Five years ago when I joined my employer we were in reactive mode when it comes to production performance. Nodes were going down daily and we would jump to fix that fire and then move to next fire. Problem with this approach was that we would be always busy and even nightly calls for customers in Europe. Similar pattern was happening when it came to production support, many issues were known only when a customer would report it. Again this would mean that we would get calls on weekend and we had to fix issues ASAP and sometimes that means putting a band-aid. Some times issues will linger in production unnoticed for 1-2 weeks and when customer would report it, its a lengthy and arcane process to hunt for issues in existing logs. More than debugging this would require a lot of patience and persistence to look for trend and form a time-line of events to hunt for root cause. Only few engineers would be willing to do so.
Over the course of last few years we became better at production support by turning to proactive production support. I wrote a exception analysis report and daily my team members and me would analyse exception report and send tickets to appropriate colleagues. Many times there will be tickets that don’t require patch and can be temporarily fixed by doing some data cleanup. If the issue was in the flow executed by background jobs or automated agents then we would fix issues even before customer would notice it. Advantage of this approach is that issues will get caught before it becomes a nightmare. Incremental fixes adds up to a lot and now the volume of production issues we have to fix has reduced because we fix many of them in the background and include it in coming patch.
Two months ago we installed new relic and we are not turning the table around on performance front also by trusting data rather than gut feelings. New relic allows us to do trend analysis and release impact analysis, we can catch the issues while its turning from Green->Yellow->Red. Off course some issues turn from Green to Red within 10-15 mins but there are many that takes time. For e.g. Last week I noticed that one data centre is behaving odd and avg response time of all apis increased from 50ms to 100ms. That means customers were having degraded performance but still they were not complaining. Today I found it was due to one customer and the problem can be fixed by some data tweaks. I did the tweaks and performance is back to normal.
Similarly from new relic we found many issues where calls to memcached would happen in a loop or database queries in a loop. Those kind of issues affect customers with 25-30K users but not all customers. Every penny counts and by taking a proactive jab at these issues we are increasing customer satisfaction and loyalty but also we are becoming more predictable by trusting metrics.
for e.g. we recently converted a service from python to java and in past we have never ramped up from 0 to 1000 customers in a week. But because of new relic I clearly saw that system was performing well with no errors even though we ramped from 30->200->1000 clients. Tomorrow we would add 500 more clients and all this is because the decision is based on data rather than gut feeling.
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Over the course of last few years we became better at production support by turning to proactive production support. I wrote a exception analysis report and daily my team members and me would analyse exception report and send tickets to appropriate colleagues. Many times there will be tickets that don’t require patch and can be temporarily fixed by doing some data cleanup. If the issue was in the flow executed by background jobs or automated agents then we would fix issues even before customer would notice it. Advantage of this approach is that issues will get caught before it becomes a nightmare. Incremental fixes adds up to a lot and now the volume of production issues we have to fix has reduced because we fix many of them in the background and include it in coming patch.
Two months ago we installed new relic and we are not turning the table around on performance front also by trusting data rather than gut feelings. New relic allows us to do trend analysis and release impact analysis, we can catch the issues while its turning from Green->Yellow->Red. Off course some issues turn from Green to Red within 10-15 mins but there are many that takes time. For e.g. Last week I noticed that one data centre is behaving odd and avg response time of all apis increased from 50ms to 100ms. That means customers were having degraded performance but still they were not complaining. Today I found it was due to one customer and the problem can be fixed by some data tweaks. I did the tweaks and performance is back to normal.
Similarly from new relic we found many issues where calls to memcached would happen in a loop or database queries in a loop. Those kind of issues affect customers with 25-30K users but not all customers. Every penny counts and by taking a proactive jab at these issues we are increasing customer satisfaction and loyalty but also we are becoming more predictable by trusting metrics.
for e.g. we recently converted a service from python to java and in past we have never ramped up from 0 to 1000 customers in a week. But because of new relic I clearly saw that system was performing well with no errors even though we ramped from 30->200->1000 clients. Tomorrow we would add 500 more clients and all this is because the decision is based on data rather than gut feeling.
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