Performance and Usability Problems-Aperture 3.1.3 and 10.7.1

Late 2009 iMac
Quad Core i7
8 GB RAM
512 ATI GPU
Nik Software Complete Collection Plugins—all updated
I’m guessing this is due to memory leaks, but Aperture is crippled with 10.7.1. On my great iMac, Aperture now runs like it did when I ran Aperture 2 on a 2004 PowerBook G4.
With Snow Leopard, I had none of these issues.
With Aperture idle, it uses up to 2 GB real memory and 3 GB virtual memory. With it idle, it generates MBs of page outs and swap used. If I start to use it for adjustments, even minor ones, page outs and swap used increase dramatically. For example, last night after editing and adjusting a project of images, page outs were just under 5 GB. I’ve reverted to the old habit of rebooting to clear page outs and swaps. Last time I had to do this was with the 2004 PowerBook.
While actually using Aperture for edits and adjustments, response to almost any input is slow, with conspicuous delays. Even checking and unchecking an adjustment brick causes a considerable delay between the click and when the change is visible.
Again, with Snow Leopard, Aperture was a bullet with current configuration. I just ordered 8 more GBs of RAM in hopes that this will help until Apple fixes Aperture.
Anyone else having such considerable performance issues with Aperture 3.1.3 and 10.7.1?
Thanks

jcrosby wrote:The "individual setup" argument is an oversimplification. With relatively current hardware, I can reproduce very poor performance when running Aperture in Lion in either 32-bit or 63-bit mode (though the patterns change a bit), and so can many others.
John-
Actually I do think it is about individual setups. The kinds of complaints expressed in this thread have been going on literally for decades.
Starting with early-days Photoshop then later Aperture I have observed the challenges of heavy graphics apps with hardware/RAM/OSs/app versions. We have always been able to reproduce poor performance, because apps/OSs generally do evolve to be more demanding.
IMO our goal is to help individual setups tweak to achieve non-poor performance while we wait for stronger hardware, or an OS improvement, or an app improvement, or money to buy more RAM, or whatever. IMO it really is an ongoing individual-setup (e.g. last year I was running a 2006 C2D MBP with a max of 3 GB RAM requiring constant tweaking) issue.
Today I run Aperture under 10.6.8 on a 2011 MBP with SSD and 8 GB RAM and it rocks. I would consider 10.6.8 to be one of the tweaks that folks might do if all the other tweaks (like add RAM and run 32-bit) fail to achieve non-poor performance.
-Allen

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    CONSTRAINT wu_ea_fk FOREIGN KEY (wu_emp_id, wu_proj_id, wu_act_id)
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    start empins
    start projins
    start actins
    start aoins
    start epins
    start eains
    start wuins
    start pmselect
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    Thanks Roger

    Hi,
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    I do see this Oracle 12c book at Amazon written over a year ago.
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    Thanks for the response. I managed to Google the 'nobody' account and found the explanation.
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    One thing that might also have an impact... my available drive space is now down to less than 5gb (runs anywhere between 1gb and 5gb free depending on what I'm running). I know this causes problems for Parallels as it keeps moaning at me, but would it cause any problems with OSX?
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    Tom

  • Iphoto usability problems

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  • EXTREMELY SLOW XQUERY PERFORMANCE AND SLOW DOCUMENT INSERTS

    EXTREMELY SLOW XQUERY PERFORMANCE AND SLOW DOCUMENT INSERTS.
    Resolution History
    12-JUN-07 15:01:17 GMT
    ### Complete Problem Description ###
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    tail><track course="26.0" speed="9.26"/></detail></event></cotEntity>

    19-JUN-07 04:34:27 GMT
    UPDATE
    =======
    Hi Arnold,
    you wrote -
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    $ which java
    /usr/bin/java
    $ java -version
    java version "1.4.2"
    gcj (GCC) 3.4.6 20060404 (Red Hat 3.4.6-3)
    sorry as I told you before I am not very knowledgeable in Java. Can you tell me what setting
    s I need to change to make use of Sun JDK 1.5. Please note I am testing on Linux
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    Thanks,
    Rakesh
    STATUS
    =======
    @CUS -- Waiting for requested information

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    Edited by: Prantor on Nov 8, 2010 10:37 PM

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  • QUERY PERFORMANCE AND DATA LOADING PERFORMANCE ISSUES

    WHAT ARE  QUERY PERFORMANCE ISSUES WE NEED TO TAKE CARE PLEASE EXPLAIN AND LET ME KNOW T CODES...PLZ URGENT
    WHAT ARE DATALOADING PERFORMANCE ISSUES  WE NEED TO TAKE CARE PLEASE EXPLAIN AND LET ME KNOW T CODES PLZ URGENT
    WILL REWARD FULL POINT S
    REGARDS
    GURU

    BW Back end
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    4)Balance loads by distributing processes onto different servers if possible. If your site uses more than one BW application server, distribute the extraction processes to different servers using transaction code SM59 — Maintain RFC Destination. Load balancing is possible only if the extraction program allows the option
    5)Set optimum parameters for data-packet size. Packet size affects the number of data requests to the database. Set the data-packet size to optimum values for an efficient data-extraction mechanism. To find the optimum value, start with a packet size in the range of 50,000 to 100,000 and gradually increase it. At some point, you will reach the threshold at which increasing packet size further does not provide any performance increase. To set the packet size, use transaction code SBIW — BW IMG Menu — on the source system. To set the data load parameters for flat-file uploads, use transaction code RSCUSTV6 in BW.
    6)Build indexes on DataSource tables based on selection criteria. Indexing DataSource tables improves the extraction performance, because it reduces the read times of those tables.
    7)Execute collection jobs in parallel. Like the Business Content extractors, generic extractors have a number of collection jobs to retrieve relevant data from DataSource tables. Scheduling these collection jobs to run in parallel reduces the total extraction time, and they can be scheduled via transaction code SM37 in the source system.
    8). Break up your data selections for InfoPackages and schedule the portions to run in parallel. This parallel upload mechanism sends different portions of the data to BW at the same time, and as a result the total upload time is reduced. You can schedule InfoPackages in the Administrator Workbench.
    You can upload data from a data target (InfoCube and ODS) to another data target within the BW system. While uploading, you can schedule more than one InfoPackage with different selection options in each one. For example, fiscal year or fiscal year period can be used as selection options. Avoid using parallel uploads for high volumes of data if hardware resources are constrained. Each InfoPacket uses one background process (if scheduled to run in the background) or dialog process (if scheduled to run online) of the application server, and too many processes could overwhelm a slow server.
    9). Building secondary indexes on the tables for the selection fields optimizes these tables for reading, reducing extraction time. If your selection fields are not key fields on the table, primary indexes are not much of a help when accessing data. In this case it is better to create secondary indexes with selection fields on the associated table using ABAP Dictionary to improve better selection performance.
    10)Analyze upload times to the PSA and identify long-running uploads. When you extract the data using PSA method, data is written into PSA tables in the BW system. If your data is on the order of tens of millions, consider partitioning these PSA tables for better performance, but pay attention to the partition sizes. Partitioning PSA tables improves data-load performance because it's faster to insert data into smaller database tables. Partitioning also provides increased performance for maintenance of PSA tables — for example, you can delete a portion of data faster. You can set the size of each partition in the PSA parameters screen, in transaction code SPRO or RSCUSTV6, so that BW creates a new partition automatically when a threshold value is reached.
    11)Debug any routines in the transfer and update rules and eliminate single selects from the routines. Using single selects in custom ABAP routines for selecting data from database tables reduces performance considerably. It is better to use buffers and array operations. When you use buffers or array operations, the system reads data from the database tables and stores it in the memory for manipulation, improving performance. If you do not use buffers or array operations, the whole reading process is performed on the database with many table accesses, and performance deteriorates. Also, extensive use of library transformations in the ABAP code reduces performance; since these transformations are not compiled in advance, they are carried out during run-time.
    12)Before uploading a high volume of transaction data into InfoCubes, activate the number-range buffer for dimension IDs. The number-range buffer is a parameter that identifies the number of sequential dimension IDs stored in the memory. If you increase the number range before high-volume data upload, you reduce the number of reads from the dimension tables and hence increase the upload performance. Do not forget to set the number-range values back to their original values after the upload. Use transaction code SNRO to maintain the number range buffer values for InfoCubes.
    13)Drop the indexes before uploading high-volume data into InfoCubes. Regenerate them after the upload. Indexes on InfoCubes are optimized for reading data from the InfoCubes. If the indexes exist during the upload, BW reads the indexes and tries to insert the records according to the indexes, resulting in poor upload performance. You can automate the dropping and regeneration of the indexes through InfoPackage scheduling. You can drop indexes in the Manage InfoCube screen in the Administrator Workbench.
    14)IDoc (intermediate document) archiving improves the extraction and loading performance and can be applied on both BW and R/3 systems. In addition to IDoc archiving, data archiving is available for InfoCubes and ODS objects.
    Hope it Helps
    Chetan
    @CP..

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