High Availability From a Business Perspective


This section contains information for those unfamiliar with HA and how it is fundamental to operational efficiency. We look at how business goals drive the configuration and operational aspects of the HA deployment.

Those familiar with these principles may skip this page.

Definition [1]

“High availability (HA) is a characteristic of a system which aims to ensure an agreed level of operational performance, usually uptime, for a higher than normal period.”

“There are three principles of systems design in reliability engineering which can help achieve high availability.

  1. Elimination of single points of failure. This means adding or building redundancy into the system so that failure of a component does not mean failure of the entire system.
  2. Reliable crossover. In redundant systems, the crossover point itself tends to become a single point of failure. Reliable systems must provide for reliable crossover.
  3. Detection of failures as they occur. If the two principles above are observed, then a user may never see a failure - but the maintenance activity must.”

Daml solution design honors these principles by:

  1. Eliminating single points of failure through redundant components.
  2. Executing reliable crossover through networking best practices, in conjunction with the Canton transaction consensus protocol, to eliminate partially processed requests.
  3. Ensuring automated failover when a single failure is detected.

Useful External Resources


Availability defines whether a system is able to fulfill its intended function over a period of time, i.e. the system works as intended 99.5% or 99.999% of the time.

The inverse is the percentage of time it is expected to fail, such as 0.5% or 0.001%.

Time-based availability

Availability is usually measured in whole system uptime percentage, rather than the uptime percentages of separate components.

A refinement of this metric is unplanned downtime, i.e. the amount of time that the system is unexpectedly unavailable. This is because well-published maintenance activities have no business impact whereas unplanned downtime can cause lost revenue, reputational harm, customers switching to a competitor, etc.

The general formula is:

\(availability = uptime / (uptime + downtime)\)

This formula calculates how many minutes of downtime are allowed in a given period. For example, a system with an availability target of 99.99% can be down for up to 52.56 minutes in an entire year and stay within its availability level.

The table below shows estimated downtimes for a number of given availability levels.

Availability level Downtime per year Downtime per quarter Downtime per month Downtime per week Downtime per day | Downtime per hour
90% 36.52 days 9.13 days 3.04 days 16.80 hours 2.40 hours | 6.00 minutes
95% 18.26 days 4.57 days 1.52 days 8.40 hours 1.20 hours 3.00 minutes
99% 3.65 days 21.91 hours 7.30 hours 1.68 hours 14.40 minutes 36.00 seconds
99.5% 1.83 days 10.96 hours 3.65 hours 50.40 minutes 7.20 minutes 18.00 seconds
99.9% 8.77 hours 2.19 hours 43.83 minutes 10.08 minutes 1.44 minutes 3.60 seconds
99.95% 4.38 hours 1.10 hours 21.91 minutes 5.04 minutes 43.20 seconds 1.80 seconds
99.99% 52.59 minutes 13.15 minutes 4.38 minutes 1.01 minutes 8.64 seconds 0.36 seconds
99.999% 5.26 minutes 1.31 minutes 26.30 seconds 6.05 seconds 0.86 seconds 0.04 seconds


For a custom availability percentage, use the availability calculator.

Data like this helps a business define an error budget or “the maximum amount of time that a technical system can fail without contractual consequences.”[2] which may also be a KPI for SREs.

For example, over a 30 day (43,200 minutes) time-window, with an availability target of 99.9%, the system must not be down for more than 43.2 minutes. This 43.2 minute figure is a concrete target to plan around, and is often referred to as the error budget. If you exceed 43.2 minutes of downtime over 30 days, you fail to meet your availability goal.

Aggregate request availability

In contrast to time-based availability, the fine-grained aggregate request availability metric considers the number of failed requests i.e. x% of total failed requests.

This metric is most useful for services that may be partially available or whose load varies over the course of a day or week rather than remaining constant, or to monitor specific, business-critical endpoints.

The general formula is:

\(availability = successfulRequests / totalRequests\)

Although not all requests have equal business value, this metric is often calculated over all requests made to the system. For example, a system that serves 2.5M requests per day, with a daily availability target of 99.99%, can serve up to 250 errors and still hit the target.


If a failing request retries and succeeds, it is not considered failed since the end-user sees no failure.


Resiliency is related to availability. “Resiliency is the capability to handle partial failures while continuing to execute and not crash. In modern application architectures — whether it be microservices running in containers on-premises or applications running in the cloud — failures are going to occur. For example, applications that communicate over networks (like services talking to a database or an API) are subject to transient failures. These temporary faults cause lesser amounts of downtime due to timeouts, overloaded resources, networking hiccups, and other problems that come and go and are hard to reproduce. These failures are usually self-correcting.” [3]

Resiliency and availability are enhanced by best practice patterns, such as the retry pattern. When a customer submits a request and receives a success response, they expect that request to succeed. If they receive an error response instead, then the user does not expect it to succeed and knows that they need to retry the request.

“Retries can be an effective way to handle transient failures that occur with cross-component communication in a system.”[3] A retry pattern is often coupled with the circuit breaker pattern, which “effectively shuts down all retries on an operation after a set number of retries have failed. This allows the system to recover from failed retries after hitting a known limit and gives it a chance to react in another way, like falling back to a cached value or returning a message to the user to try again later.”[3]

The Daml solution’s client application needs to add this type of resiliency to increase availability of the overall system consisting of platform and application.

Other Common Metrics / RTO and RPO

Recovery Time Objective (RTO) is the maximum acceptable delay between the interruption of service and restoration of service. This value determines an acceptable duration over which the service is impaired. It is a slice of the error budget but for a single instance of downtime.

Recovery Point Objective (RPO) is the maximum acceptable amount of time since the last data recovery point. This determines the acceptable data loss between the latest recovery point and a service interruption.

Financial systems often require support for an RPO of zero.

HA Cost Trade-Offs

High availability can be costly and thus require trade-offs.

To illustrate, extreme events that are highly improbable and costly to guard against - such as an asteroid strike that wipes out a continent’s data centers - may not need consideration. This highlights the trade-off between the cost of avoiding an outage, the probability of a single failure (single component redundancy), and the probability of multiple simultaneous failures (multiple component, integrated redundancy).

We can analyze the trade-offs by deriving the cost of loss of availability using unplanned downtime as follows:

\(cost = errorBudget * revenueLostPerMinuteOfDowntime\)

where the revenue lost per minute of downtime is a projected or measured statistic.

Use this formula in different configurations to compare increasing cost against availability to determine an appropriate trade-off for your business goals.


[1]https://en.wikipedia.org/wiki/High_availability as retrieved 02/22/2023
[3](1, 2, 3) https://azure.microsoft.com/en-us/blog/using-the-retry-pattern-to-make-your-cloud-application-more-resilient/