Search Results

Search found 1367 results on 55 pages for 'matthew pk'.

Page 53/55 | < Previous Page | 49 50 51 52 53 54 55  | Next Page >

  • Does this query fetch unnecessary information? Should I change the query?

    - by Camran
    I have this classifieds website, and I have about 7 tables in MySql where all data is stored. I have one main table, called "classifieds". In the classifieds table, there is a column called classified_id. This is not the PK, or a key whatsoever. It is just a number which is used for me to JOIN table records together. Ex: classifieds table: fordon table: id => 33 id => 12 classified_id => 10 classified_id => 10 ad_id => 'bmw_m3_92923' This above is linked together by the classified_id column. Now to the Q, I use this method to fetch all records WHERE the column ad_id matches any of the values inside an array, called in this case $ad_arr: SELECT mt.*, fordon.*, boende.*, elektronik.*, business.*, hem_inredning.*, hobby.* FROM classified mt LEFT JOIN fordon ON fordon.classified_id = mt.classified_id LEFT JOIN boende ON boende.classified_id = mt.classified_id LEFT JOIN elektronik ON elektronik.classified_id = mt.classified_id LEFT JOIN business ON business.classified_id = mt.classified_id LEFT JOIN hem_inredning ON hem_inredning.classified_id = mt.classified_id LEFT JOIN hobby ON hobby.classified_id = mt.classified_id WHERE mt.ad_id IN ('$ad_arr')"; Is this good or would this actually fetch unnecessary information? Check out this Q I posted couple of days ago. In the comments HLGEM is commenting that it is wrong etc etc. What do you think? http://stackoverflow.com/questions/2782275/another-rookie-question-how-to-implement-count-here Thanks

    Read the article

  • Databinding to the DataGridView (Enums + Collections)

    - by Ian
    I'm after a little help with the techniques to use for Databinding. It's been quite a while since I used any proper data binding and want to try and do something with the DataGridView. I'm trying to configure as much as possible so that I can simply designed the DatagridView through the form editor, and then use a custom class that exposes all my information. The sort of information I've got is as follows: public class Result { public String Name { get; set; } public Boolean PK { get; set; } public MyEnum EnumValue { get; set; } public IList<ResultInfos> { get; set; } } public class ResultInfos { get; set; } { public class Name { get; set; } public Int Value { get; set; } public override String ToString() { return Name + " : " Value.ToString(); } } I can bind to the simple information without any problem. I want to bind to the EnumValue with a DataGridViewComboBoxColumn, but when I set the DataPropertyName I get exceptions saying the enum values aren't valid. Then comes the ResultInfo collection. Currently I can't figure out how to bind to this and display my items, again really I want this to be a combobox, where the 1st Item is selected. Anyone any suggestions on what I'm doing wrong? Thanks

    Read the article

  • Batch processing JDBC

    - by Wai Hein
    I am practicing JDBC batch processing and having errors: error 1: Unsupported feature error 2: Execute cannot be empty or null Property files include: itemsdao.updateBookName = Update Books set bookname = ? where books.id = ? itemsdao.updateAuthorName = Update books set authorname = ? where books.id = ? I know I can execute about DML statements in one update, but I am practicing batch processing in JDBC. Below is my method public void update(Item item) { String query = null; try { connection = DbConnector.getConnection(); property = SqlPropertiesLoader.getProperties("dml.properties"); connection.setAutoCommit(false); if ( property == null ) { Logging.log.debug("dml.properties does not exist. Check property loader or file name is spelled right"); return; } query = property.getProperty("itemsdao.updateBookName"); statement = connection.prepareStatement(query); statement.setString(1, item.getBookName()); statement.setInt(2, item.getId()); statement.addBatch(query); query = property.getProperty("itemsdao.updateAuthorName"); statement = connection.prepareStatement(query); statement.setString(1, item.getAuthorName()); statement.setInt(2, item.getId()); statement.addBatch(query); statement.executeBatch(); connection.commit(); }catch (ClassNotFoundException e) { Logging.log.error("Connection class does not exist", e); } catch (SQLException e) { Logging.log.error("Violating PK constraint",e); } //helper class th finally { DbUtil.close(connection); DbUtil.closePreparedStatement(statement); }

    Read the article

  • Counting a cell up per Objects

    - by Auro
    hey i got a problem once again :D a little info first: im trying to copy data from one table to an other table(structure is the same). now one cell needs to be incremented, beginns per group at 1 (just like a histroy). i have this table: create table My_Test/My_Test2 ( my_Id Number(8,0), my_Num Number(6,0), my_Data Varchar2(100)); (my_Id, my_Num is a nested PK) if i want to insert a new row, i need to check if the value in my_id already exists. if this is true then i need to use the next my_Num for this Id. i have this in my Table: My_Id My_Num My_Data 1 1 'test1' 1 2 'test2' 2 1 'test3' if i add now a row for my_Id 1, the row would look like this: i have this in my Table: My_Id My_Num My_Data 1 3 'test4' this sounds pretty easy ,now i need to make it in a SQL and on SQL Server i had the same problem and i used this: Insert Into My_Test (My_Id,My_Num,My_Data) SELECT my_Id, ( SELECT CASE ( CASE MAX(a.my_Num) WHEN NULL THEN 0 Else Max(A.My_Num) END) + b.My_Num WHEN NULL THEN 1 ELSE ( CASE MAX(a.My_Num) WHEN NULL THEN 0 Else Max(A.My_Num) END) + b.My_Num END From My_Test A where my_id = 1 ) ,My_Data From My_Test2 B where my_id = 1; this Select gives null back if no Rows are found in the subselect is there a way so i could use max in the case? and if it give null back it should use 0 or 1? greets Auro

    Read the article

  • Unable to delete inherited entity class in EF4

    - by Coding Gorilla
    I have two entities in an EF4 model (using Model First), let's call them EntityA and EntityB. EntityA is marked as abstract, and EntityB inherits from EntityA. They are similar to the following: public class EntityA { public Guid Id; public string Name; public string Uri; } public class EntityB : EntityA { public string AnotherProperty; } The generated database tables look as I would expect them, with EntityA as on table, and then another table like: EntityA_EntityB Id (PK, FK, uniqueidentifier) AnotherProperty (varchar) There is a foreign key constraint on EntityA_EntityB that references EntityA's Id property, no cascades are configured (although I did try changing these myself). The problem is that when I attempt to do something like: Context.DeleteObject(EntityA_EntityB); EF attempts to delete the EntityA_EntityB table record before deleting the EntityA table record, which of course violates the foreign key constraint on EntityA_EntityB table. Using EFProfiler I see the following commands being sent to the database: delete [dbo].[EntityA_EntityB] where (([Id] = '5c02899f-09ea-2ed9-d44b-01aef80f6b64' /* @0 */) followed by delete [dbo].[EntityA] where ([Id] = '5c02899f-09ea-2ed9-d44b-01aef80f6b64' /* @0 */) I'm completely stumped as to how to get around this problem. I would think the EF should know that it needs to delete the base class first, before deleting the inherited class. I know I could do some triggers or other database type solutions, but I'd rather avoid doing that if I can. All my classes are POCO built using some customized T4 templates. I don't want to paste in a lot of extraneous code, but if you need more information I'll provide what I can.

    Read the article

  • Performance of stored proc when updating columns selectively based on parameters?

    - by kprobst
    I'm trying to figure out if this is relatively well-performing T-SQL (this is SQL Server 2008). I need to create a stored procedure that updates a table. The proc accepts as many parameters as there are columns in the table, and with the exception of the PK column, they all default to NULL. The body of the procedure looks like this: CREATE PROCEDURE proc_repo_update @object_id bigint ,@object_name varchar(50) = NULL ,@object_type char(2) = NULL ,@object_weight int = NULL ,@owner_id int = NULL -- ...etc AS BEGIN update object_repo set object_name = ISNULL(@object_name, object_name) ,object_type = ISNULL(@object_type, object_type) ,object_weight = ISNULL(@object_weight, object_weight) ,owner_id = ISNULL(@owner_id, owner_id) -- ...etc where object_id = @object_id return @@ROWCOUNT END So basically: Update a column only if its corresponding parameter was provided, and leave the rest alone. This works well enough, but as the ISNULL call will return the value of the column if the received parameter was null, will SQL Server optimize this somehow? This might be a performance bottleneck on the application where the table might be updated heavily (insertion will be uncommon so the performance there is not a problem). So I'm trying to figure out what's the best way to do this. Is there a way to condition the column expressions with something like CASE WHEN or something? The table will be indexed up the wazoo as well for read performance. Is this the best approach? My alternative at this point is to create the UPDATE expression in code (e.g. inline SQL) and execute it against the server. This would solve my doubts about performance, but I'd rather leave this in a stored proc if possible.

    Read the article

  • Handling primary key duplicates in a data warehouse load

    - by Meff
    I'm currently building an ETL system to load a data warehouse from a transactional system. The grain of my fact table is the transaction level. In order to ensure I don't load duplicate rows I've put a primary key on the fact table, which is the transaction ID. I've encountered a problem with transactions being reversed - In the transactional database this is done via a status, which I pick up and I can work out if the transaction is being done, or rolled back so I can load a reversal row in the warehouse. However, the reversal row will have the same transaction ID and so I get a primary key violation. I've solved this for now by negating the primary key, so transaction ID 1 would be a payment, and transaction ID -1 (In the warehouse only) would be the reversal. I have considered an alternative of generating a BIT column, where 0 is normal and 1 is reversal, then making the PK the transaction ID and the BIT column. My question is, is this a good practice, and has anyone else encountered anything like this? For reference, this is a payment processing system, so values will not be modified, so there will only ever be transactions and reversals.

    Read the article

  • Too many columns to index - use mySQL Partitions?

    - by Christopher Padfield
    We have an application with a table with 20+ columns that are all searchable. Building indexes for all these columns would make write queries very slow; and any really useful index would often have to be across multiple columns increasing the number of indexes needed. However, for 95% of these searches, only a small subset of those rows need to be searched upon, and quite a small number - say 50,000 rows. So, we have considered using mySQL Partition tables - having a column that is basically isActive which is what we divide the two partitions by. Most search queries would be run with isActive=1. Most queries would then be run against the small 50,000 row partition and be quick without other indexes. Only issue is the rows where isActive=1 is not fixed; i.e. it's not based on the date of the row or anything fixed like that; we will need to update isActive based on use of the data in that row. As I understand it that is no problem though; the data would just be moved from one partition to another during the UPDATE query. We do have a PK on id for the row though; and I am not sure if this is a problem; the manual seemed to suggest the partition had to be based on any primary keys. This would be a huge problem for us because the primary key ID has no basis on whether the row isActive.

    Read the article

  • MYSQL inserting records form table A into tables B and C (linked by foreign key) depending on column values in table A

    - by Chez
    Hi All, Have been searching high and low for a simple solution to a mysql insert problem. The problem is as follows: I am putting together an organisational database consisting of departments and desks. A department may or may not have n number of desks. Both departments and desks have their own table linked by a foreign key in desks to the relevant record in departments (i.e. the pk). I have a temporary table which I use to place all new department data (n records long)...In this table n number of desk records for a department follow the department record directly below. In the TEMP table, if a column department_name has a value,it is a department, if it doesn't it will have a value for the column desk and therefore will be a desk which is related to the above department. As I said there maybe several desk records until you get to the next department record. Ok, so what I want to do is the following: Insert the departments into the departments table and its desks into the desks table , generating a foreign key in the desk record to the relevant departments id. In pseudo-ish code: for each record in TEMP table if Department INSERT the record into Departments get the id of the newly created Department record and store it somewhere else if Desk INSERT the desk into the desks table with the relevant departments id as the foreignkey note once again that all departments desks directly follow the department in the TEMP Table Many Thanks

    Read the article

  • Joining tables with composite keys in a legacy system in hibernate

    - by Steve N
    Hi, I'm currently trying to create a pair of Hibernate annotated classes to load (read only) from a pair of tables in a legacy system. The legacy system uses a consistent (if somewhat dated) approach to keying tables. The tables I'm attempting to map are as follows: Customer CustomerAddress -------------------------- ---------------------------- customerNumber:string (pk) customerNumber:string (pk_1) name:string sequenceNumber:int (pk_2) street:string postalCode:string I've approached this by creating a CustomerAddress class like this: @Entity @Table(name="CustomerAddress") @IdClass(CustomerAddressKey.class) public class CustomerAddress { @Id @AttributeOverrides({ @AttributeOverride(name = "customerNumber", column = @Column(name="customerNumber")), @AttributeOverride(name = "sequenceNumber", column = @Column(name="sequenceNumber")) }) private String customerNumber; private int sequenceNumber; private String name; private String postalCode; ... } Where the CustomerAddressKey class is a simple Serializable object with the two key fields. The Customer object is then defined as: @Entity @Table(name = "Customer") public class Customer { private String customerNumber; private List<CustomerAddress> addresses = new ArrayList<CustomerAddress>(); private String name; ... } So, my question is: how do I express the OneToMany relationship on the Customer table?

    Read the article

  • performance issue: difference between select s.* vs select *

    - by kamil
    Recently I had some problem in performance of my query. The thing is described here: poor Hibernate select performance comparing to running directly - how debug? After long time of struggling, I've finally discovered that the query with select prefix like: select sth.* from Something as sth... Is 300x times slower then query started this way: select * from Something as sth.. Could somebody help me, and asnwer why is that so? Some external documents on this would be really useful. The table used for testing was: SALES_UNIT table contains some basic info abot sales unit node such as name and etc. The only association is to table SALES_UNIT_TYPE, as ManyToOne. The primary key is ID and field VALID_FROM_DTTM which is date. SALES_UNIT_RELATION contains relation PARENT-CHILD between sales unit nodes. Consists of SALES_UNIT_PARENT_ID, SALES_UNIT_CHILD_ID and VALID_TO_DTTM/VALID_FROM_DTTM. No association with any tables. The PK here is ..PARENT_ID, ..CHILD_ID and VALID_FROM_DTTM The actual query I've done was: select s.* from sales_unit s left join sales_unit_relation r on (s.sales_unit_id = r.sales_unit_child_id) where r.sales_unit_child_id is null select * from sales_unit s left join sales_unit_relation r on (s.sales_unit_id = r.sales_unit_child_id) where r.sales_unit_child_id is null Same query, both uses left join and only difference is with select.

    Read the article

  • Common Properties: Consolidating Loan, Purchase, Inventory and Sale tables into one Transaction tabl

    - by Frank Computer
    Pawnshop Application: I have separate tables for Loan, Purchase, Inventory & Sales transactions. Each tables rows are joined to their respective customer rows by: customer.pk [serial] = loan.fk [integer]; = purchase.fk [integer]; = inventory.fk [integer]; = sale.fk [integer]; Since there are so many common properties within the four tables, I consolidated the four tables into one table called "transaction", where a column: transaction.trx_type char(1) {L=Loan, P=Purchase, I=Inventory, S=Sale} Scenario: A customer initially pawns merchandise, makes a couple of interest payments, then decides he wants to sell the merchandise to the pawnshop, who then places merchandise in Inventory and eventually sells it to another customer. I designed a generic transaction table where for example: transaction.main_amount DECIMAL(7,2) in a loan transaction holds the pawn amount, in a purchase holds the purchase price, in inventory and sale holds sale price. This is clearly a denormalized design, but has made programming alot easier and improved performance. Any type of transaction can now be performed from within one screen, without the need to change to different tables.

    Read the article

  • Data base design with Blob

    - by mmuthu
    Hi, I have a situation where i need to store the binary data into database as blob column. There are three different table exists in my database where in i need to store a blob data for each record. Not every record will have the blob data all the time. It is time and user based. The table one will have to store the *.doc files almost for all the record The table two will have to store the *.xml optionally. The table three will have to store images (not sure what is frequency, etc) Now my questions is whether it is a good idea to maintain a separate table to store the blob data pointing it to the respective table PK's (Yes, there will be no FK's and assuming program will maintain it). It will be some thing like below, BLOB|PK_ID|TABLE_NAME Alternatively, is it a good idea to keep the blob column in respective tables. As for as my application runtime is concerned, The table 2 will be read very frequently. Though the blob column will not be required. The table 2 record will gets deleted frequently. Similarly other blob data in respective table will not be accessed frequently. All of the blob content will be read on-demand basis. I'm thinking first approach will work better for me. What do you guys think? Btw, I'm using Oracle.

    Read the article

  • Hibernate 1:M relationship ,row order, constant values table and concurrency

    - by EugeneP
    table A and B need to have 1:M relationship a and b are added during application runtime, so A created, then say 4 B's created. Each B instance has to come in order, so that I could later extract them in the same order as I added them. The app will be a web-app running on Tomcat, so 10 instances may work simultaneously. So my question are: 1) How to preserve inserting order, so that I could extract B instances that A references in the same order as I persisted them. That's tricky, because we add to a Collection and then it gets saved (am I right?). So, it depends on how Hibernate saves it, what if it changes the order in what we added instances? I've seen something like LIST instead of SET when describing relationships, is that what I need? 2) How to add a 3-rd column to B so that I could differentiate the instances, something like SEX(M,F,U) in B table. Do I need a special table, or there's and easy way to describe constants in Hibernate. What do you recommend? 3) Talking about concurrency, what methods do you recommend to use? There should be no collisions in the db and as you see, there might easily be some if rows are not inserted (PK added) right where it is invoked without delays ?

    Read the article

  • Create an index only on certain rows in mysql

    - by dhruvbird
    So, I have this funny requirement of creating an index on a table only on a certain set of rows. This is what my table looks like: USER: userid, friendid, created, blah0, blah1, ..., blahN Now, I'd like to create an index on: (userid, friendid, created) but only on those rows where userid = friendid. The reason being that this index is only going to be used to satisfy queries where the WHERE clause contains "userid = friendid". There will be many rows where this is NOT the case, and I really don't want to waste all that extra space on the index. Another option would be to create a table (query table) which is populated on insert/update of this table and create a trigger to do so, but again I am guessing an index on that table would mean that the data would be stored twice. How does mysql store Primary Keys? I mean is the table ordered on the Primary Key or is it ordered by insert order and the PK is like a normal unique index? I checked up on clustered indexes (http://dev.mysql.com/doc/refman/5.0/en/innodb-index-types.html), but it seems only InnoDB supports them. I am using MyISAM (I mention this because then I could have created a clustered index on these 3 fields in the query table). I am basically looking for something like this: ALTER TABLE USERS ADD INDEX (userid, friendid, created) WHERE userid=friendid

    Read the article

  • Foreign-key-like merge in R

    - by skyl
    I'm merging a bunch of csv with 1 row per id/pk/seqn. > full = merge(demo, lab13am, by="seqn", all=TRUE) > full = merge(full, cdq, by="seqn", all=TRUE) > full = merge(full, mcq, by="seqn", all=TRUE) > full = merge(full, cfq, by="seqn", all=TRUE) > full = merge(full, diq, by="seqn", all=TRUE) > print(length(full$ridageyr)) [1] 9965 > print(summary(full$ridageyr)) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 11.00 19.00 29.73 48.00 85.00 Everything is great. But, I have another file which has multiple rows per id like: "seqn","rxd030","rxd240b","nhcode","rxq250" 56,2,"","",NA,NA,"" 57,1,"ACETAMINOPHEN","01200",2 57,1,"BUDESONIDE","08800",1 58,1,"99999","",NA 57 has two rows. So, if I naively try to merge this file, I have a ton more rows and my data gets all skewed up. > full = merge(full, rxq, by="seqn", all=TRUE) > print(length(full$ridageyr)) [1] 15643 > print(summary(full$ridageyr)) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 14.00 41.00 40.28 66.00 85.00 Is there a normal idiomatic way to deal with data like this? Suppose I want a way to make a simple model like MYSPECIAL_FACTOR <- somehow() glm(MYSPECIAL_FACTOR ~ full$ridageyr, family=binomial) where MYSPECIAL_FACTOR is, say, whether or not rxd240b == "ACETAMINOPHEN" for the observations which are unique by seqn. You can reproduce by running the first bit of this.

    Read the article

  • MySQL Cluster 7.3: On-Demand Webinar and Q&A Available

    - by Mat Keep
    The on-demand webinar for the MySQL Cluster 7.3 Development Release is now available. You can learn more about the design, implementation and getting started with all of the new MySQL Cluster 7.3 features from the comfort and convenience of your own device, including: - Foreign Key constraints in MySQL Cluster - Node.js NoSQL API  - Auto-installation of higher performance distributed, clusters We received some great questions over the course of the webinar, and I wanted to share those for the benefit of a broader audience. Q. What Foreign Key actions are supported: A. The core referential actions defined in the SQL:2003 standard are implemented: CASCADE RESTRICT NO ACTION SET NULL Q. Where are Foreign Keys implemented, ie data nodes or SQL nodes? A. They are implemented in the data nodes, therefore can be enforced for both the SQL and NoSQL APIs Q. Are they compatible with the InnoDB Foreign Key implementation? A. Yes, with the following exceptions: - InnoDB doesn’t support “No Action” constraints, MySQL Cluster does - You can choose to suspend FK constraint enforcement with InnoDB using the FOREIGN_KEY_CHECKS parameter; at the moment, MySQL Cluster ignores that parameter. - You cannot set up FKs between 2 tables where one is stored using MySQL Cluster and the other InnoDB. - You cannot change primary keys through the NDB API which means that the MySQL Server actually has to simulate such operations by deleting and re-adding the row. If the PK in the parent table has a FK constraint on it then this causes non-ideal behaviour. With Restrict or No Action constraints, the change will result in an error. With Cascaded constraints, you’d want the rows in the child table to be updated with the new FK value but, the implicit delete of the row from the parent table would remove the associated rows from the child table and the subsequent implicit insert into the parent wouldn’t reinstate the child rows. For this reason, an attempt to add an ON UPDATE CASCADE where the parent column is a primary key will be rejected. Q. Does adding or dropping Foreign Keys cause downtime due to a schema change? A. Nope, this is an online operation. MySQL Cluster supports a number of on-line schema changes, ie adding and dropping indexes, adding columns, etc. Q. Where can I see an example of node.js with MySQL Cluster? A. Check out the tutorial and download the code from GitHub Q. Can I use the auto-installer to support remote deployments? How about setting up MySQL Cluster 7.2? A. Yes to both! Q. Can I get a demo Check out the tutorial. You can download the code from http://labs.mysql.com/ Go to Select Build drop-down box Q. What is be minimum internet speen required for Geo distributed cluster with synchronous replication? A. if you're splitting you cluster between sites then we recommend a network latency of 20ms or less. Alternatively, use MySQL asynchronous replication where the latency of your WAN doesn't impact the latency of your reads/writes. Q. Where you can one learn more about the PayPal project with MySQL Cluster? A. Take a look at the following - you'll find press coverage, a video and slides from their keynote presentation  So, if you want to learn more, listen to the new MySQL Cluster 7.3 on-demand webinar  MySQL Cluster 7.3 is still in the development phase, so it would be great to get your feedback on these new features, and things you want to see!

    Read the article

  • django/python: is one view that handles two sibling models a good idea?

    - by clime
    I am using django multi-table inheritance: Video and Image are models derived from Media. I have implemented two views: video_list and image_list, which are just proxies to media_list. media_list returns images or videos (based on input parameter model) for a certain object, which can be of type Event, Member, or Crag. The view alters its behaviour based on input parameter action (better name would be mode), which can be of value "edit" or "view". The problem is that I need to ask whether the input parameter model contains Video or Image in media_list so that I can do the right thing. Similar condition is also in helper method media_edit_list that is called from the view. I don't particularly like it but the only alternative I can think of is to have separate (but almost the same) logic for video_list and image_list and then probably also separate helper methods for videos and images: video_edit_list, image_edit_list, video_view_list, image_view_list. So four functions instead of just two. That I like even less because the video functions would be very similar to the respective image functions. What do you recommend? Here is extract of relevant parts: http://pastebin.com/07t4bdza. I'll also paste the code here: #urls url(r'^media/images/(?P<rel_model_tag>(event|member|crag))/(?P<rel_object_id>\d+)/(?P<action>(view|edit))/$', views.image_list, name='image-list') url(r'^media/videos/(?P<rel_model_tag>(event|member|crag))/(?P<rel_object_id>\d+)/(?P<action>(view|edit))/$', views.video_list, name='video-list') #views def image_list(request, rel_model_tag, rel_object_id, mode): return media_list(request, Image, rel_model_tag, rel_object_id, mode) def video_list(request, rel_model_tag, rel_object_id, mode): return media_list(request, Video, rel_model_tag, rel_object_id, mode) def media_list(request, model, rel_model_tag, rel_object_id, mode): rel_model = tag_to_model(rel_model_tag) rel_object = get_object_or_404(rel_model, pk=rel_object_id) if model == Image: star_media = rel_object.star_image else: star_media = rel_object.star_video filter_params = {} if rel_model == Event: filter_params['event'] = rel_object_id elif rel_model == Member: filter_params['members'] = rel_object_id elif rel_model == Crag: filter_params['crag'] = rel_object_id media_list = model.objects.filter(~Q(id=star_media.id)).filter(**filter_params).order_by('date_added').all() context = { 'media_list': media_list, 'star_media': star_media, } if mode == 'edit': return media_edit_list(request, model, rel_model_tag, rel_object_id, context) return media_view_list(request, model, rel_model_tag, rel_object_id, context) def media_view_list(request, model, rel_model_tag, rel_object_id, context): if request.is_ajax(): context['base_template'] = 'boxes/base-lite.html' return render(request, 'media/list-items.html', context) def media_edit_list(request, model, rel_model_tag, rel_object_id, context): if model == Image: get_media_edit_record = get_image_edit_record else: get_media_edit_record = get_video_edit_record media_list = [get_media_edit_record(media, rel_model_tag, rel_object_id) for media in context['media_list']] if context['star_media']: star_media = get_media_edit_record(context['star_media'], rel_model_tag, rel_object_id) else: star_media = None json = simplejson.dumps({ 'star_media': star_media, 'media_list': media_list, }) return HttpResponse(json, content_type=json_response_mimetype(request)) def get_image_edit_record(image, rel_model_tag, rel_object_id): record = { 'url': image.image.url, 'name': image.title or image.filename, 'type': mimetypes.guess_type(image.image.path)[0] or 'image/png', 'thumbnailUrl': image.thumbnail_2.url, 'size': image.image.size, 'id': image.id, 'media_id': image.media_ptr.id, 'starUrl':reverse('image-star', kwargs={'image_id': image.id, 'rel_model_tag': rel_model_tag, 'rel_object_id': rel_object_id}), } return record def get_video_edit_record(video, rel_model_tag, rel_object_id): record = { 'url': video.embed_url, 'name': video.title or video.url, 'type': None, 'thumbnailUrl': video.thumbnail_2.url, 'size': None, 'id': video.id, 'media_id': video.media_ptr.id, 'starUrl': reverse('video-star', kwargs={'video_id': video.id, 'rel_model_tag': rel_model_tag, 'rel_object_id': rel_object_id}), } return record # models class Media(models.Model, WebModel): title = models.CharField('title', max_length=128, default='', db_index=True, blank=True) event = models.ForeignKey(Event, null=True, default=None, blank=True) crag = models.ForeignKey(Crag, null=True, default=None, blank=True) members = models.ManyToManyField(Member, blank=True) added_by = models.ForeignKey(Member, related_name='added_images') date_added = models.DateTimeField('date added', auto_now_add=True, null=True, default=None, editable=False) class Image(Media): image = ProcessedImageField(upload_to='uploads', processors=[ResizeToFit(width=1024, height=1024, upscale=False)], format='JPEG', options={'quality': 75}) thumbnail_1 = ImageSpecField(source='image', processors=[SmartResize(width=178, height=134)], format='JPEG', options={'quality': 75}) thumbnail_2 = ImageSpecField(source='image', #processors=[SmartResize(width=256, height=192)], processors=[ResizeToFit(height=164)], format='JPEG', options={'quality': 75}) class Video(Media): url = models.URLField('url', max_length=256, default='') embed_url = models.URLField('embed url', max_length=256, default='', blank=True) author = models.CharField('author', max_length=64, default='', blank=True) thumbnail = ProcessedImageField(upload_to='uploads', processors=[ResizeToFit(width=1024, height=1024, upscale=False)], format='JPEG', options={'quality': 75}, null=True, default=None, blank=True) thumbnail_1 = ImageSpecField(source='thumbnail', processors=[SmartResize(width=178, height=134)], format='JPEG', options={'quality': 75}) thumbnail_2 = ImageSpecField(source='thumbnail', #processors=[SmartResize(width=256, height=192)], processors=[ResizeToFit(height=164)], format='JPEG', options={'quality': 75}) class Crag(models.Model, WebModel): name = models.CharField('name', max_length=64, default='', db_index=True) normalized_name = models.CharField('normalized name', max_length=64, default='', editable=False) type = models.IntegerField('crag type', null=True, default=None, choices=crag_types) description = models.TextField('description', default='', blank=True) country = models.ForeignKey('country', null=True, default=None) #TODO: make this not null when db enables it latitude = models.FloatField('latitude', null=True, default=None) longitude = models.FloatField('longitude', null=True, default=None) location_index = FixedCharField('location index', length=24, default='', editable=False, db_index=True) # handled by db, used for marker clustering added_by = models.ForeignKey('member', null=True, default=None) #route_count = models.IntegerField('route count', null=True, default=None, editable=False) date_created = models.DateTimeField('date created', auto_now_add=True, null=True, default=None, editable=False) last_modified = models.DateTimeField('last modified', auto_now=True, null=True, default=None, editable=False) star_image = models.ForeignKey('Image', null=True, default=None, related_name='star_crags', on_delete=models.SET_NULL) star_video = models.ForeignKey('Video', null=True, default=None, related_name='star_crags', on_delete=models.SET_NULL)

    Read the article

  • django/python: is one view that handles two separate models a good idea?

    - by clime
    I am using django multi-table inheritance: Video and Image are models derived from Media. I have implemented two views: video_list and image_list, which are just proxies to media_list. media_list returns images or videos (based on input parameter model) for a certain object, which can be of type Event, Member, or Crag. It alters its behaviour based on input parameter action, which can be either "edit" or "view". The problem is that I need to ask whether the input parameter model contains Video or Image in media_list so that I can do the right thing. Similar condition is also in helper method media_edit_list that is called from the view. I don't particularly like it but the only alternative I can think of is to have separate logic for video_list and image_list and then probably also separate helper methods for videos and images: video_edit_list, image_edit_list, video_view_list, image_view_list. So four functions instead of just two. That I like even less because the video functions would be very similar to the respective image functions. What do you recommend? Here is extract of relevant parts: http://pastebin.com/07t4bdza. I'll also paste the code here: #urls url(r'^media/images/(?P<rel_model_tag>(event|member|crag))/(?P<rel_object_id>\d+)/(?P<action>(view|edit))/$', views.video_list, name='image-list') url(r'^media/videos/(?P<rel_model_tag>(event|member|crag))/(?P<rel_object_id>\d+)/(?P<action>(view|edit))/$', views.image_list, name='video-list') #views def image_list(request, rel_model_tag, rel_object_id, action): return media_list(request, Image, rel_model_tag, rel_object_id, action) def video_list(request, rel_model_tag, rel_object_id, action): return media_list(request, Video, rel_model_tag, rel_object_id, action) def media_list(request, model, rel_model_tag, rel_object_id, action): rel_model = tag_to_model(rel_model_tag) rel_object = get_object_or_404(rel_model, pk=rel_object_id) if model == Image: star_media = rel_object.star_image else: star_media = rel_object.star_video filter_params = {} if rel_model == Event: filter_params['media__event'] = rel_object_id elif rel_model == Member: filter_params['media__members'] = rel_object_id elif rel_model == Crag: filter_params['media__crag'] = rel_object_id media_list = model.objects.filter(~Q(id=star_media.id)).filter(**filter_params).order_by('media__date_added').all() context = { 'media_list': media_list, 'star_media': star_media, } if action == 'edit': return media_edit_list(request, model, rel_model_tag, rel_model_id, context) return media_view_list(request, model, rel_model_tag, rel_model_id, context) def media_view_list(request, model, rel_model_tag, rel_object_id, context): if request.is_ajax(): context['base_template'] = 'boxes/base-lite.html' return render(request, 'media/list-items.html', context) def media_edit_list(request, model, rel_model_tag, rel_object_id, context): if model == Image: get_media_record = get_image_record else: get_media_record = get_video_record media_list = [get_media_record(media, rel_model_tag, rel_object_id) for media in context['media_list']] if context['star_media']: star_media = get_media_record(star_media, rel_model_tag, rel_object_id) star_media['starred'] = True else: star_media = None json = simplejson.dumps({ 'star_media': star_media, 'media_list': media_list, }) return HttpResponse(json, content_type=json_response_mimetype(request)) # models class Media(models.Model, WebModel): title = models.CharField('title', max_length=128, default='', db_index=True, blank=True) event = models.ForeignKey(Event, null=True, default=None, blank=True) crag = models.ForeignKey(Crag, null=True, default=None, blank=True) members = models.ManyToManyField(Member, blank=True) added_by = models.ForeignKey(Member, related_name='added_images') date_added = models.DateTimeField('date added', auto_now_add=True, null=True, default=None, editable=False) def __unicode__(self): return self.title def get_absolute_url(self): return self.image.url if self.image else self.video.embed_url class Image(Media): image = ProcessedImageField(upload_to='uploads', processors=[ResizeToFit(width=1024, height=1024, upscale=False)], format='JPEG', options={'quality': 75}) thumbnail_1 = ImageSpecField(source='image', processors=[SmartResize(width=178, height=134)], format='JPEG', options={'quality': 75}) thumbnail_2 = ImageSpecField(source='image', #processors=[SmartResize(width=256, height=192)], processors=[ResizeToFit(height=164)], format='JPEG', options={'quality': 75}) class Video(Media): url = models.URLField('url', max_length=256, default='') embed_url = models.URLField('embed url', max_length=256, default='', blank=True) author = models.CharField('author', max_length=64, default='', blank=True) thumbnail = ProcessedImageField(upload_to='uploads', processors=[ResizeToFit(width=1024, height=1024, upscale=False)], format='JPEG', options={'quality': 75}, null=True, default=None, blank=True) thumbnail_1 = ImageSpecField(source='thumbnail', processors=[SmartResize(width=178, height=134)], format='JPEG', options={'quality': 75}) thumbnail_2 = ImageSpecField(source='thumbnail', #processors=[SmartResize(width=256, height=192)], processors=[ResizeToFit(height=164)], format='JPEG', options={'quality': 75}) class Crag(models.Model, WebModel): name = models.CharField('name', max_length=64, default='', db_index=True) normalized_name = models.CharField('normalized name', max_length=64, default='', editable=False) type = models.IntegerField('crag type', null=True, default=None, choices=crag_types) description = models.TextField('description', default='', blank=True) country = models.ForeignKey('country', null=True, default=None) #TODO: make this not null when db enables it latitude = models.FloatField('latitude', null=True, default=None) longitude = models.FloatField('longitude', null=True, default=None) location_index = FixedCharField('location index', length=24, default='', editable=False, db_index=True) # handled by db, used for marker clustering added_by = models.ForeignKey('member', null=True, default=None) #route_count = models.IntegerField('route count', null=True, default=None, editable=False) date_created = models.DateTimeField('date created', auto_now_add=True, null=True, default=None, editable=False) last_modified = models.DateTimeField('last modified', auto_now=True, null=True, default=None, editable=False) star_image = models.OneToOneField('Image', null=True, default=None, related_name='star_crags', on_delete=models.SET_NULL) star_video = models.OneToOneField('Video', null=True, default=None, related_name='star_crags', on_delete=models.SET_NULL)

    Read the article

  • MySQL Memory usage

    - by Rob Stevenson-Leggett
    Our MySQL server seems to be using a lot of memory. I've tried looking for slow queries and queries with no index and have halved the peak CPU usage and Apache memory usage but the MySQL memory stays constantly at 2.2GB (~51% of available memory on the server). Here's the graph from Plesk. Running top in the SSH window shows the same figures. Does anyone have any ideas on why the memory usage is constant like this and not peaks and troughs with usage of the app? Here's the output of the MySQL Tuning Primer script: -- MYSQL PERFORMANCE TUNING PRIMER -- - By: Matthew Montgomery - MySQL Version 5.0.77-log x86_64 Uptime = 1 days 14 hrs 4 min 21 sec Avg. qps = 22 Total Questions = 3059456 Threads Connected = 13 Warning: Server has not been running for at least 48hrs. It may not be safe to use these recommendations To find out more information on how each of these runtime variables effects performance visit: http://dev.mysql.com/doc/refman/5.0/en/server-system-variables.html Visit http://www.mysql.com/products/enterprise/advisors.html for info about MySQL's Enterprise Monitoring and Advisory Service SLOW QUERIES The slow query log is enabled. Current long_query_time = 1 sec. You have 6 out of 3059477 that take longer than 1 sec. to complete Your long_query_time seems to be fine BINARY UPDATE LOG The binary update log is NOT enabled. You will not be able to do point in time recovery See http://dev.mysql.com/doc/refman/5.0/en/point-in-time-recovery.html WORKER THREADS Current thread_cache_size = 0 Current threads_cached = 0 Current threads_per_sec = 2 Historic threads_per_sec = 0 Threads created per/sec are overrunning threads cached You should raise thread_cache_size MAX CONNECTIONS Current max_connections = 100 Current threads_connected = 14 Historic max_used_connections = 20 The number of used connections is 20% of the configured maximum. Your max_connections variable seems to be fine. INNODB STATUS Current InnoDB index space = 6 M Current InnoDB data space = 18 M Current InnoDB buffer pool free = 0 % Current innodb_buffer_pool_size = 8 M Depending on how much space your innodb indexes take up it may be safe to increase this value to up to 2 / 3 of total system memory MEMORY USAGE Max Memory Ever Allocated : 2.07 G Configured Max Per-thread Buffers : 274 M Configured Max Global Buffers : 2.01 G Configured Max Memory Limit : 2.28 G Physical Memory : 3.84 G Max memory limit seem to be within acceptable norms KEY BUFFER Current MyISAM index space = 4 M Current key_buffer_size = 7 M Key cache miss rate is 1 : 40 Key buffer free ratio = 81 % Your key_buffer_size seems to be fine QUERY CACHE Query cache is supported but not enabled Perhaps you should set the query_cache_size SORT OPERATIONS Current sort_buffer_size = 2 M Current read_rnd_buffer_size = 256 K Sort buffer seems to be fine JOINS Current join_buffer_size = 132.00 K You have had 16 queries where a join could not use an index properly You should enable "log-queries-not-using-indexes" Then look for non indexed joins in the slow query log. If you are unable to optimize your queries you may want to increase your join_buffer_size to accommodate larger joins in one pass. Note! This script will still suggest raising the join_buffer_size when ANY joins not using indexes are found. OPEN FILES LIMIT Current open_files_limit = 1024 files The open_files_limit should typically be set to at least 2x-3x that of table_cache if you have heavy MyISAM usage. Your open_files_limit value seems to be fine TABLE CACHE Current table_cache value = 64 tables You have a total of 426 tables You have 64 open tables. Current table_cache hit rate is 1% , while 100% of your table cache is in use You should probably increase your table_cache TEMP TABLES Current max_heap_table_size = 16 M Current tmp_table_size = 32 M Of 15134 temp tables, 9% were created on disk Effective in-memory tmp_table_size is limited to max_heap_table_size. Created disk tmp tables ratio seems fine TABLE SCANS Current read_buffer_size = 128 K Current table scan ratio = 2915 : 1 read_buffer_size seems to be fine TABLE LOCKING Current Lock Wait ratio = 1 : 142213 Your table locking seems to be fine The app is a facebook game with about 50-100 concurrent users. Thanks, Rob

    Read the article

  • Base de Datos Oracle, su mejor opción para reducir costos de IT

    - by Ivan Hassig
    Por Victoria Cadavid Sr. Sales Cosultant Oracle Direct Uno de los principales desafíos en la administración de centros de datos es la reducción de costos de operación. A medida que las compañías crecen y los proveedores de tecnología ofrecen soluciones cada vez más robustas, conservar el equilibrio entre desempeño, soporte al negocio y gestión del Costo Total de Propiedad es un desafío cada vez mayor para los Gerentes de Tecnología y para los Administradores de Centros de Datos. Las estrategias más comunes para conseguir reducción en los costos de administración de Centros de Datos y en la gestión de Tecnología de una organización en general, se enfocan en la mejora del desempeño de las aplicaciones, reducción del costo de administración y adquisición de hardware, reducción de los costos de almacenamiento, aumento de la productividad en la administración de las Bases de Datos y mejora en la atención de requerimientos y prestación de servicios de mesa de ayuda, sin embargo, las estrategias de reducción de costos deben contemplar también la reducción de costos asociados a pérdida y robo de información, cumplimiento regulatorio, generación de valor y continuidad del negocio, que comúnmente se conciben como iniciativas aisladas que no siempre se adelantan con el ánimo de apoyar la reducción de costos. Una iniciativa integral de reducción de costos de TI, debe contemplar cada uno de los factores que  generan costo y pueden ser optimizados. En este artículo queremos abordar la reducción de costos de tecnología a partir de la adopción del que según los expertos es el motor de Base de Datos # del mercado.Durante años, la base de datos Oracle ha sido reconocida por su velocidad, confiabilidad, seguridad y capacidad para soportar cargas de datos tanto de aplicaciones altamente transaccionales, como de Bodegas de datos e incluso análisis de Big Data , ofreciendo alto desempeño y facilidades de administración, sin embrago, cuando pensamos en proyectos de reducción de costos de IT, además de la capacidad para soportar aplicaciones (incluso aplicaciones altamente transaccionales) con alto desempeño, pensamos en procesos de automatización, optimización de recursos, consolidación, virtualización e incluso alternativas más cómodas de licenciamiento. La Base de Datos Oracle está diseñada para proveer todas las capacidades que un área de tecnología necesita para reducir costos, adaptándose a los diferentes escenarios de negocio y a las capacidades y características de cada organización.Es así, como además del motor de Base de Datos, Oracle ofrece una serie de soluciones para optimizar la administración de la información a través de mecanismos de optimización del uso del storage, continuidad del Negocio, consolidación de infraestructura, seguridad y administración automática, que propenden por un mejor uso de los recursos de tecnología, ofrecen opciones avanzadas de configuración y direccionan la reducción de los tiempos de las tareas operativas más comunes. Una de las opciones de la base de datos que se pueden provechar para reducir costos de hardware es Oracle Real Application Clusters. Esta solución de clustering permite que varios servidores (incluso servidores de bajo costo) trabajen en conjunto para soportar Grids o Nubes Privadas de Bases de Datos, proporcionando los beneficios de la consolidación de infraestructura, los esquemas de alta disponibilidad, rápido desempeño y escalabilidad por demanda, haciendo que el aprovisionamiento, el mantenimiento de las bases de datos y la adición de nuevos nodos se lleve e cabo de una forma más rápida y con menos riesgo, además de apalancar las inversiones en servidores de menor costo. Otra de las soluciones que promueven la reducción de costos de Tecnología es Oracle In-Memory Database Cache que permite almacenar y procesar datos en la memoria de las aplicaciones, permitiendo el máximo aprovechamiento de los recursos de procesamiento de la capa media, lo que cobra mucho valor en escenarios de alta transaccionalidad. De este modo se saca el mayor provecho de los recursos de procesamiento evitando crecimiento innecesario en recursos de hardware. Otra de las formas de evitar inversiones innecesarias en hardware, aprovechando los recursos existentes, incluso en escenarios de alto crecimiento de los volúmenes de información es la compresión de los datos. Oracle Advanced Compression permite comprimir hasta 4 veces los diferentes tipos de datos, mejorando la capacidad de almacenamiento, sin comprometer el desempeño de las aplicaciones. Desde el lado del almacenamiento también se pueden conseguir reducciones importantes de los costos de IT. En este escenario, la tecnología propia de la base de Datos Oracle ofrece capacidades de Administración Automática del Almacenamiento que no solo permiten una distribución óptima de los datos en los discos físicos para garantizar el máximo desempeño, sino que facilitan el aprovisionamiento y la remoción de discos defectuosos y ofrecen balanceo y mirroring, garantizando el uso máximo de cada uno de los dispositivos y la disponibilidad de los datos. Otra de las soluciones que facilitan la administración del almacenamiento es Oracle Partitioning, una opción de la Base de Datos que permite dividir grandes tablas en estructuras más pequeñas. Esta aproximación facilita la administración del ciclo de vida de la información y permite por ejemplo, separar los datos históricos (que generalmente se convierten en información de solo lectura y no tienen un alto volumen de consulta) y enviarlos a un almacenamiento de bajo costos, conservando la data activa en dispositivos de almacenamiento más ágiles. Adicionalmente, Oracle Partitioning facilita la administración de las bases de datos que tienen un gran volumen de registros y mejora el desempeño de la base de datos gracias a la posibilidad de optimizar las consultas haciendo uso únicamente de las particiones relevantes de una tabla o índice en el proceso de búsqueda. Otros factores adicionales, que pueden generar costos innecesarios a los departamentos de Tecnología son: La pérdida, corrupción o robo de datos y la falta de disponibilidad de las aplicaciones para dar soporte al negocio. Para evitar este tipo de situaciones que pueden acarrear multas y pérdida de negocios y de dinero, Oracle ofrece soluciones que permiten proteger y auditar la base de datos, recuperar la información en caso de corrupción o ejecución de acciones que comprometan la integridad de la información y soluciones que permitan garantizar que la información de las aplicaciones tenga una disponibilidad de 7x24. Ya hablamos de los beneficios de Oracle RAC, para facilitar los procesos de Consolidación y mejorar el desempeño de las aplicaciones, sin embrago esta solución, es sumamente útil en escenarios dónde las organizaciones de quieren garantizar una alta disponibilidad de la información, ante fallo de los servidores o en eventos de desconexión planeada para realizar labores de mantenimiento. Además de Oracle RAC, existen soluciones como Oracle Data Guard y Active Data Guard que permiten replicar de forma automática las bases de datos hacia un centro de datos de contingencia, permitiendo una recuperación inmediata ante eventos que deshabiliten por completo un centro de datos. Además de lo anterior, Active Data Guard, permite aprovechar la base de datos de contingencia para realizar labores de consulta, mejorando el desempeño de las aplicaciones. Desde el punto de vista de mejora en la seguridad, Oracle cuenta con soluciones como Advanced security que permite encriptar los datos y los canales a través de los cueles se comparte la información, Total Recall, que permite visualizar los cambios realizados a la base de datos en un momento determinado del tiempo, para evitar pérdida y corrupción de datos, Database Vault que permite restringir el acceso de los usuarios privilegiados a información confidencial, Audit Vault, que permite verificar quién hizo qué y cuándo dentro de las bases de datos de una organización y Oracle Data Masking que permite enmascarar los datos para garantizar la protección de la información sensible y el cumplimiento de las políticas y normas relacionadas con protección de información confidencial, por ejemplo, mientras las aplicaciones pasan del ambiente de desarrollo al ambiente de producción. Como mencionamos en un comienzo, las iniciativas de reducción de costos de tecnología deben apalancarse en estrategias que contemplen los diferentes factores que puedan generar sobre costos, los factores de riesgo que puedan acarrear costos no previsto, el aprovechamiento de los recursos actuales, para evitar inversiones innecesarias y los factores de optimización que permitan el máximo aprovechamiento de las inversiones actuales. Como vimos, todas estas iniciativas pueden ser abordadas haciendo uso de la tecnología de Oracle a nivel de Base de Datos, lo más importante es detectar los puntos críticos a nivel de riesgo, diagnosticar las proporción en que están siendo aprovechados los recursos actuales y definir las prioridades de la organización y del área de IT, para así dar inicio a todas aquellas iniciativas que de forma gradual, van a evitar sobrecostos e inversiones innecesarias, proporcionando un mayor apoyo al negocio y un impacto significativo en la productividad de la organización. Más información http://www.oracle.com/lad/products/database/index.html?ssSourceSiteId=otnes 1Fuente: Market Share: All Software Markets, Worldwide 2011 by Colleen Graham, Joanne Correia, David Coyle, Fabrizio Biscotti, Matthew Cheung, Ruggero Contu, Yanna Dharmasthira, Tom Eid, Chad Eschinger, Bianca Granetto, Hai Hong Swinehart, Sharon Mertz, Chris Pang, Asheesh Raina, Dan Sommer, Bhavish Sood, Marianne D'Aquila, Laurie Wurster and Jie Zhang. - March 29, 2012 2Big Data: Información recopilada desde fuentes no tradicionales como blogs, redes sociales, email, sensores, fotografías, grabaciones en video, etc. que normalmente se encuentran de forma no estructurada y en un gran volumen

    Read the article

  • Benchmark MySQL Cluster using flexAsynch: No free node id found for mysqld(API)?

    - by quanta
    I am going to benchmark MySQL Cluster using flexAsynch follow this guide, details as below: mkdir /usr/local/mysqlc732/ cd /usr/local/src/mysql-cluster-gpl-7.3.2 cmake . -DCMAKE_INSTALL_PREFIX=/usr/local/mysqlc732/ -DWITH_NDB_TEST=ON make make install Everything works fine until this step: # /usr/local/mysqlc732/bin/flexAsynch -t 1 -p 80 -l 2 -o 100 -c 100 -n FLEXASYNCH - Starting normal mode Perform benchmark of insert, update and delete transactions 1 number of concurrent threads 80 number of parallel operation per thread 100 transaction(s) per round 2 iterations Load Factor is 80% 25 attributes per table 1 is the number of 32 bit words per attribute Tables are with logging Transactions are executed with hint provided No force send is used, adaptive algorithm used Key Errors are disallowed Temporary Resource Errors are allowed Insufficient Space Errors are disallowed Node Recovery Errors are allowed Overload Errors are allowed Timeout Errors are allowed Internal NDB Errors are allowed User logic reported Errors are allowed Application Errors are disallowed Using table name TAB0 NDBT_ProgramExit: 1 - Failed ndb_cluster.log: WARNING -- Failed to allocate nodeid for API at 127.0.0.1. Returned eror: 'No free node id found for mysqld(API).' I also have recompiled with -DWITH_DEBUG=1 -DWITH_NDB_DEBUG=1. How can I run flexAsynch in the debug mode? # /usr/local/mysqlc732/bin/flexAsynch -h FLEXASYNCH Perform benchmark of insert, update and delete transactions Arguments: -t Number of threads to start, default 1 -p Number of parallel transactions per thread, default 32 -o Number of transactions per loop, default 500 -l Number of loops to run, default 1, 0=infinite -load_factor Number Load factor in index in percent (40 -> 99) -a Number of attributes, default 25 -c Number of operations per transaction -s Size of each attribute, default 1 (PK is always of size 1, independent of this value) -simple Use simple read to read from database -dirty Use dirty read to read from database -write Use writeTuple in insert and update -n Use standard table names -no_table_create Don't create tables in db -temp Create table(s) without logging -no_hint Don't give hint on where to execute transaction coordinator -adaptive Use adaptive send algorithm (default) -force Force send when communicating -non_adaptive Send at a 10 millisecond interval -local 1 = each thread its own node, 2 = round robin on node per parallel trans 3 = random node per parallel trans -ndbrecord Use NDB Record -r Number of extra loops -insert Only run inserts on standard table -read Only run reads on standard table -update Only run updates on standard table -delete Only run deletes on standard table -create_table Only run Create Table of standard table -drop_table Only run Drop Table on standard table -warmup_time Warmup Time before measurement starts -execution_time Execution Time where measurement is done -cooldown_time Cooldown time after measurement completed -table Number of standard table, default 0

    Read the article

  • MySQL for Excel 1.1.0 GA has been released

    - by Javier Treviño
    The MySQL Windows Experience Team is proud to announce the release of MySQL for Excel version 1.1.0 GA, one of our newest products contained in the MySQL Installer suite. You can download it from our official Downloads page at http://dev.mysql.com/downloads/installer/. The 1.1.0 release of MySQL for Excel introduces the following features: Edit MySQL Data. Edit MySQL Data This may be the coolest feature so far; users will be able to edit the data in a MySQL table using MS Excel in a very friendly and intuitive way.  Edit Data supports inserting new rows, deleting existing rows and updating existing data as easy as playing with data in an Excel’s spreadsheet and pushing changes back to the server.  Also this version contains the following bug fixes: Enabled the following checkboxes in the Append Data's Advanced Options dialog and added code in the Append Data dialog to use the checkboxes as follows: Automatically store the column mapping for the given table     If checked the current mapping will be stored automatically after clicking the Append button if the append operation is successful and there is no mapping for the current connection.schema.table already; the new mapping is stored with a proposed name of Mapping. Reload stored column mapping for the selected table automatically     If checked the first Stored Mapping found where all column names in the source grid match all column names in the target grid is automatically selected and applied when the Append Data dialog is loaded. Fixed code in Append Data that applies a stored column mapping to skip target columns where the associated mapping is empty (saved as a -1). Enclosed the Add-In's startup code in a try-catch block in order to log any possible error thrown during startup; and added information messages to the log at the beginning of the Add-In's startup code and at the end of the shutdown code.  Also changed the wrapper method that calls the MySQLUtility to write messages to the log to make logging easier, thus changed the log call throughout all the code that contains a try-catch block. Added code to the main wix configuration file to check if a newer version is already installed and if so abort the installation Fixed code to refresh the Import Procedure Form's preview grid's data source to repaint its contents every time the Call button is pressed. Added code to re-pull connections after connections are migrated from Excel to Workbench. Fixed code so when the Append Data's Automatic Mapping is performed any subsequent change on a mapping resets the mapping to a Manual Mapping. Added code to the InfoDialog class to set the button text to "Show Details" or "Hide Details" depending on the status of the Details text container. Fixed a GUID in the main wix configuration file so now previous versions are uninstalled during a new installation. Added an option to the Export Data's Advanced Options dialog to remove columns with no data, by default the Export Dialog will only flag those columns as Excluded. Added code to display a warning and paint a column red if the column name in the Export Data dialog is not set, display a warning if the table name is not set, and stack warnings but not display them if a column is Excluded, warnings are displayed normally for columns if they are not Excluded anymore.  Added code to prevent the Append and Export of Data if more than 1 selection is made (selecting more than 1 area holding the Ctrl key while selecting Excel cells). Fixed problem that prevented MySQL for Excel from loading when Display settings in Windows 7 is set to Adjust to Best Performance (Oracle bug 14521405 - UNHANDLED EXCEPTION IS THROWN WHEN LOADING MYSQL FOR EXCEL). Fixed code that renames the auto-generated Primary Key column when the Table name changes since it was not detecting if a column with the same name already existed in the table. The column duplication was not actually happening, it looked that way because the automatically generated PK column was not detecting a column had that same name. Fixed code in Export Data dialog to always set an empty string instead of null to the MySQLDataColumn properties that stores MySQL data types (MySQLDataType, RowsFrom1stDataType and RowsFrom2ndDataType). Added code to display a warning and color red a column which Data Type has not been set by the user or has been manually cleared. Added code to output to the application log exception messages consistently in all places where exceptions are catched. A series of blog posts explaining the new Edit MySQL Data feature and the other existing features are coming in this blog. You can access the MySQL for Excel documentation at http://dev.mysql.com/doc/refman/5.5/en/mysql-for-excel.html You can also post questions on our MySQL for Excel forum found at http://forums.mysql.com/. You can also post questions on our MySQL for Excel forum found at http://forums.mysql.com/. Enjoy and thanks for the support!

    Read the article

  • NoSQL Java API for MySQL Cluster: Questions & Answers

    - by Mat Keep
    The MySQL Cluster engineering team recently ran a live webinar, available now on-demand demonstrating the ClusterJ and ClusterJPA NoSQL APIs for MySQL Cluster, and how these can be used in building real-time, high scale Java-based services that require continuous availability. Attendees asked a number of great questions during the webinar, and I thought it would be useful to share those here, so others are also able to learn more about the Java NoSQL APIs. First, a little bit about why we developed these APIs and why they are interesting to Java developers. ClusterJ and Cluster JPA ClusterJ is a Java interface to MySQL Cluster that provides either a static or dynamic domain object model, similar to the data model used by JDO, JPA, and Hibernate. A simple API gives users extremely high performance for common operations: insert, delete, update, and query. ClusterJPA works with ClusterJ to extend functionality, including - Persistent classes - Relationships - Joins in queries - Lazy loading - Table and index creation from object model By eliminating data transformations via SQL, users get lower data access latency and higher throughput. In addition, Java developers have a more natural programming method to directly manage their data, with a complete, feature-rich solution for Object/Relational Mapping. As a result, the development of Java applications is simplified with faster development cycles resulting in accelerated time to market for new services. MySQL Cluster offers multiple NoSQL APIs alongside Java: - Memcached for a persistent, high performance, write-scalable Key/Value store, - HTTP/REST via an Apache module - C++ via the NDB API for the lowest absolute latency. Developers can use SQL as well as NoSQL APIs for access to the same data set via multiple query patterns – from simple Primary Key lookups or inserts to complex cross-shard JOINs using Adaptive Query Localization Marrying NoSQL and SQL access to an ACID-compliant database offers developers a number of benefits. MySQL Cluster’s distributed, shared-nothing architecture with auto-sharding and real time performance makes it a great fit for workloads requiring high volume OLTP. Users also get the added flexibility of being able to run real-time analytics across the same OLTP data set for real-time business insight. OK – hopefully you now have a better idea of why ClusterJ and JPA are available. Now, for the Q&A. Q & A Q. Why would I use Connector/J vs. ClusterJ? A. Partly it's a question of whether you prefer to work with SQL (Connector/J) or objects (ClusterJ). Performance of ClusterJ will be better as there is no need to pass through the MySQL Server. A ClusterJ operation can only act on a single table (e.g. no joins) - ClusterJPA extends that capability Q. Can I mix different APIs (ie ClusterJ, Connector/J) in our application for different query types? A. Yes. You can mix and match all of the API types, SQL, JDBC, ODBC, ClusterJ, Memcached, REST, C++. They all access the exact same data in the data nodes. Update through one API and new data is instantly visible to all of the others. Q. How many TCP connections would a SessionFactory instance create for a cluster of 8 data nodes? A. SessionFactory has a connection to the mgmd (management node) but otherwise is just a vehicle to create Sessions. Without using connection pooling, a SessionFactory will have one connection open with each data node. Using optional connection pooling allows multiple connections from the SessionFactory to increase throughput. Q. Can you give details of how Cluster J optimizes sharding to enhance performance of distributed query processing? A. Each data node in a cluster runs a Transaction Coordinator (TC), which begins and ends the transaction, but also serves as a resource to operate on the result rows. While an API node (such as a ClusterJ process) can send queries to any TC/data node, there are performance gains if the TC is where most of the result data is stored. ClusterJ computes the shard (partition) key to choose the data node where the row resides as the TC. Q. What happens if we perform two primary key lookups within the same transaction? Are they sent to the data node in one transaction? A. ClusterJ will send identical PK lookups to the same data node. Q. How is distributed query processing handled by MySQL Cluster ? A. If the data is split between data nodes then all of the information will be transparently combined and passed back to the application. The session will connect to a data node - typically by hashing the primary key - which then interacts with its neighboring nodes to collect the data needed to fulfil the query. Q. Can I use Foreign Keys with MySQL Cluster A. Support for Foreign Keys is included in the MySQL Cluster 7.3 Early Access release Summary The NoSQL Java APIs are packaged with MySQL Cluster, available for download here so feel free to take them for a spin today! Key Resources MySQL Cluster on-line demo  MySQL ClusterJ and JPA On-demand webinar  MySQL ClusterJ and JPA documentation MySQL ClusterJ and JPA whitepaper and tutorial

    Read the article

  • SQL Table stored as a Heap - the dangers within

    - by MikeD
    Nearly all of the time I create a table, I include a primary key, and often that PK is implemented as a clustered index. Those two don't always have to go together, but in my world they almost always do. On a recent project, I was working on a data warehouse and a set of SSIS packages to import data from an OLTP database into my data warehouse. The data I was importing from the business database into the warehouse was mostly new rows, sometimes updates to existing rows, and sometimes deletes. I decided to use the MERGE statement to implement the insert, update or delete in the data warehouse, I found it quite performant to have a stored procedure that extracted all the new, updated, and deleted rows from the source database and dump it into a working table in my data warehouse, then run a stored proc in the warehouse that was the MERGE statement that took the rows from the working table and updated the real fact table. Use Warehouse CREATE TABLE Integration.MergePolicy (PolicyId int, PolicyTypeKey int, Premium money, Deductible money, EffectiveDate date, Operation varchar(5)) CREATE TABLE fact.Policy (PolicyKey int identity primary key, PolicyId int, PolicyTypeKey int, Premium money, Deductible money, EffectiveDate date) CREATE PROC Integration.MergePolicy as begin begin tran Merge fact.Policy as tgtUsing Integration.MergePolicy as SrcOn (tgt.PolicyId = Src.PolicyId) When not matched by Target then Insert (PolicyId, PolicyTypeKey, Premium, Deductible, EffectiveDate)values (src.PolicyId, src.PolicyTypeKey, src.Premium, src.Deductible, src.EffectiveDate) When matched and src.Operation = 'U' then Update set PolicyTypeKey = src.PolicyTypeKey,Premium = src.Premium,Deductible = src.Deductible,EffectiveDate = src.EffectiveDate When matched and src.Operation = 'D' then Delete ;delete from Integration.WorkPolicy commit end Notice that my worktable (Integration.MergePolicy) doesn't have any primary key or clustered index. I didn't think this would be a problem, since it was relatively small table and was empty after each time I ran the stored proc. For one of the work tables, during the initial loads of the warehouse, it was getting about 1.5 million rows inserted, processed, then deleted. Also, because of a bug in the extraction process, the same 1.5 million rows (plus a few hundred more each time) was getting inserted, processed, and deleted. This was being sone on a fairly hefty server that was otherwise unused, and no one was paying any attention to the time it was taking. This week I received a backup of this database and loaded it on my laptop to troubleshoot the problem, and of course it took a good ten minutes or more to run the process. However, what seemed strange to me was that after I fixed the problem and happened to run the merge sproc when the work table was completely empty, it still took almost ten minutes to complete. I immediately looked back at the MERGE statement to see if I had some sort of outer join that meant it would be scanning the target table (which had about 2 million rows in it), then turned on the execution plan output to see what was happening under the hood. Running the stored procedure again took a long time, and the plan output didn't show me much - 55% on the MERGE statement, and 45% on the DELETE statement, and table scans on the work table in both places. I was surprised at the relative cost of the DELETE statement, because there were really 0 rows to delete, but I was expecting to see the table scans. (I was beginning now to suspect that my problem was because the work table was being stored as a heap.) Then I turned on STATS_IO and ran the sproc again. The output was quite interesting.Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.Table 'Policy'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.Table 'MergePolicy'. Scan count 1, logical reads 433276, physical reads 60, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. I've reproduced the above from memory, the details aren't exact, but the essential bit was the very high number of logical reads on the table stored as a heap. Even just doing a SELECT Count(*) from Integration.MergePolicy incurred that sort of output, even though the result was always 0. I suppose I should research more on the allocation and deallocation of pages to tables stored as a heap, but I haven't, and my original assumption that a table stored as a heap with no rows would only need to read one page to answer any query was definitely proven wrong. It's likely that some sort of physical defragmentation of the table may have cleaned that up, but it seemed that the easiest answer was to put a clustered index on the table. After doing so, the execution plan showed a cluster index scan, and the IO stats showed only a single page read. (I aborted my first attempt at adding a clustered index on the table because it was taking too long - instead I ran TRUNCATE TABLE Integration.MergePolicy first and added the clustered index, both of which took very little time). I suspect I may not have noticed this if I had used TRUNCATE TABLE Integration.MergePolicy instead of DELETE FROM Integration.MergePolicy, since I'm guessing that the truncate operation does some rather quick releasing of pages allocated to the heap table. In the future, I will likely be much more careful to have a clustered index on every table I use, even the working tables. Mike  

    Read the article

< Previous Page | 49 50 51 52 53 54 55  | Next Page >