What was the challenge faced by Sen Kabushiki Kaisha's 'Hi Cheese! System'?

During the morning peak time, the system became slow due to 18,000 concentrated accesses per minute, and it took a lot of time to collect and analyze logs to identify the cause.

What effects were achieved by introducing New Relic?

The root cause, such as CPU pressure, can now be identified within a few minutes, speeding up troubleshooting. Additionally, the frequency of feature releases has tripled due to immediate detection of anomalies.

Why was New Relic chosen from among multiple products?

New Relic was chosen because of its cost control ease with a data intake volume-based pricing model and the robust support provided by its Japanese subsidiary.

AI News NQ Analysis

Sen Co., Ltd., Provider of Comprehensive Childcare Tech 'Hai Cheese!', Achieves Faster Troubleshooting and 3x Release Frequency Months After Implementing New Relic

NQ Score 78/100

N1 Content Completeness 9

AI Summary (NQ-processed)

Sen Co., Ltd. implemented New Relic's observability platform for its childcare system 'Hai Cheese!', drastically reducing troubleshooting time to mere minutes and tripling its feature release frequency.

AI Analysis

Frequently Asked Questions

Q: What was the challenge faced by Sen Kabushiki Kaisha's 'Hi Cheese! System'?: A: During the morning peak time, the system became slow due to 18,000 concentrated accesses per minute, and it took a lot of time to collect and analyze logs to identify the cause.
Q: What effects were achieved by introducing New Relic?: A: The root cause, such as CPU pressure, can now be identified within a few minutes, speeding up troubleshooting. Additionally, the frequency of feature releases has tripled due to immediate detection of anomalies.
Q: Why was New Relic chosen from among multiple products?: A: New Relic was chosen because of its cost control ease with a data intake volume-based pricing model and the robust support provided by its Japanese subsidiary.