Object Replication Performance

January 8, 2007 at 5:48 pm | Posted in Object Replication, Performance | 3 Comments

In a recent post on the Documentum Support Forums I alluded to some testing I had done on Documentum Object Replication. I give the test results here to demonstrate some important design points when deciding how (or if) to deploy object replication.

The test involved timing a replication run from start to finish (source and target) using different sizes of data set. In each run no changes had been made to the replicated data so you might expect that:

  1. Each replication run is quite quick
  2. The time to replicate is related to the number of changes made to objects under the replication folder

The replication used the following options:

  • Full Refresh – false
  • Fast Replication – false

Here are the results:

Objects       Duration
-------       --------
  1,000           64s
 10,000          344s
 50,000         2032s
100,000         4213s

As you can see it takes a substantial amount of time to replicate no changes! In fact the time the replication takes is proportional to the number of objects under the replication folder (nb. Don’t take these figures as some sort of guideline for how long a replication will take as this figure will depend crucially on the number of relationships between objects and the speed of the processor).

The problem is related to the way Documentum’s dump API tries to check each object and its related objects to see if they have changed; even with a very simple dataset with no user-created relationships there are usually 6 or 7 queries to run for each object and this is a substantial amout of processing when aggregated over a large number of objects to check. For more complicated datasets with multiple relationships between objects the processing times will increase markedly.

If you think this type of behaviour is unsatisfactory for your particular requirement there are a couple of things you can look at.

  1. Rather than use a single replication folder, try to split up the data across multiple folder and create a replication job for each one. Since each replication job is a single-threaded process multiple CPUs do not benefit a single replication job however multiple Replication Jobs can take advantage of multi-processor machines
  2. Consider using the Fast Replication option. Fast Replication reduces the amount of checking for Related Objects and, particularly for datasets with multiple relationships between objects, can be considerably faster for no-change replication runs. Make sure you test thoroughly though particularly if you are relying on changes to Related Objects being automatically replicated


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  1. Hi Robin,

    Were you able to test the advantage of replication using multiple jobs for multiple folders. Was it beneficial in a multi processor environment?

    Also if we are running a two-way replication, it is said that we should wait for one replication job to finish one way before we start the replication job to run the second way.

    For example:

    I have Site 1 and Site 2. I run a replication Job R1 to replicate from Site 1 to Site 2. Then when job R1 finishes, I run a replication job R2 to replicate from Site 2 to Site 1.

    In a scenario where I have multiple jobs for different folders, can I have a job R1 replicating from Site 1/Folder 1 to Site 2/Folder1 and at the same time a job replicating from Site 2/Folder 2 to Site 1/Folder 2.

    Would this work?

    Bonson Mampilli

  2. I looked at the multiple replication idea in a proof of concept for a client and it certainly gives performance advantages over the single replication job. This will be the case provided:

    1) Your bottleneck is CPU
    2) You have multiple CPUs – but ensure you retain enough CPU for other user requests

    The example you give should be fine. The problem with 2-way replication is where you start replicating objects from repository 2 before they have been fully replicated from repository 1

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