Search Results

Search found 53054 results on 2123 pages for 'sql sample database'.

Page 1026/2123 | < Previous Page | 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033  | Next Page >

  • Java Cloud Service Integration to REST Service

    - by Jani Rautiainen
    Service (JCS) provides a platform to develop and deploy business applications in the cloud. In Fusion Applications Cloud deployments customers do not have the option to deploy custom applications developed with JDeveloper to ensure the integrity and supportability of the hosted application service. Instead the custom applications can be deployed to the JCS and integrated to the Fusion Application Cloud instance. This series of articles will go through the features of JCS, provide end-to-end examples on how to develop and deploy applications on JCS and how to integrate them with the Fusion Applications instance. In this article a custom application integrating with REST service will be implemented. We will use REST services provided by Taleo as an example; however the same approach will work with any REST service. In this example the data from the REST service is used to populate a dynamic table. Pre-requisites Access to Cloud instance In order to deploy the application access to a JCS instance is needed, a free trial JCS instance can be obtained from Oracle Cloud site. To register you will need a credit card even if the credit card will not be charged. To register simply click "Try it" and choose the "Java" option. The confirmation email will contain the connection details. See this video for example of the registration.Once the request is processed you will be assigned 2 service instances; Java and Database. Applications deployed to the JCS must use Oracle Database Cloud Service as their underlying database. So when JCS instance is created a database instance is associated with it using a JDBC data source.The cloud services can be monitored and managed through the web UI. For details refer to Getting Started with Oracle Cloud. JDeveloper JDeveloper contains Cloud specific features related to e.g. connection and deployment. To use these features download the JDeveloper from JDeveloper download site by clicking the "Download JDeveloper 11.1.1.7.1 for ADF deployment on Oracle Cloud" link, this version of JDeveloper will have the JCS integration features that will be used in this article. For versions that do not include the Cloud integration features the Oracle Java Cloud Service SDK or the JCS Java Console can be used for deployment. For details on installing and configuring the JDeveloper refer to the installation guideFor details on SDK refer to Using the Command-Line Interface to Monitor Oracle Java Cloud Service and Using the Command-Line Interface to Manage Oracle Java Cloud Service. Access to a local database The database associated with the JCS instance cannot be connected to with JDBC.  Since creating ADFbc business component requires a JDBC connection we will need access to a local database. 3rd party libraries This example will use some 3rd party libraries for implementing the REST service call and processing the input / output content. Other libraries may also be used, however these are tested to work. Jersey 1.x Jersey library will be used as a client to make the call to the REST service. JCS documentation for supported specifications states: Java API for RESTful Web Services (JAX-RS) 1.1 So Jersey 1.x will be used. Download the single-JAR Jersey bundle; in this example Jersey 1.18 JAR bundle is used. Json-simple Jjson-simple library will be used to process the json objects. Download the  JAR file; in this example json-simple-1.1.1.jar is used. Accessing data in Taleo Before implementing the application it is beneficial to familiarize oneself with the data in Taleo. Easiest way to do this is by using a RESTClient on your browser. Once added to the browser you can access the UI: The client can be used to call the REST services to test the URLs and data before adding them into the application. First derive the base URL for the service this can be done with: Method: GET URL: https://tbe.taleo.net/MANAGER/dispatcher/api/v1/serviceUrl/<company name> The response will contain the base URL to be used for the service calls for the company. Next obtain authentication token with: Method: POST URL: https://ch.tbe.taleo.net/CH07/ats/api/v1/login?orgCode=<company>&userName=<user name>&password=<password> The response includes an authentication token that can be used for few hours to authenticate with the service: {   "response": {     "authToken": "webapi26419680747505890557"   },   "status": {     "detail": {},     "success": true   } } To authenticate the service calls navigate to "Headers -> Custom Header": And add a new request header with: Name: Cookie Value: authToken=webapi26419680747505890557 Once authentication token is defined the tool can be used to invoke REST services; for example: Method: GET URL: https://ch.tbe.taleo.net/CH07/ats/api/v1/object/candidate/search.xml?status=16 This data will be used on the application to be created. For details on the Taleo REST services refer to the Taleo Business Edition REST API Guide. Create Application First Fusion Web Application is created and configured. Start JDeveloper and click "New Application": Application Name: JcsRestDemo Application Package Prefix: oracle.apps.jcs.test Application Template: Fusion Web Application (ADF) Configure Local Cloud Connection Follow the steps documented in the "Java Cloud Service ADF Web Application" article to configure a local database connection needed to create the ADFbc objects. Configure Libraries Add the 3rd party libraries into the class path. Create the following directory and copy the jar files into it: <JDEV_USER_HOME>/JcsRestDemo/lib  Select the "Model" project, navigate "Application -> Project Properties -> Libraries and Classpath -> Add JAR / Directory" and add the 2 3rd party libraries: Accessing Data from Taleo To access data from Taleo using the REST service the 3rd party libraries will be used. 2 Java classes are implemented, one representing the Candidate object and another for accessing the Taleo repository Candidate Candidate object is a POJO object used to represent the candidate data obtained from the Taleo repository. The data obtained will be used to populate the ADFbc object used to display the data on the UI. The candidate object contains simply the variables we obtain using the REST services and the getters / setters for them: Navigate "New -> General -> Java -> Java Class", enter "Candidate" as the name and create it in the package "oracle.apps.jcs.test.model".  Copy / paste the following as the content: import oracle.jbo.domain.Number; public class Candidate { private Number candId; private String firstName; private String lastName; public Candidate() { super(); } public Candidate(Number candId, String firstName, String lastName) { super(); this.candId = candId; this.firstName = firstName; this.lastName = lastName; } public void setCandId(Number candId) { this.candId = candId; } public Number getCandId() { return candId; } public void setFirstName(String firstName) { this.firstName = firstName; } public String getFirstName() { return firstName; } public void setLastName(String lastName) { this.lastName = lastName; } public String getLastName() { return lastName; } } Taleo Repository Taleo repository class will interact with the Taleo REST services. The logic will query data from Taleo and populate Candidate objects with the data. The Candidate object will then be used to populate the ADFbc object used to display data on the UI. Navigate "New -> General -> Java -> Java Class", enter "TaleoRepository" as the name and create it in the package "oracle.apps.jcs.test.model".  Copy / paste the following as the content (for details of the implementation refer to the documentation in the code): import com.sun.jersey.api.client.Client; import com.sun.jersey.api.client.ClientResponse; import com.sun.jersey.api.client.WebResource; import com.sun.jersey.core.util.MultivaluedMapImpl; import java.io.StringReader; import java.util.ArrayList; import java.util.Iterator; import java.util.List; import java.util.Map; import javax.ws.rs.core.MediaType; import javax.ws.rs.core.MultivaluedMap; import oracle.jbo.domain.Number; import org.json.simple.JSONArray; import org.json.simple.JSONObject; import org.json.simple.parser.JSONParser; /** * This class interacts with the Taleo REST services */ public class TaleoRepository { /** * Connection information needed to access the Taleo services */ String _company = null; String _userName = null; String _password = null; /** * Jersey client used to access the REST services */ Client _client = null; /** * Parser for processing the JSON objects used as * input / output for the services */ JSONParser _parser = null; /** * The base url for constructing the REST URLs. This is obtained * from Taleo with a service call */ String _baseUrl = null; /** * Authentication token obtained from Taleo using a service call. * The token can be used to authenticate on subsequent * service calls. The token will expire in 4 hours */ String _authToken = null; /** * Static url that can be used to obtain the url used to construct * service calls for a given company */ private static String _taleoUrl = "https://tbe.taleo.net/MANAGER/dispatcher/api/v1/serviceUrl/"; /** * Default constructor for the repository * Authentication details are passed as parameters and used to generate * authentication token. Note that each service call will * generate its own token. This is done to avoid dealing with the expiry * of the token. Also only 20 tokens are allowed per user simultaneously. * So instead for each call there is login / logout. * * @param company the company for which the service calls are made * @param userName the user name to authenticate with * @param password the password to authenticate with. */ public TaleoRepository(String company, String userName, String password) { super(); _company = company; _userName = userName; _password = password; _client = Client.create(); _parser = new JSONParser(); _baseUrl = getBaseUrl(); } /** * This obtains the base url for a company to be used * to construct the urls for service calls * @return base url for the service calls */ private String getBaseUrl() { String result = null; if (null != _baseUrl) { result = _baseUrl; } else { try { String company = _company; WebResource resource = _client.resource(_taleoUrl + company); ClientResponse response = resource.type(MediaType.APPLICATION_FORM_URLENCODED_TYPE).get(ClientResponse.class); String entity = response.getEntity(String.class); JSONObject jsonObject = (JSONObject)_parser.parse(new StringReader(entity)); JSONObject jsonResponse = (JSONObject)jsonObject.get("response"); result = (String)jsonResponse.get("URL"); } catch (Exception ex) { ex.printStackTrace(); } } return result; } /** * Generates authentication token, that can be used to authenticate on * subsequent service calls. Note that each service call will * generate its own token. This is done to avoid dealing with the expiry * of the token. Also only 20 tokens are allowed per user simultaneously. * So instead for each call there is login / logout. * @return authentication token that can be used to authenticate on * subsequent service calls */ private String login() { String result = null; try { MultivaluedMap<String, String> formData = new MultivaluedMapImpl(); formData.add("orgCode", _company); formData.add("userName", _userName); formData.add("password", _password); WebResource resource = _client.resource(_baseUrl + "login"); ClientResponse response = resource.type(MediaType.APPLICATION_FORM_URLENCODED_TYPE).post(ClientResponse.class, formData); String entity = response.getEntity(String.class); JSONObject jsonObject = (JSONObject)_parser.parse(new StringReader(entity)); JSONObject jsonResponse = (JSONObject)jsonObject.get("response"); result = (String)jsonResponse.get("authToken"); } catch (Exception ex) { throw new RuntimeException("Unable to login ", ex); } if (null == result) throw new RuntimeException("Unable to login "); return result; } /** * Releases a authentication token. Each call to login must be followed * by call to logout after the processing is done. This is required as * the tokens are limited to 20 per user and if not released the tokens * will only expire after 4 hours. * @param authToken */ private void logout(String authToken) { WebResource resource = _client.resource(_baseUrl + "logout"); resource.header("cookie", "authToken=" + authToken).post(ClientResponse.class); } /** * This method is used to obtain a list of candidates using a REST * service call. At this example the query is hard coded to query * based on status. The url constructed to access the service is: * <_baseUrl>/object/candidate/search.xml?status=16 * @return List of candidates obtained with the service call */ public List<Candidate> getCandidates() { List<Candidate> result = new ArrayList<Candidate>(); try { // First login, note that in finally block we must have logout _authToken = "authToken=" + login(); /** * Construct the URL, the resulting url will be: * <_baseUrl>/object/candidate/search.xml?status=16 */ MultivaluedMap<String, String> formData = new MultivaluedMapImpl(); formData.add("status", "16"); JSONArray searchResults = (JSONArray)getTaleoResource("object/candidate/search", "searchResults", formData); /** * Process the results, the resulting JSON object is something like * this (simplified for readability): * * { * "response": * { * "searchResults": * [ * { * "candidate": * { * "candId": 211, * "firstName": "Mary", * "lastName": "Stochi", * logic here will find the candidate object(s), obtain the desired * data from them, construct a Candidate object based on the data * and add it to the results. */ for (Object object : searchResults) { JSONObject temp = (JSONObject)object; JSONObject candidate = (JSONObject)findObject(temp, "candidate"); Long candIdTemp = (Long)candidate.get("candId"); Number candId = (null == candIdTemp ? null : new Number(candIdTemp)); String firstName = (String)candidate.get("firstName"); String lastName = (String)candidate.get("lastName"); result.add(new Candidate(candId, firstName, lastName)); } } catch (Exception ex) { ex.printStackTrace(); } finally { if (null != _authToken) logout(_authToken); } return result; } /** * Convenience method to construct url for the service call, invoke the * service and obtain a resource from the response * @param path the path for the service to be invoked. This is combined * with the base url to construct a url for the service * @param resource the key for the object in the response that will be * obtained * @param parameters any parameters used for the service call. The call * is slightly different depending whether parameters exist or not. * @return the resource from the response for the service call */ private Object getTaleoResource(String path, String resource, MultivaluedMap<String, String> parameters) { Object result = null; try { WebResource webResource = _client.resource(_baseUrl + path); ClientResponse response = null; if (null == parameters) response = webResource.header("cookie", _authToken).get(ClientResponse.class); else response = webResource.queryParams(parameters).header("cookie", _authToken).get(ClientResponse.class); String entity = response.getEntity(String.class); JSONObject jsonObject = (JSONObject)_parser.parse(new StringReader(entity)); result = findObject(jsonObject, resource); } catch (Exception ex) { ex.printStackTrace(); } return result; } /** * Convenience method to recursively find a object with an key * traversing down from a given root object. This will traverse a * JSONObject / JSONArray recursively to find a matching key, if found * the object with the key is returned. * @param root root object which contains the key searched for * @param key the key for the object to search for * @return the object matching the key */ private Object findObject(Object root, String key) { Object result = null; if (root instanceof JSONObject) { JSONObject rootJSON = (JSONObject)root; if (rootJSON.containsKey(key)) { result = rootJSON.get(key); } else { Iterator children = rootJSON.entrySet().iterator(); while (children.hasNext()) { Map.Entry entry = (Map.Entry)children.next(); Object child = entry.getValue(); if (child instanceof JSONObject || child instanceof JSONArray) { result = findObject(child, key); if (null != result) break; } } } } else if (root instanceof JSONArray) { JSONArray rootJSON = (JSONArray)root; for (Object child : rootJSON) { if (child instanceof JSONObject || child instanceof JSONArray) { result = findObject(child, key); if (null != result) break; } } } return result; } }   Creating Business Objects While JCS application can be created without a local database, the local database is required when using ADFbc objects even if database objects are not referred. For this example we will create a "Transient" view object that will be programmatically populated based the data obtained from Taleo REST services. Creating ADFbc objects Choose the "Model" project and navigate "New -> Business Tier : ADF Business Components : View Object". On the "Initialize Business Components Project" choose the local database connection created in previous step. On Step 1 enter "JcsRestDemoVO" on the "Name" and choose "Rows populated programmatically, not based on query": On step 2 create the following attributes: CandId Type: Number Updatable: Always Key Attribute: checked Name Type: String Updatable: Always On steps 3 and 4 accept defaults and click "Next".  On step 5 check the "Application Module" checkbox and enter "JcsRestDemoAM" as the name: Click "Finish" to generate the objects. Populating the VO To display the data on the UI the "transient VO" is populated programmatically based on the data obtained from the Taleo REST services. Open the "JcsRestDemoVOImpl.java". Copy / paste the following as the content (for details of the implementation refer to the documentation in the code): import java.sql.ResultSet; import java.util.List; import java.util.ListIterator; import oracle.jbo.server.ViewObjectImpl; import oracle.jbo.server.ViewRowImpl; import oracle.jbo.server.ViewRowSetImpl; // --------------------------------------------------------------------- // --- File generated by Oracle ADF Business Components Design Time. // --- Tue Feb 18 09:40:25 PST 2014 // --- Custom code may be added to this class. // --- Warning: Do not modify method signatures of generated methods. // --------------------------------------------------------------------- public class JcsRestDemoVOImpl extends ViewObjectImpl { /** * This is the default constructor (do not remove). */ public JcsRestDemoVOImpl() { } @Override public void executeQuery() { /** * For some reason we need to reset everything, otherwise * 2nd entry to the UI screen may fail with * "java.util.NoSuchElementException" in createRowFromResultSet * call to "candidates.next()". I am not sure why this is happening * as the Iterator is new and "hasNext" is true at the point * of the execution. My theory is that since the iterator object is * exactly the same the VO cache somehow reuses the iterator including * the pointer that has already exhausted the iterable elements on the * previous run. Working around the issue * here by cleaning out everything on the VO every time before query * is executed on the VO. */ getViewDef().setQuery(null); getViewDef().setSelectClause(null); setQuery(null); this.reset(); this.clearCache(); super.executeQuery(); } /** * executeQueryForCollection - overridden for custom java data source support. */ protected void executeQueryForCollection(Object qc, Object[] params, int noUserParams) { /** * Integrate with the Taleo REST services using TaleoRepository class. * A list of candidates matching a hard coded query is obtained. */ TaleoRepository repository = new TaleoRepository(<company>, <username>, <password>); List<Candidate> candidates = repository.getCandidates(); /** * Store iterator for the candidates as user data on the collection. * This will be used in createRowFromResultSet to create rows based on * the custom iterator. */ ListIterator<Candidate> candidatescIterator = candidates.listIterator(); setUserDataForCollection(qc, candidatescIterator); super.executeQueryForCollection(qc, params, noUserParams); } /** * hasNextForCollection - overridden for custom java data source support. */ protected boolean hasNextForCollection(Object qc) { boolean result = false; /** * Determines whether there are candidates for which to create a row */ ListIterator<Candidate> candidates = (ListIterator<Candidate>)getUserDataForCollection(qc); result = candidates.hasNext(); /** * If all candidates to be created indicate that processing is done */ if (!result) { setFetchCompleteForCollection(qc, true); } return result; } /** * createRowFromResultSet - overridden for custom java data source support. */ protected ViewRowImpl createRowFromResultSet(Object qc, ResultSet resultSet) { /** * Obtain the next candidate from the collection and create a row * for it. */ ListIterator<Candidate> candidates = (ListIterator<Candidate>)getUserDataForCollection(qc); ViewRowImpl row = createNewRowForCollection(qc); try { Candidate candidate = candidates.next(); row.setAttribute("CandId", candidate.getCandId()); row.setAttribute("Name", candidate.getFirstName() + " " + candidate.getLastName()); } catch (Exception e) { e.printStackTrace(); } return row; } /** * getQueryHitCount - overridden for custom java data source support. */ public long getQueryHitCount(ViewRowSetImpl viewRowSet) { /** * For this example this is not implemented rather we always return 0. */ return 0; } } Creating UI Choose the "ViewController" project and navigate "New -> Web Tier : JSF : JSF Page". On the "Create JSF Page" enter "JcsRestDemo" as name and ensure that the "Create as XML document (*.jspx)" is checked.  Open "JcsRestDemo.jspx" and navigate to "Data Controls -> JcsRestDemoAMDataControl -> JcsRestDemoVO1" and drag & drop the VO to the "<af:form> " as a "ADF Read-only Table": Accept the defaults in "Edit Table Columns". To execute the query navigate to to "Data Controls -> JcsRestDemoAMDataControl -> JcsRestDemoVO1 -> Operations -> Execute" and drag & drop the operation to the "<af:form> " as a "Button": Deploying to JCS Follow the same steps as documented in previous article"Java Cloud Service ADF Web Application". Once deployed the application can be accessed with URL: https://java-[identity domain].java.[data center].oraclecloudapps.com/JcsRestDemo-ViewController-context-root/faces/JcsRestDemo.jspx The UI displays a list of candidates obtained from the Taleo REST Services: Summary In this article we learned how to integrate with REST services using Jersey library in JCS. In future articles various other integration techniques will be covered.

    Read the article

  • DBA Best Practices - A Blog Series: Episode 2 - Password Lists

    - by Argenis
      Digital World, Digital Locks One of the biggest digital assets that any company has is its secrets. These include passwords, key rings, certificates, and any other digital asset used to protect another asset from tampering or unauthorized access. As a DBA, you are very likely to manage some of these assets for your company - and your employer trusts you with keeping them safe. Probably one of the most important of these assets are passwords. As you well know, the can be used anywhere: for service accounts, credentials, proxies, linked servers, DTS/SSIS packages, symmetrical keys, private keys, etc., etc. Have you given some thought to what you're doing to keep these passwords safe? Are you backing them up somewhere? Who else besides you can access them? Good-Ol’ Post-It Notes Under Your Keyboard If you have a password-protected Excel sheet for your passwords, I have bad news for you: Excel's level of encryption is good for your grandma's budget spreadsheet, not for a list of enterprise passwords. I will try to summarize the main point of this best practice in one sentence: You should keep your passwords on an encrypted, access and version-controlled, backed-up, well-known shared location that every DBA on your team is aware of, and maintain copies of this password "database" on your DBA's workstations. Now I have to break down that statement to you: - Encrypted: what’s the point of saving your passwords on a file that any Windows admin with enough privileges can read? - Access controlled: This one is pretty much self-explanatory. - Version controlled: Passwords change (and I’m really hoping you do change them) and version control would allow you to track what a previous password was if the utility you’ve chosen doesn’t handle that for you. - Backed-up: You want a safe copy of the password list to be kept offline, preferably in long term storage, with relative ease of restoring. - Well-known shared location: This is critical for teams: what good is a password list if only one person in the team knows where it is? I have seen multiple examples of this that work well. They all start with an encrypted database. Certainly you could leverage SQL Server's native encryption solutions like cell encryption for this. I have found such implementations to be impractical, for the most part. Enter The World Of Utilities There are a myriad of open source/free software solutions to help you here. One of my favorites is KeePass, which creates encrypted files that can be saved to a network share, Sharepoint, etc. KeePass has UIs for most operating systems, including Windows, MacOS, iOS, Android and Windows Phone. Other solutions I've used before worth mentioning include PasswordSafe and 1Password, with the latter one being a paid solution – but wildly popular in mobile devices. There are, of course, even more "enterprise-level" solutions available from 3rd party vendors. The truth is that most of the customers that I work with don't need that level of protection of their digital assets, and something like a KeePass database on Sharepoint suits them very well. What are you doing to safeguard your passwords? Leave a comment below, and join the discussion! Cheers, -Argenis

    Read the article

  • I have to generate PL/SQL using Java. Most of the procedures are common. Only a few keeps changing.

    - by blog
    I have to generate PL-SQL code, with some common code(invariable) and a variable code. I don't want to use any external tools. Some ways that I can think: Can I go and maintain the common code in a template and with markers, where my java code will generate code in the markers and generate a new file. Maintain the common code in static constant String and then generate the whole code in StringBuffer and at last write to file. But, I am not at all satisfied with both the ideas. Can you please suggest any better ways of doing this or the use of any design patterns or anything? Thanks in Advance.

    Read the article

  • postfix with mailman

    - by Thufir
    What should happen is that [email protected] should be delivered to that users inbox on localhost, user@localhost. Thunderbird works fine at reading user@localhost. I'm just using a small portion of postfix-dovecot with Ubuntu mailman. How can I get postfix to recognize the FQDN and deliver them to a localhost inbox? root@dur:~# root@dur:~# tail /var/log/mail.err;tail /var/log/mailman/subscribe;postconf -n Aug 27 18:59:16 dur dovecot: lda(root): Error: chdir(/root) failed: Permission denied Aug 27 18:59:16 dur dovecot: lda(root): Error: user root: Initialization failed: Initializing mail storage from mail_location setting failed: stat(/root/Maildir) failed: Permission denied (euid=65534(nobody) egid=65534(nogroup) missing +x perm: /root, dir owned by 0:0 mode=0700) Aug 27 18:59:16 dur dovecot: lda(root): Fatal: Invalid user settings. Refer to server log for more information. Aug 27 20:09:16 dur postfix/trivial-rewrite[15896]: error: open database /etc/postfix/transport.db: No such file or directory Aug 27 21:19:17 dur postfix/trivial-rewrite[16569]: error: open database /etc/postfix/transport.db: No such file or directory Aug 27 22:27:00 dur postfix[17042]: fatal: usage: postfix [-c config_dir] [-Dv] command Aug 27 22:29:19 dur postfix/trivial-rewrite[17062]: error: open database /etc/postfix/transport.db: No such file or directory Aug 27 22:59:07 dur postfix/postfix-script[17459]: error: unknown command: 'restart' Aug 27 22:59:07 dur postfix/postfix-script[17460]: fatal: usage: postfix start (or stop, reload, abort, flush, check, status, set-permissions, upgrade-configuration) Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: error: open database /etc/postfix/transport.db: No such file or directory Aug 27 21:39:03 2012 (16734) cola: pending "[email protected]" <[email protected]> 127.0.0.1 Aug 27 21:40:37 2012 (16749) cola: pending "[email protected]" <[email protected]> 127.0.0.1 Aug 27 22:45:31 2012 (17288) gmane.mail.mailman.user.1: pending [email protected] 127.0.0.1 Aug 27 22:45:46 2012 (17293) gmane.mail.mailman.user.1: pending [email protected] 127.0.0.1 Aug 27 23:02:01 2012 (17588) test3: pending [email protected] 127.0.0.1 Aug 27 23:05:41 2012 (17652) test4: pending [email protected] 127.0.0.1 Aug 27 23:56:20 2012 (17985) test5: pending [email protected] 127.0.0.1 alias_database = hash:/etc/aliases alias_maps = hash:/etc/aliases, hash:/var/lib/mailman/data/aliases append_dot_mydomain = no biff = no broken_sasl_auth_clients = yes config_directory = /etc/postfix default_transport = smtp home_mailbox = Maildir/ inet_interfaces = loopback-only mailbox_command = /usr/lib/dovecot/deliver -c /etc/dovecot/conf.d/01-mail-stack-delivery.conf -m "${EXTENSION}" mailbox_size_limit = 0 mailman_destination_recipient_limit = 1 mydestination = dur, dur.bounceme.net, localhost.bounceme.net, localhost myhostname = dur.bounceme.net mynetworks = 127.0.0.0/8 [::ffff:127.0.0.0]/104 [::1]/128 readme_directory = no recipient_delimiter = + relay_domains = lists.dur.bounceme.net relay_transport = relay relayhost = smtp_tls_session_cache_database = btree:${data_directory}/smtp_scache smtp_use_tls = yes smtpd_banner = $myhostname ESMTP $mail_name (Ubuntu) smtpd_recipient_restrictions = reject_unknown_sender_domain, reject_unknown_recipient_domain, reject_unauth_pipelining, permit_mynetworks, permit_sasl_authenticated, reject_unauth_destination smtpd_sasl_auth_enable = yes smtpd_sasl_authenticated_header = yes smtpd_sasl_local_domain = $myhostname smtpd_sasl_path = private/dovecot-auth smtpd_sasl_security_options = noanonymous smtpd_sasl_type = dovecot smtpd_tls_auth_only = yes smtpd_tls_cert_file = /etc/ssl/certs/ssl-mail.pem smtpd_tls_key_file = /etc/ssl/private/ssl-mail.key smtpd_tls_mandatory_ciphers = medium smtpd_tls_mandatory_protocols = SSLv3, TLSv1 smtpd_tls_received_header = yes smtpd_tls_session_cache_database = btree:${data_directory}/smtpd_scache smtpd_use_tls = yes tls_random_source = dev:/dev/urandom transport_maps = hash:/etc/postfix/transport root@dur:~# there's definitely a transport problem: root@dur:~# root@dur:~# root@dur:~# grep transport /var/log/mail.log | tail Aug 27 22:29:19 dur postfix/trivial-rewrite[17062]: warning: hash:/etc/postfix/transport lookup error for "[email protected]" Aug 27 22:29:19 dur postfix/trivial-rewrite[17062]: warning: transport_maps lookup failure Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: error: open database /etc/postfix/transport.db: No such file or directory Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: warning: hash:/etc/postfix/transport is unavailable. open database /etc/postfix/transport.db: No such file or directory Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: warning: hash:/etc/postfix/transport lookup error for "*" Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: warning: hash:/etc/postfix/transport is unavailable. open database /etc/postfix/transport.db: No such file or directory Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: warning: hash:/etc/postfix/transport lookup error for "*" Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: warning: hash:/etc/postfix/transport is unavailable. open database /etc/postfix/transport.db: No such file or directory Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: warning: hash:/etc/postfix/transport lookup error for "[email protected]" Aug 27 23:39:17 dur postfix/trivial-rewrite[17794]: warning: transport_maps lookup failure root@dur:~# trying to add the transport file: EDIT root@dur:~# root@dur:~# touch /etc/postfix/transport root@dur:~# ll /etc/postfix/transport -rw-r--r-- 1 root root 0 Aug 28 00:16 /etc/postfix/transport root@dur:~# root@dur:~# cd /etc/postfix/ root@dur:/etc/postfix# root@dur:/etc/postfix# postmap transport root@dur:/etc/postfix# root@dur:/etc/postfix# cat transport

    Read the article

  • Parsing SQLIO Output to Excel Charts using Regex in PowerShell

    - by Jonathan Kehayias
    Today Joe Webb ( Blog | Twitter ) blogged about The Power of Regex in Powershell, and in his post he shows how to parse the SQL Server Error Log for events of interest.  At the end of his blog post Joe asked about other places where Regular Expressions have been useful in PowerShell so I thought I’d blog my script for parsing SQLIO output using Regex in PowerShell, to populate an Excel worksheet and build charts based on the results automatically. If you’ve never used SQLIO, Brent Ozar ( Blog...(read more)

    Read the article

  • Parsing SQLIO Output to Excel Charts using Regex in PowerShell

    - by Jonathan Kehayias
    Today Joe Webb ( Blog | Twitter ) blogged about The Power of Regex in Powershell, and in his post he shows how to parse the SQL Server Error Log for events of interest. At the end of his blog post Joe asked about other places where Regular Expressions have been useful in PowerShell so I thought I’d blog my script for parsing SQLIO output using Regex in PowerShell, to populate an Excel worksheet and build charts based on the results automatically. If you’ve never used SQLIO, Brent Ozar ( Blog | Twitter...(read more)

    Read the article

  • Reporting Services - It's a Wrap!

    - by smisner
    If you have any experience at all with Reporting Services, you have probably developed a report using the matrix data region. It's handy when you want to generate columns dynamically based on data. If users view a matrix report online, they can scroll horizontally to view all columns and all is well. But if they want to print the report, the experience is completely different and you'll have to decide how you want to handle dynamic columns. By default, when a user prints a matrix report for which the number of columns exceeds the width of the page, Reporting Services determines how many columns can fit on the page and renders one or more separate pages for the additional columns. In this post, I'll explain two techniques for managing dynamic columns. First, I'll show how to use the RepeatRowHeaders property to make it easier to read a report when columns span multiple pages, and then I'll show you how to "wrap" columns so that you can avoid the horizontal page break. Included with this post are the sample RDLs for download. First, let's look at the default behavior of a matrix. A matrix that has too many columns for one printed page (or output to page-based renderer like PDF or Word) will be rendered such that the first page with the row group headers and the inital set of columns, as shown in Figure 1. The second page continues by rendering the next set of columns that can fit on the page, as shown in Figure 2.This pattern continues until all columns are rendered. The problem with the default behavior is that you've lost the context of employee and sales order - the row headers - on the second page. That makes it hard for users to read this report because the layout requires them to flip back and forth between the current page and the first page of the report. You can fix this behavior by finding the RepeatRowHeaders of the tablix report item and changing its value to True. The second (and subsequent pages) of the matrix now look like the image shown in Figure 3. The problem with this approach is that the number of printed pages to flip through is unpredictable when you have a large number of potential columns. What if you want to include all columns on the same page? You can take advantage of the repeating behavior of a tablix and get repeating columns by embedding one tablix inside of another. For this example, I'm using SQL Server 2008 R2 Reporting Services. You can get similar results with SQL Server 2008. (In fact, you could probably do something similar in SQL Server 2005, but I haven't tested it. The steps would be slightly different because you would be working with the old-style matrix as compared to the new-style tablix discussed in this post.) I created a dataset that queries AdventureWorksDW2008 tables: SELECT TOP (100) e.LastName + ', ' + e.FirstName AS EmployeeName, d.FullDateAlternateKey, f.SalesOrderNumber, p.EnglishProductName, sum(SalesAmount) as SalesAmount FROM FactResellerSales AS f INNER JOIN DimProduct AS p ON p.ProductKey = f.ProductKey INNER JOIN DimDate AS d ON d.DateKey = f.OrderDateKey INNER JOIN DimEmployee AS e ON e.EmployeeKey = f.EmployeeKey GROUP BY p.EnglishProductName, d.FullDateAlternateKey, e.LastName + ', ' + e.FirstName, f.SalesOrderNumber ORDER BY EmployeeName, f.SalesOrderNumber, p.EnglishProductName To start the report: Add a matrix to the report body and drag Employee Name to the row header, which also creates a group. Next drag SalesOrderNumber below Employee Name in the Row Groups panel, which creates a second group and a second column in the row header section of the matrix, as shown in Figure 4. Now for some trickiness. Add another column to the row headers. This new column will be associated with the existing EmployeeName group rather than causing BIDS to create a new group. To do this, right-click on the EmployeeName textbox in the bottom row, point to Insert Column, and then click Inside Group-Right. Then add the SalesOrderNumber field to this new column. By doing this, you're creating a report that repeats a set of columns for each EmployeeName/SalesOrderNumber combination that appears in the data. Next, modify the first row group's expression to group on both EmployeeName and SalesOrderNumber. In the Row Groups section, right-click EmployeeName, click Group Properties, click the Add button, and select [SalesOrderNumber]. Now you need to configure the columns to repeat. Rather than use the Columns group of the matrix like you might expect, you're going to use the textbox that belongs to the second group of the tablix as a location for embedding other report items. First, clear out the text that's currently in the third column - SalesOrderNumber - because it's already added as a separate textbox in this report design. Then drag and drop a matrix into that textbox, as shown in Figure 5. Again, you need to do some tricks here to get the appearance and behavior right. We don't really want repeating rows in the embedded matrix, so follow these steps: Click on the Rows label which then displays RowGroup in the Row Groups pane below the report body. Right-click on RowGroup,click Delete Group, and select the option to delete associated rows and columns. As a result, you get a modified matrix which has only a ColumnGroup in it, with a row above a double-dashed line for the column group and a row below the line for the aggregated data. Let's continue: Drag EnglishProductName to the data textbox (below the line). Add a second data row by right-clicking EnglishProductName, pointing to Insert Row, and clicking Below. Add the SalesAmount field to the new data textbox. Now eliminate the column group row without eliminating the group. To do this, right-click the row above the double-dashed line, click Delete Rows, and then select Delete Rows Only in the message box. Now you're ready for the fit and finish phase: Resize the column containing the embedded matrix so that it fits completely. Also, the final column in the matrix is for the column group. You can't delete this column, but you can make it as small as possible. Just click on the matrix to display the row and column handles, and then drag the right edge of the rightmost column to the left to make the column virtually disappear. Next, configure the groups so that the columns of the embedded matrix will wrap. In the Column Groups pane, right-click ColumnGroup1 and click on the expression button (labeled fx) to the right of Group On [EnglishProductName]. Replace the expression with the following: =RowNumber("SalesOrderNumber" ). We use SalesOrderNumber here because that is the name of the group that "contains" the embedded matrix. The next step is to configure the number of columns to display before wrapping. Click any cell in the matrix that is not inside the embedded matrix, and then double-click the second group in the Row Groups pane - SalesOrderNumber. Change the group expression to the following expression: =Ceiling(RowNumber("EmployeeName")/3) The last step is to apply formatting. In my example, I set the SalesAmount textbox's Format property to C2 and also right-aligned the text in both the EnglishProductName and the SalesAmount textboxes. And voila - Figure 6 shows a matrix report with wrapping columns. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

    Read the article

  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

    Read the article

  • SSIS packages incompatibilities between SSIS 2008 and SSIS 2008 R2

    - by Marco Russo (SQLBI)
    When you install SQL 2008 R2 workstation components you get a newer version of BIDS (BI Developer Studio, included in the workstation components) that replaces BIDS 2008 version (BIDS 2005 still live side-by-side). Everything would be good if you can use the newer version to edit any 2008 AND 2008R2 project. SSIS editor doesn't offer a way to set the "compatibility level" of the package, becuase it is almost all unchanged. However, if a package has an ADO.NET Destination Adapter, there is a difference...(read more)

    Read the article

  • Some thoughts on interviewing….

    - by Jonathan Kehayias
    At the beginning of the year I changed jobs, leaving a very stable position where I had the opportunity to learn under an amazing mentor (who happened to be a Oracle DBA and not a SQL DBA), to take on a job that I felt was much more challenging and had better potential for personal as well as professional growth.  I wasn’t necessarily looking for another job at the time, but one that interested me was mentioned at our local user group meeting and I decided to check it out and see if it was something...(read more)

    Read the article

  • On automating a split-mirror ASM backup with EMC TimeFinder ...

    - by [email protected]
    Normal 0 21 false false false MicrosoftInternetExplorer4 st1\:*{behavior:url(#ieooui) } /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} Hi clerks,   Offloading the backup operation to another host using disk cloning could really improve the performance on highly busy databases ( 24x7, zero downtime and all this stuff ...) There are well know white papers on this subject, ASM included, but today Im showing you a nice way to automate the procedure using shell scripting with EMC TimeFinder technologies:   Assumptions: *********** ASM diskgroups name:   +data_${db_name} : asm data diskgroup +fra_${db_name} :  asm fra  diskgroup   EMC Time Finder sync groups name:   rac_${DB_NAME}_data_tf : data group rac_${DB_NAME}_fra_tf:   fra group     There are two scripts, one located on the production box ( bck_database.sh ) and the other one on the backup server node ( bck_database_mirror.sh ) The second one is remotly executed from the production host There are a bunch of variables along the code with selfexplanatory names I guess, anyway let me know if you want some help     #!/bin/ksh ### ###  Copyright (c) 1988, 2010, Oracle Corporation.  All Rights Reserved. ### ###    NAME ###     bck_database.sh ### ###    DESCRIPTION ###     Database backup on third mirror ### ###    RETURNS ### ###    NOTES ### ###    MODIFIED                                 (DD/MM/YY) ###    Oracle            28/01/10             - Creacion ###   V_DATE=`/bin/date +%Y%m%d_%H%M%S` V_FICH_LOG=`dirname $0`/trace_dir_location/`basename $0`.${V_DATE}.log exec 4>&1 tee ${V_FICH_LOG} >&4 |& exec 1>&p 2>&1     ADMIN_DIR=`dirname $0` . ${ADMIN_DIR}/setenv_instance.sh -- This script should set the instance vars like Oracle Home, Sid, db_name ... if [ $? -ne 0 ] then   echo "Error when setting the environment."   exit 1 fi   echo "${V_DATE} ####################################################" echo "Executing database backup: ${DB_NAME}" echo "####################################################################"   V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Sync asm data diskgroups ..." echo "####################################################################" sudo symmir -g rac_${DB_NAME}_data_tf establish -noprompt if [ $? -ne 0 ] then   echo "Error when sync asm data diskgroups"   exit 2 fi V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Verifying asm data disks ..." echo "####################################################################" sudo symmir -g rac_${DB_NAME}_data_tf -i 30 verify if [ $? -ne 0 ] then   echo "Error when verifying asm data diskgroups"   exit 3 fi     V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Sync asm fra diskgroups ..." echo "####################################################################" sudo symmir -g rac_${DB_NAME}_fra_tf establish -noprompt if [ $? -ne 0 ] then   echo "Error when sync asm fra diskgroups"   exit 4 fi V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Verifying asm fra disks ..." echo "####################################################################" sudo symmir -g rac_${DB_NAME}_fra_tf -i 30 verify if [ $? -ne 0 ] then   echo "Error when verifying asm fra diskgroups"   exit 5 fi   V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "ASM sync sucessfully completed!" echo "####################################################################"     V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Updating status ${DB_NAME} to BEGIN BACKUP ..." echo "####################################################################" sqlplus -s /nolog <<-!   whenever sqlerror exit 1   connect / as sysdba   whenever sqlerror exit   alter system archive log current;   alter database ${DB_NAME} begin backup; ! if [ $? -ne 0 ] then   echo "Error when updating database status to BEGIN backup"   exit 6 fi   V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Splitting asm data disks....." echo "####################################################################" sudo symmir -g rac_${DB_NAME}_data_tf split -noprompt if [ $? -ne 0 ] then   echo "Error when splitting asm data disks"   exit 7 fi   V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Updating status ${DB_NAME} to END BACKUP ..." echo "####################################################################" sqlplus -s /nolog <<-!   whenever sqlerror exit 1   connect / as sysdba   whenever sqlerror exit   alter database ${DB_NAME} end backup;   alter system archive log current; ! if [ $? -ne 0 ] then   echo "Error when updating database status to END backup"   exit 8 fi   V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Generating controlfile copies...." echo "####################################################################" rman<<-! connect target / run { allocate channel ch1 type DISK; copy current controlfile to '+FRA_${DB_NAME}/${DB_NAME}/CONTROLFILE/control_mount.ctl'; copy current controlfile to '+FRA_${DB_NAME}/${DB_NAME}/CONTROLFILE/control_backup.ctl'; } ! if [ $? -ne 0 ] then   echo "Error generating controlfile copies"   exit 9 fi V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Resync RMAN catalog ....." echo "####################################################################" rman<<-! connect target / connect catalog ${V_RMAN_USR}/${V_RMAN_PWD}@${V_DB_CATALOG} resync catalog; ! if [ $? -ne 0 ] then   echo "Error when resyncing RMAN catalog"   exit 10 fi   V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Splitting asm fra disks....." echo "####################################################################" sudo symmir -g rac_${DB_NAME}_fra_tf split -noprompt if [ $? -ne 0 ] then   echo "Error when splitting asm fra disks"   exit 11 fi     echo "WARNING!: Calling bck_database_mirror.sh host ${NODE_BCK_SERVER}..." ssh ${NODO_BCK_SERVER} ${ADMIN_DIR_BCK}/bck_database_mirror.sh if [ $? -ne 0 ] then   echo "Error, when remote executing the backup "   exit 12 fi V_DATE=`/bin/date +%Y%m%d_%H%M%S` echo "${V_DATE} ####################################################" echo "Cleaning the archived redo logs already copied to tape ..." echo "####################################################################" rman<<-! connect target / connect catalog ${V_RMAN_USR}/${V_RMAN_PWD}@${V_DB_CATALOG} run { resync catalog; delete noprompt archivelog all backed up 1 times to device type sbt; } ! if [ $? -ne 0 ] then   echo "Error when cleaning the archived redo logs"   exit 13 fi echo "${V_DATE} ####################################################" echo "Backup sucessfully executed!!" echo "####################################################################" exit 0   ------------------------------------------------------------------------------ ------------------------** BACKUP SERVER NODE ** ----------------------------- ------------------------------------------------------------------------------   #!/bin/ksh ### ###  Copyright (c) 1988, 2010, Oracle Corporation.  All Rights Reserved. ### ###    ###    NAME ###     bck_database_mirror.sh ### ###    DESCRIPTION ###      Backup @ backup server ### ###    RETURNS ### ###    NOTES ### ###    MODIFIED                                 (DD/MM/YY) ###      Oracle                    28/01/10     - Creacion         V_DATE=`/bin/date +%Y%m%d_%H%M%S`   echo "${V_DATE} ####################################################"   echo "Starting ASM instance ..."   echo "####################################################################"   ${V_ADMIN_DIR}/start_asm.sh -- This script is supposed to start the ASM instance in the backup server   if [ $? -ne 0 ]   then     echo "Error when tying to start ASM instance."     exit 1   fi       . ${V_ADMIN_DIR}/setenv_asm.sh -- This script is supposed to set the env. variables of the ASM instance   if [ $? -ne 0 ]   then     echo "Error when setting the ASM environment"     exit 1   fi       V_DATE=`/bin/date +%Y%m%d_%H%M%S`   echo "${V_DATE} ####################################################"   echo "The asm diskgroups/disks dettected are the following ..."   echo "####################################################################"     sqlplus /nolog <<-!     whenever sqlerror exit 1     connect / as sysdba     whenever sqlerror exit     SET LINES 200     COL PATH FORMAT A25     SELECT DISK.MOUNT_STATUS, DISK.PATH, DISK.NAME, DISK_GROUP.NAME, DISK_GROUP.TOTAL_MB FROM V\$ASM_DISK DISK, V\$ASM_DISKGROUP DISK_GROUP WHERE DISK.GROUP_NUMBER=DISK_GROUP.GROUP_NUMBER; !       V_ADMIN_DIR=`dirname $0`   . ${V_ADMIN_DIR}/setenv_instance.sh -- This script is supposed to set the env. variables of the database instance   if [ $? -ne 0 ]   then     echo "Error when setting the database instance environment"     exit 1   fi     V_DATE=`/bin/date +%Y%m%d_%H%M%S`   echo "${V_DATE} ####################################################"   echo "Starting ${DB_NAME} in MOUNT mode..."   echo "####################################################################"   ${V_ADMIN_DIR}/start_instance_mount.sh -- This script is supposed to do a startup mount   if [ $? -ne 0 ]   then     echo "Error starting  ${DB_NAME} in MOUNT mode"     exit 1   fi   V_DATE=`/bin/date +%Y%m%d_%H%M%S`   echo "${V_DATE} ####################################################"   echo "Executing RMAN backup..."   echo "####################################################################"   rman<<-!   connect target /   connect catalog ${V_RMAN_USR}/${V_RMAN_PWD}@${V_DB_CATALOG}   run {   allocate channel ch1 type 'SBT_TAPE' parms'ENV=(TDPO_OPTFILE=/opt/tivoli/tsm/client/oracle/bin64/tdpo.opt)'; -- TDPO Media Library   crosscheck archivelog all;   backup tag BCK_CONTROLFILE_ST_${DB_NAME}   format 'ctl_%d_%s__%p_%t'   controlfilecopy '+FRA_${DB_NAME}/${DB_NAME}/CONTROLFILE/control_backup.ctl';   backup tag BCK_DATAFILE_ST_${DB_NAME} full   format 'db_%d_%s_%p_%t'database;   backup tag BCK_ARCHLOG_ST_${DB_NAME} format 'al_%d_%s_%p_%t' archivelog all;   release channel ch1;   } !   if [ $? -ne 0 ]   then     echo "Error executing the RMAN backup"     exit 1   fi     ${V_ADMIN_DIR}/stop_instance_immediate.sh -- This script is supposed to do a shutdown immediate of the database instance   ${ADMIN_DIR}/stop_asm_immediate.sh -- This script is supposed to do a shutdown immediate of the ASM instance   exit 0     fi   Hope it helps someone! --L

    Read the article

  • Another Questionable Article Online…

    - by Jonathan Kehayias
    At the beginning of the month I blogged about my thoughts on the virtualization feedback provided by SSWUG’s newsletter , and Rich responded with some information on how the incorrect information lead him to making incorrect conclusions.  It seems like every couple of weeks an article, tip, newsletter, whatever is posted by or on a major site that has questionable if not outright incorrect material in it.  Last week MSSQLTips posted SQL Server tempdb one or multiple data files in which...(read more)

    Read the article

  • Where have I been? Speaking a lot, actually…

    - by drsql
    A few weeks back (Feb 27) I spoke at the Rocky Mountain Tech Trifecta ( http://rmtechtrifecta.pbworks.com/ ), where I gave the SQL Track keynote, and then did my Database Design session.  Great time and I had a blast giving a keynote. It was especially fun just doing a lightweight session just encouraging folks to do design. Last week, I spoke virtually for the Minnesota PASS group, giving the same presentation, plus 10% and including my patent pending Lego audience (The Minifiggers) and audience...(read more)

    Read the article

  • How to update all the SSIS packages&rsquo; Connection Managers in a BIDS project with PowerShell

    - by Luca Zavarella
    During the development of a BI solution, we all know that 80% of the time is spent during the ETL (Extract, Transform, Load) phase. If you use the BI Stack Tool provided by Microsoft SQL Server, this step is accomplished by the development of n Integration Services (SSIS) packages. In general, the number of packages made ??in the ETL phase for a non-trivial solution of BI is quite significant. An SSIS package, therefore, extracts data from a source, it "hammers" :) the data and then transfers it to a specific destination. Very often it happens that the connection to the source data is the same for all packages. Using Integration Services, this results in having the same Connection Manager (perhaps with the same name) for all packages: The source data of my BI solution comes from an Helper database (HLP), then, for each package tha import this data, I have the HLP Connection Manager (the use of a Shared Data Source is not recommended, because the Connection String is wired and therefore you have to open the SSIS project and use the proper wizard change it...). In order to change the HLP Connection String at runtime, we could use the Package Configuration, or we could run our packages with DTLoggedExec by Davide Mauri (a must-have if you are developing with SQL Server 2005/2008). But my need was to change all the HLP connections in all packages within the SSIS Visual Studio project, because I had to version them through Team Foundation Server (TFS). A good scribe with a lot of patience should have changed by hand all the connections by double-clicking the HLP Connection Manager of each package, and then changing the referenced server/database: Not being endowed with such virtues :) I took just a little of time to write a small script in PowerShell, using the fact that a SSIS package (a .dtsx file) is nothing but an xml file, and therefore can be changed quite easily. I'm not a guru of PowerShell, but I managed more or less to put together the following lines of code: $LeftDelimiterString = "Initial Catalog=" $RightDelimiterString = ";Provider=" $ToBeReplacedString = "AstarteToBeReplaced" $ReplacingString = "AstarteReplacing" $MainFolder = "C:\MySSISPackagesFolder" $files = get-childitem "$MainFolder" *.dtsx `       | Where-Object {!($_.PSIsContainer)} foreach ($file in $files) {       (Get-Content $file.FullName) `             | % {$_ -replace "($LeftDelimiterString)($ToBeReplacedString)($RightDelimiterString)", "`$1$ReplacingString`$3"} ` | Set-Content $file.FullName; } The script above just opens any SSIS package (.dtsx) in the supplied folder, then for each of them goes in search of the following text: Initial Catalog=AstarteToBeReplaced;Provider= and it replaces the text found with this: Initial Catalog=AstarteReplacing;Provider= I don’t enter into the details of each cmdlet used. I leave the reader to search for these details. Alternatively, you can use a specific object model exposed in some .NET assemblies provided by Integration Services, or you can use the Pacman utility: Enjoy! :) P.S. Using TFS as versioning system, before running the script I checked out the packages and, after the script executed succesfully, I checked in them.

    Read the article

  • Some thoughts on interviewing….

    - by Jonathan Kehayias
    At the beginning of the year I changed jobs, leaving a very stable position where I had the opportunity to learn under an amazing mentor (who happened to be a Oracle DBA and not a SQL DBA), to take on a job that I felt was much more challenging and had better potential for personal as well as professional growth.  I wasn’t necessarily looking for another job at the time, but one that interested me was mentioned at our local user group meeting and I decided to check it out and see if it was something...(read more)

    Read the article

  • ASP.NET and HTML5 Local Storage

    - by Stephen Walther
    My favorite feature of HTML5, hands-down, is HTML5 local storage (aka DOM storage). By taking advantage of HTML5 local storage, you can dramatically improve the performance of your data-driven ASP.NET applications by caching data in the browser persistently. Think of HTML5 local storage like browser cookies, but much better. Like cookies, local storage is persistent. When you add something to browser local storage, it remains there when the user returns to the website (possibly days or months later). Importantly, unlike the cookie storage limitation of 4KB, you can store up to 10 megabytes in HTML5 local storage. Because HTML5 local storage works with the latest versions of all modern browsers (IE, Firefox, Chrome, Safari), you can start taking advantage of this HTML5 feature in your applications right now. Why use HTML5 Local Storage? I use HTML5 Local Storage in the JavaScript Reference application: http://Superexpert.com/JavaScriptReference The JavaScript Reference application is an HTML5 app that provides an interactive reference for all of the syntax elements of JavaScript (You can read more about the application and download the source code for the application here). When you open the application for the first time, all of the entries are transferred from the server to the browser (all 300+ entries). All of the entries are stored in local storage. When you open the application in the future, only changes are transferred from the server to the browser. The benefit of this approach is that the application performs extremely fast. When you click the details link to view details on a particular entry, the entry details appear instantly because all of the entries are stored on the client machine. When you perform key-up searches, by typing in the filter textbox, matching entries are displayed very quickly because the entries are being filtered on the local machine. This approach can have a dramatic effect on the performance of any interactive data-driven web application. Interacting with data on the client is almost always faster than interacting with the same data on the server. Retrieving Data from the Server In the JavaScript Reference application, I use Microsoft WCF Data Services to expose data to the browser. WCF Data Services generates a REST interface for your data automatically. Here are the steps: Create your database tables in Microsoft SQL Server. For example, I created a database named ReferenceDB and a database table named Entities. Use the Entity Framework to generate your data model. For example, I used the Entity Framework to generate a class named ReferenceDBEntities and a class named Entities. Expose your data through WCF Data Services. I added a WCF Data Service to my project and modified the data service class to look like this:   using System.Data.Services; using System.Data.Services.Common; using System.Web; using JavaScriptReference.Models; namespace JavaScriptReference.Services { [System.ServiceModel.ServiceBehavior(IncludeExceptionDetailInFaults = true)] public class EntryService : DataService<ReferenceDBEntities> { // This method is called only once to initialize service-wide policies. public static void InitializeService(DataServiceConfiguration config) { config.UseVerboseErrors = true; config.SetEntitySetAccessRule("*", EntitySetRights.All); config.DataServiceBehavior.MaxProtocolVersion = DataServiceProtocolVersion.V2; } // Define a change interceptor for the Products entity set. [ChangeInterceptor("Entries")] public void OnChangeEntries(Entry entry, UpdateOperations operations) { if (!HttpContext.Current.Request.IsAuthenticated) { throw new DataServiceException("Cannot update reference unless authenticated."); } } } }     The WCF data service is named EntryService. Notice that it derives from DataService<ReferenceEntitites>. Because it derives from DataService<ReferenceEntities>, the data service exposes the contents of the ReferenceEntitiesDB database. In the code above, I defined a ChangeInterceptor to prevent un-authenticated users from making changes to the database. Anyone can retrieve data through the service, but only authenticated users are allowed to make changes. After you expose data through a WCF Data Service, you can use jQuery to retrieve the data by performing an Ajax call. For example, I am using an Ajax call that looks something like this to retrieve the JavaScript entries from the EntryService.svc data service: $.ajax({ dataType: "json", url: “/Services/EntryService.svc/Entries”, success: function (result) { var data = callback(result["d"]); } });     Notice that you must unwrap the data using result[“d”]. After you unwrap the data, you have a JavaScript array of the entries. I’m transferring all 300+ entries from the server to the client when the application is opened for the first time. In other words, I transfer the entire database from the server to the client, once and only once, when the application is opened for the first time. The data is transferred using JSON. Here is a fragment: { "d" : [ { "__metadata": { "uri": "http://superexpert.com/javascriptreference/Services/EntryService.svc/Entries(1)", "type": "ReferenceDBModel.Entry" }, "Id": 1, "Name": "Global", "Browsers": "ff3_6,ie8,ie9,c8,sf5,es3,es5", "Syntax": "object", "ShortDescription": "Contains global variables and functions", "FullDescription": "<p>\nThe Global object is determined by the host environment. In web browsers, the Global object is the same as the windows object.\n</p>\n<p>\nYou can use the keyword <code>this</code> to refer to the Global object when in the global context (outside of any function).\n</p>\n<p>\nThe Global object holds all global variables and functions. For example, the following code demonstrates that the global <code>movieTitle</code> variable refers to the same thing as <code>window.movieTitle</code> and <code>this.movieTitle</code>.\n</p>\n<pre>\nvar movieTitle = \"Star Wars\";\nconsole.log(movieTitle === this.movieTitle); // true\nconsole.log(movieTitle === window.movieTitle); // true\n</pre>\n", "LastUpdated": "634298578273756641", "IsDeleted": false, "OwnerId": null }, { "__metadata": { "uri": "http://superexpert.com/javascriptreference/Services/EntryService.svc/Entries(2)", "type": "ReferenceDBModel.Entry" }, "Id": 2, "Name": "eval(string)", "Browsers": "ff3_6,ie8,ie9,c8,sf5,es3,es5", "Syntax": "function", "ShortDescription": "Evaluates and executes JavaScript code dynamically", "FullDescription": "<p>\nThe following code evaluates and executes the string \"3+5\" at runtime.\n</p>\n<pre>\nvar result = eval(\"3+5\");\nconsole.log(result); // returns 8\n</pre>\n<p>\nYou can rewrite the code above like this:\n</p>\n<pre>\nvar result;\neval(\"result = 3+5\");\nconsole.log(result);\n</pre>", "LastUpdated": "634298580913817644", "IsDeleted": false, "OwnerId": 1 } … ]} I worried about the amount of time that it would take to transfer the records. According to Google Chome, it takes about 5 seconds to retrieve all 300+ records on a broadband connection over the Internet. 5 seconds is a small price to pay to avoid performing any server fetches of the data in the future. And here are the estimated times using different types of connections using Fiddler: Notice that using a modem, it takes 33 seconds to download the database. 33 seconds is a significant chunk of time. So, I would not use the approach of transferring the entire database up front if you expect a significant portion of your website audience to connect to your website with a modem. Adding Data to HTML5 Local Storage After the JavaScript entries are retrieved from the server, the entries are stored in HTML5 local storage. Here’s the reference documentation for HTML5 storage for Internet Explorer: http://msdn.microsoft.com/en-us/library/cc197062(VS.85).aspx You access local storage by accessing the windows.localStorage object in JavaScript. This object contains key/value pairs. For example, you can use the following JavaScript code to add a new item to local storage: <script type="text/javascript"> window.localStorage.setItem("message", "Hello World!"); </script>   You can use the Google Chrome Storage tab in the Developer Tools (hit CTRL-SHIFT I in Chrome) to view items added to local storage: After you add an item to local storage, you can read it at any time in the future by using the window.localStorage.getItem() method: <script type="text/javascript"> window.localStorage.setItem("message", "Hello World!"); </script>   You only can add strings to local storage and not JavaScript objects such as arrays. Therefore, before adding a JavaScript object to local storage, you need to convert it into a JSON string. In the JavaScript Reference application, I use a wrapper around local storage that looks something like this: function Storage() { this.get = function (name) { return JSON.parse(window.localStorage.getItem(name)); }; this.set = function (name, value) { window.localStorage.setItem(name, JSON.stringify(value)); }; this.clear = function () { window.localStorage.clear(); }; }   If you use the wrapper above, then you can add arbitrary JavaScript objects to local storage like this: var store = new Storage(); // Add array to storage var products = [ {name:"Fish", price:2.33}, {name:"Bacon", price:1.33} ]; store.set("products", products); // Retrieve items from storage var products = store.get("products");   Modern browsers support the JSON object natively. If you need the script above to work with older browsers then you should download the JSON2.js library from: https://github.com/douglascrockford/JSON-js The JSON2 library will use the native JSON object if a browser already supports JSON. Merging Server Changes with Browser Local Storage When you first open the JavaScript Reference application, the entire database of JavaScript entries is transferred from the server to the browser. Two items are added to local storage: entries and entriesLastUpdated. The first item contains the entire entries database (a big JSON string of entries). The second item, a timestamp, represents the version of the entries. Whenever you open the JavaScript Reference in the future, the entriesLastUpdated timestamp is passed to the server. Only records that have been deleted, updated, or added since entriesLastUpdated are transferred to the browser. The OData query to get the latest updates looks like this: http://superexpert.com/javascriptreference/Services/EntryService.svc/Entries?$filter=(LastUpdated%20gt%20634301199890494792L) If you remove URL encoding, the query looks like this: http://superexpert.com/javascriptreference/Services/EntryService.svc/Entries?$filter=(LastUpdated gt 634301199890494792L) This query returns only those entries where the value of LastUpdated > 634301199890494792 (the version timestamp). The changes – new JavaScript entries, deleted entries, and updated entries – are merged with the existing entries in local storage. The JavaScript code for performing the merge is contained in the EntriesHelper.js file. The merge() method looks like this:   merge: function (oldEntries, newEntries) { // concat (this performs the add) oldEntries = oldEntries || []; var mergedEntries = oldEntries.concat(newEntries); // sort this.sortByIdThenLastUpdated(mergedEntries); // prune duplicates (this performs the update) mergedEntries = this.pruneDuplicates(mergedEntries); // delete mergedEntries = this.removeIsDeleted(mergedEntries); // Sort this.sortByName(mergedEntries); return mergedEntries; },   The contents of local storage are then updated with the merged entries. I spent several hours writing the merge() method (much longer than I expected). I found two resources to be extremely useful. First, I wrote extensive unit tests for the merge() method. I wrote the unit tests using server-side JavaScript. I describe this approach to writing unit tests in this blog entry. The unit tests are included in the JavaScript Reference source code. Second, I found the following blog entry to be super useful (thanks Nick!): http://nicksnettravels.builttoroam.com/post/2010/08/03/OData-Synchronization-with-WCF-Data-Services.aspx One big challenge that I encountered involved timestamps. I originally tried to store an actual UTC time as the value of the entriesLastUpdated item. I quickly discovered that trying to work with dates in JSON turned out to be a big can of worms that I did not want to open. Next, I tried to use a SQL timestamp column. However, I learned that OData cannot handle the timestamp data type when doing a filter query. Therefore, I ended up using a bigint column in SQL and manually creating the value when a record is updated. I overrode the SaveChanges() method to look something like this: public override int SaveChanges(SaveOptions options) { var changes = this.ObjectStateManager.GetObjectStateEntries( EntityState.Modified | EntityState.Added | EntityState.Deleted); foreach (var change in changes) { var entity = change.Entity as IEntityTracking; if (entity != null) { entity.LastUpdated = DateTime.Now.Ticks; } } return base.SaveChanges(options); }   Notice that I assign Date.Now.Ticks to the entity.LastUpdated property whenever an entry is modified, added, or deleted. Summary After building the JavaScript Reference application, I am convinced that HTML5 local storage can have a dramatic impact on the performance of any data-driven web application. If you are building a web application that involves extensive interaction with data then I recommend that you take advantage of this new feature included in the HTML5 standard.

    Read the article

  • Do DBA’s not desire sleep?

    - by drsql
    Or any support people for that matter.  I constantly hear people having to support this, support that, wearing the “beeper”, etc etc.  But these people do seemingly love what they do, because I hear this on “non-essential” communications channels, like Twitter, SQL Saturday conversations, etc.  These are people who are doing what they do because they like it. I have to be honest with you though, about the second time I was awakened by a beeper with the same problem I would be outraged....(read more)

    Read the article

  • SQLBits 8 – Conor’s back

    - by simonsabin
    I recently announced the awesome line up for SQLBits 8 in which I mentioned Conor Cunningham . Yes we have Conor coming back. Conor is the most popular SQLBits speaker ever. Conor Cunningham is a Principal Software Architect at Microsoft on the SQL Server Query Processor Team.  He's worked on database technologies for Microsoft for over 10 years and is holds numerous patents related to Query Optimization and Query Processing.  Conor is the author of a number of peer-reviewed articles...(read more)

    Read the article

  • Get your Master Immersion learning on!

    - by AaronBertrand
    Time is running out to register for the Master Immersion training events being held in Dallas, TX by SQLskills . These particular events will be instructed by Paul Randal ( blog | twitter ), Kimberly Tripp ( blog | twitter ), and Brent Ozar ( blog | twitter ) and reflect the new format for the Microsoft Certified Master (MCM) program. This means training in the low 4 figures instead of close to $20K, and that you can take at your own pace. We at SQL Sentry ( twitter ) are quite proud to be the exclusive...(read more)

    Read the article

  • Get client IP address

    - by Peter Larsson
    Up until now, I have used convuluted approaches to get the current user client IP-address. This weekend I browsed Books Online for SQL Server 2008 R2 (November CTP) and found this new cool function! SELECT  CONNECTIONPROPERTY('net_transport') AS net_transport,         CONNECTIONPROPERTY('protocol_type') AS protocol_type,         CONNECTIONPROPERTY('auth_scheme') AS auth_scheme,         CONNECTIONPROPERTY('local_net_address') AS local_net_address,         CONNECTIONPROPERTY('local_tcp_port') AS local_tcp_port,         CONNECTIONPROPERTY('client_net_address') AS client_net_address  //Peter

    Read the article

  • Sybase ASE

    - by Linchi Shea
    I sat in a Sybase ASE class last week for five days. Although it didn't cover the more advanced features introduced in the more recent versions of Sybase ASE, the class did touch all the basics of administering Sybase ASE. While I was successful in suppressing any urge to openly compare Sybase ASE with Microsoft SQL Server in the class, I could not help making mental notes on the differences between the two database platforms. It's always interesting to look at how two DBMS platforms that share the...(read more)

    Read the article

  • Get your Master Immersion learning on!

    - by AaronBertrand
    Time is running out to register for the Master Immersion training events being held in Dallas, TX by SQLskills . These particular events will be instructed by Paul Randal ( blog | twitter ), Kimberly Tripp ( blog | twitter ), and Brent Ozar ( blog | twitter ) and reflect the new format for the Microsoft Certified Master (MCM) program. This means training in the low 4 figures instead of close to $20K, and that you can take at your own pace. We at SQL Sentry ( twitter ) are quite proud to be the exclusive...(read more)

    Read the article

  • AutoAudit 1.10c

    - by Paul Nielsen
    AutoAudit is a free SQL Server (2005, 2008) Code-Gen utility that creates Audit Trail Triggers with: · Created, Modified, and RowVersion (incrementing INT) columns to table · Creates View to reconstruct deleted rows · Creates UDF to reconstruct Row History · Schema Audit Trigger to track schema changes · Re-code-gens triggers when Alter Table changes the table Version 1.10c Adds: · Createdby and ModifiedBy columns. Pass the user to the column and AutoAudit records that username instead of the Suser_Sname...(read more)

    Read the article

  • Grant’s video warning – backup verification

    Grant takes a humorous (but completely serious) look at why you should be regularly verifying your backups. Get top tips for backup and recovery, and protect yourself when disaster strikes. Watch the video Schedule Azure backupsRed Gate’s Cloud Services makes it simple to create and schedule backups of your SQL Azure databases to Azure blob storage or Amazon S3. Try it for free today.

    Read the article

< Previous Page | 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033  | Next Page >