Cassandra vs Google Cloud Bigtable

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Cassandra vs Google Cloud Bigtable: What are the differences?

Introduction

Cassandra and Google Cloud Bigtable are both NoSQL databases, designed for handling large amounts of data with high scalability and performance. However, they have some key differences that set them apart from each other.

  1. Data Model: Cassandra uses a wide-column data model, which allows for flexible schema and dynamic addition of columns. On the other hand, Google Cloud Bigtable utilizes a sparse, distributed, persistent multidimensional sorted map data model, where data is structured in rows and columns similar to a traditional database table.

  2. Consistency Model: Cassandra offers tunable consistency, where users can choose between strong consistency or eventual consistency based on their requirements. In contrast, Google Cloud Bigtable provides only eventual consistency, which means that data may be inconsistent for a brief period of time before it becomes consistent across all nodes.

  3. Concurrency Control: Cassandra uses a distributed versioning approach known as "last write wins" to handle conflicts during concurrent updates. In contrast, Google Cloud Bigtable relies on optimistic concurrency control, where concurrent requests are allowed and conflicts are detected and resolved based on timestamps.

  4. Storage Architecture: Cassandra employs a distributed, peer-to-peer architecture where data is distributed across a cluster of nodes. Data in Cassandra is stored in memory and disk-based data structures, with built-in support for replication and fault-tolerance. On the other hand, Google Cloud Bigtable utilizes a distributed file system called Colossus, where data is stored in a hierarchical structure of tablets for efficient storage and retrieval.

  5. Query Language: Cassandra uses its own query language called CQL (Cassandra Query Language), which is similar to SQL but with some differences. Google Cloud Bigtable, on the other hand, does not provide a query language out of the box. Instead, it encourages the use of client libraries and frameworks to interact with the database.

  6. Scaling: Both Cassandra and Google Cloud Bigtable are designed for horizontal scalability. However, Cassandra offers automatic data partitioning and distribution across nodes for seamless scalability. Google Cloud Bigtable, on the other hand, requires manual sharding and management of tablets to achieve scalability.

In summary, Cassandra and Google Cloud Bigtable differ in their data model, consistency model, concurrency control, storage architecture, query language, and scaling mechanisms.

Advice on Cassandra and Google Cloud Bigtable
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 · 145.2K 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 Google Cloud Bigtable
  • 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
    High performance
  • 9
    Fully managed
  • 5
    High scalability

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Cons of Cassandra
Cons of Google Cloud Bigtable
  • 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 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.

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    What companies use Cassandra?
    What companies use Google Cloud Bigtable?
    See which teams inside your own company are using Cassandra or Google Cloud Bigtable.
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    What tools integrate with Cassandra?
    What tools integrate with Google Cloud Bigtable?

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    What are some alternatives to Cassandra and Google Cloud Bigtable?
    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.
    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.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    See all alternatives