recursive query - Page 6

jQuery recursive function to upload many files while giving the user some feedback

- by checcco

Hi guys, I'm trying to write a jQuery function to let users upload many files at once. Here's the function I thought to give the user some feedback about the upload process progress. function uploadFiles(numbersOfFiles, start) { $("#info").html(start + " files uploaded"); $.post('upload.php', { start: start }, function (data) { start += 5; if (start < numbersOfFiles) { $("#info").html(start + " files uploaded"); uploadFiles(numbersOfFiles, start); } else { $("#info").html("All files have been uploaded"); } }); } The function calls a php script to upload 5 files, then if there are more files to upload it calls the script again. The whole process works. I've tried it with 100 files. The only thing that doesn't work is the #info div updating. The div get updated the first time and then again only to show "All files have been uploaded". So there's no feedback for the user about the uploading process. I can't understand why... Any help?

Read the article

Recursive SQL, have the last three values increased?

- by Mike

Hi, I have a table that looks like this: ---------------------- | DateTime | Value | ---------------------- | 2010-01-01 | 26 | | 2010-02-01 | 24 | | 2010-03-01 | 23 | | 2010-04-01 | 28 | | 2010-05-01 | 30 | I need to find if the last three consecutive months have increased in value. How can I get SQL to check whether the value for each has increased? If anyone can give me any pointers for solving this problem it would be greatly appreciated! Thanks

Read the article

C# recursive programming with lists

- by David Torrey

I am working on a program where each item can hold an array of items (i'm making a menu, which has a tree-like structure) currently i have the items as a list, instead of an array, but I don't feel like I'm using it to its full potential to simplify code. I chose a list over a standard array because the interface (.add, .remove, etc...) makes a lot of sense. I have code to search through the structure and return the path of the name (i.e. Item.subitem.subsubitem.subsubsubitem). Below is my code: public class Item { //public Item[] subitem; <-- Array of Items public List<Item> subitem; // <-- List of Items public Color itemColor = Color.FromArgb(50,50,200); public Rectangle itemSize = new Rectangle(0,0,64,64); public Bitmap itemBitmap = null; public string itemName; public string LocateItem(string searchName) { string tItemName = null; //if the item name matches the search parameter, send it up) if (itemName == searchName) { return itemName; } if (subitem != null) { //spiral down a level foreach (Item tSearchItem in subitem) { tItemName = tSearchItem.LocateItem(searchName); if (tItemName != null) break; //exit for if item was found } } //do name logic (use index numbers) //if LocateItem of the subitems returned nothing and the current item is not a match, return null (not found) if (tItemName == null && itemName != searchName) { return null; } //if it's not the item being searched for and the search item was found, change the string and return it up if (tItemName != null && itemName != searchName) { tItemName.Insert(0, itemName + "."); //insert the parent name on the left --> TopItem.SubItem.SubSubItem.SubSubSubItem return tItemName; } //default not found return null; } } My question is if there is an easier way to do this with lists? I've been going back and forth in my head as to whether I should use lists or just an array. The only reason I have a list is so that I don't have to make code to resize the array each time I add or remove an item.

Read the article

In MySQL, what is the most effective query design for joining large tables with many to many relatio

- by lighthouse65

In our application, we collect data on automotive engine performance -- basically source data on engine performance based on the engine type, the vehicle running it and the engine design. Currently, the basis for new row inserts is an engine on-off period; we monitor performance variables based on a change in engine state from active to inactive and vice versa. The related engineState table looks like this: +---------+-----------+---------------+---------------------+---------------------+-----------------+ | vehicle | engine | engine_state | state_start_time | state_end_time | engine_variable | +---------+-----------+---------------+---------------------+---------------------+-----------------+ | 080025 | E01 | active | 2008-01-24 16:19:15 | 2008-01-24 16:24:45 | 720 | | 080028 | E02 | inactive | 2008-01-24 16:19:25 | 2008-01-24 16:22:17 | 304 | +---------+-----------+---------------+---------------------+---------------------+-----------------+ For a specific analysis, we would like to analyze table content based on a row granularity of minutes, rather than the current basis of active / inactive engine state. For this, we are thinking of creating a simple productionMinute table with a row for each minute in the period we are analyzing and joining the productionMinute and engineEvent tables on the date-time columns in each table. So if our period of analysis is from 2009-12-01 to 2010-02-28, we would create a new table with 129,600 rows, one for each minute of each day for that three-month period. The first few rows of the productionMinute table: +---------------------+ | production_minute | +---------------------+ | 2009-12-01 00:00 | | 2009-12-01 00:01 | | 2009-12-01 00:02 | | 2009-12-01 00:03 | +---------------------+ The join between the tables would be engineState AS es LEFT JOIN productionMinute AS pm ON es.state_start_time <= pm.production_minute AND pm.production_minute <= es.event_end_time. This join, however, brings up multiple environmental issues: The engineState table has 5 million rows and the productionMinute table has 130,000 rows When an engineState row spans more than one minute (i.e. the difference between es.state_start_time and es.state_end_time is greater than one minute), as is the case in the example above, there are multiple productionMinute table rows that join to a single engineState table row When there is more than one engine in operation during any given minute, also as per the example above, multiple engineState table rows join to a single productionMinute row In testing our logic and using only a small table extract (one day rather than 3 months, for the productionMinute table) the query takes over an hour to generate. In researching this item in order to improve performance so that it would be feasible to query three months of data, our thoughts were to create a temporary table from the engineEvent one, eliminating any table data that is not critical for the analysis, and joining the temporary table to the productionMinute table. We are also planning on experimenting with different joins -- specifically an inner join -- to see if that would improve performance. What is the best query design for joining tables with the many:many relationship between the join predicates as outlined above? What is the best join type (left / right, inner)?

Read the article

SQL SERVER – Signal Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28

- by pinaldave

In this post, let’s delve a bit more in depth regarding wait stats. The very first question: when do the wait stats occur? Here is the simple answer. When SQL Server is executing any task, and if for any reason it has to wait for resources to execute the task, this wait is recorded by SQL Server with the reason for the delay. Later on we can analyze these wait stats to understand the reason the task was delayed and maybe we can eliminate the wait for SQL Server. It is not always possible to remove the wait type 100%, but there are few suggestions that can help. Before we continue learning about wait types and wait stats, we need to understand three important milestones of the query life-cycle. Running - a query which is being executed on a CPU is called a running query. This query is responsible for CPU time. Runnable – a query which is ready to execute and waiting for its turn to run is called a runnable query. This query is responsible for Signal Wait time. (In other words, the query is ready to run but CPU is servicing another query). Suspended – a query which is waiting due to any reason (to know the reason, we are learning wait stats) to be converted to runnable is suspended query. This query is responsible for wait time. (In other words, this is the time we are trying to reduce). In simple words, query execution time is a summation of the query Executing CPU Time (Running) + Query Wait Time (Suspended) + Query Signal Wait Time (Runnable). Again, it may be possible a query goes to all these stats multiple times. Let us try to understand the whole thing with a simple analogy of a taxi and a passenger. Two friends, Tom and Danny, go to the mall together. When they leave the mall, they decide to take a taxi. Tom and Danny both stand in the line waiting for their turn to get into the taxi. This is the Signal Wait Time as they are ready to get into the taxi but the taxis are currently serving other customer and they have to wait for their turn. In other word they are in a runnable state. Now when it is their turn to get into the taxi, the taxi driver informs them he does not take credit cards and only cash is accepted. Neither Tom nor Danny have enough cash, they both cannot get into the vehicle. Tom waits outside in the queue and Danny goes to ATM to fetch the cash. During this time the taxi cannot wait, they have to let other passengers get into the taxi. As Tom and Danny both are outside in the queue, this is the Query Wait Time and they are in the suspended state. They cannot do anything till they get the cash. Once Danny gets the cash, they are both standing in the line again, creating one more Signal Wait Time. This time when their turn comes they can pay the taxi driver in cash and reach their destination. The time taken for the taxi to get from the mall to the destination is running time (CPU time) and the taxi is running. I hope this analogy is bit clear with the wait stats. You can check the Signalwait stats using following query of Glenn Berry. -- Signal Waits for instance SELECT CAST(100.0 * SUM(signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%signal (cpu) waits], CAST(100.0 * SUM(wait_time_ms - signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%resource waits] FROM sys.dm_os_wait_stats OPTION (RECOMPILE); Higher the Signal wait stats are not good for the system. Very high value indicates CPU pressure. In my experience, when systems are running smooth and without any glitch the Signal wait stat is lower than 20%. Again, this number can be debated (and it is from my experience and is not documented anywhere). In other words, lower is better and higher is not good for the system. In future articles we will discuss in detail the various wait types and wait stats and their resolution. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

Read the article

SQL SERVER – Single Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28

- by pinaldave

In this post, let’s delve a bit more in depth regarding wait stats. The very first question: when do the wait stats occur? Here is the simple answer. When SQL Server is executing any task, and if for any reason it has to wait for resources to execute the task, this wait is recorded by SQL Server with the reason for the delay. Later on we can analyze these wait stats to understand the reason the task was delayed and maybe we can eliminate the wait for SQL Server. It is not always possible to remove the wait type 100%, but there are few suggestions that can help. Before we continue learning about wait types and wait stats, we need to understand three important milestones of the query life-cycle. Running - a query which is being executed on a CPU is called a running query. This query is responsible for CPU time. Runnable – a query which is ready to execute and waiting for its turn to run is called a runnable query. This query is responsible for Single Wait time. (In other words, the query is ready to run but CPU is servicing another query). Suspended – a query which is waiting due to any reason (to know the reason, we are learning wait stats) to be converted to runnable is suspended query. This query is responsible for wait time. (In other words, this is the time we are trying to reduce). In simple words, query execution time is a summation of the query Executing CPU Time (Running) + Query Wait Time (Suspended) + Query Single Wait Time (Runnable). Again, it may be possible a query goes to all these stats multiple times. Let us try to understand the whole thing with a simple analogy of a taxi and a passenger. Two friends, Tom and Danny, go to the mall together. When they leave the mall, they decide to take a taxi. Tom and Danny both stand in the line waiting for their turn to get into the taxi. This is the Signal Wait Time as they are ready to get into the taxi but the taxis are currently serving other customer and they have to wait for their turn. In other word they are in a runnable state. Now when it is their turn to get into the taxi, the taxi driver informs them he does not take credit cards and only cash is accepted. Neither Tom nor Danny have enough cash, they both cannot get into the vehicle. Tom waits outside in the queue and Danny goes to ATM to fetch the cash. During this time the taxi cannot wait, they have to let other passengers get into the taxi. As Tom and Danny both are outside in the queue, this is the Query Wait Time and they are in the suspended state. They cannot do anything till they get the cash. Once Danny gets the cash, they are both standing in the line again, creating one more Single Wait Time. This time when their turn comes they can pay the taxi driver in cash and reach their destination. The time taken for the taxi to get from the mall to the destination is running time (CPU time) and the taxi is running. I hope this analogy is bit clear with the wait stats. You can check the single wait stats using following query of Glenn Berry. -- Signal Waits for instance SELECT CAST(100.0 * SUM(signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%signal (cpu) waits], CAST(100.0 * SUM(wait_time_ms - signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%resource waits] FROM sys.dm_os_wait_stats OPTION (RECOMPILE); Higher the single wait stats are not good for the system. Very high value indicates CPU pressure. In my experience, when systems are running smooth and without any glitch the single wait stat is lower than 20%. Again, this number can be debated (and it is from my experience and is not documented anywhere). In other words, lower is better and higher is not good for the system. In future articles we will discuss in detail the various wait types and wait stats and their resolution. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

Read the article

Query returns too few rows

- by Tareq

setup: mysql> create table product_stock( product_id integer, qty integer, branch_id integer); Query OK, 0 rows affected (0.17 sec) mysql> create table product( product_id integer, product_name varchar(255)); Query OK, 0 rows affected (0.11 sec) mysql> insert into product(product_id, product_name) values(1, 'Apsana White DX Pencil'); Query OK, 1 row affected (0.05 sec) mysql> insert into product(product_id, product_name) values(2, 'Diamond Glass Marking Pencil'); Query OK, 1 row affected (0.03 sec) mysql> insert into product(product_id, product_name) values(3, 'Apsana Black Pencil'); Query OK, 1 row affected (0.03 sec) mysql> insert into product_stock(product_id, qty, branch_id) values(1, 100, 1); Query OK, 1 row affected (0.03 sec) mysql> insert into product_stock(product_id, qty, branch_id) values(1, 50, 2); Query OK, 1 row affected (0.03 sec) mysql> insert into product_stock(product_id, qty, branch_id) values(2, 80, 1); Query OK, 1 row affected (0.03 sec) my query: mysql> SELECT IFNULL(SUM(s.qty),0) AS stock, product_name FROM product_stock s RIGHT JOIN product p ON s.product_id=p.product_id WHERE branch_id=1 GROUP BY product_name ORDER BY product_name; returns: +-------+-------------------------------+ | stock | product_name | +-------+-------------------------------+ | 100 | Apsana White DX Pencil | | 80 | Diamond Glass Marking Pencil | +-------+-------------------------------+ 1 row in set (0.00 sec) But I want to have the following result: +-------+------------------------------+ | stock | product_name | +-------+------------------------------+ | 0 | Apsana Black Pencil | | 100 | Apsana White DX Pencil | | 80 | Diamond Glass Marking Pencil | +-------+------------------------------+ To get this result what mysql query should I run?

Read the article

How do I filter one of the columns in a SQL Server SQL Query

- by Kent S. Clarkson

I have a table (that relates to a number of other tables) where I would like to filter ONE of the columns (RequesterID) - that column will be a combobox where only people that are not sales people should be selectable. Here is the "unfiltered" query, lets call it QUERY 1: SELECT RequestsID, RequesterID, ProductsID FROM dbo.Requests If using a separate query, lets call it QUERY 2, to filter RequesterID (which is a People related column, connected to People.PeopleID), it would look like this: SELECT People.PeopleID FROM People INNER JOIN Roles ON People.RolesID = Roles.RolesID INNER JOIN Requests ON People.PeopleID = Requests.RequesterID WHERE (Roles.Role <> N'SalesGuy') ORDER BY Requests.RequestsID Now, is there a way of "merging" the QUERY 2 into QUERY 1? (dbo.Requests in QUERY 1 has RequesterID populated as a Foreign Key from dbo.People, so no problem there... The connections are all right, just not know how to write the SQL query!)

Read the article

MySQL slow query log logging all queries

- by Blanka

We have a MySQL 5.1.52 Percona Server 11.6 instance that suddenly started logging every single query to the slow query log. The long_query_time configuration is set to 1, yet, suddenly we're seeing every single query (e.g. just saw one that took 0.000563s!). As a result, our log files are growing at an insane pace. We just had to truncate a 180G slow query log file. I tried setting the long_query_time variable to a really large number to see if it stopped altogether (1000000), but same result. show global variables like 'general_log%'; +------------------+--------------------------+ | Variable_name | Value | +------------------+--------------------------+ | general_log | OFF | | general_log_file | /usr2/mysql/data/db4.log | +------------------+--------------------------+ 2 rows in set (0.00 sec) show global variables like 'slow_query_log%'; +---------------------------------------+-------------------------------+ | Variable_name | Value | +---------------------------------------+-------------------------------+ | slow_query_log | ON | | slow_query_log_file | /usr2/mysql/data/db4-slow.log | | slow_query_log_microseconds_timestamp | OFF | +---------------------------------------+-------------------------------+ 3 rows in set (0.00 sec) show global variables like 'long%'; +-----------------+----------+ | Variable_name | Value | +-----------------+----------+ | long_query_time | 1.000000 | +-----------------+----------+ 1 row in set (0.00 sec)

Read the article

Recursive Query Help

- by Josh

I have two tables in my database schema that represent an entity having a many-to-many relationship with itself. Role --------------------- +RoleID +Name RoleHasChildRole --------------------- +ParentRoleID +ChildRoleID Essentially, I need to to be able to write a query such that: Given a set of roles, return the unique set of all related roles recursively. This is an MSSQL 2008 Database.

Read the article

How can I optimize this subqueried and Joined MySQL Query?

- by kevzettler

Read the article

SQL query: Delete a entry which is not present in a join table?

- by Mestika

Hi, I’m going to delete all users which has no subscription but I seem to run into problems each time I try to detect the users. My schemas look like this: Users = {userid, name} Subscriptionoffering = {userid, subscriptionname} Now, what I’m going to do is to delete all users in the user table there has a count of zero in the subscriptionoffering table. Or said in other words: All users which userid is not present in the subscriptionoffering table. I’ve tried with different queries but with no result. I’ve tried to say where user.userid <> subscriptionoffering.userid, but that doesn’t seem to work. Do anyone know how to create the correct query? Thanks Mestika

Read the article

Is it easier to write a recursive-descent parser using an EBNF or a BNF?

- by Vivin Paliath

I've got a BNF and EBNF for a grammar. The BNF is obviously more verbose. I have a fairly good idea as far as using the BNF to build a recursive-descent parser; there are many resources for this. I am having trouble finding resources to convert an EBNF to a recursive-descent parser. Is this because it's more difficult? I recall from my CS theory classes that we went over EBNFs, but we didn't go over converting them into a recursive-descent parser. We did go over converting BNF's into a recursive-descent parser. The reason I'm asking is because the EBNF is more compact. From looking at the EBNF's in general, I notice that terms enclosed between { and } can be converted into a while loop. Are there any other guidelines or rules?

Read the article

Slow MySQL Query not using filesort

- by Canadaka

I have a query on my homepage that is getting slower and slower as my database table grows larger. tablename = tweets_cache rows = 572,327 this is the query I'm currently using that is slow, over 5 seconds. SELECT * FROM tweets_cache t WHERE t.province='' AND t.mp='0' ORDER BY t.published DESC LIMIT 50; If I take out either the WHERE or the ORDER BY, then the query is super fast 0.016 seconds. I have the following indexes on the tweets_cache table. PRIMARY published mp category province author So i'm not sure why its not using the indexes since mp, provice and published all have indexes? Doing a profile of the query shows that its not using an index to sort the query and is using filesort which is really slow. possible_keys = mp,province Extra = Using where; Using filesort I tried adding a new multie-colum index with "profiles & mp". The explain shows that this new index listed under "possible_keys" and "key", but the query time is unchanged, still over 5 seconds. Here is a screenshot of the profiler info on the query. http://i355.photobucket.com/albums/r469/canadaka_bucket/slow_query_profile.png Something weird, I made a dump of my database to test on my local desktop so i don't screw up the live site. The same query on my local runs super fast, milliseconds. So I copied all the same mysql startup variables from the server to my local to make sure there wasn't some setting that might be causing this. But even after that the local query runs super fast, but the one on the live server is over 5 seconds. My database server is only using around 800MB of the 4GB it has available. here are the related my.ini settings i'm using default-storage-engine = MYISAM max_connections = 800 skip-locking key_buffer = 512M max_allowed_packet = 1M table_cache = 512 sort_buffer_size = 4M read_buffer_size = 4M read_rnd_buffer_size = 16M myisam_sort_buffer_size = 64M thread_cache_size = 8 query_cache_size = 128M # Try number of CPU's*2 for thread_concurrency thread_concurrency = 8 # Disable Federated by default skip-federated key_buffer = 512M sort_buffer_size = 256M read_buffer = 2M write_buffer = 2M key_buffer = 512M sort_buffer_size = 256M read_buffer = 2M write_buffer = 2M

Read the article

Is there anything else I can do to optimize this MySQL query?

- by Legend

I have two tables, Table A with 700,000 entries and Table B with 600,000 entries. The structure is as follows: Table A: +-----------+---------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-----------+---------------------+------+-----+---------+----------------+ | id | bigint(20) unsigned | NO | PRI | NULL | auto_increment | | number | bigint(20) unsigned | YES | | NULL | | +-----------+---------------------+------+-----+---------+----------------+ Table B: +-------------+---------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-------------+---------------------+------+-----+---------+----------------+ | id | bigint(20) unsigned | NO | PRI | NULL | auto_increment | | number_s | bigint(20) unsigned | YES | MUL | NULL | | | number_e | bigint(20) unsigned | YES | MUL | NULL | | | source | varchar(50) | YES | | NULL | | +-------------+---------------------+------+-----+---------+----------------+ I am trying to find if any of the values in Table A are present in Table B using the following code: $sql = "SELECT number from TableA"; $result = mysql_query($sql) or die(mysql_error()); while($row = mysql_fetch_assoc($result)) { $number = $row['number']; $sql = "SELECT source, count(source) FROM TableB WHERE number_s < $number AND number_e > $number GROUP BY source"; $re = mysql_query($sql) or die(mysql_error); while($ro = mysql_fetch_array($re)) { echo $number."\t".$ro[0]."\t".$ro[1]."\n"; } } I was hoping that the query would go fast but then for some reason, it isn't terrible fast. My explain on the select (with a particular value of "number") gives me the following: mysql> explain SELECT source, count(source) FROM TableB WHERE number_s < 1812194440 AND number_e > 1812194440 GROUP BY source; +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ | 1 | SIMPLE | TableB | ALL | number_s,number_e | NULL | NULL | NULL | 696325 | Using where; Using temporary; Using filesort | +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ 1 row in set (0.00 sec) Is there any optimization that I can squeeze out of this? I tried writing a stored procedure for the same task but it doesn't even seem to work in the first place... It doesn't give any syntax errors... I tried running it for a day and it was still running which felt odd. CREATE PROCEDURE Filter() Begin DECLARE number BIGINT UNSIGNED; DECLARE x INT; DECLARE done INT DEFAULT 0; DECLARE cur1 CURSOR FOR SELECT number FROM TableA; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; CREATE TEMPORARY TABLE IF NOT EXISTS Flags(number bigint unsigned, count int(11)); OPEN cur1; hist_loop: LOOP FETCH cur1 INTO number; SELECT count(*) from TableB WHERE number_s < number AND number_e > number INTO x; IF done = 1 THEN LEAVE hist_loop; END IF; IF x IS NOT NULL AND x>0 THEN INSERT INTO Flags(number, count) VALUES(number, x); END IF; END LOOP hist_loop; CLOSE cur1; END

Read the article

Linq-to-sql Compiled Query is returning result from different DataContext

- by Vladimir Kojic

Compiled query: public static Func<OperationalDataContext, short, Machine> QueryMachineById = CompiledQuery.Compile((OperationalDataContext db, short machineID) => db.Machines.Where(m => m.MachineID == machineID).SingleOrDefault()); It looks like compiled query is caching Machine object and returning the same object even if query is called from new DataContext (I’m disposing DataContext in the service but I’m getting Machine from previous DataContext). I use POCOs and XML mapping. Revised: It looks like compiled query is returning result from new data context and it is not using the one that I passed in compiled-query. Therefore I can not reuse returned object and link it to another object obtained from datacontext thru non compiled queries. I’m using unit of work pattern : // First Call Using(new DataContext) { Machine from DataContext.Table == machine from cached query } // Do some work // Second Call is failing Using(new DataContext) { Machine from DataContext.Table <> machine from cached query }

Read the article

Light-weight, free, database query tool for Windows?

- by NoCatharsis

My question is very similar to the one here except pertaining to a Windows tool. I am also referencing this table and what I found here with a Google search. However, I have no idea which tool would best meet my (very basic) purposes. I am currently using Excel with a basic ODBC connection string to query my database at work. However, Excel is pretty memory-heavy and a basic query tends to throw my computer into a 30 second stall-a-thon. Is there a free tool out there that is light-weight and can serve the same purpose when provided an ODBC connection and a SQL query? Also would prefer that it easily copies over to a spreadsheet as needed.

Read the article

Visual Query Builder

- by johnnyArt

If been using "dbForge Query Builder" lately and I'm gotten used to the ease of building and testing a query, specially for those complex ones with inner joins, aliases and multiple conditionals. The expiry date of the trial is about to come, and while wanting to remain on the legal side for this I'd rather not pay the 50USD it costs (although I must say it's pretty cheap for what it does). So my question would be: Are there any free alternatives to replace this visual query builder? I've failed to find any and fear that my only two options are paying for it, or going to the dark side.

Read the article

Query optimization using composite indexes

- by xmarch

Many times, during the process of creating a new Coherence application, developers do not pay attention to the way cache queries are constructed; they only check that these queries comply with functional specs. Later, performance testing shows that these perform poorly and it is then when developers start working on improvements until the non-functional performance requirements are met. This post describes the optimization process of a real-life scenario, where using a composite attribute index has brought a radical improvement in query execution times. The execution times went down from 4 seconds to 2 milliseconds! E-commerce solution based on Oracle ATG – Endeca In the context of a new e-commerce solution based on Oracle ATG – Endeca, Oracle Coherence has been used to calculate and store SKU prices. In this architecture, a Coherence cache stores the final SKU prices used for Endeca baseline indexing. Each SKU price is calculated from a base SKU price and a series of calculations based on information from corporate global discounts. Corporate global discounts information is stored in an auxiliary Coherence cache with over 800.000 entries. In particular, to obtain each price the process needs to execute six queries over the global discount cache. After the implementation was finished, we discovered that the most expensive steps in the price calculation discount process were the global discounts cache query. This query has 10 parameters and is executed 6 times for each SKU price calculation. The steps taken to optimise this query are described below; Starting point Initial query was: String filter = "levelId = :iLevelId AND salesCompanyId = :iSalesCompanyId AND salesChannelId = :iSalesChannelId "+ "AND departmentId = :iDepartmentId AND familyId = :iFamilyId AND brand = :iBrand AND manufacturer = :iManufacturer "+ "AND areaId = :iAreaId AND endDate >= :iEndDate AND startDate <= :iStartDate"; Map<String, Object> params = new HashMap<String, Object>(10); // Fill all parameters. params.put("iLevelId", xxxx); // Executing filter. Filter globalDiscountsFilter = QueryHelper.createFilter(filter, params); NamedCache globalDiscountsCache = CacheFactory.getCache(CacheConstants.GLOBAL_DISCOUNTS_CACHE_NAME); Set applicableDiscounts = globalDiscountsCache.entrySet(globalDiscountsFilter); With the small dataset used for development the cache queries performed very well. However, when carrying out performance testing with a real-world sample size of 800,000 entries, each query execution was taking more than 4 seconds. First round of optimizations The first optimisation step was the creation of separate Coherence index for each of the 10 attributes used by the filter. This avoided object deserialization while executing the query. Each index was created as follows: globalDiscountsCache.addIndex(new ReflectionExtractor("getXXX" ) , false, null); After adding these indexes the query execution time was reduced to between 450 ms and 1s. However, these execution times were still not good enough. Second round of optimizations In this optimisation phase a Coherence query explain plan was used to identify how many entires each index reduced the results set by, along with the cost in ms of executing that part of the query. Though the explain plan showed that all the indexes for the query were being used, it also showed that the ordering of the query parameters was "sub-optimal". Parameters associated to object attributes with high-cardinality should appear at the beginning of the filter, or more specifically, the attributes that filters out the highest of number records should be placed at the beginning. But examining corporate global discount data we realized that depending on the values of the parameters used in the query the “good” order for the attributes was different. In particular, if the attributes brand and family had specific values it was more optimal to have a different query changing the order of the attributes. Ultimately, we ended up with three different optimal variants of the query that were used in its relevant cases: String filter = "brand = :iBrand AND familyId = :iFamilyId AND departmentId = :iDepartmentId AND levelId = :iLevelId "+ "AND manufacturer = :iManufacturer AND endDate >= :iEndDate AND salesCompanyId = :iSalesCompanyId "+ "AND areaId = :iAreaId AND salesChannelId = :iSalesChannelId AND startDate <= :iStartDate"; String filter = "familyId = :iFamilyId AND departmentId = :iDepartmentId AND levelId = :iLevelId AND brand = :iBrand "+ "AND manufacturer = :iManufacturer AND endDate >= :iEndDate AND salesCompanyId = :iSalesCompanyId "+ "AND areaId = :iAreaId AND salesChannelId = :iSalesChannelId AND startDate <= :iStartDate"; String filter = "brand = :iBrand AND departmentId = :iDepartmentId AND familyId = :iFamilyId AND levelId = :iLevelId "+ "AND manufacturer = :iManufacturer AND endDate >= :iEndDate AND salesCompanyId = :iSalesCompanyId "+ "AND areaId = :iAreaId AND salesChannelId = :iSalesChannelId AND startDate <= :iStartDate"; Using the appropriate query depending on the value of brand and family parameters the query execution time dropped to between 100 ms and 150 ms. But these these execution times were still not good enough and the solution was cumbersome. Third and last round of optimizations The third and final optimization was to introduce a composite index. However, this did mean that it was not possible to use the Coherence Query Language (CohQL), as composite indexes are not currently supporte in CohQL. As the original query had 8 parameters using EqualsFilter, 1 using GreaterEqualsFilter and 1 using LessEqualsFilter, the composite index was built for the 8 attributes using EqualsFilter. The final query had an EqualsFilter for the multiple extractor, a GreaterEqualsFilter and a LessEqualsFilter for the 2 remaining attributes. All individual indexes were dropped except the ones being used for LessEqualsFilter and GreaterEqualsFilter. We were now running in an scenario with an 8-attributes composite filter and 2 single attribute filters. The composite index created was as follows: ValueExtractor[] ve = { new ReflectionExtractor("getSalesChannelId" ), new ReflectionExtractor("getLevelId" ), new ReflectionExtractor("getAreaId" ), new ReflectionExtractor("getDepartmentId" ), new ReflectionExtractor("getFamilyId" ), new ReflectionExtractor("getManufacturer" ), new ReflectionExtractor("getBrand" ), new ReflectionExtractor("getSalesCompanyId" )}; MultiExtractor me = new MultiExtractor(ve); NamedCache globalDiscountsCache = CacheFactory.getCache(CacheConstants.GLOBAL_DISCOUNTS_CACHE_NAME); globalDiscountsCache.addIndex(me, false, null); And the final query was: ValueExtractor[] ve = { new ReflectionExtractor("getSalesChannelId" ), new ReflectionExtractor("getLevelId" ), new ReflectionExtractor("getAreaId" ), new ReflectionExtractor("getDepartmentId" ), new ReflectionExtractor("getFamilyId" ), new ReflectionExtractor("getManufacturer" ), new ReflectionExtractor("getBrand" ), new ReflectionExtractor("getSalesCompanyId" )}; MultiExtractor me = new MultiExtractor(ve); // Fill composite parameters.String SalesCompanyId = xxxx;...AndFilter composite = new AndFilter(new EqualsFilter(me, Arrays.asList(iSalesChannelId, iLevelId, iAreaId, iDepartmentId, iFamilyId, iManufacturer, iBrand, SalesCompanyId)), new GreaterEqualsFilter(new ReflectionExtractor("getEndDate" ), iEndDate)); AndFilter finalFilter = new AndFilter(composite, new LessEqualsFilter(new ReflectionExtractor("getStartDate" ), iStartDate)); NamedCache globalDiscountsCache = CacheFactory.getCache(CacheConstants.GLOBAL_DISCOUNTS_CACHE_NAME); Set applicableDiscounts = globalDiscountsCache.entrySet(finalFilter); Using this composite index the query improved dramatically and the execution time dropped to between 2 ms and 4 ms. These execution times completely met the non-functional performance requirements . It should be noticed than when using the composite index the order of the attributes inside the ValueExtractor was not relevant.

Read the article

Problem in HQL query

- by Rupeshit

I written a query in my sql like this: "select * from table_name order by col_name = 101 desc " Which is working perfectly fine in mysql but when I tried to convert this query into HQl query then it is throwing an exception.So can anyone suggest me that how to write HQL query for the above SQL query.

Read the article

SQLAlchemy custom query column

- by thrillerator

I have a declarative table defined like this: class Transaction(Base): __tablename__ = "transactions" id = Column(Integer, primary_key=True) account_id = Column(Integer) transfer_account_id = Column(Integer) amount = Column(Numeric(12, 2)) ... The query should be: SELECT id, (CASE WHEN transfer_account_id=1 THEN -amount ELSE amount) AS amount FROM transactions WHERE account_id = 1 OR transfer_account_id = 1 My code is: query = Transaction.query.filter_by(account_id=1, transfer_account_id=1) query = query.add_column(func.case(...).label("amount") But it doesn't replace the amount column. Been trying to do this with for hours and I don't want to use raw SQL.

Read the article

how to design the query for non-sql database

- by Benny

can anyone give some reference for non-sql database query interface design pattern? For sql-based database, the query can be achieved by combining the query token. but for non-sql, how to design the query, given that the query could be very complex.

Read the article

SQL SERVER – Identify Most Resource Intensive Queries – SQL in Sixty Seconds #028 – Video

- by pinaldave

During performance tuning conversation the very first question people often ask is what are the queries offending the server or in another word let us identify the queries which are the most resource intensive. The resources are often described as either Memory, CPU or IO. When we talk about the queries the same is applicable for them as well. The query which is doing lots of reads or writes are for sure resource intensive as well query which are taking maximum CPU time. Performance tuning is a very deep subject and we all have our own preference regarding what should be the first step to tuning and what should be looked with the salt of grain. Though there is no denying that a query which uses more resources than what it should be using for sure require tuning. There are many ways to do identify query using intense resources (e.g. Extended events etc) but in this one we will go by simple DMV. There is a small gotcha we all have to remember about usage of DMV is that it only brings back results from existing cache. So if you have a query which is very resource intensive but is not cached or if you have explicitly removed the query from the cache it will be not part of the result returned by this DMV. It is quite possible that a query is aged and removed from the cache if your cache is not huge. If your cache is large you may want to be careful in running this query during business hours as this query itself can be resource intensive. Get Script to identify resource intensive query from Here Related Tips in SQL in Sixty Seconds: SQL SERVER – Find Most Expensive Queries Using DMV Simple Example to Configure Resource Governor – Introduction to Resource Governor SQL SERVER – DMV – sys.dm_exec_query_optimizer_info – Statistics of Optimizer SQL SERVER – Wait Stats – Wait Types – Wait Queues – Day 0 of 28 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Query, SQL Scripts, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Excel

Read the article

Query Months help

- by StealthRT

Hey all i am in need of some helpful tips/advice on how to go about my problem. I have a database that houses a "signup" table. The date for this table is formated as such: 2010-04-03 00:00:00 Now suppose i have 10 records in this database: 2010-04-03 00:00:00 2010-01-01 00:00:00 2010-06-22 00:00:00 2010-02-08 00:00:00 2010-02-05 00:00:00 2010-03-08 00:00:00 2010-09-29 00:00:00 2010-11-16 00:00:00 2010-04-09 00:00:00 2010-05-21 00:00:00 And i wanted to get each months total registers... so following the example above: Jan = 1 Feb = 2 Mar = 1 Apr = 2 May = 1 Jun = 1 Jul = 0 Aug = 0 Sep = 1 Oct = 0 Nov = 1 Dec = 0 Now how can i use a query to do that but not have to use a query like: WHERE left(date, 7) = '2010-01' and keep doing that 12 times? I would like it to be a single query call and just have it place the months visits into a array like so: do until EOF theMonthArray[0] = "total for jan" theMonthArray[1] = "total for feb" theMonthArray[2] = "total for mar" theMonthArray[3] = "total for apr" ...etc loop I just can not think of a way to do that other than the example i posted with the 12 query called-one for each month. This is my query as of right now. Again, this only populates for one month where i am trying to populate all 12 months all at once. SELECT count(idNumber) as numVisits, theAccount, signUpDate, theActive from userinfo WHERE theActive = 'YES' AND idNumber = '0203' AND theAccount = 'SUB' AND left(signUpDate, 7) = '2010-04' GROUP BY idNumber ORDER BY numVisits; The example query above outputs this: numVisits | theAccount | signUpDate | theActive 2 SUB 2010-04-16 00:00:00 YES Which is correct because i have 2 records within the month of April. But again, i am trying to do all 12 months at one time (in a single query) so i do not tax the database server as much when compared to doing 12 different query's... UPDATE I'm looking to do something like along these lines: if NOT rst.EOF if left(rst("signUpDate"), 7) = "2010-01" then theMonthArray[0] = rst("numVisits") end if if left(rst("signUpDate"), 7) = "2010-02" then theMonthArray[1] = rst("numVisits") end if etc etc.... end if Any help would be great! :) David

Read the article

SQL select descendants of a row

- by Joey Adams

Suppose a tree structure is implemented in SQL like this: CREATE TABLE nodes ( id INTEGER PRIMARY KEY, parent INTEGER -- references nodes(id) ); Although cycles can be created in this representation, let's assume we never let that happen. The table will only store a collection of roots (records where parent is null) and their descendants. The goal is to, given an id of a node on the table, find all nodes that are descendants of it. A is a descendant of B if either A's parent is B or A's parent is a descendant of B. Note the recursive definition. Here is some sample data: INSERT INTO nodes VALUES (1, NULL); INSERT INTO nodes VALUES (2, 1); INSERT INTO nodes VALUES (3, 2); INSERT INTO nodes VALUES (4, 3); INSERT INTO nodes VALUES (5, 3); INSERT INTO nodes VALUES (6, 2); which represents: 1 `-- 2 |-- 3 | |-- 4 | `-- 5 | `-- 6 We can select the (immediate) children of 1 by doing this: SELECT a.* FROM nodes AS a WHERE parent=1; We can select the children and grandchildren of 1 by doing this: SELECT a.* FROM nodes AS a WHERE parent=1 UNION ALL SELECT b.* FROM nodes AS a, nodes AS b WHERE a.parent=1 AND b.parent=a.id; We can select the children, grandchildren, and great grandchildren of 1 by doing this: SELECT a.* FROM nodes AS a WHERE parent=1 UNION ALL SELECT b.* FROM nodes AS a, nodes AS b WHERE a.parent=1 AND b.parent=a.id UNION ALL SELECT c.* FROM nodes AS a, nodes AS b, nodes AS c WHERE a.parent=1 AND b.parent=a.id AND c.parent=b.id; How can a query be constructed that gets all descendants of node 1 rather than those at a finite depth? It seems like I would need to create a recursive query or something. I'd like to know if such a query would be possible using SQLite. However, if this type of query requires features not available in SQLite, I'm curious to know if it can be done in other SQL databases.

Search Results

Search found 18245 results on 730 pages for 'recursive query'.

Page 6/730 | < Previous Page | 2 3 4 5 6 7 8 9 10 11 12 13 | Next Page >

- by checcco

- by Mike

- by David Torrey

- by lighthouse65

- by pinaldave

- by pinaldave

- by Tareq

- by Kent S. Clarkson

- by Blanka

- by Josh

- by kevzettler

- by Mestika

- by Vivin Paliath

- by Canadaka

- by Legend

- by Vladimir Kojic

- by NoCatharsis

- by johnnyArt

- by xmarch

- by Rupeshit

- by thrillerator

- by Benny

- by pinaldave

- by StealthRT

- by Joey Adams

< Previous Page | 2 3 4 5 6 7 8 9 10 11 12 13 | Next Page >