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Cassandra vs etcd: What are the differences?

Key Differences between Cassandra and etcd

Cassandra and etcd are two popular distributed databases used for different purposes. While both are designed to handle large amounts of data, they have several key differences:

  1. Data Model: Cassandra is a wide-column store database that uses a schema-free approach, allowing for flexible data structures. On the other hand, etcd is a key-value store database that stores data as key-value pairs, making it more suitable for simpler data structures.

  2. Consistency Model: Cassandra uses eventual consistency, which means that updates may take some time to propagate throughout the system, leading to potential data conflicts. In contrast, etcd offers strong consistency, ensuring that updates are visible to all nodes immediately, reducing the chances of conflicts.

  3. Partitioning: Cassandra uses consistent hashing to distribute data across multiple nodes, allowing for horizontal scaling and fault tolerance. In comparison, etcd uses range partitioning, where data is divided into ranges and stored in different nodes based on the key's range, providing efficient read and write operations.

  4. Concurrency Control: Cassandra utilizes a last-write-wins conflict resolution strategy, where the latest update to a data record is considered the valid one. In contrast, etcd supports optimistic concurrency control, allowing multiple clients to perform concurrent updates and resolving conflicts based on timestamps.

  5. Queries and Indexing: Cassandra supports a query language called CQL (Cassandra Query Language), which provides a SQL-like interface for querying data. Additionally, Cassandra allows indexing on different columns to optimize search performance. On the other hand, etcd does not support complex queries or indexing; it primarily focuses on fast key-value lookups.

  6. Distributed Consensus: Cassandra uses a decentralized peer-to-peer architecture with no single point of failure, achieving fault tolerance through data replication across multiple nodes. In contrast, etcd utilizes the Raft consensus algorithm, where a leader node coordinates updates and ensures consistency among the cluster members.

In summary, Cassandra and etcd differ in their data models, consistency models, partitioning strategies, concurrency control mechanisms, query capabilities, and distributed consensus approaches. These distinctions make each database suitable for specific use cases and requirements.

Advice on Cassandra and etcd
Vinay Mehta
Needs advice
on
CassandraCassandra
and
ScyllaDBScyllaDB

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

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Replies (4)
Recommends
on
ScyllaDBScyllaDB

Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring

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Alex Peake
Recommends
on
CassandraCassandra

Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.

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Recommends
on
ScyllaDBScyllaDB

By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.

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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 146K views
Recommends
on
CassandraCassandra

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

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Pros of Cassandra
Pros of etcd
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
    OLTP
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
    Fast
  • 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

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Cons of Cassandra
Cons of etcd
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
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    - No public GitHub repository available -

    What is 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.

    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.

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    What are some alternatives to Cassandra and etcd?
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    Google Cloud Bigtable
    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    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.
    Couchbase
    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
    See all alternatives