Software in the Field: Redundancy, Resiliency, and Failsafes
Originally posted on the Project Mwana blog
We’ve just been in Ndola for the past week building a bridge from the lab computer system to our RapidSMS server back in Lusaka. Our goal is to get fresh DBS results as they’re entered onto the (non-internet connected) lab computer, and funnel them to RapidSMS, where we can then distribute them to clinics. It’s a critical piece of the puzzle; without results, we have nothing to send. And yet, for all its importance, we have no on-site staff to leave in Ndola, the machine cannot be remotely accessed, and even if we had staff to spare, Ndola is a half-day’s ride from Lusaka and a full-day from Mansa. Whatever software we leave there will largely have to administer and maintain itself.
To that end, I cannot overstate the importance of such software giving you detailed feedback about itself. Having the software maintain thorough logs about its health and operation and having it make a heroic effort to get those logs to you is the key to remotely diagnosing and fixing any problem that may arise.
We deployed our solution on the lab computer Friday afternoon – about forty minutes left in our departure window to get back to Lusaka that night – and actually managed to squeeze in a live test or two. Upon seeing real results trickle into the Lusaka server, we combination cheered/sighed in relief, packed up, and left. The true test, however, would come on Monday. We would see if the system still worked without the magical effect of us standing right there in front of it.
The first sync is scheduled daily at 9:30am. I sat nervous at my laptop, trying to limit myself in how often I clicked ‘Refresh’. 9:30 came and went, then 9:35. The lab connection was slow; these things could take time… 9:40… 9:45… Perhaps the computer hadn’t been turned on… By 10:00, hope had faded, and I drafted an email to the lab manager asking him to assist us in troubleshooting. As I was about to click ‘Send’, a new submission from Ndola popped into the queue.
We had successfully sent results end-to-end! The team was thrilled. But something was odd. On closer inspection, there were no results. Instead, just this:
... Apr-26 09:30:01: records deleted from ZPCT database! (12-00000) Apr-26 09:30:01: querying records of interest from ZPCT: 280 total; 4 new; 257 resolved; 1 in limbo; 18 untested Apr-26 09:30:01: error accessing ZPCT database (read-only); staging database not touched Traceback (most recent call last): File "C:\unicef-mwana\script\extract.py", line 323, in pull_records records = query_prod_records() File "C:\unicef-mwana\script\extract.py", line 315, in query_prod_records records.append((source, read_sample_record(id, conn))) File "C:\unicef-mwana\script\extract.py", line 181, in read_sample_record sample_row = query_sample(sample_id, conn) File "C:\unicef-mwana\script\extract.py", line 175, in query_sample raise ValueError('sample [%s] not found' % sample_id) ValueError: sample [12-00000] not found Apr-26 09:30:01: db sync attempt 1 of 6 failed; trying again in 2 minutes ... Apr-26 10:06:04: all db sync attempts failed
We hadn’t sent results end-to-end after all. But in a way, it was even better; we had sent an exact explanation of why we couldn’t send results end-to-end. All our work of making the software keep us in the loop had paid off on the very first day.
It turns out we had a bug. During our testing on Friday, we had created a new sample result for testing, then deleted it. Our software duly noted the deleted record, but then still tried to check it for updates. As it no longer existed in the main database, the software crashed. Despite this, it still tried again another five times, with increasingly lengthy waiting periods between each attempt. This was by design – database queries can fail for any number of ephemeral reasons, so this was yet another layer of built-in resiliency. Finally, almost forty minutes later, it phoned home about its failure.
Without the reporting infrastructure we had put in place, debugging our lack of results from the lab would have been a nightmare. With many, many hours of debugging, severely depleted goodwill of the lab manager, and perhaps an emergency visit to Ndola, we would have figured it out. It also may have even been days before we realized there was actually a problem! But instead, we knew the precise cause of the failure immediately, as if we had been sitting right at the computer at the exact moment the failure happened. A fix was ready in two hours, and would be deployed hopefully within the day.
The takeaway is that if you’re deploying a critical piece of software in an environment you don’t have control over and don’t have ongoing access to, build in layer upon layer of redundancy, resiliency, and fail-safes. In particular, we employed the following tactics:
- software keeps detailed logs about its normal operation
- software traps any unexpected errors – no matter where they occur – and adds debugging information to the logs
- logs are sent along with our actual data of interest (in fact, logs are sent first and treated with higher priority)
- software phones home at regular intervals even if there is no new data to send
- software requires positive acknowledgment from the other end that the data it sends have been received safely; without this acknowledgment, it will re-send the data on each scheduled run
- software transmits data in small increments, so forward progress can be made even in the face of frequent interruptions
- the software attempts unreliable operations (querying a database, sending data over the internet) several times before giving up
There is no question these strategies are valuable. We just had no idea they would pay off so quickly.
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