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  • Increase Performance of VS 2010 by using a SSD

    - by System.Data
    After searching on the internet for performance improvements when using Visual Studio 2010 with a solid state hard drive, I heard a lot of different opinions. A lot of people said that there isn't really a benefit when using a SSD, but in contrast others said the exact opposite. I am a bit confused with the contrasting opinions and I cannot really make a decision whether buying a SSD would make a difference. What are your experiences with this issue and which SSD did you use?

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  • Using DB_PARAMS to Tune the EP_LOAD_SALES Performance

    - by user702295
    The DB_PARAMS table can be used to tune the EP_LOAD_SALES performance.  The AWR report supplied shows 16 CPUs so I imaging that you can run with 8 or more parallel threads.  This can be done by setting the following DB_PARAMS parameters.  Note that most of parameter changes are just changing a 2 or 4 into an 8: DBHintEp_Load_SalesUseParallel = TRUE DBHintEp_Load_SalesUseParallelDML = TRUE DBHintEp_Load_SalesInsertErr = + parallel(@T_SRC_SALES@ 8) full(@T_SRC_SALES@) DBHintEp_Load_SalesInsertLd  = + parallel(@T_SRC_SALES@ 8) DBHintEp_Load_SalesMergeSALES_DATA = + parallel(@T_SRC_SALES_LD@ 8) full(@T_SRC_SALES_LD@) DBHintMdp_AddUpdateIs_Fictive0SD = + parallel(s 8 ) DBHintMdp_AddUpdateIs_Fictive2SD = + parallel(s 8 )

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  • Multiple Object Instantiation

    - by Ricky Baby
    I am trying to get my head around object oriented programming as it pertains to web development (more specifically PHP). I understand inheritance and abstraction etc, and know all the "buzz-words" like encapsulation and single purpose and why I should be doing all this. But my knowledge is falling short with actually creating objects that relate to the data I have in my database, creating a single object that a representative of a single entity makes sense, but what are the best practises when creating 100, 1,000 or 10,000 objects of the same type. for instance, when trying to display a list of the items, ideally I would like to be consistent with the objects I use, but where exactly should I run the query/get the data to populate the object(s) as running 10,000 queries seems wasteful. As an example, say I have a database of cats, and I want a list of all black cats, do I need to set up a FactoryObject which grabs the data needed for each cat from my database, then passes that data into each individual CatObject and returns the results in a array/object - or should I pass each CatObject it's identifier and let it populate itself in a separate query.

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  • good literature for teaching object oriented thinking in C [closed]

    - by Dipan Mehta
    Quite often C is the primary platform for the development. And when things are large scale, I have seen partitioning of the system as different objects is quite a natural thing. Some or many of the object orientated analysis and design principles are used here very well. This is not a debate question about whether or not C is a good candidate for object oriented programming or not. This is also NOT a question how to do OO in C. You can refer to this question and there are probably many such citations. As far as I am concerned, I have learned some of this things while working with many open source and commercial projects. (libjpeg, ffmpeg, Gstreamer which is based on GObject). I can probably extend a few references that explains some of these concepts such as - 1. Event Helix article, 2. Linux Mag article 3. one of my answers which links Schreiner's reference. Unfortunately, when we induct younger folks, it seems too hard to make them learn all of it the hard way. Usually, when we say it's C, a general reaction is to throw away all of the "Object thinking". Looking for help extending above references from those who have been in the similar areas of work. Are there any good formal literature that explains how Object thinking can be made to use while you are working in C? I have seen tons of book on general "object oriented paradigms" but they all focus on advanced languages mostly not in C. You see most C books - but most focus only on the syntax and the obfuscated corners of C and that's it. There are hardly ANY good reference, specially books or any systematic (I mean formal) literature on how to apply OO in C. This is very surprising given that so many large scale open source projects use C which are truly using this very well; but we hardly see any good formal literature on this subject.

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  • Console.log in IE on an object just outputted [object Object]

    - by LookitsPuck
    All, I'm used to debugging JavaScript in Chrome or Firefox just because their built in developer tools are a lot cleaner than IE's. IE8 came along way with the Developer Tools being more polished, but they're still not completely up to snuff. I like being able to step through code as if I was in Visual Studio, and that is pretty nice about IE, however, when trying to do a simple console.log on an object that I have, in Firefox/Chrome/etc. I can actually explore that object. In IE, the console is simply outputting the following: LOG: [object Object] Is there any way to drill down into that object in IE like in Chrome/Firefox/etc.? Thanks all! -Steve

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  • Python performance: iteration and operations on nested lists

    - by J.J.
    Problem Hey folks. I'm looking for some advice on python performance. Some background on my problem: Given: A mesh of nodes of size (x,y) each with a value (0...255) starting at 0 A list of N input coordinates each at a specified location within the range (0...x, 0...y) Increment the value of the node at the input coordinate and the node's neighbors within range Z up to a maximum of 255. Neighbors beyond the mesh edge are ignored. (No wrapping) BASE CASE: A mesh of size 1024x1024 nodes, with 400 input coordinates and a range Z of 75 nodes. Processing should be O(x*y*Z*N). I expect x, y and Z to remain roughly around the values in the base case, but the number of input coordinates N could increase up to 100,000. My goal is to minimize processing time. Current results I have 2 current implementations: f1, f2 Running speed on my 2.26 GHz Intel Core 2 Duo with Python 2.6.1: f1: 2.9s f2: 1.8s f1 is the initial naive implementation: three nested for loops. f2 is replaces the inner for loop with a list comprehension. Code is included below for your perusal. Question How can I further reduce the processing time? I'd prefer sub-1.0s for the test parameters. Please, keep the recommendations to native Python. I know I can move to a third-party package such as numpy, but I'm trying to avoid any third party packages. Also, I've generated random input coordinates, and simplified the definition of the node value updates to keep our discussion simple. The specifics have to change slightly and are outside the scope of my question. thanks much! f1 is the initial naive implementation: three nested for loops. 2.9s def f1(x,y,n,z): rows = [] for i in range(x): rows.append([0 for i in xrange(y)]) for i in range(n): inputX, inputY = (int(x*random.random()), int(y*random.random())) topleft = (inputX - z, inputY - z) for i in xrange(max(0, topleft[0]), min(topleft[0]+(z*2), x)): for j in xrange(max(0, topleft[1]), min(topleft[1]+(z*2), y)): if rows[i][j] <= 255: rows[i][j] += 1 f2 is replaces the inner for loop with a list comprehension. 1.8s def f2(x,y,n,z): rows = [] for i in range(x): rows.append([0 for i in xrange(y)]) for i in range(n): inputX, inputY = (int(x*random.random()), int(y*random.random())) topleft = (inputX - z, inputY - z) for i in xrange(max(0, topleft[0]), min(topleft[0]+(z*2), x)): l = max(0, topleft[1]) r = min(topleft[1]+(z*2), y) rows[i][l:r] = [j+1 for j in rows[i][l:r] if j < 255]

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  • Difference between null==object and object==null

    - by priyank.mp
    Hi I would like to know diff between the above comparisons? I am getting null pointer exception when I check object.getItems() == null. But if I change it to null == object.getItems(), it workes fine. I did look into this http://stackoverflow.com/questions/2938476/what-is-the-difference-between-null-object-and-objectnull-closed But I didnt get satisfactory answer.

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  • Performance Enhancement in Full-Text Search Query

    - by Calvin Sun
    Ever since its first release, we are continuing consolidating and developing InnoDB Full-Text Search feature. There is one recent improvement that worth blogging about. It is an effort with MySQL Optimizer team that simplifies some common queries’ Query Plans and dramatically shorted the query time. I will describe the issue, our solution and the end result by some performance numbers to demonstrate our efforts in continuing enhancement the Full-Text Search capability. The Issue: As we had discussed in previous Blogs, InnoDB implements Full-Text index as reversed auxiliary tables. The query once parsed will be reinterpreted into several queries into related auxiliary tables and then results are merged and consolidated to come up with the final result. So at the end of the query, we’ll have all matching records on hand, sorted by their ranking or by their Doc IDs. Unfortunately, MySQL’s optimizer and query processing had been initially designed for MyISAM Full-Text index, and sometimes did not fully utilize the complete result package from InnoDB. Here are a couple examples: Case 1: Query result ordered by Rank with only top N results: mysql> SELECT FTS_DOC_ID, MATCH (title, body) AGAINST ('database') AS SCORE FROM articles ORDER BY score DESC LIMIT 1; In this query, user tries to retrieve a single record with highest ranking. It should have a quick answer once we have all the matching documents on hand, especially if there are ranked. However, before this change, MySQL would almost retrieve rankings for almost every row in the table, sort them and them come with the top rank result. This whole retrieve and sort is quite unnecessary given the InnoDB already have the answer. In a real life case, user could have millions of rows, so in the old scheme, it would retrieve millions of rows' ranking and sort them, even if our FTS already found there are two 3 matched rows. Apparently, the million ranking retrieve is done in vain. In above case, it should just ask for 3 matched rows' ranking, all other rows' ranking are 0. If it want the top ranking, then it can just get the first record from our already sorted result. Case 2: Select Count(*) on matching records: mysql> SELECT COUNT(*) FROM articles WHERE MATCH (title,body) AGAINST ('database' IN NATURAL LANGUAGE MODE); In this case, InnoDB search can find matching rows quickly and will have all matching rows. However, before our change, in the old scheme, every row in the table was requested by MySQL one by one, just to check whether its ranking is larger than 0, and later comes up a count. In fact, there is no need for MySQL to fetch all rows, instead InnoDB already had all the matching records. The only thing need is to call an InnoDB API to retrieve the count The difference can be huge. Following query output shows how big the difference can be: mysql> select count(*) from searchindex_inno where match(si_title, si_text) against ('people')  +----------+ | count(*) | +----------+ | 666877 | +----------+ 1 row in set (16 min 17.37 sec) So the query took almost 16 minutes. Let’s see how long the InnoDB can come up the result. In InnoDB, you can obtain extra diagnostic printout by turning on “innodb_ft_enable_diag_print”, this will print out extra query info: Error log: keynr=2, 'people' NL search Total docs: 10954826 Total words: 0 UNION: Searching: 'people' Processing time: 2 secs: row(s) 666877: error: 10 ft_init() ft_init_ext() keynr=2, 'people' NL search Total docs: 10954826 Total words: 0 UNION: Searching: 'people' Processing time: 3 secs: row(s) 666877: error: 10 Output shows it only took InnoDB only 3 seconds to get the result, while the whole query took 16 minutes to finish. So large amount of time has been wasted on the un-needed row fetching. The Solution: The solution is obvious. MySQL can skip some of its steps, optimize its plan and obtain useful information directly from InnoDB. Some of savings from doing this include: 1) Avoid redundant sorting. Since InnoDB already sorted the result according to ranking. MySQL Query Processing layer does not need to sort to get top matching results. 2) Avoid row by row fetching to get the matching count. InnoDB provides all the matching records. All those not in the result list should all have ranking of 0, and no need to be retrieved. And InnoDB has a count of total matching records on hand. No need to recount. 3) Covered index scan. InnoDB results always contains the matching records' Document ID and their ranking. So if only the Document ID and ranking is needed, there is no need to go to user table to fetch the record itself. 4) Narrow the search result early, reduce the user table access. If the user wants to get top N matching records, we do not need to fetch all matching records from user table. We should be able to first select TOP N matching DOC IDs, and then only fetch corresponding records with these Doc IDs. Performance Results and comparison with MyISAM The result by this change is very obvious. I includes six testing result performed by Alexander Rubin just to demonstrate how fast the InnoDB query now becomes when comparing MyISAM Full-Text Search. These tests are base on the English Wikipedia data of 5.4 Million rows and approximately 16G table. The test was performed on a machine with 1 CPU Dual Core, SSD drive, 8G of RAM and InnoDB_buffer_pool is set to 8 GB. Table 1: SELECT with LIMIT CLAUSE mysql> SELECT si_title, match(si_title, si_text) against('family') as rel FROM si WHERE match(si_title, si_text) against('family') ORDER BY rel desc LIMIT 10; InnoDB MyISAM Times Faster Time for the query 1.63 sec 3 min 26.31 sec 127 You can see for this particular query (retrieve top 10 records), InnoDB Full-Text Search is now approximately 127 times faster than MyISAM. Table 2: SELECT COUNT QUERY mysql>select count(*) from si where match(si_title, si_text) against('family‘); +----------+ | count(*) | +----------+ | 293955 | +----------+ InnoDB MyISAM Times Faster Time for the query 1.35 sec 28 min 59.59 sec 1289 In this particular case, where there are 293k matching results, InnoDB took only 1.35 second to get all of them, while take MyISAM almost half an hour, that is about 1289 times faster!. Table 3: SELECT ID with ORDER BY and LIMIT CLAUSE for selected terms mysql> SELECT <ID>, match(si_title, si_text) against(<TERM>) as rel FROM si_<TB> WHERE match(si_title, si_text) against (<TERM>) ORDER BY rel desc LIMIT 10; Term InnoDB (time to execute) MyISAM(time to execute) Times Faster family 0.5 sec 5.05 sec 10.1 family film 0.95 sec 25.39 sec 26.7 Pizza restaurant orange county California 0.93 sec 32.03 sec 34.4 President united states of America 2.5 sec 36.98 sec 14.8 Table 4: SELECT title and text with ORDER BY and LIMIT CLAUSE for selected terms mysql> SELECT <ID>, si_title, si_text, ... as rel FROM si_<TB> WHERE match(si_title, si_text) against (<TERM>) ORDER BY rel desc LIMIT 10; Term InnoDB (time to execute) MyISAM(time to execute) Times Faster family 0.61 sec 41.65 sec 68.3 family film 1.15 sec 47.17 sec 41.0 Pizza restaurant orange county california 1.03 sec 48.2 sec 46.8 President united states of america 2.49 sec 44.61 sec 17.9 Table 5: SELECT ID with ORDER BY and LIMIT CLAUSE for selected terms mysql> SELECT <ID>, match(si_title, si_text) against(<TERM>) as rel  FROM si_<TB> WHERE match(si_title, si_text) against (<TERM>) ORDER BY rel desc LIMIT 10; Term InnoDB (time to execute) MyISAM(time to execute) Times Faster family 0.5 sec 5.05 sec 10.1 family film 0.95 sec 25.39 sec 26.7 Pizza restaurant orange county califormia 0.93 sec 32.03 sec 34.4 President united states of america 2.5 sec 36.98 sec 14.8 Table 6: SELECT COUNT(*) mysql> SELECT count(*) FROM si_<TB> WHERE match(si_title, si_text) against (<TERM>) LIMIT 10; Term InnoDB (time to execute) MyISAM(time to execute) Times Faster family 0.47 sec 82 sec 174.5 family film 0.83 sec 131 sec 157.8 Pizza restaurant orange county califormia 0.74 sec 106 sec 143.2 President united states of america 1.96 sec 220 sec 112.2  Again, table 3 to table 6 all showing InnoDB consistently outperform MyISAM in these queries by a large margin. It becomes obvious the InnoDB has great advantage over MyISAM in handling large data search. Summary: These results demonstrate the great performance we could achieve by making MySQL optimizer and InnoDB Full-Text Search more tightly coupled. I think there are still many cases that InnoDB’s result info have not been fully taken advantage of, which means we still have great room to improve. And we will continuously explore the area, and get more dramatic results for InnoDB full-text searches. Jimmy Yang, September 29, 2012

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  • bootstrap: Uncaught TypeError: Object [object Object] has no method 'tooltip', 'typeahead'

    - by DarkKnightFan
    I am trying to use the tooltip, typeahead, datepicker features of bootstrap. But I keep getting these errors in the console! Uncaught TypeError: Object [object Object] has no method 'tooltip' Uncaught TypeError: Object [object Object] has no method 'typeahead' This is how my imports look like: <link href="css/bootstrap.css" rel="stylesheet"> <link href="css/datepicker.css" rel="stylesheet"> <link href="css/bootstrap-responsive.css" rel="stylesheet"> <script type="text/javascript" src="js/jquery-1.8.0.js"></script> <script type="text/javascript" src="js/bootstrap.js"></script> <script type="text/javascript" src="js/bootstrap-datepicker.js"></script> <script type="text/javascript" src="js/bootstrap-typeahead.js"></script> <script type="text/javascript" src="js/highcharts.js"></script> <script type="text/javascript" src="js/exporting.js"></script> <link type="text/css" rel="stylesheet" href="css/jquery.dropdown.css" /> <script type="text/javascript" src="js/jquery.dropdown.js"></script> Some sample code: <a class="btn btn-success" data-toggle="modal" href="#payments" rel="tooltip" data-placement="top" title="Record your payments"> <i class="icon-check icon-white"></i> Payments</a> $("#payments").tooltip('show'); Similarly I have code for datepicker and typeahead. any solution?

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  • Uncaught TypeError: Property 'dist2' of object [object Object] is not a function

    - by Radu Vlad
    I have this functions that should return me the distance from point p to segment line v-w. The problem i have is after some time i receive the following error: Uncaught TypeError: Property 'dist2' of object [object Object] is not a function. I receive it in distToSegmentSquared directly,not even calling the function dist2().Is it any other dist2() anywhere in jquery?I found none... function sqr(x) { return x * x; } function dist2(v, w) { console.log(v); console.log(w); return sqr(v.x - w.x) + sqr(v.y - w.y); } function distToSegmentSquared(p, v, w) { var l2 = dist2(v, w); if (l2 == 0) return dist2(p, v); var t = ((p.x - v.x) * (w.x - v.x) + (p.y - v.y) * (w.y - v.y)) / l2; if (t < 0) return dist2(p, v); if (t > 1) return dist2(p, w); return dist2(p, {x: v.x + t * (w.x - v.x), y: v.y + t * (w.y - v.y)}); } function distToSegment(p, v, w) { return Math.sqrt(distToSegmentSquared(p, v, w)); } The values that are given in for that error are: p: Object x: 461 y: 333 v: Object x: 80 y: 120 w: Object x: 260 y: 120

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  • SQL Monitor’s data repository: Alerts

    - by Chris Lambrou
    In my previous post, I introduced the SQL Monitor data repository, and described how the monitored objects are stored in a hierarchy in the data schema, in a series of tables with a _Keys suffix. In this post I had planned to describe how the actual data for the monitored objects is stored in corresponding tables with _StableSamples and _UnstableSamples suffixes. However, I’m going to postpone that until my next post, as I’ve had a request from a SQL Monitor user to explain how alerts are stored. In the SQL Monitor data repository, alerts are stored in tables belonging to the alert schema, which contains the following five tables: alert.Alert alert.Alert_Cleared alert.Alert_Comment alert.Alert_Severity alert.Alert_Type In this post, I’m only going to cover the alert.Alert and alert.Alert_Type tables. I may cover the other three tables in a later post. The most important table in this schema is alert.Alert, as each row in this table corresponds to a single alert. So let’s have a look at it. SELECT TOP 100 AlertId, AlertType, TargetObject, [Read], SubType FROM alert.Alert ORDER BY AlertId DESC;  AlertIdAlertTypeTargetObjectReadSubType 165550397:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,10 265549387:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,10 365548187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 11…     So what are we seeing here, then? Well, AlertId is an auto-incrementing identity column, so ORDER BY AlertId DESC ensures that we see the most recent alerts first. AlertType indicates the type of each alert, such as Job failed (6), Backup overdue (14) or Long-running query (12). The TargetObject column indicates which monitored object the alert is associated with. The Read column acts as a flag to indicate whether or not the alert has been read. And finally the SubType column is used in the case of a Custom metric (40) alert, to indicate which custom metric the alert pertains to. Okay, now lets look at some of those columns in more detail. The AlertType column is an easy one to start with, and it brings use nicely to the next table, data.Alert_Type. Let’s have a look at what’s in this table: SELECT AlertType, Event, Monitoring, Name, Description FROM alert.Alert_Type ORDER BY AlertType;  AlertTypeEventMonitoringNameDescription 1100Processor utilizationProcessor utilization (CPU) on a host machine stays above a threshold percentage for longer than a specified duration 2210SQL Server error log entryAn error is written to the SQL Server error log with a severity level above a specified value. 3310Cluster failoverThe active cluster node fails, causing the SQL Server instance to switch nodes. 4410DeadlockSQL deadlock occurs. 5500Processor under-utilizationProcessor utilization (CPU) on a host machine remains below a threshold percentage for longer than a specified duration 6610Job failedA job does not complete successfully (the job returns an error code). 7700Machine unreachableHost machine (Windows server) cannot be contacted on the network. 8800SQL Server instance unreachableThe SQL Server instance is not running or cannot be contacted on the network. 9900Disk spaceDisk space used on a logical disk drive is above a defined threshold for longer than a specified duration. 101000Physical memoryPhysical memory (RAM) used on the host machine stays above a threshold percentage for longer than a specified duration. 111100Blocked processSQL process is blocked for longer than a specified duration. 121200Long-running queryA SQL query runs for longer than a specified duration. 131400Backup overdueNo full backup exists, or the last full backup is older than a specified time. 141500Log backup overdueNo log backup exists, or the last log backup is older than a specified time. 151600Database unavailableDatabase changes from Online to any other state. 161700Page verificationTorn Page Detection or Page Checksum is not enabled for a database. 171800Integrity check overdueNo entry for an integrity check (DBCC DBINFO returns no date for dbi_dbccLastKnownGood field), or the last check is older than a specified time. 181900Fragmented indexesFragmentation level of one or more indexes is above a threshold percentage. 192400Job duration unusualThe duration of a SQL job duration deviates from its baseline duration by more than a threshold percentage. 202501Clock skewSystem clock time on the Base Monitor computer differs from the system clock time on a monitored SQL Server host machine by a specified number of seconds. 212700SQL Server Agent Service statusThe SQL Server Agent Service status matches the status specified. 222800SQL Server Reporting Service statusThe SQL Server Reporting Service status matches the status specified. 232900SQL Server Full Text Search Service statusThe SQL Server Full Text Search Service status matches the status specified. 243000SQL Server Analysis Service statusThe SQL Server Analysis Service status matches the status specified. 253100SQL Server Integration Service statusThe SQL Server Integration Service status matches the status specified. 263300SQL Server Browser Service statusThe SQL Server Browser Service status matches the status specified. 273400SQL Server VSS Writer Service statusThe SQL Server VSS Writer status matches the status specified. 283501Deadlock trace flag disabledThe monitored SQL Server’s trace flag cannot be enabled. 293600Monitoring stopped (host machine credentials)SQL Monitor cannot contact the host machine because authentication failed. 303700Monitoring stopped (SQL Server credentials)SQL Monitor cannot contact the SQL Server instance because authentication failed. 313800Monitoring error (host machine data collection)SQL Monitor cannot collect data from the host machine. 323900Monitoring error (SQL Server data collection)SQL Monitor cannot collect data from the SQL Server instance. 334000Custom metricThe custom metric value has passed an alert threshold. 344100Custom metric collection errorSQL Monitor cannot collect custom metric data from the target object. Basically, alert.Alert_Type is just a big reference table containing information about the 34 different alert types supported by SQL Monitor (note that the largest id is 41, not 34 – some alert types have been retired since SQL Monitor was first developed). The Name and Description columns are self evident, and I’m going to skip over the Event and Monitoring columns as they’re not very interesting. The AlertId column is the primary key, and is referenced by AlertId in the alert.Alert table. As such, we can rewrite our earlier query to join these two tables, in order to provide a more readable view of the alerts: SELECT TOP 100 AlertId, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType ORDER BY AlertId DESC;  AlertIdNameTargetObjectReadSubType 165550Monitoring error (SQL Server data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,00 265549Monitoring error (host machine data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,00 365548Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 Okay, the next column to discuss in the alert.Alert table is TargetObject. Oh boy, this one’s a bit tricky! The TargetObject of an alert is a serialized string representation of the position in the monitored object hierarchy of the object to which the alert pertains. The serialization format is somewhat convenient for parsing in the C# source code of SQL Monitor, and has some helpful characteristics, but it’s probably very awkward to manipulate in T-SQL. I could document the serialization format here, but it would be very dry reading, so perhaps it’s best to consider an example from the table above. Have a look at the alert with an AlertID of 65543. It’s a Backup overdue alert for the SqlMonitorData database running on the default instance of granger, my laptop. Each different alert type is associated with a specific type of monitored object in the object hierarchy (I described the hierarchy in my previous post). The Backup overdue alert is associated with databases, whose position in the object hierarchy is root → Cluster → SqlServer → Database. The TargetObject value identifies the target object by specifying the key properties at each level in the hierarchy, thus: Cluster: Name = "granger" SqlServer: Name = "" (an empty string, denoting the default instance) Database: Name = "SqlMonitorData" Well, look at the actual TargetObject value for this alert: "7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,". It is indeed composed of three parts, one for each level in the hierarchy: Cluster: "7:Cluster,1,4:Name,s7:granger," SqlServer: "9:SqlServer,1,4:Name,s0:," Database: "8:Database,1,4:Name,s14:SqlMonitorData," Each part is handled in exactly the same way, so let’s concentrate on the first part, "7:Cluster,1,4:Name,s7:granger,". It comprises the following: "7:Cluster," – This identifies the level in the hierarchy. "1," – This indicates how many different key properties there are to uniquely identify a cluster (we saw in my last post that each cluster is identified by a single property, its Name). "4:Name,s14:SqlMonitorData," – This represents the Name property, and its corresponding value, SqlMonitorData. It’s split up like this: "4:Name," – Indicates the name of the key property. "s" – Indicates the type of the key property, in this case, it’s a string. "14:SqlMonitorData," – Indicates the value of the property. At this point, you might be wondering about the format of some of these strings. Why is the string "Cluster" stored as "7:Cluster,"? Well an encoding scheme is used, which consists of the following: "7" – This is the length of the string "Cluster" ":" – This is a delimiter between the length of the string and the actual string’s contents. "Cluster" – This is the string itself. 7 characters. "," – This is a final terminating character that indicates the end of the encoded string. You can see that "4:Name,", "8:Database," and "14:SqlMonitorData," also conform to the same encoding scheme. In the example above, the "s" character is used to indicate that the value of the Name property is a string. If you explore the TargetObject property of alerts in your own SQL Monitor data repository, you might find other characters used for other non-string key property values. The different value types you might possibly encounter are as follows: "I" – Denotes a bigint value. For example, "I65432,". "g" – Denotes a GUID value. For example, "g32116732-63ae-4ab5-bd34-7dfdfb084c18,". "d" – Denotes a datetime value. For example, "d634815384796832438,". The value is stored as a bigint, rather than a native SQL datetime value. I’ll describe how datetime values are handled in the SQL Monitor data repostory in a future post. I suggest you have a look at the alerts in your own SQL Monitor data repository for further examples, so you can see how the TargetObject values are composed for each of the different types of alert. Let me give one further example, though, that represents a Custom metric alert, as this will help in describing the final column of interest in the alert.Alert table, SubType. Let me show you the alert I’m interested in: SELECT AlertId, a.AlertType, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType WHERE AlertId = 65769;  AlertIdAlertTypeNameTargetObjectReadSubType 16576940Custom metric7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 An AlertType value of 40 corresponds to the Custom metric alert type. The Name taken from the alert.Alert_Type table is simply Custom metric, but this doesn’t tell us anything about the specific custom metric that this alert pertains to. That’s where the SubType value comes in. For custom metric alerts, this provides us with the Id of the specific custom alert definition that can be found in the settings.CustomAlertDefinitions table. I don’t really want to delve into custom alert definitions yet (maybe in a later post), but an extra join in the previous query shows us that this alert pertains to the CPU pressure (avg runnable task count) custom metric alert. SELECT AlertId, a.AlertType, at.Name, cad.Name AS CustomAlertName, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType JOIN settings.CustomAlertDefinitions cad ON a.SubType = cad.Id WHERE AlertId = 65769;  AlertIdAlertTypeNameCustomAlertNameTargetObjectReadSubType 16576940Custom metricCPU pressure (avg runnable task count)7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 The TargetObject value in this case breaks down like this: "7:Cluster,1,4:Name,s7:granger," – Cluster named "granger". "9:SqlServer,1,4:Name,s0:," – SqlServer named "" (the default instance). "8:Database,1,4:Name,s6:master," – Database named "master". "12:CustomMetric,1,8:MetricId,I2," – Custom metric with an Id of 2. Note that the hierarchy for a custom metric is slightly different compared to the earlier Backup overdue alert. It’s root → Cluster → SqlServer → Database → CustomMetric. Also notice that, unlike Cluster, SqlServer and Database, the key property for CustomMetric is called MetricId (not Name), and the value is a bigint (not a string). Finally, delving into the custom metric tables is beyond the scope of this post, but for the sake of avoiding any future confusion, I’d like to point out that whilst the SubType references a custom alert definition, the MetricID value embedded in the TargetObject value references a custom metric definition. Although in this case both the custom metric definition and custom alert definition share the same Id value of 2, this is not generally the case. Okay, that’s enough for now, not least because as I’m typing this, it’s almost 2am, I have to go to work tomorrow, and my alarm is set for 6am – eek! In my next post, I’ll either cover the remaining three tables in the alert schema, or I’ll delve into the way SQL Monitor stores its monitoring data, as I’d originally planned to cover in this post.

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  • OBIEE 11.1.1 - User Interface (UI) Performance Is Slow With Internet Explorer 8

    - by Ahmed A
    The OBIEE 11g UI is performance is slow in IE 8 and faster in Firefox.  For VPN or WAN users, it takes long time to display links on Dashboards via IE 8. Cause is IE 8 generates many HTTP 304 return calls and this caused the 11g UI slower when compared to the Mozilla FireFox browser. To resolve this issue, you can implement HTTP compression and caching. This is a best practice.Why use Web Server Compression / Caching for OBIEE? Bandwidth Savings: Enabling HTTP compression can have a dramatic improvement on the latency of responses. By compressing static files and dynamic application responses, it will significantly reduce the remote (high latency) user response time. Improves request/response latency: Caching makes it possible to suppress the payload of the HTTP reply using the 304 status code.  Minimizing round trips over the Web to re-validate cached items can make a huge difference in browser page load times. This screen shot depicts the flow and where the compression and decompression occurs: Solution: a. How to Enable HTTP Caching / Compression in Oracle HTTP Server (OHS) 11.1.1.x 1. To implement HTTP compression / caching, install and configure Oracle HTTP Server (OHS) 11.1.1.x for the bi_serverN Managed Servers (refer to "OBIEE Enterprise Deployment Guide for Oracle Business Intelligence" document for details). 2. On the OHS machine, open the file HTTP Server configuration file (httpd.conf) for editing. This file is located in the OHS installation directory.For example: ORACLE_HOME/Oracle_WT1/instances/instance1/config/OHS/ohs13. In httpd.conf file, verify that the following directives are included and not commented out: LoadModule expires_module "${ORACLE_HOME}/ohs/modules/mod_expires.soLoadModule deflate_module "${ORACLE_HOME}/ohs/modules/mod_deflate.so 4. Add the following lines in httpd.conf file below the directive LoadModule section and restart the OHS: Note: For the Windows platform, you will need to enclose any paths in double quotes ("), for example:Alias "/analytics ORACLE_HOME/bifoundation/web/app"<Directory "ORACLE_HOME/bifoundation/web/app"> Alias /analytics ORACLE_HOME/bifoundation/web/app#Pls replace the ORACLE_HOME with your actual BI ORACLE_HOME path<Directory ORACLE_HOME/bifoundation/web/app>#We don't generate proper cross server ETags so disable themFileETag noneSetOutputFilter DEFLATE# Don't compress imagesSetEnvIfNoCase Request_URI \.(?:gif|jpe?g|png)$ no-gzip dont-vary<FilesMatch "\.(gif|jpeg|png|js|x-javascript|javascript|css)$">#Enable future expiry of static filesExpiresActive onExpiresDefault "access plus 1 week"     #1 week, this will stops the HTTP304 calls i.e. generated by IE 8Header set Cache-Control "max-age=604800"</FilesMatch>DirectoryIndex default.jsp</Directory>#Restrict access to WEB-INF<Location /analytics/WEB-INF>Order Allow,DenyDeny from all</Location> Note: Make sure you replace above placeholder "ORACLE_HOME" to your correct path for BI ORACLE_HOME.For example: my BI Oracle Home path is /Oracle/BIEE11g/Oracle_BI1/bifoundation/web/app Important Notes: Above caching rules restricted to static files found inside the /analytics directory(/web/app). This approach is safer instead of setting static file caching globally. In some customer environments you may not get 100% performance gains in IE 8.0 browser. So in that case you need to extend caching rules to other directories with static files content. If OHS is installed on separate dedicated machine, make sure static files in your BI ORACLE_HOME (../Oracle_BI1/bifoundation/web/app) is accessible to the OHS instance. The following screen shot summarizes the before and after results and improvements after enabling compression and caching:

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  • Merge sort versus quick sort performance

    - by Giorgio
    I have implemented merge sort and quick sort using C (GCC 4.4.3 on Ubuntu 10.04 running on a 4 GB RAM laptop with an Intel DUO CPU at 2GHz) and I wanted to compare the performance of the two algorithms. The prototypes of the sorting functions are: void merge_sort(const char **lines, int start, int end); void quick_sort(const char **lines, int start, int end); i.e. both take an array of pointers to strings and sort the elements with index i : start <= i <= end. I have produced some files containing random strings with length on average 4.5 characters. The test files range from 100 lines to 10000000 lines. I was a bit surprised by the results because, even though I know that merge sort has complexity O(n log(n)) while quick sort is O(n^2), I have often read that on average quick sort should be as fast as merge sort. However, my results are the following. Up to 10000 strings, both algorithms perform equally well. For 10000 strings, both require about 0.007 seconds. For 100000 strings, merge sort is slightly faster with 0.095 s against 0.121 s. For 1000000 strings merge sort takes 1.287 s against 5.233 s of quick sort. For 5000000 strings merge sort takes 7.582 s against 118.240 s of quick sort. For 10000000 strings merge sort takes 16.305 s against 1202.918 s of quick sort. So my question is: are my results as expected, meaning that quick sort is comparable in speed to merge sort for small inputs but, as the size of the input data grows, the fact that its complexity is quadratic will become evident? Here is a sketch of what I did. In the merge sort implementation, the partitioning consists in calling merge sort recursively, i.e. merge_sort(lines, start, (start + end) / 2); merge_sort(lines, 1 + (start + end) / 2, end); Merging of the two sorted sub-array is performed by reading the data from the array lines and writing it to a global temporary array of pointers (this global array is allocate only once). After each merge the pointers are copied back to the original array. So the strings are stored once but I need twice as much memory for the pointers. For quick sort, the partition function chooses the last element of the array to sort as the pivot and scans the previous elements in one loop. After it has produced a partition of the type start ... {elements <= pivot} ... pivotIndex ... {elements > pivot} ... end it calls itself recursively: quick_sort(lines, start, pivotIndex - 1); quick_sort(lines, pivotIndex + 1, end); Note that this quick sort implementation sorts the array in-place and does not require additional memory, therefore it is more memory efficient than the merge sort implementation. So my question is: is there a better way to implement quick sort that is worthwhile trying out? If I improve the quick sort implementation and perform more tests on different data sets (computing the average of the running times on different data sets) can I expect a better performance of quick sort wrt merge sort? EDIT Thank you for your answers. My implementation is in-place and is based on the pseudo-code I have found on wikipedia in Section In-place version: function partition(array, 'left', 'right', 'pivotIndex') where I choose the last element in the range to be sorted as a pivot, i.e. pivotIndex := right. I have checked the code over and over again and it seems correct to me. In order to rule out the case that I am using the wrong implementation I have uploaded the source code on github (in case you would like to take a look at it). Your answers seem to suggest that I am using the wrong test data. I will look into it and try out different test data sets. I will report as soon as I have some results.

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  • How to design a scalable notification system?

    - by Trent
    I need to write a notification system manager. Here is my requirements: I need to be able to send a Notification on different platforms, which may be totally different (for exemple, I need to be able to send either an SMS or an E-mail). Sometimes the notification may be the same for all recipients for a given platform, but sometimes it may be a notification per recipients (or several) per platform. Each notification can contain platform specific payload (for exemple an MMS can contains a sound or an image). The system need to be scalable, I need to be able to send a very large amount of notification without crashing either the application or the server. It is a two step process, first a customer may type a message and choose a platform to send to, and the notification(s) should be created to be processed either real-time either later. Then the system needs to send the notification to the platform provider. For now, I end up with some though but I don't know how scalable it will be or if it is a good design. I've though of the following objects (in a pseudo language): a generic Notification object: class Notification { String $message; Payload $payload; Collection<Recipient> $recipients; } The problem with the following objects is what if I've 1.000.000 recipients ? Even if the Recipient object is very small, it'll take too much memory. I could also create one Notification per recipient, but some platform providers requires me to send it in batch, meaning I need to define one Notification with several Recipients. Each created notification could be stored in a persistent storage like a DB or Redis. Would it be a good it to aggregate this later to make sure it is scalable? On the second step, I need to process this notification. But how could I distinguish the notification to the right platform provider? Should I use an object like MMSNotification extending an abstract Notification? or something like Notification.setType('MMS')? To allow to process a lot of notification at the same time, I think a messaging queue system like RabbitMQ may be the right tool. Is it? It would allow me to queue a lot of notification and have several worker to pop notification and process them. But what if I need to batch the recipients as seen above? Then I imagine a NotificationProcessor object for which I could I add NotificationHandler each NotificationHandler would be in charge to connect the platform provider and perform notification. I can also use an EventManager to allow pluggable behavior. Any feedbacks or ideas? Thanks for giving your time. Note: I'm used to work in PHP and it is likely the language of my choice.

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  • What is the most efficient way to handle points / small vectors in JavaScript?

    - by Chris
    Currently I'm creating an web based (= JavaScript) application thata is using a lot of "points" (= small, fixed size vectors). There are basically two obvious ways of representing them: var pointA = [ xValue, yValue ]; and var pointB = { x: xValue, y: yValue }; So translating my point a bit would look like: var pointAtrans = [ pointA[0] + 3, pointA[1] + 4 ]; var pointBtrans = { x: pointB.x + 3, pointB.y + 4 }; Both are easy to handle from a programmer point of view (the object variant is a bit more readable, especially as I'm mostly dealing with 2D data, seldom with 3D and hardly with 4D - but never more. It'll allways fit into x,y,z and w) But my question is now: What is the most efficient way from the language perspective - theoretically and in real implementations? What are the memory requirements? What are the setup costs of an array vs. an object? ... My target browsers are FireFox and the Webkit based ones (Chromium, Safari), but it wouldn't hurt to have a great (= fast) experience under IE and Opera as well...

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  • DDD Model Design and Repository Persistence Performance Considerations

    - by agarhy
    So I have been reading about DDD for some time and trying to figure out the best approach on several issues. I tend to agree that I should design my model in a persistent agnostic manner. And that repositories should load and persist my models in valid states. But are these approaches realistic practically? I mean its normal for a model to hold a reference to a collection of another type. Persisting that model should mean persist the entire collection. Fine. But do I really need to load the entire collection every time I load the model? Probably not. So I can have specialized repositories. Some that load maybe a subset of the object graph via DTOs and others that load the entire object graph. But when do I use which? If I have DTOs, what's stopping client code from directly calling them and completely bypassing the model? I can have mappers and factories to create my models from DTOs maybe? But depending on the design of my models that might not always work. Or it might not allow my models to be created in a valid state. What's the correct approach here?

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  • Java game object pool management

    - by Kenneth Bray
    Currently I am using arrays to handle all of my game objects in the game I am making, and I know how terrible this is for performance. My question is what is the best way to handle game objects and not hurt performance? Here is how I am creating an array and then looping through it to update the objects in the array: public static ArrayList<VboCube> game_objects = new ArrayList<VboCube>(); /* add objects to the game */ while (!Display.isCloseRequested() && !Keyboard.isKeyDown(Keyboard.KEY_ESCAPE)) { for (int i = 0; i < game_objects.size(); i++){ // draw the object game_objects.get(i).Draw(); game_objects.get(i).Update(); //world.updatePhysics(); } } I am not looking for someone to write me code for asset or object management, just point me into a better direction to get better performance. I appreciate the help you guys have provided me in the past, and I dont think I would be as far along with my project without the support on stack exchange!

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  • ATI proprietary driver performance?

    - by Axel
    I'm about to (at least, want to..) buy a laptop with an ATI Radeon HD 4250, and I haven't a good opinion on ATI's drivers. How is the actual performance of the open/proprietary driver (currently I have nVidia, and I'm very satisfied)? The intended use for the laptop is: watching videos, programming in Java/PHP/maybe Qt... but, I like to know if Compiz runs well. Yes, I'm a hardcore (?) programmer that uses Compiz. :P Someone has this GPU? Experiences? Thoughts? Thanks! :D

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  • Intel Xeon 5600 (Westmere-EP) and AMD Magny-Cours Performance Update

    - by jchang
    HP has just released TPC-C and TPC-E results for the ProLiant DL380G7 with 2 Xeon 5680 3.33GHz 6-core processor, allowing a direct comparison with their DL385G& with 2 Opteron 6176 2.3GHz 12-core processors. Last month I complained about the lack of performance results for the Intel Xeon 5600 6-core 32nm processor line for 2-way systems. This might have been deliberate to not complicate the message for the Xeon 7500 8-core 45nm (for 4-way+ systems) launch two weeks later. http://sqlblog.com/blogs/joe_chang/archive/2010/04/07/intel-xeon-5600-westmere-ep-and-7500-nehalem-ex.aspx...(read more)

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  • How does ecryptfs impact harddisk performance?

    - by Freddi
    I have my home directy encrypted with ecryptfs. Does ecryptfs lead to fragmentation? I have the feeling that reading files, displaying folders and login became continuously slower and slower (although it was not noticeably slow at the beginning). The hard disk makes a lot of seek noise even if I open only a text file. In /home/.ecryptfs I see many big archives (that probably contain the encrypted files), so I'm wondering if Linux file system online defragmentation gains anything here. What options do I have to increase performance? Should I decide whether I maybe better do without encryption?

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  • Quick ways to boost performance and scalability of ASP.NET, WCF and Desktop Clients

    - by oazabir
    There are some simple configuration changes that you can make on machine.config and IIS to give your web applications significant performance boost. These are simple harmless changes but makes a lot of difference in terms of scalability. By tweaking system.net changes, you can increase the number of parallel calls that can be made from the services hosted on your servers as well as on desktop computers and thus increase scalability. By changing WCF throttling config you can increase number of simultaneous calls WCF can accept and thus make most use of your hardware power. By changing ASP.NET process model, you can increase number of concurrent requests that can be served by your website. And finally by turning on IIS caching and dynamic compression, you can dramatically increase the page download speed on browsers and and overall responsiveness of your applications. Read the CodeProject article for more details. http://www.codeproject.com/KB/webservices/quickwins.aspx Please vote for me if you find the article useful.

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  • Java performance of StringBuilder append chains

    - by ultimate_guy
    In Java, if I am building a significant number of strings, is there any difference in performance in the following two examples? StringBuilder sb = new StringBuilder(); for (int i = 0; i < largeNumber; i++) { sb.append(var[i]); sb.append('='); sb.append(value[i]); sb.append(','); } or StringBuilder sb = new StringBuilder(); for (int i = 0; i < largeNumber; i++) { sb.append(var[i]).append('=').append(value[i]).append(','); } Thanks!

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