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Amazon DynamoDB

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Advice on Amazon DynamoDB, Cassandra, and HBase
Vinay Mehta
Needs advice
on
Scylla
and
Cassandra

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)
Alex Peake
Recommends
Cassandra

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
Scylla

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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Recommends
Scylla

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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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 50.6K views
Recommends
Cassandra

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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Decisions about Amazon DynamoDB, Cassandra, and HBase
emile salem

6 months ago we finished migrating the Workjam channels module datastore. Reasons for the switch was frustrations with AWS read/write capacities being frequently exceeded because of unplanned explosive growth, hard limitations on batch updates and interesting Cassandra features such as consistency tuning and Datastax's Solr integration. The decision to use Cassandra might not have been the most practical one as our needs would probably have been better served by a document store such as MongoDB, as we are not dealing with intense document update operations, but Cassandra was used throughout the company and the aim of stack uniformity was favoured over functional needs. We implemented the migration with the aim of incurring no downtime and the ability to rollback by sending write commands over AMQP. Overall the migration went smoothly, the devs learned all the powers of Cassandra and got acquainted with its many constraints. Datastax's Solr integration made the search implementation very simple but we were very disappointed in some of Datastax's Solr integration limitations (ex: search highlights being deprecated).

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Pros of Amazon DynamoDB
Pros of Cassandra
Pros of HBase
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
  • 3
    No sql
  • 3
    To store data
  • 2
    Serverless
  • 2
    No Stored procedures is GOOD
  • 1
    ORM with DynamoDBMapper
  • 1
    Elastic Scalability using on-demand mode
  • 1
    Elastic Scalability using autoscaling
  • 1
    DynamoDB Stream
  • 112
    Distributed
  • 93
    High performance
  • 80
    High availability
  • 74
    Easy scalability
  • 52
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 8
    OLTP
  • 7
    Schema optional
  • 6
    Open source
  • 2
    Workload separation (via MDC)
  • 9
    Performance
  • 5
    OLTP
  • 1
    Fast Point Queries

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Cons of Amazon DynamoDB
Cons of Cassandra
Cons of HBase
  • 4
    Only sequential access for paginate data
  • 1
    Scaling
  • 1
    Document Limit Size
  • 2
    Reliability of replication
  • 1
    Updates
    Be the first to leave a con

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    - No public GitHub repository available -

    What is Amazon DynamoDB?

    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

    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 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.

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    Jun 24 2020 at 4:42PM

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    What are some alternatives to Amazon DynamoDB, Cassandra, and HBase?
    Google Cloud Datastore
    Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.
    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.
    Amazon SimpleDB
    Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.
    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.
    Amazon S3
    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
    See all alternatives