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  • I have written an SQL query but I want to optimize it [closed]

    - by ankit gupta
    is there any way to do this using minimum no of joins and select? 2 tables are involved in this operation transaction_pci_details and transaction SELECT t6.transaction_pci_details_id, t6.terminal_id, t6.transaction_no, t6.transaction_id, t6.transaction_type, t6.reversal_flag, t6.transmission_date_time, t6.retrivel_ref_no, t6.card_no,t6.card_type, t6.expires_on, t6.transaction_amount, t6.currency_code, t6.response_code, t6.action_code, t6.message_reason_code, t6.merchant_id, t6.auth_code, t6.actual_trans_amnt, t6.bal_card_amnt, t5.sales_person_id FROM TRANSACTION AS t5 INNER JOIN ( SELECT t4.transaction_pci_details_id, t4.terminal_id, t4.transaction_no, t4.transaction_id, t4.transaction_type, t4.reversal_flag, t4.transmission_date_time, t4.retrivel_ref_no, t4.card_no, t4.card_type, t4.expires_on, t4.transaction_amount, t4.currency_code, t4.response_code, t4.action_code, t3.message_reason_code, t4.merchant_id, t4.auth_code, t4.actual_trans_amnt, t4.bal_card_amnt FROM ( SELECT* FROM transaction_pci_details WHERE message_reason_code LIKE '%OUT%'|| message_reason_code LIKE '%FAILED%' /*we can add date here*/ UNION ALL SELECT t2.transaction_pci_details_id, t2.terminal_id, t2.transaction_no, t2.transaction_id, t2.transaction_type, t2.reversal_flag, t2.transmission_date_time, t2.retrivel_ref_no, t2.card_no, t2.card_type, t2.expires_on, t2.transaction_amount, t2.currency_code, t2.response_code, t2.action_code, t2.message_reason_code, t2.merchant_id, t2.auth_code, t2.actual_trans_amnt, t2.bal_card_amnt FROM ( SELECT transaction_id FROM TRANSACTION WHERE transaction_type_id = 8 ) AS t1 INNER JOIN ( SELECT * FROM transaction_pci_details WHERE message_reason_code LIKE '%appro%' /*we can add date here*/ ) AS t2 ON t1.transaction_id = t2.transaction_id ) AS t3 INNER JOIN ( SELECT* FROM transaction_pci_details WHERE action_code LIKE '%REQ%' /*we can add date here*/ ) AS t4 ON t3.transaction_pci_details_id - t4.transaction_pci_details_id = 1 ) AS t6 ON t5.transaction_id = t6.transaction_id

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  • Error 404 after rewrite query strings with htaccess

    - by Cristian
    I'm trying to redirect the URLs of a client's website like this: www.localsite.com/immobile.php?id_immobile=24 In something like this: www.localsite.com/immobile/24.php I'm using this rule in .htaccess but it returns a 404 error page. RewriteEngine On RewriteCond %{QUERY_STRING} ^id_immobile=([0-9]*)$ RewriteRule ^immobile\.php$ http://localsite.com/immobile/%1.php? [L] I have tried many other rules, but none work. What can I do?

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  • Sub query pass through

    - by SQL and the like
    Occasionally in forums and on client sites I see conditional subqueries in statements. This is where the developer has decided that it is only necessary to process some data under a certain condition.  By way of example, something like this : Create Procedure GetOrder @SalesOrderId integer, @CountDetails tinyint as Select SOH.salesorderid , case when @CountDetails = 1 then (Select count(*) from Sales.SalesOrderDetail SOD where SOH.SalesOrderID = SOD.SalesOrderID) end from sales.SalesOrderHeader...(read more)

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  • Does the order of columns in a query matter?

    - by James Simpson
    When selecting columns from a MySQL table, is performance affected by the order that you select the columns as compared to their order in the table (not considering indexes that may cover the columns)? For example, you have a table with rows uid, name, bday, and you have the following query. SELECT uid, name, bday FROM table Does MySQL see the following query any differently and thus cause any sort of performance hit? SELECT uid, bday, name FROM table

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  • Problem with JMX query of Coherence node MBeans visible in JConsole

    - by Quinn Taylor
    I'm using JMX to build a custom tool for monitoring remote Coherence clusters at work. I'm able to connect just fine and query MBeans directly, and I've acquired nearly all the information I need. However, I've run into a snag when trying to query MBeans for specific caches within a cluster, which is where I can find stats about total number of gets/puts, average time for each, etc. The MBeans I'm trying to access programatically are visible when I connect to the remote process using JConsole, and have names like this: Coherence:type=Cache,service=SequenceQueue,name=SEQ%GENERATOR,nodeId=1,tier=back It would make it more flexible if I can dynamically grab all type=Cache MBeans for a particular node ID without specifying all the caches. I'm trying to query them like this: QueryExp specifiedNodeId = Query.eq(Query.attr("nodeId"), Query.value(nodeId)); QueryExp typeIsCache = Query.eq(Query.attr("type"), Query.value("Cache")); QueryExp cacheNodes = Query.and(specifiedNodeId, typeIsCache); ObjectName coherence = new ObjectName("Coherence:*"); Set<ObjectName> cacheMBeans = mBeanServer.queryMBeans(coherence, cacheNodes); However, regardless of whether I use queryMBeans() or queryNames(), the query returns a Set containing... ...0 objects if I pass the arguments shown above ...0 objects if I pass null for the first argument ...all MBeans in the Coherence:* domain (112) if I pass null for the second argument ...every single MBean (128) if I pass null for both arguments The first two results are the unexpected ones, and suggest a problem in the QueryExp I'm passing, but I can't figure out what the problem is. I even tried just passing typeIsCache or specifiedNodeId for the second parameter (with either coherence or null as the first parameter) and I always get 0 results. I'm pretty green with JMX — any insight on what the problem is? (FYI, the monitoring tool will be run on Java 5, so things like JMX 2.0 won't help me at this point.)

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  • MySQL select query result set changes based on column order

    - by user197191
    I have a drupal 7 site using the Views module to back-end site content search results. The same query with the same dataset returns different results from MySQL 5.5.28 to MySQL 5.6.14. The results from 5.5.28 are the correct, expected results. The results from 5.6.14 are not. If, however, I simply move a column in the select statement, the query returns the correct results. Here is the code-generated query in question (modified for readability). I apologize for the length; I couldn't find a way to reproduce it without the whole query: SELECT DISTINCT node_node_revision.nid AS node_node_revision_nid, node_revision.title AS node_revision_title, node_field_revision_field_position_institution_ref.nid AS node_field_revision_field_position_institution_ref_nid, node_revision.vid AS vid, node_revision.nid AS node_revision_nid, node_node_revision.title AS node_node_revision_title, SUM(search_index.score * search_total.count) AS score, 'node' AS field_data_field_system_inst_name_node_entity_type, 'node' AS field_revision_field_position_college_division_node_entity_t, 'node' AS field_revision_field_position_department_node_entity_type, 'node' AS field_revision_field_search_lvl_degree_lvls_node_entity_type, 'node' AS field_revision_field_position_app_deadline_node_entity_type, 'node' AS field_revision_field_position_start_date_node_entity_type, 'node' AS field_revision_body_node_entity_type FROM node_revision node_revision LEFT JOIN node node_node_revision ON node_revision.nid = node_node_revision.nid LEFT JOIN field_revision_field_position_institution_ref field_revision_field_position_institution_ref ON node_revision.vid = field_revision_field_position_institution_ref.revision_id AND (field_revision_field_position_institution_ref.entity_type = 'node' AND field_revision_field_position_institution_ref.deleted = '0') LEFT JOIN node node_field_revision_field_position_institution_ref ON field_revision_field_position_institution_ref.field_position_institution_ref_target_id = node_field_revision_field_position_institution_ref.nid LEFT JOIN field_revision_field_position_cip_code field_revision_field_position_cip_code ON node_revision.vid = field_revision_field_position_cip_code.revision_id AND (field_revision_field_position_cip_code.entity_type = 'node' AND field_revision_field_position_cip_code.deleted = '0') LEFT JOIN node node_field_revision_field_position_cip_code ON field_revision_field_position_cip_code.field_position_cip_code_target_id = node_field_revision_field_position_cip_code.nid LEFT JOIN node node_node_revision_1 ON node_revision.nid = node_node_revision_1.nid LEFT JOIN field_revision_field_position_vacancy_status field_revision_field_position_vacancy_status ON node_revision.vid = field_revision_field_position_vacancy_status.revision_id AND (field_revision_field_position_vacancy_status.entity_type = 'node' AND field_revision_field_position_vacancy_status.deleted = '0') LEFT JOIN search_index search_index ON node_revision.nid = search_index.sid LEFT JOIN search_total search_total ON search_index.word = search_total.word WHERE ( ( (node_node_revision.status = '1') AND (node_node_revision.type IN ('position')) AND (field_revision_field_position_vacancy_status.field_position_vacancy_status_target_id IN ('38')) AND( (search_index.type = 'node') AND( (search_index.word = 'accountant') ) ) AND ( (node_revision.vid=node_node_revision.vid AND node_node_revision.status=1) ) ) ) GROUP BY search_index.sid, vid, score, field_data_field_system_inst_name_node_entity_type, field_revision_field_position_college_division_node_entity_t, field_revision_field_position_department_node_entity_type, field_revision_field_search_lvl_degree_lvls_node_entity_type, field_revision_field_position_app_deadline_node_entity_type, field_revision_field_position_start_date_node_entity_type, field_revision_body_node_entity_type HAVING ( ( (COUNT(*) >= '1') ) ) ORDER BY node_node_revision_title ASC LIMIT 20 OFFSET 0; Again, this query returns different sets of results from MySQL 5.5.28 (correct) to 5.6.14 (incorrect). If I move the column named "score" (the SUM() column) to the end of the column list, the query returns the correct set of results in both versions of MySQL. My question is: Is this expected behavior (and why), or is this a bug? I'm on the verge of reverting my entire environment back to 5.5 because of this.

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  • 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)?

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  • 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

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  • 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

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  • Meta package / quick reference for string manipulation commands?

    - by Dylan McCall
    The latest version of the Scribes text editor lets us select some text, hit Alt+X, and then run an arbitrary command. For example, I can run the sort command and the selected text is replaced appropriately. This is quite useful but I am also not very well-versed in awk and the like. Is there something I can grab that will provide more of these commands like sort? Maybe a package with a whole bunch of handy, task-specific string manipulation commands?

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  • SQL Server and the XML Data Type : Data Manipulation

    The introduction of the xml data type, with its own set of methods for processing xml data, made it possible for SQL Server developers to create columns and variables of the type xml. Deanna Dicken examines the modify() method, which provides for data manipulation of the XML data stored in the xml data type via XML DML statements.

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  • 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?

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  • 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!)

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  • 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)

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  • How can I optimize this subqueried and Joined MySQL Query?

    - by kevzettler
    I'm pretty green on mysql and I need some tips on cleaning up a query. It is used in several variations through out a site. Its got some subquerys derived tables and fun going on. Heres the query: # Query_time: 2 Lock_time: 0 Rows_sent: 0 Rows_examined: 0 SELECT * FROM ( SELECT products . *, categories.category_name AS category, ( SELECT COUNT( * ) FROM distros WHERE distros.product_id = products.product_id) AS distro_count, (SELECT COUNT(*) FROM downloads WHERE downloads.product_id = products.product_id AND WEEK(downloads.date) = WEEK(curdate())) AS true_downloads, (SELECT COUNT(*) FROM views WHERE views.product_id = products.product_id AND WEEK(views.date) = WEEK(curdate())) AS true_views FROM products INNER JOIN categories ON products.category_id = categories.category_id ORDER BY created_date DESC, true_views DESC ) AS count_table WHERE count_table.distro_count > 0 AND count_table.status = 'published' AND count_table.active = 1 LIMIT 0, 8 Heres the explain: +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ | 1 | PRIMARY | <derived2> | ALL | NULL | NULL | NULL | NULL | 232 | Using where | | 2 | DERIVED | categories | index | PRIMARY | idx_name | 47 | NULL | 13 | Using index; Using temporary; Using filesort | | 2 | DERIVED | products | ref | category_id | category_id | 4 | digizald_db.categories.category_id | 9 | | | 5 | DEPENDENT SUBQUERY | views | ref | product_id | product_id | 4 | digizald_db.products.product_id | 46 | Using where | | 4 | DEPENDENT SUBQUERY | downloads | ref | product_id | product_id | 4 | digizald_db.products.product_id | 14 | Using where | | 3 | DEPENDENT SUBQUERY | distros | ref | product_id | product_id | 4 | digizald_db.products.product_id | 1 | Using index | +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ 6 rows in set (0.04 sec) And the Tables: mysql> describe products; +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ | Field | Type | Null | Key | Default | Extra | +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ | product_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_key | char(32) | NO | | NULL | | | title | varchar(150) | NO | | NULL | | | company | varchar(150) | NO | | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | description | text | NO | | NULL | | | video_code | text | NO | | NULL | | | category_id | int(10) unsigned | NO | MUL | NULL | | | price | decimal(10,2) | NO | | NULL | | | quantity | int(10) unsigned | NO | | NULL | | | downloads | int(10) unsigned | NO | | NULL | | | views | int(10) unsigned | NO | | NULL | | | status | enum('pending','published','rejected','removed') | NO | | NULL | | | active | tinyint(1) | NO | | NULL | | | deleted | tinyint(1) | NO | | NULL | | | created_date | datetime | NO | | NULL | | | modified_date | timestamp | NO | | CURRENT_TIMESTAMP | | | scrape_source | varchar(215) | YES | | NULL | | +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ 18 rows in set (0.00 sec) mysql> describe categories -> ; +------------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+------------------+------+-----+---------+----------------+ | category_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | category_name | varchar(45) | NO | MUL | NULL | | | parent_id | int(10) unsigned | YES | MUL | NULL | | | category_type_id | int(10) unsigned | NO | | NULL | | +------------------+------------------+------+-----+---------+----------------+ 4 rows in set (0.00 sec) mysql> describe compatibilities -> ; +------------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+------------------+------+-----+---------+----------------+ | compatibility_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | name | varchar(45) | NO | | NULL | | | code_name | varchar(45) | NO | | NULL | | | description | varchar(128) | NO | | NULL | | | position | int(10) unsigned | NO | | NULL | | +------------------+------------------+------+-----+---------+----------------+ 5 rows in set (0.01 sec) mysql> describe distros -> ; +------------------+--------------------------------------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+--------------------------------------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | compatibility_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | | NULL | | | status | enum('pending','published','rejected','removed') | NO | | NULL | | | distro_type | enum('file','url') | NO | | NULL | | | version | varchar(150) | NO | | NULL | | | filename | varchar(50) | YES | | NULL | | | url | varchar(250) | YES | | NULL | | | virus | enum('READY','PASS','FAIL') | YES | | NULL | | | downloads | int(10) unsigned | NO | | 0 | | +------------------+--------------------------------------------------+------+-----+---------+----------------+ 11 rows in set (0.01 sec) mysql> describe downloads; +------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | distro_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | ip_address | varchar(15) | NO | | NULL | | | date | datetime | NO | | NULL | | +------------+------------------+------+-----+---------+----------------+ 6 rows in set (0.01 sec) mysql> describe views -> ; +------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | ip_address | varchar(15) | NO | | NULL | | | date | datetime | NO | | NULL | | +------------+------------------+------+-----+---------+----------------+ 5 rows in set (0.00 sec)

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  • Pro/con of using Angular directives for complex form validation/ GUI manipulation

    - by tengen
    I am building a new SPA front end to replace an existing enterprise's legacy hodgepodge of systems that are outdated and in need of updating. I am new to angular, and wanted to see if the community could give me some perspective. I'll state my problem, and then ask my question. I have to generate several series of check boxes based on data from a .js include, with data like this: $scope.fieldMappings.investmentObjectiveMap = [ {'id':"CAPITAL PRESERVATION", 'name':"Capital Preservation"}, {'id':"STABLE", 'name':"Moderate"}, {'id':"BALANCED", 'name':"Moderate Growth"}, // etc {'id':"NONE", 'name':"None"} ]; The checkboxes are created using an ng-repeat, like this: <div ng-repeat="investmentObjective in fieldMappings.investmentObjectiveMap"> ... </div> However, I needed the values represented by the checkboxes to map to a different model (not just 2-way-bound to the fieldmappings object). To accomplish this, I created a directive, which accepts a destination array destarray which is eventually mapped to the model. I also know I need to handle some very specific gui controls, such as unchecking "None" if anything else gets checked, or checking "None" if everything else gets unchecked. Also, "None" won't be an option in every group of checkboxes, so the directive needs to be generic enough to accept a validation function that can fiddle with the checked state of the checkbox group's inputs based on what's already clicked, but smart enough not to break if there is no option called "NONE". I started to do that by adding an ng-click which invoked a function in the controller, but in looking around Stack Overflow, I read people saying that its bad to put DOM manipulation code inside your controller - it should go in directives. So do I need another directive? So far: (html): <input my-checkbox-group type="checkbox" fieldobj="investmentObjective" ng-click="validationfunc()" validationfunc="clearOnNone()" destarray="investor.investmentObjective" /> Directive code: .directive("myCheckboxGroup", function () { return { restrict: "A", scope: { destarray: "=", // the source of all the checkbox values fieldobj: "=", // the array the values came from validationfunc: "&" // the function to be called for validation (optional) }, link: function (scope, elem, attrs) { if (scope.destarray.indexOf(scope.fieldobj.id) !== -1) { elem[0].checked = true; } elem.bind('click', function () { var index = scope.destarray.indexOf(scope.fieldobj.id); if (elem[0].checked) { if (index === -1) { scope.destarray.push(scope.fieldobj.id); } } else { if (index !== -1) { scope.destarray.splice(index, 1); } } }); } }; }) .js controller snippet: .controller( 'SuitabilityCtrl', ['$scope', function ( $scope ) { $scope.clearOnNone = function() { // naughty jQuery DOM manipulation code that // looks at checkboxes and checks/unchecks as needed }; The above code is done and works fine, except the naughty jquery code in clearOnNone(), which is why I wrote this question. And here is my question: after ALL this, I think to myself - I could be done already if I just manually handled all this GUI logic and validation junk with jQuery written in my controller. At what point does it become foolish to write these complicated directives that future developers will have to puzzle over more than if I had just written jQuery code that 99% of us would understand with a glance? How do other developers draw the line? I see this all over Stack Overflow. For example, this question seems like it could be answered with a dozen lines of straightforward jQuery, yet he has opted to do it the angular way, with a directive and a partial... it seems like a lot of work for a simple problem. Specifically, I suppose I would like to know: how SHOULD I be writing the code that checks whether "None" has been selected (if it exists as an option in this group of checkboxes), and then check/uncheck the other boxes accordingly? A more complex directive? I can't believe I'm the only developer that is having to implement code that is more complex than needed just to satisfy an opinionated framework.

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