The terms scalability and high availability (HA) have never been more popular than now when there is a rising demand for stable and performance infrastructures designed to serve critical systems. While increasing system load is a common concern, reducing downtime and eliminating individual failure points are equally critical. High availability is a characteristic of large-scale infrastructure planning that takes these factors into account.
What is high availability?
High availability is often synonymous with high availability systems, high availability environments or high availability servers. Simply put, when some of the components of your IT infrastructure fail, high availability enables the system to maintain functionality.
High availability is critical for mission-critical systems, as service disruption can hurt the business, leading to increased expenses or financial losses. While high availability may not completely eliminate the risk of system downtime, it does ensure that the IT team takes all necessary precautions to ensure business continuity.
Why is high availability important?
Today, downtime and disruptions equate to loss of income. From a business standpoint, high availability has become too critical. Customers are frustrated when services go down, which can lead loyal customers to look for alternatives and use competing services.
IT teams always work to reduce downtime and ensure system availability at all times. Downtime can have a wide range of consequences, including loss of productivity, missed business opportunities, data loss and a damaged brand image.
Businesses value high availability because it makes their services more reliable. Unexpected situations can cause even the most reliable systems and servers to fail. Therefore, it is essential to use high availability to reduce service interruptions, outages and downtime. Highly available systems can recover from data loss and server crashes automatically.
One of the many reasons for high availability is to avoid downtime. Here are some of the other reasons:
Ensuring information security – By reducing system downtime through high availability, you can dramatically reduce the likelihood of accessing your essential business data or being illegally acquired.
Management of SLAs Maintaining operation time is a must for managed service providers who want to provide quality service to their customers. Managed service providers can use high-availability solutions to ensure that they meet their SLA 100 percent of the time and that their customers’ networks do not go down.
Maintaining a brand reputation – System availability is a key to the quality of your service. As a result, managed service providers can take advantage of high availability environments to ensure system uptime and establish a strong brand in the market.
What makes a system particularly available?
Eliminating individual failure points in your infrastructure is one of the goals of high availability. A single failure point is a component in your technology stack that, if not available, will cause a service outage. As a result, any component required for the proper but redundant functionality of your app is considered as a single failure point.
There are no single failure points
Each layer of your stack should be prepared for excess to avoid individual failure points. Consider the following scenario: You have a load balance and two identical, redundant web servers. The client traffic will be evenly distributed among the web servers, but if one of them goes down, the load balance will redirect all the traffic to the remaining online server.
In this situation, the web server layer is not a single failure point because unnecessary components for the same task are available. The load balance, which sits on this layer, can detect component failures and change its behavior to ensure rapid recovery.
Hardware redundancy
Building two or more computer systems or physical copies of a hardware component can ensure redundancy. The system can have redundant servers, power supply, memory and other components. You can reduce the risk of interruption in the event of higher loads by using redundancies in high-level components.
Reliable crossover
When a server breaks or stops responding, the reliable crossover must be assembled to ensure that failure systems take over. This is another component of redundancy implementation in HA systems. This allows a backup component to replace a failed component. A reliable crossover is a process of correctly moving from one component to another without losing data or affecting performance.
Software and application redundancy
Software and application redundancy functions similarly to hardware redundancy in that it performs the same function by running different parts if another instance is affected. In high availability engineering, fault tolerance and reliability, this is a critical concept. It also includes self-healing programs. Applying redundancy ensures that it meets reliability targets while maintaining technological limitations.
Data redundancy
Data redundancy is guaranteed through a high availability system, meaning the same data is stored in multiple locations. This reduces the risk of data loss and ensures that the data can be recovered if one of the memory locations or servers fails. It also allows for the correction of inaccuracies in data transmitted or stored.
Self-monitoring of failure
Highly available systems contain self-healing and self-monitoring capabilities that can detect abnormal fault rates or damaged cases. This ensures that the error is detected and corrected immediately, with minimal impact on system performance. Your operating time will increase as your self-monitoring feature becomes more effective.
SCAND best practices
Process analysis needs are the first step in building a high availability system. This includes identifying key processes, the types of data they interact with, how data should be transferred, stored and their retention period.
The first step in designing the system is defining the domains. A domain identifies a single service or subset of a service. Processor- and memory-intensive tasks, necessary communication, and integrations with defined third-party systems are then determined. It is also identified where synchronous and asynchronous communication will take place. The system architecture is created with the capabilities of hosting providers in mind. Undecided whether original cloud services or a third-party solution (for example, Kafka vs. Google Pub / Sub) would be a great choice.
The minimum architecture capable of handling high availability should consist of a set of duplicate nodes / services (at least two copies, as required by PCI-DSS), load balancing for routing nodes, a failed database, and a monitoring facility capable of detecting environmental issues .
Message queues and message queues are used when direct synchronous HTTP communication between services is not provided and data processing is distributed. By delegating messages to each service copy, MQ makes it easy to properly handle competing resources. When event streaming is used, MQ brokers assist in the development of a powerful distributed system. This makes it possible to clearly separate operations like recording, monitoring and collecting analysis in settings. It also allows all subscriber services to keep track of the most up-to-date sections of data they require and create scalable processing clusters.
Depending on the needs of the project, fast memory storage like Redis, Memcached can be introduced to be able to maintain quick access to key-value features like tokens or JSON processed user interface parts and data structures.
Kubernetes technology enables container monitoring and orchestration in zero-confidence environments. It helps to identify broken containers and replace them in a matter of minutes, helps to increase horizontally with new replicas of services that help maintain load dispersal among stateless clones.
The database is created with a failover capable of replacing the main instance immediately at the hardware level in the event of a failure. In order to be able to work in high load conditions, we create read-only copies of an initial write-only database. Data processing is divided into main DB writing operations and query operations that work with multiple database clones (CRQS). In case of failure in the main database, one of the copy copies becomes the main one. In the event of a read copy failure, a new one is created and synchronized to be updated using a specific time interval before airing.
To work with a higher load identified by PCI-DSS Level 1 (over 6 million transactions per year), processing clusters are created that work with their own unique data partitions (services and databases). The data cluster is identified by the incoming request parameters (or by a user token) in front of the most efficient API gateway layer. Consumption processes such as collecting statistics and producing reports are carried out simultaneously in separate clusters.
The availability of services is monitored through built-in cloud hosting or stand-alone professional solutions such as TICK, ELK stacks that allow you to collect large sections of logs, metrics and use various alert channels to run incidents to the technical support team (email, SMS, slack, telegram, etc.)
Development teams work on service databases, branches of which are used to update the development / phase / production environment manually or automatically by commits (optional with tags). The main development principle is to keep the CVS industry working and stable (CI). Before moving on to the object construction phase, unit tests / regression / automation / pressure / security are activated in the piping. If the system does not meet the metrics identified with the stress tests, the delivery is canceled and returned to the developers. Stress tests operate in CI / CD piping and consist of scenarios written using the following common tools: JMeter, artillery.io, k6, Gatling and others.
We provide a smooth system update without downtime using CD principles. We recommend the following deployment strategies: reincarnation, in which new versions of containers operate continuously and old versions are gradually replaced; canary, where the launch of a new version is controlled for a subset of users and is published in full after the tests are completed (check out our software quality assurance services). Delivery can be made manually or automatically. Depending on the complexity of database transfers, this can be done immediately or gradually.
The most important thing in HA is to make sure that all the technical means are still working and protecting you from risks. According to regulations or not, our customers need to get HA maintenance policy.
According to our instruction the following approach is usually applied:
- Policies on how staff should act in different infrastructure disaster scenarios;
- A policy that determines how often, which team should conduct HA tests; How the process should be documented, and what / how messages should be saved;
- Disaster recovery tests;
- Recovery training.
In short, we make sure infrastructure disasters are known, described and the operational team knows what to do to get into SLA requirements.
The test is done by simulating different breakout scenarios: service level, DB level, domain level, etc. Test sessions are held for each component or platform at once. A short-term environment is used for this purpose.
Summary
High availability is no longer a luxury in today’s competitive market. Failure of critical IT systems can result in significant costs for the company, ranging from a decline in user productivity to a loss of revenue and trust on the part of customers. As a result, IT and business leaders in SMEs must prioritize high availability for core applications.
High-availability infrastructure consists of hardware, software and applications designed to recover quickly in the event of a malfunction and maintain functionality with little downtime. A company with a well-defined set of high-availability best practices, including high-availability analysis frameworks, business drivers and system capabilities, will have operational resilience and better business agility.