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  • Make services not start automatically after reboot (as they require access to an encrypted partition)

    - by Binary255
    Hi, I use Ubuntu Server 10.04. I more or less only want the server to be accessible over SSH after a reboot. I will then login and mount the encrypted partition myself, after which I start the services which uses it. How would I go about setting something like that up? (My first idea was to have everything except /boot in an encrypted LVM, but I never got logging in through SSH and mounting the LVM to work. Initramfs was a bit too complicated for me. Otherwise I think this would have been the best solution.)

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  • error: no such partition. Grub rescue

    - by user1440731
    I deleted Linux (Ubuntu) partition from windows Ii forgot to repair mbr. The thing is I don't have my windows boot CD with me now and not CD drive. so the solution I needed is that is there any small utility software that can be easily downloaded (I knew about hiren CD but that's about 500 MB and I don't need that whole stuff) and can work through USB? Also explain steps to perform. Thank you UPDATED: Any small Linux live CD can also b taken into consideration as i can boot that on USB using pendrivelinux software

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  • not able to open files in windows partition from linux on a dual boot system

    - by user1237244
    I have installed dual boot on my laptop, windows XP && Fedora linux. For some unknown reason windows XP is not booting up. Through fedora I'm able to see the windows XP partition and files in it. But the problem here is, I'm not able to open those files from linux. Does anybody hit this kind of issue, if so and incase you figured out the soluton, can you please share the solution? Thanks in advance.

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  • Best way to partition 1 TB (Linux and Windows 7)

    - by Simon
    Is there an intelligent way to partition 1 TB and be prepared for resizing/adding/deleting partitions? I was thinking about LVM, but as far as I remember, Windows 7 can't be installed on logical volume right? For now my plan is: - ~150 GB for Windows 7 and other stuff (Visual Studio..., maybe I'll split it 100/50 or something like that) - simple NTFS - 850 GB = LVM - disk for Linux (Ubuntu) and other stuff virtual machines, etc. I'm mostly interested in how and what tools should I use to get easy in maintain partitions for both systems.

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  • BIOS recognizes HD, but Ubuntu doesn't recognize it as partition

    - by user23792
    Hello, I just stuck in a new 64 GB SSD (literally out of the box) into my Lenovo X61 laptop, replacing an old 5400 RPM 80 GB drive. When I boot the system, my motherboard successfully sees the SATA hard drive. Now I want to install Ubuntu on it. I stick it in the CD drive, bootup the system, and it gets to step 4 (choose partition), but sees no available partitions. Do I need to do something to the hard drive before installing Ubuntu? Many thanks.

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  • Optimal partition setup for Windows 7 on SSD

    - by Mike C.
    Hello, I'm setting up my system with Windows 7 right now, with knowledge that I am going to be getting a SSD in the future. What optimizations/setup should I do now to make a smoother transition in the future? Should I created two partitions - one for the OS and one for the data? Assuming this is the case, I would be able to easily ghost my OS partition onto the SSD in the future. If so, what should go on the OS drive besides the OS? Program files? If I install games or Visual Studio, should it go on the OS drive or the data drive? I can see the SSD filling up fast if I install all my program files on there. I've seen a few posts where people talk about leaving a portion of the SSD unformatted - is this something I should do? Thanks!

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  • Is there a way to read the contents of the master boot record?

    - by Codezilla
    Reading another question on here it made me curious if it's possible to actually read the contents of the mbr. As I understand it, there's a certain area at the very front of the partition that lists this information. I'm curious if it's sort of like an ini file or some sort of script that runs and tells the computer what it needs to know about where to boot from and other information like sectors, heads, cylinders that's important. I don't know much about what would be in it, but I thought it'd be interesting to learn more about the specifics.

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  • Best way to have linux setup for changing distros

    - by Wizard
    Hi there. I am currently using Fedora and looking at switching to Linux Mint Debian Edition. What is the best way to have the machine setup, so changing distro causes the least issues. In that I mean; people usually say having /home on it's own partition is good because then you just format the other partitions and you don't loose anything in home. However what happens then with say Evolution (or other program) configs etc with one version and then when you move to another distro it has other files, this could cause issues? Is there another way to have machine setup?

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  • How do brand laptop manufacturers restrict hard disk drive?

    - by user176705
    I'm curious to know, when I bought a brand new laptop there are limitations to create or change the HDD partitions, except the following partitions: c:\ drive (Main partition + OS drive) NTFS. 400 Gb. Recovery drive NTFS. 15 Gb. Tools drive FAT32. 2 Gb. System drive NTFS. 0.3 Gb. My questions are: How do manufacturers restrict HDDs ? What is the term for these restrictions? Can this be applied to desktop PCs? Is it possible to modify the restrictions by an end-user?

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  • permanently mount multiple directories from different disks under root [on hold]

    - by piotrek
    I have SSD and HDD. Some directories like /var /srv /tmp should be on hdd while /boot /usr /lib on ssd. But do I have to create separate partition for every single directory? i want to have 2 or so partitions. one for each disk and distribute directories as needed. is it possible? and how? i've heard about symlinks, mount --bind, mhddfs but: symlinks are treated differently by tools like cp so i'm not sure if it's safe to have main system directories symlinked i have no idea how can I use mount --bind or mhddfs in fstab

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  • SQL SERVER – Simple Example of Incremental Statistics – Performance improvements in SQL Server 2014 – Part 2

    - by Pinal Dave
    This is the second part of the series Incremental Statistics. Here is the index of the complete series. What is Incremental Statistics? – Performance improvements in SQL Server 2014 – Part 1 Simple Example of Incremental Statistics – Performance improvements in SQL Server 2014 – Part 2 DMV to Identify Incremental Statistics – Performance improvements in SQL Server 2014 – Part 3 In part 1 we have understood what is incremental statistics and now in this second part we will see a simple example of incremental statistics. This blog post is heavily inspired from my friend Balmukund’s must read blog post. If you have partitioned table and lots of data, this feature can be specifically very useful. Prerequisite Here are two things you must know before you start with the demonstrations. AdventureWorks – For the demonstration purpose I have installed AdventureWorks 2012 as an AdventureWorks 2014 in this demonstration. Partitions – You should know how partition works with databases. Setup Script Here is the setup script for creating Partition Function, Scheme, and the Table. We will populate the table based on the SalesOrderDetails table from AdventureWorks. -- Use Database USE AdventureWorks2014 GO -- Create Partition Function CREATE PARTITION FUNCTION IncrStatFn (INT) AS RANGE LEFT FOR VALUES (44000, 54000, 64000, 74000) GO -- Create Partition Scheme CREATE PARTITION SCHEME IncrStatSch AS PARTITION [IncrStatFn] TO ([PRIMARY], [PRIMARY], [PRIMARY], [PRIMARY], [PRIMARY]) GO -- Create Table Incremental_Statistics CREATE TABLE [IncrStatTab]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [ModifiedDate] [datetime] NOT NULL) ON IncrStatSch(SalesOrderID) GO -- Populate Table INSERT INTO [IncrStatTab]([SalesOrderID], [SalesOrderDetailID], [CarrierTrackingNumber], [OrderQty], [ProductID], [SpecialOfferID], [UnitPrice],   [UnitPriceDiscount], [ModifiedDate]) SELECT     [SalesOrderID], [SalesOrderDetailID], [CarrierTrackingNumber], [OrderQty], [ProductID], [SpecialOfferID], [UnitPrice],   [UnitPriceDiscount], [ModifiedDate] FROM       [Sales].[SalesOrderDetail] WHERE      SalesOrderID < 54000 GO Check Details Now we will check details in the partition table IncrStatSch. -- Check the partition SELECT * FROM sys.partitions WHERE OBJECT_ID = OBJECT_ID('IncrStatTab') GO You will notice that only a few of the partition are filled up with data and remaining all the partitions are empty. Now we will create statistics on the Table on the column SalesOrderID. However, here we will keep adding one more keyword which is INCREMENTAL = ON. Please note this is the new keyword and feature added in SQL Server 2014. It did not exist in earlier versions. -- Create Statistics CREATE STATISTICS IncrStat ON [IncrStatTab] (SalesOrderID) WITH FULLSCAN, INCREMENTAL = ON GO Now we have successfully created statistics let us check the statistical histogram of the table. Now let us once again populate the table with more data. This time the data are entered into a different partition than earlier populated partition. -- Populate Table INSERT INTO [IncrStatTab]([SalesOrderID], [SalesOrderDetailID], [CarrierTrackingNumber], [OrderQty], [ProductID], [SpecialOfferID], [UnitPrice],   [UnitPriceDiscount], [ModifiedDate]) SELECT     [SalesOrderID], [SalesOrderDetailID], [CarrierTrackingNumber], [OrderQty], [ProductID], [SpecialOfferID], [UnitPrice],   [UnitPriceDiscount], [ModifiedDate] FROM       [Sales].[SalesOrderDetail] WHERE      SalesOrderID > 54000 GO Let us check the status of the partition once again with following script. -- Check the partition SELECT * FROM sys.partitions WHERE OBJECT_ID = OBJECT_ID('IncrStatTab') GO Statistics Update Now here has the new feature come into action. Previously, if we have to update the statistics, we will have to FULLSCAN the entire table irrespective of which partition got the data. However, in SQL Server 2014 we can just specify which partition we want to update in terms of Statistics. Here is the script for the same. -- Update Statistics Manually UPDATE STATISTICS IncrStatTab (IncrStat) WITH RESAMPLE ON PARTITIONS(3, 4) GO Now let us check the statistics once again. -- Show Statistics DBCC SHOW_STATISTICS('IncrStatTab', IncrStat) WITH HISTOGRAM GO Upon examining statistics histogram, you will notice that now the distribution has changed and there is way more rows in the histogram. Summary The new feature of Incremental Statistics is indeed a boon for the scenario where there are partitions and statistics needs to be updated frequently on the partitions. In earlier version to update statistics one has to do FULLSCAN on the entire table which was wasting too many resources. With the new feature in SQL Server 2014, now only those partitions which are significantly changed can be specified in the script to update statistics. Cleanup You can clean up the database by executing following scripts. -- Clean up DROP TABLE [IncrStatTab] DROP PARTITION SCHEME [IncrStatSch] DROP PARTITION FUNCTION [IncrStatFn] GO Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SQL Statistics, Statistics

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  • Developing Schema Compare for Oracle (Part 6): 9i Query Performance

    - by Simon Cooper
    All throughout the EAP and beta versions of Schema Compare for Oracle, our main request was support for Oracle 9i. After releasing version 1.0 with support for 10g and 11g, our next step was then to get version 1.1 of SCfO out with support for 9i. However, there were some significant problems that we had to overcome first. This post will concentrate on query execution time. When we first tested SCfO on a 9i server, after accounting for various changes to the data dictionary, we found that database registration was taking a long time. And I mean a looooooong time. The same database that on 10g or 11g would take a couple of minutes to register would be taking upwards of 30 mins on 9i. Obviously, this is not ideal, so a poke around the query execution plans was required. As an example, let's take the table population query - the one that reads ALL_TABLES and joins it with a few other dictionary views to get us back our list of tables. On 10g, this query takes 5.6 seconds. On 9i, it takes 89.47 seconds. The difference in execution plan is even more dramatic - here's the (edited) execution plan on 10g: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 108K| 939 || 1 | SORT ORDER BY | | 108K| 939 || 2 | NESTED LOOPS OUTER | | 108K| 938 ||* 3 | HASH JOIN RIGHT OUTER | | 103K| 762 || 4 | VIEW | ALL_EXTERNAL_LOCATIONS | 2058 | 3 ||* 20 | HASH JOIN RIGHT OUTER | | 73472 | 759 || 21 | VIEW | ALL_EXTERNAL_TABLES | 2097 | 3 ||* 34 | HASH JOIN RIGHT OUTER | | 39920 | 755 || 35 | VIEW | ALL_MVIEWS | 51 | 7 || 58 | NESTED LOOPS OUTER | | 39104 | 748 || 59 | VIEW | ALL_TABLES | 6704 | 668 || 89 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2025 | 5 || 106 | VIEW | ALL_PART_TABLES | 277 | 11 |------------------------------------------------------------------------------- And the same query on 9i: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 16P| 55G|| 1 | SORT ORDER BY | | 16P| 55G|| 2 | NESTED LOOPS OUTER | | 16P| 862M|| 3 | NESTED LOOPS OUTER | | 5251G| 992K|| 4 | NESTED LOOPS OUTER | | 4243M| 2578 || 5 | NESTED LOOPS OUTER | | 2669K| 1440 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 ||* 50 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2043 | ||* 66 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_TABLES | 1777K| ||* 80 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_LOCATIONS | 1744K| ||* 96 | VIEW | ALL_PART_TABLES | 852K| |------------------------------------------------------------------------------- Have a look at the cost column. 10g's overall query cost is 939, and 9i is 55,000,000,000 (or more precisely, 55,496,472,769). It's also having to process far more data. What on earth could be causing this huge difference in query cost? After trawling through the '10g New Features' documentation, we found item 1.9.2.21. Before 10g, Oracle advised that you do not collect statistics on data dictionary objects. From 10g, it advised that you do collect statistics on the data dictionary; for our queries, Oracle therefore knows what sort of data is in the dictionary tables, and so can generate an efficient execution plan. On 9i, no statistics are present on the system tables, so Oracle has to use the Rule Based Optimizer, which turns most LEFT JOINs into nested loops. If we force 9i to use hash joins, like 10g, we get a much better plan: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 7587K| 3704 || 1 | SORT ORDER BY | | 7587K| 3704 ||* 2 | HASH JOIN OUTER | | 7587K| 822 ||* 3 | HASH JOIN OUTER | | 5262K| 616 ||* 4 | HASH JOIN OUTER | | 2980K| 465 ||* 5 | HASH JOIN OUTER | | 710K| 432 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 || 50 | VIEW | ALL_PART_TABLES | 852K| 104 || 78 | VIEW | ALL_TAB_COMMENTS | 2043 | 14 || 93 | VIEW | ALL_EXTERNAL_LOCATIONS | 1744K| 31 || 106 | VIEW | ALL_EXTERNAL_TABLES | 1777K| 28 |------------------------------------------------------------------------------- That's much more like it. This drops the execution time down to 24 seconds. Not as good as 10g, but still an improvement. There are still several problems with this, however. 10g introduced a new join method - a right outer hash join (used in the first execution plan). The 9i query optimizer doesn't have this option available, so forcing a hash join means it has to hash the ALL_TABLES table, and furthermore re-hash it for every hash join in the execution plan; this could be thousands and thousands of rows. And although forcing hash joins somewhat alleviates this problem on our test systems, there's no guarantee that this will improve the execution time on customers' systems; it may even increase the time it takes (say, if all their tables are partitioned, or they've got a lot of materialized views). Ideally, we would want a solution that provides a speedup whatever the input. To try and get some ideas, we asked some oracle performance specialists to see if they had any ideas or tips. Their recommendation was to add a hidden hook into the product that allowed users to specify their own query hints, or even rewrite the queries entirely. However, we would prefer not to take that approach; as well as a lot of new infrastructure & a rewrite of the population code, it would have meant that any users of 9i would have to spend some time optimizing it to get it working on their system before they could use the product. Another approach was needed. All our population queries have a very specific pattern - a base table provides most of the information we need (ALL_TABLES for tables, or ALL_TAB_COLS for columns) and we do a left join to extra subsidiary tables that fill in gaps (for instance, ALL_PART_TABLES for partition information). All the left joins use the same set of columns to join on (typically the object owner & name), so we could re-use the hash information for each join, rather than re-hashing the same columns for every join. To allow us to do this, along with various other performance improvements that could be done for the specific query pattern we were using, we read all the tables individually and do a hash join on the client. Fortunately, this 'pure' algorithmic problem is the kind that can be very well optimized for expected real-world situations; as well as storing row data we're not using in the hash key on disk, we use very specific memory-efficient data structures to store all the information we need. This allows us to achieve a database population time that is as fast as on 10g, and even (in some situations) slightly faster, and a memory overhead of roughly 150 bytes per row of data in the result set (for schemas with 10,000 tables in that means an extra 1.4MB memory being used during population). Next: fun with the 9i dictionary views.

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  • Optimize an SQL statement

    - by kovshenin
    Hey, I'm running WordPress, the database diagram could be found here: http://codex.wordpress.org/Database_Description After doing tonnes of filters and applying some hooks to the core, I'm left with the following query: SELECT SQL_CALC_FOUND_ROWS wp_posts.* FROM wp_posts JOIN wp_postmeta ppmeta_beds ON (ppmeta_beds.post_id = wp_posts.ID AND ppmeta_beds.meta_key = 'pp-general-beds' AND ppmeta_beds.meta_value >= 2) JOIN wp_postmeta ppmeta_baths ON (ppmeta_baths.post_id = wp_posts.ID AND ppmeta_baths.meta_key = 'pp-general-baths' AND ppmeta_baths.meta_value >= 3) JOIN wp_postmeta ppmeta_furnished ON (ppmeta_furnished.post_id = wp_posts.ID AND ppmeta_furnished.meta_key = 'pp-general-furnished' AND ppmeta_furnished.meta_value = 'yes') JOIN wp_postmeta ppmeta_pool ON (ppmeta_pool.post_id = wp_posts.ID AND ppmeta_pool.meta_key = 'pp-facilities-pool' AND ppmeta_pool.meta_value = 'yes') JOIN wp_postmeta ppmeta_pool_type ON (ppmeta_pool_type.post_id = wp_posts.ID AND ppmeta_pool_type.meta_key = 'pp-facilities-pool-type' AND ppmeta_pool_type.meta_value IN ('tennis', 'voleyball', 'basketball', 'fitness')) JOIN wp_postmeta ppmeta_sport ON (ppmeta_sport.post_id = wp_posts.ID AND ppmeta_sport.meta_key = 'pp-facilities-sport' AND ppmeta_sport.meta_value = 'yes') JOIN wp_postmeta ppmeta_sport_type ON (ppmeta_sport_type.post_id = wp_posts.ID AND ppmeta_sport_type.meta_key = 'pp-facilities-sport-type' AND ppmeta_sport_type.meta_value IN ('tennis', 'voleyball', 'basketball', 'fitness')) JOIN wp_postmeta ppmeta_parking ON (ppmeta_parking.post_id = wp_posts.ID AND ppmeta_parking.meta_key = 'pp-facilities-parking' AND ppmeta_parking.meta_value = 'yes') JOIN wp_postmeta ppmeta_parking_type ON (ppmeta_parking_type.post_id = wp_posts.ID AND ppmeta_parking_type.meta_key = 'pp-facilities-parking-type' AND ppmeta_parking_type.meta_value IN ('street', 'off-street', 'garage')) JOIN wp_postmeta ppmeta_garden ON (ppmeta_garden.post_id = wp_posts.ID AND ppmeta_garden.meta_key = 'pp-facilities-garden' AND ppmeta_garden.meta_value = 'yes') JOIN wp_postmeta ppmeta_garden_type ON (ppmeta_garden_type.post_id = wp_posts.ID AND ppmeta_garden_type.meta_key = 'pp-facilities-garden-type' AND ppmeta_garden_type.meta_value IN ('private', 'communal')) JOIN wp_postmeta ppmeta_type ON (ppmeta_type.post_id = wp_posts.ID AND ppmeta_type.meta_key = 'pp-general-type' AND ppmeta_type.meta_value IN ('villa', 'apartment', 'penthouse')) JOIN wp_postmeta ppmeta_status ON (ppmeta_status.post_id = wp_posts.ID AND ppmeta_status.meta_key = 'pp-general-status' AND ppmeta_status.meta_value IN ('off-plan', 'resale')) JOIN wp_postmeta ppmeta_location_type ON (ppmeta_location_type.post_id = wp_posts.ID AND ppmeta_location_type.meta_key = 'pp-location-type' AND ppmeta_location_type.meta_value IN ('beachfront', 'countryside', 'town-center', 'near-the-sea', 'hillside', 'private-resort')) JOIN wp_postmeta ppmeta_price_range ON (ppmeta_price_range.post_id = wp_posts.ID AND ppmeta_price_range.meta_key = 'pp-general-price' AND ppmeta_price_range.meta_value BETWEEN 10000 AND 50000) JOIN wp_postmeta ppmeta_area_range ON (ppmeta_area_range.post_id = wp_posts.ID AND ppmeta_area_range.meta_key = 'pp-general-area' AND ppmeta_area_range.meta_value BETWEEN 50 AND 150) WHERE 1=1 AND (((wp_posts.post_title LIKE '%fdsfsad%') OR (wp_posts.post_content LIKE '%fdsfsad%'))) AND wp_posts.post_type = 'property' AND (wp_posts.post_status = 'publish' OR wp_posts.post_status = 'private') ORDER BY wp_posts.post_date DESC LIMIT 0, 10 It's way too big. Could anybody please show me a way of optimizing all those joins into fewer statements? As you can see they all use the same tables but under different names. I'm not an SQL guru but I think there should be a way, because this is insane ;) Thanks! Update Here's what explain returns: http://twitpic.com/1cd36p

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  • How do you add more space to a Fedora (LVM) partition?

    - by Trevor Boyd Smith
    In a nutshell, i have a VM that ran out of space. I increased the size of the VM's harddrive to be 4 times bigger but the OS partition is still only using 1x the space. I need to change the LVM partition to take up the extra 4x space but I don't know how to extend the LVM partition. (NOTE: To make the screenshots given below I had to boot from a live-cd for gnome-partition-manager (aka gparted). Very unfortunately gparted is only able to "detect LVM" and can't do any LVM operations.) Here is what "gparted" shows. Please notice that the "resize" option is not available: The Problem: I can't find good directions<1 on how to grow the LVM partition via GUI or command-line! How do you grow a LVM partition that was created by the default Fedora install? If you are giving command line directions. Please explain what each line of commands does.

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  • How to create a dynamic Linq Join extension method

    - by Royd Brayshay
    There was a library of dynamic Linq extensions methods released as a sample with VS2008. I'd like to extend it with a Join method. The code below fails with a parameter miss match exception at run time. Can anyone find the problem? public static IQueryable Join(this IQueryable outer, IEnumerable inner, string outerSelector, string innerSelector, string resultsSelector, params object[] values) { if (inner == null) throw new ArgumentNullException("inner"); if (outerSelector == null) throw new ArgumentNullException("outerSelector"); if (innerSelector == null) throw new ArgumentNullException("innerSelector"); if (resultsSelector == null) throw new ArgumentNullException("resultsSelctor"); LambdaExpression outerSelectorLambda = DynamicExpression.ParseLambda(outer.ElementType, null, outerSelector, values); LambdaExpression innerSelectorLambda = DynamicExpression.ParseLambda(inner.AsQueryable().ElementType, null, innerSelector, values); ParameterExpression[] parameters = new ParameterExpression[] { Expression.Parameter(outer.ElementType, "outer"), Expression.Parameter(inner.AsQueryable().ElementType, "inner") }; LambdaExpression resultsSelectorLambda = DynamicExpression.ParseLambda(parameters, null, resultsSelector, values); return outer.Provider.CreateQuery( Expression.Call( typeof(Queryable), "Join", new Type[] { outer.ElementType, inner.AsQueryable().ElementType, outerSelectorLambda.Body.Type, innerSelectorLambda.Body.Type, resultsSelectorLambda.Body.Type }, outer.Expression, inner.AsQueryable().Expression, Expression.Quote(outerSelectorLambda), Expression.Quote(innerSelectorLambda), Expression.Quote(resultsSelectorLambda))); } I've now fixed it myself, here's the answer. Please vote it up or add a better one.

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  • Replicating SQL's 'Join' in Python

    - by Daniel Mathews
    I'm in the process of trying to switch from R to Python (mainly issues around general flexibility). With Numpy, matplotlib and ipython, I've am able to cover all my use cases save for merging 'datasets'. I would like to simulate SQL's join by clause (inner, outer, full) purely in python. R handles this with the 'merge' function. I've tried the numpy.lib.recfunctions join_by, but it critical issues with duplicates along the 'key': join_by(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None, usemask=True, asrecarray=False) Join arrays r1 and r2 on key key. The key should be either a string or a sequence of string corresponding to the fields used to join the array. An exception is raised if the key field cannot be found in the two input arrays. Neither r1 nor r2 should have any duplicates along key: the presence of duplicates will make the output quite unreliable. Note that duplicates are not looked for by the algorithm. source: http://presbrey.mit.edu:1234/numpy.lib.recfunctions.html Any pointers or help will be most appreciated!

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  • JPA native query join returns object but dereference throws class cast exception

    - by masato-san
    I'm using JPQL Native query to join table and query result is stored in List<Object[]>. public String getJoinJpqlNativeQuery() { String final SQL_JOIN = "SELECT v1.bitbit, v1.numnum, v1.someTime, t1.username, t1.anotherNum FROM MasatosanTest t1 JOIN MasatoView v1 ON v1.username = t1.username;" System.out.println("get join jpql native query is being called ============================"); EntityManager em = null; List<Object[]> out = null; try { em = EmProvider.getDefaultManager(); Query query = em.createNativeQuery(SQL_JOIN); out = query.getResultList(); System.out.println("return object ==========>" + out); System.out.println(out.get(0)); String one = out.get(0).toString(); //LINE 77 where ClassCastException System.out.println(one); } catch(Exception e) { } finally { if(em != null) { em.close; } } } The problem is System.out.println("return object ==========>" + out); outputs: return object ==========> [[true, 0, 2010-12-21 15:32:53.0, masatosan, 0.020], [false, 0, 2010-12-21 15:32:53.0, koga, 0.213]] System.out.println(out.get(0)) outputs: [true, 0, 2010-12-21 15:32:53.0, masatosan, 0.020] So I assumed that I can assign return value of out.get(0) which should be String: String one = out.get(0).toString(); But I get weird ClassCastException. java.lang.ClassCastException: java.util.Vector cannot be cast to [Ljava.lang.Object; at local.test.jaxrs.MasatosanTestResource.getJoinJpqlNativeQuery (MasatosanTestResource.java:77) So what's really going on? Even Object[] foo = out.get(0); would throw an ClassCastException :(

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  • Select * from 'many to many' SQL relationship

    - by Rampant Creative Group
    I'm still learning SQL and my brain is having a hard time with this one. Say I have 3 tables: teams players and teams_players as my link table All I want to do is run a query to get each team and the players on them. I tried this: SELECT * FROM teams INNER JOIN teams_players ON teams.id = teams_players.team_id INNER JOIN players ON teams_players.player_id = players.id But it returned a separate row for each player on each team. Is JOIN the right way to do it or should I be doing something else? ----------------------------------------- Edit Ok, so from what I'm hearing, this isn't necessarily a bad way to do it. I'll just have to group the data by team while I'm doing my loop. I have not yet tried the modified SQL statements provided, but I will today and get back to you. To answer the question about structure - I guess I wasn't thinking about the returned row structure which is part of what lead to my confusion. In this particular case, each team is limited to 4 players (or less) so I guess the structure that would be helpful to me is something like the following: teams.id, teams.name, players.id, players.name, players.id, players.name, players.id, players.name, players.id, players.name, 1 Team ABC 1 Jim 2 Bob 3 Ned 4 Roy 2 Team XYZ 2 Bob 3 Ned 5 Ralph 6 Tom

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  • Using Partitions for a large MySQL table

    - by user293594
    An update on my attempts to implement a 505,000,000-row table on MySQL on my MacBook Pro: Following the advice given, I have partitioned my table, tr: i UNSIGNED INT NOT NULL, j UNSIGNED INT NOT NULL, A FLOAT(12,8) NOT NULL, nu BIGINT NOT NULL, KEY (nu), key (A) with a range on nu. nu ought to be a real number, but because I only have 6-d.p. accuracy and the maximum value of nu is 30000. I multiplied it by 10^8 made it a BIGINT - I gather one can't use FLOAT or DOUBLE values to PARTITION a MySQL table. Anyway, I have 15 partitions (p0: nu<25,000,000,000, p1: nu<50,000,000,000, etc.). I was thinking that this should speed up a typical to SELECT: SELECT * FROM tr WHERE nu>95000000000 AND nu<100000000000 AND A.>1. to something of the order of the same query on a table consisting of only the data in the relevant partition (<30 secs). But it's taking 30mins+ to return rows for queries within a partition and double that if the query is for rows spanning two (contiguous) partitions. I realise I could just have 15 different tables, and query them separately, but is there a way to do this 'automatically' with partitions? Has anyone got any suggestions?

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  • Eclipselink: Create objects from JOIN query

    - by Raven
    Hi, I have a SQL query SELECT * FROM Thing AS a JOIN Thing_Property AS b ON a.id=b.Thing_ID JOIN Property AS c ON b.properties_ID = c.id JOIN Item AS d ON c.item_ID = d.id ORDER BY a.name, d.name and I Eclipselink to create my object model with it. Here is the model: @SuppressWarnings("serial") @Entity public class Thing implements Serializable { @Id @GeneratedValue(strategy = GenerationType.TABLE) private int id; private String name; @OneToMany(cascade=CascadeType.ALL) @PrivateOwned private List<Property> properties = new ArrayList<Property>(); ... // getter and setter following here } public class Property implements Serializable { @Id @GeneratedValue(strategy = GenerationType.TABLE) private int id; @OneToOne private Item item; private String value; ... // getter and setter following here } public class Item implements Serializable { @Id @GeneratedValue(strategy = GenerationType.TABLE) private int id; private String name; .... // getter and setter following here } // Code end but I can not figure out, how to make Eclipselink create the model from that query. Can you help?

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  • Mysql many to many problem (leaderborad/scoreboard)

    - by zoko2902
    Hi all! I'm working on a small project in regards of the upcoming World Cup. I'm building a roster/leaderboard/scoredboard based on groups with national teams. The idea is to have information on all upcoming matches within the group or in the knockout phase (scores, time of the match, match stats etc.). Currently I'm stuck with the DB in that I can't come up with a query that would return paired teams in a row. I have these 3 tables: CREATE TABLE IF NOT EXISTS `wc_team` ( `id` INT NOT NULL AUTO_INCREMENT , `name` VARCHAR(45) NULL , `description` VARCHAR(250) NULL , `flag` VARCHAR(45) NULL , `image` VARCHAR(45) NULL , `added` TIMESTAMP NULL DEFAULT CURRENT_TIMESTAMP , PRIMARY KEY (`id`) , CREATE TABLE IF NOT EXISTS `wc_match` ( `id` INT NOT NULL AUTO_INCREMENT , `score` VARCHAR(6) NULL , `date` DATE NULL , `time` VARCHAR(45) NULL , `added` TIMESTAMP NULL DEFAULT CURRENT_TIMESTAMP , PRIMARY KEY (`id`) , CREATE TABLE IF NOT EXISTS `wc_team_has_match` ( `wc_team_id` INT NOT NULL , `wc_match_id` INT NOT NULL , PRIMARY KEY (`wc_team_id`, `wc_match_id`) , I've simplified the tables so we don't go in the wrong direction. Now I've tried al kinds of joins and groupings I could think of, but I never seem to get. Example guery: SELECT t.wc_team_id,t.wc_match_id,c.id.c.name,d.id,d.name FROM wc_team_has_match AS t LEFT JOIN wc_match AS s ON t.wc_match_id = s.id LEFT JOIN wc_team AS c ON t.wc_team_id = c.id LEFT JOIN wc_team AS d ON t.wc_team_id = d.id Which returns: wc_team_id wc_match_id id name id name 16 5 16 Brazil 16 Brazil 18 5 18 Argentina 18 Argentina But what I really want is: wc_team_id wc_match_id id name id name 16 5 16 Brazil 18 Argentina Keep in mind that a group has more matches I want to see all those matches not only one. Any pointer or suggestion would be extremly appreciated since I'm stuck like a duck on this one :).

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  • how to use two count or more on one selecting statament ..?

    - by jjj
    i develop this code: SELECT COUNT(NewEmployee.EmployeeID), NewEmployee.EmployeeId,EmployeeName FROM NewEmployee INNER JOIN NewTimeAttendance ON NewEmployee.EmployeeID = NewTimeAttendance.EmployeeID and NewTimeAttendance.TotalTime is null and (NewTimeAttendance.note = '' or NewTimeAttendance.note is null ) and (month=1 or month =2 or month = 3) GROUP BY NewEmployee.EmployeeID, EmployeeName order by EmployeeID from my previous two question selecting null stuff and counting issue...that amazing code is working beautifully fine..but now i need to select more than one count... ...searched (google) .... found alias...tried: SELECT COUNT(NewEmployee.EmployeeID) as attenddays, COUNT(NewEmployee.EmployeeID) as empabsent , NewEmployee.EmployeeId,EmployeeName FROM NewEmployee INNER JOIN NewTimeAttendance ON empabsent =NewEmployee.EmployeeID = NewTimeAttendance.EmployeeID and NewTimeAttendance.TotalTime is null and (NewTimeAttendance.note = '' or NewTimeAttendance.note is null ) and (month=1 or month =2 or month = 3) , attenddays = NewTimeAttendance.EmployeeID and NewTimeAttendance.TotalTime is null and (NewTimeAttendance.note = '' or NewTimeAttendance.note is null ) and (month=1 or month =2 or month = 3) GROUP BY NewEmployee.EmployeeID, EmployeeName order by EmployeeID Incorrect syntax near '='. second try: SELECT COUNT(NewEmployee.EmployeeID) as attenddays, COUNT(NewEmployee.EmployeeID) as absentdays, NewEmployee.EmployeeId,EmployeeName FROM NewEmployee INNER JOIN NewTimeAttendance ON attenddays(NewEmployee.EmployeeID = NewTimeAttendance.EmployeeID and NewTimeAttendance.TotalTime is null and (NewTimeAttendance.note = '' or NewTimeAttendance.note is null ) and (month=1 or month =2 or month = 3)) , absentdays(NewEmployee.EmployeeID = NewTimeAttendance.EmployeeID and NewTimeAttendance.TotalTime is null and (NewTimeAttendance.note = '' or NewTimeAttendance.note is null ) and (month=1 or month =2 or month = 3)) GROUP BY NewEmployee.EmployeeID, EmployeeName order by EmployeeID Incorrect syntax near '='. not very good ideas... so ...help thanks in advance

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  • Queries within queries: Is there a better way?

    - by mririgo
    As I build bigger, more advanced web applications, I'm finding myself writing extremely long and complex queries. I tend to write queries within queries a lot because I feel making one call to the database from PHP is better than making several and correlating the data. However, anyone who knows anything about SQL knows about JOINs. Personally, I've used a JOIN or two before, but quickly stopped when I discovered using subqueries because it felt easier and quicker for me to write and maintain. Commonly, I'll do subqueries that may contain one or more subqueries from relative tables. Consider this example: SELECT (SELECT username FROM users WHERE records.user_id = user_id) AS username, (SELECT last_name||', '||first_name FROM users WHERE records.user_id = user_id) AS name, in_timestamp, out_timestamp FROM records ORDER BY in_timestamp Rarely, I'll do subqueries after the WHERE clause. Consider this example: SELECT user_id, (SELECT name FROM organizations WHERE (SELECT organization FROM locations WHERE records.location = location_id) = organization_id) AS organization_name FROM records ORDER BY in_timestamp In these two cases, would I see any sort of improvement if I decided to rewrite the queries using a JOIN? As more of a blanket question, what are the advantages/disadvantages of using subqueries or a JOIN? Is one way more correct or accepted than the other?

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  • In Perl, how can I wait for threads to end in parallel?

    - by Pmarcoen
    I have a Perl script that launches 2 threads,one for each processor. I need it to wait for a thread to end, if one thread ends a new one is spawned. It seems that the join method blocks the rest of the program, therefore the second thread can't end until everything the first thread does is done which sort of defeats its purpose. I tried the is_joinable method but that doesn't seem to do it either. Here is some of my code : use threads; use threads::shared; @file_list = @ARGV; #Our file list $nofiles = $#file_list + 1; #Real number of files $currfile = 1; #Current number of file to process my %MSG : shared; #shared hash $thr0 = threads->new(\&process, shift(@file_list)); $currfile++; $thr1 = threads->new(\&process, shift(@file_list)); $currfile++; while(1){ if ($thr0->is_joinable()) { $thr0->join; #check if there are files left to process if($currfile <= $nofiles){ $thr0 = threads->new(\&process, shift(@file_list)); $currfile++; } } if ($thr1->is_joinable()) { $thr1->join; #check if there are files left to process if($currfile <= $nofiles){ $thr1 = threads->new(\&process, shift(@file_list)); $currfile++; } } } sub process{ print "Opening $currfile of $nofiles\n"; #do some stuff if(some condition){ lock(%MSG); #write stuff to hash } print "Closing $currfile of $nofiles\n"; } The output of this is : Opening 1 of 4 Opening 2 of 4 Closing 1 of 4 Opening 3 of 4 Closing 3 of 4 Opening 4 of 4 Closing 2 of 4 Closing 4 of 4

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  • How Optimize sql query make it faster

    - by user502083
    Hello every one : I have a very simple small database, 2 of tables are: Node (Node_ID, Node_name, Node_Date) : Node_ID is primary key Citation (Origin_Id, Target_Id) : PRIMARY KEY (Origin_Id, Target_Id) each is FK in Node Now I write a query that first find all citations that their Origin_Id has a specific date and then I want to know what are the target dates of these records. I'm using sqlite in python the Node table has 3000 record and Citation has 9000 records, and my query is like this in a function: def cited_years_list(self, date): c=self.cur try: c.execute("""select n.Node_Date,count(*) from Node n INNER JOIN (select c.Origin_Id AS Origin_Id, c.Target_Id AS Target_Id, n.Node_Date AS Date from CITATION c INNER JOIN NODE n ON c.Origin_Id=n.Node_Id where CAST(n.Node_Date as INT)={0}) VW ON VW.Target_Id=n.Node_Id GROUP BY n.Node_Date;""".format(date)) cited_years=c.fetchall() self.conn.commit() print('Cited Years are : \n ',str(cited_years)) except Exception as e: print('Cited Years retrival failed ',e) return cited_years Then I call this function for some specific years, But it's crazy slowwwwwwwww :( (around 1 min for a specific year) Although my query works fine, it is slow. would you please give me a suggestion to make it faster? I'd appreciate any idea about optimizing this query :) I also should mention that I have indices on Origin_Id and Target_Id, so the inner join should be pretty fast, but it's not!!!

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