Amazon QLDB vs Cassandra

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Amazon QLDB

Stacks5

Followers17

Votes0

Amazon QLDB vs Cassandra: What are the differences?

Introduction

This Markdown code provides a comparison between Amazon QLDB and Cassandra, highlighting their key differences.

Scalability and Availability: Amazon QLDB is a fully managed service that automatically scales to accommodate customers' demands, ensuring high availability and performance. On the other hand, Cassandra relies on a peer-to-peer architecture, allowing it to scale horizontally across multiple nodes for increased throughput and fault tolerance.
Consistency Model: Amazon QLDB follows an ACID-compliant consistency model, providing strong consistency guarantees for transactions. It ensures that all reads observe the latest committed state. In contrast, Cassandra offers tunable consistency, allowing users to choose between consistency and availability based on their application requirements.
Data Model: Amazon QLDB adopts a document-like model, where data is stored as structured documents called "tables." It supports SQL-like queries and provides efficient indexing and filtering of data. On the other hand, Cassandra employs a columnar data model, storing data in tables consisting of columns and rows. It offers a flexible schema, allowing easy addition or removal of columns.
Data Replication: Amazon QLDB automatically replicates data with built-in data durability and automatic failover, ensuring data is preserved even in the event of hardware failures. Cassandra, on the other hand, allows users to configure data replication strategies to achieve fault tolerance and resilience. Users have more control over how and where data is replicated.
Transaction Support: Amazon QLDB provides built-in support for multi-document ACID transactions, ensuring data consistency and integrity. It allows users to group multiple actions into a transaction and roll back changes if necessary. In Cassandra, transactions are not natively supported, and users need to implement custom strategies to achieve similar functionality.
Query Language: Amazon QLDB uses PartiQL, which is a SQL-compatible query language. It allows users to perform SQL-like queries on the structured data stored in QLDB tables. Cassandra, on the other hand, uses CQL (Cassandra Query Language), which is specifically designed for Cassandra's columnar data model. CQL is similar to SQL but has some distinct syntax and functionalities.

In summary, Amazon QLDB offers a fully managed, scalable, and highly available service with strong consistency guarantees and built-in support for transactions. It has a document-like data model and uses PartiQL as the query language. On the other hand, Cassandra provides a peer-to-peer architecture with tunable consistency, a columnar data model, customizable data replication strategies, custom transaction implementations, and uses CQL as the query language.

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Advice on Cassandra, Amazon QLDB

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

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.

174k views174k

Comments

Detailed Comparison

Cassandra	Amazon QLDB
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.	It is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log ‎owned by a central trusted authority. It can be used to track each and every application data change and maintains a complete and verifiable history of changes over time.
-	Immutable and Transparent; Cryptographically Verifiable; Serverless; Easy to Use; Streaming Capability
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 5
Followers 3.5K	Followers 17
Votes 507	Votes 0
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	No community feedback yet
Integrations
No integrations available	AWS Lambda Amazon Redshift Amazon Kinesis Amazon Elasticsearch Service

What are some alternatives to Cassandra, Amazon QLDB?

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.

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.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Amazon QLDB vs Cassandra: What are the differences?

Introduction

This Markdown code provides a comparison between Amazon QLDB and Cassandra, highlighting their key differences.

Scalability and Availability: Amazon QLDB is a fully managed service that automatically scales to accommodate customers' demands, ensuring high availability and performance. On the other hand, Cassandra relies on a peer-to-peer architecture, allowing it to scale horizontally across multiple nodes for increased throughput and fault tolerance.
Consistency Model: Amazon QLDB follows an ACID-compliant consistency model, providing strong consistency guarantees for transactions. It ensures that all reads observe the latest committed state. In contrast, Cassandra offers tunable consistency, allowing users to choose between consistency and availability based on their application requirements.
Data Model: Amazon QLDB adopts a document-like model, where data is stored as structured documents called "tables." It supports SQL-like queries and provides efficient indexing and filtering of data. On the other hand, Cassandra employs a columnar data model, storing data in tables consisting of columns and rows. It offers a flexible schema, allowing easy addition or removal of columns.
Data Replication: Amazon QLDB automatically replicates data with built-in data durability and automatic failover, ensuring data is preserved even in the event of hardware failures. Cassandra, on the other hand, allows users to configure data replication strategies to achieve fault tolerance and resilience. Users have more control over how and where data is replicated.
Transaction Support: Amazon QLDB provides built-in support for multi-document ACID transactions, ensuring data consistency and integrity. It allows users to group multiple actions into a transaction and roll back changes if necessary. In Cassandra, transactions are not natively supported, and users need to implement custom strategies to achieve similar functionality.
Query Language: Amazon QLDB uses PartiQL, which is a SQL-compatible query language. It allows users to perform SQL-like queries on the structured data stored in QLDB tables. Cassandra, on the other hand, uses CQL (Cassandra Query Language), which is specifically designed for Cassandra's columnar data model. CQL is similar to SQL but has some distinct syntax and functionalities.

Amazon QLDB vs Cassandra

Overview

Amazon QLDB vs Cassandra: What are the differences?