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With the rise of a distributed system, the organization has one more concern to handle and it’s reducing the incidence of server failure in a multi-server ecosystem while having a robust key storage solution. etcd shows us as a viable solution here. Often known as the backbone of all the leading distributed platforms, etcd is a fault-tolerant resource resolving tons of hassles. Let’s explore more about it.
There are multiple attention-worthy concepts as one deals with distributed systems. The distributed key holds the greatest significance as it plays a crucial role in connecting distributed systems. etcd exists wherever distributed keys are present and acts like a highly –viable distributed key –value.
This is an open-source store critical to store and manage every bit of data/information that a distributed system will require to remain functional. In general, it deals with state, meta, and configuration data for k8s.
Be it containerized or distributed workloads, they both have steep management curves as the workloads tend to become complex with each scale. Here, Kubernetes is a great option as it simplifies these resource handling by coordinating effectively between key operational fronts like load-balancing, health auditing, job scheduling, service discovery, and so on.
While handling all these, Kubernetes is something that can easily manage all of the concerned pods and clusters while acting like a single source of truth displaying real-time system state. etcd is just that resource. Here, everything that Kubernetes requires is to achieve coordination in the distributed network.
Along with Kubernetes, etcd does the same job for Cloud Foundry and can be easily used in any other distributed system that demands constant coordination between cluster metadata spread across the system.
As far its name’s reference is concerned, d here denotes ‘distributed’. As the name is based on Linux directory structure, etc denotes that the config. file is stored in the “/etc;” folder.
etcd tuning is the default setting supporting its installation on a local network in case of low latency. When etcd is used on a network with high latency, etcd tuning is required for internal timeout and heartbeat interval. In Docker, the etcd Docker server runs inside a container and is accessible via the etcd client.
One might tend to think that why only etcd? There could be other key store systems as well. However, as you will know more about etcd, you’ll get to learn that etcd is called the spine of a distributed system. Reasons for this are very legit:
It’s a highly replicated resource ensuring that every concerned node is capable of accessing the database completely: with no exceptions.
It has almost zero downtime and comes with great availability. Under no conditions, it will be unavailable. It’s designed to be highly fault-tolerant and keep on working even when there is hardware or network failure.
With Kubernetes etcd, it’s possible to have your hands on only updated information as every data reads the information available and delivers the updated information. The great thing is that this updated data is written over any single cluster.
It’s a speedy resource as it has been observed that it managed to write 10,000 data per second.
As long as K8s etcd is in use, one doesn’t have to worry about API security. With compatibility with SSL and TLS encryption, it promises to keep data theft under control. Need more data security? Don’t worry as you can easily apply access control.
etcd is so simple to use that it has become a part of web applications, mobile applications, and container orchestration engines. Whether you’re developing a SPA or using Kubernetes, etcd permits you to read and write data with any standard tool. This simplicity almost meant that developers of all technical competencies could work with it.
While you plan to use it, you must understand one aspect of storage: disk speed has a high influence on etcd performance. Better disk storage speed means high performance. Hence, it’s highly recommended to use an SSD.
CoreOS, history and etcd support
CoreOS is closely linked with etcd as the same team developed these two tools. Originally, etcd was built on Raft with the sole aim of easy coordination between multiple Container Linux copies so that applications have continuous runtime.
After the early years, etcd was handed over to CNCF so that container-based cloud development can be simplified for everyone. On the other hand, CoreOS merged with Red Hat.
Etcd and Kubernetes
As mentioned above, etcd is one of many fundamental Kubernetes components. Here, it acts like a customary key-value store resource that aids greatly in constructing highly functional Kubernetes clusters. Effectively, every cluster’s state information is stored in etcd via k8s API server.
Kubernetes monitors the data using the ‘watch’ function of etcd. Also, the ‘watch’ function is useful for Kubernetes reconfiguration in case of any changes.
The etcd Operator
It is the human operational knowledge used to ease down the etcd usage on Kubernetes. In addition, it works on a container platform. It manages etcd as per the guidelines or instructions of Operator Framework that explains the strategy to remove complexities from etcd management and configurations.
With one-command installation, etcd Operator uses a unified declarative configuration. It inherits below-mentioned features.
etcd Operator constantly takes backup in real time at regular intervals. However, users have to define the backup policies according to their needs and the requirements.
etcd Operator allows users to specify the cluster size once and experience uniform configuration settings.
Resizing is easy as changing specs in settings is enough to apply new change details in development, destruction, and redesigning.
Without any downtime, etcd Operator permits etcd upgrades.
Operator in action
The use of the etcd Operator makes etcd usage simpler than ever, as explained above. How does it achieve this? Basically, the process is based on approaches.
The Observation involves close monitoring of the present cluster state with the help of Kubernetes API.
Differentiating means finding differences between past and present cluster states.
Lastly, the Act that involves resolving the differences using API of various kinds like k8s API and etcd cluster management API.
Raft consensus algorithm
The foundation of etcd lies upon the Raft consensus algorithm when it comes to ensuring data storage consistency on all the involved nodes. Now, let’s try to understand the core functionality of the etcd Raft algorithm.
It uses a selected leader node for creating and managing follower node replications within a cluster. The leader receives requests from the clients and forwards them to the related followers. Each follower node forwarding is logged and when the leader finds that most of these nodes have the latest data, it takes and writes the data on the client’s side, as instructed.
In case of any crash or network packet misplacement, the leader will become inactive until every single concerned follower node features updated logs.
Suppose there are some issues in receiving the leader message for a specific time, the algorithm considers it as a leader failure incidence and starts looking for a new leader, for which an election is conducted.
The concerned follower will also prove their candidacy as a leader. As soon as a new leader is appointed, replication management starts and continues in a cycle.
This way, continuous etcd availability is ensured.
etcd vs. Redis
Both are well-known open-source resources having distinctive functionalities. For instance, Redis can also store in-memory data but etcd Kubernetes stores every cluster-crucial data. On a deeper level, Redis works as a message broker, cache, and database. etcd is always a key store for distributed systems.
Expansion capability-wise, Redis is more flexible as it supports multiple data varieties and structures. When fault tolerance is concerned, definitely etcd is a better performing option. Additionally, it supports continuous data availability.
They both have different key usages. Try Redis for distributed memory caching systems and Kubernetes etcd for distributed systems.
ZooKeeper vs. Consul vs. etcd
As all these three terms are part of distributed systems, it’s obvious that they will have overlapping characteristics. Here, we’re helping you to spot the key differences.
When ZooKeeper was created, its key functionality was to help coordinate the metadata and configuration data for Apache Hadoop clusters. It came into being before etcd and played a crucial role in its development.
Lessons learned for Zookeeper become the forming elements for etcd clusters. Hence, etcd is often considered an advanced version of Zookeeper. The only difference here is that Zookeeper is used for Apache while etcd deals in Kubernetes mainly.
It’s easy to attain dynamic reconfiguration with etcd but Zookeeper doesn’t support this. Stability-wise, etcd is more stable and performs perfectly fine when the traffic load is high.
Zookeeper supports only a few custom Jute RPC protocols but etcd is highly flexible. It offers support for a good number of frameworks as well as languages.
Next is Consul v/s etcd. Consul is entirely different from etcd as it’s the dedicated service networking solution. You may consider it more capable than Istio. However, on the other hand, etcd is more efficient than it.
Even though Consul is also based on the Raft algorithm and features a key-value store, it’s not as strong as etcd when controlling comes.
The surged use of distributed systems has given a new vertical to software development. etcd is a key resource to use in Kubernetes and multiple other distributed systems. Acting like a distributed key store for this use scenario, etcd plays a crucial role to connect disintegrated systems.
It’s fast, highly secure, and always ready to help you. Because of all these features, etcd Github is a growing community. As it’s an open-source tool and has an easy learning curve, developers of all sorts can use it in their distributed system development projects.