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  • nginx: how do I track down a random 500 from nginx (not my application). Potentially has something to do with load?

    - by kaleidomedallion
    We recently had some 500's from nginx itself that somehow were not logged (we have screenshots, but nothing in the logs). That is weird in itself, because usually errors show up there. Regardless, I am wondering if there is something like a connection pool size that if maxed out would result in a 500? We have correlated it potentially to a recent spike in traffic, but it is not conclusive. Anyone have any ideas of how to begin to approach such an issue?

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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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  • Are CK Metrics still considered useful? Is there an open source tool to help?

    - by DeveloperDon
    Chidamber & Kemerer proposed several metrics for object oriented code. Among them, depth of inheritance tree, weighted number of methods, number of member functions, number of children, and coupling between objects. Using a base of code, they tried to correlated these metrics to the defect density and maintenance effort using covariant analysis. Are these metrics actionable in projects? Perhaps they can guide refactoring. For example weighted number of methods might show which God classes needed to be broken into more cohesive classes that address a single concern. Is there approach superseded by a better method, and is there a tool that can identify problem code, particularly in moderately large project being handed off to a new developer or team?

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  • My Latest Hare-Brained Scheme

    - by Liam McLennan
    I have not had a significant side project for a while but I have been working on a product idea. Its an analytics application that analyses twitter data and reports on market sentiment. The target market is companies who want to track trends in consumer sentiment. My idea is to teach the application to divide relevant tweets into ‘positive’ and ‘negative’ categories. If the input was the set of tweets featuring the word ‘telstra’ the application would find the following tweet:   and put it in the ‘negative’ category. Collecting data in this fashion facilitates the creation of graphs such as: which can then be correlated against events, such as a share offer or new product release. I may go ahead and build this, just because I am a programmer and it amuses me to do so. My concerns are: There  is no market for this tool There is a market, but I don’t understand it and have no way to reach it.

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  • Page load speeds effect on crawl rate

    - by Sam Pegler
    We've noticed a big drop in the total pages crawled per day on our site, we have no control over the crawl rate in google webmaster tools so it's possible this has been changed by google. However it's a fairly large site and I wouldn't of thought that the crawl rate would've been decreased. What we have noticed though is a sizeable increase in page load times, in my mind this would be the cause. Can anyone else confirm if the crawl rate is directly correlated to page load time? Seems logical, longer page load time, less pages crawled. Any decent documentation on this would be appreciated, I don't normally have any input on SEO so this is new to me.

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  • Program to Help Order Undated Photos

    - by Richard
    I have a large number of photos which have the correct DateTimeOriginal set in EXIF. I have about 300 photos for which the DateTimeOriginal is completely wrong. The DateTimeOriginals of these photos are not correlated, so I cannot change their time en masse. It must be done individually. I'm looking for a program that would essentially allow me to drag and drop the incorrectly time stamped photos into their place in the sequence of correctly time stamped photos. It would be nice to then be able to have the DateTimeOriginal tag updated, or the photos renamed chronologically. Thanks!

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  • Twitter Customer Sentiment Analysis

    - by Liam McLennan
    The breakable toy that I am currently working on is a twitter customer sentiment analyser. It scrapes twitter for tweets relating to a particular organisation, applies a machine learning algorithm to determine if the content of tweet is positive or negative, and generates reports of the sentiment data over time, correlated to dates, events and news feeds. I’m having lots of fun building this, but I would also like to learn if there is a market for quantified sentiment data. So that I can start to show people what I have in mind I have created a mockup of the simplest and most important report. It shows customer sentiment over time, with important events highlighted. As the user moves their mouse to the right (forward in time) the source data area scrolls up to display the tweets from that time. The tweets are colour coded based on sentiment rating. After I started working on this project I discovered that a team of students have already built something similar. It is a lot of fun to enter your employers name and see what it says.

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  • ProgrammingError when aggregating over an annotated & grouped Django ORM query

    - by ento
    I'm trying to construct a query to get the "average, maximum, minimum number of items purchased by a single user". The data source is this simple sales record table: class SalesRecord(models.Model): id = models.IntegerField(primary_key=True) user_id = models.IntegerField() product_code = models.CharField() price = models.IntegerField() created_at = models.DateTimeField() A new record is inserted into this table for every item purchased by a user. Here's my attempt at building the query: q = SalesRecord.objects.all() q = q.values('user_id').annotate( # group by user and count the # of records count=Count('id'), # (= # of items) ).order_by() result = q.aggregate(Max('count'), Min('count'), Avg('count')) When I try to execute the code, a ProgrammingError is raised at the last line: (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'FROM (SELECT sales_records.user_id AS user_id, COUNT(sales_records.`' at line 1") Django's error screen shows that the SQL is SELECT FROM (SELECT `sales_records`.`player_id` AS `player_id`, COUNT(`sales_records`.`id`) AS `count` FROM `sales_records` WHERE (`sales_records`.`created_at` >= %s AND `sales_records`.`created_at` <= %s ) GROUP BY `sales_records`.`player_id` ORDER BY NULL) subquery It's not selecting anything! Can someone please show me the right way to do this? Hacking Django I've found that clearing the cache of selected fields in django.db.models.sql.BaseQuery.get_aggregation() seems to solve the problem. Though I'm not really sure this is a fix or a workaround. @@ -327,10 +327,13 @@ # Remove any aggregates marked for reduction from the subquery # and move them to the outer AggregateQuery. + self._aggregate_select_cache = None + self.aggregate_select_mask = None for alias, aggregate in self.aggregate_select.items(): if aggregate.is_summary: query.aggregate_select[alias] = aggregate - del obj.aggregate_select[alias] + if alias in obj.aggregate_select: + del obj.aggregate_select[alias] ... yields result: {'count__max': 267, 'count__avg': 26.2563, 'count__min': 1}

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  • Linq Getting Customers group by date and then by their type

    - by Nitin varpe
    I am working on generating report for showing customer using LINQ in C#. I want to show no. of customers of each type. There are 3 types of customer registered, guest and manager. I want to group by customers by registered date and then by type of customer. i.e If today 3 guest, 4 registered and 2 manager are inserted. and tomorrow 4,5 and 6 are registered resp. then report should show Number of customers registerd on the day . separate row for each type. DATE TYPEOF CUSTOMER COUNT 31-10-2013 GUEST 3 31-10-2013 REGISTERED 4 31-10-2013 MANAGER 2 30-10-2013 GUEST 5 30-10-2013 REGISTERED 10 30-10-2013 MANAGER 3 LIKE THIS . var subquery = from eat in _customerRepo.Table group eat by new { yy = eat.CreatedOnUTC.Value.Year, mm = eat.CreatedOnUTC.Value.Month, dd = eat.CreatedOnUTC.Value.Day } into g select new { Id = g.Min(x => x.Id) }; var query = from c in _customerRepo.Table join cin in subquery.Distinct() on c.Id equals cin.Id select c; By above query I get minimum cutomers registerd on that day Thanks in advance

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  • wordpress generating slow mysql queries - is it index problem?

    - by tash
    Hello Stack Overflow I've got very slow Mysql queries coming up from my wordpress site. It's making everything slow and I think this is eating up CPU usage. I've pasted the Explain results for the two most frequently problematic queries below. This is a typical result - although very occasionally teh queries do seem to be performed at a more normal speed. I have the usual wordpress indexes on the database tables. You will see that one of the queries is generated from wordpress core code, and not from anything specific - like the theme - for my site. I have a vague feeling that the database is not always using the indexes/is not using them properly... Is this right? Does anyone know how to fix it? Or is it a different problem entirely? Many thanks in advance for any help anyone can offer - it is hugely appreciated Query: [wp-blog-header.php(14): wp()] SELECT SQL_CALC_FOUND_ROWS wp_posts.* FROM wp_posts WHERE 1=1 AND wp_posts.post_type = 'post' AND (wp_posts.post_status = 'publish' OR wp_posts.post_status = 'private') ORDER BY wp_posts.post_date DESC LIMIT 0, 6 id select_type table type possible_keys key key_len ref rows Extra 1 SIMPLE wp_posts ref type_status_date type_status_date 63 const 427 Using where; Using filesort Query time: 34.2829 (ms) 9) Query: [wp-content/themes/LMHR/index.php(40): query_posts()] SELECT SQL_CALC_FOUND_ROWS wp_posts.* FROM wp_posts WHERE 1=1 AND wp_posts.ID NOT IN ( SELECT tr.object_id FROM wp_term_relationships AS tr INNER JOIN wp_term_taxonomy AS tt ON tr.term_taxonomy_id = tt.term_taxonomy_id WHERE tt.taxonomy = 'category' AND tt.term_id IN ('217', '218', '223', '224') ) AND wp_posts.post_type = 'post' AND (wp_posts.post_status = 'publish' OR wp_posts.post_status = 'private') ORDER BY wp_posts.post_date DESC LIMIT 0, 6 id select_type table type possible_keys key key_len ref rows Extra 1 PRIMARY wp_posts ref type_status_date type_status_date 63 const 427 Using where; Using filesort 2 DEPENDENT SUBQUERY tr ref PRIMARY,term_taxonomy_id PRIMARY 8 func 1 Using index 2 DEPENDENT SUBQUERY tt eq_ref PRIMARY,term_id_taxonomy,taxonomy PRIMARY 8 antin1_lovemusic2010.tr.term_taxonomy_id 1 Using where Query time: 70.3900 (ms)

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  • SQL - table alias scope.

    - by Support - multilanguage SO
    I've just learned ( yesterday ) to use "exists" instead of "in". BAD select * from table where nameid in ( select nameid from othertable where otherdesc = 'SomeDesc' ) GOOD select * from table t where exists ( select nameid from othertable o where t.nameid = o.nameid and otherdesc = 'SomeDesc' ) And I have some questions about this: 1) The explanation as I understood was: "The reason why this is better is because only the matching values will be returned instead of building a massive list of possible results". Does that mean that while the first subquery might return 900 results the second will return only 1 ( yes or no )? 2) In the past I have had the RDBMS complainin: "only the first 1000 rows might be retrieved", this second approach would solve that problem? 3) What is the scope of the alias in the second subquery?... does the alias only lives in the parenthesis? for example select * from table t where exists ( select nameid from othertable o where t.nameid = o.nameid and otherdesc = 'SomeDesc' ) AND select nameid from othertable o where t.nameid = o.nameid and otherdesc = 'SomeOtherDesc' ) That is, if I use the same alias ( o for table othertable ) In the second "exist" will it present any problem with the first exists? or are they totally independent? Is this something Oracle only related or it is valid for most RDBMS? Thanks a lot

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  • Linq-to-sql join/where?

    - by Curtis White
    I've the following table structures Users id Types id isBool UsersTypes userid types I want to select all the UserTypes based on id and isBool. I tried this query var q = from usertype in usertypes from type in types where type.isBool == false where userstypes.user == id select usertype; But this did not work as expected. My questions are: Why? Is there any difference in using the join on syntax vs where, where vs where cond1 && cond2? My understanding is query optimizer will optimize. Is there any difference in using where cond1 == var1 && cond2 == var2 with and without the parenthesis? This seems peculiar that it is possible to build this without parenthesis What type of query do I need in this case? I can see that I could do a subquery or use a group but not 100% sure if it is required. An example might be helpful. I'm thinking a subquery may be required in this case.

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  • Can I use @table variable in SQL Server Report Builder?

    - by edosoft
    Using MS SQL 2008 Reporting services: I'm trying to write a report that displays some correlated data so I thought to use a @table variable like so DECLARE @Results TABLE (Number int, Name nvarchar(250), Total1 money, Total2 money) insert into @Results(Number, Name, Total1) select number, name, sum(total) from table1 group by number, name update @Results set total2 = total from (select number, sum(total) from table2) s where s.number = number select from @results However, Report Builder keeps asking to enter a value for the variable @Results. It this at all possible?

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  • Basics of Join Predicate Pushdown in Oracle

    - by Maria Colgan
    Happy New Year to all of our readers! We hope you all had a great holiday season. We start the new year by continuing our series on Optimizer transformations. This time it is the turn of Predicate Pushdown. I would like to thank Rafi Ahmed for the content of this blog.Normally, a view cannot be joined with an index-based nested loop (i.e., index access) join, since a view, in contrast with a base table, does not have an index defined on it. A view can only be joined with other tables using three methods: hash, nested loop, and sort-merge joins. Introduction The join predicate pushdown (JPPD) transformation allows a view to be joined with index-based nested-loop join method, which may provide a more optimal alternative. In the join predicate pushdown transformation, the view remains a separate query block, but it contains the join predicate, which is pushed down from its containing query block into the view. The view thus becomes correlated and must be evaluated for each row of the outer query block. These pushed-down join predicates, once inside the view, open up new index access paths on the base tables inside the view; this allows the view to be joined with index-based nested-loop join method, thereby enabling the optimizer to select an efficient execution plan. The join predicate pushdown transformation is not always optimal. The join predicate pushed-down view becomes correlated and it must be evaluated for each outer row; if there is a large number of outer rows, the cost of evaluating the view multiple times may make the nested-loop join suboptimal, and therefore joining the view with hash or sort-merge join method may be more efficient. The decision whether to push down join predicates into a view is determined by evaluating the costs of the outer query with and without the join predicate pushdown transformation under Oracle's cost-based query transformation framework. The join predicate pushdown transformation applies to both non-mergeable views and mergeable views and to pre-defined and inline views as well as to views generated internally by the optimizer during various transformations. The following shows the types of views on which join predicate pushdown is currently supported. UNION ALL/UNION view Outer-joined view Anti-joined view Semi-joined view DISTINCT view GROUP-BY view Examples Consider query A, which has an outer-joined view V. The view cannot be merged, as it contains two tables, and the join between these two tables must be performed before the join between the view and the outer table T4. A: SELECT T4.unique1, V.unique3 FROM T_4K T4,            (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3) VWHERE T4.unique3 = V.hundred(+) AND       T4.ten = V.ten(+) AND       T4.thousand = 5; The following shows the non-default plan for query A generated by disabling join predicate pushdown. When query A undergoes join predicate pushdown, it yields query B. Note that query B is expressed in a non-standard SQL and shows an internal representation of the query. B: SELECT T4.unique1, V.unique3 FROM T_4K T4,           (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3             AND T4.unique3 = V.hundred(+)             AND T4.ten = V.ten(+)) V WHERE T4.thousand = 5; The execution plan for query B is shown below. In the execution plan BX, note the keyword 'VIEW PUSHED PREDICATE' indicates that the view has undergone the join predicate pushdown transformation. The join predicates (shown here in red) have been moved into the view V; these join predicates open up index access paths thereby enabling index-based nested-loop join of the view. With join predicate pushdown, the cost of query A has come down from 62 to 32.  As mentioned earlier, the join predicate pushdown transformation is cost-based, and a join predicate pushed-down plan is selected only when it reduces the overall cost. Consider another example of a query C, which contains a view with the UNION ALL set operator.C: SELECT R.unique1, V.unique3 FROM T_5K R,            (SELECT T1.unique3, T2.unique1+T1.unique1             FROM T_5K T1, T_10K T2             WHERE T1.unique1 = T2.unique1             UNION ALL             SELECT T1.unique3, T2.unique2             FROM G_4K T1, T_10K T2             WHERE T1.unique1 = T2.unique1) V WHERE R.unique3 = V.unique3 and R.thousand < 1; The execution plan of query C is shown below. In the above, 'VIEW UNION ALL PUSHED PREDICATE' indicates that the UNION ALL view has undergone the join predicate pushdown transformation. As can be seen, here the join predicate has been replicated and pushed inside every branch of the UNION ALL view. The join predicates (shown here in red) open up index access paths thereby enabling index-based nested loop join of the view. Consider query D as an example of join predicate pushdown into a distinct view. We have the following cardinalities of the tables involved in query D: Sales (1,016,271), Customers (50,000), and Costs (787,766).  D: SELECT C.cust_last_name, C.cust_city FROM customers C,            (SELECT DISTINCT S.cust_id             FROM sales S, costs CT             WHERE S.prod_id = CT.prod_id and CT.unit_price > 70) V WHERE C.cust_state_province = 'CA' and C.cust_id = V.cust_id; The execution plan of query D is shown below. As shown in XD, when query D undergoes join predicate pushdown transformation, the expensive DISTINCT operator is removed and the join is converted into a semi-join; this is possible, since all the SELECT list items of the view participate in an equi-join with the outer tables. Under similar conditions, when a group-by view undergoes join predicate pushdown transformation, the expensive group-by operator can also be removed. With the join predicate pushdown transformation, the elapsed time of query D came down from 63 seconds to 5 seconds. Since distinct and group-by views are mergeable views, the cost-based transformation framework also compares the cost of merging the view with that of join predicate pushdown in selecting the most optimal execution plan. Summary We have tried to illustrate the basic ideas behind join predicate pushdown on different types of views by showing example queries that are quite simple. Oracle can handle far more complex queries and other types of views not shown here in the examples. Again many thanks to Rafi Ahmed for the content of this blog post.

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  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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  • SQL SERVER – Solution to Puzzle – Simulate LEAD() and LAG() without Using SQL Server 2012 Analytic Function

    - by pinaldave
    Earlier I wrote a series on SQL Server Analytic Functions of SQL Server 2012. During the series to keep the learning maximum and having fun, we had few puzzles. One of the puzzle was simulating LEAD() and LAG() without using SQL Server 2012 Analytic Function. Please read the puzzle here first before reading the solution : Write T-SQL Self Join Without Using LEAD and LAG. When I was originally wrote the puzzle I had done small blunder and the question was a bit confusing which I corrected later on but wrote a follow up blog post on over here where I describe the give-away. Quick Recap: Generate following results without using SQL Server 2012 analytic functions. I had received so many valid answers. Some answers were similar to other and some were very innovative. Some answers were very adaptive and some did not work when I changed where condition. After selecting all the valid answer, I put them in table and ran RANDOM function on the same and selected winners. Here are the valid answers. No Joins and No Analytic Functions Excellent Solution by Geri Reshef – Winner of SQL Server Interview Questions and Answers (India | USA) WITH T1 AS (SELECT Row_Number() OVER(ORDER BY SalesOrderDetailID) N, s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663)) SELECT SalesOrderID,SalesOrderDetailID,OrderQty, CASE WHEN N%2=1 THEN MAX(CASE WHEN N%2=0 THEN SalesOrderDetailID END) OVER (Partition BY (N+1)/2) ELSE MAX(CASE WHEN N%2=1 THEN SalesOrderDetailID END) OVER (Partition BY N/2) END LeadVal, CASE WHEN N%2=1 THEN MAX(CASE WHEN N%2=0 THEN SalesOrderDetailID END) OVER (Partition BY N/2) ELSE MAX(CASE WHEN N%2=1 THEN SalesOrderDetailID END) OVER (Partition BY (N+1)/2) END LagVal FROM T1 ORDER BY SalesOrderID, SalesOrderDetailID, OrderQty; GO No Analytic Function and Early Bird Excellent Solution by DHall – Winner of Pluralsight 30 days Subscription -- a query to emulate LEAD() and LAG() ;WITH s AS ( SELECT 1 AS ldOffset, -- equiv to 2nd param of LEAD 1 AS lgOffset, -- equiv to 2nd param of LAG NULL AS ldDefVal, -- equiv to 3rd param of LEAD NULL AS lgDefVal, -- equiv to 3rd param of LAG ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS row, SalesOrderID, SalesOrderDetailID, OrderQty FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty, ISNULL( sLd.SalesOrderDetailID, s.ldDefVal) AS LeadValue, ISNULL( sLg.SalesOrderDetailID, s.lgDefVal) AS LagValue FROM s LEFT OUTER JOIN s AS sLd ON s.row = sLd.row - s.ldOffset LEFT OUTER JOIN s AS sLg ON s.row = sLg.row + s.lgOffset ORDER BY s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty No Analytic Function and Partition By Excellent Solution by DHall – Winner of Pluralsight 30 days Subscription /* a query to emulate LEAD() and LAG() */ ;WITH s AS ( SELECT 1 AS LeadOffset, /* equiv to 2nd param of LEAD */ 1 AS LagOffset, /* equiv to 2nd param of LAG */ NULL AS LeadDefVal, /* equiv to 3rd param of LEAD */ NULL AS LagDefVal, /* equiv to 3rd param of LAG */ /* Try changing the values of the 4 integer values above to see their effect on the results */ /* The values given above of 0, 0, null and null behave the same as the default 2nd and 3rd parameters to LEAD() and LAG() */ ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS row, SalesOrderID, SalesOrderDetailID, OrderQty FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty, ISNULL( sLead.SalesOrderDetailID, s.LeadDefVal) AS LeadValue, ISNULL( sLag.SalesOrderDetailID, s.LagDefVal) AS LagValue FROM s LEFT OUTER JOIN s AS sLead ON s.row = sLead.row - s.LeadOffset /* Try commenting out this next line when LeadOffset != 0 */ AND s.SalesOrderID = sLead.SalesOrderID /* The additional join criteria on SalesOrderID above is equivalent to PARTITION BY SalesOrderID in the OVER clause of the LEAD() function */ LEFT OUTER JOIN s AS sLag ON s.row = sLag.row + s.LagOffset /* Try commenting out this next line when LagOffset != 0 */ AND s.SalesOrderID = sLag.SalesOrderID /* The additional join criteria on SalesOrderID above is equivalent to PARTITION BY SalesOrderID in the OVER clause of the LAG() function */ ORDER BY s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty No Analytic Function and CTE Usage Excellent Solution by Pravin Patel - Winner of SQL Server Interview Questions and Answers (India | USA) --CTE based solution ; WITH cteMain AS ( SELECT SalesOrderID, SalesOrderDetailID, OrderQty, ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS sn FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty, sLead.SalesOrderDetailID AS leadvalue, sLeg.SalesOrderDetailID AS leagvalue FROM cteMain AS m LEFT OUTER JOIN cteMain AS sLead ON sLead.sn = m.sn+1 LEFT OUTER JOIN cteMain AS sLeg ON sLeg.sn = m.sn-1 ORDER BY m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty No Analytic Function and Co-Related Subquery Usage Excellent Solution by Pravin Patel – Winner of SQL Server Interview Questions and Answers (India | USA) -- Co-Related subquery SELECT m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty, ( SELECT MIN(SalesOrderDetailID) FROM Sales.SalesOrderDetail AS l WHERE l.SalesOrderID IN (43670, 43669, 43667, 43663) AND l.SalesOrderID >= m.SalesOrderID AND l.SalesOrderDetailID > m.SalesOrderDetailID ) AS lead, ( SELECT MAX(SalesOrderDetailID) FROM Sales.SalesOrderDetail AS l WHERE l.SalesOrderID IN (43670, 43669, 43667, 43663) AND l.SalesOrderID <= m.SalesOrderID AND l.SalesOrderDetailID < m.SalesOrderDetailID ) AS leag FROM Sales.SalesOrderDetail AS m WHERE m.SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty This was one of the most interesting Puzzle on this blog. Giveaway Winners will get following giveaways. Geri Reshef and Pravin Patel SQL Server Interview Questions and Answers (India | USA) DHall Pluralsight 30 days Subscription Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Function, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Form Validation, Dependant Drop Downs, Data Level Security in OWS for DotNetNuke - 5 Videos

    In this tutorial we demonstrate some very advanced techniques for building a car parts application in Open Web Studio. Throughout the tutorial we cover form input, validation, how to use dependant drop down lists, populating checkbox lists and introduce a new concept of data level security. Data level security allows you to control which data a user can access within a module. The videos contain: Video 1 - How to Setup Form Validation Video 2 - Car Parts Application, Assigning Security Roles into a Global Session Variable Video 3 - How to Build the Categories Module with Data Level Security Video 4 - How to Build the SubCategories Module and Use SubQuery Video 5 - How to Build the Car Parts List Module Total Time Length: 44min 19secsDid you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • query to select topic with highest number of comment +support+oppose+views

    - by chetan
    table schema title description desid replyto support oppose views browser used a1 none 1 1 12 - bad topic b2 1 2 3 14 sql database a3 none 4 5 34 - crome b4 1 3 4 12 Topic desid starts with a and comment desid starts with b .For comment replyto is the desid of topic . Its easy to select * with highest number of support+oppose+views by query "select * from [DB_user1212].[dbo].[discussions] where desid like 'a%' order by (sup+opp+visited) desc" For highest (comment +support+oppose+views ) i tried "select * from [DB_user1212].[dbo].[discussions] where desid like 'a%' order by ((select count(*) from [DB_user1212].[dbo].[discussions] where replyto = desid )+sup+opp+visited) desc" but it didn't work . Because its not possible to send desid from outer query to innner subquery .

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  • PHP/APC fatal error, apc_mmap: mmap failed

    - by Sudowned
    I'm seeing some intermittent CPU usage spikes to 100%, sort of correlated to these log entries: [27-Feb-2012 13:29:29] PHP Fatal error: PHP Startup: apc_mmap: mmap failed: in Unknown on line 0 [27-Feb-2012 13:29:30] PHP Fatal error: PHP Startup: apc_mmap: mmap failed: in Unknown on line 0 [27-Feb-2012 13:29:31] PHP Fatal error: PHP Startup: apc_mmap: mmap failed: in Unknown on line 0 [27-Feb-2012 13:29:31] PHP Fatal error: PHP Startup: apc_mmap: mmap failed: in Unknown on line 0 phpinfo() indicates that APC is set up, and as far as I can tell this error doesn't cause visible 500 errors on the live site, which is a WordPress install that gets about 600k views monthly. Google's been unhelpful so far, and I was hoping that someone here had some insight as to what's causing this and how to fix it. Curiously, this error only shows up /usr/local/apache2/logs/error_log and not the error_log for the cpanel-configured site.

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  • lighttpd silently stops logging

    - by Max Cantor
    I'm on a Slicehost 256MB VPS with Ubuntu 9.04 (Jaunty). lighttpd is the only web server process running; it listens on port 80. My lighttpd.conf can be found here. I'm using Ubuntu's default logrotate setup for lighty. At seemingly random times, lighttpd will stop logging. It is not correlated with log rotation--that is, the errors do not occur when logrotate kicks in. What happens is, I will verify that the server is serving files by hitting a URL with my browser, and I will verify that it is not logging by checking access.log and seeing that the GET request I just made is not there. Using init.d to restart the process starts logging again, without truncating or rotating the log file. That is, new requests will be logged at the end of the existing access.log file. There are no cron jobs running on this box. Any ideas?

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  • How can I make Excel correlate data from two data sets into a single graph?

    - by Tom Ritter
    I have two datasets, one being sparser than the other. They look like this: Data Set 1: 4 50 5 55 6 60 7 70 8 80 Data Set 2: 4 10 6 20 8 30 I have several hundred points instead of this few. I want them in the same graph, the X axis being 4-8, the y axis being 0-100ish, and two lines, one for each data set. What I get is two lines, not correlated at all along the X axis, and the X axis being labeled from one of the two datasets, with the labels being wrong for the other. The smaller data set is one-point-per-tick on the x axis, when I need it to skip ticks and actually line up with the other data set. Not married to excel, willing to try this in something else if it's free.

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  • Remote I/O costs with a Content Delivery Network

    - by x711Li
    As far as I know, the time complexity of scanning a directory and the amount of files in said directory are correlated due to I/O costs. Would the administrative costs of placing the files in a hashed directory tree for uploading/downloading files through a CDN API be worth it for the added efficiency? For instance, given a filename foo.mp3, the MD5 hash for this is 10ebb1120767e9de166e0f5905077cb1. Thus, storing foo.mp3 in ./10/eb/foo.mp3 would allow for less files per directory (assuming MD5 generates patterns with in Base36, this allows for 36^2 root directories with 36^2 subdirectories each and little chance of hash collision) Considering the directories themselves are not loaded, would the I/O costs of directory scanning still exist with direct uploading/downloading?

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