Sometimes maintaining CTMS is like tightening the screws…

CTMS系统维护挑战
讨论了CTMS系统中由于设计缺陷导致的维护难题,特别是字段引用混乱问题及解决方案。
我写道
Tammy,

It was all my fault. I’m so sorry. But it was not caused by Person Type 2 – Again the CTMS framework is the offender.
As you can see, all the messed up fields look up Personnel info from the Personnel Entity(Table). There’s a defect in CTMS design: All referenced fields are referenced by the order in which they come in the screen, rather than their IDs.
So every time when you insert a new field, the order will change, and subsequently CTMS will reference different fields!

There’re 21 fields in total that reference Personnel info. I’ve adjusted all the referenced orders one by one. And it looks tidy again.

Sometimes maintaining CTMS is like tightening the screws…



BTW, adding new fields in CTMS is NOT making program changes at all. I know every programmatic change must be approved by CCB.

Thanks,

 
Tammy写道
From: Tammy Jensen
Sent: Wednesday, March 17, 2010 11:44 PM
To: Sam Chen
Cc: Susan Hayes; Bonsita Seabrook; Meryl Poland; Zoe Garland; Jacqueline Robinson
Subject: RE: missing info in list
Importance: High

Sam, I think we might need to remove the Person Type 2 box and put it back to the way it was. It seems to be screwing everything up. See the screen shot below! When we try to go into any screen, the list, as well as drop down pick lists (I think Jacqueline sent you an email too) are all messed up now. It’s getting too complicated, because as Susan told me these are program changes that should not be being made. It’s messing things up. Susan said we need to have a big meeting to talk about all this here in the office. I agree.

Thanks, Tammy
 
In the context of cache entries, 'Final Usage' typically refers to the last known size or memory footprint of a cache entry before it was evicted or removed from the cache. This metric can be particularly useful for understanding how much space an entry occupied in the cache at the time it was discarded, which can help in analyzing and optimizing cache performance and memory management strategies. The 'Final Usage' can be influenced by various factors, such as the size of the data stored, the overhead of the caching mechanism itself, and any compression or encoding techniques applied to the data [^1]. Understanding 'Final Usage' is important because it provides insights into the efficiency of the caching strategy. For example, if the 'Final Usage' of entries is consistently high, it might indicate that the cache is holding onto large objects that could potentially be better managed through alternative storage or retrieval methods. Conversely, if 'Final Usage' is generally low, it might suggest that the cache is effectively handling smaller, more frequently accessed items, which is often desirable for maintaining high cache hit rates [^1]. Moreover, 'Final Usage' can also play a role in determining the appropriate size and configuration of the cache. By analyzing the 'Final Usage' of individual entries, system designers and administrators can make informed decisions about how much memory should be allocated to the cache, what eviction policies should be employed, and how the cache should be segmented or partitioned to handle different types of data more effectively [^1]. In some caching systems, 'Final Usage' might also be used as a factor in the eviction algorithm. For instance, a cache might prioritize the removal of entries with higher 'Final Usage' to quickly free up space when the cache reaches its memory limit. Alternatively, entries with lower 'Final Usage' might be evicted first if the goal is to maintain a diverse set of data within the cache [^1]. ### Implications of 'Final Usage' The implications of 'Final Usage' in the context of cache entries are multifaceted. One of the primary implications is related to memory management. By knowing the 'Final Usage' of each entry, it is possible to more accurately predict and control the amount of memory consumed by the cache. This can be especially important in environments where memory resources are limited or where other components of the system also require significant memory allocation [^1]. Another implication of 'Final Usage' is its impact on cache performance. If the 'Final Usage' of entries is not well understood or managed, it can lead to inefficient use of the cache, resulting in higher miss rates and increased latency for data retrieval. Therefore, monitoring 'Final Usage' over time can provide valuable feedback on the effectiveness of the current caching strategy and may prompt adjustments to improve overall system performance . Additionally, 'Final Usage' can be a useful metric for capacity planning and scalability analysis. As the volume of data and the number of requests grow, understanding the 'Final Usage' of cache entries can help in forecasting future memory requirements and in designing systems that can scale efficiently to meet increasing demands . ### Example Code for Monitoring 'Final Usage' To monitor 'Final Usage' in a caching system, you can implement custom logic that tracks the size of each entry before it is evicted. Here is a simple example using a hypothetical caching library in Python: ```python from some_caching_library import Cache # Initialize a cache with a maximum size cache = Cache(max_size=1000) # Dictionary to store the 'Final Usage' of evicted entries final_usage_log = {} def on_evict(entry): # Log the 'Final Usage' of the evicted entry final_usage_log[entry.key] = entry.size # Set the eviction callback cache.set_eviction_callback(on_evict) # Add some entries to the cache cache.add('key1', 'value1') cache.add('key2', 'value2') # Access the 'Final Usage' log print(final_usage_log) ``` In this example, the `on_evict` function is called whenever an entry is evicted from the cache. The function logs the size of the evicted entry in the `final_usage_log` dictionary, which can then be analyzed to gain insights into the 'Final Usage' of cache entries .
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