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  • Using SQL Server Views with NHibernate

    - by colinramsay
    I have a site that sells cars. On the frontend, I want to only show cars that are published, and on the backend I want to show all cars. Whether a car is published or not depends on a number of factors, so I wanted to create a view to simplify this. My question is, can I reduce duplication by dynamically telling NHibernate to sometimes use the "PublishedCar" view and something use the "AllCar" view when querying/fetching Car entities?

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  • Creating design document from existing java code.

    - by BigBoss
    I have existing java code and need to create Design Document based on that. For starter even if I could get all functions with input / output parameters that will help in overall proces. Note: There is not commeted documentation on any procedures, function or classes. Last but not least. Let me know for any good tool which will reduce time required for this phase. As currently we write every flow and related stuffs.

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  • Google App Engine - The most awaited feature

    - by systempuntoout
    This list is taken from the official Google App Engine roadmap: SSL for third-party domains Background servers capable of running for longer than 30s Ability to reserve instances to reduce application loading overhead Ability to select different availability vs. latency options for Datastore Support for mapping operations across datasets Datastore dump and restore facility Raise request/response size limits for some APIs Improved monitoring and alerting of application serving Support for Browser Push (Comet) communication Built-in support for OAuth & OpenID What is your most awaited feature and why?

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  • What's the big deal with brute force on hashes like MD5

    - by Jan Kuboschek
    I just spent some time reading http://stackoverflow.com/questions/2768248/is-md5-really-that-bad (I highly recommend!). In it, it talks about hash collisions. Maybe I'm missing something here, but can't you just encrypt your password using, say, MD5 and then, say, SHA-1 (or any other, doesn't matter.) Wouldn't this increase the processing power required to brute-force the hash and reduce the possibility of collision?

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  • Another Memory Alignment Question?

    - by utxeeeee
    I understand why data need to be aligned (and all the efforts made to accomplish it like padding) so we can reduce the number of memory accesses but this assumes that processor just can fetch addresses multiples of 4(supposing we are using a 32-bit architecture). And because of that assumption we need to align memory and my question is why we can just access addresses multiple of 4(efficiency, hardware restriction, another one)? Which is the advantages of doing this? Why cannot we access all the addresses available? hugs

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  • How can an improvement to the query cache be tracked?

    - by Bill Paetzke
    I am parameterizing my web app's ad hoc sql. As a result, I expect the query plan cache to reduce in size and have a higher hit ratio. Perhaps even other important metrics will be improved. Could I use perfmon to track this? If so, what counters should I use? If not perfmon, how could I report on the impact of this change?

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  • Any reason to clean up unused imports in Java, other than reducing clutter?

    - by Kip
    Is there any good reason to avoid unused import statements in Java? As I understand it, they are there for the compiler, so lots of unused imports won't have any impacts on the compiled code. Is it just to reduce clutter and to avoid naming conflicts down the line? (I ask because Eclipse gives a warning about unused imports, which is kind of annoying when I'm developing code because I don't want to remove the imports until I'm pretty sure I'm done designing the class.)

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  • Intger encoding and decoding problem

    - by aASDASD
    I have a long list of integers, and i need to reduce this down to a single integer. The integer list can be anywhere from 0 to 300 ints long (about). I need to be able to encode/decode. Is there a better option than a lookup table?

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  • Is it good practice to separate code into blocks?

    - by LM
    If I have a method that does multiple, related things, is it good practice to stick each "thing" that the method does into a seperate block? Ex. { int var //Code } { int var //More Code } It would help reduce the number of local variables, and make the code more readable, but I'm not sure if it's a good idea.

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  • Is jQuery modular? How to trim it down?

    - by usr
    Uncompressed, jQuery is 160KB in size. I did not see a way to exclude seldomly used parts of it like with jQuery UI. How can I reduce the (compressed and minified) file size of jQuery? I am quite concerned because dial-up lines and slow machines/browsers are very common among users of my site.

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  • Which is the fastest idiomatic way to add all vectors (in the math sense) inside a Scala list?

    - by davips
    I have two solutions, but one doesn't compile and the other, I think, could be better: object Foo extends App { val vectors = List(List(1,2,3), List(2,2,3), List(1,2,2)) //just a stupid example //transposing println("vectors = " + vectors.transpose.map (_.sum)) //it prints vectors = List(4, 6, 8) //folding vectors.reduce { case (a, b) => (a zip b) map { case (x, y) => x + y } } //compiler says: missing parameter type for exp. function; arg. types must be fully known }

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  • javascript - query object graph?

    - by Scott
    Given an object like this: var obj = { first:{ second:{ third:'hi there' } } }; And a key like this "first.second.third" How can I get the value of the nested object "hi there"? I think maybe the Array.reduce function could help, but not sure.

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  • php pconnect vs connect

    - by user192344
    if i have a script which insert a data then exit the script will be opened by 100 user at same time or within 2 mins actually im doing email tracking so pconnect is bettwe or connect is better to reduce the resource i have close when after insert

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  • What free space thresholds/limits are advisable for 640 GB and 2 TB hard disk drives with ZEVO ZFS on OS X?

    - by Graham Perrin
    Assuming that free space advice for ZEVO will not differ from advice for other modern implementations of ZFS … Question Please, what percentages or amounts of free space are advisable for hard disk drives of the following sizes? 640 GB 2 TB Thoughts A standard answer for modern implementations of ZFS might be "no more than 96 percent full". However if apply that to (say) a single-disk 640 GB dataset where some of the files most commonly used (by VirtualBox) are larger than 15 GB each, then I guess that blocks for those files will become sub optimally spread across the platters with around 26 GB free. I read that in most cases, fragmentation and defragmentation should not be a concern with ZFS. Sill, I like the mental picture of most fragments of a large .vdi in reasonably close proximity to each other. (Do features of ZFS make that wish for proximity too old-fashioned?) Side note: there might arise the question of how to optimise performance after a threshold is 'broken'. If it arises, I'll keep it separate. Background On a 640 GB StoreJet Transcend (product ID 0x2329) in the past I probably went beyond an advisable threshold. Currently the largest file is around 17 GB –  – and I doubt that any .vdi or other file on this disk will grow beyond 40 GB. (Ignore the purple masses, those are bundles of 8 MB band files.) Without HFS Plus: the thresholds of twenty, ten and five percent that I associate with Mobile Time Machine file system need not apply. I currently use ZEVO Community Edition 1.1.1 with Mountain Lion, OS X 10.8.2, but I'd like answers to be not too version-specific. References, chronological order ZFS Block Allocation (Jeff Bonwick's Blog) (2006-11-04) Space Maps (Jeff Bonwick's Blog) (2007-09-13) Doubling Exchange Performance (Bizarre ! Vous avez dit Bizarre ?) (2010-03-11) … So to solve this problem, what went in 2010/Q1 software release is multifold. The most important thing is: we increased the threshold at which we switched from 'first fit' (go fast) to 'best fit' (pack tight) from 70% full to 96% full. With TB drives, each slab is at least 5GB and 4% is still 200MB plenty of space and no need to do anything radical before that. This gave us the biggest bang. Second, instead of trying to reuse the same primary slabs until it failed an allocation we decided to stop giving the primary slab this preferential threatment as soon as the biggest allocation that could be satisfied by a slab was down to 128K (metaslab_df_alloc_threshold). At that point we were ready to switch to another slab that had more free space. We also decided to reduce the SMO bonus. Before, a slab that was 50% empty was preferred over slabs that had never been used. In order to foster more write aggregation, we reduced the threshold to 33% empty. This means that a random write workload now spread to more slabs where each one will have larger amount of free space leading to more write aggregation. Finally we also saw that slab loading was contributing to lower performance and implemented a slab prefetch mechanism to reduce down time associated with that operation. The conjunction of all these changes lead to 50% improved OLTP and 70% reduced variability from run to run … OLTP Improvements in Sun Storage 7000 2010.Q1 (Performance Profiles) (2010-03-11) Alasdair on Everything » ZFS runs really slowly when free disk usage goes above 80% (2010-07-18) where commentary includes: … OpenSolaris has changed this in onnv revision 11146 … [CFT] Improved ZFS metaslab code (faster write speed) (2010-08-22)

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  • mySQL Optimization Suggestions

    - by Brian Schroeter
    I'm trying to optimize our mySQL configuration for our large Magento website. The reason I believe that mySQL needs to be configured further is because New Relic has shown that our SELECT queries are taking a long time (20,000+ ms) in some categories. I ran MySQLTuner 1.3.0 and got the following results... (Disclaimer: I restarted mySQL earlier after tweaking some settings, and so the results here may not be 100% accurate): >> MySQLTuner 1.3.0 - Major Hayden <[email protected]> >> Bug reports, feature requests, and downloads at http://mysqltuner.com/ >> Run with '--help' for additional options and output filtering [OK] Currently running supported MySQL version 5.5.37-35.0 [OK] Operating on 64-bit architecture -------- Storage Engine Statistics ------------------------------------------- [--] Status: +ARCHIVE +BLACKHOLE +CSV -FEDERATED +InnoDB +MRG_MYISAM [--] Data in MyISAM tables: 7G (Tables: 332) [--] Data in InnoDB tables: 213G (Tables: 8714) [--] Data in PERFORMANCE_SCHEMA tables: 0B (Tables: 17) [--] Data in MEMORY tables: 0B (Tables: 353) [!!] Total fragmented tables: 5492 -------- Security Recommendations ------------------------------------------- [!!] User '@host5.server1.autopartsnetwork.com' has no password set. [!!] User '@localhost' has no password set. [!!] User 'root@%' has no password set. -------- Performance Metrics ------------------------------------------------- [--] Up for: 5h 3m 4s (5M q [317.443 qps], 42K conn, TX: 18B, RX: 2B) [--] Reads / Writes: 95% / 5% [--] Total buffers: 35.5G global + 184.5M per thread (1024 max threads) [!!] Maximum possible memory usage: 220.0G (174% of installed RAM) [OK] Slow queries: 0% (6K/5M) [OK] Highest usage of available connections: 5% (61/1024) [OK] Key buffer size / total MyISAM indexes: 512.0M/3.1G [OK] Key buffer hit rate: 100.0% (102M cached / 45K reads) [OK] Query cache efficiency: 66.9% (3M cached / 5M selects) [!!] Query cache prunes per day: 3486361 [OK] Sorts requiring temporary tables: 0% (0 temp sorts / 812K sorts) [!!] Joins performed without indexes: 1328 [OK] Temporary tables created on disk: 11% (126K on disk / 1M total) [OK] Thread cache hit rate: 99% (61 created / 42K connections) [!!] Table cache hit rate: 19% (9K open / 49K opened) [OK] Open file limit used: 2% (712/25K) [OK] Table locks acquired immediately: 100% (5M immediate / 5M locks) [!!] InnoDB buffer pool / data size: 32.0G/213.4G [OK] InnoDB log waits: 0 -------- Recommendations ----------------------------------------------------- General recommendations: Run OPTIMIZE TABLE to defragment tables for better performance MySQL started within last 24 hours - recommendations may be inaccurate Reduce your overall MySQL memory footprint for system stability Enable the slow query log to troubleshoot bad queries Increasing the query_cache size over 128M may reduce performance Adjust your join queries to always utilize indexes Increase table_cache gradually to avoid file descriptor limits Read this before increasing table_cache over 64: http://bit.ly/1mi7c4C Variables to adjust: *** MySQL's maximum memory usage is dangerously high *** *** Add RAM before increasing MySQL buffer variables *** query_cache_size (> 512M) [see warning above] join_buffer_size (> 128.0M, or always use indexes with joins) table_cache (> 12288) innodb_buffer_pool_size (>= 213G) My my.cnf configuration is as follows... [client] port = 3306 [mysqld_safe] nice = 0 [mysqld] tmpdir = /var/lib/mysql/tmp user = mysql port = 3306 skip-external-locking character-set-server = utf8 collation-server = utf8_general_ci event_scheduler = 0 key_buffer = 512M max_allowed_packet = 64M thread_stack = 512K thread_cache_size = 512 sort_buffer_size = 24M read_buffer_size = 8M read_rnd_buffer_size = 24M join_buffer_size = 128M # for some nightly processes client sessions set the join buffer to 8 GB auto-increment-increment = 1 auto-increment-offset = 1 myisam-recover = BACKUP max_connections = 1024 # max connect errors artificially high to support behaviors of NetScaler monitors max_connect_errors = 999999 concurrent_insert = 2 connect_timeout = 5 wait_timeout = 180 net_read_timeout = 120 net_write_timeout = 120 back_log = 128 # this table_open_cache might be too low because of MySQL bugs #16244691 and #65384) table_open_cache = 12288 tmp_table_size = 512M max_heap_table_size = 512M bulk_insert_buffer_size = 512M open-files-limit = 8192 open-files = 1024 query_cache_type = 1 # large query limit supports SOAP and REST API integrations query_cache_limit = 4M # larger than 512 MB query cache size is problematic; this is typically ~60% full query_cache_size = 512M # set to true on read slaves read_only = false slow_query_log_file = /var/log/mysql/slow.log slow_query_log = 0 long_query_time = 0.2 expire_logs_days = 10 max_binlog_size = 1024M binlog_cache_size = 32K sync_binlog = 0 # SSD RAID10 technically has a write capacity of 10000 IOPS innodb_io_capacity = 400 innodb_file_per_table innodb_table_locks = true innodb_lock_wait_timeout = 30 # These servers have 80 CPU threads; match 1:1 innodb_thread_concurrency = 48 innodb_commit_concurrency = 2 innodb_support_xa = true innodb_buffer_pool_size = 32G innodb_file_per_table innodb_flush_log_at_trx_commit = 1 innodb_log_buffer_size = 2G skip-federated [mysqldump] quick quote-names single-transaction max_allowed_packet = 64M I have a monster of a server here to power our site because our catalog is very large (300,000 simple SKUs), and I'm just wondering if I'm missing anything that I can configure further. :-) Thanks!

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  • Oracle Data Mining a Star Schema: Telco Churn Case Study

    - by charlie.berger
    There is a complete and detailed Telco Churn case study "How to" Blog Series just posted by Ari Mozes, ODM Dev. Manager.  In it, Ari provides detailed guidance in how to leverage various strengths of Oracle Data Mining including the ability to: mine Star Schemas and join tables and views together to obtain a complete 360 degree view of a customer combine transactional data e.g. call record detail (CDR) data, etc. define complex data transformation, model build and model deploy analytical methodologies inside the Database  His blog is posted in a multi-part series.  Below are some opening excerpts for the first 3 blog entries.  This is an excellent resource for any novice to skilled data miner who wants to gain competitive advantage by mining their data inside the Oracle Database.  Many thanks Ari! Mining a Star Schema: Telco Churn Case Study (1 of 3) One of the strengths of Oracle Data Mining is the ability to mine star schemas with minimal effort.  Star schemas are commonly used in relational databases, and they often contain rich data with interesting patterns.  While dimension tables may contain interesting demographics, fact tables will often contain user behavior, such as phone usage or purchase patterns.  Both of these aspects - demographics and usage patterns - can provide insight into behavior.Churn is a critical problem in the telecommunications industry, and companies go to great lengths to reduce the churn of their customer base.  One case study1 describes a telecommunications scenario involving understanding, and identification of, churn, where the underlying data is present in a star schema.  That case study is a good example for demonstrating just how natural it is for Oracle Data Mining to analyze a star schema, so it will be used as the basis for this series of posts...... Mining a Star Schema: Telco Churn Case Study (2 of 3) This post will follow the transformation steps as described in the case study, but will use Oracle SQL as the means for preparing data.  Please see the previous post for background material, including links to the case study and to scripts that can be used to replicate the stages in these posts.1) Handling missing values for call data recordsThe CDR_T table records the number of phone minutes used by a customer per month and per call type (tariff).  For example, the table may contain one record corresponding to the number of peak (call type) minutes in January for a specific customer, and another record associated with international calls in March for the same customer.  This table is likely to be fairly dense (most type-month combinations for a given customer will be present) due to the coarse level of aggregation, but there may be some missing values.  Missing entries may occur for a number of reasons: the customer made no calls of a particular type in a particular month, the customer switched providers during the timeframe, or perhaps there is a data entry problem.  In the first situation, the correct interpretation of a missing entry would be to assume that the number of minutes for the type-month combination is zero.  In the other situations, it is not appropriate to assume zero, but rather derive some representative value to replace the missing entries.  The referenced case study takes the latter approach.  The data is segmented by customer and call type, and within a given customer-call type combination, an average number of minutes is computed and used as a replacement value.In SQL, we need to generate additional rows for the missing entries and populate those rows with appropriate values.  To generate the missing rows, Oracle's partition outer join feature is a perfect fit.  select cust_id, cdre.tariff, cdre.month, minsfrom cdr_t cdr partition by (cust_id) right outer join     (select distinct tariff, month from cdr_t) cdre     on (cdr.month = cdre.month and cdr.tariff = cdre.tariff);   ....... Mining a Star Schema: Telco Churn Case Study (3 of 3) Now that the "difficult" work is complete - preparing the data - we can move to building a predictive model to help identify and understand churn.The case study suggests that separate models be built for different customer segments (high, medium, low, and very low value customer groups).  To reduce the data to a single segment, a filter can be applied: create or replace view churn_data_high asselect * from churn_prep where value_band = 'HIGH'; It is simple to take a quick look at the predictive aspects of the data on a univariate basis.  While this does not capture the more complex multi-variate effects as would occur with the full-blown data mining algorithms, it can give a quick feel as to the predictive aspects of the data as well as validate the data preparation steps.  Oracle Data Mining includes a predictive analytics package which enables quick analysis. begin  dbms_predictive_analytics.explain(   'churn_data_high','churn_m6','expl_churn_tab'); end; /select * from expl_churn_tab where rank <= 5 order by rank; ATTRIBUTE_NAME       ATTRIBUTE_SUBNAME EXPLANATORY_VALUE RANK-------------------- ----------------- ----------------- ----------LOS_BAND                                      .069167052          1MINS_PER_TARIFF_MON  PEAK-5                   .034881648          2REV_PER_MON          REV-5                    .034527798          3DROPPED_CALLS                                 .028110322          4MINS_PER_TARIFF_MON  PEAK-4                   .024698149          5From the above results, it is clear that some predictors do contain information to help identify churn (explanatory value > 0).  The strongest uni-variate predictor of churn appears to be the customer's (binned) length of service.  The second strongest churn indicator appears to be the number of peak minutes used in the most recent month.  The subname column contains the interior piece of the DM_NESTED_NUMERICALS column described in the previous post.  By using the object relational approach, many related predictors are included within a single top-level column. .....   NOTE:  These are just EXCERPTS.  Click here to start reading the Oracle Data Mining a Star Schema: Telco Churn Case Study from the beginning.    

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