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• I Know What I Did This Summer: Put Down Trex Decking

  - by thatjeffsmith

  If you’re wondering why I would bore everyone with my pictures and frequent status updates/tweets from the past week – it’s so I could document the process of refurbishing my deck, or what some would call a porch. When we go to take a vacation, buy a car, do anything – we also read personal blogs to get the real story. So, if you’re curious about what it takes to tackle this sort of project, read on. Skills/Equipment/Manpower We Possessed I took the old decking out by myself. I’m about 230 lbs, more than 6′ tall, and I’m pretty healthy. This took about 8 hours over two afternoons. Three of us put the deck back together. My wife has two engineering degrees. Her father also has two engineering degrees. Lots of brainpower available here. Also, her dad ran the public works department for a country for more than 20 years – so lots and lots of practical experience on hand. We had a compound mitre saw, a skilsaw, 2-3 crowbars, a framing hammer, 3 cordless drills, a corded drill, lots of sawhorses, a power sander, an angle grinder, a 10×10 Coleman canopy tent, a Ford F-150 pickup truck, outdoor speakers and lots of iTunes playlists, plenty of water and cold beer. Why We Did This Our deck was relatively young – it was built in 2005. However, the pressure treated boards must not have been adequately maintained before we bought the house. I had powerwashed the deck every other year and had it stained a few times. The boards just rotted. We’re going to be in the house for a long time, and we wanted something that would look nice and require little maintenance. More bad deck boards The deck boards were in bad shape Things We Learned The two most important things: The hidden fasteners have to be put in JUST right. Wedge them into the grooved board, then bend down the bit that is screwed down. We didn’t do this on the first board and couldn’t get the second board to fit nearly close enough. Watching the official TREX YouTube video helped immensely, and we should have watched that first. When pre-drilling holes for the boards that need screwed down – DO NOT pre-drill through the underlying framing wood. ONLY pre-drill through the TREX itself. The screw won’t seat in the board properly. Instead of sitting down flush with the board, it will stop at the top of the board and just spin. I had to call the the place that sold me the screws to find this out. So about a third of our screws look like crap. If it doesn’t look or feel right – stop everything and pick up your computer or your phone. It’s not right, and it will be much easier to stop and find out why. We didn’t do this, and now I’m going to see every screw that’s not flush with the boards and get upset. Oh well. The Process How much time did it take? Well I spent about 8 hours taking the deck apart. And then the 3 of use spent 8 hours the first day, 10 hours the second day, 8 hours the third, and another 6 hours on the fourth day. That’s like 104 man-hours. We supposedly saved four or five thousand dollars in labor, but don’t do the math here or you might get a bit upset. The main thing is that we got what we wanted, and there won’t be any surprises later. Now for some pictures… This 6”+ pry bar made the destruction of the old deck much easier Most of the joists, once exposed, were OK. This joist wasn’t sitting on ANYTHING before. We think a lazy gas person cut the board to sneak a gas line in. Awesome… These monster lag bolts had to be accounted for when putting in the additional framing The border pattern Sheri wanted to put in required a lot more framing. These were the first boards to go down – we screwed them in as there was no way to attach clips I sat, kicked in the boards, and then drilled these clips in – but my wife was able to go MUCH faster by using her hands to lock the boards in and drill on her knees. I liked locking the board in with my feet when they needed to be ‘encouraged’ to go straight. The first board took FOREVER to go in, but then when we got rolling, we were able to put in a 20′ board in less than 10 minutes. This was end of construction day #2 – we got much further than we thought we would. Ah, the dreaded last 10% – what to do here? Remember those ‘floating’ stringers? Yeah, we fixed that up a bit, too. My wife used a website (and her brain) to calculate exactly how to cut the stringers to give us the rise/run we needed with the proper clearance and all that jazz. The stairs with stringers and toe kicks – this was worth the effort It started raining on us as I screwed down the steps – this we managed to get our shade tent up on the deck to protect us from the rain too The stairs, finished Finished, mostly Good corner shot The top of the stairs Stairs, looking down Celebratory beer In Summary There are a few things we’re not happy with. I think we can fix them up – but later. I have a few things left to finish, rewire the lighting, get the gas grille put back in, and rehang some screen doors. I was expecting this to be a lot worse than it was. If I didn’t have the help, I would have never done it myself. But I’m glad that I did have that help and did do that project. It’s not often you get to spend that kind of qualify time with family and building cool stuff.

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• quick look at: dm_db_index_physical_stats

  - by fatherjack

  A quick look at the key data from this dmv that can help a DBA keep databases performing well and systems online as the users need them. When the dynamic management views relating to index statistics became available in SQL Server 2005 there was much hype about how they can help a DBA keep their servers running in better health than ever before. This particular view gives an insight into the physical health of the indexes present in a database. Whether they are use or unused, complete or missing some columns is irrelevant, this is simply the physical stats of all indexes; disabled indexes are ignored however. In it’s simplest form this dmv can be executed as:   The results from executing this contain a record for every index in every database but some of the columns will be NULL. The first parameter is there so that you can specify which database you want to gather index details on, rather than scan every database. Simply specifying DB_ID() in place of the first NULL achieves this. In order to avoid the NULLS, or more accurately, in order to choose when to have the NULLS you need to specify a value for the last parameter. It takes one of 4 values – DEFAULT, ‘SAMPLED’, ‘LIMITED’ or ‘DETAILED’. If you execute the dmv with each of these values you can see some interesting details in the times taken to complete each step. DECLARE @Start DATETIME DECLARE @First DATETIME DECLARE @Second DATETIME DECLARE @Third DATETIME DECLARE @Finish DATETIME SET @Start = GETDATE() SELECT * FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, DEFAULT) AS ddips SET @First = GETDATE() SELECT * FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, 'SAMPLED') AS ddips SET @Second = GETDATE() SELECT * FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, 'LIMITED') AS ddips SET @Third = GETDATE() SELECT * FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, 'DETAILED') AS ddips SET @Finish = GETDATE() SELECT DATEDIFF(ms, @Start, @First) AS [DEFAULT] , DATEDIFF(ms, @First, @Second) AS [SAMPLED] , DATEDIFF(ms, @Second, @Third) AS [LIMITED] , DATEDIFF(ms, @Third, @Finish) AS [DETAILED] Running this code will give you 4 result sets; DEFAULT will have 12 columns full of data and then NULLS in the remainder. SAMPLED will have 21 columns full of data. LIMITED will have 12 columns of data and the NULLS in the remainder. DETAILED will have 21 columns full of data. So, from this we can deduce that the DEFAULT value (the same one that is also applied when you query the view using a NULL parameter) is the same as using LIMITED. Viewing the final result set has some details that are worth noting: Running queries against this view takes significantly longer when using the SAMPLED and DETAILED values in the last parameter. The duration of the query is directly related to the size of the database you are working in so be careful running this on big databases unless you have tried it on a test server first. Let’s look at the data we get back with the DEFAULT value first of all and then progress to the extra information later. We know that the first parameter that we supply has to be a database id and for the purposes of this blog we will be providing that value with the DB_ID function. We could just as easily put a fixed value in there or a function such as DB_ID (‘AnyDatabaseName’). The first columns we get back are database_id and object_id. These are pretty explanatory and we can wrap those in some code to make things a little easier to read: SELECT DB_NAME([ddips].[database_id]) AS [DatabaseName] , OBJECT_NAME([ddips].[object_id]) AS [TableName] … FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, NULL) AS ddips  gives us   SELECT DB_NAME([ddips].[database_id]) AS [DatabaseName] , OBJECT_NAME([ddips].[object_id]) AS [TableName], [i].[name] AS [IndexName] , ….. FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, NULL) AS ddips INNER JOIN [sys].[indexes] AS i ON [ddips].[index_id] = [i].[index_id] AND [ddips].[object_id] = [i].[object_id]     These handily tie in with the next parameters in the query on the dmv. If you specify an object_id and an index_id in these then you get results limited to either the table or the specific index. Once again we can place a  function in here to make it easier to work with a specific table. eg. SELECT * FROM [sys].[dm_db_index_physical_stats] (DB_ID(), OBJECT_ID(‘AdventureWorks2008.Person.Address’) , 1, NULL, NULL) AS ddips   Note: Despite me showing that functions can be placed directly in the parameters for this dmv, best practice recommends that functions are not used directly in the function as it is possible that they will fail to return a valid object ID. To be certain of not passing invalid values to this function, and therefore setting an automated process off on the wrong path, declare variables for the OBJECT_IDs and once they have been validated, use them in the function: DECLARE @db_id SMALLINT; DECLARE @object_id INT; SET @db_id = DB_ID(N’AdventureWorks_2008′); SET @object_id = OBJECT_ID(N’AdventureWorks_2008.Person.Address’); IF @db_id IS NULL BEGINPRINT N’Invalid database’; ENDELSE IF @object_id IS NULL BEGINPRINT N’Invalid object’; ENDELSE BEGINSELECT * FROM sys.dm_db_index_physical_stats (@db_id, @object_id, NULL, NULL , ‘LIMITED’); END; GO In cases where the results of querying this dmv don’t have any effect on other processes (i.e. simply viewing the results in the SSMS results area)  then it will be noticed when the results are not consistent with the expected results and in the case of this blog this is the method I have used. So, now we can relate the values in these columns to something that we recognise in the database lets see what those other values in the dmv are all about. The next columns are: We’ll skip partition_number, index_type_desc, alloc_unit_type_desc, index_depth and index_level  as this is a quick look at the dmv and they are pretty self explanatory. The final columns revealed by querying this view in the DEFAULT mode are avg_fragmentation_in_percent. This is the amount that the index is logically fragmented. It will show NULL when the dmv is queried in SAMPLED mode. fragment_count. The number of pieces that the index is broken into. It will show NULL when the dmv is queried in SAMPLED mode. avg_fragment_size_in_pages. The average size, in pages, of a single fragment in the leaf level of the IN_ROW_DATA allocation unit. It will show NULL when the dmv is queried in SAMPLED mode. page_count. Total number of index or data pages in use. OK, so what does this give us? Well, there is an obvious correlation between fragment_count, page_count and avg_fragment_size-in_pages. We see that an index that takes up 27 pages and is in 3 fragments has an average fragment size of 9 pages (27/3=9). This means that for this index there are 3 separate places on the hard disk that SQL Server needs to locate and access to gather the data when it is requested by a DML query. If this index was bigger than 72KB then having it’s data in 3 pieces might not be too big an issue as each piece would have a significant piece of data to read and the speed of access would not be too poor. If the number of fragments increases then obviously the amount of data in each piece decreases and that means the amount of work for the disks to do in order to retrieve the data to satisfy the query increases and this would start to decrease performance. This information can be useful to keep in mind when considering the value in the avg_fragmentation_in_percent column. This is arrived at by an internal algorithm that gives a value to the logical fragmentation of the index taking into account the multiple files, type of allocation unit and the previously mentioned characteristics if index size (page_count) and fragment_count. Seeing an index with a high avg_fragmentation_in_percent value will be a call to action for a DBA that is investigating performance issues. It is possible that tables will have indexes that suffer from rapid increases in fragmentation as part of normal daily business and that regular defragmentation work will be needed to keep it in good order. In other cases indexes will rarely become fragmented and therefore not need rebuilding from one end of the year to another. Keeping this in mind DBAs need to use an ‘intelligent’ process that assesses key characteristics of an index and decides on the best, if any, defragmentation method to apply should be used. There is a simple example of this in the sample code found in the Books OnLine content for this dmv, in example D. There are also a couple of very popular solutions created by SQL Server MVPs Michelle Ufford and Ola Hallengren which I would wholly recommend that you review for much further detail on how to care for your SQL Server indexes. Right, let’s get back on track then. Querying the dmv with the fifth parameter value as ‘DETAILED’ takes longer because it goes through the index and refreshes all data from every level of the index. As this blog is only a quick look a we are going to skate right past ghost_record_count and version_ghost_record_count and discuss avg_page_space_used_in_percent, record_count, min_record_size_in_bytes, max_record_size_in_bytes and avg_record_size_in_bytes. We can see from the details below that there is a correlation between the columns marked. Column 1 (Page_Count) is the number of 8KB pages used by the index, column 2 is how full each page is (how much of the 8KB has actual data written on it), column 3 is how many records are recorded in the index and column 4 is the average size of each record. This approximates to: ((Col1*8) * 1024*(Col2/100))/Col3 = Col4*. avg_page_space_used_in_percent is an important column to review as this indicates how much of the disk that has been given over to the storage of the index actually has data on it. This value is affected by the value given for the FILL_FACTOR parameter when creating an index. avg_record_size_in_bytes is important as you can use it to get an idea of how many records are in each page and therefore in each fragment, thus reinforcing how important it is to keep fragmentation under control. min_record_size_in_bytes and max_record_size_in_bytes are exactly as their names set them out to be. A detail of the smallest and largest records in the index. Purely offered as a guide to the DBA to better understand the storage practices taking place. So, keeping an eye on avg_fragmentation_in_percent will ensure that your indexes are helping data access processes take place as efficiently as possible. Where fragmentation recurs frequently then potentially the DBA should consider; the fill_factor of the index in order to leave space at the leaf level so that new records can be inserted without causing fragmentation so rapidly. the columns used in the index should be analysed to avoid new records needing to be inserted in the middle of the index but rather always be added to the end. * – it’s approximate as there are many factors associated with things like the type of data and other database settings that affect this slightly.  Another great resource for working with SQL Server DMVs is Performance Tuning with SQL Server Dynamic Management Views by Louis Davidson and Tim Ford – a free ebook or paperback from Simple Talk. Disclaimer – Jonathan is a Friend of Red Gate and as such, whenever they are discussed, will have a generally positive disposition towards Red Gate tools. Other tools are often available and you should always try others before you come back and buy the Red Gate ones. All code in this blog is provided “as is” and no guarantee, warranty or accuracy is applicable or inferred, run the code on a test server and be sure to understand it before you run it on a server that means a lot to you or your manager.

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• ?Oracle Database 12c????Information Lifecycle Management ILM?Storage Enhancements

  - by Liu Maclean(???)

  Oracle Database 12c????Information Lifecycle Management ILM ?????????Storage Enhancements ???????? Lifecycle Management ILM ????????? Automatic Data Placement ??????, ??ADP? ?????? 12c???????Datafile??? Online Move Datafile, ????????????????datafile???????,??????????????? ????(12.1.0.1)Automatic Data Optimization?heat map????????: ????????? (CDB)?????Automatic Data Optimization?heat map Row-level policies for ADO are not supported for Temporal Validity. Partition-level ADO and compression are supported if partitioned on the end-time columns. Row-level policies for ADO are not supported for in-database archiving. Partition-level ADO and compression are supported if partitioned on the ORA_ARCHIVE_STATE column. Custom policies (user-defined functions) for ADO are not supported if the policies default at the tablespace level. ADO does not perform checks for storage space in a target tablespace when using storage tiering. ADO is not supported on tables with object types or materialized views. ADO concurrency (the number of simultaneous policy jobs for ADO) depends on the concurrency of the Oracle scheduler. If a policy job for ADO fails more than two times, then the job is marked disabled and the job must be manually enabled later. Policies for ADO are only run in the Oracle Scheduler maintenance windows. Outside of the maintenance windows all policies are stopped. The only exceptions are those jobs for rebuilding indexes in ADO offline mode. ADO has restrictions related to moving tables and table partitions. ??????row,segment???????????ADO??,?????create table?alter table?????? ????ADO??,??????????????,???????????????? storage tier , ?????????storage tier?????????, ??????????????ADO??????????? segment?row??group? ?CREATE TABLE?ALERT TABLE???ILM???,??????????????????ADO policy? ??ILM policy???????????????? ??????? ????ADO policy, ?????alter table  ???????,?????????????? CREATE TABLE sales_ado (PROD_ID NUMBER NOT NULL, CUST_ID NUMBER NOT NULL, TIME_ID DATE NOT NULL, CHANNEL_ID NUMBER NOT NULL, PROMO_ID NUMBER NOT NULL, QUANTITY_SOLD NUMBER(10,2) NOT NULL, AMOUNT_SOLD NUMBER(10,2) NOT NULL ) ILM ADD POLICY COMPRESS FOR ARCHIVE HIGH SEGMENT AFTER 6 MONTHS OF NO ACCESS; SQL> SELECT SUBSTR(policy_name,1,24) AS POLICY_NAME, policy_type, enabled 2 FROM USER_ILMPOLICIES; POLICY_NAME POLICY_TYPE ENABLED -------------------- -------------------------- -------------- P41 DATA MOVEMENT YES ALTER TABLE sales MODIFY PARTITION sales_1995 ILM ADD POLICY COMPRESS FOR ARCHIVE HIGH SEGMENT AFTER 6 MONTHS OF NO ACCESS; SELECT SUBSTR(policy_name,1,24) AS POLICY_NAME, policy_type, enabled FROM USER_ILMPOLICIES; POLICY_NAME POLICY_TYPE ENABLE ------------------------ ------------- ------ P1 DATA MOVEMENT YES P2 DATA MOVEMENT YES /* You can disable an ADO policy with the following */ ALTER TABLE sales_ado ILM DISABLE POLICY P1; /* You can delete an ADO policy with the following */ ALTER TABLE sales_ado ILM DELETE POLICY P1; /* You can disable all ADO policies with the following */ ALTER TABLE sales_ado ILM DISABLE_ALL; /* You can delete all ADO policies with the following */ ALTER TABLE sales_ado ILM DELETE_ALL; /* You can disable an ADO policy in a partition with the following */ ALTER TABLE sales MODIFY PARTITION sales_1995 ILM DISABLE POLICY P2; /* You can delete an ADO policy in a partition with the following */ ALTER TABLE sales MODIFY PARTITION sales_1995 ILM DELETE POLICY P2; ILM ???????: ?????ILM ADP????,???????: ?????? ???? activity tracking, ????2????????,???????????????????: SEGMENT-LEVEL???????????????????? ROW-LEVEL????????,??????? ????????: 1??????? SEGMENT-LEVEL activity tracking ALTER TABLE interval_sales ILM  ENABLE ACTIVITY TRACKING SEGMENT ACCESS ???????INTERVAL_SALES??segment level  activity tracking,?????????????????? 2? ??????????? ALTER TABLE emp ILM ENABLE ACTIVITY TRACKING (CREATE TIME , WRITE TIME); 3????????? ALTER TABLE emp ILM ENABLE ACTIVITY TRACKING  (READ TIME); ?12.1.0.1.0?????? ??HEAT_MAP??????????, ?????system??session?????heap_map????????????? ?????????HEAT MAP??,? ALTER SYSTEM SET HEAT_MAP = ON; ?HEAT MAP??????,??????????????????????????  ??SYSTEM?SYSAUX????????????? ???????HEAT MAP??: ALTER SYSTEM SET HEAT_MAP = OFF; ????? HEAT_MAP????, ?HEAT_MAP??? ?????????????????????? ?HEAT_MAP?????????Automatic Data Optimization (ADO)??? ??ADO??,Heat Map ?????????? ????V$HEAT_MAP_SEGMENT ??????? HEAT MAP?? SQL> select * from V$heat_map_segment; no rows selected SQL> alter session set heat_map=on; Session altered. SQL> select * from scott.emp; EMPNO ENAME JOB MGR HIREDATE SAL COMM DEPTNO ---------- ---------- --------- ---------- --------- ---------- ---------- ---------- 7369 SMITH CLERK 7902 17-DEC-80 800 20 7499 ALLEN SALESMAN 7698 20-FEB-81 1600 300 30 7521 WARD SALESMAN 7698 22-FEB-81 1250 500 30 7566 JONES MANAGER 7839 02-APR-81 2975 20 7654 MARTIN SALESMAN 7698 28-SEP-81 1250 1400 30 7698 BLAKE MANAGER 7839 01-MAY-81 2850 30 7782 CLARK MANAGER 7839 09-JUN-81 2450 10 7788 SCOTT ANALYST 7566 19-APR-87 3000 20 7839 KING PRESIDENT 17-NOV-81 5000 10 7844 TURNER SALESMAN 7698 08-SEP-81 1500 0 30 7876 ADAMS CLERK 7788 23-MAY-87 1100 20 7900 JAMES CLERK 7698 03-DEC-81 950 30 7902 FORD ANALYST 7566 03-DEC-81 3000 20 7934 MILLER CLERK 7782 23-JAN-82 1300 10 14 rows selected. SQL> select * from v$heat_map_segment; OBJECT_NAME SUBOBJECT_NAME OBJ# DATAOBJ# TRACK_TIM SEG SEG FUL LOO CON_ID -------------------- -------------------- ---------- ---------- --------- --- --- --- --- ---------- EMP 92997 92997 23-JUL-13 NO NO YES NO 0 ??v$heat_map_segment???,?v$heat_map_segment??????????????X$HEATMAPSEGMENT V$HEAT_MAP_SEGMENT displays real-time segment access information. Column Datatype Description OBJECT_NAME VARCHAR2(128) Name of the object SUBOBJECT_NAME VARCHAR2(128) Name of the subobject OBJ# NUMBER Object number DATAOBJ# NUMBER Data object number TRACK_TIME DATE Timestamp of current activity tracking SEGMENT_WRITE VARCHAR2(3) Indicates whether the segment has write access: (YES or NO) SEGMENT_READ VARCHAR2(3) Indicates whether the segment has read access: (YES or NO) FULL_SCAN VARCHAR2(3) Indicates whether the segment has full table scan: (YES or NO) LOOKUP_SCAN VARCHAR2(3) Indicates whether the segment has lookup scan: (YES or NO) CON_ID NUMBER The ID of the container to which the data pertains. Possible values include:   0: This value is used for rows containing data that pertain to the entire CDB. This value is also used for rows in non-CDBs. 1: This value is used for rows containing data that pertain to only the root n: Where n is the applicable container ID for the rows containing data The Heat Map feature is not supported in CDBs in Oracle Database 12c, so the value in this column can be ignored. ??HEAP MAP??????????????????,????DBA_HEAT_MAP_SEGMENT???????? ???????HEAT_MAP_STAT$?????? ??Automatic Data Optimization??????: ????1: SQL> alter system set heat_map=on; ?????? ????????????? scott?? http://www.askmaclean.com/archives/scott-schema-script.html SQL> grant all on dbms_lock to scott; ????? SQL> grant dba to scott; ????? @ilm_setup_basic C:\APP\XIANGBLI\ORADATA\MACLEAN\ilm.dbf @tktgilm_demo_env_setup SQL> connect scott/tiger ; ???? SQL> select count(*) from scott.employee; COUNT(*) ---------- 3072 ??? 1 ?? SQL> set serveroutput on SQL> exec print_compression_stats('SCOTT','EMPLOYEE'); Compression Stats ------------------ Uncmpressed : 3072 Adv/basic compressed : 0 Others : 0 PL/SQL ???????? ???????3072?????? ????????? ????policy ???????????? alter table employee ilm add policy row store compress advanced row after 3 days of no modification / SQL> set serveroutput on SQL> execute list_ilm_policies; -------------------------------------------------- Policies defined for SCOTT -------------------------------------------------- Object Name------ : EMPLOYEE Subobject Name--- : Object Type------ : TABLE Inherited from--- : POLICY NOT INHERITED Policy Name------ : P1 Action Type------ : COMPRESSION Scope------------ : ROW Compression level : ADVANCED Tier Tablespace-- : Condition type--- : LAST MODIFICATION TIME Condition days--- : 3 Enabled---------- : YES -------------------------------------------------- PL/SQL ???????? SQL> select sysdate from dual; SYSDATE -------------- 29-7? -13 SQL> execute set_back_chktime(get_policy_name('EMPLOYEE',null,'COMPRESSION','ROW','ADVANCED',3,null,null),'EMPLOYEE',null,6); Object check time reset ... -------------------------------------- Object Name : EMPLOYEE Object Number : 93123 D.Object Numbr : 93123 Policy Number : 1 Object chktime : 23-7? -13 08.13.42.000000 ?? Distnt chktime : 0 -------------------------------------- PL/SQL ???????? ?policy?chktime???6??, ????set_back_chktime???????????????“????”?,?????????,???????? ?????? alter system flush buffer_cache; alter system flush buffer_cache; alter system flush shared_pool; alter system flush shared_pool; SQL> execute set_window('MONDAY_WINDOW','OPEN'); Set Maint. Window OPEN ----------------------------- Window Name : MONDAY_WINDOW Enabled? : TRUE Active? : TRUE ----------------------------- PL/SQL ???????? SQL> exec dbms_lock.sleep(60) ; PL/SQL ???????? SQL> exec print_compression_stats('SCOTT', 'EMPLOYEE'); Compression Stats ------------------ Uncmpressed : 338 Adv/basic compressed : 2734 Others : 0 PL/SQL ???????? ??????????????? Adv/basic compressed : 2734 ??????? SQL> col object_name for a20 SQL> select object_id,object_name from dba_objects where object_name='EMPLOYEE'; OBJECT_ID OBJECT_NAME ---------- -------------------- 93123 EMPLOYEE SQL> execute list_ilm_policy_executions ; -------------------------------------------------- Policies execution details for SCOTT -------------------------------------------------- Policy Name------ : P22 Job Name--------- : ILMJOB48 Start time------- : 29-7? -13 08.37.45.061000 ?? End time--------- : 29-7? -13 08.37.48.629000 ?? ----------------- Object Name------ : EMPLOYEE Sub_obj Name----- : Obj Type--------- : TABLE ----------------- Exec-state------- : SELECTED FOR EXECUTION Job state-------- : COMPLETED SUCCESSFULLY Exec comments---- : Results comments- : --- -------------------------------------------------- PL/SQL ???????? ILMJOB48?????policy?JOB,?12.1.0.1??J00x???? ?MMON_SLAVE???M00x???15????????? select sample_time,program,module,action from v$active_session_history where action ='KDILM background EXEcution' order by sample_time; 29-7? -13 08.16.38.369000000 ?? ORACLE.EXE (M000) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.17.38.388000000 ?? ORACLE.EXE (M000) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.17.39.390000000 ?? ORACLE.EXE (M000) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.23.38.681000000 ?? ORACLE.EXE (M002) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.32.38.968000000 ?? ORACLE.EXE (M000) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.33.39.993000000 ?? ORACLE.EXE (M003) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.33.40.993000000 ?? ORACLE.EXE (M003) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.36.40.066000000 ?? ORACLE.EXE (M000) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.37.42.258000000 ?? ORACLE.EXE (M000) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.37.43.258000000 ?? ORACLE.EXE (M000) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.37.44.258000000 ?? ORACLE.EXE (M000) MMON_SLAVE KDILM background EXEcution 29-7? -13 08.38.42.386000000 ?? ORACLE.EXE (M001) MMON_SLAVE KDILM background EXEcution select distinct action from v$active_session_history where action like 'KDILM%' KDILM background CLeaNup KDILM background EXEcution SQL> execute set_window('MONDAY_WINDOW','CLOSE'); Set Maint. Window CLOSE ----------------------------- Window Name : MONDAY_WINDOW Enabled? : TRUE Active? : FALSE ----------------------------- PL/SQL ???????? SQL> drop table employee purge ; ????? ???? ????? spool ilm_usecase_1_cleanup.lst @ilm_demo_cleanup ; spool off

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• Optimizing a 3D World Javascript Animation

  - by johnny

  Hi! I've recently come up with the idea to create a tag cloud like animation shaped like the earth. I've extracted the coastline coordinates from ngdc.noaa.gov and wrote a little script that displayed it in my browser. Now as you can imagine, the whole coastline consists of about 48919 points, which my script would individually render (each coordinate being represented by one span). Obviously no browser is capable of rendering this fluently - but it would be nice if I could render as much as let's say 200 spans (twice as much as now) on my old p4 2.8 Ghz (as a representative benchmark). Are there any javascript optimizations I could use in order to speed up the display of those spans? One 'coordinate': <div id="world_pixels"> <span id="wp_0" style="position:fixed; top:0px; left:0px; z-index:1; font-size:20px; cursor:pointer;cursor:hand;" onmouseover="magnify_world_pixel('wp_0');" onmouseout="shrink_world_pixel('wp_0');" onClick="set_askcue_bar('', 'new york')">new york</span> </div> The script: $(document).ready(function(){ world_pixels = $("#world_pixels span"); world_pixels.spin(); setInterval("world_pixels.spin()",1500); }); z = new Array(); $.fn.spin = function () { for(i=0; i<this.length; i++) { /*actual screen coordinates: x/y/z --> left/font-size/top 300/13/0 300/6/300 | / |/ 0/13/300 ----|---- 600/13/300 /| / | 300/20/300 300/13/600 */ /*scale font size*/ var resize_x = 1; /*scale width*/ var resize_y = 2.5; /*scale height*/ var resize_z = 2.5; var from_left = 300; var from_top = 20; /*actual math coordinates: 1 -1 | / |/ 1 ----|---- -1 /| / | 1 -1 */ //var get_element = document.getElementById(); //var font_size = parseInt(this.style.fontSize); var font_size = parseInt($(this[i]).css("font-size")); var left = parseInt($(this[i]).css("left")); if (coast_line_array[i][1]) { } else { var top = parseInt($(this[i]).css("top")); z[i] = from_top + (top - (300 * resize_z)) / (300 * resize_z); //global beacause it's used in other functions later on var top_new = from_top + Math.round(Math.cos(coast_line_array[i][2]/90*Math.PI) * (300 * resize_z) + (300 * resize_z)); $(this[i]).css("top", top_new); coast_line_array[i][3] = 1; } var x = resize_x * (font_size - 13) / 7; var y = from_left + (left- (300 * resize_y)) / (300 * resize_y); if (y >= 0) { this[i].phi = Math.acos(x/(Math.sqrt(x^2 + y^2))); } else { this[i].phi = 2*Math.PI - Math.acos(x/(Math.sqrt(x^2 + y^2))); i } this[i].theta = Math.acos(z[i]/Math.sqrt(x^2 + y^2 + z[i]^2)); var font_size_new = resize_x * Math.round(Math.sin(coast_line_array[i][4]/90*Math.PI) * Math.cos(coast_line_array[i][0]/180*Math.PI) * 7 + 13); var left_new = from_left + Math.round(Math.sin(coast_line_array[i][5]/90*Math.PI) * Math.sin(coast_line_array[i][0]/180*Math.PI) * (300 * resize_y) + (300 * resize_y)); //coast_line_array[i][6] = coast_line_array[i][7]+1; if ((coast_line_array[i][0] + 1) > 180) { coast_line_array[i][0] = -180; } else { coast_line_array[i][0] = coast_line_array[i][0] + 0.25; } $(this[i]).css("font-size", font_size_new); $(this[i]).css("left", left_new); } } resize_x = 1; function magnify_world_pixel(element) { $("#"+element).animate({ fontSize: resize_x*30+"px" }, { duration: 1000 }); } function shrink_world_pixel(element) { $("#"+element).animate({ fontSize: resize_x*6+"px" }, { duration: 1000 }); } I'd appreciate any suggestions to optimize my script, maybe there is even a totally different approach on how to go about this. The whole .js file which stores the array for all the coordinates is available on my page, the file is about 2.9 mb, so you might consider pulling the .zip for local testing: metaroulette.com/files/31218.zip metaroulette.com/files/31218.js P.S. the php I use to create the spans: <?php //$arbitrary_characters = array('a','b','c','ddsfsdfsdf','e','f','g','h','isdfsdffd','j','k','l','mfdgcvbcvbs','n','o','p','q','r','s','t','uasdfsdf','v','w','x','y','z','0','1','2','3','4','5','6','7','8','9',); $arbitrary_characters = array('cat','table','cool','deloitte','askcue','what','more','less','adjective','nice','clinton','mars','jupiter','testversion','beta','hilarious','lolcatz','funny','obama','president','nice','what','misplaced','category','people','religion','global','skyscraper','new york','dubai','helsinki','volcano','iceland','peter','telephone','internet', 'dialer', 'cord', 'movie', 'party', 'chris', 'guitar', 'bentley', 'ford', 'ferrari', 'etc', 'de facto'); for ($i=0; $i<96; $i++) { $arb_digits = rand (0,45); $arbitrary_character = $arbitrary_characters[$arb_digits]; //$arbitrary_character = "."; echo "<span id=\"wp_$i\" style=\"position:fixed; top:0px; left:0px; z-index:1; font-size:20px; cursor:pointer;cursor:hand;\" onmouseover=\"magnify_world_pixel('wp_$i');\" onmouseout=\"shrink_world_pixel('wp_$i');\" onClick=\"set_askcue_bar('', '$arbitrary_character')\">$arbitrary_character</span>\n"; } ?>

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