPerson Table KVSingleOfferID KVSingleOfferName 1 Person Name I want to have following possible output in KVCalcuationWish table. KVCalcuationWishID KVCalcuationWishName KVSingleOfferID KVCalcuationsForPersonID 1 NULL 1 NULL 2 NULL NULL 1 so what i want to achieve is ...... when i am save KVSingleOffer object i want separate record to be inserted and when i save KVCalcuationsForPerson object another separate record should be save to KVCalcuationwish table. Is that possible? Sorry for long description... but i really hang on this situation... Thanks & Regards, Joyous Suhas

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  • SQLite UPSERT - ON DUPLICATE KEY UPDATE

    - by Alix Axel
    MySQL has something like this: INSERT INTO visits (ip, hits) VALUES ('127.0.0.1', 1) ON DUPLICATE KEY UPDATE hits = hits + 1; As far as I'm know this feature doesn't exist in SQLite, what I want to know is if there is any way to archive the same effect without having to execute two queries. Also, if this is not possible, what do you prefer: SELECT + (INSERT or UPDATE) or UPDATE (+ INSERT if UPDATE fails)

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