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  • Severe mysqldump performance degradation using Centos Linux, 8GB PAE and MySQL 5.0.77

    - by Duncan Harris
    We use MySQL 5.0.77 on CentOS 5.5 on VMWare: Linux dev.ic.soschildrensvillages.org.uk 2.6.18-194.11.4.el5PAE #1 SMP Tue Sep 21 05:48:23 EDT 2010 i686 i686 i386 GNU/Linux We have recently upgraded from 4GB RAM to 8GB. When we did this the time of our mysqldump overnight backup jumped from under 10 minutes to over 2 hours. It also caused unresponsiveness on our plone based web site due to database load. The dump is using the optimized mysqldump format and is spooled directly through a socket to another server. Any ideas on what we could do to fix gratefully appreciated. Would a MySQL upgrade help? Anything we can do to MySQL config? Anything we can do to Linux config? Or do we have to add another server or go to 64-bit? We ran a previous (non-virtual) server on 6GB PAE and didn't notice a similar issue. This was on same MySQL version, but Centos 4.4. Server config file: [mysqld] port=3307 socket=/tmp/mysql_live.sock wait_timeout=31536000 interactive_timeout=31536000 datadir=/var/mysql/live/data user=mysql max_connections = 200 max_allowed_packet = 64M table_cache = 2048 binlog_cache_size = 128K max_heap_table_size = 32M sort_buffer_size = 2M join_buffer_size = 2M lower_case_table_names = 1 innodb_data_file_path = ibdata1:10M:autoextend innodb_buffer_pool_size=1G innodb_log_file_size=300M innodb_log_buffer_size=8M innodb_flush_log_at_trx_commit=1 innodb_file_per_table [mysqldump] # Do not buffer the whole result set in memory before writing it to # file. Required for dumping very large tables quick max_allowed_packet = 64M [mysqld_safe] # Increase the amount of open files allowed per process. Warning: Make # sure you have set the global system limit high enough! The high value # is required for a large number of opened tables open-files-limit = 8192 Server variables: mysql> show variables; +---------------------------------+------------------------------------------------------------------+ | Variable_name | Value | +---------------------------------+------------------------------------------------------------------+ | auto_increment_increment | 1 | | auto_increment_offset | 1 | | automatic_sp_privileges | ON | | back_log | 50 | | basedir | /usr/local/mysql-5.0.77-linux-i686-glibc23/ | | binlog_cache_size | 131072 | | bulk_insert_buffer_size | 8388608 | | character_set_client | latin1 | | character_set_connection | latin1 | | character_set_database | latin1 | | character_set_filesystem | binary | | character_set_results | latin1 | | character_set_server | latin1 | | character_set_system | utf8 | | character_sets_dir | /usr/local/mysql-5.0.77-linux-i686-glibc23/share/mysql/charsets/ | | collation_connection | latin1_swedish_ci | | collation_database | latin1_swedish_ci | | collation_server | latin1_swedish_ci | | completion_type | 0 | | concurrent_insert | 1 | | connect_timeout | 10 | | datadir | /var/mysql/live/data/ | | date_format | %Y-%m-%d | | datetime_format | %Y-%m-%d %H:%i:%s | | default_week_format | 0 | | delay_key_write | ON | | delayed_insert_limit | 100 | | delayed_insert_timeout | 300 | | delayed_queue_size | 1000 | | div_precision_increment | 4 | | keep_files_on_create | OFF | | engine_condition_pushdown | OFF | | expire_logs_days | 0 | | flush | OFF | | flush_time | 0 | | ft_boolean_syntax | + -><()~*:""&| | | ft_max_word_len | 84 | | ft_min_word_len | 4 | | ft_query_expansion_limit | 20 | | ft_stopword_file | (built-in) | | group_concat_max_len | 1024 | | have_archive | YES | | have_bdb | NO | | have_blackhole_engine | YES | | have_compress | YES | | have_crypt | YES | | have_csv | YES | | have_dynamic_loading | YES | | have_example_engine | NO | | have_federated_engine | YES | | have_geometry | YES | | have_innodb | YES | | have_isam | NO | | have_merge_engine | YES | | have_ndbcluster | DISABLED | | have_openssl | DISABLED | | have_ssl | DISABLED | | have_query_cache | YES | | have_raid | NO | | have_rtree_keys | YES | | have_symlink | YES | | hostname | app.ic.soschildrensvillages.org.uk | | init_connect | | | init_file | | | init_slave | | | innodb_additional_mem_pool_size | 1048576 | | innodb_autoextend_increment | 8 | | innodb_buffer_pool_awe_mem_mb | 0 | | innodb_buffer_pool_size | 1073741824 | | innodb_checksums | ON | | innodb_commit_concurrency | 0 | | innodb_concurrency_tickets | 500 | | innodb_data_file_path | ibdata1:10M:autoextend | | innodb_data_home_dir | | | innodb_adaptive_hash_index | ON | | innodb_doublewrite | ON | | innodb_fast_shutdown | 1 | | innodb_file_io_threads | 4 | | innodb_file_per_table | ON | | innodb_flush_log_at_trx_commit | 1 | | innodb_flush_method | | | innodb_force_recovery | 0 | | innodb_lock_wait_timeout | 50 | | innodb_locks_unsafe_for_binlog | OFF | | innodb_log_arch_dir | | | innodb_log_archive | OFF | | innodb_log_buffer_size | 8388608 | | innodb_log_file_size | 314572800 | | innodb_log_files_in_group | 2 | | innodb_log_group_home_dir | ./ | | innodb_max_dirty_pages_pct | 90 | | innodb_max_purge_lag | 0 | | innodb_mirrored_log_groups | 1 | | innodb_open_files | 300 | | innodb_rollback_on_timeout | OFF | | innodb_support_xa | ON | | innodb_sync_spin_loops | 20 | | innodb_table_locks | ON | | innodb_thread_concurrency | 8 | | innodb_thread_sleep_delay | 10000 | | interactive_timeout | 31536000 | | join_buffer_size | 2097152 | | key_buffer_size | 8384512 | | key_cache_age_threshold | 300 | | key_cache_block_size | 1024 | | key_cache_division_limit | 100 | | language | /usr/local/mysql-5.0.77-linux-i686-glibc23/share/mysql/english/ | | large_files_support | ON | | large_page_size | 0 | | large_pages | OFF | | lc_time_names | en_US | | license | GPL | | local_infile | ON | | locked_in_memory | OFF | | log | OFF | | log_bin | OFF | | log_bin_trust_function_creators | OFF | | log_error | | | log_queries_not_using_indexes | OFF | | log_slave_updates | OFF | | log_slow_queries | OFF | | log_warnings | 1 | | long_query_time | 10 | | low_priority_updates | OFF | | lower_case_file_system | OFF | | lower_case_table_names | 1 | | max_allowed_packet | 67108864 | | max_binlog_cache_size | 4294963200 | | max_binlog_size | 1073741824 | | max_connect_errors | 10 | | max_connections | 200 | | max_delayed_threads | 20 | | max_error_count | 64 | | max_heap_table_size | 33554432 | | max_insert_delayed_threads | 20 | | max_join_size | 18446744073709551615 | | max_length_for_sort_data | 1024 | | max_prepared_stmt_count | 16382 | | max_relay_log_size | 0 | | max_seeks_for_key | 4294967295 | | max_sort_length | 1024 | | max_sp_recursion_depth | 0 | | max_tmp_tables | 32 | | max_user_connections | 0 | | max_write_lock_count | 4294967295 | | multi_range_count | 256 | | myisam_data_pointer_size | 6 | | myisam_max_sort_file_size | 2146435072 | | myisam_recover_options | OFF | | myisam_repair_threads | 1 | | myisam_sort_buffer_size | 8388608 | | myisam_stats_method | nulls_unequal | | ndb_autoincrement_prefetch_sz | 1 | | ndb_force_send | ON | | ndb_use_exact_count | ON | | ndb_use_transactions | ON | | ndb_cache_check_time | 0 | | ndb_connectstring | | | net_buffer_length | 16384 | | net_read_timeout | 30 | | net_retry_count | 10 | | net_write_timeout | 60 | | new | OFF | | old_passwords | OFF | | open_files_limit | 8192 | | optimizer_prune_level | 1 | | optimizer_search_depth | 62 | | pid_file | /var/mysql/live/mysqld.pid | | plugin_dir | | | port | 3307 | | preload_buffer_size | 32768 | | profiling | OFF | | profiling_history_size | 15 | | protocol_version | 10 | | query_alloc_block_size | 8192 | | query_cache_limit | 1048576 | | query_cache_min_res_unit | 4096 | | query_cache_size | 0 | | query_cache_type | ON | | query_cache_wlock_invalidate | OFF | | query_prealloc_size | 8192 | | range_alloc_block_size | 4096 | | read_buffer_size | 131072 | | read_only | OFF | | read_rnd_buffer_size | 262144 | | relay_log | | | relay_log_index | | | relay_log_info_file | relay-log.info | | relay_log_purge | ON | | relay_log_space_limit | 0 | | rpl_recovery_rank | 0 | | secure_auth | OFF | | secure_file_priv | | | server_id | 0 | | skip_external_locking | ON | | skip_networking | OFF | | skip_show_database | OFF | | slave_compressed_protocol | OFF | | slave_load_tmpdir | /tmp/ | | slave_net_timeout | 3600 | | slave_skip_errors | OFF | | slave_transaction_retries | 10 | | slow_launch_time | 2 | | socket | /tmp/mysql_live.sock | | sort_buffer_size | 2097152 | | sql_big_selects | ON | | sql_mode | | | sql_notes | ON | | sql_warnings | OFF | | ssl_ca | | | ssl_capath | | | ssl_cert | | | ssl_cipher | | | ssl_key | | | storage_engine | MyISAM | | sync_binlog | 0 | | sync_frm | ON | | system_time_zone | GMT | | table_cache | 2048 | | table_lock_wait_timeout | 50 | | table_type | MyISAM | | thread_cache_size | 0 | | thread_stack | 196608 | | time_format | %H:%i:%s | | time_zone | SYSTEM | | timed_mutexes | OFF | | tmp_table_size | 33554432 | | tmpdir | /tmp/ | | transaction_alloc_block_size | 8192 | | transaction_prealloc_size | 4096 | | tx_isolation | REPEATABLE-READ | | updatable_views_with_limit | YES | | version | 5.0.77 | | version_comment | MySQL Community Server (GPL) | | version_compile_machine | i686 | | version_compile_os | pc-linux-gnu | | wait_timeout | 31536000 | +---------------------------------+------------------------------------------------------------------+ 237 rows in set (0.00 sec)

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  • Corrupted mysql table, cause crash in mysql.h (c++)

    - by Francesco
    i've created a very simple mysql class in c+, but when happen that mysql crash , indexes of tables become corrupted, and all my c++ programs crash too because seems that are unable to recognize corrupted table and allowing me to handle the issue .. Q_RES = mysql_real_query(MY_mysql, tmp_query.c_str(), (unsigned int) tmp_query.size()); if (Q_RES != 0) { if (Q_RES == CR_COMMANDS_OUT_OF_SYNC) cout << "errorquery : CR_COMMANDS_OUT_OF_SYNC " << endl; if (Q_RES == CR_SERVER_GONE_ERROR) cout << "errorquery : CR_SERVER_GONE_ERROR " << endl; if (Q_RES == CR_SERVER_LOST) cout << "errorquery : CR_SERVER_LOST " << endl; LAST_ERROR = mysql_error(MY_mysql); if (n_retrycount < n_retry_limit) { // RETRY! n_retrycount++; sleep(1); cout << "SLEEP - query retry! " << endl; ping(); return select_sql(tmp_query); } return false; } MY_result = mysql_store_result(MY_mysql); B_stored_results = true; cout << "b8" << endl; LAST_affected_rows = (mysql_num_rows(MY_result) + 1); // coult return -1 cout << "b8-1" << endl; the program terminate with a "segmentation fault" after doing the "b8" and before the "b8-1" , Q_RES have no issue even if the table is corrupted.. i would like to know if there is a way to recognize that the table have problems and so then i can run a mysql repair or mysql check .. thanks, Francesco

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  • SQL SERVER – Why Do We Need Data Quality Services – Importance and Significance of Data Quality Services (DQS)

    - by pinaldave
    Databases are awesome.  I’m sure my readers know my opinion about this – I have made SQL Server my life’s work after all!  I love technology and all things computer-related.  Of course, even with my love for technology, I have to admit that it has its limits.  For example, it takes a human brain to notice that data has been input incorrectly.  Computer “brains” might be faster than humans, but human brains are still better at pattern recognition.  For example, a human brain will notice that “300” is a ridiculous age for a human to be, but to a computer it is just a number.  A human will also notice similarities between “P. Dave” and “Pinal Dave,” but this would stump most computers. In a database, these sorts of anomalies are incredibly important.  Databases are often used by multiple people who rely on this data to be true and accurate, so data quality is key.  That is why the improved SQL Server features Master Data Management talks about Data Quality Services.  This service has the ability to recognize and flag anomalies like out of range numbers and similarities between data.  This allows a human brain with its pattern recognition abilities to double-check and ensure that P. Dave is the same as Pinal Dave. A nice feature of Data Quality Services is that once you set the rules for the program to follow, it will not only keep your data organized in the future, but go to the past and “fix up” any data that has already been entered.  It also allows you do combine data from multiple places and it will apply these rules across the board, so that you don’t have any weird issues that crop up when trying to fit a round peg into a square hole. There are two parts of Data Quality Services that help you accomplish all these neat things.  The first part is DQL Server, which you can think of as the hardware component of the system.  It is installed on the side of (it needs to install separately after SQL Server is installed) SQL Server and runs quietly in the background, performing all its cleanup services. DQS Client is the user interface that you can interact with to set the rules and check over your data.  There are three main aspects of Client: knowledge base management, data quality projects and administration.  Knowledge base management is the part of the system that allows you to set the rules, or program the “knowledge base,” so that your database is clean and consistent. Data Quality projects are what run in the background and clean up the data that is already present.  The administration allows you to check out what DQS Client is doing, change rules, and generally oversee the entire process.  The whole process is user-friendly and a pleasure to use.  I highly recommend implementing Data Quality Services in your database. Here are few of my blog posts which are related to Data Quality Services and I encourage you to try this out. SQL SERVER – Installing Data Quality Services (DQS) on SQL Server 2012 SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS SQL SERVER – DQS Error – Cannot connect to server – A .NET Framework error occurred during execution of user-defined routine or aggregate “SetDataQualitySessions” – SetDataQualitySessionPhaseTwo SQL SERVER – Configuring Interactive Cleansing Suggestion Min Score for Suggestions in Data Quality Services (DQS) – Sensitivity of Suggestion SQL SERVER – Unable to DELETE Project in Data Quality Projects (DQS) Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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  • Welcome Oracle Data Integration 12c: Simplified, Future-Ready Solutions with Extreme Performance

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 The big day for the Oracle Data Integration team has finally arrived! It is my honor to introduce you to Oracle Data Integration 12c. Today we announced the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. With the 12c release Oracle becomes the new leader in the data integration and replication technologies as no other vendor offers such a complete set of data integration capabilities for pervasive, continuous access to trusted data across Oracle platforms as well as third-party systems and applications. Oracle Data Integration 12c release addresses data-driven organizations’ critical and evolving data integration requirements under 3 key themes: Future-Ready Solutions Extreme Performance Fast Time-to-Value       There are many new features that support these key differentiators for Oracle Data Integrator 12c and for Oracle GoldenGate 12c. In this first 12c blog post, I will highlight only a few:·Future-Ready Solutions to Support Current and Emerging Initiatives: Oracle Data Integration offer robust and reliable solutions for key technology trends including cloud computing, big data analytics, real-time business intelligence and continuous data availability. Via the tight integration with Oracle’s database, middleware, and application offerings Oracle Data Integration will continue to support the new features and capabilities right away as these products evolve and provide advance features. E    Extreme Performance: Both GoldenGate and Data Integrator are known for their high performance. The new release widens the gap even further against competition. Oracle GoldenGate 12c’s Integrated Delivery feature enables higher throughput via a special application programming interface into Oracle Database. As mentioned in the press release, customers already report up to 5X higher performance compared to earlier versions of GoldenGate. Oracle Data Integrator 12c introduces parallelism that significantly increases its performance as well. Fast Time-to-Value via Higher IT Productivity and Simplified Solutions:  Oracle Data Integrator 12c’s new flow-based declarative UI brings superior developer productivity, ease of use, and ultimately fast time to market for end users.  It also gives the ability to seamlessly reuse mapping logic speeds development.Oracle GoldenGate 12c ‘s Integrated Delivery feature automatically optimally tunes the process, saving time while improving performance. This is just a quick glimpse into Oracle Data Integrator 12c and Oracle GoldenGate 12c. On November 12th we will reveal much more about the new release in our video webcast "Introducing 12c for Oracle Data Integration". Our customer and partner speakers, including SolarWorld, BT, Rittman Mead will join us in launching the new release. Please join us at this free event to learn more from our executives about the 12c release, hear our customers’ perspectives on the new features, and ask your questions to our experts in the live Q&A. Also, please continue to follow our blogs, tweets, and Facebook updates as we unveil more about the new features of the latest release. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • MySQL table data transformation -- how can I dis-aggregate MySQL time data?

    - by lighthouse65
    We are coding for a MySQL data warehousing application that stores descriptive data (User ID, Work ID, Machine ID, Start and End Time columns in the first table below) associated with time and production quantity data (Output and Time columns in the first table below) upon which aggregate (SUM, COUNT, AVG) functions are applied. We now wish to dis-aggregate time data for another type of analysis. Our current data table design: +---------+---------+------------+---------------------+---------------------+--------+------+ | User ID | Work ID | Machine ID | Event Start Time | Event End Time | Output | Time | +---------+---------+------------+---------------------+---------------------+--------+------+ | 080025 | ABC123 | M01 | 2008-01-24 16:19:15 | 2008-01-24 16:34:45 | 2120 | 930 | +---------+---------+------------+---------------------+---------------------+--------+------+ Reprocessing dis-aggregation that we would like to do would be to transform table content based on a granularity of minutes, rather than the current production event ("Event Start Time" and "Event End Time") granularity. The resulting reprocessing of existing table rows would look like: +---------+---------+------------+---------------------+--------+ | User ID | Work ID | Machine ID | Production Minute | Output | +---------+---------+------------+---------------------+--------+ | 080025 | ABC123 | M01 | 2010-01-24 16:19 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:20 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:21 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:22 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:23 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:24 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:25 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:26 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:27 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:28 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:29 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:30 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:31 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:22 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:33 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:34 | 133 | +---------+---------+------------+---------------------+--------+ So the reprocessing would take an existing row of data created at the granularity of production event and modify the granularity to minutes, eliminating redundant (Event End Time, Time) columns while doing so. It assumes a constant rate of production and divides output by the difference in minutes plus one to populate the new table's Output column. I know this can be done in code...but can it be done entirely in a MySQL insert statement (or otherwise entirely in MySQL)? I am thinking of a INSERT ... INTO construction but keep getting stuck. An additional complexity is that there are hundreds of machines to include in the operation so there will be multiple rows (one for each machine) for each minute of the day. Any ideas would be much appreciated. Thanks.

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  • random data using php & mysql

    - by Prakash
    I have mysql database structure like below: CREATE TABLE test ( id int(11) NOT NULL auto_increment, title text NULL, tags text NULL, PRIMARY KEY (id) ); data on field tags is stored as a comma separated text like html,php,mysql,website,html etc... now I need create an array that contains around 50 randomly selected tags from random records. currently I am using rand() to select 15 random mysql data from database and then holding all the tags from 15 records in an array. Then I am using array_rand() for randomizing the array and selecting only 50 random records. $query=mysql_query("select * from test order by id asc, RAND() limit 15"); $tags=""; while ($eachData=mysql_fetch_array($query)) { $additionalTags=$eachData['tags']; if ($tags=="") { $tags.=$additionalTags; } else { $tags.=$tags.",".$additionalTags; } } $tags=explode(",", $tags); $newTags=array(); foreach ($tags as $tag) { $tag=trim($tag); if ($tag!="") { if (!in_array($tag, $newTags)) { $newTags[]=$tag; } } } $random_newTags=array_rand($newTags, 50); Now I have huge records on the database, and because of that; rand() is performing very slow and sometimes it doesn't work. So can anyone let me know how to handle this situation correctly so that my page will work normally.

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  • MySQL table data transformation -- how can I dis-aggreate MySQL time data?

    - by lighthouse65
    We are coding for a MySQL data warehousing application that stores descriptive data (User ID, Work ID, Machine ID, Start and End Time columns in the first table below) associated with time and production quantity data (Output and Time columns in the first table below) upon which aggregate (SUM, COUNT, AVG) functions are applied. We now wish to dis-aggregate time data for another type of analysis. Our current data table design: +---------+---------+------------+---------------------+---------------------+--------+------+ | User ID | Work ID | Machine ID | Event Start Time | Event End Time | Output | Time | +---------+---------+------------+---------------------+---------------------+--------+------+ | 080025 | ABC123 | M01 | 2008-01-24 16:19:15 | 2008-01-24 16:34:45 | 2120 | 930 | +---------+---------+------------+---------------------+---------------------+--------+------+ Reprocessing dis-aggregation that we would like to do would be to transform table content based on a granularity of minutes, rather than the current production event ("Event Start Time" and "Event End Time") granularity. The resulting reprocessing of existing table rows would look like: +---------+---------+------------+---------------------+--------+ | User ID | Work ID | Machine ID | Production Minute | Output | +---------+---------+------------+---------------------+--------+ | 080025 | ABC123 | M01 | 2010-01-24 16:19 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:20 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:21 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:22 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:23 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:24 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:25 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:26 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:27 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:28 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:29 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:30 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:31 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:22 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:33 | 133 | | 080025 | ABC123 | M01 | 2010-01-24 16:34 | 133 | +---------+---------+------------+---------------------+--------+ So the reprocessing would take an existing row of data created at the granularity of production event and modify the granularity to minutes, eliminating redundant (Event End Time, Time) columns while doing so. It assumes a constant rate of production and divides output by the difference in minutes plus one to populate the new table's Output column. I know this can be done in code...but can it be done entirely in a MySQL insert statement (or otherwise entirely in MySQL)? I am thinking of a INSERT ... INTO construction but keep getting stuck. An additional complexity is that there are hundreds of machines to include in the operation so there will be multiple rows (one for each machine) for each minute of the day. Any ideas would be much appreciated. Thanks.

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  • MySQL, An Ideal Choice for The Cloud

    - by Bertrand Matthelié
    As the world's most popular web database, MySQL has quickly become the leading database for the cloud, with most providers offering MySQL-based services. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Access our Resource Kit to discover: Why MySQL has become the leading database in the cloud, and how it addresses the critical attributes of cloud-based deployments How ISVs rely on MySQL to power their SaaS offerings Best practices to deploy the world’s most popular open source database in public and private clouds Normal 0 false false false EN-US X-NONE X-NONE You will also find out how you can leverage MySQL together with Hadoop and other technologies to unlock the value of Big Data, either on-premise or in the cloud. Access white papers, webinars, case studies and other resources in /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} our Resource Kit now!

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  • How to proceed setting up a secondary mysql linux slave?

    - by Algorist
    I have a mysql database master and slave in production. I want to setup additional mysql slave. There is around 15 Terabyte of data in the database and there are MYISAM and InnoDB tables in the database. I am thinking of below options: Shutdown master database and copy the mysql data folder to secondary slave. Can Innodb tables be copied like this? Run flush table with read lock, scp the file to new slave and unlock the table and this is possible for myisam tables, can I do the same for innodb tables too? Thanks for looking at the question.

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  • How to generate the right password format for Apache2 authentication in use with DBD and MySQL 5.1?

    - by Walkman
    I want to authenticate users for a folder from a MySQL 5.1 database with AuthType Basic. The passwords are stored in plain text (they are not really passwords, so doesn't matter). The password format for apache however only allows for SHA1, MD5 on Linux systems as described here. How could I generate the right format with an SQL query ? Seems like apache format is a binary format with a lenght of 20, but the mysql SHA1 function return 40 long. My SQL query is something like this: SELECT CONCAT('{SHA}', BASE64_ENCODE(SHA1(access_key))) FROM user_access_keys INNER JOIN users ON user_access_keys.user_id = users.id WHERE name = %s where base64_encode is a stored function (Mysql 5.1 doesn't have TO_BASE64 yet). This query returns a 61 byte BLOB which is not the same format that apache uses. How could I generate the same format ? You can suggest other method for this too. The point is that I want to authenticate users from a MySQL5.1 database using plain text as password.

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  • What could cause sudden crash of a MySQL 5.0.67 installation?

    - by Alex R
    I have an old Ubuntu 8.10 32-bit with MySQL 5.0.67. There's 5.7GB of data in it and it grows by about 100MB every day. About 3 days ago, the MySQL instance begin dying suddenly and quitely (no log entry) during the nightly mysqldump. What could be causing it? Upgrading MySQL is a long-term project for me, unless there happens to be a specific bug in 5.0.67 then I guess I'll just need to reprioritize. I'm hoping somebody might be familiar with this problem since this is a fairly popular version bundled with Ubuntu 8.10. Thanks

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  • Open Source Survey: Oracle Products on Top

    - by trond-arne.undheim
    Oracle continues to work with the open source community to bring the most innovative and productive software to market (more). Oracle products received the most votes in several key categories of the 2010 Linux Journal Reader's Choice Awards. With over 12,000 technologists reporting, these product earned top spots: Best Office Suite: OpenOffice.org Best Single Office Program: OpenOffice.org Writer Best Database: MySQL Best Virtualization Solution: VirtualBox "As the leading open source technology and service provider, Oracle continues to work with the community stakeholders to rapidly innovate many open source products for use in fully tested production environments," says Edward Screven, Oracle's chief corporate architect. "Supporting open source is important to Oracle and our customers, and we continue to invest in it." According to a recent report by the Linux Foundation, Oracle is one of the top ten contributors to the Linux Kernel. Oracle also contributes millions of lines of code to these important projects: OpenJDK: 7,002,579 Eclipse: 1,800,000 (#3 in active committers) MySQL: 5,073,113 NetBeans: 7,870,446 JSF: 701,980 Apache MyFaces Trinidad: 1,316,840 Hudson: 1,209,779 OpenOffice.org: 7,500,000

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  • Pitfalls of using MySQL as your database choice?

    - by Sergio
    I've read online on multiple occassions that MySQL is a bad database. The places I've read this include some threads on Reddit, but they never seem to delve in on why it's a poor product. Is there any truth to this claim? I've never used it beyond a very simple CRUD scenario, and that was for a university project during my second year. What pitfalls, if any, are there when choosing MySQL as your database?

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  • Big Data – Final Wrap and What Next – Day 21 of 21

    - by Pinal Dave
    In yesterday’s blog post we explored various resources related to learning Big Data and in this blog post we will wrap up this 21 day series on Big Data. I have been exploring various terms and technology related to Big Data this entire month. It was indeed fun to write about Big Data in 21 days but the subject of Big Data is much bigger and larger than someone can cover it in 21 days. My first goal was to write about the basics and I think we have got that one covered pretty well. During this 21 days I have received many questions and answers related to Big Data. I have covered a few of the questions in this series and a few more I will be covering in the next coming months. Now after understanding Big Data basics. I am personally going to do a list of the things next. I thought I will share the same with you as this will give you a good idea how to continue the journey of the Big Data. Build a schedule to read various Apache documentations Watch all Pluralsight Courses Explore HortonWorks Sandbox Start building presentation about Big Data – this is a great way to learn something new Present in User Groups Meetings on Big Data Topics Write more blog posts about Big Data I am going to continue learning about Big Data – I want you to continue learning Big Data. Please leave a comment how you are going to continue learning about Big Data. I will publish all the informative comments on this blog with due credit. I want to end this series with the infographic by UMUC. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Tap into MySQL's Amazing Performance Results with the Performance Tuning Course

    - by Antoinette O'Sullivan
    Want to leverage the high-speed load utilities, distinctive memory caches, full text indexes, and other performance-enhancing mechanisms that MySQL offers to fuel today's critical business systems. The authentic MySQL Performance Tuning course, in 4 days, teaches you to evaluate the MySQL architecture, learn to use the tools, configure the database for performance, tune application and SQL code, tune the server, examine the storage engines, assess the application architecture, and learn general tuning concepts. You can take this course in one the following three ways: Training-on-Demand: Access the streaming video, instructor delivery of this course from your own desk, at your own pace. Book time for hands-on practice when it suits you. Live-Virtual Class: Take this instructor-led class live from your own desk. With 700 events on the schedule you are sure to find a time and date to suit you! In-Class: Travel to a classroom to take this class. A sample of events on the schedule are as follows.  Location  Date  Delivery Language  Hamburg, Germany  22 October 2012  German  Prague, Czech Republic  1 October 2012  Czech  Warsaw, Poland  3 December 2012  Polish  London, England  19 November 2012  English  Rome, Italy  23 October 2012  Italian Lisbon, Portugal  6 November 2012  European Portugese  Aix en Provence, France  4 September 2012   French  Strasbourg, France 16 October 2012   French  Nieuwegein, Netherlands 26 November 2012   Dutch  Madrid, Spain 17 December 2012   Spanish  Mechelen, Belgium  1 October 2012  English  Riga, Latvia  10 December 2012  Latvian  Petaling Jaya, Malaysia  10 September 2012 English   Edmonton, Canada 10 December 2012   English  Vancouver, Canada 10 December 2012   English  Ottawa, Canada 26 November 2012   English  Toronto, Canada 26 November 2012   English  Montreal, Canada 26 November 2012   English  Mexico City, Mexico 10 September 2012   Spanish  Sao Paolo, Brazil 26 November 2012  Brazilian Portugese   Tokyo, Japan 19 November 2012   Japanese  Tokyo, Japan  19 November 2012  Japanese For further information on this class, or to register your interest in additional events, go to the Oracle University Portal: http://oracle.com/education/mysql

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  • Partner Webcast - Focus on Oracle Data Profiling and Data Quality 11g

    - by lukasz.romaszewski(at)oracle.com
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:RO;} Partner Webcast Focus on Oracle Data Profiling and Data Quality 11g February 24th, 12am  CET   Oracle offers an integrated suite Data Quality software architected to discover and correct today's data quality problems and establish a platform prepared for tomorrow's yet unknown data challenges. Oracle Data Profiling provides data investigation, discovery, and profiling in support of quality, migration, integration, stewardship, and governance initiatives. It includes a broad range of features that expand upon basic profiling, including automated monitoring, business-rule validation, and trend analysis. Oracle Data Quality for Data Integrator provides cleansing, standardization, matching, address validation, location enrichment, and linking functions for global customer data and operational business data. It ensures that data adheres to established standards that are adaptable to fit each organization's specific needs.  Both single - and double - byte data are processed in local languages to provide a unique and centralized view of customers, products and services.   During this in-person briefing, Data Integration Solution Specialists will be providing a technical overview and a walkthrough.   Agenda ·         Oracle Data Integration Strategy overview ·         A focus on Oracle Data Profiling and Oracle Data Quality for Data Integrator: o   Oracle Data Profiling o   Oracle Data Quality for Data Integrator o   Live demoo   Q&A Delivery Format  This FREE online LIVE eSeminar will be delivered over the Web and Conference Call. Registrations   received less than 24hours  prior to start time may not receive confirmation to attend. To register , click here. For any questions please contact [email protected]

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  • Using Upstart after building Apache & Mysql from source

    - by Tek
    I'm using Ubuntu 10.10, Apache 2.2.17 and Mysql 5.5.10. I need some assistance getting Apache and Mysql running on boot. Reading the getting started over at upstart website attempting to get it to work. I added /etc/init/apache2.conf along with the following line: exec /usr/local/apache2/bin/apachectl I'm probably doing everything wrong, could someone point me in the right direction? Thanks. :)

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  • Using Upstart after building Apache & Mysql from source.

    - by Tek
    I'm using Ubuntu 10.10, Apache 2.2.17 and Mysql 5.5.10. I need some assistance getting Apache and Mysql running on boot. Reading the getting started over at upstart website attempting to get it to work. I added /etc/init/apache2.conf along with the following line: exec /usr/local/apache2/bin/apachectl I'm probably doing everything wrong, could someone point me in the right direction? Thanks. :)

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  • i got mysql error on this statement i don't know why [closed]

    - by John Smiith
    i got mysql error on this statement i don't know why error is: #1064 - You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'CONSTRAINT fk_objet_code FOREIGN KEY (objet_code) REFERENCES objet(code) ) ENG' at line 6 sql code is CREATE TABLE IF NOT EXISTS `class` ( `numero` int(11) NOT NULL AUTO_INCREMENT, `type_class` varchar(100) DEFAULT NULL, `images` varchar(200) NOT NULL, PRIMARY KEY (`numero`) CONSTRAINT fk_objet_code FOREIGN KEY (objet_code) REFERENCES objet(code) ) ENGINE=InnoDB;;

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  • apostrophe in mysql/php

    - by fusion
    i'm trying to learn php/mysql. inserting data into mysql works fine but inserting those with apostrophe is generating an error. i tried using mysql_real_escape_string, yet this doesn't work. would appreciate any help. <?php include 'config.php'; echo "Connected <br />"; $auth = $_POST['author']; $quo = $_POST['quote']; $author = mysql_real_escape_string($auth); $quote = mysql_real_escape_string($quo); //************************** //inserting data $sql="INSERT INTO Quotes (vauthor, cquotes) VALUES ($author, $quote)"; if (!mysql_query($sql,$conn)) { die('Error: ' . mysql_error()); } echo "1 record added"; ... what am i doing wrong?

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  • Mac OS X, MySQL Preference Pane doesn't work

    - by Steve Kuo
    I downloaded and installed MySQL 5.1.47 for OS X 10.6 using the DMG archive: mysql-5.1.47-osx10.6-x86_64.dmg I also installed MySQL.prefPane and MySQLStartupItem.pkg. MySQL.prefPane is a Preference Pane. The problem is, whenever I attempt to start/stop MySQL from the Preference Pane, System Preferences just hangs. It runs at about 50% CPU forever, eventually I have for force quit System Preferences. The same thing happens if I toggle "Automatically Start MySQL Server on Startup". Basically the MySQL Preference Pane is not functional. Note that I have no problem starting MySQL from the command line: sudo /usr/local/mysql/bin/mysqld_safe I have tried reinstalling MySQL and the Preference Pane. I'm using the standard installation location, nothing out of the ordinary. Every time the MySQL Preference Pane just hangs. I'm doing this on a Macbook Pro (Intel) running OS X 10.6.3. There are no old versions of MySQL on this machine.

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  • Can't connect to local MySQL server through socket

    - by Martin
    I was trying to tune the performance of a running mysql-server by running this command: mysqld_safe --key_buffer_size=64M --table_cache=256 --sort_buffer_size=4M --read_buffer_size=1M & After this i'm unable to connect mysql from the server where mysql is running. I get this error: ERROR 2002 (HY000): Can't connect to local MySQL server through socket '/var/run/mysqld/mysqld.sock' (111) However, luckily i can still connect to mysql remotely. So all my webservers still have access to mysql and are running without any problems. Because of this though i don't want to try to restart the mysql server since that will probably fuck everything up. Now i know that mysqld_safe is starting the mysql-server, and since the mysql server was already running i guess it's some kind of problem with two mysql servers running and listening to the same port. Is there some way to solve this problem without restarting the initial mysql server? UPDATE: This is what ps xa | grep "mysql" says: 11672 ? S 0:00 /bin/sh /usr/bin/mysqld_safe 11780 ? Sl 175:04 /usr/sbin/mysqld --basedir=/usr --datadir=/var/lib/mysql --user=mysql --pid-file=/var/run/mysqld/mysqld.pid --socket=/var/run/mysqld/mysqld.sock --port=3306 11781 ? S 0:00 logger -t mysqld -p daemon.error 12432 pts/0 R+ 0:00 grep mysql

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  • MySQL "OR MATCH" hangs (long pause with no answer) on multiple tables

    - by Kerry
    After learning how to do MySQL Full-Text search, the recommended solution for multiple tables was OR MATCH and then do the other database call. You can see that in my query below. When I do this, it just gets stuck in a "busy" state, and I can't access the MySQL database. SELECT a.`product_id`, a.`name`, a.`slug`, a.`description`, b.`list_price`, b.`price`, c.`image`, c.`swatch`, e.`name` AS industry, MATCH( a.`name`, a.`sku`, a.`description` ) AGAINST ( '%s' IN BOOLEAN MODE ) AS relevance FROM `products` AS a LEFT JOIN `website_products` AS b ON (a.`product_id` = b.`product_id`) LEFT JOIN ( SELECT `product_id`, `image`, `swatch` FROM `product_images` WHERE `sequence` = 0) AS c ON (a.`product_id` = c.`product_id`) LEFT JOIN `brands` AS d ON (a.`brand_id` = d.`brand_id`) INNER JOIN `industries` AS e ON (a.`industry_id` = e.`industry_id`) WHERE b.`website_id` = %d AND b.`status` = %d AND b.`active` = %d AND MATCH( a.`name`, a.`sku`, a.`description` ) AGAINST ( '%s' IN BOOLEAN MODE ) OR MATCH ( d.`name` ) AGAINST ( '%s' IN BOOLEAN MODE ) GROUP BY a.`product_id` ORDER BY relevance DESC LIMIT 0, 9 Any help would be greatly appreciated.

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  • MySQL Trigger creation

    - by Bruce Garlock
    I have an application where I need to INSERT an auto_increment value from a PK in another table. I know how to do this in PHP, but I need to have this done at the DB level, since I cannot change the program logic. I am new to triggers, so I'm sure this will be an easy answer for someone. Here is what I have so far: DELIMITER // CREATE TRIGGER new_project AFTER INSERT ON m_quality_header FOR EACH ROW BEGIN INSERT INTO m_quality_detail (d_matl_qa_ID) VALUES (NEW.h_matl_qa_ID); END// DELIMITER ; I just want the value of the auto_increment value from h_matl_qa_ID to be inserted as a new record into d_matl_qa_ID. The error I get is: "This version of MySQL doesn't yet support 'multiple triggers with the same action time and event for one table' But, I don't want to update the table that has the trigger, so why is my current code considered a 'multiple' trigger? This is on MySQL 5.0.45-7.el5 running on a CentOS 5 server (64-bit Intel) If I have to, I can modify the PHP code, but that needs to be the last resort.

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