{"id":319,"date":"2020-05-12T23:44:52","date_gmt":"2020-05-12T22:44:52","guid":{"rendered":"https:\/\/blog.wnohang.net\/?p=319"},"modified":"2020-05-12T23:52:17","modified_gmt":"2020-05-12T22:52:17","slug":"circuit-breakers-stock-markets-and-distributed-systems","status":"publish","type":"post","link":"https:\/\/blog.wnohang.net\/index.php\/2020\/05\/12\/circuit-breakers-stock-markets-and-distributed-systems\/","title":{"rendered":"Circuit Breakers: Stock Markets and Distributed Systems"},"content":{"rendered":"Reading Time: <\/span> 4<\/span> minutes<\/span><\/span>\n

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There are many parallels between the stock markets and the distributed systems in computer science. This post, in particular, is about circuit breakers prevalent in them for better resilience against irrational exuberance<\/a> and upstream service errors respectively. In particular, this is about exploring breakers in stock markets from a distributed systems perspective. <\/p>\n\n\n\n

Of late, the US stock market (and the non-US ones too) has been quite volatile, to say the least. CBOE VIX index<\/a>  (also known as the \u2018fear index\u2019) which measures implied volatility<\/a> in the S&P 500<\/a> index, has traversed a wide range between highs and lows, fluctuating several points on many of the days in March and April, peaking at 82.69 on 16th March (stock market bottomed out on 23 March, both as of May 10, 2020).  <\/p>\n\n\n\n

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In recent times, similar levels of volatility were last observed during the Global Financial Crisis<\/a> (GFC) of 2008. Markets are driven by emotions and speculation as much they are driven by algorithms and numbers.<\/p>\n\n\n\n

During periods of high volatility, a \u201cthundering herd<\/a>\u201d of panic sell-off can be potentially triggered. To put a stop to this herd, a circuit breaker<\/strong> or a trading curb<\/a> kicks in. Many would have experienced electrical circuit breakers<\/a> in daily life when dealing with not-so-pleasant electrical issues wherein an overload would have triggered the breaker, requiring a manual reset. Similarly, these circuit breakers are triggered corresponding to a fall of 7%, 13%, and 20% in levels of the S&P 500 index, and markets are halted for 15 minutes each for the first two and the entirety of the trading day for a 20% drop. The goal of each of these curbs is to prevent the need for the next one. World Economic Forum<\/a> and Reuters<\/a> have crisp short reads on the recent circuit breaker events. <\/p>\n\n\n\n

In March alone, these circuit breakers were triggered four times. This is very significant since, before these clustered breaker events, the circuit breakers were triggered only once in 1997 after being instituted to avoid a repeat of the 1987 Black Monday crash<\/a>.<\/p>\n\n\n\n

On one of these days in March, with these breakers triggered, other than dread at the roller coaster-like volatility, this reminded me of circuit breakers in distributed systems. In these systems, these breakers are a design pattern (popularized by Michael Nygard in the book Release It!<\/a>) used often by services\/clients when their dependent service is unavailable. It works by isolating the unavailable\/erroring service from calling clients and periodically monitoring the service before allowing some (half-open) or all the traffic (open) states.  The overarching goal here is to avoid escalation of an error state to a crash and cascading failures in general.<\/p>\n\n\n\n

Following diagram from Azure Circuit Breaker pattern – Cloud Design Patterns<\/a> illustrates the state transitions better: <\/p>\n\n\n\n

\"Circuit<\/p>\n\n\n\n

Cascading failures are those wherein a failure in one service avalanches into larger problems in multiple dependent services. One hapless git commit somewhere deep in the stack ends up breaking your photo-sharing service. It would be hard to miss this class of failures from postmortems of popular website\/service incidents. This clip from Ice Age sort of exemplifies a cascading failure in my mind:<\/p>\n\n\n\n

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