Search Results

Search found 13889 results on 556 pages for 'results'.

Page 62/556 | < Previous Page | 58 59 60 61 62 63 64 65 66 67 68 69  | Next Page >

  • Android How do i overwrite the filter for my ArrayAdapter?

    - by alan
    Hey guys my first post here... Im trying to write a custom filter to filter the arraylist in my arrayadapter such that my listview is filtered when i click on the button. For instance when i click on my button public void onClick(View arg0) { String abc = "abc"; m_adapter.getFilter().filter(abc); } However, when i click on my button, my app terminate unexpectedly. Here is my code for the arrayadapter and filter. Please help me. package com.ntu.rosemobile.searchlist; public class ResultsAdapter extends ArrayAdapter<SearchItem> implements Filterable{ public ArrayList<SearchItem> subItems; public ArrayList<SearchItem> allItems; private LayoutInflater inflater; private PTypeFilter filter; public ResultsAdapter(Context context, int textViewResourceId, ArrayList<SearchItem> items) { super(context, textViewResourceId, items); this.subItems = items; this.allItems = this.subItems; inflater= LayoutInflater.from(context); } @Override public Filter getFilter() { if (filter == null){ filter = new PTypeFilter(); } return filter; } //@Override public View getView(int position, View convertView, ViewGroup parent) { View v = convertView; if (v == null) { v = inflater.inflate(R.layout.listrow, null); } SearchItem o = subItems.get(position); if (o != null) { TextView pname = (TextView) v.findViewById(R.id.productname); TextView neg = (TextView) v.findViewById(R.id.negNum); TextView pos = (TextView) v.findViewById(R.id.posNum); TextView neu = (TextView) v.findViewById(R.id.neuNum); WebImageView productPhoto = (WebImageView)v.findViewById(R.id.pPhoto); if(productPhoto!=null){ productPhoto.setImageUrl(o.getImageUrl().toString()); productPhoto.loadImage(); } if(pname!= null){ pname.setText(o.getProductName().toString()); } if (neg != null) { String a = "" + o.getNegativeReviews(); neg.setText(a); } if(neu != null){ String a = "" + o.getNeutralReviews(); neu.setText(a); } if(pos != null){ String a = "" + o.getPositiveReviews(); pos.setText(a); } } return v; } private class PTypeFilter extends Filter{ @SuppressWarnings("unchecked") @Override protected void publishResults(CharSequence prefix, FilterResults results) { // NOTE: this function is *always* called from the UI thread. subItems = (ArrayList<SearchItem>)results.values; notifyDataSetChanged(); } @SuppressWarnings("unchecked") protected FilterResults performFiltering(CharSequence prefix) { // NOTE: this function is *always* called from a background thread, and // not the UI thread. FilterResults results = new FilterResults(); ArrayList<SearchItem> i = new ArrayList<SearchItem>(); if (prefix!= null && prefix.toString().length() > 0) { for (int index = 0; index < allItems.size(); index++) { SearchItem si = allItems.get(index); if(si.getPType().compareTo(prefix.toString()) == 0){ i.add(si); } } results.values = i; results.count = i.size(); } else{ synchronized (allItems){ results.values = allItems; results.count = allItems.size(); } } return results; } } }

    Read the article

  • Java AD Authentication across Trusted Domains

    - by benjiisnotcool
    I am trying to implement Active Directory authentication in Java which will be ran from a Linux machine. Our AD set-up will consist of multiple servers that share trust relationships with one another so for our test environment we have two domain controllers: test1.ad1.foo.com who trusts test2.ad2.bar.com. Using the code below I can successfully authenticate a user from test1 but not on test2: public class ADDetailsProvider implements ResultSetProvider { private String domain; private String user; private String password; public ADDetailsProvider(String user, String password) { //extract domain name if (user.contains("\\")) { this.user = user.substring((user.lastIndexOf("\\") + 1), user.length()); this.domain = user.substring(0, user.lastIndexOf("\\")); } else { this.user = user; this.domain = ""; } this.password = password; } /* Test from the command line */ public static void main (String[] argv) throws SQLException { ResultSetProvider res = processADLogin(argv[0], argv[1]); ResultSet results = null; res.assignRowValues(results, 0); System.out.println(argv[0] + " " + argv[1]); } public boolean assignRowValues(ResultSet results, int currentRow) throws SQLException { // Only want a single row if (currentRow >= 1) return false; try { ADAuthenticator adAuth = new ADAuthenticator(); LdapContext ldapCtx = adAuth.authenticate(this.domain, this.user, this.password); NamingEnumeration userDetails = adAuth.getUserDetails(ldapCtx, this.user); // Fill the result set (throws SQLException). while (userDetails.hasMoreElements()) { Attribute attr = (Attribute)userDetails.next(); results.updateString(attr.getID(), attr.get().toString()); } results.updateInt("authenticated", 1); return true; } catch (FileNotFoundException fnf) { Logger.getAnonymousLogger().log(Level.WARNING, "Caught File Not Found Exception trying to read cris_authentication.properties"); results.updateInt("authenticated", 0); return false; } catch (IOException ioe) { Logger.getAnonymousLogger().log(Level.WARNING, "Caught IO Excpetion processing login"); results.updateInt("authenticated", 0); return false; } catch (AuthenticationException aex) { Logger.getAnonymousLogger().log(Level.WARNING, "Caught Authentication Exception attempting to bind to LDAP for [{0}]", this.user); results.updateInt("authenticated", 0); return true; } catch (NamingException ne) { Logger.getAnonymousLogger().log(Level.WARNING, "Caught Naming Exception performing user search or LDAP bind for [{0}]", this.user); results.updateInt("authenticated", 0); return true; } } public void close() { // nothing needed here } /** * This method is called via a Postgres function binding to access the * functionality provided by this class. */ public static ResultSetProvider processADLogin(String user, String password) { return new ADDetailsProvider(user, password); } } public class ADAuthenticator { public ADAuthenticator() throws FileNotFoundException, IOException { Properties props = new Properties(); InputStream inStream = this.getClass().getClassLoader(). getResourceAsStream("com/bar/foo/ad/authentication.properties"); props.load(inStream); this.domain = props.getProperty("ldap.domain"); inStream.close(); } public LdapContext authenticate(String domain, String user, String pass) throws AuthenticationException, NamingException, IOException { Hashtable env = new Hashtable(); this.domain = domain; env.put(Context.INITIAL_CONTEXT_FACTORY, com.sun.jndi.ldap.LdapCtxFactory); env.put(Context.PROVIDER_URL, "ldap://" + test1.ad1.foo.com + ":" + 3268); env.put(Context.SECURITY_AUTHENTICATION, simple); env.put(Context.REFERRAL, follow); env.put(Context.SECURITY_PRINCIPAL, (domain + "\\" + user)); env.put(Context.SECURITY_CREDENTIALS, pass); // Bind using specified username and password LdapContext ldapCtx = new InitialLdapContext(env, null); return ldapCtx; } public NamingEnumeration getUserDetails(LdapContext ldapCtx, String user) throws NamingException { // List of attributes to return from LDAP query String returnAttributes[] = {"ou", "sAMAccountName", "givenName", "sn", "memberOf"}; //Create the search controls SearchControls searchCtls = new SearchControls(); searchCtls.setReturningAttributes(returnAttributes); //Specify the search scope searchCtls.setSearchScope(SearchControls.SUBTREE_SCOPE); // Specify the user to search against String searchFilter = "(&(objectClass=*)(sAMAccountName=" + user + "))"; //Perform the search NamingEnumeration answer = ldapCtx.search("dc=dev4,dc=dbt,dc=ukhealth,dc=local", searchFilter, searchCtls); // Only care about the first tuple Attributes userAttributes = ((SearchResult)answer.next()).getAttributes(); if (userAttributes.size() <= 0) throw new NamingException(); return (NamingEnumeration) userAttributes.getAll(); } From what I understand of the trust relationship, if trust1 receives a login attempt for a user in trust2, then it should forward the login attempt on to it and it works this out from the user's domain name. Is this correct or am I missing something or is this not possible using the method above? --EDIT-- The stack trace from the LDAP bind is {java.naming.provider.url=ldap://test1.ad1.foo.com:3268, java.naming.factory.initial=com.sun.jndi.ldap.LdapCtxFactory, java.naming.security.authentication=simple, java.naming.referral=follow} 30-Oct-2012 13:16:02 ADDetailsProvider assignRowValues WARNING: Caught Authentication Exception attempting to bind to LDAP for [trusttest] Auth error is [LDAP: error code 49 - 80090308: LdapErr: DSID-0C0903A9, comment: AcceptSecurityContext error, data 52e, v1db0]

    Read the article

  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

    Read the article

  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

    Read the article

  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

    Read the article

  • West Wind WebSurge - an easy way to Load Test Web Applications

    - by Rick Strahl
    A few months ago on a project the subject of load testing came up. We were having some serious issues with a Web application that would start spewing SQL lock errors under somewhat heavy load. These sort of errors can be tough to catch, precisely because they only occur under load and not during typical development testing. To replicate this error more reliably we needed to put a load on the application and run it for a while before these SQL errors would flare up. It’s been a while since I’d looked at load testing tools, so I spent a bit of time looking at different tools and frankly didn’t really find anything that was a good fit. A lot of tools were either a pain to use, didn’t have the basic features I needed, or are extravagantly expensive. In  the end I got frustrated enough to build an initially small custom load test solution that then morphed into a more generic library, then gained a console front end and eventually turned into a full blown Web load testing tool that is now called West Wind WebSurge. I got seriously frustrated looking for tools every time I needed some quick and dirty load testing for an application. If my aim is to just put an application under heavy enough load to find a scalability problem in code, or to simply try and push an application to its limits on the hardware it’s running I shouldn’t have to have to struggle to set up tests. It should be easy enough to get going in a few minutes, so that the testing can be set up quickly so that it can be done on a regular basis without a lot of hassle. And that was the goal when I started to build out my initial custom load tester into a more widely usable tool. If you’re in a hurry and you want to check it out, you can find more information and download links here: West Wind WebSurge Product Page Walk through Video Download link (zip) Install from Chocolatey Source on GitHub For a more detailed discussion of the why’s and how’s and some background continue reading. How did I get here? When I started out on this path, I wasn’t planning on building a tool like this myself – but I got frustrated enough looking at what’s out there to think that I can do better than what’s available for the most common simple load testing scenarios. When we ran into the SQL lock problems I mentioned, I started looking around what’s available for Web load testing solutions that would work for our whole team which consisted of a few developers and a couple of IT guys both of which needed to be able to run the tests. It had been a while since I looked at tools and I figured that by now there should be some good solutions out there, but as it turns out I didn’t really find anything that fit our relatively simple needs without costing an arm and a leg… I spent the better part of a day installing and trying various load testing tools and to be frank most of them were either terrible at what they do, incredibly unfriendly to use, used some terminology I couldn’t even parse, or were extremely expensive (and I mean in the ‘sell your liver’ range of expensive). Pick your poison. There are also a number of online solutions for load testing and they actually looked more promising, but those wouldn’t work well for our scenario as the application is running inside of a private VPN with no outside access into the VPN. Most of those online solutions also ended up being very pricey as well – presumably because of the bandwidth required to test over the open Web can be enormous. When I asked around on Twitter what people were using– I got mostly… crickets. Several people mentioned Visual Studio Load Test, and most other suggestions pointed to online solutions. I did get a bunch of responses though with people asking to let them know what I found – apparently I’m not alone when it comes to finding load testing tools that are effective and easy to use. As to Visual Studio, the higher end skus of Visual Studio and the test edition include a Web load testing tool, which is quite powerful, but there are a number of issues with that: First it’s tied to Visual Studio so it’s not very portable – you need a VS install. I also find the test setup and terminology used by the VS test runner extremely confusing. Heck, it’s complicated enough that there’s even a Pluralsight course on using the Visual Studio Web test from Steve Smith. And of course you need to have one of the high end Visual Studio Skus, and those are mucho Dinero ($$$) – just for the load testing that’s rarely an option. Some of the tools are ultra extensive and let you run analysis tools on the target serves which is useful, but in most cases – just plain overkill and only distracts from what I tend to be ultimately interested in: Reproducing problems that occur at high load, and finding the upper limits and ‘what if’ scenarios as load is ramped up increasingly against a site. Yes it’s useful to have Web app instrumentation, but often that’s not what you’re interested in. I still fondly remember early days of Web testing when Microsoft had the WAST (Web Application Stress Tool) tool, which was rather simple – and also somewhat limited – but easily allowed you to create stress tests very quickly. It had some serious limitations (mainly that it didn’t work with SSL),  but the idea behind it was excellent: Create tests quickly and easily and provide a decent engine to run it locally with minimal setup. You could get set up and run tests within a few minutes. Unfortunately, that tool died a quiet death as so many of Microsoft’s tools that probably were built by an intern and then abandoned, even though there was a lot of potential and it was actually fairly widely used. Eventually the tools was no longer downloadable and now it simply doesn’t work anymore on higher end hardware. West Wind Web Surge – Making Load Testing Quick and Easy So I ended up creating West Wind WebSurge out of rebellious frustration… The goal of WebSurge is to make it drop dead simple to create load tests. It’s super easy to capture sessions either using the built in capture tool (big props to Eric Lawrence, Telerik and FiddlerCore which made that piece a snap), using the full version of Fiddler and exporting sessions, or by manually or programmatically creating text files based on plain HTTP headers to create requests. I’ve been using this tool for 4 months now on a regular basis on various projects as a reality check for performance and scalability and it’s worked extremely well for finding small performance issues. I also use it regularly as a simple URL tester, as it allows me to quickly enter a URL plus headers and content and test that URL and its results along with the ability to easily save one or more of those URLs. A few weeks back I made a walk through video that goes over most of the features of WebSurge in some detail: Note that the UI has slightly changed since then, so there are some UI improvements. Most notably the test results screen has been updated recently to a different layout and to provide more information about each URL in a session at a glance. The video and the main WebSurge site has a lot of info of basic operations. For the rest of this post I’ll talk about a few deeper aspects that may be of interest while also giving a glance at how WebSurge works. Session Capturing As you would expect, WebSurge works with Sessions of Urls that are played back under load. Here’s what the main Session View looks like: You can create session entries manually by individually adding URLs to test (on the Request tab on the right) and saving them, or you can capture output from Web Browsers, Windows Desktop applications that call services, your own applications using the built in Capture tool. With this tool you can capture anything HTTP -SSL requests and content from Web pages, AJAX calls, SOAP or REST services – again anything that uses Windows or .NET HTTP APIs. Behind the scenes the capture tool uses FiddlerCore so basically anything you can capture with Fiddler you can also capture with Web Surge Session capture tool. Alternately you can actually use Fiddler as well, and then export the captured Fiddler trace to a file, which can then be imported into WebSurge. This is a nice way to let somebody capture session without having to actually install WebSurge or for your customers to provide an exact playback scenario for a given set of URLs that cause a problem perhaps. Note that not all applications work with Fiddler’s proxy unless you configure a proxy. For example, .NET Web applications that make HTTP calls usually don’t show up in Fiddler by default. For those .NET applications you can explicitly override proxy settings to capture those requests to service calls. The capture tool also has handy optional filters that allow you to filter by domain, to help block out noise that you typically don’t want to include in your requests. For example, if your pages include links to CDNs, or Google Analytics or social links you typically don’t want to include those in your load test, so by capturing just from a specific domain you are guaranteed content from only that one domain. Additionally you can provide url filters in the configuration file – filters allow to provide filter strings that if contained in a url will cause requests to be ignored. Again this is useful if you don’t filter by domain but you want to filter out things like static image, css and script files etc. Often you’re not interested in the load characteristics of these static and usually cached resources as they just add noise to tests and often skew the overall url performance results. In my testing I tend to care only about my dynamic requests. SSL Captures require Fiddler Note, that in order to capture SSL requests you’ll have to install the Fiddler’s SSL certificate. The easiest way to do this is to install Fiddler and use its SSL configuration options to get the certificate into the local certificate store. There’s a document on the Telerik site that provides the exact steps to get SSL captures to work with Fiddler and therefore with WebSurge. Session Storage A group of URLs entered or captured make up a Session. Sessions can be saved and restored easily as they use a very simple text format that simply stored on disk. The format is slightly customized HTTP header traces separated by a separator line. The headers are standard HTTP headers except that the full URL instead of just the domain relative path is stored as part of the 1st HTTP header line for easier parsing. Because it’s just text and uses the same format that Fiddler uses for exports, it’s super easy to create Sessions by hand manually or under program control writing out to a simple text file. You can see what this format looks like in the Capture window figure above – the raw captured format is also what’s stored to disk and what WebSurge parses from. The only ‘custom’ part of these headers is that 1st line contains the full URL instead of the domain relative path and Host: header. The rest of each header are just plain standard HTTP headers with each individual URL isolated by a separator line. The format used here also uses what Fiddler produces for exports, so it’s easy to exchange or view data either in Fiddler or WebSurge. Urls can also be edited interactively so you can modify the headers easily as well: Again – it’s just plain HTTP headers so anything you can do with HTTP can be added here. Use it for single URL Testing Incidentally I’ve also found this form as an excellent way to test and replay individual URLs for simple non-load testing purposes. Because you can capture a single or many URLs and store them on disk, this also provides a nice HTTP playground where you can record URLs with their headers, and fire them one at a time or as a session and see results immediately. It’s actually an easy way for REST presentations and I find the simple UI flow actually easier than using Fiddler natively. Finally you can save one or more URLs as a session for later retrieval. I’m using this more and more for simple URL checks. Overriding Cookies and Domains Speaking of HTTP headers – you can also overwrite cookies used as part of the options. One thing that happens with modern Web applications is that you have session cookies in use for authorization. These cookies tend to expire at some point which would invalidate a test. Using the Options dialog you can actually override the cookie: which replaces the cookie for all requests with the cookie value specified here. You can capture a valid cookie from a manual HTTP request in your browser and then paste into the cookie field, to replace the existing Cookie with the new one that is now valid. Likewise you can easily replace the domain so if you captured urls on west-wind.com and now you want to test on localhost you can do that easily easily as well. You could even do something like capture on store.west-wind.com and then test on localhost/store which would also work. Running Load Tests Once you’ve created a Session you can specify the length of the test in seconds, and specify the number of simultaneous threads to run each session on. Sessions run through each of the URLs in the session sequentially by default. One option in the options list above is that you can also randomize the URLs so each thread runs requests in a different order. This avoids bunching up URLs initially when tests start as all threads run the same requests simultaneously which can sometimes skew the results of the first few minutes of a test. While sessions run some progress information is displayed: By default there’s a live view of requests displayed in a Console-like window. On the bottom of the window there’s a running total summary that displays where you’re at in the test, how many requests have been processed and what the requests per second count is currently for all requests. Note that for tests that run over a thousand requests a second it’s a good idea to turn off the console display. While the console display is nice to see that something is happening and also gives you slight idea what’s happening with actual requests, once a lot of requests are processed, this UI updating actually adds a lot of CPU overhead to the application which may cause the actual load generated to be reduced. If you are running a 1000 requests a second there’s not much to see anyway as requests roll by way too fast to see individual lines anyway. If you look on the options panel, there is a NoProgressEvents option that disables the console display. Note that the summary display is still updated approximately once a second so you can always tell that the test is still running. Test Results When the test is done you get a simple Results display: On the right you get an overall summary as well as breakdown by each URL in the session. Both success and failures are highlighted so it’s easy to see what’s breaking in your load test. The report can be printed or you can also open the HTML document in your default Web Browser for printing to PDF or saving the HTML document to disk. The list on the right shows you a partial list of the URLs that were fired so you can look in detail at the request and response data. The list can be filtered by success and failure requests. Each list is partial only (at the moment) and limited to a max of 1000 items in order to render reasonably quickly. Each item in the list can be clicked to see the full request and response data: This particularly useful for errors so you can quickly see and copy what request data was used and in the case of a GET request you can also just click the link to quickly jump to the page. For non-GET requests you can find the URL in the Session list, and use the context menu to Test the URL as configured including any HTTP content data to send. You get to see the full HTTP request and response as well as a link in the Request header to go visit the actual page. Not so useful for a POST as above, but definitely useful for GET requests. Finally you can also get a few charts. The most useful one is probably the Request per Second chart which can be accessed from the Charts menu or shortcut. Here’s what it looks like:   Results can also be exported to JSON, XML and HTML. Keep in mind that these files can get very large rather quickly though, so exports can end up taking a while to complete. Command Line Interface WebSurge runs with a small core load engine and this engine is plugged into the front end application I’ve shown so far. There’s also a command line interface available to run WebSurge from the Windows command prompt. Using the command line you can run tests for either an individual URL (similar to AB.exe for example) or a full Session file. By default when it runs WebSurgeCli shows progress every second showing total request count, failures and the requests per second for the entire test. A silent option can turn off this progress display and display only the results. The command line interface can be useful for build integration which allows checking for failures perhaps or hitting a specific requests per second count etc. It’s also nice to use this as quick and dirty URL test facility similar to the way you’d use Apache Bench (ab.exe). Unlike ab.exe though, WebSurgeCli supports SSL and makes it much easier to create multi-URL tests using either manual editing or the WebSurge UI. Current Status Currently West Wind WebSurge is still in Beta status. I’m still adding small new features and tweaking the UI in an attempt to make it as easy and self-explanatory as possible to run. Documentation for the UI and specialty features is also still a work in progress. I plan on open-sourcing this product, but it won’t be free. There’s a free version available that provides a limited number of threads and request URLs to run. A relatively low cost license  removes the thread and request limitations. Pricing info can be found on the Web site – there’s an introductory price which is $99 at the moment which I think is reasonable compared to most other for pay solutions out there that are exorbitant by comparison… The reason code is not available yet is – well, the UI portion of the app is a bit embarrassing in its current monolithic state. The UI started as a very simple interface originally that later got a lot more complex – yeah, that never happens, right? Unless there’s a lot of interest I don’t foresee re-writing the UI entirely (which would be ideal), but in the meantime at least some cleanup is required before I dare to publish it :-). The code will likely be released with version 1.0. I’m very interested in feedback. Do you think this could be useful to you and provide value over other tools you may or may not have used before? I hope so – it already has provided a ton of value for me and the work I do that made the development worthwhile at this point. You can leave a comment below, or for more extensive discussions you can post a message on the West Wind Message Board in the WebSurge section Microsoft MVPs and Insiders get a free License If you’re a Microsoft MVP or a Microsoft Insider you can get a full license for free. Send me a link to your current, official Microsoft profile and I’ll send you a not-for resale license. Send any messages to [email protected]. Resources For more info on WebSurge and to download it to try it out, use the following links. West Wind WebSurge Home Download West Wind WebSurge Getting Started with West Wind WebSurge Video© Rick Strahl, West Wind Technologies, 2005-2014Posted in ASP.NET   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

    Read the article

  • SQL SERVER – SQL in Sixty Seconds – 5 Videos from Joes 2 Pros Series – SQL Exam Prep Series 70-433

    - by pinaldave
    Joes 2 Pros SQL Server Learning series is indeed fun. Joes 2 Pros series is written for beginners and who wants to build expertise for SQL Server programming and development from fundamental. In the beginning of the series author Rick Morelan is not shy to explain the simplest concept of how to open SQL Server Management Studio. Honestly the book starts with that much basic but as it progresses further Rick discussing about various advanced concepts from query tuning to Core Architecture. This five part series is written with keeping SQL Server Exam 70-433. Instead of just focusing on what will be there in exam, this series is focusing on learning the important concepts thoroughly. This book no way take short cut to explain any concepts and at times, will go beyond the topic at length. The best part is that all the books has many companion videos explaining the concepts and videos. Every Wednesday I like to post a video which explains something in quick few seconds. Today we will go over five videos which I posted in my earlier posts related to Joes 2 Pros series. Introduction to XML Data Type Methods – SQL in Sixty Seconds #015 The XML data type was first introduced with SQL Server 2005. This data type continues with SQL Server 2008 where expanded XML features are available, most notably is the power of the XQuery language to analyze and query the values contained in your XML instance. There are five XML data type methods available in SQL Server 2008: query() – Used to extract XML fragments from an XML data type. value() – Used to extract a single value from an XML document. exist() – Used to determine if a specified node exists. Returns 1 if yes and 0 if no. modify() – Updates XML data in an XML data type. node() – Shreds XML data into multiple rows (not covered in this blog post). [Detailed Blog Post] | [Quiz with Answer] Introduction to SQL Error Actions – SQL in Sixty Seconds #014 Most people believe that when SQL Server encounters an error severity level 11 or higher the remaining SQL statements will not get executed. In addition, people also believe that if any error severity level of 11 or higher is hit inside an explicit transaction, then the whole statement will fail as a unit. While both of these beliefs are true 99% of the time, they are not true in all cases. It is these outlying cases that frequently cause unexpected results in your SQL code. To understand how to achieve consistent results you need to know the four ways SQL Error Actions can react to error severity levels 11-16: Statement Termination – The statement with the procedure fails but the code keeps on running to the next statement. Transactions are not affected. Scope Abortion – The current procedure, function or batch is aborted and the next calling scope keeps running. That is, if Stored Procedure A calls B and C, and B fails, then nothing in B runs but A continues to call C. @@Error is set but the procedure does not have a return value. Batch Termination – The entire client call is terminated. XACT_ABORT – (ON = The entire client call is terminated.) or (OFF = SQL Server will choose how to handle all errors.) [Detailed Blog Post] | [Quiz with Answer] Introduction to Basics of a Query Hint – SQL in Sixty Seconds #013 Query hints specify that the indicated hints should be used throughout the query. Query hints affect all operators in the statement and are implemented using the OPTION clause. Cautionary Note: Because the SQL Server Query Optimizer typically selects the best execution plan for a query, it is highly recommended that hints be used as a last resort for experienced developers and database administrators to achieve the desired results. [Detailed Blog Post] | [Quiz with Answer] Introduction to Hierarchical Query – SQL in Sixty Seconds #012 A CTE can be thought of as a temporary result set and are similar to a derived table in that it is not stored as an object and lasts only for the duration of the query. A CTE is generally considered to be more readable than a derived table and does not require the extra effort of declaring a Temp Table while providing the same benefits to the user. However; a CTE is more powerful than a derived table as it can also be self-referencing, or even referenced multiple times in the same query. A recursive CTE requires four elements in order to work properly: Anchor query (runs once and the results ‘seed’ the Recursive query) Recursive query (runs multiple times and is the criteria for the remaining results) UNION ALL statement to bind the Anchor and Recursive queries together. INNER JOIN statement to bind the Recursive query to the results of the CTE. [Detailed Blog Post] | [Quiz with Answer] Introduction to SQL Server Security – SQL in Sixty Seconds #011 Let’s get some basic definitions down first. Take the workplace example where “Tom” needs “Read” access to the “Financial Folder”. What are the Securable, Principal, and Permissions from that last sentence? A Securable is a resource that someone might want to access (like the Financial Folder). A Principal is anything that might want to gain access to the securable (like Tom). A Permission is the level of access a principal has to a securable (like Read). [Detailed Blog Post] | [Quiz with Answer] Please leave a comment explain which one was your favorite video as that will help me understand what works and what needs improvement. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video

    Read the article

  • Why aren't we all doing model driven development yet ?

    - by KeesDijk
    I am a true believer in Model Driven Development, I think it has the possibility to increase productivity, quality and predictability. When looking at MetaEdit the results are amazing. Mendix in the Netherlands is growing very very fast and has great results. I also know there are a lot of problems versioning of generators, templates and framework projects that just aren't right for model driven development (not enough repetition) higher risks (when the first project fails, you have less results than you would have with more traditional development) etc But still these problems seem solvable and the benefits should outweigh the effort needed. Question: What do you see as the biggest problems that make you not even consider model driven development ? I want to use these answers not just for my own understanding but also as a possible source for a series of internal articles I plan to write.

    Read the article

  • IBM "per core" comparisons for SPECjEnterprise2010

    - by jhenning
    I recently stumbled upon a blog entry from Roman Kharkovski (an IBM employee) comparing some SPECjEnterprise2010 results for IBM vs. Oracle. Mr. Kharkovski's blog claims that SPARC delivers half the transactions per core vs. POWER7. Prior to any argument, I should say that my predisposition is to like Mr. Kharkovski, because he says that his blog is intended to be factual; that the intent is to try to avoid marketing hype and FUD tactic; and mostly because he features a picture of himself wearing a bike helmet (me too). Therefore, in a spirit of technical argument, rather than FUD fight, there are a few areas in his comparison that should be discussed. Scaling is not free For any benchmark, if a small system scores 13k using quantity R1 of some resource, and a big system scores 57k using quantity R2 of that resource, then, sure, it's tempting to divide: is  13k/R1 > 57k/R2 ? It is tempting, but not necessarily educational. The problem is that scaling is not free. Building big systems is harder than building small systems. Scoring  13k/R1  on a little system provides no guarantee whatsoever that one can sustain that ratio when attempting to handle more than 4 times as many users. Choosing the denominator radically changes the picture When ratios are used, one can vastly manipulate appearances by the choice of denominator. In this case, lots of choices are available for the resource to be compared (R1 and R2 above). IBM chooses to put cores in the denominator. Mr. Kharkovski provides some reasons for that choice in his blog entry. And yet, it should be noted that the very concept of a core is: arbitrary: not necessarily comparable across vendors; fluid: modern chips shift chip resources in response to load; and invisible: unless you have a microscope, you can't see it. By contrast, one can actually see processor chips with the naked eye, and they are a bit easier to count. If we put chips in the denominator instead of cores, we get: 13161.07 EjOPS / 4 chips = 3290 EjOPS per chip for IBM vs 57422.17 EjOPS / 16 chips = 3588 EjOPS per chip for Oracle The choice of denominator makes all the difference in the appearance. Speaking for myself, dividing by chips just seems to make more sense, because: I can see chips and count them; and I can accurately compare the number of chips in my system to the count in some other vendor's system; and Tthe probability of being able to continue to accurately count them over the next 10 years of microprocessor development seems higher than the probability of being able to accurately and comparably count "cores". SPEC Fair use requirements Speaking as an individual, not speaking for SPEC and not speaking for my employer, I wonder whether Mr. Kharkovski's blog article, taken as a whole, meets the requirements of the SPEC Fair Use rule www.spec.org/fairuse.html section I.D.2. For example, Mr. Kharkovski's footnote (1) begins Results from http://www.spec.org as of 04/04/2013 Oracle SUN SPARC T5-8 449 EjOPS/core SPECjEnterprise2010 (Oracle's WLS best SPECjEnterprise2010 EjOPS/core result on SPARC). IBM Power730 823 EjOPS/core (World Record SPECjEnterprise2010 EJOPS/core result) The questionable tactic, from a Fair Use point of view, is that there is no such metric at the designated location. At www.spec.org, You can find the SPEC metric 57422.17 SPECjEnterprise2010 EjOPS for Oracle and You can also find the SPEC metric 13161.07 SPECjEnterprise2010 EjOPS for IBM. Despite the implication of the footnote, you will not find any mention of 449 nor anything that says 823. SPEC says that you can, under its fair use rule, derive your own values; but it emphasizes: "The context must not give the appearance that SPEC has created or endorsed the derived value." Substantiation and transparency Although SPEC disclaims responsibility for non-SPEC information (section I.E), it says that non-SPEC data and methods should be accurate, should be explained, should be substantiated. Unfortunately, it is difficult or impossible for the reader to independently verify the pricing: Were like units compared to like (e.g. list price to list price)? Were all components (hw, sw, support) included? Were all fees included? Note that when tpc.org shows IBM pricing, there are often items such as "PROCESSOR ACTIVATION" and "MEMORY ACTIVATION". Without the transparency of a detailed breakdown, the pricing claims are questionable. T5 claim for "Fastest Processor" Mr. Kharkovski several times questions Oracle's claim for fastest processor, writing You see, when you publish industry benchmarks, people may actually compare your results to other vendor's results. Well, as we performance people always say, "it depends". If you believe in performance-per-core as the primary way of looking at the world, then yes, the POWER7+ is impressive, spending its chip resources to support up to 32 threads (8 cores x 4 threads). Or, it just might be useful to consider performance-per-chip. Each SPARC T5 chip allows 128 hardware threads to be simultaneously executing (16 cores x 8 threads). The Industry Standard Benchmark that focuses specifically on processor chip performance is SPEC CPU2006. For this very well known and popular benchmark, SPARC T5: provides better performance than both POWER7 and POWER7+, for 1 chip vs. 1 chip, for 8 chip vs. 8 chip, for integer (SPECint_rate2006) and floating point (SPECfp_rate2006), for Peak tuning and for Base tuning. For example, at the 8-chip level, integer throughput (SPECint_rate2006) is: 3750 for SPARC 2170 for POWER7+. You can find the details at the March 2013 BestPerf CPU2006 page SPEC is a trademark of the Standard Performance Evaluation Corporation, www.spec.org. The two specific results quoted for SPECjEnterprise2010 are posted at the URLs linked from the discussion. Results for SPEC CPU2006 were verified at spec.org 1 July 2013, and can be rechecked here.

    Read the article

  • Need Help With Finding SEO Company/Individual

    - by three3
    Hi everyone, I am fairly new to SEO and I have done all of the tactics and operations that I know to do to help my site rank to the number 1 spot on Google. I know that no one can guarantee the number 1 spot on Google or on any other search engine but I cannot even seem to get my website to the first page of Google's search results. My company is looking to hire a company or individual to work on our SEO. Does anyone here know of an SEO company or individual that has had good results in the past with getting a website to the front of Google, and preferably to the number 1 spot on Google? We are willing to pay a large sum of money for our keywords to rank on the front of Google search results. Any suggestions are welcome. Thanks John

    Read the article

  • Need Help With Finding SEO Company/Individual

    - by three3
    I am fairly new to SEO and I have done all of the tactics and operations that I know to do to help my site rank to the number 1 spot on Google. I know that no one can guarantee the number 1 spot on Google or on any other search engine but I cannot even seem to get my website to the first page of Google's search results. My company is looking to hire a company or individual to work on our SEO. Does anyone here know of an SEO company or individual that has had good results in the past with getting a website to the front of Google, and preferably to the number 1 spot on Google? We are willing to pay a large sum of money for our keywords to rank on the front of Google search results. Any suggestions are welcome. Thanks John

    Read the article

  • Benchmark Against 160 Identity and Access Programs Worldwide

    - by Naresh Persaud
    Aberdeen documented the results of taking a "platform approach" to Identity and Access Management in a recent study - you can read the complete report here. Aberdeen has created an assessment tool that allows organizations to take a similar survey and compare their performance to companies surveyed in the original report. The assessment takes 5 minutes to complete and provides a complete printable report with a statistical comparison for each performance indicator. In addition, the assessment report provides guidance on improvements that organizations can take to achieve better results based on the benchmark. Take the assessment by clicking here.  You can also attend one of the physical events and discuss the results of the survey with Derek Brink the author. In the events, Derek discusses how organizations take advantage of the report. Register here. 

    Read the article

  • Why write clean, refactored code?

    - by Shamal Karunarathne
    Hi programming lovers, This is a question I've been asking myself for a long time. Thought of throwing out it to you. From my experience of working on several Java based projects, I've seen tons of codes which we call 'dirty'. The unconventional class/method/field naming, wrong way of handling of exceptions, unnecessarily heavy loops and recursion etc. But the code gives the intended results. Though I hate to see dirty code, it's time taking to clean them up and eventually comes the question of "is it worth? it's giving the desired results so what's the point of cleaning?" In team projects, should there be someone specifically to refactor and check for clean code? Or are there situations where the 'dirty' codes fail to give intended results or make the customers unhappy? Do feel free to comment and reply. And tell me if I'm missing something here. Thanks.

    Read the article

  • await, WhenAll, WaitAll, oh my!!

    - by cibrax
    If you are dealing with asynchronous work in .NET, you might know that the Task class has become the main driver for wrapping asynchronous calls. Although this class was officially introduced in .NET 4.0, the programming model for consuming tasks was much more simplified in C# 5.0 in .NET 4.5 with the addition of the new async/await keywords. In a nutshell, you can use these keywords to make asynchronous calls as if they were sequential, and avoiding in that way any fork or callback in the code. The compiler takes care of the rest. I was yesterday writing some code for making multiple asynchronous calls to backend services in parallel. The code looked as follow, var allResults = new List<Result>(); foreach(var provider in providers) { var results = await provider.GetResults(); allResults.AddRange(results); } return allResults; You see, I was using the await keyword to make multiple calls in parallel. Something I did not consider was the overhead this code implied after being compiled. I started an interesting discussion with some smart folks in twitter. One of them, Tugberk Ugurlu, had the brilliant idea of actually write some code to make a performance comparison with another approach using Task.WhenAll. There are two additional methods you can use to wait for the results of multiple calls in parallel, WhenAll and WaitAll. WhenAll creates a new task and waits for results in that new task, so it does not block the calling thread. WaitAll, on the other hand, blocks the calling thread. This is the code Tugberk initially wrote, and I modified afterwards to also show the results of WaitAll. class Program { private static Func<Stopwatch, Task>[] funcs = new Func<Stopwatch, Task>[] { async (watch) => { watch.Start(); await Task.Delay(1000); Console.WriteLine("1000 one has been completed."); }, async (watch) => { await Task.Delay(1500); Console.WriteLine("1500 one has been completed."); }, async (watch) => { await Task.Delay(2000); Console.WriteLine("2000 one has been completed."); watch.Stop(); Console.WriteLine(watch.ElapsedMilliseconds + "ms has been elapsed."); } }; static void Main(string[] args) { Console.WriteLine("Await in loop work starts..."); DoWorkAsync().ContinueWith(task => { Console.WriteLine("Parallel work starts..."); DoWorkInParallelAsync().ContinueWith(t => { Console.WriteLine("WaitAll work starts..."); WaitForAll(); }); }); Console.ReadLine(); } static async Task DoWorkAsync() { Stopwatch watch = new Stopwatch(); foreach (var func in funcs) { await func(watch); } } static async Task DoWorkInParallelAsync() { Stopwatch watch = new Stopwatch(); await Task.WhenAll(funcs[0](watch), funcs[1](watch), funcs[2](watch)); } static void WaitForAll() { Stopwatch watch = new Stopwatch(); Task.WaitAll(funcs[0](watch), funcs[1](watch), funcs[2](watch)); } } After running this code, the results were very concluding. Await in loop work starts... 1000 one has been completed. 1500 one has been completed. 2000 one has been completed. 4532ms has been elapsed. Parallel work starts... 1000 one has been completed. 1500 one has been completed. 2000 one has been completed. 2007ms has been elapsed. WaitAll work starts... 1000 one has been completed. 1500 one has been completed. 2000 one has been completed. 2009ms has been elapsed. The await keyword in a loop does not really make the calls in parallel.

    Read the article

  • Google shows "search instead for" when searching for our website

    - by Athanatos
    Our website is new and the name is similar (only one letter different than another website) completely different type and company though. searching for xxxxxA works OK in Google and we find relatively good results. However searching xxxxxA.com finds results for the other website and gives us the following options: Showing results for xxxxxE.com Search instead for xxxxxA.com (hyperlink when clicked then it is correctly searching for our site) Questions: Do we need to contact Google to correct this and if yes how ? if not will it be corrected automatically when the site becomes more popular and what is the process? How do we make the process quicker?

    Read the article

  • What kind of connection this is?

    - by Rohit
    I happened to use `netstat' program this morning.. And what I couldn't understand its results. The snap-shot of the results window is given below: Screen-shot is at http://s2.postimg.org/3zt058415/image.png ( I can't post images! :/ ) Can somebody explain me the last entry in the results, where I've pointed them with arrows? [1] I don't understand why `canonical' servers are connected with my computer even if I am not currently using anything that I would expect such a connection to exist. [2] Second, I don't see the PID/Program name of the Program which has made this connection. I would at-least expect a PID.

    Read the article

  • I've changed my URL schema. How do I tell Google to index the new schema and forget the old one?

    - by growse
    I had a site where the urls were constructed like this /index.php/Topic /index.php/AnotherTopic These were indexed in google, and search results returned that pointed to these. However, I've recently replatformed that site, and reconfigured it so the above urls would be: /index.php?title=Topic /index.php?title=AnotherTopic The original urls are returning 404s. The site is linking to the correct URL schema internally, but Google is retaining the original schema in its search results. I've updated and resubmitted the sitemap which only contains the new schema. Also, Google's webmasters tool is going slightly bananas at the fact there's now a spike in 404 errors in its crawl results. What would be the best approach to get Google to 'forget' about the old schema, and instead index the new schema? Should I try blocking /index.php/ in robots.txt? Should I be returning 301 codes instead of 404 for the original urls?

    Read the article

  • Whats steps can I suggest to achieve the best Geolocation Result [migrated]

    - by Matt
    We are using Geolocation (getCurrentPosition()) in a website to determine a users position when using our site from a mobile device. I want to write an article explaining how the user can obtain the best results. Am I correct in assuming: Enabling GPS will yield the best result when in rural areas (less buildings to obscure line of sight to the satelites) Enabling Wi-Fi will yield the best results when in urban areas (generally more Wi-Fi hotspots available) Is it true that Android phones have better results from silently harvesting Wi-Fi hotspot details? Any links to reference material on this are appreciated

    Read the article

  • Developing a search algorithm

    - by Richart Bremer
    I want to create a basic search engine, and I want you to give me some ideas how to filter out the best results for my visitors. I have three fields regarding a product the user can search in: Title Category Description I came up with these ideas and I ask you to either competently criticize them or add to them. If the search term occurs in all three fields it should be among the first results. If it is in two of the fields it is below the results of 1. Combine the amount of occurences and output a value in per cent. For instance if in all fields together the term clock appeared 50 times and in all fields together there are 200 words, then the per cent value is 50/200*100 = 25%. Another product entry amounts to say 20% so product one having 25% is listed before product two having 20%.

    Read the article

  • Why is FTP file transfer from Android Phone Slow?

    - by Frychiko
    To transfer files from my Android phone to Ubuntu, I use an app that creates a FTP server on the phone. Copying files to Ubuntu 12.04 (same with 12.10) I get up to 260 KB/s. Copying files to Windows 7 I get up to 1050 KB/s. I am currently on a fresh install of 12.10 with barely anything installed with the same results. I have tested with both a Galaxy S3 and HTC Desire HD with identical results. I have tested about 5 apps with the same results. Why is it slow on Ubuntu?

    Read the article

  • Displaying JSON in your Browser

    - by Rick Strahl
    Do you work with AJAX requests a lot and need to quickly check URLs for JSON results? Then you probably know that it’s a fairly big hassle to examine JSON results directly in the browser. Yes, you can use FireBug or Fiddler which work pretty well for actual AJAX requests, but if you just fire off a URL for quick testing in the browser you usually get hit by the Save As dialog and the download manager, followed by having to open the saved document in a text editor in FireFox. Enter JSONView which allows you to simply display JSON results directly in the browser. For example, imagine I have a URL like this: http://localhost/westwindwebtoolkitweb/RestService.ashx?Method=ReturnObject&format=json&Name1=Rick&Name2=John&date=12/30/2010 typed directly into the browser and that that returns a complex JSON object. With JSONView the result looks like this: No fuss, no muss. It just works. Here the result is an array of Person objects that contain additional address child objects displayed right in the browser. JSONView basically adds content type checking for application/json results and when it finds a JSON result takes over the rendering and formats the display in the browser. Note that it re-formats the raw JSON as well for a nicer display view along with collapsible regions for objects. You can still use View Source to see the raw JSON string returned. For me this is a huge time-saver. As I work with AJAX result data using GET and REST style URLs quite a bit it’s a big timesaver. To quickly and easily display JSON is a key feature in my development day and JSONView for all its simplicity fits that bill for me. If you’re doing AJAX development and you often review URL based JSON results do yourself a favor and pick up a copy of JSONView. Other Browsers JSONView works only with FireFox – what about other browsers? Chrome Chrome actually displays raw JSON responses as plain text without any plug-ins. There’s no plug-in or configuration needed, it just works, although you won’t get any fancy formatting. [updated from comments] There’s also a port of JSONView available for Chrome from here: https://chrome.google.com/webstore/detail/chklaanhfefbnpoihckbnefhakgolnmc It looks like it works just about the same as the JSONView plug-in for FireFox. Thanks for all that pointed this out… Internet Explorer Internet Explorer probably has the worst response to JSON encoded content: It displays an error page as it apparently tries to render JSON as XML: Yeah that seems real smart – rendering JSON as an XML document. WTF? To get at the actual JSON output, you can use View Source. To get IE to display JSON directly as text you can add a Mime type mapping in the registry:   Create a new application/json key in: HKEY_CLASSES_ROOT\MIME\Database\ContentType\application/json Add a string value of CLSID with a value of {25336920-03F9-11cf-8FD0-00AA00686F13} Add a DWORD value of Encoding with a value of 80000 I can’t take credit for this tip – found it here first on Sky Sander’s Blog. Note that the CLSID can be used for just about any type of text data you want to display as plain text in the IE. It’s the in-place display mechanism and it should work for most text content. For example it might also be useful for looking at CSS and JS files inside of the browser instead of downloading those documents as well. © Rick Strahl, West Wind Technologies, 2005-2011Posted in ASP.NET  AJAX  

    Read the article

  • Enable Multi-Column Google Searches with a User Script

    - by Asian Angel
    Are you wanting to improve the search results view at Google and make better use of the webpage space? With a little user script magic you can make those search results look and fit better in your favorite browser. Note: This user script may conflict with the AutoPager extension if you have it installed in your favorite browser. Before Here is the standard single column view of search results at Google. Not too bad but the available space could certainly be better utilized. Note: For the purposes of our example we are using Google Chrome but this user script can be easily added to other browsers. After If you have never installed a user script in Chrome before it is just as simple as the regular extensions at the official Google website. Here you can see the details for the user script we are installing. Notice that you can view the source code if desired. To add the user script to Chrome click on “Install”. Once you start the install process you will see an intermediary message asking if you wish to continue in the lower left corner of your browser. Click “Continue” to move to the next step in the install process. From this point on the install process is practically identical to the official extensions. You can see the final confirmation window here…click “Install” to finish adding the user script to Chrome. As with regular extensions you will see a post-install message in the upper right corner. So, what does a user script look like in the “Extensions Page”? You can see the user script entry here…outside of an icon it looks rather identical to a normal extension. After refreshing the search page shown above we now have two columns of search results (default setting). This looks much much better than a single column view and there is little to no page scrolling required now. To switch to a three column view simply use the keyboard shortcut “Alt + 3”. To return to a single column view use “Alt + 1” and for the default two column view use “Alt + 2”. Three keyboard shortcuts for three different views…definitely a good thing. Note: On our test system we needed to use the number keys at the top of our keyboard to switch views…this is most likely the result of unique settings on our test system. Conclusion If you are wanting a better viewing experience when conducting searches at Google then this user script will make a very nice addition to your favorite browser. For those using Firefox you can add user scripts with the Greasemonkey & Stylish extensions. Using Opera Browser? See our how-to for adding user scripts to Opera here. Links Install the Multi-Column View of Google Search Results User Script Similar Articles Productive Geek Tips Hide Flash Animations in Google ChromeEnable Google Search From Shortcut Key in KDE on (k)UbuntuSet Gmail as Default Mail Client in UbuntuSet Up User Scripts in Opera BrowserHow To Enable Favicons for Google Reader Subscriptions TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Yes, it’s Patch Tuesday Generate Stunning Tag Clouds With Tagxedo Install, Remove and HIDE Fonts in Windows 7 Need Help with Your Home Network? Awesome Lyrics Finder for Winamp & Windows Media Player Download Videos from Hulu

    Read the article

  • SPARC T4-2 Produces World Record Oracle Essbase Aggregate Storage Benchmark Result

    - by Brian
    Significance of Results Oracle's SPARC T4-2 server configured with a Sun Storage F5100 Flash Array and running Oracle Solaris 10 with Oracle Database 11g has achieved exceptional performance for the Oracle Essbase Aggregate Storage Option benchmark. The benchmark has upwards of 1 billion records, 15 dimensions and millions of members. Oracle Essbase is a multi-dimensional online analytical processing (OLAP) server and is well-suited to work well with SPARC T4 servers. The SPARC T4-2 server (2 cpus) running Oracle Essbase 11.1.2.2.100 outperformed the previous published results on Oracle's SPARC Enterprise M5000 server (4 cpus) with Oracle Essbase 11.1.1.3 on Oracle Solaris 10 by 80%, 32% and 2x performance improvement on Data Loading, Default Aggregation and Usage Based Aggregation, respectively. The SPARC T4-2 server with Sun Storage F5100 Flash Array and Oracle Essbase running on Oracle Solaris 10 achieves sub-second query response times for 20,000 users in a 15 dimension database. The SPARC T4-2 server configured with Oracle Essbase was able to aggregate and store values in the database for a 15 dimension cube in 398 minutes with 16 threads and in 484 minutes with 8 threads. The Sun Storage F5100 Flash Array provides more than a 20% improvement out-of-the-box compared to a mid-size fiber channel disk array for default aggregation and user-based aggregation. The Sun Storage F5100 Flash Array with Oracle Essbase provides the best combination for large Oracle Essbase databases leveraging Oracle Solaris ZFS and taking advantage of high bandwidth for faster load and aggregation. Oracle Fusion Middleware provides a family of complete, integrated, hot pluggable and best-of-breed products known for enabling enterprise customers to create and run agile and intelligent business applications. Oracle Essbase's performance demonstrates why so many customers rely on Oracle Fusion Middleware as their foundation for innovation. Performance Landscape System Data Size(millions of items) Database Load(minutes) Default Aggregation(minutes) Usage Based Aggregation(minutes) SPARC T4-2, 2 x SPARC T4 2.85 GHz 1000 149 398* 55 Sun M5000, 4 x SPARC64 VII 2.53 GHz 1000 269 526 115 Sun M5000, 4 x SPARC64 VII 2.4 GHz 400 120 448 18 * – 398 mins with CALCPARALLEL set to 16; 484 mins with CALCPARALLEL threads set to 8 Configuration Summary Hardware Configuration: 1 x SPARC T4-2 2 x 2.85 GHz SPARC T4 processors 128 GB memory 2 x 300 GB 10000 RPM SAS internal disks Storage Configuration: 1 x Sun Storage F5100 Flash Array 40 x 24 GB flash modules SAS HBA with 2 SAS channels Data Storage Scheme Striped - RAID 0 Oracle Solaris ZFS Software Configuration: Oracle Solaris 10 8/11 Installer V 11.1.2.2.100 Oracle Essbase Client v 11.1.2.2.100 Oracle Essbase v 11.1.2.2.100 Oracle Essbase Administration services 64-bit Oracle Database 11g Release 2 (11.2.0.3) HP's Mercury Interactive QuickTest Professional 9.5.0 Benchmark Description The objective of the Oracle Essbase Aggregate Storage Option benchmark is to showcase the ability of Oracle Essbase to scale in terms of user population and data volume for large enterprise deployments. Typical administrative and end-user operations for OLAP applications were simulated to produce benchmark results. The benchmark test results include: Database Load: Time elapsed to build a database including outline and data load. Default Aggregation: Time elapsed to build aggregation. User Based Aggregation: Time elapsed of the aggregate views proposed as a result of tracked retrieval queries. Summary of the data used for this benchmark: 40 flat files, each of size 1.2 GB, 49.4 GB in total 10 million rows per file, 1 billion rows total 28 columns of data per row Database outline has 15 dimensions (five of them are attribute dimensions) Customer dimension has 13.3 million members 3 rule files Key Points and Best Practices The Sun Storage F5100 Flash Array has been used to accelerate the application performance. Setting data load threads (DLTHREADSPREPARE) to 64 and Load Buffer to 6 improved dataloading by about 9%. Factors influencing aggregation materialization performance are "Aggregate Storage Cache" and "Number of Threads" (CALCPARALLEL) for parallel view materialization. The optimal values for this workload on the SPARC T4-2 server were: Aggregate Storage Cache: 32 GB CALCPARALLEL: 16   See Also Oracle Essbase Aggregate Storage Option Benchmark on Oracle's SPARC T4-2 Server oracle.com Oracle Essbase oracle.com OTN SPARC T4-2 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 28 August 2012.

    Read the article

  • SQL SERVER – How to Roll Back SQL Server Database Changes

    - by Pinal Dave
    In a perfect scenario, no unexpected and unplanned changes occur. There are no unpleasant surprises, no inadvertent changes. However, even with all precautions and testing, there is sometimes a need to revert a structure or data change. One of the methods that can be used in this situation is to use an older database backup that has the records or database object structure you want to revert to. For this method, you have to have the adequate full database backup and a tool that will help you with comparison and synchronization is preferred. In this article, we will focus on another method: rolling back the changes. This can be done by using: An option in SQL Server Management Studio T-SQL, or ApexSQL Log The first two solutions have been described in this article The disadvantages of these methods are that you have to know when exactly the change you want to revert happened and that all transactions on the database executed in a specific time range are rolled back – the ones you want to undo and the ones you don’t. How to easily roll back SQL Server database changes using ApexSQL Log? The biggest challenge is to roll back just specific changes, not all changes that happened in a specific time range. While SQL Server Management Studio option and T-SQL read and roll forward all transactions in the transaction log files, I will show you a solution that finds and scripts only the specific changes that match your criteria. Therefore, you don’t need to worry about all other database changes that you don’t want to roll back. ApexSQL Log is a SQL Server disaster recovery tool that reads transaction logs and provides a wide range of filters that enable you to easily rollback only specific data changes. First, connect to the online database where you want to roll back the changes. Once you select the database, ApexSQL Log will show its recovery model. Note that changes can be rolled back even for a database in the Simple recovery model, when no database and transaction log backups are available. However, ApexSQL Log achieves best results when the database is in the Full recovery model and you have a chain of subsequent transaction log backups, back to the moment when the change occurred. In this example, we will use only the online transaction log. In the next step, use filters to read only the transactions that happened in a specific time range. To remove noise, it’s recommended to use as many filters as possible. Besides filtering by the time of the transaction, ApexSQL Log can filter by the operation type: Table name: As well as transaction state (committed, aborted, running, and unknown), name of the user who committed the change, specific field values, server process IDs, and transaction description. You can select only the tables affected by the changes you want to roll back. However, if you’re not certain which tables were affected, you can leave them all selected and once the results are shown in the main grid, analyze them to find the ones you to roll back. When you set the filters, you can select how to present the results. ApexSQL Log can automatically create undo or redo scripts, export the transactions into an XML, HTML, CSV, SQL, or SQL Bulk file, and create a batch file that you can use for unattended transaction log reading. In this example, I will open the results in the grid, as I want to analyze them before rolling back the transactions. The results contain information about the transaction, as well as who and when made it. For UPDATEs, ApexSQL Log shows both old and new values, so you can easily see what has happened. To create an UNDO script that rolls back the changes, select the transactions you want to roll back and click Create undo script in the menu. For the DELETE statement selected in the screenshot above, the undo script is: INSERT INTO [Sales].[PersonCreditCard] ([BusinessEntityID], [CreditCardID], [ModifiedDate]) VALUES (297, 8010, '20050901 00:00:00.000') When it comes to rolling back database changes, ApexSQL Log has a big advantage, as it rolls back only specific transactions, while leaving all other transactions that occurred at the same time range intact. That makes ApexSQL Log a good solution for rolling back inadvertent data and schema changes on your SQL Server databases. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: ApexSQL

    Read the article

  • Logging errors caused by exceptions deep in the application

    - by Kaleb Pederson
    What are best-practices for logging deep within an application's source? Is it bad practice to have multiple event log entries for a single error? For example, let's say that I have an ETL system whose transform step involves: a transformer, pipeline, processing algorithm, and processing engine. In brief, the transformer takes in an input file, parses out records, and sends the records through the pipeline. The pipeline aggregates the results of the processing algorithm (which could do serial or parallel processing). The processing algorithm sends each record through one or more processing engines. So, I have at least four levels: Transformer - Pipeline - Algorithm - Engine. My code might then look something like the following: class Transformer { void Process(InputSource input) { try { var inRecords = _parser.Parse(input.Stream); var outRecords = _pipeline.Transform(inRecords); } catch (Exception ex) { var inner = new ProcessException(input, ex); _logger.Error("Unable to parse source " + input.Name, inner); throw inner; } } } class Pipeline { IEnumerable<Result> Transform(IEnumerable<Record> records) { // NOTE: no try/catch as I have no useful information to provide // at this point in the process var results = _algorithm.Process(records); // examine and do useful things with results return results; } } class Algorithm { IEnumerable<Result> Process(IEnumerable<Record> records) { var results = new List<Result>(); foreach (var engine in Engines) { foreach (var record in records) { try { engine.Process(record); } catch (Exception ex) { var inner = new EngineProcessingException(engine, record, ex); _logger.Error("Engine {0} unable to parse record {1}", engine, record); throw inner; } } } } } class Engine { Result Process(Record record) { for (int i=0; i<record.SubRecords.Count; ++i) { try { Validate(record.subRecords[i]); } catch (Exception ex) { var inner = new RecordValidationException(record, i, ex); _logger.Error( "Validation of subrecord {0} failed for record {1}", i, record ); } } } } There's a few important things to notice: A single error at the deepest level causes three log entries (ugly? DOS?) Thrown exceptions contain all important and useful information Logging only happens when failure to do so would cause loss of useful information at a lower level. Thoughts and concerns: I don't like having so many log entries for each error I don't want to lose important, useful data; the exceptions contain all the important but the stacktrace is typically the only thing displayed besides the message. I can log at different levels (e.g., warning, informational) The higher level classes should be completely unaware of the structure of the lower-level exceptions (which may change as the different implementations are replaced). The information available at higher levels should not be passed to the lower levels. So, to restate the main questions: What are best-practices for logging deep within an application's source? Is it bad practice to have multiple event log entries for a single error?

    Read the article

< Previous Page | 58 59 60 61 62 63 64 65 66 67 68 69  | Next Page >