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• php in background exec() function

  - by albertopriore

  Hi! I made this script to test the execution of php in background foreach($tests as $test) { exec("php test.php ".$test["id"]); } to run php in background like suggested in php process background and How to add large number of event notification reminder via Google Calendar API using PHP? and php execute a background process But the script do not run faster than when it was all in one script without the addition of test.php. what I'm doing wrong? thanks in advance!

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• name of the class that contains the method code

  - by kdlp

  I'm trying to find the name of the class that contains method code. In the example underneath I use self.__class__.__name__, but of course this returns the name of the class of which self is an instance and not class that contains the test() method code. b.test() will print 'B' while I would like to get 'A'. I looked into the inspect module documentation but did not find anything directly useful. class A: def __init__(self): pass def test(self): print self.__class__.__name__ class B(A): def __init__(self): A.__init__(self) a = A() b = B() a.test() b.test()

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• Nashorn ?? JDBC ? Oracle DB ?????

  - by Homma

  ???? ????????????Nashorn ?? JavaScript ??????? JDBC ? API ??????Oracle DB ?????????????????????? ?????????????????????JDBC ? API ??????????????? ????????? URL ? https://blogs.oracle.com/nashorn_ja/entry/nashorn_jdbc_1 ??? ???? ???? DB ????Oracle Linux 6.5 ?? Oracle 11.2.0.3.0 ?????????????? JDBC ????????????? DB ????????????????? ???? ?Oracle Database JDBC ???????????????????????Nashorn ?? JavaScript ?????????????????????? JDBC ? Oracle DB ??????? Nashorn ?? JavaScript ??????? JDBC ? Oracle DB ?????? JavaScript ?????? DB ???????????????? JavaScript ?????? oracle ????????? JavaScript ?????? DB ?????????????????????????????????DB ???????????? JavaScript ???????????????????????? oracle ?????????? JDBC ??????????????????????? ???? DB ?????? ?????? DB ???????????? SQL> create user test identified by "test"; SQL> grant connect, resource to test; Java 8 ??????? ???? JDK 8 ?????????????????????????????? 8u5 ???? Java 8 ??????? ???????? JDK ? yum ??????????????? # yum install ./jdk-8u5-linux-x64.rpm JDK ????????????????????? # java -version java version "1.8.0_05" Java(TM) SE Runtime Environment (build 1.8.0_05-b13) Java HotSpot(TM) 64-Bit Server VM (build 25.5-b02, mixed mode) Nashorn ????? oracle ??????????PATH ??????? $ vi ~/.bash_profile PATH=${PATH}:/usr/java/latest/bin export PATH $ . ~/.bash_profile jjs ?????????????????? $ jjs -fv nashorn full version 1.8.0_05-b13 ????????????? JDBC ?????????????? JDBC ?????????JDBC ?????? ??????????????????? ???????? JDBC ????????????????????????? ?????????????? JavaScript ??????????jjs ???????????????????? Nashorn ? JavaScript ?????????????????? JDBC ??????? jjs ????? -cp ?????? JDBC ????? JAR ??????????? $ vi version.js var OracleDataSource = Java.type("oracle.jdbc.pool.OracleDataSource"); var ods = new OracleDataSource(); ods.setURL("jdbc:oracle:thin:test/test@localhost:1521:orcl"); var conn = ods.getConnection(); var meta = conn.getMetaData(); print("JDBC driver version is " + meta.getDriverVersion()); $ jjs -cp ${ORACLE_HOME}/jdbc/lib/ojdbc6.jar version.js JDBC driver version is 11.2.0.3.0 ??????JavaScript ???????? JDBC ?????????? (11.2.0.3.0) ????????? Java.type() ??????? JavaClass ??????? new ????? Java ??????????????????????????? Java ???????????????????? ????????????????????????????????????????????????????? ?????????????????????????????????????? Java ??????????????? JavaScript ???????????????????????????????? ?????? ???????????????? jjs ???????????Nashorn ??????????????jjs ??????????????????????????? $ jjs -cp ${ORACLE_HOME}/jdbc/lib/ojdbc6.jar jjs> var OracleDataSource = Java.type("oracle.jdbc.pool.OracleDataSource"); jjs> var ods = new OracleDataSource(); jjs> ods.setURL("jdbc:oracle:thin:test/test@localhost:1521:orcl"); null jjs> var conn = ods.getConnection(); jjs> var meta = conn.getMetaData(); jjs> print("JDBC driver version is " + meta.getDriverVersion()); JDBC driver version is 11.2.0.3.0 ???????? JDBC ?????????? (11.2.0.3.0) ????????? ?????????????????????????????????????????????????????????JDBC ?????????????????????? ??? Nashorn ???????? JDBC ? API ????????????? API ???????????????? ???????? JavaScript ?????????????????????????????????? ???????????? JDBC ? DB ???????????????? JDBC ??????????????????????????? ???? Oracle Database JDBC?????? 11g????2(11.2) ??????? jjs ?????????? Nashorn User's Guide Java Scripting Programmer's Guide Oracle Nashorn: A Next-Generation JavaScript Engine for the JVM

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• MySQL is running VERY slow

  - by user1032531

  I have two servers: a VPS and a laptop. I recently re-built both of them, and MySQL is running about 20 times slower on the laptop. Both servers used to run CentOS 5.8 and I think MySQL 5.1, and the laptop used to do great so I do not think it is the hardware. For the VPS, my provider installed CentOS 6.4, and then I installed MySQL 5.1.69 using yum with the CentOS repo. For the laptop, I installed CentOS 6.4 basic server and then installed MySQL 5.1.69 using yum with the CentOS repo. my.cnf for both servers are identical, and I have shown below. For both servers, I've also included below the output from SHOW VARIABLES; as well as output from sysbench, file system information, and cpu information. I have tried adding skip-name-resolve, but it didn't help. The matrix below shows the SHOW VARIABLES output from both servers which is different. Again, MySQL was installed the same way, so I do not know why it is different, but it is and I think this might be why the laptop is executing MySQL so slowly. Why is the laptop running MySQL slowly, and how do I fix it? Differences between SHOW VARIABLES on both servers +---------------------------+-----------------------+-------------------------+ | Variable | Value-VPS | Value-Laptop | +---------------------------+-----------------------+-------------------------+ | hostname | vps.site1.com | laptop.site2.com | | max_binlog_cache_size | 4294963200 | 18446744073709500000 | | max_seeks_for_key | 4294967295 | 18446744073709500000 | | max_write_lock_count | 4294967295 | 18446744073709500000 | | myisam_max_sort_file_size | 2146435072 | 9223372036853720000 | | myisam_mmap_size | 4294967295 | 18446744073709500000 | | plugin_dir | /usr/lib/mysql/plugin | /usr/lib64/mysql/plugin | | pseudo_thread_id | 7568 | 2 | | system_time_zone | EST | PDT | | thread_stack | 196608 | 262144 | | timestamp | 1372252112 | 1372252046 | | version_compile_machine | i386 | x86_64 | +---------------------------+-----------------------+-------------------------+ my.cnf for both servers [root@server1 ~]# cat /etc/my.cnf [mysqld] datadir=/var/lib/mysql socket=/var/lib/mysql/mysql.sock user=mysql # Disabling symbolic-links is recommended to prevent assorted security risks symbolic-links=0 [mysqld_safe] log-error=/var/log/mysqld.log pid-file=/var/run/mysqld/mysqld.pid innodb_strict_mode=on sql_mode=TRADITIONAL # sql_mode=STRICT_TRANS_TABLES,NO_ZERO_DATE,NO_ZERO_IN_DATE character-set-server=utf8 collation-server=utf8_general_ci log=/var/log/mysqld_all.log [root@server1 ~]# VPS SHOW VARIABLES Info Same as Laptop shown below but changes per above matrix (removed to allow me to be under the 30000 characters as required by ServerFault) Laptop SHOW VARIABLES Info auto_increment_increment 1 auto_increment_offset 1 autocommit ON automatic_sp_privileges ON back_log 50 basedir /usr/ big_tables OFF binlog_cache_size 32768 binlog_direct_non_transactional_updates OFF binlog_format STATEMENT bulk_insert_buffer_size 8388608 character_set_client utf8 character_set_connection utf8 character_set_database latin1 character_set_filesystem binary character_set_results utf8 character_set_server latin1 character_set_system utf8 character_sets_dir /usr/share/mysql/charsets/ collation_connection utf8_general_ci collation_database latin1_swedish_ci collation_server latin1_swedish_ci completion_type 0 concurrent_insert 1 connect_timeout 10 datadir /var/lib/mysql/ 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 engine_condition_pushdown ON error_count 0 event_scheduler OFF expire_logs_days 0 flush OFF flush_time 0 foreign_key_checks ON ft_boolean_syntax + -><()~*:""&| ft_max_word_len 84 ft_min_word_len 4 ft_query_expansion_limit 20 ft_stopword_file (built-in) general_log OFF general_log_file /var/run/mysqld/mysqld.log group_concat_max_len 1024 have_community_features YES have_compress YES have_crypt YES have_csv YES have_dynamic_loading YES have_geometry YES have_innodb YES have_ndbcluster NO have_openssl DISABLED have_partitioning YES have_query_cache YES have_rtree_keys YES have_ssl DISABLED have_symlink DISABLED hostname server1.site2.com identity 0 ignore_builtin_innodb OFF init_connect init_file init_slave innodb_adaptive_hash_index ON innodb_additional_mem_pool_size 1048576 innodb_autoextend_increment 8 innodb_autoinc_lock_mode 1 innodb_buffer_pool_size 8388608 innodb_checksums ON innodb_commit_concurrency 0 innodb_concurrency_tickets 500 innodb_data_file_path ibdata1:10M:autoextend innodb_data_home_dir innodb_doublewrite ON innodb_fast_shutdown 1 innodb_file_io_threads 4 innodb_file_per_table OFF 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_buffer_size 1048576 innodb_log_file_size 5242880 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_stats_method nulls_equal innodb_stats_on_metadata ON innodb_support_xa ON innodb_sync_spin_loops 20 innodb_table_locks ON innodb_thread_concurrency 8 innodb_thread_sleep_delay 10000 innodb_use_legacy_cardinality_algorithm ON insert_id 0 interactive_timeout 28800 join_buffer_size 131072 keep_files_on_create OFF key_buffer_size 8384512 key_cache_age_threshold 300 key_cache_block_size 1024 key_cache_division_limit 100 language /usr/share/mysql/english/ large_files_support ON large_page_size 0 large_pages OFF last_insert_id 0 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_bin_trust_routine_creators OFF log_error /var/log/mysqld.log log_output FILE log_queries_not_using_indexes OFF log_slave_updates OFF log_slow_queries OFF log_warnings 1 long_query_time 10.000000 low_priority_updates OFF lower_case_file_system OFF lower_case_table_names 0 max_allowed_packet 1048576 max_binlog_cache_size 18446744073709547520 max_binlog_size 1073741824 max_connect_errors 10 max_connections 151 max_delayed_threads 20 max_error_count 64 max_heap_table_size 16777216 max_insert_delayed_threads 20 max_join_size 18446744073709551615 max_length_for_sort_data 1024 max_long_data_size 1048576 max_prepared_stmt_count 16382 max_relay_log_size 0 max_seeks_for_key 18446744073709551615 max_sort_length 1024 max_sp_recursion_depth 0 max_tmp_tables 32 max_user_connections 0 max_write_lock_count 18446744073709551615 min_examined_row_limit 0 multi_range_count 256 myisam_data_pointer_size 6 myisam_max_sort_file_size 9223372036853727232 myisam_mmap_size 18446744073709551615 myisam_recover_options OFF myisam_repair_threads 1 myisam_sort_buffer_size 8388608 myisam_stats_method nulls_unequal myisam_use_mmap OFF net_buffer_length 16384 net_read_timeout 30 net_retry_count 10 net_write_timeout 60 new OFF old OFF old_alter_table OFF old_passwords OFF open_files_limit 1024 optimizer_prune_level 1 optimizer_search_depth 62 optimizer_switch index_merge=on,index_merge_union=on,index_merge_sort_union=on,index_merge_intersection=on pid_file /var/run/mysqld/mysqld.pid plugin_dir /usr/lib64/mysql/plugin port 3306 preload_buffer_size 32768 profiling OFF profiling_history_size 15 protocol_version 10 pseudo_thread_id 3 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 rand_seed1 rand_seed2 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 report_host report_password report_port 3306 report_user rpl_recovery_rank 0 secure_auth OFF secure_file_priv server_id 0 skip_external_locking ON skip_name_resolve OFF skip_networking OFF skip_show_database OFF slave_compressed_protocol OFF slave_exec_mode STRICT slave_load_tmpdir /tmp slave_max_allowed_packet 1073741824 slave_net_timeout 3600 slave_skip_errors OFF slave_transaction_retries 10 slow_launch_time 2 slow_query_log OFF slow_query_log_file /var/run/mysqld/mysqld-slow.log socket /var/lib/mysql/mysql.sock sort_buffer_size 2097144 sql_auto_is_null ON sql_big_selects ON sql_big_tables OFF sql_buffer_result OFF sql_log_bin ON sql_log_off OFF sql_log_update ON sql_low_priority_updates OFF sql_max_join_size 18446744073709551615 sql_mode sql_notes ON sql_quote_show_create ON sql_safe_updates OFF sql_select_limit 18446744073709551615 sql_slave_skip_counter 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 PDT table_definition_cache 256 table_lock_wait_timeout 50 table_open_cache 64 table_type MyISAM thread_cache_size 0 thread_handling one-thread-per-connection thread_stack 262144 time_format %H:%i:%s time_zone SYSTEM timed_mutexes OFF timestamp 1372254399 tmp_table_size 16777216 tmpdir /tmp transaction_alloc_block_size 8192 transaction_prealloc_size 4096 tx_isolation REPEATABLE-READ unique_checks ON updatable_views_with_limit YES version 5.1.69 version_comment Source distribution version_compile_machine x86_64 version_compile_os redhat-linux-gnu wait_timeout 28800 warning_count 0 VPS Sysbench Info [root@vps ~]# cat sysbench.txt sysbench 0.4.12: multi-threaded system evaluation benchmark Running the test with following options: Number of threads: 8 Doing OLTP test. Running mixed OLTP test Doing read-only test Using Special distribution (12 iterations, 1 pct of values are returned in 75 pct cases) Using "BEGIN" for starting transactions Using auto_inc on the id column Threads started! Time limit exceeded, exiting... (last message repeated 7 times) Done. OLTP test statistics: queries performed: read: 1449966 write: 0 other: 207138 total: 1657104 transactions: 103569 (1726.01 per sec.) deadlocks: 0 (0.00 per sec.) read/write requests: 1449966 (24164.08 per sec.) other operations: 207138 (3452.01 per sec.) Test execution summary: total time: 60.0050s total number of events: 103569 total time taken by event execution: 479.1544 per-request statistics: min: 1.98ms avg: 4.63ms max: 330.73ms approx. 95 percentile: 8.26ms Threads fairness: events (avg/stddev): 12946.1250/381.09 execution time (avg/stddev): 59.8943/0.00 [root@vps ~]# Laptop Sysbench Info [root@server1 ~]# cat sysbench.txt sysbench 0.4.12: multi-threaded system evaluation benchmark Running the test with following options: Number of threads: 8 Doing OLTP test. Running mixed OLTP test Doing read-only test Using Special distribution (12 iterations, 1 pct of values are returned in 75 pct cases) Using "BEGIN" for starting transactions Using auto_inc on the id column Threads started! Time limit exceeded, exiting... (last message repeated 7 times) Done. OLTP test statistics: queries performed: read: 634718 write: 0 other: 90674 total: 725392 transactions: 45337 (755.56 per sec.) deadlocks: 0 (0.00 per sec.) read/write requests: 634718 (10577.78 per sec.) other operations: 90674 (1511.11 per sec.) Test execution summary: total time: 60.0048s total number of events: 45337 total time taken by event execution: 479.4912 per-request statistics: min: 2.04ms avg: 10.58ms max: 85.56ms approx. 95 percentile: 19.70ms Threads fairness: events (avg/stddev): 5667.1250/42.18 execution time (avg/stddev): 59.9364/0.00 [root@server1 ~]# VPS File Info [root@vps ~]# df -T Filesystem Type 1K-blocks Used Available Use% Mounted on /dev/simfs simfs 20971520 16187440 4784080 78% / none tmpfs 6224432 4 6224428 1% /dev none tmpfs 6224432 0 6224432 0% /dev/shm [root@vps ~]# Laptop File Info [root@server1 ~]# df -T Filesystem Type 1K-blocks Used Available Use% Mounted on /dev/mapper/vg_server1-lv_root ext4 72383800 4243964 64462860 7% / tmpfs tmpfs 956352 0 956352 0% /dev/shm /dev/sdb1 ext4 495844 60948 409296 13% /boot [root@server1 ~]# VPS CPU Info Removed to stay under the 30000 character limit required by ServerFault Laptop CPU Info [root@server1 ~]# cat /proc/cpuinfo processor : 0 vendor_id : GenuineIntel cpu family : 6 model : 15 model name : Intel(R) Core(TM)2 Duo CPU T7100 @ 1.80GHz stepping : 13 cpu MHz : 800.000 cache size : 2048 KB physical id : 0 siblings : 2 core id : 0 cpu cores : 2 apicid : 0 initial apicid : 0 fpu : yes fpu_exception : yes cpuid level : 10 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx lm constant_tsc arch_perfmon pebs bts rep_good aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm lahf_lm ida dts tpr_shadow vnmi flexpriority bogomips : 3591.39 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: processor : 1 vendor_id : GenuineIntel cpu family : 6 model : 15 model name : Intel(R) Core(TM)2 Duo CPU T7100 @ 1.80GHz stepping : 13 cpu MHz : 800.000 cache size : 2048 KB physical id : 0 siblings : 2 core id : 1 cpu cores : 2 apicid : 1 initial apicid : 1 fpu : yes fpu_exception : yes cpuid level : 10 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx lm constant_tsc arch_perfmon pebs bts rep_good aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm lahf_lm ida dts tpr_shadow vnmi flexpriority bogomips : 3591.39 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: [root@server1 ~]# EDIT New Info requested by shakalandy [root@localhost ~]# cat /proc/meminfo MemTotal: 2044804 kB MemFree: 761464 kB Buffers: 68868 kB Cached: 369708 kB SwapCached: 0 kB Active: 881080 kB Inactive: 246016 kB Active(anon): 688312 kB Inactive(anon): 4416 kB Active(file): 192768 kB Inactive(file): 241600 kB Unevictable: 0 kB Mlocked: 0 kB SwapTotal: 4095992 kB SwapFree: 4095992 kB Dirty: 0 kB Writeback: 0 kB AnonPages: 688428 kB Mapped: 65156 kB Shmem: 4216 kB Slab: 92428 kB SReclaimable: 31260 kB SUnreclaim: 61168 kB KernelStack: 2392 kB PageTables: 28356 kB NFS_Unstable: 0 kB Bounce: 0 kB WritebackTmp: 0 kB CommitLimit: 5118392 kB Committed_AS: 1530212 kB VmallocTotal: 34359738367 kB VmallocUsed: 343604 kB VmallocChunk: 34359372920 kB HardwareCorrupted: 0 kB AnonHugePages: 520192 kB HugePages_Total: 0 HugePages_Free: 0 HugePages_Rsvd: 0 HugePages_Surp: 0 Hugepagesize: 2048 kB DirectMap4k: 8556 kB DirectMap2M: 2078720 kB [root@localhost ~]# ps aux | grep mysql root 2227 0.0 0.0 108332 1504 ? S 07:36 0:00 /bin/sh /usr/bin/mysqld_safe --datadir=/var/lib/mysql --pid-file=/var/lib/mysql/localhost.badobe.com.pid mysql 2319 0.1 24.5 1470068 501360 ? Sl 07:36 0:57 /usr/sbin/mysqld --basedir=/usr --datadir=/var/lib/mysql --plugin-dir=/usr/lib64/mysql/plugin --user=mysql --log-error=/var/lib/mysql/localhost.badobe.com.err --pid-file=/var/lib/mysql/localhost.badobe.com.pid root 3579 0.0 0.1 201840 3028 pts/0 S+ 07:40 0:00 mysql -u root -p root 13887 0.0 0.1 201840 3036 pts/3 S+ 18:08 0:00 mysql -uroot -px xxxxxxxxxx root 14449 0.0 0.0 103248 840 pts/2 S+ 18:16 0:00 grep mysql [root@localhost ~]# ps aux | grep mysql root 2227 0.0 0.0 108332 1504 ? S 07:36 0:00 /bin/sh /usr/bin/mysqld_safe --datadir=/var/lib/mysql --pid-file=/var/lib/mysql/localhost.badobe.com.pid mysql 2319 0.1 24.5 1470068 501356 ? Sl 07:36 0:57 /usr/sbin/mysqld --basedir=/usr --datadir=/var/lib/mysql --plugin-dir=/usr/lib64/mysql/plugin --user=mysql --log-error=/var/lib/mysql/localhost.badobe.com.err --pid-file=/var/lib/mysql/localhost.badobe.com.pid root 3579 0.0 0.1 201840 3028 pts/0 S+ 07:40 0:00 mysql -u root -p root 13887 0.0 0.1 201840 3048 pts/3 S+ 18:08 0:00 mysql -uroot -px xxxxxxxxxx root 14470 0.0 0.0 103248 840 pts/2 S+ 18:16 0:00 grep mysql [root@localhost ~]# vmstat 1 procs -----------memory---------- ---swap-- -----io---- --system-- -----cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 0 0 0 742172 76376 371064 0 0 6 6 78 202 2 1 97 1 0 0 0 0 742164 76380 371060 0 0 0 16 191 467 2 1 93 5 0 0 0 0 742164 76380 371064 0 0 0 0 148 388 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 159 418 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 145 380 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 166 429 2 1 97 0 0 1 0 0 742164 76380 371064 0 0 0 0 148 373 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 149 382 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 168 408 2 0 97 0 0 0 0 0 742164 76380 371064 0 0 0 0 165 394 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 159 354 2 1 98 0 0 0 0 0 742164 76388 371060 0 0 0 16 180 447 2 0 91 6 0 0 0 0 742164 76388 371064 0 0 0 0 143 344 2 1 98 0 0 0 1 0 742784 76416 370044 0 0 28 580 360 678 3 1 74 23 0 1 0 0 744768 76496 367772 0 0 40 1036 437 865 3 1 53 43 0 0 1 0 747248 76596 365412 0 0 48 1224 561 923 3 2 53 43 0 0 1 0 749232 76696 363092 0 0 32 1132 512 883 3 2 52 44 0 0 1 0 751340 76772 361020 0 0 32 1008 472 872 2 1 52 45 0 0 1 0 753448 76840 358540 0 0 36 1088 512 860 2 1 51 46 0 0 1 0 755060 76936 357636 0 0 28 1012 481 922 2 2 52 45 0 0 1 0 755060 77064 357988 0 0 12 896 444 902 2 1 53 45 0 0 1 0 754688 77148 358448 0 0 16 1096 506 1007 1 1 56 42 0 0 2 0 754192 77268 358932 0 0 12 1060 481 957 1 2 53 44 0 0 1 0 753696 77380 359392 0 0 12 1052 512 1025 2 1 55 42 0 0 1 0 751028 77480 359828 0 0 8 984 423 909 2 2 52 45 0 0 1 0 750524 77620 360200 0 0 8 788 367 869 1 2 54 44 0 0 1 0 749904 77700 360664 0 0 8 928 439 924 2 2 55 43 0 0 1 0 749408 77796 361084 0 0 12 976 468 967 1 1 56 43 0 0 1 0 748788 77896 361464 0 0 12 992 453 944 1 2 54 43 0 1 1 0 748416 77992 361996 0 0 12 784 392 868 2 1 52 46 0 0 1 0 747920 78092 362336 0 0 4 896 382 874 1 1 52 46 0 0 1 0 745252 78172 362780 0 0 12 1040 444 923 1 1 56 42 0 0 1 0 744764 78288 363220 0 0 8 1024 448 934 2 1 55 43 0 0 1 0 744144 78408 363668 0 0 8 1000 461 982 2 1 53 44 0 0 1 0 743648 78488 364148 0 0 8 872 443 888 2 1 54 43 0 0 1 0 743152 78548 364468 0 0 16 1020 511 995 2 1 55 43 0 0 1 0 742656 78632 365024 0 0 12 928 431 913 1 2 53 44 0 0 1 0 742160 78728 365468 0 0 12 996 470 955 2 2 54 44 0 1 1 0 739492 78840 365896 0 0 8 988 447 939 1 2 52 46 0 0 1 0 738872 78996 366352 0 0 12 972 442 928 1 1 55 44 0 1 1 0 738244 79148 366812 0 0 8 948 549 1126 2 2 54 43 0 0 1 0 737624 79312 367188 0 0 12 996 456 953 2 2 54 43 0 0 1 0 736880 79456 367660 0 0 12 960 444 918 1 1 53 46 0 0 1 0 736260 79584 368124 0 0 8 884 414 921 1 1 54 44 0 0 1 0 735648 79716 368488 0 0 12 976 450 955 2 1 56 41 0 0 1 0 733104 79840 368988 0 0 12 932 453 918 1 2 55 43 0 0 1 0 732608 79996 369356 0 0 16 916 444 889 1 2 54 43 0 1 1 0 731476 80128 369800 0 0 16 852 514 978 2 2 54 43 0 0 1 0 731244 80252 370200 0 0 8 904 398 870 2 1 55 43 0 1 1 0 730624 80384 370612 0 0 12 1032 447 977 1 2 57 41 0 0 1 0 730004 80524 371096 0 0 12 984 469 941 2 2 52 45 0 0 1 0 729508 80636 371544 0 0 12 928 438 922 2 1 52 46 0 0 1 0 728888 80756 371948 0 0 16 972 439 943 2 1 55 43 0 0 1 0 726468 80900 372272 0 0 8 960 545 1024 2 1 54 43 0 1 1 0 726344 81024 372272 0 0 8 464 490 1057 1 2 53 44 0 0 1 0 726096 81148 372276 0 0 4 328 441 1063 2 1 53 45 0 1 1 0 726096 81256 372292 0 0 0 296 387 975 1 1 53 45 0 0 1 0 725848 81380 372284 0 0 4 332 425 1034 2 1 54 44 0 1 1 0 725848 81496 372300 0 0 4 308 386 992 2 1 54 43 0 0 1 0 725600 81616 372296 0 0 4 328 404 1060 1 1 54 44 0 procs -----------memory---------- ---swap-- -----io---- --system-- -----cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 0 1 0 725600 81732 372296 0 0 4 328 439 1011 1 1 53 44 0 0 1 0 725476 81848 372308 0 0 0 316 441 1023 2 2 52 46 0 1 1 0 725352 81972 372300 0 0 4 344 451 1021 1 1 55 43 0 2 1 0 725228 82088 372320 0 0 0 328 427 1058 1 1 54 44 0 1 1 0 724980 82220 372300 0 0 4 336 419 999 2 1 54 44 0 1 1 0 724980 82328 372320 0 0 4 320 430 1019 1 1 54 44 0 1 1 0 724732 82436 372328 0 0 0 388 363 942 2 1 54 44 0 1 1 0 724608 82560 372312 0 0 4 308 419 993 1 2 54 44 0 1 0 0 724360 82684 372320 0 0 0 304 421 1028 2 1 55 42 0 1 0 0 724360 82684 372388 0 0 0 0 158 416 2 1 98 0 0 1 1 0 724236 82720 372360 0 0 0 6464 243 855 3 2 84 12 0 1 0 0 724112 82748 372360 0 0 0 5356 266 895 3 1 84 12 0 2 1 0 724112 82764 372380 0 0 0 3052 221 511 2 2 93 4 0 1 0 0 724112 82796 372372 0 0 0 4548 325 1067 2 2 81 16 0 1 0 0 724112 82816 372368 0 0 0 3240 259 829 3 1 90 6 0 1 0 0 724112 82836 372380 0 0 0 3260 309 822 3 2 88 8 0 1 1 0 724112 82876 372364 0 0 0 4680 326 978 3 1 77 19 0 1 0 0 724112 82884 372380 0 0 0 512 207 508 2 1 95 2 0 1 0 0 724112 82884 372388 0 0 0 0 138 361 2 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 158 397 2 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 146 395 2 1 98 0 0 2 0 0 724112 82884 372388 0 0 0 0 160 395 2 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 163 382 1 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 176 422 2 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 134 351 2 1 98 0 0 0 0 0 724112 82884 372388 0 0 0 0 190 429 2 1 97 0 0 0 0 0 724104 82884 372392 0 0 0 0 139 358 2 1 98 0 0 0 0 0 724848 82884 372392 0 0 0 4 211 432 2 1 97 0 0 1 0 0 724980 82884 372392 0 0 0 0 166 370 2 1 98 0 0 0 0 0 724980 82884 372392 0 0 0 0 164 397 2 1 98 0 0 ^C [root@localhost ~]#

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• Is Berkeley DB a NoSQL solution?

  - by Gregory Burd

  Berkeley DB is a library. To use it to store data you must link the library into your application. You can use most programming languages to access the API, the calls across these APIs generally mimic the Berkeley DB C-API which makes perfect sense because Berkeley DB is written in C. The inspiration for Berkeley DB was the DBM library, a part of the earliest versions of UNIX written by AT&T's Ken Thompson in 1979. DBM was a simple key/value hashtable-based storage library. In the early 1990s as BSD UNIX was transitioning from version 4.3 to 4.4 and retrofitting commercial code owned by AT&T with unencumbered code, it was the future founders of Sleepycat Software who wrote libdb (aka Berkeley DB) as the replacement for DBM. The problem it addressed was fast, reliable local key/value storage. At that time databases almost always lived on a single node, even the most sophisticated databases only had simple fail-over two node solutions. If you had a lot of data to store you would choose between the few commercial RDBMS solutions or to write your own custom solution. Berkeley DB took the headache out of the custom approach. These basic market forces inspired other DBM implementations. There was the "New DBM" (ndbm) and the "GNU DBM" (GDBM) and a few others, but the theme was the same. Even today TokyoCabinet calls itself "a modern implementation of DBM" mimicking, and improving on, something first created over thirty years ago. In the mid-1990s, DBM was the name for what you needed if you were looking for fast, reliable local storage. Fast forward to today. What's changed? Systems are connected over fast, very reliable networks. Disks are cheep, fast, and capable of storing huge amounts of data. CPUs continued to follow Moore's Law, processing power that filled a room in 1990 now fits in your pocket. PCs, servers, and other computers proliferated both in business and the personal markets. In addition to the new hardware entire markets, social systems, and new modes of interpersonal communication moved onto the web and started evolving rapidly. These changes cause a massive explosion of data and a need to analyze and understand that data. Taken together this resulted in an entirely different landscape for database storage, new solutions were needed. A number of novel solutions stepped up and eventually a category called NoSQL emerged. The new market forces inspired the CAP theorem and the heated debate of BASE vs. ACID. But in essence this was simply the market looking at what to trade off to meet these new demands. These new database systems shared many qualities in common. There were designed to address massive amounts of data, millions of requests per second, and scale out across multiple systems. The first large-scale and successful solution was Dynamo, Amazon's distributed key/value database. Dynamo essentially took the next logical step and added a twist. Dynamo was to be the database of record, it would be distributed, data would be partitioned across many nodes, and it would tolerate failure by avoiding single points of failure. Amazon did this because they recognized that the majority of the dynamic content they provided to customers visiting their web store front didn't require the services of an RDBMS. The queries were simple, key/value look-ups or simple range queries with only a few queries that required more complex joins. They set about to use relational technology only in places where it was the best solution for the task, places like accounting and order fulfillment, but not in the myriad of other situations. The success of Dynamo, and it's design, inspired the next generation of Non-SQL, distributed database solutions including Cassandra, Riak and Voldemort. The problem their designers set out to solve was, "reliability at massive scale" so the first focal point was distributed database algorithms. Underneath Dynamo there is a local transactional database; either Berkeley DB, Berkeley DB Java Edition, MySQL or an in-memory key/value data structure. Dynamo was an evolution of local key/value storage onto networks. Cassandra, Riak, and Voldemort all faced similar design decisions and one, Voldemort, choose Berkeley DB Java Edition for it's node-local storage. Riak at first was entirely in-memory, but has recently added write-once, append-only log-based on-disk storage similar type of storage as Berkeley DB except that it is based on a hash table which must reside entirely in-memory rather than a btree which can live in-memory or on disk. Berkeley DB evolved too, we added high availability (HA) and a replication manager that makes it easy to setup replica groups. Berkeley DB's replication doesn't partitioned the data, every node keeps an entire copy of the database. For consistency, there is a single node where writes are committed first - a master - then those changes are delivered to the replica nodes as log records. Applications can choose to wait until all nodes are consistent, or fire and forget allowing Berkeley DB to eventually become consistent. Berkeley DB's HA scales-out quite well for read-intensive applications and also effectively eliminates the central point of failure by allowing replica nodes to be elected (using a PAXOS algorithm) to mastership if the master should fail. This implementation covers a wide variety of use cases. MemcacheDB is a server that implements the Memcache network protocol but uses Berkeley DB for storage and HA to replicate the cache state across all the nodes in the cache group. Google Accounts, the user authentication layer for all Google properties, was until recently running Berkeley DB HA. That scaled to a globally distributed system. That said, most NoSQL solutions try to partition (shard) data across nodes in the replication group and some allow writes as well as reads at any node, Berkeley DB HA does not. So, is Berkeley DB a "NoSQL" solution? Not really, but it certainly is a component of many of the existing NoSQL solutions out there. Forgetting all the noise about how NoSQL solutions are complex distributed databases when you boil them down to a single node you still have to store the data to some form of stable local storage. DBMs solved that problem a long time ago. NoSQL has more to do with the layers on top of the DBM; the distributed, sometimes-consistent, partitioned, scale-out storage that manage key/value or document sets and generally have some form of simple HTTP/REST-style network API. Does Berkeley DB do that? Not really. Is Berkeley DB a "NoSQL" solution today? Nope, but it's the most robust solution on which to build such a system. Re-inventing the node-local data storage isn't easy. A lot of people are starting to come to appreciate the sophisticated features found in Berkeley DB, even mimic them in some cases. Could Berkeley DB grow into a NoSQL solution? Absolutely. Our key/value API could be extended over the net using any of a number of existing network protocols such as memcache or HTTP/REST. We could adapt our node-local data partitioning out over replicated nodes. We even have a nice query language and cost-based query optimizer in our BDB XML product that we could reuse were we to build out a document-based NoSQL-style product. XML and JSON are not so different that we couldn't adapt one to work with the other interchangeably. Without too much effort we could add what's missing, we could jump into this No SQL market withing a single product development cycle. Why isn't Berkeley DB already a NoSQL solution? Why aren't we working on it? Why indeed...

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• Fragmented Log files could be slowing down your database

  - by Fatherjack

  Something that is sometimes forgotten by a lot of DBAs is the fact that database log files get fragmented in the same way that you get fragmentation in a data file. The cause is very different but the effect is the same – too much effort reading and writing data. Data files get fragmented as data is changed through normal system activity, INSERTs, UPDATEs and DELETEs cause fragmentation and most experienced DBAs are monitoring their indexes for fragmentation and dealing with it accordingly. However, you don’t hear about so many working on their log files. How can a log file get fragmented? I’m glad you asked. When you create a database there are at least two files created on the disk storage; an mdf for the data and an ldf for the log file (you can also have ndf files for extra data storage but that’s off topic for now). It is wholly possible to have more than one log file but in most cases there is little point in creating more than one as the log file is written to in a ‘wrap-around’ method (more on that later). When a log file is created at the time that a database is created the file is actually sub divided into a number of virtual log files (VLFs). The number and size of these VLFs depends on the size chosen for the log file. VLFs are also created in the space added to a log file when a log file growth event takes place. Do you have your log files set to auto grow? Then you have potentially been introducing many VLFs into your log file. Let’s get to see how many VLFs we have in a brand new database. USE master GO CREATE DATABASE VLF_Test ON ( NAME = VLF_Test, FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL10.ROCK_2008\MSSQL\DATA\VLF_Test.mdf', SIZE = 100, MAXSIZE = 500, FILEGROWTH = 50 ) LOG ON ( NAME = VLF_Test_Log, FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL10.ROCK_2008\MSSQL\DATA\VLF_Test_log.ldf', SIZE = 5MB, MAXSIZE = 250MB, FILEGROWTH = 5MB ); go USE VLF_Test go DBCC LOGINFO; The results of this are firstly a new database is created with specified files sizes and the the DBCC LOGINFO results are returned to the script editor. The DBCC LOGINFO results have plenty of interesting information in them but lets first note there are 4 rows of information, this relates to the fact that 4 VLFs have been created in the log file. The values in the FileSize column are the sizes of each VLF in bytes, you will see that the last one to be created is slightly larger than the others. So, a 5MB log file has 4 VLFs of roughly 1.25 MB. Lets alter the CREATE DATABASE script to create a log file that’s a bit bigger and see what happens. Alter the code above so that the log file details are replaced by LOG ON ( NAME = VLF_Test_Log, FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL10.ROCK_2008\MSSQL\DATA\VLF_Test_log.ldf', SIZE = 1GB, MAXSIZE = 25GB, FILEGROWTH = 1GB ); With a bigger log file specified we get more VLFs What if we make it bigger again? LOG ON ( NAME = VLF_Test_Log, FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL10.ROCK_2008\MSSQL\DATA\VLF_Test_log.ldf', SIZE = 5GB, MAXSIZE = 250GB, FILEGROWTH = 5GB ); This time we see more VLFs are created within our log file. We now have our 5GB log file comprised of 16 files of 320MB each. In fact these sizes fall into all the ranges that control the VLF creation criteria – what a coincidence! The rules that are followed when a log file is created or has it’s size increased are pretty basic. If the file growth is lower than 64MB then 4 VLFs are created If the growth is between 64MB and 1GB then 8 VLFs are created If the growth is greater than 1GB then 16 VLFs are created. Now the potential for chaos comes if the default values and settings for log file growth are used. By default a database log file gets a 1MB log file with unlimited growth in steps of 10%. The database we just created is 6 MB, let’s add some data and see what happens. USE vlf_test go -- we need somewhere to put the data so, a table is in order IF OBJECT_ID('A_Table') IS NOT NULL DROP TABLE A_Table go CREATE TABLE A_Table ( Col_A int IDENTITY, Col_B CHAR(8000) ) GO -- Let's check the state of the log file -- 4 VLFs found EXECUTE ('DBCC LOGINFO'); go -- We can go ahead and insert some data and then check the state of the log file again INSERT A_Table (col_b) SELECT TOP 500 REPLICATE('a',2000) FROM sys.columns AS sc, sys.columns AS sc2 GO -- insert 500 rows and we get 22 VLFs EXECUTE ('DBCC LOGINFO'); go -- Let's insert more rows INSERT A_Table (col_b) SELECT TOP 2000 REPLICATE('a',2000) FROM sys.columns AS sc, sys.columns AS sc2 GO 10 -- insert 2000 rows, in 10 batches and we suddenly have 107 VLFs EXECUTE ('DBCC LOGINFO'); Well, that escalated quickly! Our log file is split, internally, into 107 fragments after a few thousand inserts. The same happens with any logged transactions, I just chose to illustrate this with INSERTs. Having too many VLFs can cause performance degradation at times of database start up, log backup and log restore operations so it’s well worth keeping a check on this property. How do we prevent excessive VLF creation? Creating the database with larger files and also with larger growth steps and actively choosing to grow your databases rather than leaving it to the Auto Grow event can make sure that the growths are made with a size that is optimal. How do we resolve a situation of a database with too many VLFs? This process needs to be done when the database is under little or no stress so that you don’t affect system users. The steps are: BACKUP LOG YourDBName TO YourBackupDestinationOfChoice Shrink the log file to its smallest possible size DBCC SHRINKFILE(FileNameOfTLogHere, TRUNCATEONLY) * Re-size the log file to the size you want it to, taking in to account your expected needs for the coming months or year. ALTER DATABASE YourDBName MODIFY FILE ( NAME = FileNameOfTLogHere, SIZE = TheSizeYouWantItToBeIn_MB) * – If you don’t know the file name of your log file then run sp_helpfile while you are connected to the database that you want to work on and you will get the details you need. The resize step can take quite a while This is already detailed far better than I can explain it by Kimberley Tripp in her blog 8-Steps-to-better-Transaction-Log-throughput.aspx. The result of this will be a log file with a VLF count according to the bullet list above. Knowing when VLFs are being created By complete coincidence while I have been writing this blog (it’s been quite some time from it’s inception to going live) Jonathan Kehayias from SQLSkills.com has written a great article on how to track database file growth using Event Notifications and Service Broker. I strongly recommend taking a look at it as this is going to catch any sneaky auto grows that take place and let you know about them right away. Hassle free monitoring of VLFs If you are lucky or wise enough to be using SQL Monitor or another monitoring tool that let’s you write your own custom metrics then you can keep an eye on this very easily. There is a custom metric for VLFs (written by Stuart Ainsworth) already on the site and there are some others there are very useful so take a moment or two to look around while you are there. Resources MSDN – http://msdn.microsoft.com/en-us/library/ms179355(v=sql.105).aspx Kimberly Tripp from SQLSkills.com – http://www.sqlskills.com/BLOGS/KIMBERLY/post/8-Steps-to-better-Transaction-Log-throughput.aspx Thomas LaRock at Simple-Talk.com – http://www.simple-talk.com/sql/database-administration/monitoring-sql-server-virtual-log-file-fragmentation/ Disclosure I am a Friend of Red Gate. This means that I am more than likely to say good things about Red Gate DBA and Developer tools. No matter how awesome I make them sound, take the time to compare them with other products before you contact the Red Gate sales team to make your order.

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• SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

  - by Brian

  Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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• Pre-rentrée Oracle Open World 2012 : à vos agendas

  - by Eric Bezille

  A maintenant moins d'un mois de l’événement majeur d'Oracle, qui se tient comme chaque année à San Francisco, fin septembre, début octobre, les spéculations vont bon train sur les annonces qui vont y être dévoilées... Et sans lever le voile, je vous engage à prendre connaissance des sujets des "Key Notes" qui seront tenues par Larry Ellison, Mark Hurd, Thomas Kurian (responsable des développements logiciels) et John Fowler (responsable des développements systèmes) afin de vous donner un avant goût. Stratégie et Roadmaps Oracle Bien entendu, au-delà des séances plénières qui vous donnerons  une vision précise de la stratégie, et pour ceux qui seront sur place, je vous engage à ne pas manquer les séances d'approfondissement qui auront lieu dans la semaine, dont voici quelques morceaux choisis : "Accelerate your Business with the Oracle Hardware Advantage" avec John Fowler, le lundi 1er Octobre, 3:15pm-4:15pm "Why Oracle Softwares Runs Best on Oracle Hardware" , avec Bradley Carlile, le responsable des Benchmarks, le lundi 1er Octobre, 12:15pm-13:15pm "Engineered Systems - from Vision to Game-changing Results", avec Robert Shimp, le lundi 1er Octobre 1:45pm-2:45pm "Database and Application Consolidation on SPARC Supercluster", avec Hugo Rivero, responsable dans les équipes d'intégration matériels et logiciels, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle’s SPARC Server Strategy Update", avec Masood Heydari, responsable des développements serveurs SPARC, le mardi 2 Octobre, 10:15am - 11:15am "Oracle Solaris 11 Strategy, Engineering Insights, and Roadmap", avec Markus Flier, responsable des développements Solaris, le mercredi 3 Octobre, 10:15am - 11:15am "Oracle Virtualization Strategy and Roadmap", avec Wim Coekaerts, responsable des développement Oracle VM et Oracle Linux, le lundi 1er Octobre, 12:15pm-1:15pm "Big Data: The Big Story", avec Jean-Pierre Dijcks, responsable du développement produits Big Data, le lundi 1er Octobre, 3:15pm-4:15pm "Scaling with the Cloud: Strategies for Storage in Cloud Deployments", avec Christine Rogers,  Principal Product Manager, et Chris Wood, Senior Product Specialist, Stockage , le lundi 1er Octobre, 10:45am-11:45am Retours d'expériences et témoignages Si Oracle Open World est l'occasion de partager avec les équipes de développement d'Oracle en direct, c'est aussi l'occasion d'échanger avec des clients et experts qui ont mis en oeuvre  nos technologies pour bénéficier de leurs retours d'expériences, comme par exemple : "Oracle Optimized Solution for Siebel CRM at ACCOR", avec les témoignages d'Eric Wyttynck, directeur IT Multichannel & CRM  et Pascal Massenet, VP Loyalty & CRM systems, sur les bénéfices non seulement métiers, mais également projet et IT, le mercredi 3 Octobre, 1:15pm-2:15pm "Tips from AT&T: Oracle E-Business Suite, Oracle Database, and SPARC Enterprise", avec le retour d'expérience des experts Oracle, le mardi 2 Octobre, 11:45am-12:45pm "Creating a Maximum Availability Architecture with SPARC SuperCluster", avec le témoignage de Carte Wright, Database Engineer à CKI, le mercredi 3 Octobre, 11:45am-12:45pm "Multitenancy: Everybody Talks It, Oracle Walks It with Pillar Axiom Storage", avec le témoignage de Stephen Schleiger, Manager Systems Engineering de Navis, le lundi 1er Octobre, 1:45pm-2:45pm "Oracle Exadata for Database Consolidation: Best Practices", avec le retour d'expérience des experts Oracle ayant participé à la mise en oeuvre d'un grand client du monde bancaire, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle Exadata Customer Panel: Packaged Applications with Oracle Exadata", animé par Tim Shetler, VP Product Management, mardi 2 Octobre, 1:15pm-2:15pm "Big Data: Improving Nearline Data Throughput with the StorageTek SL8500 Modular Library System", avec le témoignage du CTO de CSC, Alan Powers, le jeudi 4 Octobre, 12:45pm-1:45pm "Building an IaaS Platform with SPARC, Oracle Solaris 11, and Oracle VM Server for SPARC", avec le témoignage de Syed Qadri, Lead DBA et Michael Arnold, System Architect d'US Cellular, le mardi 2 Octobre, 10:15am-11:15am "Transform Data Center TCO with Oracle Optimized Servers: A Customer Panel", avec les témoignages notamment d'AT&T et Liberty Global, le mardi 2 Octobre, 11:45am-12:45pm "Data Warehouse and Big Data Customers’ View of the Future", avec The Nielsen Company US, Turkcell, GE Retail Finance, Allianz Managed Operations and Services SE, le lundi 1er Octobre, 4:45pm-5:45pm "Extreme Storage Scale and Efficiency: Lessons from a 100,000-Person Organization", le témoignage de l'IT interne d'Oracle sur la transformation et la migration de l'ensemble de notre infrastructure de stockage, mardi 2 Octobre, 1:15pm-2:15pm Echanges avec les groupes d'utilisateurs et les équipes de développement Oracle Si vous avez prévu d'arriver suffisamment tôt, vous pourrez également échanger dès le dimanche avec les groupes d'utilisateurs, ou tous les soirs avec les équipes de développement Oracle sur des sujets comme : "To Exalogic or Not to Exalogic: An Architectural Journey", avec Todd Sheetz - Manager of DBA and Enterprise Architecture, Veolia Environmental Services, le dimanche 30 Septembre, 2:30pm-3:30pm "Oracle Exalytics and Oracle TimesTen for Exalytics Best Practices", avec Mark Rittman, de Rittman Mead Consulting Ltd, le dimanche 30 Septembre, 10:30am-11:30am "Introduction of Oracle Exadata at Telenet: Bringing BI to Warp Speed", avec Rudy Verlinden & Eric Bartholomeus - Managers IT infrastructure à Telenet, le dimanche 30 Septembre, 1:15pm-2:00pm "The Perfect Marriage: Sun ZFS Storage Appliance with Oracle Exadata", avec Melanie Polston, directeur, Data Management, de Novation et Charles Kim, Managing Director de Viscosity, le dimanche 30 Septembre, 9:00am-10am "Oracle’s Big Data Solutions: NoSQL, Connectors, R, and Appliance Technologies", avec Jean-Pierre Dijcks et les équipes de développement Oracle, le lundi 1er Octobre, 6:15pm-7:00pm Testez et évaluez les solutions Et pour finir, vous pouvez même tester les technologies au travers du Oracle DemoGrounds, (1133 Moscone South pour la partie Systèmes Oracle, OS, et Virtualisation) et des "Hands-on-Labs", comme : "Deploying an IaaS Environment with Oracle VM", le mardi 2 Octobre, 10:15am-11:15am "Virtualize and Deploy Oracle Applications in Minutes with Oracle VM: Hands-on Lab", le mardi 2 Octobre, 11:45am-12:45pm (il est fortement conseillé d'avoir suivi le "Hands-on-Labs" précédent avant d'effectuer ce Lab. "x86 Enterprise Cloud Infrastructure with Oracle VM 3.x and Sun ZFS Storage Appliance", le mercredi 3 Octobre, 5:00pm-6:00pm "StorageTek Tape Analytics: Managing Tape Has Never Been So Simple", le mercredi 3 Octobre, 1:15pm-2:15pm "Oracle’s Pillar Axiom 600 Storage System: Power and Ease", le lundi 1er Octobre, 12:15pm-1:15pm "Enterprise Cloud Infrastructure for SPARC with Oracle Enterprise Manager Ops Center 12c", le lundi 1er Octobre, 1:45pm-2:45pm "Managing Storage in the Cloud", le mardi 2 Octobre, 5:00pm-6:00pm "Learn How to Write MapReduce on Oracle’s Big Data Platform", le lundi 1er Octobre, 12:15pm-1:15pm "Oracle Big Data Analytics and R", le mardi 2 Octobre, 1:15pm-2:15pm "Reduce Risk with Oracle Solaris Access Control to Restrain Users and Isolate Applications", le lundi 1er Octobre, 10:45am-11:45am "Managing Your Data with Built-In Oracle Solaris ZFS Data Services in Release 11", le lundi 1er Octobre, 4:45pm-5:45pm "Virtualizing Your Oracle Solaris 11 Environment", le mardi 2 Octobre, 1:15pm-2:15pm "Large-Scale Installation and Deployment of Oracle Solaris 11", le mercredi 3 Octobre, 3:30pm-4:30pm En conclusion, une semaine très riche en perspective, et qui vous permettra de balayer l'ensemble des sujets au coeur de vos préoccupations, de la stratégie à l'implémentation... Cette semaine doit se préparer, pour tailler votre agenda sur mesure, à travers les plus de 2000 sessions dont je ne vous ai fait qu'un extrait, et dont vous pouvez retrouver l'ensemble en ligne.

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• Service Broker, not ETL

  - by jamiet

  I have been very quiet on this blog of late and one reason for that is I have been very busy on a client project that I would like to talk about a little here. The client that I have been working for has a website that runs on a distributed architecture utilising a messaging infrastructure for communication between different endpoints. My brief was to build a system that could consume these messages and produce analytical information in near-real-time. More specifically I basically had to deliver a data warehouse however it was the real-time aspect of the project that really intrigued me. This real-time requirement meant that using an Extract transformation, Load (ETL) tool was out of the question and so I had no choice but to write T-SQL code (i.e. stored-procedures) to process the incoming messages and load the data into the data warehouse. This concerned me though – I had no way to control the rate at which data would arrive into the system yet we were going to have end-users querying the system at the same time that those messages were arriving; the potential for contention in such a scenario was pretty high and and was something I wanted to minimise as much as possible. Moreover I did not want the processing of data inside the data warehouse to have any impact on the customer-facing website. As you have probably guessed from the title of this blog post this is where Service Broker stepped in! For those that have not heard of it Service Broker is a queuing technology that has been built into SQL Server since SQL Server 2005. It provides a number of features however the one that was of interest to me was the fact that it facilitates asynchronous data processing which, in layman’s terms, means the ability to process some data without requiring the system that supplied the data having to wait for the response. That was a crucial feature because on this project the customer-facing website (in effect an OLTP system) would be calling one of our stored procedures with each message – we did not want to cause the OLTP system to wait on us every time we processed one of those messages. This asynchronous nature also helps to alleviate the contention problem because the asynchronous processing activity is handled just like any other task in the database engine and hence can wait on another task (such as an end-user query). Service Broker it was then! The stored procedure called by the OLTP system would simply put the message onto a queue and we would use a feature called activation to pick each message off the queue in turn and process it into the warehouse. At the time of writing the system is not yet up to full capacity but so far everything seems to be working OK (touch wood) and crucially our users are seeing data in near-real-time. By near-real-time I am talking about latencies of a few minutes at most and to someone like me who is used to building systems that have overnight latencies that is a huge step forward! So then, am I advocating that you all go out and dump your ETL tools? Of course not, no! What this project has taught me though is that in certain scenarios there may be better ways to implement a data warehouse system then the traditional “load data in overnight” approach that we are all used to. Moreover I have really enjoyed getting to grips with a new technology and even if you don’t want to use Service Broker you might want to consider asynchronous messaging architectures for your BI/data warehousing solutions in the future. This has been a very high level overview of my use of Service Broker and I have deliberately left out much of the minutiae of what has been a very challenging implementation. Nonetheless I hope I have caused you to reflect upon your own approaches to BI and question whether other approaches may be more tenable. All comments and questions gratefully received! Lastly, if you have never used Service Broker before and want to kick the tyres I have provided below a very simple “Service Broker Hello World” script that will create all of the objects required to facilitate Service Broker communications and then send the message “Hello World” from one place to anther! This doesn’t represent a “proper” implementation per se because it doesn’t close down down conversation objects (which you should always do in a real-world scenario) but its enough to demonstrate the capabilities! @Jamiet ----------------------------------------------------------------------------------------------- /*This is a basic Service Broker Hello World app. Have fun! -Jamie */ USE MASTER GO CREATE DATABASE SBTest GO --Turn Service Broker on! ALTER DATABASE SBTest SET ENABLE_BROKER GO USE SBTest GO -- 1) we need to create a message type. Note that our message type is -- very simple and allowed any type of content CREATE MESSAGE TYPE HelloMessage VALIDATION = NONE GO -- 2) Once the message type has been created, we need to create a contract -- that specifies who can send what types of messages CREATE CONTRACT HelloContract (HelloMessage SENT BY INITIATOR) GO --We can query the metadata of the objects we just created SELECT * FROM   sys.service_message_types WHERE name = 'HelloMessage'; SELECT * FROM   sys.service_contracts WHERE name = 'HelloContract'; SELECT * FROM   sys.service_contract_message_usages WHERE  service_contract_id IN (SELECT service_contract_id FROM sys.service_contracts WHERE name = 'HelloContract') AND        message_type_id IN (SELECT message_type_id FROM sys.service_message_types WHERE name = 'HelloMessage'); -- 3) The communication is between two endpoints. Thus, we need two queues to -- hold messages CREATE QUEUE SenderQueue CREATE QUEUE ReceiverQueue GO --more querying metatda SELECT * FROM sys.service_queues WHERE name IN ('SenderQueue','ReceiverQueue'); --we can also select from the queues as if they were tables SELECT * FROM SenderQueue   SELECT * FROM ReceiverQueue   -- 4) Create the required services and bind them to be above created queues CREATE SERVICE Sender   ON QUEUE SenderQueue CREATE SERVICE Receiver   ON QUEUE ReceiverQueue (HelloContract) GO --more querying metadata SELECT * FROM sys.services WHERE name IN ('Receiver','Sender'); -- 5) At this point, we can begin the conversation between the two services by -- sending messages DECLARE @conversationHandle UNIQUEIDENTIFIER DECLARE @message NVARCHAR(100) BEGIN   BEGIN TRANSACTION;   BEGIN DIALOG @conversationHandle         FROM SERVICE Sender         TO SERVICE 'Receiver'         ON CONTRACT HelloContract WITH ENCRYPTION=OFF   -- Send a message on the conversation   SET @message = N'Hello, World';   SEND  ON CONVERSATION @conversationHandle         MESSAGE TYPE HelloMessage (@message)   COMMIT TRANSACTION END GO --check contents of queues SELECT * FROM SenderQueue   SELECT * FROM ReceiverQueue   GO -- Receive a message from the queue RECEIVE CONVERT(NVARCHAR(MAX), message_body) AS MESSAGE FROM ReceiverQueue GO --If no messages were received and/or you can't see anything on the queues you may wish to check the following for clues: SELECT * FROM sys.transmission_queue -- Cleanup DROP SERVICE Sender DROP SERVICE Receiver DROP QUEUE SenderQueue DROP QUEUE ReceiverQueue DROP CONTRACT HelloContract DROP MESSAGE TYPE HelloMessage GO USE MASTER GO DROP DATABASE SBTest GO

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• C# Performance Pitfall – Interop Scenarios Change the Rules

  - by Reed

  C# and .NET, overall, really do have fantastic performance in my opinion.  That being said, the performance characteristics dramatically differ from native programming, and take some relearning if you’re used to doing performance optimization in most other languages, especially C, C++, and similar.  However, there are times when revisiting tricks learned in native code play a critical role in performance optimization in C#. I recently ran across a nasty scenario that illustrated to me how dangerous following any fixed rules for optimization can be… The rules in C# when optimizing code are very different than C or C++.  Often, they’re exactly backwards.  For example, in C and C++, lifting a variable out of loops in order to avoid memory allocations often can have huge advantages.  If some function within a call graph is allocating memory dynamically, and that gets called in a loop, it can dramatically slow down a routine. This can be a tricky bottleneck to track down, even with a profiler.  Looking at the memory allocation graph is usually the key for spotting this routine, as it’s often “hidden” deep in call graph.  For example, while optimizing some of my scientific routines, I ran into a situation where I had a loop similar to: for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i]); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This loop was at a fairly high level in the call graph, and often could take many hours to complete, depending on the input data.  As such, any performance optimization we could achieve would be greatly appreciated by our users. After a fair bit of profiling, I noticed that a couple of function calls down the call graph (inside of ProcessElement), there was some code that effectively was doing: // Allocate some data required DataStructure* data = new DataStructure(num); // Call into a subroutine that passed around and manipulated this data highly CallSubroutine(data); // Read and use some values from here double values = data->Foo; // Cleanup delete data; // ... return bar; Normally, if “DataStructure” was a simple data type, I could just allocate it on the stack.  However, it’s constructor, internally, allocated it’s own memory using new, so this wouldn’t eliminate the problem.  In this case, however, I could change the call signatures to allow the pointer to the data structure to be passed into ProcessElement and through the call graph, allowing the inner routine to reuse the same “data” memory instead of allocating.  At the highest level, my code effectively changed to something like: DataStructure* data = new DataStructure(numberToProcess); for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i], data); } delete data; Granted, this dramatically reduced the maintainability of the code, so it wasn’t something I wanted to do unless there was a significant benefit.  In this case, after profiling the new version, I found that it increased the overall performance dramatically – my main test case went from 35 minutes runtime down to 21 minutes.  This was such a significant improvement, I felt it was worth the reduction in maintainability. In C and C++, it’s generally a good idea (for performance) to: Reduce the number of memory allocations as much as possible, Use fewer, larger memory allocations instead of many smaller ones, and Allocate as high up the call stack as possible, and reuse memory I’ve seen many people try to make similar optimizations in C# code.  For good or bad, this is typically not a good idea.  The garbage collector in .NET completely changes the rules here. In C#, reallocating memory in a loop is not always a bad idea.  In this scenario, for example, I may have been much better off leaving the original code alone.  The reason for this is the garbage collector.  The GC in .NET is incredibly effective, and leaving the allocation deep inside the call stack has some huge advantages.  First and foremost, it tends to make the code more maintainable – passing around object references tends to couple the methods together more than necessary, and overall increase the complexity of the code.  This is something that should be avoided unless there is a significant reason.  Second, (unlike C and C++) memory allocation of a single object in C# is normally cheap and fast.  Finally, and most critically, there is a large advantage to having short lived objects.  If you lift a variable out of the loop and reuse the memory, its much more likely that object will get promoted to Gen1 (or worse, Gen2).  This can cause expensive compaction operations to be required, and also lead to (at least temporary) memory fragmentation as well as more costly collections later. As such, I’ve found that it’s often (though not always) faster to leave memory allocations where you’d naturally place them – deep inside of the call graph, inside of the loops.  This causes the objects to stay very short lived, which in turn increases the efficiency of the garbage collector, and can dramatically improve the overall performance of the routine as a whole. In C#, I tend to: Keep variable declarations in the tightest scope possible Declare and allocate objects at usage While this tends to cause some of the same goals (reducing unnecessary allocations, etc), the goal here is a bit different – it’s about keeping the objects rooted for as little time as possible in order to (attempt) to keep them completely in Gen0, or worst case, Gen1.  It also has the huge advantage of keeping the code very maintainable – objects are used and “released” as soon as possible, which keeps the code very clean.  It does, however, often have the side effect of causing more allocations to occur, but keeping the objects rooted for a much shorter time. Now – nowhere here am I suggesting that these rules are hard, fast rules that are always true.  That being said, my time spent optimizing over the years encourages me to naturally write code that follows the above guidelines, then profile and adjust as necessary.  In my current project, however, I ran across one of those nasty little pitfalls that’s something to keep in mind – interop changes the rules. In this case, I was dealing with an API that, internally, used some COM objects.  In this case, these COM objects were leading to native allocations (most likely C++) occurring in a loop deep in my call graph.  Even though I was writing nice, clean managed code, the normal managed code rules for performance no longer apply.  After profiling to find the bottleneck in my code, I realized that my inner loop, a innocuous looking block of C# code, was effectively causing a set of native memory allocations in every iteration.  This required going back to a “native programming” mindset for optimization.  Lifting these variables and reusing them took a 1:10 routine down to 0:20 – again, a very worthwhile improvement. Overall, the lessons here are: Always profile if you suspect a performance problem – don’t assume any rule is correct, or any code is efficient just because it looks like it should be Remember to check memory allocations when profiling, not just CPU cycles Interop scenarios often cause managed code to act very differently than “normal” managed code. Native code can be hidden very cleverly inside of managed wrappers

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• Oracle Flashback Technologies - Overview

  - by Sridhar_R-Oracle

  Oracle Flashback Technologies - IntroductionIn his May 29th 2014 blog, my colleague Joe Meeks introduced Oracle Maximum Availability Architecture (MAA) and discussed both planned and unplanned outages. Let’s take a closer look at unplanned outages. These can be caused by physical failures (e.g., server, storage, network, file deletion, physical corruption, site failures) or by logical failures – cases where all components and files are physically available, but data is incorrect or corrupt. These logical failures are usually caused by human errors or application logic errors. This blog series focuses on these logical errors – what causes them and how to address and recover from them using Oracle Database Flashback. In this introductory blog post, I’ll provide an overview of the Oracle Database Flashback technologies and will discuss the features in detail in future blog posts. Let’s get started. We are all human beings (unless a machine is reading this), and making mistakes is a part of what we do…often what we do best!  We “fat finger”, we spill drinks on keyboards, unplug the wrong cables, etc.  In addition, many of us, in our lives as DBAs or developers, must have observed, caused, or corrected one or more of the following unpleasant events: Accidentally updated a table with wrong values !! Performed a batch update that went wrong - due to logical errors in the code !! Dropped a table !! How do DBAs typically recover from these types of errors? First, data needs to be restored and recovered to the point-in-time when the error occurred (incomplete or point-in-time recovery).  Moreover, depending on the type of fault, it’s possible that some services – or even the entire database – would have to be taken down during the recovery process.Apart from error conditions, there are other questions that need to be addressed as part of the investigation. For example, what did the data look like in the morning, prior to the error? What were the various changes to the row(s) between two timestamps? Who performed the transaction and how can it be reversed?  Oracle Database includes built-in Flashback technologies, with features that address these challenges and questions, and enable you to perform faster, easier, and convenient recovery from logical corruptions. HistoryFlashback Query, the first Flashback Technology, was introduced in Oracle 9i. It provides a simple, powerful and completely non-disruptive mechanism for data verification and recovery from logical errors, and enables users to view the state of data at a previous point in time.Flashback Technologies were further enhanced in Oracle 10g, to provide fast, easy recovery at the database, table, row, and even at a transaction level.Oracle Database 11g introduced an innovative method to manage and query long-term historical data with Flashback Data Archive. The 11g release also introduced Flashback Transaction, which provides an easy, one-step operation to back out a transaction. Oracle Database versions 11.2.0.2 and beyond further enhanced the performance of these features. Note that all the features listed here work without requiring any kind of restore operation.In addition, Flashback features are fully supported with the new multi-tenant capabilities introduced with Oracle Database 12c, Flashback Features Oracle Flashback Database enables point-in-time-recovery of the entire database without requiring a traditional restore and recovery operation. It rewinds the entire database to a specified point in time in the past by undoing all the changes that were made since that time.Oracle Flashback Table enables an entire table or a set of tables to be recovered to a point in time in the past.Oracle Flashback Drop enables accidentally dropped tables and all dependent objects to be restored.Oracle Flashback Query enables data to be viewed at a point-in-time in the past. This feature can be used to view and reconstruct data that was lost due to unintentional change(s) or deletion(s). This feature can also be used to build self-service error correction into applications, empowering end-users to undo and correct their errors.Oracle Flashback Version Query offers the ability to query the historical changes to data between two points in time or system change numbers (SCN) Oracle Flashback Transaction Query enables changes to be examined at the transaction level. This capability can be used to diagnose problems, perform analysis, audit transactions, and even revert the transaction by undoing SQLOracle Flashback Transaction is a procedure used to back-out a transaction and its dependent transactions.Flashback technologies eliminate the need for a traditional restore and recovery process to fix logical corruptions or make enquiries. Using these technologies, you can recover from the error in the same amount of time it took to generate the error. All the Flashback features can be accessed either via SQL command line (or) via Enterprise Manager.  Most of the Flashback technologies depend on the available UNDO to retrieve older data. The following table describes the various Flashback technologies: their purpose, dependencies and situations where each individual technology can be used.   Example Syntax Error investigation related:The purpose is to investigate what went wrong and what the values were at certain points in timeFlashback Queries  ( select .. as of SCN | Timestamp )   - Helps to see the value of a row/set of rows at a point in timeFlashback Version Queries  ( select .. versions between SCN | Timestamp and SCN | Timestamp)  - Helps determine how the value evolved between certain SCNs or between timestamps Flashback Transaction Queries (select .. XID=)   - Helps to understand how the transaction caused the changes.Error correction related:The purpose is to fix the error and correct the problems,Flashback Table  (flashback table .. to SCN | Timestamp)  - To rewind the table to a particular timestamp or SCN to reverse unwanted updates Flashback Drop (flashback table ..  to before drop )  - To undrop or undelete a table Flashback Database (flashback database to SCN  | Restore Point )  - This is the rewind button for Oracle databases. You can revert the entire database to a particular point in time. It is a fast way to perform a PITR (point-in-time recovery). Flashback Transaction (DBMS_FLASHBACK.TRANSACTION_BACKOUT(XID..))  - To reverse a transaction and its related transactions Advanced use cases Flashback technology is integrated into Oracle Recovery Manager (RMAN) and Oracle Data Guard. So, apart from the basic use cases mentioned above, the following use cases are addressed using Oracle Flashback. Block Media recovery by RMAN - to perform block level recovery Snapshot Standby - where the standby is temporarily converted to a read/write environment for testing, backup, or migration purposes Re-instate old primary in a Data Guard environment – this avoids the need to restore an old backup and perform a recovery to make it a new standby. Guaranteed Restore Points - to bring back the entire database to an older point-in-time in a guaranteed way. and so on..I hope this introductory overview helps you understand how Flashback features can be used to investigate and recover from logical errors.  As mentioned earlier, I will take a deeper-dive into to some of the critical Flashback features in my upcoming blogs and address common use cases.

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• SQL SERVER – Create a Very First Report with the Report Wizard

  - by Pinal Dave

  This example is from the Beginning SSRS by Kathi Kellenberger. Supporting files are available with a free download from the www.Joes2Pros.com web site. What is the report Wizard? In today’s world automation is all around you. Henry Ford began building his Model T automobiles on a moving assembly line a century ago and changed the world. The moving assembly line allowed Ford to build identical cars quickly and cheaply. Henry Ford said in his autobiography “Any customer can have a car painted any color that he wants so long as it is black.” Today you can buy a car straight from the factory with your choice of several colors and with many options like back up cameras, built-in navigation systems and heated leather seats. The assembly lines now use robots to perform some tasks along with human workers. When you order your new car, if you want something special, not offered by the manufacturer, you will have to find a way to add it later. In computer software, we also have “assembly lines” called wizards. A wizard will ask you a series of questions, often branching to specific questions based on earlier answers, until you get to the end of the wizard. These wizards are used for many things, from something simple like setting up a rule in Outlook to performing administrative tasks on a server. Often, a wizard will get you part of the way to the end result, enough to get much of the tedious work out of the way. Once you get the product from the wizard, if the wizard is not capable of doing something you need, you can tweak the results. Create a Report with the Report Wizard Let’s get started with your first report!  Launch SQL Server Data Tools (SSDT) from the Start menu under SQL Server 2012. Once SSDT is running, click New Project to launch the New Project dialog box. On the left side of the screen expand Business Intelligence and select Reporting Services. Configure the properties as shown in . Be sure to select Report Server Project Wizard as the type of report and to save the project in the C:\Joes2Pros\SSRSCompanionFiles\Chapter3\Project folder. Click OK and wait for the Report Wizard to launch. Click Next on the Welcome screen.  On the Select the Data Source screen, make sure that New data source is selected. Type JProCo as the data source name. Make sure that Microsoft SQL Server is selected in the Type dropdown. Click Edit to configure the connection string on the Connection Properties dialog box. If your SQL Server database server is installed on your local computer, type in localhost for the Server name and select the JProCo database from the Select or enter a database name dropdown. Click OK to dismiss the Connection Properties dialog box. Check Make this a shared data source and click Next. On the Design the Query screen, you can use the query builder to build a query if you wish. Since this post is not meant to teach you T-SQL queries, you will copy all queries from files that have been provided for you. In the C:\Joes2Pros\SSRSCompanionFiles\Chapter3\Resources folder open the sales by employee.sql file. Copy and paste the code from the file into the Query string Text Box. Click Next. On the Select the Report Type screen, choose Tabular and click Next. On the Design the Table screen, you have to figure out the groupings of the report. How do you do this? Well, you often need to know a bit about the data and report requirements. I often draw the report out on paper first to help me determine the groups. In the case of this report, I could group the data several ways. Do I want to see the data grouped by Year and Month? Do I want to see the data grouped by Employee or Category? The only thing I know for sure about this ahead of time is that the TotalSales goes in the Details section. Let’s assume that the CIO asked to see the data grouped first by Year and Month, then by Category. Let’s move the fields to the right-hand side. This is done by selecting Page > Group or Details >, as shown in, and click Next. On the Choose the Table Layout screen, select Stepped and check Include subtotals and Enable drilldown, as shown in. On the Choose the Style screen, choose any color scheme you wish (unlike the Model T) and click Next. I chose the default, Slate. On the Choose the Deployment Location screen, change the Deployment folder to Chapter 3 and click Next. At the Completing the Wizard screen, name your report Employee Sales and click Finish. After clicking Finish, the report and a shared data source will appear in the Solution Explorer and the report will also be visible in Design view. Click the Preview tab at the top. This report expects the user to supply a year which the report will then use as a filter. Type in a year between 2006 and 2013 and click View Report. Click the plus sign next to the Sales Year to expand the report to see the months, then expand again to see the categories and finally the details. You now have the assembly line report completed, and you probably already have some ideas on how to improve the report. Tomorrow’s Post Tomorrow’s blog post will show how to create your own data sources and data sets in SSRS. If you want to learn SSRS in easy to simple words – I strongly recommend you to get Beginning SSRS book from Joes 2 Pros. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Reporting Services, SSRS

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• Twitter ?? Nashorn ????(??)

  - by Homma

  ???? Nashorn ? Java ??????? Twitter ???????????????????? JavaFX ??????????????? ????? ??? jlaskey ??? Nashorn Blog ????????????? https://blogs.oracle.com/nashorn/entry/nashorn_in_the_twitterverse_continued ???????? ?? Twitter ???????????????????????? JavaFX ??????????????????????????????? Nashorn ?? JavaFX ??????????????JavaFX ???????????????????????????????????????Nashorn ? Java ????????????????????????????????????(JavaFX ?????????????????????)? ?????????????????????????????????????????????? Twitter ????????????????????????? var twitter4j = Packages.twitter4j; var TwitterFactory = twitter4j.TwitterFactory; var Query = twitter4j.Query; function getTrendingData() { var twitter = new TwitterFactory().instance; var query = new Query("nashorn OR nashornjs"); query.since("2012-11-21"); query.count = 100; var data = {}; do { var result = twitter.search(query); var tweets = result.tweets; for each (var tweet in tweets) { var date = tweet.createdAt; var key = (1900 + date.year) + "/" + (1 + date.month) + "/" + date.date; data[key] = (data[key] || 0) + 1; } } while (query = result.nextQuery()); return data; } ??????????????????getTrendingData() ??????????????(??????????Nashorn ???????? OpenJDK ?????? 2012 ? 11 ? 21 ???)??????????????????????????????????? ????JavaFX ? BarChart ??????????? var javafx = Packages.javafx; var Stage = javafx.stage.Stage var Scene = javafx.scene.Scene; var Group = javafx.scene.Group; var Chart = javafx.scene.chart.Chart; var FXCollections = javafx.collections.FXCollections; var ObservableList = javafx.collections.ObservableList; var CategoryAxis = javafx.scene.chart.CategoryAxis; var NumberAxis = javafx.scene.chart.NumberAxis; var BarChart = javafx.scene.chart.BarChart; var XYChart = javafx.scene.chart.XYChart; var Series = javafx.scene.chart.XYChart.Series; var Data = javafx.scene.chart.XYChart.Data; function graph(stage, data) { var root = new Group(); stage.scene = new Scene(root); var dates = Object.keys(data); var xAxis = new CategoryAxis(); xAxis.categories = FXCollections.observableArrayList(dates); var yAxis = new NumberAxis("Tweets", 0.0, 200.0, 50.0); var series = FXCollections.observableArrayList(); for (var date in data) { series.add(new Data(date, data[date])); } var tweets = new Series("Tweets", series); var barChartData = FXCollections.observableArrayList(tweets); var chart = new BarChart(xAxis, yAxis, barChartData, 25.0); root.children.add(chart); } ????????????????????????????????stage.scene = new Scene(root) ? stage.setScene(new Scene(root)) ????????????????????Nashorn ? stage ??????? scene ???????????????????(Dynalink ?????????)Java Beans ???????????????? (setScene()) ???????????????????????????????Nashorn ? FXCollections ??????????????????????????????observableArrayList(dates) ??????????Nashorn ? JavaScript ??? (dates) ? Java ???????????????????????????? JavaScript ?????????????????? Java ????????????????????????????????????????????????????????????? ????????????????????????????????? JavaFX ???????????????????????? JavaFX ??????????????javafx.application.Application ??????????????????????????? JavaFX ????????????????????????????????????????????????? import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import javafx.application.Application; import javafx.stage.Stage; import javax.script.ScriptEngine; import javax.script.ScriptEngineManager; import javax.script.ScriptException; public class TrendingMain extends Application { private static final ScriptEngineManager MANAGER = new ScriptEngineManager(); private final ScriptEngine engine = MANAGER.getEngineByName("nashorn"); private Trending trending; public static void main(String[] args) { launch(args); } @Override public void start(Stage stage) throws Exception { trending = (Trending) load("Trending.js"); trending.start(stage); } @Override public void stop() throws Exception { trending.stop(); } private Object load(String script) throws IOException, ScriptException { try (final InputStream is = TrendingMain.class.getResourceAsStream(script)) { return engine.eval(new InputStreamReader(is, "utf-8")); } } } ???? Nashorn ??????? JSR-223 ? javax.script ????????? private static final ScriptEngineManager MANAGER = new ScriptEngineManager(); private final ScriptEngine engine = MANAGER.getEngineByName("nashorn"); ????????? JavaScript ???????? Nashorn ???????????????????? load ???????????????????????engine ???????????????load ????????????? ???????????????Java ???????????????????????????????????????????????????? Java ????????????????JavaFX ???????? start ????? stop ?????????????????????????????????????? public interface Trending { public void start(Stage stage) throws Exception; public void stop() throws Exception; } ?????????????????????????????? function newTrending() { return new Packages.Trending() { start: function(stage) { var data = getTrendingData(); graph(stage, data); stage.show(); }, stop: function() { } } } newTrending(); ?????? Trending ?????????????????????start ????? stop ??????????????????????????????????? eval ???? Java ??????????????? trending = (Trending) load("Trending.js"); ????????????????Trending.js ??????? getTrendingData ???????????? newTrending ????????????????????? Java ?????????newTrending ????????? eval ????????? Trending ????????????????????????????????????????????????????????? trending.start(stage); ???????? ???? Nashorn ????????? http://www.myexpospace.com/JavaOne2012/SessionFiles/CON5251_PDF_5251_0001.pdf ???????? Dynalink ??????? https://github.com/szegedi/dynalink ????????

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• Gnome 3 gdm fails to start after preupgrade from fedora 14 to 15

  - by digital illusion

  I'm not able to boot fedora 15 in runlevel 5. After all services start, when the login screen should appear, gdm just show a mouse waiting cursor and keeps restarting itself. From /var/log/gdm/\:0-greeter.log Gtk-Message: Failed to load module "pk-gtk-module" /usr/bin/gnome-session: symbol lookup error: /usr/lib/gtk-3.0/modules/libatk-bridge.so: undefined symbol: atk_plug_get_type /usr/libexec/gnome-setting-daemon: symbol lookup error: /usr/lib/gtk-3.0modules/libatk-bridge.so: undefined symbol: atk_plug_get_type Where should atk_plug_get_type be defined? Edit: Here a better description of the error (system-config-network-gui:2643): Gnome-WARNING **: Accessibility: failed to find module 'libgail-gnome' which is needed to make this application accessible /usr/bin/python: symbol lookup error: /usr/lib/gtk-2.0/modules/libatk-bridge.so: undefined symbol: atk_plug_get_type Why there are still references to gtk2? Did preupgrade fail? Attaching upgrade log... it seems gdm was not added, but it is present in the users and groups list. May 26 11:25:52 sysimage sendmail[1076]: alias database /etc/aliases rebuilt by root May 26 11:25:52 sysimage sendmail[1076]: /etc/aliases: 77 aliases, longest 23 bytes, 795 bytes total May 26 11:46:09 sysimage useradd[1793]: failed adding user 'dbus', data deleted May 26 11:53:37 sysimage systemd-machine-id-setup[2443]: Initializing machine ID from D-Bus machine ID. May 26 11:55:28 sysimage useradd[2835]: failed adding user 'apache', data deleted May 26 11:55:38 sysimage useradd[2842]: failed adding user 'haldaemon', data deleted May 26 11:55:43 sysimage useradd[2848]: failed adding user 'smolt', data deleted May 26 11:57:32 sysimage sendmail[3032]: alias database /etc/aliases rebuilt by root May 26 11:57:32 sysimage sendmail[3032]: /etc/aliases: 77 aliases, longest 23 bytes, 795 bytes total May 26 11:57:46 sysimage groupadd[3066]: group added to /etc/group: name=cgred, GID=482 May 26 11:57:47 sysimage groupadd[3066]: group added to /etc/gshadow: name=cgred May 26 11:57:47 sysimage groupadd[3066]: new group: name=cgred, GID=482 May 26 11:58:42 sysimage useradd[3086]: failed adding user 'ntp', data deleted May 26 12:00:13 sysimage dbus: avc: received policyload notice (seqno=2) May 26 12:15:08 sysimage useradd[4950]: failed adding user 'gdm', data deleted May 26 12:24:39 sysimage dbus: avc: received policyload notice (seqno=3) May 26 12:25:24 sysimage useradd[5522]: failed adding user 'mysql', data deleted May 26 12:25:37 sysimage useradd[5533]: failed adding user 'rpcuser', data deleted May 26 12:26:31 sysimage useradd[5592]: failed adding user 'tcpdump', data deleted Any suggestions before I revert installation to F14?

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• Tomcat6 getting crashed at regular intervals installed in Ubuntu

  - by Milesh Rout

  I have installed Tomcat6 in Ubuntu OS and when I run my web application the server gets crashed at regular intervals. I have tried a lot but not getting the solution. I have increased the memory upto 2048mb but still getting such error. Following is the error I am getting. Any help would be really appreciated. org.apache.tomcat.util.http.Parameters processParametersINFO: Invalid chunk starting at byte [312] and ending at byte [312] with a value of [null] ignoredException in thread "Timer-1" Exception in thread "com.mchange.v2.async.ThreadPoolAsynchronousRunner$PoolThread-#0" Exception in thread "com.mchange.v2.async.ThreadPoolAsynchronousRunner$PoolThread-#2" Exception in thread "com.mchange.v2.async.ThreadPoolAsynchronousRunner$PoolThread-#1" Exception in thread "Timer-2" Exception in thread "http-8080-4" Exception in thread "http-8080-8" Exception in thread "http-8080-17" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.UpdateTimestampsCache.data" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.UpdateTimestampsCache.data" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.UpdateTimestampsCache.data" Exception in thread "com.safenet.usermgmt.User.data" Exception in thread "http-8080-7" Exception in thread "http-8080-12" Exception in thread "http-8080-16" Exception in thread "http-8080-14" Exception in thread "http-8080-13" Exception in thread "http-8080-15" Exception in thread "http-8080-6" OpenJDK Client VM warning: Exception java.lang.OutOfMemoryError occurred dispatching signal SIGTERM to handler- the VM may need to be forcibly terminated

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• Slowdown upon router/modem setup change

  - by Ollie Saunders

  I’ve been using a Belkin FSD7632-4 modem router to connect to my TalkTalk provided ADSL internet connection for some time and been pretty happy with it. Recently, however, the connection has been failing and I decided to get a ASUS RT-N16 instead, which is also a much more capable router generally. The ASUS RT-N16 doesn’t come with a modem built-in so I purchased as Zoom modem as well. I’ve set them both up and am using them to post this message. But I’m a bit miffed to find that I get a significantly and consistently slower downstream rate from the new configuration than with the old Belkin. Belkin modem router: downstream: 3.45 mbps upstream: 0.73 mbps ASUS router + Zoom modem: downstream: 2.71 mbps upstream: 0.66 mbps Any ideas why this is? The really weird thing about this is that the Zoom supports ADSL2 and ADSL2+ but I don’t think the old Belkin does. At first I thought it might be due to the Zoom modem being limited to PPPoE instead of PPPoA, which my ISP supports, but then I tried using PPPoE with the Belkin and that still gave a high speed. I’m using VC-Mux encapsulation with both. VPI of 0 and VCI of 38. I pulled this data off the Zoom: Mode: ADSL2 Line Coding: Trellis On Status: No Defect Link Power State: L0 Downstream Upstream SNR Margin (dB): 12.3 11.8 Attenuation (dB): 43.0 24.9 Output Power (dBm): 12.9 0.0 Attainable Rate (Kbps): 3936 844 Rate (Kbps): 3194 840 MSGc (number of bytes in overhead channel message): 59 10 B (number of bytes in Mux Data Frame): 99 14 M (number of Mux Data Frames in FEC Data Frame): 2 16 T (Mux Data Frames over sync bytes): 1 8 R (number of check bytes in FEC Data Frame): 8 8 S (ratio of FEC over PMD Data Frame length): 1.9833 9.0594 L (number of bits in PMD Data Frame): 839 219 D (interleaver depth): 32 2 Delay (msec): 15 4 Super Frames: 15808 14078 Super Frame Errors: 0 4294967232 RS Words: 513778 111753 RS Correctable Errors: 126 4294967238 RS Uncorrectable Errors: 0 N/A HEC Errors: 0 4294967279 OCD Errors: 0 0 LCD Errors: 0 0 Total Cells: 1920175 237597 Data Cells: 205993 392 Bit Errors: 0 0 Total ES: 0 0 Total SES: 0 0 Total UAS: 34 0

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• Using Truecrypt to secure mySQL database, any pitfalls?

  - by Saul

  The objective is to secure my database data from server theft, i.e. the server is at a business office location with normal premises lock and burglar alarm, but because the data is personal healthcare data I want to ensure that if the server was stolen the data would be unavailable as encrypted. I'm exploring installing mySQL on a mounted Truecrypt encrypted volume. It all works fine, and when I power off, or just cruelly pull the plug the encrypted drive disappears. This seems a load easier than encrypting data to the database, and I understand that if there is a security hole in the web app , or a user gets physical access to a plugged in server the data is compromised, but as a sanity check , is there any good reason not to do this? @James I'm thinking in a theft scenario, its not going to be powered down nicely and so is likely to crash any DB transactions running. But then if someone steals the server I'm going to need to rely on my off site backup anyway. @tomjedrz, its kind of all sensitive, individual personal and address details linked to medical referrals/records. Would be as bad in our field as losing credit card data, but means that almost everything in the database would need encryption... so figured better to run the whole DB in an encrypted partition. If encrypt data in the tables there's got to be a key somewhere on the server I'm presuming, which seems more of a risk if the box walks. At the moment the app is configured to drop a dump of data (weekly full and then deltas only hourly using rdiff) into a directory also on the Truecrypt disk. I have an off site box running WS_FTP Pro scheduled to connect by FTPs and synch down the backup, again into a Truecrypt mounted partition.

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• MySQL : table organisation for very large sets with high update frequency

  - by Remiz

  I'm facing a dilemma in the choice of my MySQL schema application. So before I start here is a picture extremely simplified of my database : Schema here : http://i43.tinypic.com/2wp5lxz.png In one sentence : for each customer, the application harvest text data and attached tags to each data collected. As approximation of the usage of each table, here is what I expect : customer : ~5000, shouldn't grow fast data : 5 millions per customer, could double or triple for big customers. tag : ~1000, quite fixed size data_tag : hundred of millions per customer easily. Each data can be tagged a lot. The harvesting process is permanent, that means that around every 15 minutes new data come and are tagged, that require a very constant index refreshing. A lot of my queries are a SELECT COUNT of DATA between specific DATES and tagged with a specific TAG on a specific CUSTOMER (very rarely it will involve several customers). Here is the situation, you can imagine with this kind of volume of data I'm facing a challenge in term of data organization and indexing. Again, it's a very minimalistic and simplified version of my structure. My question is, is it better: to stick with this model and to manage crazy index optimization ? (which involves potentially having billions of rows in the data_tag table) change the schema and use one data table and one data_tag table per customer ? (which involves having 5000 tables on my database) I'm running all of this on a MySQL 5.0 dedicated server (quad-core, 8Go of ram) replicated. I only use InnoDB, I also have another server that run Sphinx. So knowing all of this, I can't wait to hear your opinion about this. Thanks.

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• Does image block (firefox addon) save internet bandwidth usage?

  - by dkjain

  Does image block save internet bandwidth usage. I have a data capped plan from my ISP ( 5GB at 2mbps and thereafter 256 kpbs / pm). I doubt if the addon or other similar addon actually saves bandwidht. Here is my point of view, pls correct if that is wrong. When a request is sent to the server, the server sends out whatever page it's requested to serve with all its text and images etc. So essentially my ISP has made his pipe available for the data to reach me thus he would count those bytes under my data plan. When the data arrives it's all first stored to my browser cache (folder) area which means all the data has actually been received by me/computer using my ISP's pipe. The browser then fetches those data from the cache and displays it. By hitting the stop button or blocking images via ur addon I am just choosing not to display the data which would remain in the cache or eventually be discarded if still on the network pipe after a timeout limit. The point is the data request have been completed by the ISP and so the data would be metered and thus using addon such as image block or hitting stop button while page is loading does not in any way save internet bandwidth. Your comments plz....... Regards dk.

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• Are my web server permissions for uploading correct?

  - by user1699176

  I'm on debian and I have my website in the directory /srv/www/mysite.com/public_html I set chown for www-data:www-data on /srv/www. I have root disabled and created a sudo user which is id 1000:1000. I would also like to use this user to upload to /srv/www so I added my sudo user to the www-data group. I originally got a message saying that I didn't have permissions to upload a file to that directory. After playing around with multiple permissions for a while I finally was able to upload properly, but I'm not sure if this set up is correct. I'm hesitant to change it for now since it actually works, so I thought I'd ask for advice. I think what I ended up doing was this: sudo chown -R www-data:www-data /srv/www sudo chmod g+s /srv/www sudo usermod -aG www-data myuser sudo chgrp -R www-data /srv/www sudo chmod -R g+w /srv/www When I was finally able to successfully upload a file (with FileZilla) it showed the owner as myuser myuser. Shouldn't it have been www-data myuser? My question is whether this is correct and if there are any potential security issues? For example, I wasn't sure if I was actually supposed to use "myuser" to own the /srv/www directory instead sudo chown -R myuser:myuser /srv/www or maybe sudo chown -R www-data:myuser /srv/www If you need more info, let me know, thanks.

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• How to configure Visual Studio 2010 code coverage for ASP.NET MVC unit tests

  - by DigiMortal

  I just got Visual Studio 2010 code coverage work with ASP.NET MVC application unit tests. Everything is simple after you have spent some time with forums, blogs and Google. To save your valuable time I wrote this posting to guide you through the process of making code coverage work with ASP.NET MVC application unit tests. After some fighting with Visual Studio I got everything to work as expected. I am still not very sure why users must deal with this mess, but okay – I survived it. Before you start configuring Visual Studio I expect your solution meets the following needs: there are at least one library that will be tested, there is at least on library that contains tests to be run, there are some classes and some tests for them, and, of course, you are using version of Visual Studio 2010 that supports tests (I have Visual Studio 2010 Ultimate). Now open the following screenshot to separate windows and follow the steps given below. Visual Studio 2010 Test Settings window. Click on image to see it at original size.  Double click on Local.testsettings under Solution Items. Test settings window will be opened. Select “Data and Diagnostics” from left pane. Mark checkboxes “ASP.NET Profiler” and “Code Coverage”. Move cursor to “Code Coverage” line and press Configure button or make double click on line. Assemblies selection window will be opened. Mark checkboxes that are located before assemblies about what you want code coverage reports and apply settings. Save your project and close Visual Studio. Run Visual Studio as Administrator and run tests. NB! Select Test => Run => Tests in Current Context from menu. When tests are run you can open code coverage results by selecting Test => Windows => Code Coverage Results from menu. Here you can see my example test results. Visual Studio 2010 Test Results window. All my tests passed this time. :) Click on image to see it at original size.  And here are the code coverage results. Visual Studio 2101 Code Coverage Results. I need a lot more tests for sure. Click on image to see it at original size.  As you can see everything was pretty simple. But it took me sometime to figure out how to get everything work as expected. Problems? You may face some problems when making code coverage work. Here is my short list of possible problems. Make sure you have all assemblies available for code coverage. In some cases it needs more libraries to be referenced as you currently have. By example, I had to add some more Enterprise Library assemblies to my project. You can use EventViewer to discover errors that where given during testing. Make sure you selected all testable assemblies from Code Coverage settings like shown above. Otherwise you may get empty results. Tests with code coverage are slower because we need ASP.NET profiler. If your machine slows down then try to free more resources.

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• Knockout.js - Filtering, Sorting, and Paging

  - by jtimperley

  Originally posted on: http://geekswithblogs.net/jtimperley/archive/2013/07/28/knockout.js---filtering-sorting-and-paging.aspxKnockout.js is fantastic! Maybe I missed it but it appears to be missing flexible filtering, sorting, and pagination of its grids. This is a summary of my attempt at creating this functionality which has been working out amazingly well for my purposes. Before you continue, this post is not intended to teach you the basics of Knockout. They have already created a fantastic tutorial for this purpose. You'd be wise to review this before you continue. http://learn.knockoutjs.com/ Please view the full source code and functional example on jsFiddle. Below you will find a brief explanation of some of the components. http://jsfiddle.net/JTimperley/pyCTN/13/ First we need to create a model to represent our records. This model is a simple container with defined and guaranteed members. function CustomerModel(data) { if (!data) { data = {}; } var self = this; self.id = data.id; self.name = data.name; self.status = data.status; } Next we need a model to represent the page as a whole with an array of the previously defined records. I have intentionally overlooked the filtering and sorting options for now. Note how the filtering, sorting, and pagination are chained together to accomplish all three goals. This strategy allows each of these pieces to be used selectively based on the page's needs. If you only need sorting, just sort, etc. function CustomerPageModel(data) { if (!data) { data = {}; } var self = this; self.customers = ExtractModels(self, data.customers, CustomerModel); var filters = […]; var sortOptions = […]; self.filter = new FilterModel(filters, self.customers); self.sorter = new SorterModel(sortOptions, self.filter.filteredRecords); self.pager = new PagerModel(self.sorter.orderedRecords); } The code currently supports text box and drop down filters. Text box filters require defining the current 'Value' and the 'RecordValue' function to retrieve the filterable value from the provided record. Drop downs allow defining all possible values, the current option, and the 'RecordValue' as before. Once defining these filters, they are automatically added to the screen and any changes to their values will automatically update the results, causing their sort and pagination to be re-evaluated. var filters = [ { Type: "text", Name: "Name", Value: ko.observable(""), RecordValue: function(record) { return record.name; } }, { Type: "select", Name: "Status", Options: [ GetOption("All", "All", null), GetOption("New", "New", true), GetOption("Recently Modified", "Recently Modified", false) ], CurrentOption: ko.observable(), RecordValue: function(record) { return record.status; } } ]; Sort options are more simplistic and are also automatically added to the screen. Simply provide each option's name and value for the sort drop down as well as function to allow defining how the records are compared. This mechanism can easily be adapted for using table headers as the sort triggers. That strategy hasn't crossed my functionality needs at this point. var sortOptions = [ { Name: "Name", Value: "Name", Sort: function(left, right) { return CompareCaseInsensitive(left.name, right.name); } } ]; Paging options are completely contained by the pager model. Because we will be chaining arrays between our filtering, sorting, and pagination models, the following utility method is used to prevent errors when handing an observable array to another observable array. function GetObservableArray(array) { if (typeof(array) == 'function') { return array; }   return ko.observableArray(array); }

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• When to implement: Together with or after the source product?

  - by Jeremy Oosthuizen

  Somebody recently relayed a prospect's question to me: How hard would it be to implement OUBI after the source product (CC&B, WAM or NMS) has already been implemented? Fact is that MOST non-OUBI Data Warehouse / Business Intelligence implementations take place after the source application(s) are in place and hopefully stable. If an organization decides that they need better reporting and management information, then the logical path (see The Data Warehouse Institute's Data Warehouse Maturity Model) is to a Data Warehouse -- no matter when their last applications were implemented. If there is a pre-built Data Warehouse for their specific application, or even for the desired business process in their industry, they're in luck. Else they have to design and build from scratch, using a toolset. The implementation of a toolset is unlike the implementation of OUBI which, like OBI Apps, contain pre-built ETL routines and user content. Much has been written before about the advantages of that. So, because OUBI is designed specifically for Oracle Utilities transactional products, we often implement them in parallel -- with OUBI lagging a little behind by necessity, like Reporting. Customers know from the start they're going to need the solution, and therefore purchase the products at the same time. My biggest argument FOR a parallel installation/implementation of OUBI with the source product is two-fold: - There could be things (which is the technical term for data elements) that customers figure out they need when implementing OUBI, which are often easier added to the source product's implementation project, than to add later; - OUBI's ETL often points out errors (severe or not) with converted data, which are easier to fix during the source product's implementation project, or it may even be impossible to fix afterwards. The Conversion routines sometimes miss these errors, because the source system can live with the not-quite-perfect converted data. If the data can't be properly extracted, i.e. the proper Dimensions linked to the Facts, then it can't get into OUBI. That means it can't be analyzed effectively along with the rest of the organization's data. Then there is also the throw-away-work argument, which may be significant. The operational / transactional system cannot go live without reports on Day 1. A lot of those reports would be taken care of by the implementation of OUBI. If OUBI is implemented after go-live, those reports STILL have to be built during the source product's implementation project, but they become throw-away after the OUBI implementation. I have sometimes been told that it is better to implement OUBI after the source product, because it cuts down on scope and risk for the source product's implementation project. All I can say to that, is bah humbug. No, seriously, given the arguments above, planning has to include the OUBI implementation and it has to be managed properly -- just like any other implementation. If so, it should not add any risk and it should be included in the scope from the start. The answer to the prospect's question is therefore that it is not that much more difficult; after all, most DW/BI implemenations are done like that. They just have to consider the points above.

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• SQL SERVER – Query Hint – Contest Win Joes 2 Pros Combo (USD 198) – Day 1 of 5

  - by pinaldave

  August 2011 we ran a contest where every day we give away one book for an entire month. The contest had extreme success. Lots of people participated and lots of give away. I have received lots of questions if we are doing something similar this month. Absolutely, instead of running a contest a month long we are doing something more interesting. We are giving away USD 198 worth gift every day for this week. We are giving away Joes 2 Pros 5 Volumes (BOOK) SQL 2008 Development Certification Training Kit every day. One copy in India and One in USA. Total 2 of the giveaway (worth USD 198). All the gifts are sponsored from the Koenig Training Solution and Joes 2 Pros. The books are available here Amazon | Flipkart | Indiaplaza How to Win: Read the Question Read the Hints Answer the Quiz in Contact Form in following format Question Answer Name of the country (The contest is open for USA and India residents only) 2 Winners will be randomly selected announced on August 20th. Question of the Day: Which of the following queries will return dirty data? a) SELECT * FROM Table1 (READUNCOMMITED) b) SELECT * FROM Table1 (NOLOCK) c) SELECT * FROM Table1 (DIRTYREAD) d) SELECT * FROM Table1 (MYLOCK) Query Hints: BIG HINT POST Most SQL people know what a “Dirty Record” is. You might also call that an “Intermediate record”. In case this is new to you here is a very quick explanation. The simplest way to describe the steps of a transaction is to use an example of updating an existing record into a table. When the insert runs, SQL Server gets the data from storage, such as a hard drive, and loads it into memory and your CPU. The data in memory is changed and then saved to the storage device. Finally, a message is sent confirming the rows that were affected. For a very short period of time the update takes the data and puts it into memory (an intermediate state), not a permanent state. For every data change to a table there is a brief moment where the change is made in the intermediate state, but is not committed. During this time, any other DML statement needing that data waits until the lock is released. This is a safety feature so that SQL Server evaluates only official data. For every data change to a table there is a brief moment where the change is made in this intermediate state, but is not committed. During this time, any other DML statement (SELECT, INSERT, DELETE, UPDATE) needing that data must wait until the lock is released. This is a safety feature put in place so that SQL Server evaluates only official data. Additional Hints: I have previously discussed various concepts from SQL Server Joes 2 Pros Volume 1. SQL Joes 2 Pros Development Series – Dirty Records and Table Hints SQL Joes 2 Pros Development Series – Row Constructors SQL Joes 2 Pros Development Series – Finding un-matching Records SQL Joes 2 Pros Development Series – Efficient Query Writing Strategy SQL Joes 2 Pros Development Series – Finding Apostrophes in String and Text SQL Joes 2 Pros Development Series – Wildcard – Querying Special Characters SQL Joes 2 Pros Development Series – Wildcard Basics Recap Next Step: Answer the Quiz in Contact Form in following format Question Answer Name of the country (The contest is open for USA and India) Bonus Winner Leave a comment with your favorite article from the “additional hints” section and you may be eligible for surprise gift. There is no country restriction for this Bonus Contest. Do mention why you liked it any particular blog post and I will announce the winner of the same along with the main contest. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Joes 2 Pros, PostADay, SQL, SQL Authority, SQL Puzzle, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• My Tech Ed North America Preview - Certification Edition

  - by Chris Gardner

  In my previous TechEd North America Preview, I addressed all the content I wanted to see at the show. This time, we shall turn our attention to the certifications I might try to pick up. If you have never been to TechEd North America before, one of the greatest things about the event is an on-site certification center. If you have a couple hours to spare, you can walk up to a test. The first test on my agenda is 70-5231. I took this update test once, but did not do well on the MVC portion2. A few practice tests later, and I think I'm ready to fake that section. After that, I need to complete my road to being a master. The good folks here at work have been having a real love / hate relationship with the idea of me become an MCM in SQL Server3. Of course, before I do that, I need to finally take the SQL Administration tests. Thus, we shall add 70-4324 and 70-4505 to the list. Speaking of MCM, TechEd North America will have a special on test 88-9706. This test is normally $500, and you have to find a place to take it7. However, there is a special 50% off rate for people who take it on location. With those kind of prices, I may just take it as a form of study guide. As a final push, I may take some Windows Phone exams. I mentioned in my previous post that I may attend the 70-5998 Exam Cram session. Unfortunately, I will be staffing the Hands-On-Lab at that time. As we know, this has never stopped me from taking a test. This may lead to fits of 70-5069, but after we've come this far... That should complete my list. Do I really think I'll find time to take 6 tests at TechEd North America? Probably not. I have done it at TechEd North America before, but that was before I was TechEd North America staff. I also had a co-worker pass 9 in one year, but he basically did nothing but travel to Orlando in 2007 to take tests. And what's the point of attending a HUGE conference if you don't network? Of course, networking will have to wait for Friday's post... 1 Upgrade: Transition Your MCPD .NET Framework 3.5 Web Developer Skills to MCPD .NET Framework 4 Web Developer 2Because I never have used, nor do I really think I ever will use, MVC... 3By that, I mean they love the idea, and they hate the price 4Microsoft SQL Server 2008, Implementation and Maintenance 5PRO: Designing, Optimizing and Maintaining a Database Administrative Solution Using Microsoft SQL Server 2008 6SQL Server 2008 Microsoft Certified Master: Knowledge Exam 7Which isn't nearly as expensive as the Lab Exam, nor as difficult to find a location. However, it is not offered at every testing facility. 8PRO: Designing and Developing Windows Phone Applications 9TS: Silverlight 4, Development

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