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  • Demantra Performance Clustering Factor Out of Order Ratio TABLE_REORG CHECK_REORG (Doc ID 1594372.1)

    - by user702295
    Hello!   There is a new document available: Demantra Performance Clustering Factor Out of Order Ratio TABLE_REORG CHECK_REORG (Doc ID 1594372.1) Demantra Performance Clustering Factor Out of Order Ratio TABLE_REORG CHECK_REORG The table reorganization can be setup to automatically run in version 7.3.1.5.  In version 12.2.2 we run the TABLE_REORG.CHECK_REORG function at every appserver restart. If the function recommends a reorg then we strongly encourage to reorg the database object.  This is documented in the official docs. In versions 7.3.1.3 and 7.3.1.4, the TABLE_REORG module exists and can be used. It has two main functions that are documented in the Implementation Guide Supplement, Release 7.3, Part No. E26760-03, chapter 4. In short, if you are using version 7.3.1.3 or higher, you can check for the need to run a reorg by doing the following 2 steps: 1. Run TABLE_REORG.CHECK_REORG('T'); 2. Check the table LOG_TABLE_REORG for recommendations If you are on a version before 7.3.1.3, you will need to follow the instructions below to determine if you need to do a manual reorg. How to determine if a table reorg is needed 1. It is strongly encouraged by DEV that You gather statistics on the required table.  The prefered percentage for the gather is 100%. 2. Run the following SQL to evaluate how table reorg might affect Primary Key (PK) based access:   SELECT ui.index_name,trunc((ut.num_rows/ui.clustering_factor)/(ut.num_rows/ut.blocks),2) FROM user_indexes ui, user_tables ut, user_constraints uc WHERE ui.table_name=ut.table_name AND ut.table_name=uc.table_name AND ui.index_name=uc.index_name AND UC.CONSTRAINT_TYPE='P' AND ut.table_name=upper('&enter_table_name');   3. Based on the result: VALUE ABOVE 0.75 - DOES NOT REQUIRE REORG VALUE BETWEEN 0.5 AND 0.75 - REORG IS RECOMMENDED VALUE LOWER THAN 0.5 - IT IS HIGHLY RECOMMENDED TO REORG

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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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  • SQL server virtual memory usage and performance

    - by user365035
    Hello, I have a very large DB used mostly for analytics. The performance overall is very sluggish. I just noticed that when running the query below, the amount of virtual memory used greatly exceeds the amount of physical memory available. Currently, physical memory is 10GB (10238k bytes) whereas the virtual memory returns significantly more - 8388607k bytes. That seems really wrong, but I'm at a bit of a loss on how to proceed. USE [master]; GO select cpu_count , hyperthread_ratio , physical_memory_in_bytes / 1048576 as 'mem_MB' , virtual_memory_in_bytes / 1048576 as 'virtual_mem_MB' , max_workers_count , os_error_mode , os_priority_class from sys.dm_os_sys_info

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  • .NET Reflector 7.2 Early Access Build 2 Released: Performance Critical

    - by Bart Read
    I've just posted a write-up of some of the performance tuning I've done to improve .NET Reflector 7.2's start-up time here: http://www.reflector.net/2011/05/net-reflector-7-start-up-time-running-out-of-gas-or-pedal-to-the-metal/ You can get the new build from the .NET Reflector homepage at http://www.reflector.net/. Please remember to give us your feedback in the forum, at http://forums.reflector.net/, using the tags #7.2 and #eap. Technorati Tags: reflector,early access,7.2

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  • OBIEE 11.1.1 - Built-in BI Metrics for Performance Monitoring

    - by Ahmed Awan
    You can use Fusion Middleware Control metrics to monitor System Components (BI processes) and WebLogic Server processes.   Tip: ·         Use Oracle Enterprise Manager (EM) URL to monitor end to end OBIEE real time performance: :7001/em"http://<server>:7001/em ·         In Oracle Business Intelligence 11g, the perfmon URL is still valid to use i.e. :9704/analytics/saw.dll?Perfmon"http://<server>:9704/analytics/saw.dll?Perfmon

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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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  • 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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  • 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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  • 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(@[email protected] 8) full(@[email protected]) DBHintEp_Load_SalesInsertLd  = + parallel(@[email protected] 8) DBHintEp_Load_SalesMergeSALES_DATA = + parallel(@[email protected] 8) full(@[email protected]) DBHintMdp_AddUpdateIs_Fictive0SD = + parallel(s 8 ) DBHintMdp_AddUpdateIs_Fictive2SD = + parallel(s 8 )

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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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