How Inconsistent Timestamps Affect Trust

Almost all the big apps - like Facebook, Instagram have millions of servers. These servers also have user caches and also holds the feed caches. Now, each update to any post/user data has a timestamp. But, this timestamp if interpreted differently by servers can lead to huge issues. ? If you’re running a distributed system, it’s incredibly important to keep the system clocks of the machines synchronized. If the machines are off by a few seconds, this will cause a huge variety of different issues. Some issues which may occur: Data Consistency - If the system clocks aren’t synchronized, then one node’s timestamp might conflict with another. This can cause confusion about which update is the most recent, leading to data inconsistency, frustration and anger. Observability - Keeping logs, storing metrics, traces, etc is extremely important for debugging issues. This is crucial for understanding what’s happening in your distributed system. However, all of these things are useless if your machines don’t have synchronized clocks and the timestamps are all messed up. Network Security - Many cryptographic protocols rely on synchronized clocks for correctness. The Kerberos authentication protocol, for example, uses timestamps to prevent replay attacks (where an attacker intercepts a network message and replays it later). Solely relying on a machine’s internal clock for the time is not a good idea from a security perspective. Event Ordering - You might have one event that debits an account and another that credits it. Processing these transactions in the wrong order at scale will lead to incorrect account balances and unhappy customers (or happy customers who will become very, very unhappy). Facebook solves this with a set of PTP servers. Earlier they relied on NTP servers. Now, what are they and how they work ? Will discuss this in the next post.

Trust in shift work dies when people argue about the clock. Worker's record: “Full shift until 5:30pm." Site manager: “Gone by 4:30pm.” I see this all the time from our clients. Different records. Same shift. Same mess that breaks trust on both sides. One of our clients Traba (staffing platform) kept running into this issue. They built their own system that documented location at clock-in, but it didn't capture live location during shifts or automate clock-out. With HyperTrack location intelligence, we were able to provide: →Geofences that automate clock in and clock out. →Orders API sends notifications when workers are en route. →Real-time dashboards give complete field visibility. Now they're tracking 100% of shifts with rock-solid accuracy and hitting fill rates of >98% Nobody wants to be the bad guy questioning timesheets. But when you have reliable data protecting everyone, payments happen faster because disputes just disappear. Shift work still has its challenges: unpredictable schedules, constant juggling. But now when someone asks "Did they actually show up?" Everyone's looking at the same answer.

Howdy! This week's Ashby Hidden Gem is focused on highlighting a small but very important nuance related to Ashby report customization. Namely - the ability to pick and choose what date stamp you want to use when calculating time ranges. Sounds boring, I get it 🤓. But in speaking to customers on other systems, one of the persistent challenges is a lack of trust in the data they're putting in front of leadership. When we pull on that thread - in many cases it's because there's a fundamental disconnect between how THEY define a time range, and how their ATS defines a time range. For instance - maybe you think about "hires last year" as "how many people signed an offer last year" or "how many hires had a start date last year" - but your ATS thinks of it as "how many hires had an application start date last year" Typically there's no way to adjust the date time stamp to map the customer's understanding - and in some cases it's actually totally opaque what the time stamp being applied even represents (i.e. it's not clear what the ATS is anchoring on). Which, again, leads to a fundamental mistrust of the data. Video here walks through how Ashby customers can easily manipulate the date time stamp field on all of our reports, to exactly mirror how THEY think about time ranges! Ashby #buyersdelight ---------------------------------------------------------------------------- #ashbyhiddengems is an ongoing series I'm curating - the goal is to post 1 or 2 of these a week. Follow the hashtag if you don't want to miss anything! The core audience is existing Ashby customers, and each post will highlight an existing Ashby feature which I think is underutilized or 'slept on' because of the depth and breadth of the platform (and how often our product team ships new features).