Referencia:
http://www.akadia.com/services/ora_statspack_survival_guide.html

Installing and Configuring STATSPACK
Create PERFSTAT Tablespace

The STATSPACK utility requires an isolated tablespace to obtain all of the objects and data. For uniformity, it is suggested that the tablespace be called PERFSTAT, the same name as the schema owner for the STATSPACK tables. It is important to closely watch the STATSPACK data to ensure that the stats$sql_summary table is not taking an inordinate amount of space.

SQL> CREATE TABLESPACE perfstat
DATAFILE ‘/u01/oracle/db/AKI1_perfstat.dbf’ SIZE 1000M REUSE
EXTENT MANAGEMENT LOCAL UNIFORM SIZE 512K
SEGMENT SPACE MANAGEMENT AUTO
PERMANENT
ONLINE;

Run the Create Scripts

Now that the tablespace exists, we can begin the installation process of the STATSPACK software. Note that you must have performed the following before attempting to install STATSPACK.

Run catdbsyn.sql as SYS

Run dbmspool.sql as SYS

$ cd $ORACLE_HOME/rdbms/admin
$ sqlplus “/ as sysdba”
SQL> start spcreate.sql

Choose the PERFSTAT user’s password
———————————–
Not specifying a password will result in the installation FAILING

Enter value for perfstat_password: perfstat

Choose the Default tablespace for the PERFSTAT user
—————————————————
Below is the list of online tablespaces in this database which can
store user data. Specifying the SYSTEM tablespace for the user’s
default tablespace will result in the installation FAILING, as
using SYSTEM for performance data is not supported.

Choose the PERFSTAT users’s default tablespace. This is the tablespace
in which the STATSPACK tables and indexes will be created.

TABLESPACE_NAME CONTENTS STATSPACK DEFAULT TABLESPACE
—————————— ——— —————————-
PERFSTAT PERMANENT
SYSAUX PERMANENT *
USERS PERMANENT

Pressing will result in STATSPACK’s recommended default
tablespace (identified by *) being used.

Enter value for default_tablespace: PERFSTAT

Choose the Temporary tablespace for the PERFSTAT user
—————————————————–
Below is the list of online tablespaces in this database which can
store temporary data (e.g. for sort workareas). Specifying the SYSTEM
tablespace for the user’s temporary tablespace will result in the
installation FAILING, as using SYSTEM for workareas is not supported.

Choose the PERFSTAT user’s Temporary tablespace.

TABLESPACE_NAME CONTENTS DB DEFAULT TEMP TABLESPACE
—————————— ——— ————————–
TEMP TEMPORARY *

Pressing will result in the database’s default Temporary
tablespace (identified by *) being used.

Enter value for temporary_tablespace: TEMP

…..
…..
Creating Package STATSPACK…

Package created.

No errors.
Creating Package Body STATSPACK…

Package body created.

No errors.

NOTE:
SPCPKG complete. Please check spcpkg.lis for any errors.

Check the Logfiles: spcpkg.lis, spctab.lis, spcusr.lis

Adjusting the STATSPACK Collection Level

STATSPACK has two types of collection options, level and threshold. The level parameter controls the type of data collected from Oracle, while the threshold parameter acts as a filter for the collection of SQL statements into the stats$sql_summary table.

SQL> SELECT * FROM stats$level_description ORDER BY snap_level;

Level 0 This level captures general statistics, including rollback segment, row cache, SGA, system events, background events, session events, system statistics, wait statistics, lock statistics, and Latch information.
Level 5 This level includes capturing high resource usage SQL Statements, along with all data captured by lower levels.
Level 6 This level includes capturing SQL plan and SQL plan usage information for high resource usage SQL Statements, along with all data captured by lower levels.
Level 7 This level captures segment level statistics, including logical and physical reads, row lock, itl and buffer busy waits, along with all data captured by lower levels.
Level 10 This level includes capturing Child Latch statistics, along with all data captured by lower levels.

You can change the default level of a snapshot with the statspack.snap function. The i_modify_parameter => ‘true’ changes the level permanent for all snapshots in the future.

SQL> exec statspack.snap(i_snap_level => 6, i_modify_parameter => ‘true’);

Create, View and Delete Snapshots

sqlplus perfstat/perfstat
SQL> exec statspack.snap;
SQL> select name,snap_id,to_char(snap_time,’DD.MM.YYYY:HH24:MI:SS’)
“Date/Time” from stats$snapshot,v$database;

NAME SNAP_ID Date/Time
——— ———- ——————-
AKI1 4 14.11.2004:10:56:01
AKI1 1 13.11.2004:08:48:47
AKI1 2 13.11.2004:09:00:01
AKI1 3 13.11.2004:09:01:48

SQL> @?/rdbms/admin/sppurge;
Enter the Lower and Upper Snapshot ID

Create the Report

sqlplus perfstat/perfstat
SQL> @?/rdbms/admin/spreport.sql

Statspack at a Glance

What if you have this long STATSPACK report and you want to figure out if everything is running smoothly? Here, we will review what we look for in the report, section by section. We will use an actual STATSPACK report from our own Oracle 10g system.

Statspack Report Header

STATSPACK report for

DB Name DB Id Instance Inst Num Release RAC Host
———— ———– ———— ——– ———– — —————-
AKI1 2006521736 AKI1 1 10.1.0.2.0 NO akira

Snap Id Snap Time Sessions Curs/Sess Comment
——— —————— ——– ——— ——————-
Begin Snap: 5 14-Nov-04 11:18:00 15 14.3
End Snap: 6 14-Nov-04 11:33:00 15 10.2
Elapsed: 15.00 (mins)

Cache Sizes (end)
~~~~~~~~~~~~~~~~~
Buffer Cache: 24M Std Block Size: 4K
Shared Pool Size: 764M Log Buffer: 1,000K

Note that this section may appear slightly different depending on your version of Oracle. For example, the Curs/Sess column, which shows the number of open cursors per session, is new with Oracle9i (an 8i Statspack report would not show this data).

Here, the item we are most interested in is the elapsed time. We want that to be large enough to be meaningful, but small enough to be relevant (15 to 30 minutes is OK). If we use longer times, we begin to lose the needle in the haystack.

Statspack Load Profile

Load Profile
~~~~~~~~~~~~ Per Second Per Transaction
————— —————
Redo size: 425,649.84 16,600,343.64
Logical reads: 1,679.69 65,508.00
Block changes: 2,546.17 99,300.45
Physical reads: 77.81 3,034.55
Physical writes: 78.35 3,055.64
User calls: 0.24 9.55
Parses: 2.90 113.00
Hard parses: 0.16
6.27
Sorts: 0.76 29.82
Logons: 0.01 0.36
Executes: 4.55 177.64
Transactions: 0.03

% Blocks changed per Read: 151.59 Recursive Call %: 99.56
Rollback per transaction %: 0.00 Rows per Sort: 65.61

Here, we are interested in a variety of things, but if we are looking at a “health check”, three items are important:

The Hard parses (we want very few of them)
Executes (how many statements we are executing per second / transaction)
Transactions (how many transactions per second we process).
This gives an overall view of the load on the server. In this case, we are looking at a very good hard parse number and a fairly light system load (1 – 4 transactions per second is low).

Statspack Instance Efficiency Percentage

Next, we move onto the Instance Efficiency Percentages section, which includes perhaps the only ratios we look at in any detail:

Instance Efficiency Percentages (Target 100%)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Buffer Nowait %: 100.00 Redo NoWait %: 99.99
Buffer Hit %: 95.39 In-memory Sort %: 100.00
Library Hit %: 99.42 Soft Parse %: 94.45
Execute to Parse %: 36.39 Latch Hit %: 100.00
Parse CPU to Parse Elapsd %: 59.15 % Non-Parse CPU: 99.31

Shared Pool Statistics Begin End
—— ——
Memory Usage %: 10.28 10.45
% SQL with executions>1: 70.10 71.08
% Memory for SQL w/exec>1: 44.52 44.70

The three in bold are the most important: Library Hit, Soft Parse % and Execute to Parse. All of these have to do with how well the shared pool is being utilized. Time after time, we find this to be the area of greatest payback, where we can achieve some real gains in performance.

Here, in this report, we are quite pleased with the Library Hit and the Soft Parse % values. If the library Hit ratio was low, it could be indicative of a shared pool that is too small, or just as likely, that the system did not make correct use of bind variables in the application. It would be an indicator to look at issues such as those.

OLTP System

The Soft Parse % value is one of the most important (if not the only important) ratio in the database. For a typical OLTP system, it should be as near to 100% as possible. You quite simply do not hard parse after the database has been up for a while in your typical transactional / general-purpose database. The way you achieve that is with bind variables. In a regular system like this, we are doing many executions per second, and hard parsing is something to be avoided.

Data Warehouse

In a data warehouse, we would like to generally see the Soft Parse ratio lower. We don’t necessarily want to use bind variables in a data warehouse. This is because they typically use materialized views, histograms, and other things that are easily thwarted by bind variables. In a data warehouse, we may have many seconds between executions, so hard parsing is not evil; in fact, it is good in those environments.

The moral of this is …

… to look at these ratios and look at how the system operates. Then, using that knowledge, determine if the ratio is okay given the conditions. If we just said that the execute-to-parse ratio for your system should be 95% or better, that would be unachievable in many web-based systems. If you have a routine that will be executed many times to generate a page, you should definitely parse once per page and execute it over and over, closing the cursor if necessary before your connection is returned to the connection pool.

Statspack Top 5 Timed Events

Moving on, we get to the Top 5 Timed Events section (in Oracle9i Release 2 and later) or Top 5 Wait Events (in Oracle9i Release 1 and earlier).

Top 5 Timed Events
~~~~~~~~~~~~~~~~~~ % Total
Event Waits Time (s) Call Time
——————————————– ———— ———– ———
CPU time 122 91.65
db file sequential read 1,571 2 1.61
db file scattered read 1,174 2 1.59
log file sequential read 342 2 1.39
control file parallel write 450 2 1.39
————————————————————-
Wait Events DB/Inst: AKI1/AKI1 Snaps: 5-6

-> s – second
-> cs – centisecond – 100th of a second
-> ms – millisecond – 1000th of a second
-> us – microsecond – 1000000th of a second
-> ordered by wait time desc, waits desc (idle events last)

This section is among the most important and relevant sections in the Statspack report. Here is where you find out what events (typically wait events) are consuming the most time. In Oracle9i Release 2, this section is renamed and includes a new event: CPU time.

CPU time is not really a wait event (hence, the new name), but rather the sum of the CPU used by this session, or the amount of CPU time used during the snapshot window. In a heavily loaded system, if the CPU time event is the biggest event, that could point to some CPU-intensive processing (for example, forcing the use of an index when a full scan should have been used), which could be the cause of the bottleneck.

Db file sequential read – This wait event will be generated while waiting for writes to TEMP space generally (direct loads, Parallel DML (PDML) such as parallel updates. You may tune the PGA AGGREGATE TARGET parameter to reduce waits on sequential reads.

Db file scattered read – Next is the db file scattered read wait value. That generally happens during a full scan of a table. You can use the Statspack report to help identify the query in question and fix it.
SQL ordered by Gets

Here you will find the most CPU-Time consuming SQL statements

SQL ordered by Gets DB/Inst: AKI1/AKI1 Snaps: 5-6
-> Resources reported for PL/SQL code includes the resources used by all SQL
statements called by the code.
-> End Buffer Gets Threshold: 10000 Total Buffer Gets: 720,588
-> Captured SQL accounts for 3.1% of Total Buffer Gets
-> SQL reported below exceeded 1.0% of Total Buffer Gets

CPU Elapsd Old
Buffer Gets Executions Gets per Exec %Total Time (s) Time (s) Hash Value
————— ———— ————– —— ——– ——— ———-
16,926 1 16,926.0 2.3 2.36 3.46 1279400914
Module: SQL*Plus
create table test as select * from all_objects

Tablespace IO Stats

Tablespace
——————————
Av Av Av Av Buffer Av Buf
Reads Reads/s Rd(ms) Blks/Rd Writes Writes/s Waits Wt(ms)
————– ——- —— ——- ———— ——– ———- ——
TAB 1,643 4 1.0 19.2 16,811 39 0 0.0
UNDO 166 0 0.5 1.0 5,948 14 0 0.0
SYSTEM 813 2 2.5 1.6 167 0 0 0.0
STATSPACK 146 0 0.3 1.1 277 1 0 0.0
SYSAUX 18 0 0.0 1.0
29 0 0 0.0
IDX 18 0 0.0 1.0 18 0 0 0.0
USER 18 0 0.0 1.0 18 0 0 0.0
————————————————————-

Rollback Segment Stats

->A high value for “Pct Waits” suggests more rollback segments may be required
->RBS stats may not be accurate between begin and end snaps when using Auto Undo
managment, as RBS may be dynamically created and dropped as needed

Trans Table Pct Undo Bytes
RBS No Gets Waits Written Wraps Shrinks Extends
—— ————– ——- ————— ——– ——– ——–
0 8.0 0.00 0 0 0 0
1 3,923.0 0.00 14,812,586 15 0 14
2 5,092.0 0.00 19,408,996 19 0 19
3 295.0 0.00 586,760 1 0 0
4 1,312.0 0.00 4,986,920 5 0 5
5 9.0 0.00 0 0 0 0
6 9.0 0.00 0 0 0 0
7 9.0 0.00 0 0 0 0
8 9.0 0.00 0 0 0 0
9 9.0 0.00 0 0 0 0
10 9.0 0.00 0 0 0 0
————————————————————-

Rollback Segment Storage

->Optimal Size should be larger than Avg Active

RBS No Segment Size Avg Active Optimal Size Maximum Size
—— ————— ————— ————— —————
0 364,544 0 364,544
1 17,952,768 8,343,482 17,952,768
2 25,292,800 11,854,857 25,292,800
3 4,321,280 617,292 6,418,432
4 8,515,584 1,566,623 8,515,584
5 126,976 0 126,976
6 126,976 0 126,976
7 126,976 0 126,976
8 126,976 0 126,976
9 126,976 0 126,976
10 126,976 0 126,976
————————————————————-

Generate Execution Plan for given SQL statement

If you have identified one or more problematic SQL statement, you may want to check the execution plan. Remember the “Old Hash Value” from the report above (1279400914), then execute the scrip to generate the execution plan.

sqlplus perfstat/perfstat
SQL> @?/rdbms/admin/sprepsql.sql
Enter the Hash Value, in this example: 1279400914

SQL Text
~~~~~~~~
create table test as select * from all_objects

Known Optimizer Plan(s) for this Old Hash Value
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Shows all known Optimizer Plans for this database instance, and the Snap Id’s
they were first found in the shared pool. A Plan Hash Value will appear
multiple times if the cost has changed
-> ordered by Snap Id

First First Plan
Snap Id Snap Time Hash Value Cost
——— ————— ———— ———-
6 14 Nov 04 11:26 1386862634 52

Plans in shared pool between Begin and End Snap Ids
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Shows the Execution Plans found in the shared pool between the begin and end
snapshots specified. The values for Rows, Bytes and Cost shown below are those
which existed at the time the first-ever snapshot captured this plan – these
values often change over time, and so may not be indicative of current values
-> Rows indicates Cardinality, PHV is Plan Hash Value
-> ordered by Plan Hash Value

——————————————————————————–
| Operation | PHV/Object Name | Rows | Bytes| Cost |
——————————————————————————–
|CREATE TABLE STATEMENT |—– 1386862634 —-| | | 52 |
|LOAD AS SELECT | | | | |
| VIEW | | 1K| 216K| 44 |
| FILTER | | | | |
| HASH JOIN | | 1K| 151K| 38 |
| TABLE ACCESS FULL |USER$ | 29 | 464 | 2 |
| TABLE ACCESS FULL |OBJ$ | 3K| 249K| 35 |
| TABLE ACCESS BY INDEX ROWID |IND$ | 1 | 7 | 2 |
| INDEX UNIQUE SCAN |I_IND1 | 1 | | 1 |
| NESTED LOOPS | | 5 | 115 | 16 |
| INDEX RANGE SCAN |I_OBJAUTH1 | 1 | 10 | 2 |
| FIXED TABLE FULL |X$KZSRO | 5 | 65 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| FIXED TABLE FULL |X$KZSPR | 1 | 26 | 14 |
| VIEW | | 1 | 13 | 2 |
| FAST DUAL | | 1 | | 2 |
——————————————————————————–

Resolving Your Wait Events

The following are 10 of the most common causes for wait events, along with explanations and potential solutions:

1. DB File Scattered Read

This generally indicates waits related to full table scans. As full table scans are pulled into memory, they rarely fall into contiguous buffers but instead are scattered throughout the buffer cache. A
large number here indicates that your table may have missing or suppressed indexes. Although it may be more efficient in your situation to perform a full table scan than an index scan, check to ensure that full table scans are necessary when you see these waits. Try to cache small tables to avoid reading them in over and over again, since a full table scan is put at the cold end of the LRU (Least Recently Used) list.

2. DB File Sequential Read

This event generally indicates a single block read (an index read, for example). A large number of waits here could indicate poor joining orders of tables, or unselective indexing. It is normal for this number to be large for a high-transaction, well-tuned system, but it can indicate problems in some circumstances. You should correlate this wait statistic with other known issues within the Statspack report, such as inefficient SQL. Check to ensure that index scans are necessary, and check join orders for multiple table joins. The DB_CACHE_SIZE will also be a determining factor in how often these waits show up. Problematic hash-area joins should show up in the PGA memory, but they’re also memory hogs that could cause high wait numbers for sequential reads. They can also show up as direct path read/write waits.

3. Free Buffer

This indicates your system is waiting for a buffer in memory, because none is currently available. Waits in this category may indicate that you need to increase the DB_BUFFER_CACHE, if all your SQL is tuned. Free buffer waits could also indicate that unselective SQL is causing data to flood the buffer cache with index blocks, leaving none for this particular statement that is waiting for the system to process. This normally indicates that there is a substantial amount of DML (insert/update/delete) being done and that the Database Writer (DBWR) is not writing quickly enough; the buffer cache could be full of multiple versions of the same buffer, causing great inefficiency. To address this, you may want to consider accelerating incremental checkpointing, using more DBWR processes, or increasing the number of physical disks.

4. Buffer Busy

This is a wait for a buffer that is being used in an unshareable way or is being read into the buffer cache. Buffer busy waits should not be greater than 1 percent. Check the Buffer Wait Statistics section (or V$WAITSTAT) to find out if the wait is on a segment header. If this is the case, increase the freelist groups or increase the pctused to pctfree gap. If the wait is on an undo header, you can address this by adding rollback segments; if it’s on an undo block, you need to reduce the data density on the table driving this consistent read or increase the DB_CACHE_SIZE. If the wait is on a data block, you can move data to another block to avoid this hot block, increase the freelists on the table, or use Locally Managed Tablespaces (LMTs). If it’s on an index block, you should rebuild the index, partition the index, or use a reverse key index. To prevent buffer busy waits related to data blocks, you can also use a smaller block size: fewer records fall within a single block in this case, so it’s not as “hot.” When a DML (insert/update/ delete) occurs, Oracle Database writes information into the block, including all users who are “interested” in the state of the block (Interested Transaction List, ITL). To decrease waits in this area, you can increase the initrans, which will create the space in the block to allow multiple ITL slots. You can also increase the pctfree on the table where this block exists (this writes the ITL information up to the number specified by maxtrans, when there are not enough slots built with the initrans that is specified).

5. Latch Free

Latches are low-level queuing mechanisms (they’re accurately referred to as mutual exclusion mechanisms) used to protect shared memory structures in the system global area (SGA). Latches are like locks on memory that are very quickly obtained and released. Latches are used to prevent concurrent access to a shared memory structure. If the latch is not available, a latch free miss is recorded. Most latch problems are related to the failure to use bind variables (library cache latch), redo generation issues (redo allocation latch), buffer cache contention issues (cache buffers LRU chain), and hot blocks in the buffer cache (cache buffers chain). There are also latch waits related to bugs; check MetaLink for bug reports if you suspect this is the case. When latch miss ratios are greater than 0.5 percent, you should investigate the issue.

6. Enqueue

An enqueue is a lock that protects a shared resource. Locks protect shared resources, such as data in a record, to prevent two people from updating the same data at the same time. An enqueue includes a queuing mechanism, which is FIFO (first in, first out). Note that Oracle’s latching mechanism is not FIFO. Enqueue waits usually point to the ST enqueue, the HW enqueue, the TX4 enqueue, and the TM enqueue. The ST enqueue is used for space management and allocation for dictionary-managed tablespaces. Use LMTs, or try to preallocate extents or at least make the next extent larger for problematic dictionary-managed tablespaces. HW enqueues are used with the high-water mark of a segment; manually allocating the extents can circumvent this wait. TX4s are the most common enqueue waits. TX4 enqueue waits are usually the result of one of three issues. The first issue is duplicates in a unique index; you need to commit/rollback to free the enqueue. The second is multiple updates to the same bitmap index fragment. Since a single bitmap fragment may contain multiple rowids, you need to issue a commit or rollback to free the enqueue when multiple users are trying to update the same fragment. The third and most likely issue is when multiple users are updating the same block. If there are no free ITL slots, a block-level lock could occur. You can easily avoid this scenario by increasing the initrans and/or maxtrans to allow multiple ITL slots and/or by increasing the pctfree on the table. Finally, TM enqueues occur during DML to prevent DDL to the affected object. If you have foreign keys, be sure to index them to avoid this general locking issue.

7. Log Buffer Space

This wait occurs because you are writing the log buffer faster than LGWR can write it to the redo logs, or because log switches are too slow. To address this problem, increase the size of the log files, or increase the size of the log buffer, or get faster disks to write to. You might even consider using solid-state disks, for their high speed.

8. Log File Switch

All commit requests are waiting for “logfile switch (archiving needed)” or “logfile switch (Checkpoint. Incomplete).” Ensure that the archive disk is not full or slow. DBWR may be too slow because of I/O. You may need to add more or larger redo logs, and you may potentially need to add database writers if the DBWR is the problem.

9. Log File Sync

When a user commits or rolls back data, the LGWR flushes the session’s redo from the log buffer to the redo logs. The log file sync process must wait for this to successfully complete. To reduce wait events here, try to commit more records (try to commit a batch of 50 instead of one at a time, for example). Put redo logs on a faster disk, or alternate redo logs on different physical disks, to reduce the archiving effect on LGWR. Don’t use RAID 5, since it is very slow for applications that write a lot; potentially consider using file system direct I/O or raw devices, which are very fast at writing information.

10. Idle Event.

There are several idle wait events listed after the output; you can ignore them. Idle events are generally listed at the bottom of each section and include such things as SQL*Net message to/from client and other background-related timings. Idle events are listed in the stats$idle_event table.

Remove STATSPACK from the Database

After a STATSPACK session you wa
nt to remove the STATSPACK tables.

sqlplus “/ as sysdba”
SQL> @?/rdbms/admin/spdrop.sql
SQL> DROP TABLESPACE perfstat INCLUDING CONTENTS AND DATAFILES;