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  • NHibernate many-to-many with composite element

    - by E1
    Hello everyone. I encountered the following problem. In my application uses the entities Owner and Area binding as many to many. public class Area : DomainObject<int> { private ISet<OwnersPeriod> _owners = new HashedSet<OwnersPeriod>(); public ICollection<OwnersPeriod> Owners { get { return _owners; } set { Check.Require(value != null); _owners = new HashedSet<OwnersPeriod>(value); } } } Table Owner2Area has the following fields: CREATE TABLE [dbo].[Owner2Area]( [ID] [int] IDENTITY(1,1) NOT NULL, [IDOwner] [int] NOT NULL, [IDArea] [int] NOT NULL, [FirstDate] [datetime] NOT NULL, [LastDate] [datetime] NULL, CONSTRAINT [PK_Owner2Area] PRIMARY KEY CLUSTERED) Therefore corresponds to the class OwnersPeriod public class OwnersPeriod { private Owner _member; private Area _area; public Owner Owner { get {...} set{...} } public Area Area { get { ... } set { ... } } public DateTime FirstDate { get; set; } public DateTime? LastDate { get; set; } } I wrote mappings <class lazy="false" name="Core.Domain.Area, Core" table="Areas"> ... <set name="Owners" table="Owner2Area" inverse="true" lazy="true" > <key column="IDArea"/> <composite-element class="Core.Domain.OwnersPeriod, Core" > <parent name="Area" /> <property name="FirstDate" type="datetime"/> <property name="LastDate" type="datetime"/> <many-to-one name="Owner" class="Core.Domain.Owner, Core" column="IDOwner"/> </composite-element> </set> </class> For each area existing data are successfully loaded into Owners, but when I add new record in Owner2Area through CreateSQLQuery, these data are not updated for instance of area. If I re-opened the form and got all areas, added link successfully loaded into the collection. How can be forced to load added thus recording of relation many-to-many? Nhibernate v.2.0.1, db MSSQL 2005

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  • Can LINQ-to-SQL omit unspecified columns on insert so a database default value is used?

    - by Todd Ropog
    I have a non-nullable database column which has a default value set. When inserting a row, sometimes a value is specified for the column, sometimes one is not. This works fine in TSQL when the column is omitted. For example, given the following table: CREATE TABLE [dbo].[Table1]( [id] [int] IDENTITY(1,1) NOT NULL, [col1] [nvarchar](50) NOT NULL, [col2] [nvarchar](50) NULL, CONSTRAINT [PK_Table1] PRIMARY KEY CLUSTERED ([id] ASC) ) GO ALTER TABLE [dbo].[Table1] ADD CONSTRAINT [DF_Table1_col1] DEFAULT ('DB default') FOR [col1] The following two statements will work: INSERT INTO Table1 (col1, col2) VALUES ('test value', '') INSERT INTO Table1 (col2) VALUES ('') In the second statement, the default value is used for col1. The problem I have is when using LINQ-to-SQL (L2S) with a table like this. I want to produce the same behavior, but I can't figure out how to make L2S do that. I want to be able to run the following code and have the first row get the value I specify and the second row get the default value from the database: var context = new DataClasses1DataContext(); var row1 = new Table1 { col1 = "test value", col2 = "" }; context.Table1s.InsertOnSubmit(row1); context.SubmitChanges(); var row2 = new Table1 { col2 = "" }; context.Table1s.InsertOnSubmit(row2); context.SubmitChanges(); If the Auto Generated Value property of col1 is False, the first row is created as desired, but the second row fails with a null error on col1. If Auto Generated Value is True, both rows are created with the default value from the database. I've tried various combinations of Auto Generated Value, Auto-Sync and Nullable, but nothing I've tried gives the behavior I want. L2S does not omit the column from the insert statement when no value is specified. Instead it does something like this: INSERT INTO Table1 (col1, col2) VALUES (null, '') ...which of course causes a null error on col1. Is there some way to get L2S to omit a column from the insert statement if no value is given? Or is there some other way to get the behavior I want? I need the default value at the database level because not all row inserts are done via L2S, and in some cases the default value is a little more complex than a hard coded value (e.g. creating the default based on another field) so I'd rather avoid duplicating that logic.

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  • What is causing this SQL 2005 Primary Key Deadlock between two real-time bulk upserts?

    - by skimania
    Here's the scenario: I've got a table called MarketDataCurrent (MDC) that has live updating stock prices. I've got one process called 'LiveFeed' which reads prices streaming from the wire, queues up inserts, and uses a 'bulk upload to temp table then insert/update to MDC table.' (BulkUpsert) I've got another process which then reads this data, computes other data, and then saves the results back into the same table, using a similar BulkUpsert stored proc. Thirdly, there are a multitude of users running a C# Gui polling the MDC table and reading updates from it. Now, during the day when the data is changing rapidly, things run pretty smoothly, but then, after market hours, we've recently started seeing an increasing number of Deadlock exceptions coming out of the database, nowadays we see 10-20 a day. The imporant thing to note here is that these happen when the values are NOT changing. Here's all the relevant info: Table Def: CREATE TABLE [dbo].[MarketDataCurrent]( [MDID] [int] NOT NULL, [LastUpdate] [datetime] NOT NULL, [Value] [float] NOT NULL, [Source] [varchar](20) NULL, CONSTRAINT [PK_MarketDataCurrent] PRIMARY KEY CLUSTERED ( [MDID] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] - stackoverflow wont let me post images until my reputation goes up to 10, so i'll add them as soon as you bump me up, hopefully as a result of this question. ![alt text][1] [1]: http://farm5.static.flickr.com/4049/4690759452_6b94ff7b34.jpg I've got a Sql Profiler Trace Running, catching the deadlocks, and here's what all the graphs look like. stackoverflow wont let me post images until my reputation goes up to 10, so i'll add them as soon as you bump me up, hopefully as a result of this question. ![alt text][2] [2]: http://farm5.static.flickr.com/4035/4690125231_78d84c9e15_b.jpg Process 258 is called the following 'BulkUpsert' stored proc, repeatedly, while 73 is calling the next one: ALTER proc [dbo].[MarketDataCurrent_BulkUpload] @updateTime datetime, @source varchar(10) as begin transaction update c with (rowlock) set LastUpdate = getdate(), Value = t.Value, Source = @source from MarketDataCurrent c INNER JOIN #MDTUP t ON c.MDID = t.mdid where c.lastUpdate < @updateTime and c.mdid not in (select mdid from MarketData where LiveFeedTicker is not null and PriceSource like 'LiveFeed.%') and c.value <> t.value insert into MarketDataCurrent with (rowlock) select MDID, getdate(), Value, @source from #MDTUP where mdid not in (select mdid from MarketDataCurrent with (nolock)) and mdid not in (select mdid from MarketData where LiveFeedTicker is not null and PriceSource like 'LiveFeed.%') commit And the other one: ALTER PROCEDURE [dbo].[MarketDataCurrent_LiveFeedUpload] AS begin transaction -- Update existing mdid UPDATE c WITH (ROWLOCK) SET LastUpdate = t.LastUpdate, Value = t.Value, Source = t.Source FROM MarketDataCurrent c INNER JOIN #TEMPTABLE2 t ON c.MDID = t.mdid; -- Insert new MDID INSERT INTO MarketDataCurrent with (ROWLOCK) SELECT * FROM #TEMPTABLE2 WHERE MDID NOT IN (SELECT MDID FROM MarketDataCurrent with (NOLOCK)) -- Clean up the temp table DELETE #TEMPTABLE2 commit To clarify, those Temp Tables are being created by the C# code on the same connection and are populated using the C# SqlBulkCopy class. To me it looks like it's deadlocking on the PK of the table, so I tried removing that PK and switching to a Unique Constraint instead but that increased the number of deadlocks 10-fold. I'm totally lost as to what to do about this situation and am open to just about any suggestion. HELP!!

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  • BULK INSERT from one table to another all on the server

    - by steve_d
    I have to copy a bunch of data from one database table into another. I can't use SELECT ... INTO because one of the columns is an identity column. Also, I have some changes to make to the schema. I was able to use the export data wizard to create an SSIS package, which I then edited in Visual Studio 2005 to make the changes desired and whatnot. It's certainly faster than an INSERT INTO, but it seems silly to me to download the data to a different computer just to upload it back again. (Assuming that I am correct that that's what the SSIS package is doing). Is there an equivalent to BULK INSERT that runs directly on the server, allows keeping identity values, and pulls data from a table? (as far as I can tell, BULK INSERT can only pull data from a file) Edit: I do know about IDENTITY_INSERT, but because there is a fair amount of data involved, INSERT INTO ... SELECT is kinda of slow. SSIS/BULK INSERT dumps the data into the table without regards to indexes and logging and whatnot, so it's faster. (Of course creating the clustered index on the table once it's populated is not fast, but it's still faster than the INSERT INTO...SELECT that I tried in my first attempt) Edit 2: The schema changes include (but are not limited to) the following: 1. Splitting one table into two new tables. In the future each will have its own IDENTITY column, but for the migration I think it will be simplest to use the identity from the original table as the identity for the both new tables. Once the migration is over one of the tables will have a one-to-many relationship to the other. 2. Moving columns from one table to another. 3. Deleting some cross reference tables that only cross referenced 1-to-1. Instead the reference will be a foreign key in one of the two tables. 4. Some new columns will be created with default values. 5. Some tables aren’t changing at all, but I have to copy them over due to the "put it all in a new DB" request.

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  • Manipulating values from database table with php

    - by charliecodex23
    I currently have 5 tables in MySQL database. Some of them share foreign keys and are interdependent of each other. I am displaying classes accordingly to their majors. Each class is taught during the fall, spring or all_year. In my database I have a table named semester which has an id, year, and semester fields. The semester field in particular is a tinyint that has three values 0, 1, 2. This signifies the fall, spring or all_year. When I display the query instead of having it show 0 or 1 or 2 can I have it show fall, spring etc? Extra: How can I add space to the end of each loop so the data doesn't look clustered? Key 0 Fall 1 Spring 2 All-year PHP <? try { $pdo = new PDO ("mysql:host=$hostname;dbname=$dbname","$username","$pw"); } catch (PDOException $e) { echo "Failed to get DB handle: " . $e->getMessage() . "\n"; exit; } $query = $pdo->prepare("SELECT course.name, course.code, course.description, course.hours, semester.semester, semester.year FROM course LEFT JOIN major_course_xref ON course.id = major_course_xref.course_id LEFT JOIN major ON major.id = major_course_xref.major_id LEFT JOIN course_semester_xref ON course.id = course_semester_xref.course_id LEFT JOIN semester ON course_semester_xref.semester_id = semester.id"); $query->execute(); if ($query->execute()){ while ($row = $query->fetch(PDO::FETCH_ASSOC)){ print $row['name'] . "<br>"; print $row['code'] . "<br>"; print $row['description'] . "<br>"; print $row['hours'] . " hrs.<br>"; print $row['semester'] . "<br>"; print $row['year'] . "<br>"; } } else echo 'Could not fetch results.'; unset($pdo); unset($query); ?> Current Display Computer Programming I CPSC1400 Introduction to disciplined, object-oriented program development. 4 hrs. 0 2013 Desire Display Computer Programming I CPSC1400 Introduction to disciplined, object-oriented program development. 4 hrs. Fall 2013

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  • Exchange Server 2007 Setup

    - by AlamedaDad
    Hi, I'm working on a upgrade to Exchange 2007 and I wanted to get some advise on hardware choices. We currently have an Exchange 2003 STD server with 400 users split between 6 AD Sites, that is housed on a single server. We need to move to a redundant, fault tolerant system to support our users. I'm planning on installing 2 Dell 1950 servers with W2k8-std to act as CAS and Hub servers, with NLB to allow abstraction of the actual server name to the users. There won't be an edge system since we have a Barracuda box already that will handle in/out spam/virus filtering. Backend I'm planning on 2 mailbox servers which will be Dell 2950s with 16GB RAM, 2 either dual-core or quad-core CPUs and 6 300GB SAS drives in some RAID config. These systems will be clustered using W2k8 Ent clustering and running CCR in Exchange. My questions are as follows: Is 16GB enough RAM for serving that many mailboxes along with the windows clustering and ccr? I'm trying to figure out disk layouts and I'm unsure of whether to use all local disk or some local and some SAN, via an OpenFiler iSCSI server. The SAN would be a Dell 2850 with 6 - 300GB SCSI drives and a PERC controller to slice as I want, with 8GB RAM. Option 1: 2 drives, RAID 1 - OS 2 drives, RAID 1 - Logs 2 drives, RAID 1 - Mail stores Option 2: 2 drives, RAID 1 - OS and logs 4 drives, RAID 5 - Mail Stores and scratch space for eseutil. Option 3: 2 drives, RAID 1 - OS 2 drives, RAID 1 - Logs 2 drives, RAID 0 - scratch space ~300GB iSCSI volume for mail stores Option 4: 2 drives, RAID 1 - OS 4 drives, RAID 5 - scratch space ~300GB iSCSI volume for mail stores ~300GB iSCSI volume for logs I have 2 sockets for CPUs and need to chose between dual and quad cores. The dual core have faster clocks but less cache and I'm thinking older architecture. Am I better off with more cores and cache while sacraficing clock speed? I am planning on adding the new E2K7 cluster to the E2K3 server and then move each mailbox over, all at once, then remove the old server. This seems more complicated than simply getting rid of the 2003 server and then adding the 2007 cluster and restoring the mailboxes using PowerControls or exmerge. The migration option lets me do this on my time, where a cutover means it all needs to work at once. If I go with the cutover method, how can I prebuild the servers and add them to the domain right after removing the 2003 server, or can't I? I think the answer is no and the migration is my only real option if I want to prebuild. I need to also migrate about 30GB of Public Folders. Is there anything special about this, other than specifying in the E2K7 install that I want older Outlook clients and PF's setup? I guess I could even keep the E2K3 server to host just the PFs? Lastly, if I have a mix of Outlook 200, 2003 and 2007 what do I need to do to make sure they all have access to the GAL and OAB? At time of cutover, we'll be at like 90% 2007, but we will have some older stuff around. My plan is to use Outlook Anywhere on laptops that are used outside the physical network. Are there any gotchas involved in that? I'm even thinking about using is for all Outlook clients, does anyone do that? The reason I'm considering it is that our WAN is really VPN tunnels over internet connections, so not a fully messhed, stable WAN. Thank you all very much for the assistance in advance and I look forward to discussion of these points! Regards...Michael

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  • Recommendations for distributed processing/distributed storage systems

    - by Eddie
    At my organization we have a processing and storage system spread across two dozen linux machines that handles over a petabyte of data. The system right now is very ad-hoc; processing automation and data management is handled by a collection of large perl programs on independent machines. I am looking at distributed processing and storage systems to make it easier to maintain, evenly distribute load and data with replication, and grow in disk space and compute power. The system needs to be able to handle millions of files, varying in size between 50 megabytes to 50 gigabytes. Once created, the files will not be appended to, only replaced completely if need be. The files need to be accessible via HTTP for customer download. Right now, processing is automated by perl scripts (that I have complete control over) which call a series of other programs (that I don't have control over because they are closed source) that essentially transforms one data set into another. No data mining happening here. Here is a quick list of things I am looking for: Reliability: These data must be accessible over HTTP about 99% of the time so I need something that does data replication across the cluster. Scalability: I want to be able to add more processing power and storage easily and rebalance the data on across the cluster. Distributed processing: Easy and automatic job scheduling and load balancing that fits with processing workflow I briefly described above. Data location awareness: Not strictly required but desirable. Since data and processing will be on the same set of nodes I would like the job scheduler to schedule jobs on or close to the node that the data is actually on to cut down on network traffic. Here is what I've looked at so far: Storage Management: GlusterFS: Looks really nice and easy to use but doesn't seem to have a way to figure out what node(s) a file actually resides on to supply as a hint to the job scheduler. GPFS: Seems like the gold standard of clustered filesystems. Meets most of my requirements except, like glusterfs, data location awareness. Ceph: Seems way to immature right now. Distributed processing: Sun Grid Engine: I have a lot of experience with this and it's relatively easy to use (once it is configured properly that is). But Oracle got its icy grip around it and it no longer seems very desirable. Both: Hadoop/HDFS: At first glance it looked like hadoop was perfect for my situation. Distributed storage and job scheduling and it was the only thing I found that would give me the data location awareness that I wanted. But I don't like the namename being a single point of failure. Also, I'm not really sure if the MapReduce paradigm fits the type of processing workflow that I have. It seems like you need to write all your software specifically for MapReduce instead of just using Hadoop as a generic job scheduler. OpenStack: I've done some reading on this but I'm having trouble deciding if it fits well with my problem or not. Does anyone have opinions or recommendations for technologies that would fit my problem well? Any suggestions or advise would be greatly appreciated. Thanks!

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  • Is the Cloud ready for an Enterprise Java web application? Seeking a JEE hosting advice.

    - by Jakub Holý
    Greetings to all the smart people around here! I'd like to ask whether it is feasible or a good idea at all to deploy a Java enterprise web application to a Cloud such as Amazon EC2. More exactly, I'm looking for infrastructure options for an application that shall handle few hundred users with long but neither CPU nor memory intensive sessions. I'm considering dedicated servers, virtual private servers (VPSs) and EC2. I've noticed that there is a project called JBoss Cloud so people are working on enabling such a deployment, on the other hand it doesn't seem to be mature yet and I'm not sure that the cloud is ready for this kind of applications, which differs from the typical cloud-based applications like Twitter. Would you recommend to deploy it to the cloud? What are the pros and cons? The application is a Java EE 5 web application whose main function is to enable users to compose their own customized Product by combining the available Parts. It uses stateless and stateful session beans and JPA for persistence of entities to a RDBMS and fetches information about Parts from the company's inventory system via a web service. Aside of external users it's used also by few internal ones, who are authenticated against the company's LDAP. The application should handle around 300-400 concurrent users building their product and should be reasonably scalable and available though these qualities are only of a medium importance at this stage. I've proposed an architecture consisting of a firewall (FW) and load balancer supporting sticky sessions and https (in the Cloud this would be replaced with EC2's Elastic Load Balancing service and FW on the app. servers, in a physical architecture the load-balancer would be a HW), then two physical clustered application servers combined with web servers (so that if one fails, a user doesn't loose his/her long built product) and finally a database server. The DB server would need a slave backup instance that can replace the master instance if it fails. This should provide reasonable availability and fault tolerance and provide good scalability as long as a single RDBMS can keep with the load, which should be OK for quite a while because most of the operations are done in the memory using a stateful bean and only occasionally stored or retrieved from the DB and the amount of data is low too. A problematic part could be the dependency on the remote inventory system webservice but with good caching of its outputs in the application it should be OK too. Unfortunately I've only vague idea of the system resources (memory size, number and speed of CPUs/cores) that such an "average Java EE application" for few hundred users needs. My rough and mostly unfounded estimate based on actual Amazon offerings is that 1.7GB and a single, 2-core "modern CPU" with speed around 2.5GHz (the High-CPU Medium Instance) should be sufficient for any of the two application servers (since we can handle higher load by provisioning more of them). Alternatively I would consider using the Large instance (64b, 7.5GB RAM, 2 cores at 1GHz) So my question is whether such a deployment to the cloud is technically and financially feasible or whether dedicated/VPS servers would be a better option and whether there are some real-world experiences with something similar. Thank you very much! /Jakub Holy PS: I've found the JBoss EAP in a Cloud Case Study that shows that it is possible to deploy a real-world Java EE application to the EC2 cloud but unfortunately there're no details regarding topology, instance types, or anything :-(

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  • Strange enduser experience with Liferay, Glassfish and Apache on RedHat

    - by Pete Helgren
    Tried multiple forums to get to the bottom of this. I hope I can get some direction here: Here is the stack I am working with: Red Hat Enterprise Linux Server release 5.6 (Tikanga) Liferay 6.0.6 on Glassfish 3.0.1 MySQL 5.0.77 Apache 2.2.3 The Liferay portal provides a variety of portlets to end users. Static content (web pages), static resources (primarily pdf and mp3 files 1mb - 80mb in size), File upload and download capabilities (primarily 40-60mb mp3 files) and online streaming of those MP3 files. Here is the strange end user experiences: Under normal load: (20-30) users uploading, downloading or streaming files and 20-30 accessing static content (some of it downloads), we see the following: 1) Clicking a link triggers the download of a portion of an MP3 (the portion is a few seconds long). 2) Clicking on a link tiggers the download of the page content rather than rendering. 3) Clicking a link causes the page to dump binary data to the end user rather than the expected content. 4) Clicking a link returns the text of a javascript file rather than rendering the page. Each occurrence is totally random (or appears so). Sometimes it works, sometimes it doesn't. It seems to have no relation to browser or client OS. The strange events seem to occur much more frequently when using an SSL connection rather than regular http. Apache serves as a proxy server only (reverse). It basically passes all the requests through to Glassfish. There isn't any static content proxy served by Apache. We rebuilt the entire stack from scratch and redeployed the portlet wars and still have the same issues. Liferay is running as a single server (not clustered). We disabled mod_cache in Apache. The problems are more frequent as the server load grows. This morning the load is pretty light and we are seeing few problems but the use of the site will grow, particularly tonight around 9pm CST through Wednesday morning. You could try the site (http://preview.bsfinternational.org) during those times and I would expect that you might experience one of the weirdnesses as you randomly click links on the site (https is invoked only when signed in). Again, https seems to exacerbate the issue. This seems very much like a caching issue but I don't know where in the stack to start peeling the onion. Apache? Liferay? Glassfish? MySQL? Maybe even Redhat? We are stumped and most forums we have posted to (LifeRay and Glassfish) have returned very few suggestions. I just need an idea of where to start looking. I understand that we could have a portlet EDIT: Opening the files in a Hex editor that appear to be pages that download rather than render, we see that the first 4000 characters are "junk" and then the "HTTP/1.1 ...." 'normal' header is seen. So something is dumping a jumble of characters up to offset 4000 (when viewing it in a Hex editor). Perhaps a clue? Ideas?

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  • RHCS: GFS2 in A/A cluster with common storage. Configuring GFS with rgmanager

    - by Pavel A
    I'm configuring a two node A/A cluster with a common storage attached via iSCSI, which uses GFS2 on top of clustered LVM. So far I have prepared a simple configuration, but am not sure which is the right way to configure gfs resource. Here is the rm section of /etc/cluster/cluster.conf: <rm> <failoverdomains> <failoverdomain name="node1" nofailback="0" ordered="0" restricted="1"> <failoverdomainnode name="rhc-n1"/> </failoverdomain> <failoverdomain name="node2" nofailback="0" ordered="0" restricted="1"> <failoverdomainnode name="rhc-n2"/> </failoverdomain> </failoverdomains> <resources> <script file="/etc/init.d/clvm" name="clvmd"/> <clusterfs name="gfs" fstype="gfs2" mountpoint="/mnt/gfs" device="/dev/vg-cs/lv-gfs"/> </resources> <service name="shared-storage-inst1" autostart="0" domain="node1" exclusive="0" recovery="restart"> <script ref="clvmd"> <clusterfs ref="gfs"/> </script> </service> <service name="shared-storage-inst2" autostart="0" domain="node2" exclusive="0" recovery="restart"> <script ref="clvmd"> <clusterfs ref="gfs"/> </script> </service> </rm> This is what I mean: when using clusterfs resource agent to handle GFS partition, it is not unmounted by default (unless force_unmount option is given). This way when I issue clusvcadm -s shared-storage-inst1 clvm is stopped, but GFS is not unmounted, so a node cannot alter LVM structure on shared storage anymore, but can still access data. And even though a node can do it quite safely (dlm is still running), this seems to be rather inappropriate to me, since clustat reports that the service on a particular node is stopped. Moreover if I later try to stop cman on that node, it will find a dlm locking, produced by GFS, and fail to stop. I could have simply added force_unmount="1", but I would like to know what is the reason behind the default behavior. Why is it not unmounted? Most of the examples out there silently use force_unmount="0", some don't, but none of them give any clue on how the decision was made. Apart from that I have found sample configurations, where people manage GFS partitions with gfs2 init script - https://alteeve.ca/w/2-Node_Red_Hat_KVM_Cluster_Tutorial#Defining_The_Resources or even as simply as just enabling services such as clvm and gfs2 to start automatically at boot (http://pbraun.nethence.com/doc/filesystems/gfs2.html), like: chkconfig gfs2 on If I understand the latest approach correctly, such cluster only controls whether nodes are still alive and can fence errant ones, but such cluster has no control over the status of its resources. I have some experience with Pacemaker and I'm used to that all resources are controlled by a cluster and an action can be taken when not only there are connectivity issues, but any of the resources misbehave. So, which is the right way for me to go: leave GFS partition mounted (any reasons to do so?) set force_unmount="1". Won't this break anything? Why this is not the default? use script resource <script file="/etc/init.d/gfs2" name="gfs"/> to manage GFS partition. start it at boot and don't include in cluster.conf (any reasons to do so?) This may be a sort of question that cannot be answered unambiguously, so it would be also of much value for me if you shared your experience or expressed your thoughts on the issue. How does for example /etc/cluster/cluster.conf look like when configuring gfs with Conga or ccs (they are not available to me since for now I have to use Ubuntu for the cluster)? Thanks you very much!

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  • SQL SERVER – Weekly Series – Memory Lane – #051

    - by Pinal Dave
    Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 Explanation and Understanding NOT NULL Constraint NOT NULL is integrity CONSTRAINT. It does not allow creating of the row where column contains NULL value. Most discussed questions about NULL is what is NULL? I will not go in depth analysis it. Simply put NULL is unknown or missing data. When NULL is present in database columns, it can affect the integrity of the database. I really do not prefer NULL in the database unless they are absolutely necessary. Three T-SQL Script to Create Primary Keys on Table I have always enjoyed writing about three topics Constraint and Keys, Backup and Restore and Datetime Functions. Primary Keys constraints prevent duplicate values for columns and provides a unique identifier to each column, as well it creates clustered index on the columns. 2008 Get Numeric Value From Alpha Numeric String – UDF for Get Numeric Numbers Only SQL is great with String operations. Many times, I use T-SQL to do my string operation. Let us see User Defined Function, which I wrote a few days ago, which will return only Numeric values from Alpha Numeric values. Introduction and Example of UNION and UNION ALL It is very much interesting when I get requests from blog reader to re-write my previous articles. I have received few requests to rewrite my article SQL SERVER – Union vs. Union All – Which is better for performance? with examples. I request you to read my previous article first to understand what is the concept and read this article to understand the same concept with an example. Downgrade Database for Previous Version The main questions is how they can downgrade the from SQL Server 2005 to SQL Server 2000? The answer is : Not Possible. Get Common Records From Two Tables Without Using Join Following is my scenario, Suppose Table 1 and Table 2 has same column e.g. Column1 Following is the query, 1. Select column1,column2 From Table1 2. Select column1 From Table2 I want to find common records from these tables, but I don’t want to use the Join clause because for that I need to specify the column name for Join condition. Will you help me to get common records without using Join condition? I am using SQL Server 2005. Retrieve – Select Only Date Part From DateTime – Best Practice – Part 2 A year ago I wrote a post about SQL SERVER – Retrieve – Select Only Date Part From DateTime – Best Practice where I have discussed two different methods of getting the date part from datetime. Introduction to CLR – Simple Example of CLR Stored Procedure CLR is an abbreviation of Common Language Runtime. In SQL Server 2005 and later version of it database objects can be created which are created in CLR. Stored Procedures, Functions, Triggers can be coded in CLR. CLR is faster than T-SQL in many cases. CLR is mainly used to accomplish tasks which are not possible by T-SQL or can use lots of resources. The CLR can be usually implemented where there is an intense string operation, thread management or iteration methods which can be complicated for T-SQL. Implementing CLR provides more security to the Extended Stored Procedure. 2009 Comic Slow Query – SQL Joke Before Presentation After Presentation Enable Automatic Statistic Update on Database In one of the recent projects, I found out that despite putting good indexes and optimizing the query, I could not achieve an optimized performance and I still received an unoptimized response from the SQL Server. On examination, I figured out that the culprit was statistics. The database that I was trying to optimize had auto update of the statistics was disabled. Recently Executed T-SQL Query Please refer to blog post  query to recently executed T-SQL query on database. Change Collation of Database Column – T-SQL Script – Consolidating Collations – Extention Script At some time in your DBA career, you may find yourself in a position when you sit back and realize that your database collations have somehow run amuck, or are faced with the ever annoying CANNOT RESOLVE COLLATION message when trying to join data of varying collation settings. 2010 Visiting Alma Mater – Delivering Session on Database Performance and Career – Nirma Institute of Technology Everyone always dreams of visiting their school and college, where they have studied once. It is a great feeling to see the college once again – where you have spent the wonderful golden years of your time. College time is filled with studies, education, emotions and several plans to build a future. I consider myself fortunate as I got the opportunity to study at some of the best places in the world. Change Column DataTypes There are times when I feel like writing that I am a day older in SQL Server. In fact, there are many who are looking for a solution that is simple enough. Have you ever searched online for something very simple. I often do and enjoy doing things which are straight forward and easy to change. 2011 Three DMVs – sys.dm_server_memory_dumps – sys.dm_server_services – sys.dm_server_registry In this blog post we will see three new DMVs which are introduced in Denali. The DMVs are very simple and there is not much to describe them. So here is the simple game. I will be asking a question back to you after seeing the result of the each of the DMV and you help me to complete this blog post. A Simple Quiz – T-SQL Brain Trick If you have some time, I strongly suggest you try this quiz out as it is for sure twists your brain. 2012 List All The Column With Specific Data Types in Database 5 years ago I wrote script SQL SERVER – 2005 – List All The Column With Specific Data Types, when I read it again, it is very much relevant and I liked it. This is one of the script which every developer would like to keep it handy. I have upgraded the script bit more. I have included few additional information which I believe I should have added from the beginning. It is difficult to visualize the final script when we are writing it first time. Find First Non-Numeric Character from String The function PATINDEX exists for quite a long time in SQL Server but I hardly see it being used. Well, at least I use it and I am comfortable using it. Here is a simple script which I use when I have to identify first non-numeric character. Finding Different ColumnName From Almost Identitical Tables Well here is the interesting example of how we can use sys.column catalogue views and get the details of the newly added column. I have previously written about EXCEPT over here which is very similar to MINUS of Oracle. Storing Data and Files in Cloud – Dropbox – Personal Technology Tip I thought long and hard about doing a Personal Technology Tips series for this blog.  I have so many tips I’d like to share.  I am on my computer almost all day, every day, so I have a treasure trove of interesting tidbits I like to share if given the chance.  The only thing holding me back – which tip to share first?  The first tip obviously has the weight of seeming like the most important.  But this would mean choosing amongst my favorite tricks and shortcuts.  This is a hard task. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL – Migrate Database from SQL Server to NuoDB – A Quick Tutorial

    - by Pinal Dave
    Data is growing exponentially and every organization with growing data is thinking of next big innovation in the world of Big Data. Big data is a indeed a future for every organization at one point of the time. Just like every other next big thing, big data has its own challenges and issues. The biggest challenge associated with the big data is to find the ideal platform which supports the scalability and growth of the data. If you are a regular reader of this blog, you must be familiar with NuoDB. I have been working with NuoDB for a while and their recent release is the best thus far. NuoDB is an elastically scalable SQL database that can run on local host, datacenter and cloud-based resources. A key feature of the product is that it does not require sharding (read more here). Last week, I was able to install NuoDB in less than 90 seconds and have explored their Explorer and Admin sections. You can read about my experiences in these posts: SQL – Step by Step Guide to Download and Install NuoDB – Getting Started with NuoDB SQL – Quick Start with Admin Sections of NuoDB – Manage NuoDB Database SQL – Quick Start with Explorer Sections of NuoDB – Query NuoDB Database Many SQL Authority readers have been following me in my journey to evaluate NuoDB. One of the frequently asked questions I’ve received from you is if there is any way to migrate data from SQL Server to NuoDB. The fact is that there is indeed a way to do so and NuoDB provides a fantastic tool which can help users to do it. NuoDB Migrator is a command line utility that supports the migration of Microsoft SQL Server, MySQL, Oracle, and PostgreSQL schemas and data to NuoDB. The migration to NuoDB is a three-step process: NuoDB Migrator generates a schema for a target NuoDB database It loads data into the target NuoDB database It dumps data from the source database Let’s see how we can migrate our data from SQL Server to NuoDB using a simple three-step approach. But before we do that we will create a sample database in MSSQL and later we will migrate the same database to NuoDB: Setup Step 1: Build a sample data CREATE DATABASE [Test]; CREATE TABLE [Department]( [DepartmentID] [smallint] NOT NULL, [Name] VARCHAR(100) NOT NULL, [GroupName] VARCHAR(100) NOT NULL, [ModifiedDate] [datetime] NOT NULL, CONSTRAINT [PK_Department_DepartmentID] PRIMARY KEY CLUSTERED ( [DepartmentID] ASC ) ) ON [PRIMARY]; INSERT INTO Department SELECT * FROM AdventureWorks2012.HumanResources.Department; Note that I am using the SQL Server AdventureWorks database to build this sample table but you can build this sample table any way you prefer. Setup Step 2: Install Java 64 bit Before you can begin the migration process to NuoDB, make sure you have 64-bit Java installed on your computer. This is due to the fact that the NuoDB Migrator tool is built in Java. You can download 64-bit Java for Windows, Mac OSX, or Linux from the following link: http://java.com/en/download/manual.jsp. One more thing to remember is that you make sure that the path in your environment settings is set to your JAVA_HOME directory or else the tool will not work. Here is how you can do it: Go to My Computer >> Right Click >> Select Properties >> Click on Advanced System Settings >> Click on Environment Variables >> Click on New and enter the following values. Variable Name: JAVA_HOME Variable Value: C:\Program Files\Java\jre7 Make sure you enter your Java installation directory in the Variable Value field. Setup Step 3: Install JDBC driver for SQL Server. There are two JDBC drivers available for SQL Server.  Select the one you prefer to use by following one of the two links below: Microsoft JDBC Driver jTDS JDBC Driver In this example we will be using jTDS JDBC driver. Once you download the driver, move the driver to your NuoDB installation folder. In my case, I have moved the JAR file of the driver into the C:\Program Files\NuoDB\tools\migrator\jar folder as this is my NuoDB installation directory. Now we are all set to start the three-step migration process from SQL Server to NuoDB: Migration Step 1: NuoDB Schema Generation Here is the command I use to generate a schema of my SQL Server Database in NuoDB. First I go to the folder C:\Program Files\NuoDB\tools\migrator\bin and execute the nuodb-migrator.bat file. Note that my database name is ‘test’. Additionally my username and password is also ‘test’. You can see that my SQL Server database is running on my localhost on port 1433. Additionally, the schema of the table is ‘dbo’. nuodb-migrator schema –source.driver=net.sourceforge.jtds.jdbc.Driver –source.url=jdbc:jtds:sqlserver://localhost:1433/ –source.username=test –source.password=test –source.catalog=test –source.schema=dbo –output.path=/tmp/schema.sql The above script will generate a schema of all my SQL Server tables and will put it in the folder C:\tmp\schema.sql . You can open the schema.sql file and execute this file directly in your NuoDB instance. You can follow the link here to see how you can execute the SQL script in NuoDB. Please note that if you have not yet created the schema in the NuoDB database, you should create it before executing this step. Step 2: Generate the Dump File of the Data Once you have recreated your schema in NuoDB from SQL Server, the next step is very easy. Here we create a CSV format dump file, which will contain all the data from all the tables from the SQL Server database. The command to do so is very similar to the above command. Be aware that this step may take a bit of time based on your database size. nuodb-migrator dump –source.driver=net.sourceforge.jtds.jdbc.Driver –source.url=jdbc:jtds:sqlserver://localhost:1433/ –source.username=test –source.password=test –source.catalog=test –source.schema=dbo –output.type=csv –output.path=/tmp/dump.cat Once the above command is successfully executed you can find your CSV file in the C:\tmp\ folder. However, you do not have to do anything manually. The third and final step will take care of completing the migration process. Migration Step 3: Load the Data into NuoDB After building schema and taking a dump of the data, the very next step is essential and crucial. It will take the CSV file and load it into the NuoDB database. nuodb-migrator load –target.url=jdbc:com.nuodb://localhost:48004/mytest –target.schema=dbo –target.username=test –target.password=test –input.path=/tmp/dump.cat Please note that in the above script we are now targeting the NuoDB database, which we have already created with the name of “MyTest”. If the database does not exist, create it manually before executing the above script. I have kept the username and password as “test”, but please make sure that you create a more secure password for your database for security reasons. Voila!  You’re Done That’s it. You are done. It took 3 setup and 3 migration steps to migrate your SQL Server database to NuoDB.  You can now start exploring the database and build excellent, scale-out applications. In this blog post, I have done my best to come up with simple and easy process, which you can follow to migrate your app from SQL Server to NuoDB. Download NuoDB I strongly encourage you to download NuoDB and go through my 3-step migration tutorial from SQL Server to NuoDB. Additionally here are two very important blog post from NuoDB CTO Seth Proctor. He has written excellent blog posts on the concept of the Administrative Domains. NuoDB has this concept of an Administrative Domain, which is a collection of hosts that can run one or multiple databases.  Each database has its own TEs and SMs, but all are managed within the Admin Console for that particular domain. http://www.nuodb.com/techblog/2013/03/11/getting-started-provisioning-a-domain/ http://www.nuodb.com/techblog/2013/03/14/getting-started-running-a-database/ Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

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  • Using SQL Execution Plans to discover the Swedish alphabet

    - by Rob Farley
    SQL Server is quite remarkable in a bunch of ways. In this post, I’m using the way that the Query Optimizer handles LIKE to keep it SARGable, the Execution Plans that result, Collations, and PowerShell to come up with the Swedish alphabet. SARGability is the ability to seek for items in an index according to a particular set of criteria. If you don’t have SARGability in play, you need to scan the whole index (or table if you don’t have an index). For example, I can find myself in the phonebook easily, because it’s sorted by LastName and I can find Farley in there by moving to the Fs, and so on. I can’t find everyone in my suburb easily, because the phonebook isn’t sorted that way. I can’t even find people who have six letters in their last name, because also the book is sorted by LastName, it’s not sorted by LEN(LastName). This is all stuff I’ve looked at before, including in the talk I gave at SQLBits in October 2010. If I try to find everyone who’s names start with F, I can do that using a query a bit like: SELECT LastName FROM dbo.PhoneBook WHERE LEFT(LastName,1) = 'F'; Unfortunately, the Query Optimizer doesn’t realise that all the entries that satisfy LEFT(LastName,1) = 'F' will be together, and it has to scan the whole table to find them. But if I write: SELECT LastName FROM dbo.PhoneBook WHERE LastName LIKE 'F%'; then SQL is smart enough to understand this, and performs an Index Seek instead. To see why, I look further into the plan, in particular, the properties of the Index Seek operator. The ToolTip shows me what I’m after: You’ll see that it does a Seek to find any entries that are at least F, but not yet G. There’s an extra Predicate in there (a Residual Predicate if you like), which checks that each LastName is really LIKE F% – I suppose it doesn’t consider that the Seek Predicate is quite enough – but most of the benefit is seen by its working out the Seek Predicate, filtering to just the “at least F but not yet G” section of the data. This got me curious though, particularly about where the G comes from, and whether I could leverage it to create the Swedish alphabet. I know that in the Swedish language, there are three extra letters that appear at the end of the alphabet. One of them is ä that appears in the word Västerås. It turns out that Västerås is quite hard to find in an index when you’re looking it up in a Swedish map. I talked about this briefly in my five-minute talk on Collation from SQLPASS (the one which was slightly less than serious). So by looking at the plan, I can work out what the next letter is in the alphabet of the collation used by the column. In other words, if my alphabet were Swedish, I’d be able to tell what the next letter after F is – just in case it’s not G. It turns out it is… Yes, the Swedish letter after F is G. But I worked this out by using a copy of my PhoneBook table that used the Finnish_Swedish_CI_AI collation. I couldn’t find how the Query Optimizer calculates the G, and my friend Paul White (@SQL_Kiwi) tells me that it’s frustratingly internal to the QO. He’s particularly smart, even if he is from New Zealand. To investigate further, I decided to do some PowerShell, leveraging the Get-SqlPlan function that I blogged about recently (make sure you also have the SqlServerCmdletSnapin100 snap-in added). I started by indicating that I was going to use Finnish_Swedish_CI_AI as my collation of choice, and that I’d start whichever letter cam straight after the number 9. I figure that this is a cheat’s way of guessing the first letter of the alphabet (but it doesn’t actually work in Unicode – luckily I’m using varchar not nvarchar. Actually, there are a few aspects of this code that only work using ASCII, so apologies if you were wanting to apply it to Greek, Japanese, etc). I also initialised my $alphabet variable. $collation = 'Finnish_Swedish_CI_AI'; $firstletter = '9'; $alphabet = ''; Now I created the table for my test. A single field would do, and putting a Clustered Index on it would suffice for the Seeks. Invoke-Sqlcmd -server . -data tempdb -query "create table dbo.collation_test (col varchar(10) collate $collation primary key);" Now I get into the looping. $c = $firstletter; $stillgoing = $true; while ($stillgoing) { I construct the query I want, seeking for entries which start with whatever $c has reached, and get the plan for it: $query = "select col from dbo.collation_test where col like '$($c)%';"; [xml] $pl = get-sqlplan $query "." "tempdb"; At this point, my $pl variable is a scary piece of XML, representing the execution plan. A bit of hunting through it showed me that the EndRange element contained what I was after, and that if it contained NULL, then I was done. $stillgoing = ($pl.ShowPlanXML.BatchSequence.Batch.Statements.StmtSimple.QueryPlan.RelOp.IndexScan.SeekPredicates.SeekPredicateNew.SeekKeys.EndRange -ne $null); Now I could grab the value out of it (which came with apostrophes that needed stripping), and append that to my $alphabet variable.   if ($stillgoing)   {  $c=$pl.ShowPlanXML.BatchSequence.Batch.Statements.StmtSimple.QueryPlan.RelOp.IndexScan.SeekPredicates.SeekPredicateNew.SeekKeys.EndRange.RangeExpressions.ScalarOperator.ScalarString.Replace("'","");     $alphabet += $c;   } Finally, finishing the loop, dropping the table, and showing my alphabet! } Invoke-Sqlcmd -server . -data tempdb -query "drop table dbo.collation_test;"; $alphabet; When I run all this, I see that the Swedish alphabet is ABCDEFGHIJKLMNOPQRSTUVXYZÅÄÖ, which matches what I see at Wikipedia. Interesting to see that the letters on the end are still there, even with Case Insensitivity. Turns out they’re not just “letters with accents”, they’re letters in their own right. I’m sure you gave up reading long ago, and really aren’t that fazed about the idea of doing this using PowerShell. I chose PowerShell because I’d already come up with an easy way of grabbing the estimated plan for a query, and PowerShell does allow for easy navigation of XML. I find the most interesting aspect of this as the fact that the Query Optimizer uses the next letter of the alphabet to maintain the SARGability of LIKE. I’m hoping they do something similar for a whole bunch of operations. Oh, and the fact that you know how to find stuff in the IKEA catalogue. Footnote: If you are interested in whether this works in other languages, you might want to consider the following screenshot, which shows that in principle, it should work with Japanese. It might be a bit harder to run this in PowerShell though, as I’m not sure how it translates. In Hiragana, the Japanese alphabet starts ?, ?, ?, ?, ?, ...

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  • Thread placement policies on NUMA systems - update

    - by Dave
    In a prior blog entry I noted that Solaris used a "maximum dispersal" placement policy to assign nascent threads to their initial processors. The general idea is that threads should be placed as far away from each other as possible in the resource topology in order to reduce resource contention between concurrently running threads. This policy assumes that resource contention -- pipelines, memory channel contention, destructive interference in the shared caches, etc -- will likely outweigh (a) any potential communication benefits we might achieve by packing our threads more densely onto a subset of the NUMA nodes, and (b) benefits of NUMA affinity between memory allocated by one thread and accessed by other threads. We want our threads spread widely over the system and not packed together. Conceptually, when placing a new thread, the kernel picks the least loaded node NUMA node (the node with lowest aggregate load average), and then the least loaded core on that node, etc. Furthermore, the kernel places threads onto resources -- sockets, cores, pipelines, etc -- without regard to the thread's process membership. That is, initial placement is process-agnostic. Keep reading, though. This description is incorrect. On Solaris 10 on a SPARC T5440 with 4 x T2+ NUMA nodes, if the system is otherwise unloaded and we launch a process that creates 20 compute-bound concurrent threads, then typically we'll see a perfect balance with 5 threads on each node. We see similar behavior on an 8-node x86 x4800 system, where each node has 8 cores and each core is 2-way hyperthreaded. So far so good; this behavior seems in agreement with the policy I described in the 1st paragraph. I recently tried the same experiment on a 4-node T4-4 running Solaris 11. Both the T5440 and T4-4 are 4-node systems that expose 256 logical thread contexts. To my surprise, all 20 threads were placed onto just one NUMA node while the other 3 nodes remained completely idle. I checked the usual suspects such as processor sets inadvertently left around by colleagues, processors left offline, and power management policies, but the system was configured normally. I then launched multiple concurrent instances of the process, and, interestingly, all the threads from the 1st process landed on one node, all the threads from the 2nd process landed on another node, and so on. This happened even if I interleaved thread creating between the processes, so I was relatively sure the effect didn't related to thread creation time, but rather that placement was a function of process membership. I this point I consulted the Solaris sources and talked with folks in the Solaris group. The new Solaris 11 behavior is intentional. The kernel is no longer using a simple maximum dispersal policy, and thread placement is process membership-aware. Now, even if other nodes are completely unloaded, the kernel will still try to pack new threads onto the home lgroup (socket) of the primordial thread until the load average of that node reaches 50%, after which it will pick the next least loaded node as the process's new favorite node for placement. On the T4-4 we have 64 logical thread contexts (strands) per socket (lgroup), so if we launch 48 concurrent threads we will find 32 placed on one node and 16 on some other node. If we launch 64 threads we'll find 32 and 32. That means we can end up with our threads clustered on a small subset of the nodes in a way that's quite different that what we've seen on Solaris 10. So we have a policy that allows process-aware packing but reverts to spreading threads onto other nodes if a node becomes too saturated. It turns out this policy was enabled in Solaris 10, but certain bugs suppressed the mixed packing/spreading behavior. There are configuration variables in /etc/system that allow us to dial the affinity between nascent threads and their primordial thread up and down: see lgrp_expand_proc_thresh, specifically. In the OpenSolaris source code the key routine is mpo_update_tunables(). This method reads the /etc/system variables and sets up some global variables that will subsequently be used by the dispatcher, which calls lgrp_choose() in lgrp.c to place nascent threads. Lgrp_expand_proc_thresh controls how loaded an lgroup must be before we'll consider homing a process's threads to another lgroup. Tune this value lower to have it spread your process's threads out more. To recap, the 'new' policy is as follows. Threads from the same process are packed onto a subset of the strands of a socket (50% for T-series). Once that socket reaches the 50% threshold the kernel then picks another preferred socket for that process. Threads from unrelated processes are spread across sockets. More precisely, different processes may have different preferred sockets (lgroups). Beware that I've simplified and elided details for the purposes of explication. The truth is in the code. Remarks: It's worth noting that initial thread placement is just that. If there's a gross imbalance between the load on different nodes then the kernel will migrate threads to achieve a better and more even distribution over the set of available nodes. Once a thread runs and gains some affinity for a node, however, it becomes "stickier" under the assumption that the thread has residual cache residency on that node, and that memory allocated by that thread resides on that node given the default "first-touch" page-level NUMA allocation policy. Exactly how the various policies interact and which have precedence under what circumstances could the topic of a future blog entry. The scheduler is work-conserving. The x4800 mentioned above is an interesting system. Each of the 8 sockets houses an Intel 7500-series processor. Each processor has 3 coherent QPI links and the system is arranged as a glueless 8-socket twisted ladder "mobius" topology. Nodes are either 1 or 2 hops distant over the QPI links. As an aside the mapping of logical CPUIDs to physical resources is rather interesting on Solaris/x4800. On SPARC/Solaris the CPUID layout is strictly geographic, with the highest order bits identifying the socket, the next lower bits identifying the core within that socket, following by the pipeline (if present) and finally the logical thread context ("strand") on the core. But on Solaris on the x4800 the CPUID layout is as follows. [6:6] identifies the hyperthread on a core; bits [5:3] identify the socket, or package in Intel terminology; bits [2:0] identify the core within a socket. Such low-level details should be of interest only if you're binding threads -- a bad idea, the kernel typically handles placement best -- or if you're writing NUMA-aware code that's aware of the ambient placement and makes decisions accordingly. Solaris introduced the so-called critical-threads mechanism, which is expressed by putting a thread into the FX scheduling class at priority 60. The critical-threads mechanism applies to placement on cores, not on sockets, however. That is, it's an intra-socket policy, not an inter-socket policy. Solaris 11 introduces the Power Aware Dispatcher (PAD) which packs threads instead of spreading them out in an attempt to be able to keep sockets or cores at lower power levels. Maximum dispersal may be good for performance but is anathema to power management. PAD is off by default, but power management polices constitute yet another confounding factor with respect to scheduling and dispatching. If your threads communicate heavily -- one thread reads cache lines last written by some other thread -- then the new dense packing policy may improve performance by reducing traffic on the coherent interconnect. On the other hand if your threads in your process communicate rarely, then it's possible the new packing policy might result on contention on shared computing resources. Unfortunately there's no simple litmus test that says whether packing or spreading is optimal in a given situation. The answer varies by system load, application, number of threads, and platform hardware characteristics. Currently we don't have the necessary tools and sensoria to decide at runtime, so we're reduced to an empirical approach where we run trials and try to decide on a placement policy. The situation is quite frustrating. Relatedly, it's often hard to determine just the right level of concurrency to optimize throughput. (Understanding constructive vs destructive interference in the shared caches would be a good start. We could augment the lines with a small tag field indicating which strand last installed or accessed a line. Given that, we could augment the CPU with performance counters for misses where a thread evicts a line it installed vs misses where a thread displaces a line installed by some other thread.)

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  • Oracle Expands Sun Blade Portfolio for Cloud and Highly Virtualized Environments

    - by Ferhat Hatay
    Oracle announced the expansion of Sun Blade Portfolio for cloud and highly virtualized environments that deliver powerful performance and simplified management as tightly integrated systems.  Along with the SPARC T3-1B blade server, Oracle VM blade cluster reference configuration and Oracle's optimized solution for Oracle WebLogic Suite, Oracle introduced the dual-node Sun Blade X6275 M2 server module with some impressive benchmark results.   Benchmarks on the Sun Blade X6275 M2 server module demonstrate the outstanding performance characteristics critical for running varied commercial applications used in cloud and highly virtualized environments.  These include best-in-class SPEC CPU2006 results with the Intel Xeon processor 5600 series, six Fluent world records and 1.8 times the price-performance of the IBM Power 755 running NAMD, a prominent bio-informatics workload.   Benchmarks for Sun Blade X6275 M2 server module  SPEC CPU2006  The Sun Blade X6275 M2 server module demonstrated best in class SPECint_rate2006 results for all published results using the Intel Xeon processor 5600 series, with a result of 679.  This result is 97% better than the HP BL460c G7 blade, 80% better than the IBM HS22V blade, and 79% better than the Dell M710 blade.  This result demonstrates the density advantage of the new Oracle's server module for space-constrained data centers.     Sun Blade X6275M2 (2 Nodes, Intel Xeon X5670 2.93GHz) - 679 SPECint_rate2006; HP ProLiant BL460c G7 (2.93 GHz, Intel Xeon X5670) - 347 SPECint_rate2006; IBM BladeCenter HS22V (Intel Xeon X5680)  - 377 SPECint_rate2006; Dell PowerEdge M710 (Intel Xeon X5680, 3.33 GHz) - 380 SPECint_rate2006.  SPEC, SPECint, SPECfp reg tm of Standard Performance Evaluation Corporation. Results from www.spec.org as of 11/24/2010 and this report.    For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   Fluent The Sun Fire X6275 M2 server module produced world-record results on each of the six standard cases in the current "FLUENT 12" benchmark test suite at 8-, 12-, 24-, 32-, 64- and 96-core configurations. These results beat the most recent QLogic score with IBM DX 360 M series platforms and QLogic "Truescale" interconnects.  Results on sedan_4m test case on the Sun Blade X6275 M2 server module are 23% better than the HP C7000 system, and 20% better than the IBM DX 360 M2; Dell has not posted a result for this test case.  Results can be found at the FLUENT website.   ANSYS's FLUENT software solves fluid flow problems, and is based on a numerical technique called computational fluid dynamics (CFD), which is used in the automotive, aerospace, and consumer products industries. The FLUENT 12 benchmark test suite consists of seven models that are well suited for multi-node clustered environments and representative of modern engineering CFD clusters. Vendors benchmark their systems with the principal objective of providing comparative performance information for FLUENT software that, among other things, depends on compilers, optimization, interconnect, and the performance characteristics of the hardware.   FLUENT application performance is representative of other commercial applications that require memory and CPU resources to be available in a scalable cluster-ready format.  FLUENT benchmark has six conventional test cases (eddy_417k, turbo_500k, aircraft_2m, sedan_4m, truck_14m, truck_poly_14m) at various core counts.   All information on the FLUENT website (http://www.fluent.com) is Copyrighted1995-2010 by ANSYS Inc. Results as of November 24, 2010. For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   NAMD Results on the Sun Blade X6275 M2 server module running NAMD (a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems) show up to a 1.8X better price/performance than IBM's Power 7-based system.  For space-constrained environments, the ultra-dense Sun Blade X6275 M2 server module provides a 1.7X better price/performance per rack unit than IBM's system.     IBM Power 755 4-way Cluster (16U). Total price for cluster: $324,212. See IBM United States Hardware Announcement 110-008, dated February 9, 2010, pp. 4, 21 and 39-46.  Sun Blade X6275 M2 8-Blade Cluster (10U). Total price for cluster:  $193,939. Price/performance and performance/RU comparisons based on f1ATPase molecule test results. Sun Blade X6275 M2 cluster: $3,568/step/sec, 5.435 step/sec/RU. IBM Power 755 cluster: $6,355/step/sec, 3.189 step/sec/U. See http://www-03.ibm.com/systems/power/hardware/reports/system_perf.html. See http://www.ks.uiuc.edu/Research/namd/performance.html for more information, results as of 11/24/10.   For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   Reverse Time Migration The Reverse Time Migration is heavily used in geophysical imaging and modeling for Oil & Gas Exploration.  The Sun Blade X6275 M2 server module showed up to a 40% performance improvement over the previous generation server module with super-linear scalability to 16 nodes for the 9-Point Stencil used in this Reverse Time Migration computational kernel.  The balanced combination of Oracle's Sun Storage 7410 system with the Sun Blade X6275 M2 server module cluster showed linear scalability for the total application throughput, including the I/O and MPI communication, to produce a final 3-D seismic depth imaged cube for interpretation. The final image write time from the Sun Blade X6275 M2 server module nodes to Oracle's Sun Storage 7410 system achieved 10GbE line speed of 1.25 GBytes/second or better performance. Between subsequent runs, the effects of I/O buffer caching on the Sun Blade X6275 M2 server module nodes and write optimized caching on the Sun Storage 7410 system gave up to 1.8 GBytes/second effective write performance. The performance results and characterization of this Reverse Time Migration benchmark could serve as a useful measure for many other I/O intensive commercial applications. 3D VTI Reverse Time Migration Seismic Depth Imaging, see http://blogs.sun.com/BestPerf/entry/3d_vti_reverse_time_migration for more information, results as of 11/14/2010.                            

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  • SQL SERVER – Weekly Series – Memory Lane – #032

    - by Pinal Dave
    Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 Complete Series of Database Coding Standards and Guidelines SQL SERVER Database Coding Standards and Guidelines – Introduction SQL SERVER – Database Coding Standards and Guidelines – Part 1 SQL SERVER – Database Coding Standards and Guidelines – Part 2 SQL SERVER Database Coding Standards and Guidelines Complete List Download Explanation and Example – SELF JOIN When all of the data you require is contained within a single table, but data needed to extract is related to each other in the table itself. Examples of this type of data relate to Employee information, where the table may have both an Employee’s ID number for each record and also a field that displays the ID number of an Employee’s supervisor or manager. To retrieve the data tables are required to relate/join to itself. Insert Multiple Records Using One Insert Statement – Use of UNION ALL This is very interesting question I have received from new developer. How can I insert multiple values in table using only one insert? Now this is interesting question. When there are multiple records are to be inserted in the table following is the common way using T-SQL. Function to Display Current Week Date and Day – Weekly Calendar Straight blog post with script to find current week date and day based on the parameters passed in the function.  2008 In my beginning years, I have almost same confusion as many of the developer had in their earlier years. Here are two of the interesting question which I have attempted to answer in my early year. Even if you are experienced developer may be you will still like to read following two questions: Order Of Column In Index Order of Conditions in WHERE Clauses Example of DISTINCT in Aggregate Functions Have you ever used DISTINCT with the Aggregation Function? Here is a simple example about how users can do it. Create a Comma Delimited List Using SELECT Clause From Table Column Straight to script example where I explained how to do something easy and quickly. Compound Assignment Operators SQL SERVER 2008 has introduced new concept of Compound Assignment Operators. Compound Assignment Operators are available in many other programming languages for quite some time. Compound Assignment Operators is operator where variables are operated upon and assigned on the same line. PIVOT and UNPIVOT Table Examples Here is a very interesting question – the answer to the question can be YES or NO both. “If we PIVOT any table and UNPIVOT that table do we get our original table?” Read the blog post to get the explanation of the question above. 2009 What is Interim Table – Simple Definition of Interim Table The interim table is a table that is generated by joining two tables and not the final result table. In other words, when two tables are joined they create an interim table as resultset but the resultset is not final yet. It may be possible that more tables are about to join on the interim table, and more operations are still to be applied on that table (e.g. Order By, Having etc). Besides, it may be possible that there is no interim table; sometimes final table is what is generated when the query is run. 2010 Stored Procedure and Transactions If Stored Procedure is transactional then, it should roll back complete transactions when it encounters any errors. Well, that does not happen in this case, which proves that Stored Procedure does not only provide just the transactional feature to a batch of T-SQL. Generate Database Script for SQL Azure When talking about SQL Azure the most common complaint I hear is that the script generated from stand-along SQL Server database is not compatible with SQL Azure. This was true for some time for sure but not any more. If you have SQL Server 2008 R2 installed you can follow the guideline below to generate a script which is compatible with SQL Azure. Convert IN to EXISTS – Performance Talk It is NOT necessary that every time when IN is replaced by EXISTS it gives better performance. However, in our case listed above it does for sure give better performance. You can read about this subject in the associated blog post. Subquery or Join – Various Options – SQL Server Engine Knows the Best Every single time whenever there is a performance tuning exercise, I hear the conversation from developer where some prefer subquery and some prefer join. In this two part blog post, I explain the same in the detail with examples. Part 1 | Part 2 Merge Operations – Insert, Update, Delete in Single Execution MERGE is a new feature that provides an efficient way to do multiple DML operations. In earlier versions of SQL Server, we had to write separate statements to INSERT, UPDATE, or DELETE data based on certain conditions; however, at present, by using the MERGE statement, we can include the logic of such data changes in one statement that even checks when the data is matched and then just update it, and similarly, when the data is unmatched, it is inserted. 2011 Puzzle – Statistics are not updated but are Created Once Here is the quick scenario about my setup. Create Table Insert 1000 Records Check the Statistics Now insert 10 times more 10,000 indexes Check the Statistics – it will be NOT updated – WHY? Question to You – When to use Function and When to use Stored Procedure Personally, I believe that they are both different things - they cannot be compared. I can say, it will be like comparing apples and oranges. Each has its own unique use. However, they can be used interchangeably at many times and in real life (i.e., production environment). I have personally seen both of these being used interchangeably many times. This is the precise reason for asking this question. 2012 In year 2012 I had two interesting series ran on the blog. If there is no fun in learning, the learning becomes a burden. For the same reason, I had decided to build a three part quiz around SEQUENCE. The quiz was to identify the next value of the sequence. I encourage all of you to take part in this fun quiz. Guess the Next Value – Puzzle 1 Guess the Next Value – Puzzle 2 Guess the Next Value – Puzzle 3 Guess the Next Value – Puzzle 4 Simple Example to Configure Resource Governor – Introduction to Resource Governor Resource Governor is a feature which can manage SQL Server Workload and System Resource Consumption. We can limit the amount of CPU and memory consumption by limiting /governing /throttling on the SQL Server. If there are different workloads running on SQL Server and each of the workload needs different resources or when workloads are competing for resources with each other and affecting the performance of the whole server resource governor is a very important task. Tricks to Replace SELECT * with Column Names – SQL in Sixty Seconds #017 – Video  Retrieves unnecessary columns and increases network traffic When a new columns are added views needs to be refreshed manually Leads to usage of sub-optimal execution plan Uses clustered index in most of the cases instead of using optimal index It is difficult to debug SQL SERVER – Load Generator – Free Tool From CodePlex The best part of this SQL Server Load Generator is that users can run multiple simultaneous queries again SQL Server using different login account and different application name. The interface of the tool is extremely easy to use and very intuitive as well. A Puzzle – Swap Value of Column Without Case Statement Let us assume there is a single column in the table called Gender. The challenge is to write a single update statement which will flip or swap the value in the column. For example if the value in the gender column is ‘male’ swap it with ‘female’ and if the value is ‘female’ swap it with ‘male’. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Cloud Computing = Elasticity * Availability

    - by Herve Roggero
    What is cloud computing? Is hosting the same thing as cloud computing? Are you running a cloud if you already use virtual machines? What is the difference between Infrastructure as a Service (IaaS) and a cloud provider? And the list goes on… these questions keep coming up and all try to fundamentally explain what “cloud” means relative to other concepts. At the risk of over simplification, answering these questions becomes simpler once you understand the primary foundations of cloud computing: Elasticity and Availability.   Elasticity The basic value proposition of cloud computing is to pay as you go, and to pay for what you use. This implies that an application can expand and contract on demand, across all its tiers (presentation layer, services, database, security…).  This also implies that application components can grow independently from each other. So if you need more storage for your database, you should be able to grow that tier without affecting, reconfiguring or changing the other tiers. Basically, cloud applications behave like a sponge; when you add water to a sponge, it grows in size; in the application world, the more customers you add, the more it grows. Pure IaaS providers will provide certain benefits, specifically in terms of operating costs, but an IaaS provider will not help you in making your applications elastic; neither will Virtual Machines. The smallest elasticity unit of an IaaS provider and a Virtual Machine environment is a server (physical or virtual). While adding servers in a datacenter helps in achieving scale, it is hardly enough. The application has yet to use this hardware.  If the process of adding computing resources is not transparent to the application, the application is not elastic.   As you can see from the above description, designing for the cloud is not about more servers; it is about designing an application for elasticity regardless of the underlying server farm.   Availability The fact of the matter is that making applications highly available is hard. It requires highly specialized tools and trained staff. On top of it, it's expensive. Many companies are required to run multiple data centers due to high availability requirements. In some organizations, some data centers are simply on standby, waiting to be used in a case of a failover. Other organizations are able to achieve a certain level of success with active/active data centers, in which all available data centers serve incoming user requests. While achieving high availability for services is relatively simple, establishing a highly available database farm is far more complex. In fact it is so complex that many companies establish yearly tests to validate failover procedures.   To a certain degree certain IaaS provides can assist with complex disaster recovery planning and setting up data centers that can achieve successful failover. However the burden is still on the corporation to manage and maintain such an environment, including regular hardware and software upgrades. Cloud computing on the other hand removes most of the disaster recovery requirements by hiding many of the underlying complexities.   Cloud Providers A cloud provider is an infrastructure provider offering additional tools to achieve application elasticity and availability that are not usually available on-premise. For example Microsoft Azure provides a simple configuration screen that makes it possible to run 1 or 100 web sites by clicking a button or two on a screen (simplifying provisioning), and soon SQL Azure will offer Data Federation to allow database sharding (which allows you to scale the database tier seamlessly and automatically). Other cloud providers offer certain features that are not available on-premise as well, such as the Amazon SC3 (Simple Storage Service) which gives you virtually unlimited storage capabilities for simple data stores, which is somewhat equivalent to the Microsoft Azure Table offering (offering a server-independent data storage model). Unlike IaaS providers, cloud providers give you the necessary tools to adopt elasticity as part of your application architecture.    Some cloud providers offer built-in high availability that get you out of the business of configuring clustered solutions, or running multiple data centers. Some cloud providers will give you more control (which puts some of that burden back on the customers' shoulder) and others will tend to make high availability totally transparent. For example, SQL Azure provides high availability automatically which would be very difficult to achieve (and very costly) on premise.   Keep in mind that each cloud provider has its strengths and weaknesses; some are better at achieving transparent scalability and server independence than others.    Not for Everyone Note however that it is up to you to leverage the elasticity capabilities of a cloud provider, as discussed previously; if you build a website that does not need to scale, for which elasticity is not important, then you can use a traditional host provider unless you also need high availability. Leveraging the technologies of cloud providers can be difficult and can become a journey for companies that build their solutions in a scale up fashion. Cloud computing promises to address cost containment and scalability of applications with built-in high availability. If your application does not need to scale or you do not need high availability, then cloud computing may not be for you. In fact, you may pay a premium to run your applications with cloud providers due to the underlying technologies built specifically for scalability and availability requirements. And as such, the cloud is not for everyone.   Consistent Customer Experience, Predictable Cost With all its complexities, buzz and foggy definition, cloud computing boils down to a simple objective: consistent customer experience at a predictable cost.  The objective of a cloud solution is to provide the same user experience to your last customer than the first, while keeping your operating costs directly proportional to the number of customers you have. Making your applications elastic and highly available across all its tiers, with as much automation as possible, achieves the first objective of a consistent customer experience. And the ability to expand and contract the infrastructure footprint of your application dynamically achieves the cost containment objectives.     Herve Roggero is a SQL Azure MVP and co-author of Pro SQL Azure (APress).  He is the co-founder of Blue Syntax Consulting (www.bluesyntax.net), a company focusing on cloud computing technologies helping customers understand and adopt cloud computing technologies. For more information contact herve at hroggero @ bluesyntax.net .

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  • The SSIS tuning tip that everyone misses

    - by Rob Farley
    I know that everyone misses this, because I’m yet to find someone who doesn’t have a bit of an epiphany when I describe this. When tuning Data Flows in SQL Server Integration Services, people see the Data Flow as moving from the Source to the Destination, passing through a number of transformations. What people don’t consider is the Source, getting the data out of a database. Remember, the source of data for your Data Flow is not your Source Component. It’s wherever the data is, within your database, probably on a disk somewhere. You need to tune your query to optimise it for SSIS, and this is what most people fail to do. I’m not suggesting that people don’t tune their queries – there’s plenty of information out there about making sure that your queries run as fast as possible. But for SSIS, it’s not about how fast your query runs. Let me say that again, but in bolder text: The speed of an SSIS Source is not about how fast your query runs. If your query is used in a Source component for SSIS, the thing that matters is how fast it starts returning data. In particular, those first 10,000 rows to populate that first buffer, ready to pass down the rest of the transformations on its way to the Destination. Let’s look at a very simple query as an example, using the AdventureWorks database: We’re picking the different Weight values out of the Product table, and it’s doing this by scanning the table and doing a Sort. It’s a Distinct Sort, which means that the duplicates are discarded. It'll be no surprise to see that the data produced is sorted. Obvious, I know, but I'm making a comparison to what I'll do later. Before I explain the problem here, let me jump back into the SSIS world... If you’ve investigated how to tune an SSIS flow, then you’ll know that some SSIS Data Flow Transformations are known to be Blocking, some are Partially Blocking, and some are simply Row transformations. Take the SSIS Sort transformation, for example. I’m using a larger data set for this, because my small list of Weights won’t demonstrate it well enough. Seven buffers of data came out of the source, but none of them could be pushed past the Sort operator, just in case the last buffer contained the data that would be sorted into the first buffer. This is a blocking operation. Back in the land of T-SQL, we consider our Distinct Sort operator. It’s also blocking. It won’t let data through until it’s seen all of it. If you weren’t okay with blocking operations in SSIS, why would you be happy with them in an execution plan? The source of your data is not your OLE DB Source. Remember this. The source of your data is the NCIX/CIX/Heap from which it’s being pulled. Picture it like this... the data flowing from the Clustered Index, through the Distinct Sort operator, into the SELECT operator, where a series of SSIS Buffers are populated, flowing (as they get full) down through the SSIS transformations. Alright, I know that I’m taking some liberties here, because the two queries aren’t the same, but consider the visual. The data is flowing from your disk and through your execution plan before it reaches SSIS, so you could easily find that a blocking operation in your plan is just as painful as a blocking operation in your SSIS Data Flow. Luckily, T-SQL gives us a brilliant query hint to help avoid this. OPTION (FAST 10000) This hint means that it will choose a query which will optimise for the first 10,000 rows – the default SSIS buffer size. And the effect can be quite significant. First let’s consider a simple example, then we’ll look at a larger one. Consider our weights. We don’t have 10,000, so I’m going to use OPTION (FAST 1) instead. You’ll notice that the query is more expensive, using a Flow Distinct operator instead of the Distinct Sort. This operator is consuming 84% of the query, instead of the 59% we saw from the Distinct Sort. But the first row could be returned quicker – a Flow Distinct operator is non-blocking. The data here isn’t sorted, of course. It’s in the same order that it came out of the index, just with duplicates removed. As soon as a Flow Distinct sees a value that it hasn’t come across before, it pushes it out to the operator on its left. It still has to maintain the list of what it’s seen so far, but by handling it one row at a time, it can push rows through quicker. Overall, it’s a lot more work than the Distinct Sort, but if the priority is the first few rows, then perhaps that’s exactly what we want. The Query Optimizer seems to do this by optimising the query as if there were only one row coming through: This 1 row estimation is caused by the Query Optimizer imagining the SELECT operation saying “Give me one row” first, and this message being passed all the way along. The request might not make it all the way back to the source, but in my simple example, it does. I hope this simple example has helped you understand the significance of the blocking operator. Now I’m going to show you an example on a much larger data set. This data was fetching about 780,000 rows, and these are the Estimated Plans. The data needed to be Sorted, to support further SSIS operations that needed that. First, without the hint. ...and now with OPTION (FAST 10000): A very different plan, I’m sure you’ll agree. In case you’re curious, those arrows in the top one are 780,000 rows in size. In the second, they’re estimated to be 10,000, although the Actual figures end up being 780,000. The top one definitely runs faster. It finished several times faster than the second one. With the amount of data being considered, these numbers were in minutes. Look at the second one – it’s doing Nested Loops, across 780,000 rows! That’s not generally recommended at all. That’s “Go and make yourself a coffee” time. In this case, it was about six or seven minutes. The faster one finished in about a minute. But in SSIS-land, things are different. The particular data flow that was consuming this data was significant. It was being pumped into a Script Component to process each row based on previous rows, creating about a dozen different flows. The data flow would take roughly ten minutes to run – ten minutes from when the data first appeared. The query that completes faster – chosen by the Query Optimizer with no hints, based on accurate statistics (rather than pretending the numbers are smaller) – would take a minute to start getting the data into SSIS, at which point the ten-minute flow would start, taking eleven minutes to complete. The query that took longer – chosen by the Query Optimizer pretending it only wanted the first 10,000 rows – would take only ten seconds to fill the first buffer. Despite the fact that it might have taken the database another six or seven minutes to get the data out, SSIS didn’t care. Every time it wanted the next buffer of data, it was already available, and the whole process finished in about ten minutes and ten seconds. When debugging SSIS, you run the package, and sit there waiting to see the Debug information start appearing. You look for the numbers on the data flow, and seeing operators going Yellow and Green. Without the hint, I’d sit there for a minute. With the hint, just ten seconds. You can imagine which one I preferred. By adding this hint, it felt like a magic wand had been waved across the query, to make it run several times faster. It wasn’t the case at all – but it felt like it to SSIS.

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  • Das T5-4 TPC-H Ergebnis naeher betrachtet

    - by Stefan Hinker
    Inzwischen haben vermutlich viele das neue TPC-H Ergebnis der SPARC T5-4 gesehen, das am 7. Juni bei der TPC eingereicht wurde.  Die wesentlichen Punkte dieses Benchmarks wurden wie gewohnt bereits von unserer Benchmark-Truppe auf  "BestPerf" zusammengefasst.  Es gibt aber noch einiges mehr, das eine naehere Betrachtung lohnt. Skalierbarkeit Das TPC raet von einem Vergleich von TPC-H Ergebnissen in unterschiedlichen Groessenklassen ab.  Aber auch innerhalb der 3000GB-Klasse ist es interessant: SPARC T4-4 mit 4 CPUs (32 Cores mit 3.0 GHz) liefert 205,792 QphH. SPARC T5-4 mit 4 CPUs (64 Cores mit 3.6 GHz) liefert 409,721 QphH. Das heisst, es fehlen lediglich 1863 QphH oder 0.45% zu 100% Skalierbarkeit, wenn man davon ausgeht, dass die doppelte Anzahl Kerne das doppelte Ergebnis liefern sollte.  Etwas anspruchsvoller, koennte man natuerlich auch einen Faktor von 2.4 erwarten, wenn man die hoehere Taktrate mit beruecksichtigt.  Das wuerde die Latte auf 493901 QphH legen.  Dann waere die SPARC T5-4 bei 83%.  Damit stellt sich die Frage: Was hat hier nicht skaliert?  Vermutlich der Plattenspeicher!  Auch hier lohnt sich eine naehere Betrachtung: Plattenspeicher Im Bericht auf BestPerf und auch im Full Disclosure Report der TPC stehen einige interessante Details zum Plattenspeicher und der Konfiguration.   In der Konfiguration der SPARC T4-4 wurden 12 2540-M2 Arrays verwendet, die jeweils ca. 1.5 GB/s Durchsatz liefert, insgesamt also eta 18 GB/s.  Dabei waren die Arrays offensichtlich mit jeweils 2 Kabeln pro Array direkt an die 24 8GBit FC-Ports des Servers angeschlossen.  Mit den 2x 8GBit Ports pro Array koennte man so ein theoretisches Maximum von 2GB/s erreichen.  Tatsaechlich wurden 1.5GB/s geliefert, was so ziemlich dem realistischen Maximum entsprechen duerfte. Fuer den Lauf mit der SPARC T5-4 wurden doppelt so viele Platten verwendet.  Dafuer wurden die 2540-M2 Arrays mit je einem zusaetzlichen Plattentray erweitert.  Mit dieser Konfiguration wurde dann (laut BestPerf) ein Maximaldurchsatz von 33 GB/s erreicht - nicht ganz das doppelte des SPARC T4-4 Laufs.  Um tatsaechlich den doppelten Durchsatz (36 GB/s) zu liefern, haette jedes der 12 Arrays 3 GB/s ueber seine 4 8GBit Ports liefern muessen.  Im FDR stehen nur 12 dual-port FC HBAs, was die Verwendung der Brocade FC Switches erklaert: Es wurden alle 4 8GBit ports jedes Arrays an die Switches angeschlossen, die die Datenstroeme dann in die 24 16GBit HBA ports des Servers buendelten.  Das theoretische Maximum jedes Storage-Arrays waere nun 4 GB/s.  Wenn man jedoch den Protokoll- und "Realitaets"-Overhead mit einrechnet, sind die tatsaechlich gelieferten 2.75 GB/s gar nicht schlecht.  Mit diesen Zahlen im Hinterkopf ist die Verdopplung des SPARC T4-4 Ergebnisses eine gute Leistung - und gleichzeitig eine gute Erklaerung, warum nicht bis zum 2.4-fachen skaliert wurde. Nebenbei bemerkt: Weder die SPARC T4-4 noch die SPARC T5-4 hatten in der gemessenen Konfiguration irgendwelche Flash-Devices. Mitbewerb Seit die T4 Systeme auf dem Markt sind, bemuehen sich unsere Mitbewerber redlich darum, ueberall den Eindruck zu hinterlassen, die Leistung des SPARC CPU-Kerns waere weiterhin mangelhaft.  Auch scheinen sie ueberzeugt zu sein, dass (ueber)grosse Caches und hohe Taktraten die einzigen Schluessel zu echter Server Performance seien.  Wenn ich mir nun jedoch die oeffentlichen TPC-H Ergebnisse ansehe, sehe ich dies: TPC-H @3000GB, Non-Clustered Systems System QphH SPARC T5-4 3.6 GHz SPARC T5 4/64 – 2048 GB 409,721.8 SPARC T4-4 3.0 GHz SPARC T4 4/32 – 1024 GB 205,792.0 IBM Power 780 4.1 GHz POWER7 8/32 – 1024 GB 192,001.1 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64 – 512 GB 162,601.7 Kurz zusammengefasst: Mit 32 Kernen (mit 3 GHz und 4MB L3 Cache), liefert die SPARC T4-4 mehr QphH@3000GB ab als IBM mit ihrer 32 Kern Power7 (bei 4.1 GHz und 32MB L3 Cache) und auch mehr als HP mit einem 64 Kern Intel Xeon System (2.27 GHz und 24MB L3 Cache).  Ich frage mich, wo genau SPARC hier mangelhaft ist? Nun koennte man natuerlich argumentieren, dass beide Ergebnisse nicht gerade neu sind.  Nun, in Ermangelung neuerer Ergebnisse kann man ja mal ein wenig spekulieren: IBMs aktueller Performance Report listet die o.g. IBM Power 780 mit einem rPerf Wert von 425.5.  Ein passendes Nachfolgesystem mit Power7+ CPUs waere die Power 780+ mit 64 Kernen, verfuegbar mit 3.72 GHz.  Sie wird mit einem rPerf Wert von  690.1 angegeben, also 1.62x mehr.  Wenn man also annimmt, dass Plattenspeicher nicht der limitierende Faktor ist (IBM hat mit 177 SSDs getestet, sie duerfen das gerne auf 400 erhoehen) und IBMs eigene Leistungsabschaetzung zugrunde legt, darf man ein theoretisches Ergebnis von 311398 QphH@3000GB erwarten.  Das waere dann allerdings immer noch weit von dem Ergebnis der SPARC T5-4 entfernt, und gerade in der von IBM so geschaetzen "per core" Metric noch weniger vorteilhaft. In der x86-Welt sieht es nicht besser aus.  Leider gibt es von Intel keine so praktischen rPerf-Tabellen.  Daher muss ich hier fuer eine Schaetzung auf SPECint_rate2006 zurueckgreifen.  (Ich bin kein grosser Fan von solchen Kreuz- und Querschaetzungen.  Insb. SPECcpu ist nicht besonders geeignet, um Datenbank-Leistung abzuschaetzen, da fast kein IO im Spiel ist.)  Das o.g. HP System wird bei SPEC mit 1580 CINT2006_rate gelistet.  Das bis einschl. 2013-06-14 beste Resultat fuer den neuen Intel Xeon E7-4870 mit 8 CPUs ist 2180 CINT2006_rate.  Das ist immerhin 1.38x besser.  (Wenn man nur die Taktrate beruecksichtigen wuerde, waere man bei 1.32x.)  Hier weiter zu rechnen, ist muessig, aber fuer die ungeduldigen Leser hier eine kleine tabellarische Zusammenfassung: TPC-H @3000GB Performance Spekulationen System QphH* Verbesserung gegenueber der frueheren Generation SPARC T4-4 32 cores SPARC T4 205,792 2x SPARC T5-464 cores SPARC T5 409,721 IBM Power 780 32 cores Power7 192,001 1.62x IBM Power 780+ 64 cores Power7+  311,398* HP ProLiant DL980 G764 cores Intel Xeon X7560 162,601 1.38x HP ProLiant DL980 G780 cores Intel Xeon E7-4870    224,348* * Keine echten Resultate  - spekulative Werte auf der Grundlage von rPerf (Power7+) oder SPECint_rate2006 (HP) Natuerlich sind IBM oder HP herzlich eingeladen, diese Werte zu widerlegen.  Aber stand heute warte ich noch auf aktuelle Benchmark Veroffentlichungen in diesem Datensegment. Was koennen wir also zusammenfassen? Es gibt einige Hinweise, dass der Plattenspeicher der begrenzende Faktor war, der die SPARC T5-4 daran hinderte, auf jenseits von 2x zu skalieren Der Mythos, dass SPARC Kerne keine Leistung bringen, ist genau das - ein Mythos.  Wie sieht es umgekehrt eigentlich mit einem TPC-H Ergebnis fuer die Power7+ aus? Cache ist nicht der magische Performance-Schalter, fuer den ihn manche Leute offenbar halten. Ein System, eine CPU-Architektur und ein Betriebsystem jenseits einer gewissen Grenze zu skalieren ist schwer.  In der x86-Welt scheint es noch ein wenig schwerer zu sein. Was fehlt?  Nun, das Thema Preis/Leistung ueberlasse ich gerne den Verkaeufern ;-) Und zu guter Letzt: Nein, ich habe mich nicht ins Marketing versetzen lassen.  Aber manchmal kann ich mich einfach nicht zurueckhalten... Disclosure Statements The views expressed on this blog are my own and do not necessarily reflect the views of Oracle. TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org, results as of 6/7/13. Prices are in USD. SPARC T5-4 409,721.8 QphH@3000GB, $3.94/QphH@3000GB, available 9/24/13, 4 processors, 64 cores, 512 threads; SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads. SPEC and the benchmark names SPECfp and SPECint are registered trademarks of the Standard Performance Evaluation Corporation. Results as of June 18, 2013 from www.spec.org. HP ProLiant DL980 G7 (2.27 GHz, Intel Xeon X7560): 1580 SPECint_rate2006; HP ProLiant DL980 G7 (2.4 GHz, Intel Xeon E7-4870): 2180 SPECint_rate2006,

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  • Automating deployments with the SQL Compare command line

    - by Jonathan Hickford
    In my previous article, “Five Tips to Get Your Organisation Releasing Software Frequently” I looked at how teams can automate processes to speed up release frequency. In this post, I’m looking specifically at automating deployments using the SQL Compare command line. SQL Compare compares SQL Server schemas and deploys the differences. It works very effectively in scenarios where only one deployment target is required – source and target databases are specified, compared, and a change script is automatically generated and applied. But if multiple targets exist, and pressure to increase the frequency of releases builds, this solution quickly becomes unwieldy.   This is where SQL Compare’s command line comes into its own. I’ve put together a PowerShell script that loops through the Servers table and pulls out the server and database, these are then passed to sqlcompare.exe to be used as target parameters. In the example the source database is a scripts folder, a folder structure of scripted-out database objects used by both SQL Source Control and SQL Compare. The script can easily be adapted to use schema snapshots.     -- Create a DeploymentTargets database and a Servers table CREATE DATABASE DeploymentTargets GO USE DeploymentTargets GO CREATE TABLE [dbo].[Servers]( [id] [int] IDENTITY(1,1) NOT NULL, [serverName] [nvarchar](50) NULL, [environment] [nvarchar](50) NULL, [databaseName] [nvarchar](50) NULL, CONSTRAINT [PK_Servers] PRIMARY KEY CLUSTERED ([id] ASC) ) GO -- Now insert your target server and database details INSERT INTO dbo.Servers ( serverName , environment , databaseName) VALUES ( N'myserverinstance' , N'myenvironment1' , N'mydb1') INSERT INTO dbo.Servers ( serverName , environment , databaseName) VALUES ( N'myserverinstance' , N'myenvironment2' , N'mydb2') Here’s the PowerShell script you can adapt for yourself as well. # We're holding the server names and database names that we want to deploy to in a database table. # We need to connect to that server to read these details $serverName = "" $databaseName = "DeploymentTargets" $authentication = "Integrated Security=SSPI" #$authentication = "User Id=xxx;PWD=xxx" # If you are using database authentication instead of Windows authentication. # Path to the scripts folder we want to deploy to the databases $scriptsPath = "SimpleTalk" # Path to SQLCompare.exe $SQLComparePath = "C:\Program Files (x86)\Red Gate\SQL Compare 10\sqlcompare.exe" # Create SQL connection string, and connection $ServerConnectionString = "Data Source=$serverName;Initial Catalog=$databaseName;$authentication" $ServerConnection = new-object system.data.SqlClient.SqlConnection($ServerConnectionString); # Create a Dataset to hold the DataTable $dataSet = new-object "System.Data.DataSet" "ServerList" # Create a query $query = "SET NOCOUNT ON;" $query += "SELECT serverName, environment, databaseName " $query += "FROM dbo.Servers; " # Create a DataAdapter to populate the DataSet with the results $dataAdapter = new-object "System.Data.SqlClient.SqlDataAdapter" ($query, $ServerConnection) $dataAdapter.Fill($dataSet) | Out-Null # Close the connection $ServerConnection.Close() # Populate the DataTable $dataTable = new-object "System.Data.DataTable" "Servers" $dataTable = $dataSet.Tables[0] #For every row in the DataTable $dataTable | FOREACH-OBJECT { "Server Name: $($_.serverName)" "Database Name: $($_.databaseName)" "Environment: $($_.environment)" # Compare the scripts folder to the database and synchronize the database to match # NB. Have set SQL Compare to abort on medium level warnings. $arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/AbortOnWarnings:Medium") # + @("/sync" ) # Commented out the 'sync' parameter for safety, write-host $arguments & $SQLComparePath $arguments "Exit Code: $LASTEXITCODE" # Some interesting variations # Check that every database matches a folder. # For example this might be a pre-deployment step to validate everything is at the same baseline state. # Or a post deployment script to validate the deployment worked. # An exit code of 0 means the databases are identical. # # $arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/Assertidentical") # Generate a report of the difference between the folder and each database. Generate a SQL update script for each database. # For example use this after the above to generate upgrade scripts for each database # Examine the warnings and the HTML diff report to understand how the script will change objects # #$arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/ScriptFile:update_$($_.environment+"_"+$_.databaseName).sql", "/report:update_$($_.environment+"_"+$_.databaseName).html" , "/reportType:Interactive", "/showWarnings", "/include:Identical") } It’s worth noting that the above example generates the deployment scripts dynamically. This approach should be problem-free for the vast majority of changes, but it is still good practice to review and test a pre-generated deployment script prior to deployment. An alternative approach would be to pre-generate a single deployment script using SQL Compare, and run this en masse to multiple targets programmatically using sqlcmd, or using a tool like SQL Multi Script.  You can use the /ScriptFile, /report, and /showWarnings flags to generate change scripts, difference reports and any warnings.  See the commented out example in the PowerShell: #$arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/ScriptFile:update_$($_.environment+"_"+$_.databaseName).sql", "/report:update_$($_.environment+"_"+$_.databaseName).html" , "/reportType:Interactive", "/showWarnings", "/include:Identical") There is a drawback of running a pre-generated deployment script; it assumes that a given database target hasn’t drifted from its expected state. Often there are (rightly or wrongly) many individuals within an organization who have permissions to alter the production database, and changes can therefore be made outside of the prescribed development processes. The consequence is that at deployment time, the applied script has been validated against a target that no longer represents reality. The solution here would be to add a check for drift prior to running the deployment script. This is achieved by using sqlcompare.exe to compare the target against the expected schema snapshot using the /Assertidentical flag. Should this return any differences (sqlcompare.exe Exit Code 79), a drift report is outputted instead of executing the deployment script.  See the commented out example. # $arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/Assertidentical") Any checks and processes that should be undertaken prior to a manual deployment, should also be happen during an automated deployment. You might think about triggering backups prior to deployment – even better, automate the verification of the backup too.   You can use SQL Compare’s command line interface along with PowerShell to automate multiple actions and checks that you need in your deployment process. Automation is a practical solution where multiple targets and a higher release cadence come into play. As we know, with great power comes great responsibility – responsibility to ensure that the necessary checks are made so deployments remain trouble-free.  (The code sample supplied in this post automates the simple dynamic deployment case – if you are considering more advanced automation, e.g. the drift checks, script generation, deploying to large numbers of targets and backup/verification, please email me at [email protected] for further script samples or if you have further questions)

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  • I see no LOBs!

    - by Paul White
    Is it possible to see LOB (large object) logical reads from STATISTICS IO output on a table with no LOB columns? I was asked this question today by someone who had spent a good fraction of their afternoon trying to work out why this was occurring – even going so far as to re-run DBCC CHECKDB to see if any corruption had taken place.  The table in question wasn’t particularly pretty – it had grown somewhat organically over time, with new columns being added every so often as the need arose.  Nevertheless, it remained a simple structure with no LOB columns – no TEXT or IMAGE, no XML, no MAX types – nothing aside from ordinary INT, MONEY, VARCHAR, and DATETIME types.  To add to the air of mystery, not every query that ran against the table would report LOB logical reads – just sometimes – but when it did, the query often took much longer to execute. Ok, enough of the pre-amble.  I can’t reproduce the exact structure here, but the following script creates a table that will serve to demonstrate the effect: IF OBJECT_ID(N'dbo.Test', N'U') IS NOT NULL DROP TABLE dbo.Test GO CREATE TABLE dbo.Test ( row_id NUMERIC IDENTITY NOT NULL,   col01 NVARCHAR(450) NOT NULL, col02 NVARCHAR(450) NOT NULL, col03 NVARCHAR(450) NOT NULL, col04 NVARCHAR(450) NOT NULL, col05 NVARCHAR(450) NOT NULL, col06 NVARCHAR(450) NOT NULL, col07 NVARCHAR(450) NOT NULL, col08 NVARCHAR(450) NOT NULL, col09 NVARCHAR(450) NOT NULL, col10 NVARCHAR(450) NOT NULL, CONSTRAINT [PK dbo.Test row_id] PRIMARY KEY CLUSTERED (row_id) ) ; The next script loads the ten variable-length character columns with one-character strings in the first row, two-character strings in the second row, and so on down to the 450th row: WITH Numbers AS ( -- Generates numbers 1 - 450 inclusive SELECT TOP (450) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) INSERT dbo.Test WITH (TABLOCKX) SELECT REPLICATE(N'A', N.n), REPLICATE(N'B', N.n), REPLICATE(N'C', N.n), REPLICATE(N'D', N.n), REPLICATE(N'E', N.n), REPLICATE(N'F', N.n), REPLICATE(N'G', N.n), REPLICATE(N'H', N.n), REPLICATE(N'I', N.n), REPLICATE(N'J', N.n) FROM Numbers AS N ORDER BY N.n ASC ; Once those two scripts have run, the table contains 450 rows and 10 columns of data like this: Most of the time, when we query data from this table, we don’t see any LOB logical reads, for example: -- Find the maximum length of the data in -- column 5 for a range of rows SELECT result = MAX(DATALENGTH(T.col05)) FROM dbo.Test AS T WHERE row_id BETWEEN 50 AND 100 ; But with a different query… -- Read all the data in column 1 SELECT result = MAX(DATALENGTH(T.col01)) FROM dbo.Test AS T ; …suddenly we have 49 LOB logical reads, as well as the ‘normal’ logical reads we would expect. The Explanation If we had tried to create this table in SQL Server 2000, we would have received a warning message to say that future INSERT or UPDATE operations on the table might fail if the resulting row exceeded the in-row storage limit of 8060 bytes.  If we needed to store more data than would fit in an 8060 byte row (including internal overhead) we had to use a LOB column – TEXT, NTEXT, or IMAGE.  These special data types store the large data values in a separate structure, with just a small pointer left in the original row. Row Overflow SQL Server 2005 introduced a feature called row overflow, which allows one or more variable-length columns in a row to move to off-row storage if the data in a particular row would otherwise exceed 8060 bytes.  You no longer receive a warning when creating (or altering) a table that might need more than 8060 bytes of in-row storage; if SQL Server finds that it can no longer fit a variable-length column in a particular row, it will silently move one or more of these columns off the row into a separate allocation unit. Only variable-length columns can be moved in this way (for example the (N)VARCHAR, VARBINARY, and SQL_VARIANT types).  Fixed-length columns (like INTEGER and DATETIME for example) never move into ‘row overflow’ storage.  The decision to move a column off-row is done on a row-by-row basis – so data in a particular column might be stored in-row for some table records, and off-row for others. In general, if SQL Server finds that it needs to move a column into row-overflow storage, it moves the largest variable-length column record for that row.  Note that in the case of an UPDATE statement that results in the 8060 byte limit being exceeded, it might not be the column that grew that is moved! Sneaky LOBs Anyway, that’s all very interesting but I don’t want to get too carried away with the intricacies of row-overflow storage internals.  The point is that it is now possible to define a table with non-LOB columns that will silently exceed the old row-size limit and result in ordinary variable-length columns being moved to off-row storage.  Adding new columns to a table, expanding an existing column definition, or simply storing more data in a column than you used to – all these things can result in one or more variable-length columns being moved off the row. Note that row-overflow storage is logically quite different from old-style LOB and new-style MAX data type storage – individual variable-length columns are still limited to 8000 bytes each – you can just have more of them now.  Having said that, the physical mechanisms involved are very similar to full LOB storage – a column moved to row-overflow leaves a 24-byte pointer record in the row, and the ‘separate storage’ I have been talking about is structured very similarly to both old-style LOBs and new-style MAX types.  The disadvantages are also the same: when SQL Server needs a row-overflow column value it needs to follow the in-row pointer a navigate another chain of pages, just like retrieving a traditional LOB. And Finally… In the example script presented above, the rows with row_id values from 402 to 450 inclusive all exceed the total in-row storage limit of 8060 bytes.  A SELECT that references a column in one of those rows that has moved to off-row storage will incur one or more lob logical reads as the storage engine locates the data.  The results on your system might vary slightly depending on your settings, of course; but in my tests only column 1 in rows 402-450 moved off-row.  You might like to play around with the script – updating columns, changing data type lengths, and so on – to see the effect on lob logical reads and which columns get moved when.  You might even see row-overflow columns moving back in-row if they are updated to be smaller (hint: reduce the size of a column entry by at least 1000 bytes if you hope to see this). Be aware that SQL Server will not warn you when it moves ‘ordinary’ variable-length columns into overflow storage, and it can have dramatic effects on performance.  It makes more sense than ever to choose column data types sensibly.  If you make every column a VARCHAR(8000) or NVARCHAR(4000), and someone stores data that results in a row needing more than 8060 bytes, SQL Server might turn some of your column data into pseudo-LOBs – all without saying a word. Finally, some people make a distinction between ordinary LOBs (those that can hold up to 2GB of data) and the LOB-like structures created by row-overflow (where columns are still limited to 8000 bytes) by referring to row-overflow LOBs as SLOBs.  I find that quite appealing, but the ‘S’ stands for ‘small’, which makes expanding the whole acronym a little daft-sounding…small large objects anyone? © Paul White 2011 email: [email protected] twitter: @SQL_Kiwi

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  • Online ALTER TABLE in MySQL 5.6

    - by Marko Mäkelä
    This is the low-level view of data dictionary language (DDL) operations in the InnoDB storage engine in MySQL 5.6. John Russell gave a more high-level view in his blog post April 2012 Labs Release – Online DDL Improvements. MySQL before the InnoDB Plugin Traditionally, the MySQL storage engine interface has taken a minimalistic approach to data definition language. The only natively supported operations were CREATE TABLE, DROP TABLE and RENAME TABLE. Consider the following example: CREATE TABLE t(a INT); INSERT INTO t VALUES (1),(2),(3); CREATE INDEX a ON t(a); DROP TABLE t; The CREATE INDEX statement would be executed roughly as follows: CREATE TABLE temp(a INT, INDEX(a)); INSERT INTO temp SELECT * FROM t; RENAME TABLE t TO temp2; RENAME TABLE temp TO t; DROP TABLE temp2; You could imagine that the database could crash when copying all rows from the original table to the new one. For example, it could run out of file space. Then, on restart, InnoDB would roll back the huge INSERT transaction. To fix things a little, a hack was added to ha_innobase::write_row for committing the transaction every 10,000 rows. Still, it was frustrating that even a simple DROP INDEX would make the table unavailable for modifications for a long time. Fast Index Creation in the InnoDB Plugin of MySQL 5.1 MySQL 5.1 introduced a new interface for CREATE INDEX and DROP INDEX. The old table-copying approach can still be forced by SET old_alter_table=0. This interface is used in MySQL 5.5 and in the InnoDB Plugin for MySQL 5.1. Apart from the ability to do a quick DROP INDEX, the main advantage is that InnoDB will execute a merge-sort algorithm before inserting the index records into each index that is being created. This should speed up the insert into the secondary index B-trees and potentially result in a better B-tree fill factor. The 5.1 ALTER TABLE interface was not perfect. For example, DROP FOREIGN KEY still invoked the table copy. Renaming columns could conflict with InnoDB foreign key constraints. Combining ADD KEY and DROP KEY in ALTER TABLE was problematic and not atomic inside the storage engine. The ALTER TABLE interface in MySQL 5.6 The ALTER TABLE storage engine interface was completely rewritten in MySQL 5.6. Instead of introducing a method call for every conceivable operation, MySQL 5.6 introduced a handful of methods, and data structures that keep track of the requested changes. In MySQL 5.6, online ALTER TABLE operation can be requested by specifying LOCK=NONE. Also LOCK=SHARED and LOCK=EXCLUSIVE are available. The old-style table copying can be requested by ALGORITHM=COPY. That one will require at least LOCK=SHARED. From the InnoDB point of view, anything that is possible with LOCK=EXCLUSIVE is also possible with LOCK=SHARED. Most ALGORITHM=INPLACE operations inside InnoDB can be executed online (LOCK=NONE). InnoDB will always require an exclusive table lock in two phases of the operation. The execution phases are tied to a number of methods: handler::check_if_supported_inplace_alter Checks if the storage engine can perform all requested operations, and if so, what kind of locking is needed. handler::prepare_inplace_alter_table InnoDB uses this method to set up the data dictionary cache for upcoming CREATE INDEX operation. We need stubs for the new indexes, so that we can keep track of changes to the table during online index creation. Also, crash recovery would drop any indexes that were incomplete at the time of the crash. handler::inplace_alter_table In InnoDB, this method is used for creating secondary indexes or for rebuilding the table. This is the ‘main’ phase that can be executed online (with concurrent writes to the table). handler::commit_inplace_alter_table This is where the operation is committed or rolled back. Here, InnoDB would drop any indexes, rename any columns, drop or add foreign keys, and finalize a table rebuild or index creation. It would also discard any logs that were set up for online index creation or table rebuild. The prepare and commit phases require an exclusive lock, blocking all access to the table. If MySQL times out while upgrading the table meta-data lock for the commit phase, it will roll back the ALTER TABLE operation. In MySQL 5.6, data definition language operations are still not fully atomic, because the data dictionary is split. Part of it is inside InnoDB data dictionary tables. Part of the information is only available in the *.frm file, which is not covered by any crash recovery log. But, there is a single commit phase inside the storage engine. Online Secondary Index Creation It may occur that an index needs to be created on a new column to speed up queries. But, it may be unacceptable to block modifications on the table while creating the index. It turns out that it is conceptually not so hard to support online index creation. All we need is some more execution phases: Set up a stub for the index, for logging changes. Scan the table for index records. Sort the index records. Bulk load the index records. Apply the logged changes. Replace the stub with the actual index. Threads that modify the table will log the operations to the logs of each index that is being created. Errors, such as log overflow or uniqueness violations, will only be flagged by the ALTER TABLE thread. The log is conceptually similar to the InnoDB change buffer. The bulk load of index records will bypass record locking. We still generate redo log for writing the index pages. It would suffice to log page allocations only, and to flush the index pages from the buffer pool to the file system upon completion. Native ALTER TABLE Starting with MySQL 5.6, InnoDB supports most ALTER TABLE operations natively. The notable exceptions are changes to the column type, ADD FOREIGN KEY except when foreign_key_checks=0, and changes to tables that contain FULLTEXT indexes. The keyword ALGORITHM=INPLACE is somewhat misleading, because certain operations cannot be performed in-place. For example, changing the ROW_FORMAT of a table requires a rebuild. Online operation (LOCK=NONE) is not allowed in the following cases: when adding an AUTO_INCREMENT column, when the table contains FULLTEXT indexes or a hidden FTS_DOC_ID column, or when there are FOREIGN KEY constraints referring to the table, with ON…CASCADE or ON…SET NULL option. The FOREIGN KEY limitations are needed, because MySQL does not acquire meta-data locks on the child or parent tables when executing SQL statements. Theoretically, InnoDB could support operations like ADD COLUMN and DROP COLUMN in-place, by lazily converting the table to a newer format. This would require that the data dictionary keep multiple versions of the table definition. For simplicity, we will copy the entire table, even for DROP COLUMN. The bulk copying of the table will bypass record locking and undo logging. For facilitating online operation, a temporary log will be associated with the clustered index of table. Threads that modify the table will also write the changes to the log. When altering the table, we skip all records that have been marked for deletion. In this way, we can simply discard any undo log records that were not yet purged from the original table. Off-page columns, or BLOBs, are an important consideration. We suspend the purge of delete-marked records if it would free any off-page columns from the old table. This is because the BLOBs can be needed when applying changes from the log. We have special logging for handling the ROLLBACK of an INSERT that inserted new off-page columns. This is because the columns will be freed at rollback.

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  • Organization &amp; Architecture UNISA Studies &ndash; Chap 4

    - by MarkPearl
    Learning Outcomes Explain the characteristics of memory systems Describe the memory hierarchy Discuss cache memory principles Discuss issues relevant to cache design Describe the cache organization of the Pentium Computer Memory Systems There are key characteristics of memory… Location – internal or external Capacity – expressed in terms of bytes Unit of Transfer – the number of bits read out of or written into memory at a time Access Method – sequential, direct, random or associative From a users perspective the two most important characteristics of memory are… Capacity Performance – access time, memory cycle time, transfer rate The trade off for memory happens along three axis… Faster access time, greater cost per bit Greater capacity, smaller cost per bit Greater capacity, slower access time This leads to people using a tiered approach in their use of memory   As one goes down the hierarchy, the following occurs… Decreasing cost per bit Increasing capacity Increasing access time Decreasing frequency of access of the memory by the processor The use of two levels of memory to reduce average access time works in principle, but only if conditions 1 to 4 apply. A variety of technologies exist that allow us to accomplish this. Thus it is possible to organize data across the hierarchy such that the percentage of accesses to each successively lower level is substantially less than that of the level above. A portion of main memory can be used as a buffer to hold data temporarily that is to be read out to disk. This is sometimes referred to as a disk cache and improves performance in two ways… Disk writes are clustered. Instead of many small transfers of data, we have a few large transfers of data. This improves disk performance and minimizes processor involvement. Some data designed for write-out may be referenced by a program before the next dump to disk. In that case the data is retrieved rapidly from the software cache rather than slowly from disk. Cache Memory Principles Cache memory is substantially faster than main memory. A caching system works as follows.. When a processor attempts to read a word of memory, a check is made to see if this in in cache memory… If it is, the data is supplied, If it is not in the cache, a block of main memory, consisting of a fixed number of words is loaded to the cache. Because of the phenomenon of locality of references, when a block of data is fetched into the cache, it is likely that there will be future references to that same memory location or to other words in the block. Elements of Cache Design While there are a large number of cache implementations, there are a few basic design elements that serve to classify and differentiate cache architectures… Cache Addresses Cache Size Mapping Function Replacement Algorithm Write Policy Line Size Number of Caches Cache Addresses Almost all non-embedded processors support virtual memory. Virtual memory in essence allows a program to address memory from a logical point of view without needing to worry about the amount of physical memory available. When virtual addresses are used the designer may choose to place the cache between the MMU (memory management unit) and the processor or between the MMU and main memory. The disadvantage of virtual memory is that most virtual memory systems supply each application with the same virtual memory address space (each application sees virtual memory starting at memory address 0), which means the cache memory must be completely flushed with each application context switch or extra bits must be added to each line of the cache to identify which virtual address space the address refers to. Cache Size We would like the size of the cache to be small enough so that the overall average cost per bit is close to that of main memory alone and large enough so that the overall average access time is close to that of the cache alone. Also, larger caches are slightly slower than smaller ones. Mapping Function Because there are fewer cache lines than main memory blocks, an algorithm is needed for mapping main memory blocks into cache lines. The choice of mapping function dictates how the cache is organized. Three techniques can be used… Direct – simplest technique, maps each block of main memory into only one possible cache line Associative – Each main memory block to be loaded into any line of the cache Set Associative – exhibits the strengths of both the direct and associative approaches while reducing their disadvantages For detailed explanations of each approach – read the text book (page 148 – 154) Replacement Algorithm For associative and set associating mapping a replacement algorithm is needed to determine which of the existing blocks in the cache must be replaced by a new block. There are four common approaches… LRU (Least recently used) FIFO (First in first out) LFU (Least frequently used) Random selection Write Policy When a block resident in the cache is to be replaced, there are two cases to consider If no writes to that block have happened in the cache – discard it If a write has occurred, a process needs to be initiated where the changes in the cache are propagated back to the main memory. There are several approaches to achieve this including… Write Through – all writes to the cache are done to the main memory as well at the point of the change Write Back – when a block is replaced, all dirty bits are written back to main memory The problem is complicated when we have multiple caches, there are techniques to accommodate for this but I have not summarized them. Line Size When a block of data is retrieved and placed in the cache, not only the desired word but also some number of adjacent words are retrieved. As the block size increases from very small to larger sizes, the hit ratio will at first increase because of the principle of locality, which states that the data in the vicinity of a referenced word are likely to be referenced in the near future. As the block size increases, more useful data are brought into cache. The hit ratio will begin to decrease as the block becomes even bigger and the probability of using the newly fetched information becomes less than the probability of using the newly fetched information that has to be replaced. Two specific effects come into play… Larger blocks reduce the number of blocks that fit into a cache. Because each block fetch overwrites older cache contents, a small number of blocks results in data being overwritten shortly after they are fetched. As a block becomes larger, each additional word is farther from the requested word and therefore less likely to be needed in the near future. The relationship between block size and hit ratio is complex, and no set approach is judged to be the best in all circumstances.   Pentium 4 and ARM cache organizations The processor core consists of four major components: Fetch/decode unit – fetches program instruction in order from the L2 cache, decodes these into a series of micro-operations, and stores the results in the L2 instruction cache Out-of-order execution logic – Schedules execution of the micro-operations subject to data dependencies and resource availability – thus micro-operations may be scheduled for execution in a different order than they were fetched from the instruction stream. As time permits, this unit schedules speculative execution of micro-operations that may be required in the future Execution units – These units execute micro-operations, fetching the required data from the L1 data cache and temporarily storing results in registers Memory subsystem – This unit includes the L2 and L3 caches and the system bus, which is used to access main memory when the L1 and L2 caches have a cache miss and to access the system I/O resources

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  • Running a simple integration scenario using the Oracle Big Data Connectors on Hadoop/HDFS cluster

    - by hamsun
    Between the elephant ( the tradional image of the Hadoop framework) and the Oracle Iron Man (Big Data..) an english setter could be seen as the link to the right data Data, Data, Data, we are living in a world where data technology based on popular applications , search engines, Webservers, rich sms messages, email clients, weather forecasts and so on, have a predominant role in our life. More and more technologies are used to analyze/track our behavior, try to detect patterns, to propose us "the best/right user experience" from the Google Ad services, to Telco companies or large consumer sites (like Amazon:) ). The more we use all these technologies, the more we generate data, and thus there is a need of huge data marts and specific hardware/software servers (as the Exadata servers) in order to treat/analyze/understand the trends and offer new services to the users. Some of these "data feeds" are raw, unstructured data, and cannot be processed effectively by normal SQL queries. Large scale distributed processing was an emerging infrastructure need and the solution seemed to be the "collocation of compute nodes with the data", which in turn leaded to MapReduce parallel patterns and the development of the Hadoop framework, which is based on MapReduce and a distributed file system (HDFS) that runs on larger clusters of rather inexpensive servers. Several Oracle products are using the distributed / aggregation pattern for data calculation ( Coherence, NoSql, times ten ) so once that you are familiar with one of these technologies, lets says with coherence aggregators, you will find the whole Hadoop, MapReduce concept very similar. Oracle Big Data Appliance is based on the Cloudera Distribution (CDH), and the Oracle Big Data Connectors can be plugged on a Hadoop cluster running the CDH distribution or equivalent Hadoop clusters. In this paper, a "lab like" implementation of this concept is done on a single Linux X64 server, running an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0, and a single node Apache hadoop-1.2.1 HDFS cluster, using the SQL connector for HDFS. The whole setup is fairly simple: Install on a Linux x64 server ( or virtual box appliance) an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0 server Get the Apache Hadoop distribution from: http://mir2.ovh.net/ftp.apache.org/dist/hadoop/common/hadoop-1.2.1. Get the Oracle Big Data Connectors from: http://www.oracle.com/technetwork/bdc/big-data-connectors/downloads/index.html?ssSourceSiteId=ocomen. Check the java version of your Linux server with the command: java -version java version "1.7.0_40" Java(TM) SE Runtime Environment (build 1.7.0_40-b43) Java HotSpot(TM) 64-Bit Server VM (build 24.0-b56, mixed mode) Decompress the hadoop hadoop-1.2.1.tar.gz file to /u01/hadoop-1.2.1 Modify your .bash_profile export HADOOP_HOME=/u01/hadoop-1.2.1 export PATH=$PATH:$HADOOP_HOME/bin export HIVE_HOME=/u01/hive-0.11.0 export PATH=$PATH:$HADOOP_HOME/bin:$HIVE_HOME/bin (also see my sample .bash_profile) Set up ssh trust for Hadoop process, this is a mandatory step, in our case we have to establish a "local trust" as will are using a single node configuration copy the new public keys to the list of authorized keys connect and test the ssh setup to your localhost: We will run a "pseudo-Hadoop cluster", in what is called "local standalone mode", all the Hadoop java components are running in one Java process, this is enough for our demo purposes. We need to "fine tune" some Hadoop configuration files, we have to go at our $HADOOP_HOME/conf, and modify the files: core-site.xml hdfs-site.xml mapred-site.xml check that the hadoop binaries are referenced correctly from the command line by executing: hadoop -version As Hadoop is managing our "clustered HDFS" file system we have to create "the mount point" and format it , the mount point will be declared to core-site.xml as: The layout under the /u01/hadoop-1.2.1/data will be created and used by other hadoop components (MapReduce = /mapred/...) HDFS is using the /dfs/... layout structure format the HDFS hadoop file system: Start the java components for the HDFS system As an additional check, you can use the GUI Hadoop browsers to check the content of your HDFS configurations: Once our HDFS Hadoop setup is done you can use the HDFS file system to store data ( big data : )), and plug them back and forth to Oracle Databases by the means of the Big Data Connectors ( which is the next configuration step). You can create / use a Hive db, but in our case we will make a simple integration of "raw data" , through the creation of an External Table to a local Oracle instance ( on the same Linux box, we run the Hadoop HDFS one node cluster and one Oracle DB). Download some public "big data", I use the site: http://france.meteofrance.com/france/observations, from where I can get *.csv files for my big data simulations :). Here is the data layout of my example file: Download the Big Data Connector from the OTN (oraosch-2.2.0.zip), unzip it to your local file system (see picture below) Modify your environment in order to access the connector libraries , and make the following test: [oracle@dg1 bin]$./hdfs_stream Usage: hdfs_stream locationFile [oracle@dg1 bin]$ Load the data to the Hadoop hdfs file system: hadoop fs -mkdir bgtest_data hadoop fs -put obsFrance.txt bgtest_data/obsFrance.txt hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$ hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$hadoop fs -ls hdfs:///user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt Check the content of the HDFS with the browser UI: Start the Oracle database, and run the following script in order to create the Oracle database user, the Oracle directories for the Oracle Big Data Connector (dg1 it’s my own db id replace accordingly yours): #!/bin/bash export ORAENV_ASK=NO export ORACLE_SID=dg1 . oraenv sqlplus /nolog <<EOF CONNECT / AS sysdba; CREATE OR REPLACE DIRECTORY osch_bin_path AS '/u01/orahdfs-2.2.0/bin'; CREATE USER BGUSER IDENTIFIED BY oracle; GRANT CREATE SESSION, CREATE TABLE TO BGUSER; GRANT EXECUTE ON sys.utl_file TO BGUSER; GRANT READ, EXECUTE ON DIRECTORY osch_bin_path TO BGUSER; CREATE OR REPLACE DIRECTORY BGT_LOG_DIR as '/u01/BG_TEST/logs'; GRANT READ, WRITE ON DIRECTORY BGT_LOG_DIR to BGUSER; CREATE OR REPLACE DIRECTORY BGT_DATA_DIR as '/u01/BG_TEST/data'; GRANT READ, WRITE ON DIRECTORY BGT_DATA_DIR to BGUSER; EOF Put the following in a file named t3.sh and make it executable, hadoop jar $OSCH_HOME/jlib/orahdfs.jar \ oracle.hadoop.exttab.ExternalTable \ -D oracle.hadoop.exttab.tableName=BGTEST_DP_XTAB \ -D oracle.hadoop.exttab.defaultDirectory=BGT_DATA_DIR \ -D oracle.hadoop.exttab.dataPaths="hdfs:///user/oracle/bgtest_data/obsFrance.txt" \ -D oracle.hadoop.exttab.columnCount=7 \ -D oracle.hadoop.connection.url=jdbc:oracle:thin:@//localhost:1521/dg1 \ -D oracle.hadoop.connection.user=BGUSER \ -D oracle.hadoop.exttab.printStackTrace=true \ -createTable --noexecute then test the creation fo the external table with it: [oracle@dg1 samples]$ ./t3.sh ./t3.sh: line 2: /u01/orahdfs-2.2.0: Is a directory Oracle SQL Connector for HDFS Release 2.2.0 - Production Copyright (c) 2011, 2013, Oracle and/or its affiliates. All rights reserved. Enter Database Password:] The create table command was not executed. The following table would be created. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081035-74-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files would be created. osch-20131022081035-74-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt Then remove the --noexecute flag and create the external Oracle table for the Hadoop data. Check the results: The create table command succeeded. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081719-3239-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files were created. osch-20131022081719-3239-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt This is the view from the SQL Developer: and finally the number of lines in the oracle table, imported from our Hadoop HDFS cluster SQL select count(*) from "BGUSER"."BGTEST_DP_XTAB"; COUNT(*) ---------- 1151 In a next post we will integrate data from a Hive database, and try some ODI integrations with the ODI Big Data connector. Our simplistic approach is just a step to show you how these unstructured data world can be integrated to Oracle infrastructure. Hadoop, BigData, NoSql are great technologies, they are widely used and Oracle is offering a large integration infrastructure based on these services. Oracle University presents a complete curriculum on all the Oracle related technologies: NoSQL: Introduction to Oracle NoSQL Database Using Oracle NoSQL Database Big Data: Introduction to Big Data Oracle Big Data Essentials Oracle Big Data Overview Oracle Data Integrator: Oracle Data Integrator 12c: New Features Oracle Data Integrator 11g: Integration and Administration Oracle Data Integrator: Administration and Development Oracle Data Integrator 11g: Advanced Integration and Development Oracle Coherence 12c: Oracle Coherence 12c: New Features Oracle Coherence 12c: Share and Manage Data in Clusters Oracle Coherence 12c: Oracle GoldenGate 11g: Fundamentals for Oracle Oracle GoldenGate 11g: Fundamentals for SQL Server Oracle GoldenGate 11g Fundamentals for Oracle Oracle GoldenGate 11g Fundamentals for DB2 Oracle GoldenGate 11g Fundamentals for Teradata Oracle GoldenGate 11g Fundamentals for HP NonStop Oracle GoldenGate 11g Management Pack: Overview Oracle GoldenGate 11g Troubleshooting and Tuning Oracle GoldenGate 11g: Advanced Configuration for Oracle Other Resources: Apache Hadoop : http://hadoop.apache.org/ is the homepage for these technologies. "Hadoop Definitive Guide 3rdEdition" by Tom White is a classical lecture for people who want to know more about Hadoop , and some active "googling " will also give you some more references. About the author: Eugene Simos is based in France and joined Oracle through the BEA-Weblogic Acquisition, where he worked for the Professional Service, Support, end Education for major accounts across the EMEA Region. He worked in the banking sector, ATT, Telco companies giving him extensive experience on production environments. Eugen currently specializes in Oracle Fusion Middleware teaching an array of courses on Weblogic/Webcenter, Content,BPM /SOA/Identity-Security/GoldenGate/Virtualisation/Unified Comm Suite) throughout the EMEA region.

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  • Denali Paging–Key seek lookups

    - by Dave Ballantyne
    In my previous post “Denali Paging – is it win.win ?” I demonstrated the use of using the Paging functionality within Denali.  On reflection,  I think i may of been a little unfair and should of continued always planned to continue my investigations to the next step. In Pauls article, he uses a combination of ctes to first scan the ordered keys which is then filtered using TOP and rownumber and then uses those keys to seek the data.  So what happens if we replace the scanning portion of the code with the denali paging functionality. Heres the original procedure,  we are going to replace the functionality of the Keys and SelectedKeys ctes : CREATE  PROCEDURE dbo.FetchPageKeySeek         @PageSize   BIGINT,         @PageNumber BIGINT AS BEGIN         -- Key-Seek algorithm         WITH    Keys         AS      (                 -- Step 1 : Number the rows from the non-clustered index                 -- Maximum number of rows = @PageNumber * @PageSize                 SELECT  TOP (@PageNumber * @PageSize)                         rn = ROW_NUMBER() OVER (ORDER BY P1.post_id ASC),                         P1.post_id                 FROM    dbo.Post P1                 ORDER   BY                         P1.post_id ASC                 ),                 SelectedKeys         AS      (                 -- Step 2 : Get the primary keys for the rows on the page we want                 -- Maximum number of rows from this stage = @PageSize                 SELECT  TOP (@PageSize)                         SK.rn,                         SK.post_id                 FROM    Keys SK                 WHERE   SK.rn > ((@PageNumber - 1) * @PageSize)                 ORDER   BY                         SK.post_id ASC                 )         SELECT  -- Step 3 : Retrieve the off-index data                 -- We will only have @PageSize rows by this stage                 SK.rn,                 P2.post_id,                 P2.thread_id,                 P2.member_id,                 P2.create_dt,                 P2.title,                 P2.body         FROM    SelectedKeys SK         JOIN    dbo.Post P2                 ON  P2.post_id = SK.post_id         ORDER   BY                 SK.post_id ASC; END; and here is the replacement procedure using paging: CREATE  PROCEDURE dbo.FetchOffsetPageKeySeek         @PageSize   BIGINT,         @PageNumber BIGINT AS BEGIN         -- Key-Seek algorithm         WITH    SelectedKeys         AS      (                 SELECT  post_id                 FROM    dbo.Post P1                 ORDER   BY post_id ASC                 OFFSET  @PageSize * (@PageNumber-1) ROWS                 FETCH NEXT @PageSize ROWS ONLY                 )         SELECT  P2.post_id,                 P2.thread_id,                 P2.member_id,                 P2.create_dt,                 P2.title,                 P2.body         FROM    SelectedKeys SK         JOIN    dbo.Post P2                 ON  P2.post_id = SK.post_id         ORDER   BY                 SK.post_id ASC; END; Notice how all i have done is replace the functionality with the Keys and SelectedKeys CTEs with the paging functionality. So , what is the comparative performance now ?. Exactly the same amount of IO and memory usage , but its now pretty obvious that in terms of CPU and overall duration we are onto a winner.    

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