ETCD is a key-value store used by the CoreOS Linux distributions to store data that needs to be shared across a cluster of machines. It is used for storing things such as configuration files, service discovery information, and other metadata.
ETCD is designed to be highly available and can tolerate network partitions and machine failures. It uses the Raft consensus algorithm to ensure data is replicated across all machines in a cluster.
There are a few reasons you might want to use ETCD:
To store and share configuration files across a cluster of machines
For service discovery (e.g. keeping track of which IP address service is running on)
As a metadata store for Kubernetes or other orchestration frameworks
ETCD stores data in the form of key-value pairs. Keys can be organized into directories (similar to a file system) and each key can have a value associated with it. Values can be anything from simple strings to more complex data structures such as JSON or Protobufs.
ETCD uses the Raft consensus algorithm to ensure that all machines in a cluster have the same data. Raft is a protocol that dictates how data should be replicated across a cluster of machines. It is designed to be tolerant of network partitions and machine failures.
There are a few ways you can interact with ETCD:
Via the command line
Via the HTTP API
Using one of the many language-specific clients (e.g. etcd-java, etcd-go)
One potential drawback of using ETCD is that it is a single point of failure for your cluster. If the machine that is running ETCD goes down, the entire cluster will go down with it.
Another drawback is that ETCD does not support automatic failover. If the machine running ETCD fails, you will need to manually promote another node to be the leader before the cluster can start functioning again.
A distributed system is a network of computers that are located on different physical machines. These computers communicate with each other over a network and work together to perform tasks that cannot be completed by a single computer.
There are many benefits to using a distributed system:
Scalability: A distributed system can scale horizontally, meaning that new nodes can be added to the system as needed. This is in contrast to a centralized system, which would need to be completely rebuilt if more capacity was needed.
- Fault tolerance: A distributed system can tolerate the failure of individual nodes without affecting the availability of the entire system.
- Location independence: Nodes in a distributed system can be located anywhere in the world, which makes them easier to deploy and manage.
- Improved performance: By distributing tasks across multiple nodes, a distributed system can often complete tasks faster than a centralized system.
ETCD uses a simple quorum-based replication model to provide high availability. A quorum is defined as a majority of the servers in the cluster, and all write operations must be accepted by a quorum of servers before they are considered successful. This ensures that data is replicated to a majority of servers before it is considered committed, which prevents data loss in the event of server failures. Read operations can be processed by any server in the cluster, so there is no need for a quorum of servers to be available for reads. This allows ETCD to scale horizontally without sacrificing availability.
The raft consensus algorithm is used to maintain consistency across the cluster.
The RAFT Protocol is a set of rules that govern how message passing works in a distributed system. It is designed to ensure that messages are delivered in the correct order, even if the network is subject to failure or delays. The protocol consists of four main components:
Reliable delivery: Messages are guaranteed to be delivered in the order they are sent, even if individual nodes experience failures. This is achieved through a process of message logging and retransmission.
Atomic broadcast: All nodes in the system receive the same messages in the same order. This ensures that all nodes have consistent information about the state of the system.
Fault tolerance: The system is able to recover from node failures without losing any data. This is achieved by replicating data across multiple nodes.
Performance: The system is designed to minimize latency and maximize throughput. This is achieved by batching messages and using efficient algorithms for message delivery.
The RAFT Protocol has been used in a number of distributed systems, including Apache Kafka, Cassandra, and Hadoop. It is a well-established protocol with a proven track record of reliability and performance.
As anyone who has worked with a distributed system knows, one of the most important considerations is the number of nodes in the system. Too few nodes and the system may be unstable; too many nodes and the overhead of maintaining the system may become prohibitive. For a platform like ETCD, which is responsible for storing data in a distributed fashion, the number of nodes is especially important. In general, it is best to have an odd number of nodes (three, five, seven, etc.) to ensure that there is always a majority in case of any network partitions. In addition, it is generally recommended to have at least five nodes for good performance and stability. Of course, every situation is different, so it is important to consider the specific needs of your application when deciding on the number of nodes for your ETCD deployment.
- Download binaries
curl -LO https://github.com/etcd-io/etcd/releases/download/v3.5.4/etcd-v3.5.4-linux-amd64.tar.gz
tar xzvf etcd-v3.5.4-linux-amd64.tar.gz
- Run ETCD Service
- Download binaries
curl -LO https://github.com/etcd-io/etcd/releases/download/v3.5.4/etcd-v3.5.4-darwin-amd64.zip
- Run ETCD Service
cd etcd-v3.5.4-darwin-amd64 ./etcd
./etcdctl put key1 value1
./etcdctl get key1