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  • SQL SERVER – Speed Up! – Parallel Processes and Unparalleled Performance – TechEd 2012 India

    - by pinaldave
    TechEd India 2012 is just around the corner and I will be presenting there on two different session. SQL Server Performance Tuning is a very challenging subject that requires expertise in Database Administration and Database Development. I always have enjoyed talking about SQL Server Performance tuning subject. Just like doctors I like to call my every attempt to improve the performance of SQL Server queries and database server as a practice too. I have been working with SQL Server for more than 8 years and I believe that many of the performance tuning concept I have mastered. However, performance tuning is not a simple subject. However there are occasions when I feel stumped, there are occasional when I am not sure what should be the next step. When I face situation where I cannot figure things out easily, it makes me most happy because I clearly see this as a learning opportunity. I have been presenting in TechEd India for last three years. This is my fourth time opportunity to present a technical session on SQL Server. Just like every other year, I decided to present something different, something which I have spend years of learning. This time, I am going to present about parallel processes. It is widely believed that more the CPU will improve performance of the server. It is true in many cases. However, there are cases when limiting the CPU usages have improved overall health of the server. I will be presenting on the subject of Parallel Processes and its effects. I have spent more than a year working on this subject only. After working on various queries on multi-CPU systems I have personally learned few things. In coming TechEd session, I am going to share my experience with parallel processes and performance tuning. Session Details Title: Speed Up! – Parallel Processes and Unparalleled Performance (Add to Calendar) Abstract: “More CPU More Performance” – A  very common understanding is that usage of multiple CPUs can improve the performance of the query. To get maximum performance out of any query – one has to master various aspects of the parallel processes. In this deep dive session, we will explore this complex subject with a very simple interactive demo. An attendee will walk away with proper understanding of CX_PACKET wait types, MAXDOP, parallelism threshold and various other concepts. Date and Time: March 23, 2012, 12:15 to 13:15 Location: Hotel Lalit Ashok - Kumara Krupa High Grounds, Bengaluru – 560001, Karnataka, India. Add to Calendar Please submit your questions in the comments area and I will be for sure discussing them during my session. If I pick your question to discuss during my session, here is your gift I commit right now – SQL Server Interview Questions and Answers Book. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology Tagged: TechEd, TechEdIn

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  • Performance profiler for a java application

    - by Nitin Garg
    I need to optimize a java application. It makes some 3rd party calls. I need some good tool to accurately measure the time taken by individual api calls. To give an idea of complexity- the application takes a data source file containing 10 lakh rows, and it takes around one hour to complete the processing. As a part of processing , it makes some 3rd party calls (including some network calls). I need to identify which calls are taking more time then others, and based on that, find out a way to optimize the application. Any suggestions would be appreciated.

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  • SQL 2008 Database tuning advisor won’t start

    - by Andrew Hancox
    For some reason I can't get DTA to connect to my development machine. It connects to a remote DB just fine but when I point it to my dev machine I get an error saying: Failed to initialize MSDB database for tuning (exit code: -1073741819). I'm pretty sure it's not a permissions issue since I've used profiler to capture what it's doing and all of the commands it's run so far look fine and are being run under my account which is associated with the sysadmin role, when I run them in sql management studio they go through fine. I'm pretty convinced that the problem is related to creating the objects in MSDB that are used by DTA but I tried creating these manually (I found scripts on the web) and it just seems to push the problem along the line slightly. I'm going out of my mind - have even tried reinstalling SQL but that's not fixed it. I'm using SQL 2008 with SP1 (10.0.2531) on windows server 2008 (patched up to date). SAVE ME!!!!!

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  • Performance Tuning a High-Load Apache Server

    - by futureal
    I am looking to understand some server performance problems I am seeing with a (for us) heavily loaded web server. The environment is as follows: Debian Lenny (all stable packages + patched to security updates) Apache 2.2.9 PHP 5.2.6 Amazon EC2 large instance The behavior we're seeing is that the web typically feels responsive, but with a slight delay to begin handling a request -- sometimes a fraction of a second, sometimes 2-3 seconds in our peak usage times. The actual load on the server is being reported as very high -- often 10.xx or 20.xx as reported by top. Further, running other things on the server during these times (even vi) is very slow, so the load is definitely up there. Oddly enough Apache remains very responsive, other than that initial delay. We have Apache configured as follows, using prefork: StartServers 5 MinSpareServers 5 MaxSpareServers 10 MaxClients 150 MaxRequestsPerChild 0 And KeepAlive as: KeepAlive On MaxKeepAliveRequests 100 KeepAliveTimeout 5 Looking at the server-status page, even at these times of heavy load we are rarely hitting the client cap, usually serving between 80-100 requests and many of those in the keepalive state. That tells me to rule out the initial request slowness as "waiting for a handler" but I may be wrong. Amazon's CloudWatch monitoring tells me that even when our OS is reporting a load of 15, our instance CPU utilization is between 75-80%. Example output from top: top - 15:47:06 up 31 days, 1:38, 8 users, load average: 11.46, 7.10, 6.56 Tasks: 221 total, 28 running, 193 sleeping, 0 stopped, 0 zombie Cpu(s): 66.9%us, 22.1%sy, 0.0%ni, 2.6%id, 3.1%wa, 0.0%hi, 0.7%si, 4.5%st Mem: 7871900k total, 7850624k used, 21276k free, 68728k buffers Swap: 0k total, 0k used, 0k free, 3750664k cached The majority of the processes look like: 24720 www-data 15 0 202m 26m 4412 S 9 0.3 0:02.97 apache2 24530 www-data 15 0 212m 35m 4544 S 7 0.5 0:03.05 apache2 24846 www-data 15 0 209m 33m 4420 S 7 0.4 0:01.03 apache2 24083 www-data 15 0 211m 35m 4484 S 7 0.5 0:07.14 apache2 24615 www-data 15 0 212m 35m 4404 S 7 0.5 0:02.89 apache2 Example output from vmstat at the same time as the above: procs -----------memory---------- ---swap-- -----io---- -system-- ----cpu---- r b swpd free buff cache si so bi bo in cs us sy id wa 8 0 0 215084 68908 3774864 0 0 154 228 5 7 32 12 42 9 6 21 0 198948 68936 3775740 0 0 676 2363 4022 1047 56 16 9 15 23 0 0 169460 68936 3776356 0 0 432 1372 3762 835 76 21 0 0 23 1 0 140412 68936 3776648 0 0 280 0 3157 827 70 25 0 0 20 1 0 115892 68936 3776792 0 0 188 8 2802 532 68 24 0 0 6 1 0 133368 68936 3777780 0 0 752 71 3501 878 67 29 0 1 0 1 0 146656 68944 3778064 0 0 308 2052 3312 850 38 17 19 24 2 0 0 202104 68952 3778140 0 0 28 90 2617 700 44 13 33 5 9 0 0 188960 68956 3778200 0 0 8 0 2226 475 59 17 6 2 3 0 0 166364 68956 3778252 0 0 0 21 2288 386 65 19 1 0 And finally, output from Apache's server-status: Server uptime: 31 days 2 hours 18 minutes 31 seconds Total accesses: 60102946 - Total Traffic: 974.5 GB CPU Usage: u209.62 s75.19 cu0 cs0 - .0106% CPU load 22.4 requests/sec - 380.3 kB/second - 17.0 kB/request 107 requests currently being processed, 6 idle workers C.KKKW..KWWKKWKW.KKKCKK..KKK.KKKK.KK._WK.K.K.KKKKK.K.R.KK..C.C.K K.C.K..WK_K..KKW_CK.WK..W.KKKWKCKCKW.W_KKKKK.KKWKKKW._KKK.CKK... KK_KWKKKWKCKCWKK.KKKCK.......................................... ................................................................ From my limited experience I draw the following conclusions/questions: We may be allowing far too many KeepAlive requests I do see some time spent waiting for IO in the vmstat although not consistently and not a lot (I think?) so I am not sure this is a big concern or not, I am less experienced with vmstat Also in vmstat, I see in some iterations a number of processes waiting to be served, which is what I am attributing the initial page load delay on our web server to, possibly erroneously We serve a mixture of static content (75% or higher) and script content, and the script content is often fairly processor intensive, so finding the right balance between the two is important; long term we want to move statics elsewhere to optimize both servers but our software is not ready for that today I am happy to provide additional information if anybody has any ideas, the other note is that this is a high-availability production installation so I am wary of making tweak after tweak, and is why I haven't played with things like the KeepAlive value myself yet.

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  • High Performance Storage Systems for SQL Server

    Rod Colledge turns his pessimistic mindset to storage systems, and describes the best way to configure the storage systems of SQL Servers for both performance and reliability. Even Rod gets a glint in his eye when he then goes on to describe the dazzling speed of solid-state storage, though he is quick to identify the risks.

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  • Compute Scalars, Expressions and Execution Plan Performance

    - by Paul White
    The humble Compute Scalar is one of the least well-understood of the execution plan operators, and usually the last place people look for query performance problems. It often appears in execution plans with a very low (or even zero) cost, which goes some way to explaining why people ignore it. Some readers will already know that a Compute Scalar can contain a call to a user-defined function, and that any T-SQL function with a BEGIN…END block in its definition can have truly disastrous consequences...(read more)

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  • Let the RAM improves performance

    - by user1717079
    I have a low profile machine but with a lot of fast RAM, 4 Gb, which is really an amount of memory that i probably will never use, not even an half, since i just use this machine for coding and browsing the web. The HDD is really slow and so the overall performance are bad when booting, caching or starting new program, I'm wondering if Ubuntu can provide some setting or utility to solve this situation and let my system rely more on the RAM usage.

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  • Can frequent state changes decrease rendering performance?

    - by Miro
    Can frequent texture and shader binding decrease rendering performance? "Frequent" binding example: for object for material in object render part of object using that material "Low count" binding example: for material for object in material render part of object using that material I'm planning to use an octree later and with this "low count" method of rendering it can drastically increase memory consumption. So is it good idea?

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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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  • Brendan Gregg's "Systems Performance: Enterprise and the Cloud"

    - by user12608550
    Long ago, the prerequisite UNIX performance book was Adrian Cockcroft's 1994 classic, Sun Performance and Tuning: Sparc & Solaris, later updated in 1998 as Java and the Internet. As Solaris evolved to include the invaluable DTrace observability features, new essential performance references have been published, such as Solaris Performance and Tools: DTrace and MDB Techniques for Solaris 10 and OpenSolaris (2006)  by McDougal, Mauro, and Gregg, and DTrace: Dynamic Tracing in Oracle Solaris, Mac OS X and FreeBSD (2011), also by Mauro and Gregg. Much has occurred in Solaris Land since those books appeared, notably Oracle's acquisition of Sun Microsystems in 2010 and the demise of the OpenSolaris community. But operating system technologies have continued to improve markedly in recent years, driven by stunning advances in multicore processor architecture, virtualization, and the massive scalability requirements of cloud computing. A new performance reference was needed, and I eagerly waited for something that thoroughly covered modern, distributed computing performance issues from the ground up. Well, there's a new classic now, authored yet again by Brendan Gregg, former Solaris kernel engineer at Sun and now Lead Performance Engineer at Joyent. Systems Performance: Enterprise and the Cloud is a modern, very comprehensive guide to general system performance principles and practices, as well as a highly detailed reference for specific UNIX and Linux observability tools used to examine and diagnose operating system behaviour.  It provides thorough definitions of terms, explains performance diagnostic Best Practices and "Worst Practices" (called "anti-methods"), and covers key observability tools including DTrace, SystemTap, and all the traditional UNIX utilities like vmstat, ps, iostat, and many others. The book focuses on operating system performance principles and expands on these with respect to Linux (Ubuntu, Fedora, and CentOS are cited), and to Solaris and its derivatives [1]; it is not directed at any one OS so it is extremely useful as a broad performance reference. The author goes beyond the intricacies of performance analysis and shows how to interpret and visualize statistical information gathered from the observability tools.  It's often difficult to extract understanding from voluminous rows of text output, and techniques are provided to assist with summarizing, visualizing, and interpreting the performance data. Gregg includes myriad useful references from the system performance literature, including a "Who's Who" of contributors to this great body of diagnostic tools and methods. This outstanding book should be required reading for UNIX and Linux system administrators as well as anyone charged with diagnosing OS performance issues.  Moreover, the book can easily serve as a textbook for a graduate level course in operating systems [2]. [1] Solaris 11, of course, and Joyent's SmartOS (developed from OpenSolaris) [2] Gregg has taught system performance seminars for many years; I have also taught such courses...this book would be perfect for the OS component of an advanced CS curriculum.

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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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  • DB2 increase bufferpool size and compressed tables not equal better performance. Why?

    - by Mestika
    Hi, I’m working on tuning and increasing the performance of my IBM DB2 version 9.7 database. I’ve been searching around the net for the last couple of days and learned that if I created my tables in COMPRESS mode and created one more bufferpool and set both of them to access 1024mb, then the performance in my queries should increase because of the less I/Os to the disks. However, when I run my time analysis, the performance Decrease. I added the new additions to my regular database with the indexes I’ve used all the time. Each time I search google I come up with the statement that: Increased bufferpool size and several bufferpools AND a table compression SHOULD prove to get better performance. I’m very puzzled about the total unexpected result. Are there some tuning mechanisms I’ve forgot or does anyone have a explanation for this odd behavior? Sincerely Mestika

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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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  • 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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  • SQL SERVER – Maximize Database Performance with DB Optimizer – SQL in Sixty Seconds #054

    - by Pinal Dave
    Performance tuning is an interesting concept and everybody evaluates it differently. Every developer and DBA have different opinion about how one can do performance tuning. I personally believe performance tuning is a three step process Understanding the Query Identifying the Bottleneck Implementing the Fix While, we are working with large database application and it suddenly starts to slow down. We are all under stress about how we can get back the database back to normal speed. Most of the time we do not have enough time to do deep analysis of what is going wrong as well what will fix the problem. Our primary goal at that time is to just fix the database problem as fast as we can. However, here is one very important thing which we need to keep in our mind is that when we do quick fix, it should not create any further issue with other parts of the system. When time is essence and we want to do deep analysis of our system to give us the best solution we often tend to make mistakes. Sometimes we make mistakes as we do not have proper time to analysis the entire system. Here is what I do when I face such a situation – I take the help of DB Optimizer. It is a fantastic tool and does superlative performance tuning of the system. Everytime when I talk about performance tuning tool, the initial reaction of the people is that they do not want to try this as they believe it requires lots of the learning of the tool before they use it. It is absolutely not true with the case of the DB optimizer. It is a very easy to use and self intuitive tool. Once can get going with the product, in no time. Here is a quick video I have build where I demonstrate how we can identify what index is missing for query and how we can quickly create the index. Entire three steps of the query tuning are completed in less than 60 seconds. If you are into performance tuning and query optimization you should download DB Optimizer and give it a go. Let us see the same concept in following SQL in Sixty Seconds Video: You can Download DB Optimizer and reproduce the same Sixty Seconds experience. Related Tips in SQL in Sixty Seconds: Performance Tuning – Part 1 of 2 – Getting Started and Configuration Performance Tuning – Part 2 of 2 – Analysis, Detection, Tuning and Optimizing What would you like to see in the next SQL in Sixty Seconds video? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Interview Questions and Answers, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Identity

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  • Is there a PHP benchmark that meets these specific criteria? [closed]

    - by Alex R
    I'm working on a tool which converts PHP code to Scala. As one of the finishing touches, I'm in need of a really good (er, somewhat biased) benchmark. By dumb luck my first benchmark attempt was with some code which uses bcmath extensively, which unfortunately is 1000x slower in Java, making the Scala code 22x slower overall than the original PHP. So I'm looking for some meaningful PHP benchmark with the following characteristics: The PHP source needs to be in a single file. It should solve a real-world problem. No silly looping over empty methods etc. I need it to be simple to setup - no databases, hard-to-find input files, etc. Simple text input and output preferred. It should not use features that are slow in Java (BigInteger, trigonometric functions, etc). It should not use exoteric or dynamic PHP functions (e.g. no "eval" or "variable vars"). It should not over-rely on built-in libraries, e.g. MD5, crypt, etc. It should not be I/O bound. A CPU-bound memory-hungry algorithm is preferred. Basically, intensive OO operations, integer and string manipulation, recursion, etc would be great. Thanks

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  • What's the largest (most complex) PHP algorithm ever implemented in a single monolithic PHP script?

    - by Alex R
    I'm working on a tool which converts PHP code to Scala. As one of the finishing touches, I'm in need of a really good (er, somewhat biased) benchmark. By dumb luck my first benchmark attempt was with some code which uses bcmath extensively, which unfortunately is 1000x slower in Java, making the Scala code 22x slower overall than the original PHP. So I'm looking for some meaningful PHP benchmark with the following characteristics: The source needs to be in a single file. I need it to be simple to setup - no databases, hard-to-find input files, etc. Simple text input and output preferred. It should not use features that are slow in Java (BigInteger, trigonometric functions, etc). It should not use exoteric or dynamic PHP functions (e.g. no "eval" or "variable vars"). It should not over-rely on built-in libraries, e.g. MD5, crypt, etc. It should not be I/O bound. A CPU-bound memory-hungry algorithm is preferred. Basically, intensive OO operations, integer and string manipulation, recursion, etc would be great. Thanks

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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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  • SQL SERVER – Video – Performance Improvement in Columnstore Index

    - by pinaldave
    I earlier wrote an article about SQL SERVER – Fundamentals of Columnstore Index and it got very well accepted in community. However, one of the suggestion I keep on receiving for that article is that many of the reader wanted to see columnstore index in the action but they were not able to do that. Some of the readers did not install SQL Server 2012 or some did not have good machine to recreate the big table involved in the demo. For the same reason, I have created small video for that. I have written two more article on columstore index. Please read them as followup to the video: SQL SERVER – How to Ignore Columnstore Index Usage in Query SQL SERVER – Updating Data in A Columnstore Index Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video

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  • How can I improve overall system performance?

    - by Decio Lira
    What are your tips for improving overall system performance on ubuntu? Inspired by this question I realized that some default settings may be rather conservative on Ubuntu and that it's possible to tweak it with little or no risk if you wish to make it faster. This is not meant to be application specific (e.g. make firefox load pages faster), but system wide. Preferably 1 tip per answer, with enough detail for people to implement it. A couple of mine would be: Install Preload (via Software Center or sudo apt-get install preload); Change Swappiness value - "which controls the degree to which the kernel prefers to swap when it tries to free memory"; What are yours? PS: Since this is not intended to have a unique answer but rather, several useful tips, I'm making this community wiki out-of-the-box.

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