Is there a big performance difference between HD's

Hi All
Is there a big performance difference between a 5400 & 7200 Rpm hardrive? I'm asking because I want to pick up a new Imac and am stuck between choosing the higher end 21.5" and lower end 27" and am trying to determine if the difference in price is worth the extra sheckles.

I was wanting to know the same question (5400rpm vs. 7200rpm) in a 15" standard MBP, deciding on whether to get a 1TB serial ATA drive @ 5400rpm vs. a 750GB serial ATA drive @ 7200rpm. (Sorry to jump in)
For the most part, I'm a general user - web surfing/research, Word processing/Excel/Powerpoint (pretty basic), etc. BUT I do like to take alot of photos and plan on doing some editing on them (nothing advanced) and creating slideshows with my photos/music (ex. my Europe trip of photos or a slideshow of the grandchildren as a gift to my parents, etc.)
Some folks mention "video editing" in reference to gonig with the faster speed (7200rpm) if that's what you plan on doing. But, what do they exactly mean by "video editing"? Is slideshow creation the same?
Just wondering for my needs, whether I should go with the 750GB serial ATA drive @ 7200 rpm or the 1TB serial ATA drive @ 5400rpm ($50 more yet with more storage space which would help with my increasing photo files every year).
Thanks

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Hi all,
I wanted to test different kind of map listener available in Coherence 3.3.1 as I would like to use it as an event bus. The result was that I found huge performance differences between them. In my use case, I have data which are time stamped so the full key of the data is the key which identifies its type and the time stamp. Unfortunately, when I had my map listener to the cache, I only know the type id but not the time stamp, thus I cannot add a listener for a key but for a filter which will test the value of the type id. When I launch my test, I got terrible performance results then I tried a listener for a key which gave me much better results but in my case I cannot use it.
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Cache.putAll
Test 10: Total 1078 millis, Avg 26, Total Tries 40, Cache Size 2000
Please help me to find what is wrong with my code because for now it is unusable.
Best Regards,
Nicolas
Here is my code
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import com.tangosol.io.ExternalizableLite;
import com.tangosol.net.CacheFactory;
import com.tangosol.net.NamedCache;
import com.tangosol.util.Filter;
import com.tangosol.util.MapEvent;
import com.tangosol.util.MapListener;
import com.tangosol.util.extractor.ReflectionExtractor;
import com.tangosol.util.filter.EqualsFilter;
import com.tangosol.util.filter.MapEventFilter;
public class TestFilter {
      * To run a specific test, just launch the program with one parameter which
      * is the test index
     public static void main(String[] args) {
          if (args.length != 1) {
               System.out.println("Usage : java TestFilter 1-10|all");
               System.exit(1);
          final String arg = args[0];
          if (arg.endsWith("all")) {
               for (int i = 1; i <= 10; i++) {
                    test(i);
          } else {
               final int testIndex = Integer.parseInt(args[0]);
               if (testIndex < 1 || testIndex > 10) {
                    System.out.println("Usage : java TestFilter 1-10|all");
                    System.exit(1);
               test(testIndex);
     @SuppressWarnings("unchecked")
     private static void test(int testIndex) {
          final NamedCache cache = CacheFactory.getCache("test-cache");
          final int totalObjects = 2000;
          final int totalTries = 40;
          if (testIndex >= 5 && testIndex <= 8) {
               // Add index
               cache.addIndex(new ReflectionExtractor("getKey"), false, null);
          // Add listeners
          for (int i = 0; i < totalObjects; i++) {
               final MapListener listener = new SimpleMapListener();
               if (testIndex < 9) {
                    // Listen to data with a given filter
                    final Filter filter = new EqualsFilter("getKey", i);
                    cache.addMapListener(listener, new MapEventFilter(filter), false);
               } else {
                    // Listen to data with a given key
                    cache.addMapListener(listener, new TestObjectSimple(i), false);
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          for (int iTry = 0; iTry < totalTries; iTry++) {
               final long currentTime = System.currentTimeMillis();
               final Map<Object, Object> buffer = new HashMap<Object, Object>(totalObjects);
               for (int i = 0; i < totalObjects; i++) {
                    final Object obj;
                    if (testIndex == 1 || testIndex == 2 || testIndex == 5 || testIndex == 6) {
                         // Create data with key with time stamp
                         obj = new TestObjectComplete(i, currentTime);
                    } else {
                         // Create data with key without time stamp
                         obj = new TestObjectSimple(i);
                    if ((testIndex & 1) == 1) {
                         // Load data directly into the cache
                         cache.put(obj, obj);
                    } else {
                         // Load data into a buffer first
                         buffer.put(obj, obj);
               if (!buffer.isEmpty()) {
                    cache.putAll(buffer);
          time = System.currentTimeMillis() - time;
          System.out.println("Test " + testIndex + ": Total " + time + " millis, Avg " + (time / totalTries) + ", Total Tries " + totalTries + ", Cache Size " + cache.size());
          cache.destroy();
     public static class SimpleMapListener implements MapListener {
          public void entryDeleted(MapEvent evt) {}
          public void entryInserted(MapEvent evt) {}
          public void entryUpdated(MapEvent evt) {}
     public static class TestObjectComplete implements ExternalizableLite {
          private static final long serialVersionUID = -400722070328560360L;
          private int key;
          private long time;
          public TestObjectComplete() {}
          public TestObjectComplete(int key, long time) {
               this.key = key;
               this.time = time;
          public int getKey() {
               return key;
          public void readExternal(DataInput in) throws IOException {
               this.key = in.readInt();
               this.time = in.readLong();
          public void writeExternal(DataOutput out) throws IOException {
               out.writeInt(key);
               out.writeLong(time);
     public static class TestObjectSimple implements ExternalizableLite {
          private static final long serialVersionUID = 6154040491849669837L;
          private int key;
          public TestObjectSimple() {}
          public TestObjectSimple(int key) {
               this.key = key;
          public int getKey() {
               return key;
          public void readExternal(DataInput in) throws IOException {
               this.key = in.readInt();
          public void writeExternal(DataOutput out) throws IOException {
               out.writeInt(key);
          public int hashCode() {
               return key;
          public boolean equals(Object o) {
               return o instanceof TestObjectSimple && key == ((TestObjectSimple) o).key;
}Here is my coherence config file
<?xml version="1.0"?>
<!DOCTYPE cache-config SYSTEM "cache-config.dtd">
<cache-config>
     <caching-scheme-mapping>
          <cache-mapping>
               <cache-name>test-cache</cache-name>
               <scheme-name>default-distributed</scheme-name>
          </cache-mapping>
     </caching-scheme-mapping>
     <caching-schemes>
          <distributed-scheme>
               <scheme-name>default-distributed</scheme-name>
               <backing-map-scheme>
                    <class-scheme>
                         <scheme-ref>default-backing-map</scheme-ref>
                    </class-scheme>
               </backing-map-scheme>
          </distributed-scheme>
          <class-scheme>
               <scheme-name>default-backing-map</scheme-name>
               <class-name>com.tangosol.util.SafeHashMap</class-name>
          </class-scheme>
     </caching-schemes>
</cache-config>Message was edited by:
user620763

Hi Robert,
Indeed, only the Filter.evaluate(Object obj)
method is invoked, but the object passed to it is a
MapEvent.<< In fact, I do not need to implement EntryFilter to
get a MapEvent, I could get the same result (in my
last message) by writting
cache.addMapListener(listener, filter,
true)instead of
cache.addMapListener(listener, new
MapEventFilter(filter) filter, true)
I believe, when the MapEventFilter delegates to your filter it always passes a value object to your filter (old or new), meaning a value will be deserialized.
If you instead used your own filter, you could avoid deserializing the value which usually is much larger, and go to only the key object. This would of course only be noticeable if you indeed used a much heavier cached value class.
The hashCode() and equals() does not matter on
the filter class<< I'm not so sure since I noticed that these methods
were implemented in the EqualsFilter class, that they
are called at runtime and that the performance
results are better when you add them
That interests me... In what circumstances did you see them invoked? On the storage node before sending an event, or upon registering a filtered listener?
If the second, then I guess the listeners are stored in a hash-based map of collections keyed by a filter, and indeed that might be relevant as in that case it will cause less passes on the filter for multiple listeners with an equalling filter.
DataOutput.writeInt(int) writes 4 bytes.
ExternalizableHelper.writeInt(DataOutput, int) writes
1-5 bytes (or 1-6?), with numbers with small absolute
values consuming less bytes.Similar differences exist
for the long type as well, but your stamp attribute
probably will be a large number...<< I tried it but in my use case, I got the same
results. I guess that it must be interesting, if I
serialiaze/deserialiaze many more objects.
Also, if Coherence serializes an
ExternalizableLite object, it writes out its
class-name (except if it is a Coherence XmlBean). If
you define your key as an XmlBean, and add your class
into the classname cache configuration in
ExternalizableHelper.xml, then instead of the
classname, only an int will be written. This way you
can spare a large percentage of bandwidth consumed by
transferring your key instance as it has only a small
number of attributes. For the value object, it might
or might not be so relevant, considering that it will
probably contain many more attributes. However, in
case of a lite event, the value is not transferred at
all.<< I tried it too and in my use case, I noticed that
we get objects nearly twice lighter than an
ExternalizableLite object but it's slower to get
them. But it is very intersting to keep in mind, if
we would like to reduce the network traffic.
Yes, these are minor differences at the moment.
As for the performance of XMLBean, it is a hack, but you might try overriding the readExternal/writeExternal method with your own usual ExternalizableLite implementation stuff. That way you get the advantages of the xmlbean classname cache, and avoid its reflection-based operation, at the cost of having to extend XMLBean.
Also, sooner or later the TCMP protocol and the distributed cache storages will also support using PortableObject as a transmission format, which enables using your own classname resolution and allow you to omit the classname from your objects. Unfortunately, I don't know when it will be implemented.
>
But finally, I guess that I found the best solution
for my specific use case which is to use a map
listener for a key which has no time stamp, but since
the time stamp is never null, I had just to check
properly the time stamp in the equals method.
I would still recommend to use a separate key class, use a custom filter which accesses only the key and not the value, and if possible register a lite listener instead of a heavy one. Try it with a much heavier cached value class where the differences are more pronounced.
Best regards,
Robert

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| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 1 | 288 | 1441 (1)| 00:00:18 |
| 1 | HASH GROUP BY | | 1 | 288 | 1441 (1)| 00:00:18 |
| 2 | NESTED LOOPS OUTER | | 1 | 288 | 1440 (1)| 00:00:18 |
| 3 | NESTED LOOPS | | 1 | 261 | 1438 (1)| 00:00:18 |
| 4 | NESTED LOOPS | | 318 | 74412 | 508 (1)| 00:00:07 |
| 5 | NESTED LOOPS | | 318 | 51834 | 189 (0)| 00:00:03 |
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|* 7 | INDEX UNIQUE SCAN | RESURCE_PRINCIPAL_NAME_INDEX | 1 | | 0 (0)| 00:00:01 |
| 8 | TABLE ACCESS BY INDEX ROWID| TASK_USES_RESURCE | 318 | 18126 | 188 (0)| 00:00:03 |
|* 9 | INDEX RANGE SCAN | TASK_USES_RESUR_IDX$$_0CDC0002 | 318 | | 3 (0)| 00:00:01 |
| 10 | TABLE ACCESS BY INDEX ROWID | TASK | 1 | 71 | 1 (0)| 00:00:01 |
|* 11 | INDEX UNIQUE SCAN | TASK_PK | 1 | | 0 (0)| 00:00:01 |
| 12 | TABLE ACCESS BY INDEX ROWID | TASK_WORK_HISTORY | 1 | 27 | 3 (0)| 00:00:01 |
|* 13 | INDEX RANGE SCAN | TASK_WORK_HISTORY_INDEX1 | 1 | | 2 (0)| 00:00:01 |
| 14 | TABLE ACCESS BY INDEX ROWID | TASK_USES_RESURCE | 1 | 27 | 2 (0)| 00:00:01 |
|* 15 | INDEX UNIQUE SCAN | TASK_USES_RESURCE_UK1 | 1 | | 1 (0)| 00:00:01 |
For left outer join:
| Id | Operation | Name | Rows | Bytes |TempSpc| Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 318 | 1596K| | 28544 (2)| 00:05:43 |
|* 1 | HASH JOIN OUTER | | 318 | 1596K| 1584K| 28544 (2)| 00:05:43 |
| 2 | VIEW | | 318 | 1580K| | 508 (1)| 00:00:07 |
| 3 | NESTED LOOPS | | 318 | 74412 | | 508 (1)| 00:00:07 |
| 4 | NESTED LOOPS | | 318 | 51834 | | 189 (0)| 00:00:03 |
| 5 | TABLE ACCESS BY INDEX ROWID| RESURCE | 1 | 106 | | 1 (0)| 00:00:01 |
|* 6 | INDEX UNIQUE SCAN | RESURCE_PRINCIPAL_NAME_INDEX | 1 | | | 0 (0)| 00:00:01 |
| 7 | TABLE ACCESS BY INDEX ROWID| TASK_USES_RESURCE | 318 | 18126 | | 188 (0)| 00:00:03 |
|* 8 | INDEX RANGE SCAN | TASK_USES_RESUR_IDX$$_0CDC0002 | 318 | | | 3 (0)| 00:00:01 |
| 9 | TABLE ACCESS BY INDEX ROWID | TASK | 1 | 71 | | 1 (0)| 00:00:01 |
|* 10 | INDEX UNIQUE SCAN | TASK_PK | 1 | | | 0 (0)| 00:00:01 |
| 11 | VIEW | | 1480K| 73M| | 23431 (2)| 00:04:42 |
|* 12 | HASH JOIN RIGHT OUTER | | 1480K| 76M| 38M| 23431 (2)| 00:04:42 |
| 13 | TABLE ACCESS FULL | TASK_USES_RESURCE | 1486K| 21M| | 2938 (2)| 00:00:36 |
| 14 | VIEW | | 1445K| 53M| | 15031 (2)| 00:03:01 |
| 15 | HASH GROUP BY | | 1445K| 37M| 110M| 15031 (2)| 00:03:01 |
| 16 | TABLE ACCESS FULL | TASK_WORK_HISTORY | 1445K| 37M| | 3897 (2)| 00:00:47 |
--------------------------------------------------------------------------------------------------------------------------

...continued
Complete execution plan for left join:
| Id | Operation                       | Name                           | Rows | Bytes |TempSpc| Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT                |                                |   318 | 1594K|       | 28544   (2)| 00:05:43 |
|* 1 | HASH JOIN OUTER                |                                |   318 | 1594K| 1584K| 28544   (2)| 00:05:43 |
|   2 |   VIEW                          |                                |   318 | 1578K|       |   508   (1)| 00:00:07 |
|   3 |    NESTED LOOPS                 |                                |   318 | 74412 |       |   508   (1)| 00:00:07 |
|   4 |     NESTED LOOPS                |                                |   318 | 51834 |       |   189   (0)| 00:00:03 |
|   5 |      TABLE ACCESS BY INDEX ROWID| RESURCE                        |     1 |   106 |       |     1   (0)| 00:00:01 |
|* 6 |       INDEX UNIQUE SCAN         | RESURCE_PRINCIPAL_NAME_INDEX   |     1 |       |       |     0   (0)| 00:00:01 |
|   7 |      TABLE ACCESS BY INDEX ROWID| TASK_USES_RESURCE              |   318 | 18126 |       |   188   (0)| 00:00:03 |
|* 8 |       INDEX RANGE SCAN          | TASK_USES_RESUR_IDX$$_0CDC0002 |   318 |       |       |     3   (0)| 00:00:01 |
|   9 |     TABLE ACCESS BY INDEX ROWID | TASK                           |     1 |    71 |       |     1   (0)| 00:00:01 |
|* 10 |      INDEX UNIQUE SCAN          | TASK_PK                        |     1 |       |       |     0   (0)| 00:00:01 |
| 11 |   VIEW                          |                                | 1480K|    73M|       | 23431   (2)| 00:04:42 |
|* 12 |    HASH JOIN RIGHT OUTER        |                                | 1480K|    76M|    38M| 23431   (2)| 00:04:42 |
| 13 |     TABLE ACCESS FULL           | TASK_USES_RESURCE              | 1486K|    21M|       | 2938   (2)| 00:00:36 |
| 14 |     VIEW                        |                                | 1445K|    53M|       | 15031   (2)| 00:03:01 |
| 15 |      HASH GROUP BY              |                                | 1445K|    37M|   110M| 15031   (2)| 00:03:01 |
| 16 |       TABLE ACCESS FULL         | TASK_WORK_HISTORY              | 1445K|    37M|       | 3897   (2)| 00:00:47 |
Query Block Name / Object Alias (identified by operation id):
   1 - SEL$1AFB0324
   2 - SEL$58A6D7F6 / from$_subquery$_005@SEL$8
   3 - SEL$58A6D7F6
   5 - SEL$58A6D7F6 / RESURCEWORKER@SEL$2
   6 - SEL$58A6D7F6 / RESURCEWORKER@SEL$2
   7 - SEL$58A6D7F6 / TASKUSESRESURCE@SEL$1
   8 - SEL$58A6D7F6 / TASKUSESRESURCE@SEL$1
   9 - SEL$58A6D7F6 / TASK@SEL$1
10 - SEL$58A6D7F6 / TASK@SEL$1
11 - SEL$7EBCC247 / TRW@SEL$3
12 - SEL$7EBCC247
13 - SEL$7EBCC247 / TUR@SEL$4
14 - SEL$6        / TRW_IN@SEL$5
15 - SEL$6
16 - SEL$6        / TWH@SEL$6
Predicate Information (identified by operation id):
   1 - access("TRW"."RESURCE_ID"(+)="TASKUSESRESURCE"."RESURCE_ID" AND "TRW"."TASK_ID"(+)="TASK"."ID")
   6 - access("RESURCEWORKER"."USER_PRINCIPAL_NAME"=U'jernej')
   8 - access("TASKUSESRESURCE"."RESURCE_ID"="RESURCEWORKER"."ID")
10 - access("TASKUSESRESURCE"."TASK_ID"="TASK"."ID")
12 - access("TUR"."RESURCE_ID"(+)="TRW_IN"."RESURCE_ID" AND "TUR"."TASK_ID"(+)="TRW_IN"."TASK_ID")                                                                                                                                                                                                         Jonathan, I've been to one of your workshops in Ljubljana. I'm still trying to understand everything you explained and use it, but there's much I have to learn and understand.
The way I see this query it should fist join and filter the first three tables and only then join the trw subquery. The problem with this subqrey is task_work_history table, which is accessed in very different ways in different places, so there will always be many reads to gather required data. For now, however, I'd be hapy to just bring the performance of inner join to left join...

Performance Difference Between Dual 2.7 and Quad, and Logic Pro

I used to have a G5 DP 2.7 before it went bad and Apple offered me a Quad in exchange. I'd like to understand some details of the specs between these two computers and how they might translate with respect to running Logic Pro (which I'll get to in a minute)...
Listed below are the specs which I believe illustrate the significant differences between these two machines (this info is based on info gleaned from Macworld, since the specific specs of the DP 2.7 no longer appear on Apple's website):
Dual 2.7
(2x) 2.7 GHz, cache = 512K per processor, frontside bus speed = 1.35 GHz per processor
Quad
(4x) 2.5 GHz, cache = 2MB on-chip L2 per chip, frontside bus speed = 1.25 GHz per processor
So here's the link with Logic: it's become apparent of late that Logic Pro is not able to fully utilize the processing power of all 4 cores in the Quad. Reports are that performance tops out at 50% per processor, resulting in approx. 200% possible processing usage from the 4 cores in the Quad.
However, such a limitation in processor usage wasn't present in the DP 2.7.
So based on the information I've learned so far, it seems that the Quad and the DP 2.7 are almost equivalent in terms of processing power when running Logic Pro. Now, this is what I'm surmising, but, is this actually the case? At the end of the day, I'd like to know if the Quad indeed a more powerful computer than the DP 2.7 when running Logic Pro, or, did my exchange of DP 2.7 for the Quad constitute a "downgrade" of any proportion?

The only logical answer to Why Intels do and PPC don't is:
Apple want everybody to transition to their Intel boxes. The only way to do this is offer better performance in software tweaks for one system over the another, which begs the question what else will Apple do to software for PPC owners?
Having bought a Mac Pro, primerily for testing out certain software apz under Rosetta, I did a real time test between my 8GB RAM G5 Quad and my stock Mac Pro upgraded to 2GB RAM, batch processing 204 Canon 1DsMKII RAW files into 16-Bitt TIFFs. I put a thread on the Mac Pro forum here:
http://discussions.apple.com/thread.jspa?threadID=627916&tstart=15
I so wanted my Quad to squash the Intel invaded Mac, but results speak for themselves. The Mac Pro flattened the G5 Quad by over 23 minutes (it worked out as 1.7 times faster than the G5Quad).
If that's how it performs with 2GB RAM it's gonna really fly when I max it out. So, for musicians, I would imagine with the Quad core support for Intel Macs in Logic 7.2.2 makes a fairly straight forward decision. . .buy a Mac Pro and sell your PowerMac. More time composing less time waiting for renders (never used music software so if it doesn't render your composition place whatever it does do into the 'renders' slot )
P.S I hated, really hated having to say the Mac Pro toasted my G5 Quad, I so wanted the Intel to fall flat on it's aluminium as$, but it didn't. It's a quick machine. A very quick machine which toasted my Quad in real time tests using a universal Pro app from a 3rd Party (who would have no incentive to dumb down the PPC version as they sell their product to Windows, PPC OS X and Intel OS X)

Is there a big performance difference between HD's

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