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Keepalived vs etcd: What are the differences?

Key Differences between Keepalived and etcd

Keepalived and etcd are both widely used in the field of distributed systems for ensuring availability and high reliability. However, they have key differences that set them apart from each other.

  1. Consistency vs High Availability: While etcd focuses on providing a consistent key-value store for distributed systems, Keepalived's primary goal is to ensure high availability of services. Etcd achieves consistency through a distributed consensus algorithm, whereas Keepalived employs mechanisms such as VRRP (Virtual Router Redundancy Protocol) for high availability.

  2. Scope of Usage: While Keepalived is primarily used for managing the high availability of network services such as load balancers, etcd serves a broader range of use cases like service discovery, centralized configuration, and coordination between distributed systems.

  3. Data Persistence: Etcd is designed to provide durable storage of system metadata even in the event of node failures, ensuring the consistency of data across the cluster. In contrast, Keepalived does not focus on data persistence; its main purpose is to handle failover of services between active and backup nodes.

  4. Clustering Approach: Etcd uses a leader-based approach to manage a highly available distributed key-value store, where a leader node is elected to coordinate and replicate data across the cluster. Keepalived, on the other hand, uses a more straightforward passive-active model, where one node remains active while others act as backup nodes.

  5. Wider Ecosystem Integration: Etcd has gained popularity as a key component in cloud-native ecosystems like Kubernetes, where it is used for service discovery, coordination, and configuration management. Keepalived, although it has its own ecosystem of tools, does not have the same level of integration as etcd in the cloud-native landscape.

  6. Consensus Algorithm: Etcd employs a distributed consensus algorithm called Raft, which ensures strong consistency across the cluster by electing leaders and replicating data. Keepalived, being more focused on high availability, does not use a consensus algorithm like Raft.

In summary, etcd focuses on providing a consistent distributed key-value store with strong data persistence and plays a significant role in cloud-native ecosystems. Keepalived, on the other hand, prioritizes high availability of network services and is commonly used for managing failover scenarios.

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Pros of etcd
Pros of Keepalived
  • 11
    Service discovery
  • 6
    Fault tolerant key value store
  • 2
    Secure
  • 2
    Bundled with coreos
  • 1
    Consol integration
  • 1
    Privilege Access Management
  • 1
    Open Source
  • 2
    Load Balancer
  • 2
    Extremely fast (IPVS)
  • 1
    2 nodes HA cluster management
  • 1
    Virtual IP HA with VRRP

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What is etcd?

etcd is a distributed key value store that provides a reliable way to store data across a cluster of machines. It’s open-source and available on GitHub. etcd gracefully handles master elections during network partitions and will tolerate machine failure, including the master.

What is Keepalived?

The main goal of this project is to provide simple and robust facilities for loadbalancing and high-availability to Linux system and Linux based infrastructures.

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What are some alternatives to etcd and Keepalived?
Consul
Consul is a tool for service discovery and configuration. Consul is distributed, highly available, and extremely scalable.
Zookeeper
A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications.
Redis
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
See all alternatives