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References
Jira Legacy server System Jira serverId 4733707d-2057-3a0f-ae5e-4fd8aff50176 key CPS-2478
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Parameter | Value | Notes |
|---|---|---|
Cm Handles | 500 | |
Module Set Tags | 2 | 250 CM Handles Each |
Worker Threads (parallelism) | 2 | |
Environment | Windows 11. 13th Gen Intel(R) Core(TM) i9-13900H 2.60 GHz |
Registration: Measurements Before & After PoC
Method | Before (avg. 4 runs) | After (avg. 6 runs) | Notes (improvements) | ||||
|---|---|---|---|---|---|---|---|
# Calls | Time Spent (ms) | % | # Calls | Time Spent (ms) | % | ||
query module references | 500 | 1,017 | 7% | 2 | 5 | 0% | Used ‘privateModuleSetCache’ map to locally store required data on each thread. Data discarded when thread finishes but this eliminates vast majority of DB calls. |
get modules from DMI | 100-200 | 1,326 | 9% | 2 | 13 | 0% | Use a Hazelcast distributed Set: ‘moduleSetTagsBeingProcessed’ to prevent multiple threads/instances attempting to process the same new tag. |
store schema set | 500 | 10,449 | 73% | 500 | 5,156 | 86% | 2 x faster. Probably due to less contention with read queries |
update states | 5+ | 1,429 | 10% | 5+ | 833 | 14% | 1.7 x faster |
Total | 14,221 | 6,006 | > 2 x faster! | ||||
Registration: Extrapolated Results for 20,000 Nodes and DMI Delay
below figures are calculated by multiplying the total time and adding fix delays for DMI requests
Methods | Before | After | Notes | ||
|---|---|---|---|---|---|
Time Spent (ms) | % | Time Spent (ms) | % | ||
query module references | 40,670 | 7% | 5 | 0% | |
get modules from DMI | 54,050 | 9% | 2,667 | 1% | add 200ms delay for first 10 batch of 100 |
store schema set | 417,960 | 73% | 206,220 | 85% | |
update states | 57,170 | 10% | 33,313 | 14% | |
Total | 569,850 | ~9m30s | 242,205 | ~4m3s | Need to add 2 minutes for initial delay: ~6m ~ 55 CM Handles/sec |
Registration: K6 Historical and current results, detailed analysis (excell)
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De-Registration: Test Measurements With and Without Orphanage removal
During de-registration the system remove ‘orphaned’ yang resources ie modules that are not in use any more after each batch using an expensive query.
Instrumentation showed a very high percentage of time was spent in this method when >20K cm handles were added to the system. This is because of the exponential growth of the relations between modules (schema sets) and yang resources.
The idea of this experiment/PoC is to no longer do this deletion during de-registration for each bath but do it on a much less frequent basis e.g. system start-up. In practice a module will rarely become orphanaged and even if it does there is no harm for that data to be present in the DB until the next restart of the system.
To see the impact of this change I simply removed the relevant call in the de-registration algorithm (during schema set deletion). The effect is very small (within the margin of error) for the original test sample size of 500. SO I temporally increased the sample size to 20,000 cm handles and record the following data:
Before | After (without orphan removal) | Notes (improvements) | ||
|---|---|---|---|---|
Milliseconds | CM Handles/Sec | Milliseconds | CM Handles/Sec | |
144,720 | 138 | 54,225 | 369 | |
151,189 | 132 | 57,061 | 351 | |
141,082 | 142 | 53,981 | 371 | |
Average | 137 | Average | 363 | > 2.6 x faster! |