Loading time into memory for a large datastore ?

Is there some analysis/statistics about what would be the loading time for a timesten data store according to the size of the data store.
We have a problem with one of our clients where loading of datastore into memory takes a long time. but only certain instances it takes this long.. maximum size for data store is set to be 8GB (64bit AIX with 45GB physical memory), is it something to do with transactions which are not committed?
Also is it advisable to have multiple smaller datastores or one single large datastore...

When a TimesTen datastore is loaded into memory it has to go through the following steps. If the datastore was shut down (unloaded from memory) cleanly, then the recovery steps essentially are no-ops; if not then they may take a considerable time:
1. Allocate appropriately sized shared memory segment from the O/S (on some O/S this can take a significant time if the segment is large)
2. Read the most recent checkpoint file into the shared memory segment from disk. The time for this step depends on the size of the checkpoint file and the sustained read performance of the storage subsystem; a large datastore, slow disks or a lot of I/O contention on the disks can all slow down this step.
3. Replay all outstanding transaction log files from the point corresposnding to the checkpoint until the end of the log stream is reached. Then rollback any still open transactions. If there is a very large amount of log data to replay then this can take quite some time. This step is skipped if the datastore was shut down cleanly.
4. Any indices that would have been modified during the log replay are dropped and rebuilt. If there are many indices, on large tables, that need to be rebuilt then this step can also take some time. This phase can be done in parallel (see the RecoveryThreads DSN attribute).
Once these 4 steps have been done the datastore is usable, but if recovery had to be done then we will immediately take a checkpoint which will happen in the background.
As you can see from the above there are several variables and so it is hard to give general metrics. For a clean restart (no recovery) then the time should be very close to size of datastore divided by disk sustained read rate.
The best ways to minimise restart times are to (a) ensure that checkpoints are occurring frequently enough and (b) ensure that the datastore(s) are always shutdown cleanly before e.g. stopping the TimesTen main daemon or rebooting the machine.
As to whether it is better to have multiple smaller stores or one large one - that depends on several factors.
- A single large datastore may be more convenient for the application (since all the data is in one place). If the data is split across multiple datastores then transactions cannot span the datastores and if cross-datastorestore queries/joins are needed they must be coded in the application.
- Smaller datastores can be loaded/unloaded/recovered faster than larger datastores but the increased number of datastores could make system management more complex and/or error prone.
- For very intensive workloads (especially write workloads) on large SMP machines overall better throughput and scalability will be seen from multiple small datastores compared to a single large datastore.
I hope that helps.
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