Apache Ignite vs Cassandra

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Apache Ignite

Stacks110

Followers168

Votes41

GitHub Stars5.0K

Forks1.9K

Apache Ignite vs Cassandra: What are the differences?

Introduction

Apache Ignite and Cassandra are both popular open-source distributed databases that are commonly used in modern web applications. While they share some similarities, there are several key differences between them. In this article, we will explore the main differences between Apache Ignite and Cassandra.

Data Structure: One of the primary differences between Apache Ignite and Cassandra lies in their data structure. Apache Ignite is an in-memory computing system that stores data in a distributed in-memory cache. This allows for extremely fast read and write operations. On the other hand, Cassandra is a distributed database that stores data on disk, offering high scalability and fault tolerance.
Consistency Model: Another important difference is their consistency model. Apache Ignite provides strong consistency by default, ensuring that all reads and writes are immediately reflected across the cluster. Cassandra, on the other hand, offers tunable consistency, allowing developers to choose between strong consistency, eventual consistency, or something in between.
Query Languages: Apache Ignite supports SQL as its primary query language, allowing developers to interact with the data using familiar SQL syntax. Cassandra, on the other hand, uses its own query language called CQL (Cassandra Query Language), which is similar to SQL but has some differences. This difference in query languages can affect the ease of development and migration of applications.
Fault Tolerance: In terms of fault tolerance, both Apache Ignite and Cassandra are designed to handle failures and ensure data integrity. However, their approaches differ. Apache Ignite uses a replication-based approach, where data is replicated across multiple nodes to ensure redundancy and availability. Cassandra, on the other hand, uses a distributed peer-to-peer architecture with a tunable consistency model, allowing it to achieve fault tolerance and high availability.
Data Model: Apache Ignite supports a wide variety of data models, including key-value, SQL, and file-based models. This allows developers to choose the data model that best fits their application requirements. Cassandra, on the other hand, is primarily a wide-column store that is optimized for write-heavy workloads with a flexible schema.
Secondary Indexes: Lastly, Apache Ignite provides support for secondary indexes, allowing developers to index and query non-primary key columns efficiently. Cassandra, on the other hand, does not natively support secondary indexes. Instead, developers need to use workarounds or third-party tools to achieve similar functionality.

In summary, Apache Ignite and Cassandra have several key differences. Apache Ignite is an in-memory computing system with strong consistency and SQL support, while Cassandra is a distributed database optimized for high scalability and fault tolerance with tunable consistency. Apache Ignite supports a wider variety of data models and provides native support for secondary indexes, whereas Cassandra excels in write-heavy workloads and offers flexible schemas. The choice between these two databases depends on the specific requirements and use case of the application.

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Advice on Cassandra, Apache Ignite

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	Apache Ignite
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 memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale
-	Memory-Centric Storage; Distributed SQL; Distributed Key-Value
Statistics
GitHub Stars 9.5K	GitHub Stars 5.0K
GitHub Forks 3.8K	GitHub Forks 1.9K
Stacks 3.6K	Stacks 110
Followers 3.5K	Followers 168
Votes 507	Votes 41
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	Pros 5 High Avaliability 5 Multiple client language support 5 Free 5 Written in java. runs on jvm 4 Rest interface
Integrations
No integrations available	MongoDB MySQL Apache Spark

What are some alternatives to Cassandra, Apache Ignite?

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.

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.

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.

Related Comparisons

Apache Ignite vs Cassandra: What are the differences?

Introduction

Data Structure: One of the primary differences between Apache Ignite and Cassandra lies in their data structure. Apache Ignite is an in-memory computing system that stores data in a distributed in-memory cache. This allows for extremely fast read and write operations. On the other hand, Cassandra is a distributed database that stores data on disk, offering high scalability and fault tolerance.
Consistency Model: Another important difference is their consistency model. Apache Ignite provides strong consistency by default, ensuring that all reads and writes are immediately reflected across the cluster. Cassandra, on the other hand, offers tunable consistency, allowing developers to choose between strong consistency, eventual consistency, or something in between.
Query Languages: Apache Ignite supports SQL as its primary query language, allowing developers to interact with the data using familiar SQL syntax. Cassandra, on the other hand, uses its own query language called CQL (Cassandra Query Language), which is similar to SQL but has some differences. This difference in query languages can affect the ease of development and migration of applications.
Fault Tolerance: In terms of fault tolerance, both Apache Ignite and Cassandra are designed to handle failures and ensure data integrity. However, their approaches differ. Apache Ignite uses a replication-based approach, where data is replicated across multiple nodes to ensure redundancy and availability. Cassandra, on the other hand, uses a distributed peer-to-peer architecture with a tunable consistency model, allowing it to achieve fault tolerance and high availability.
Data Model: Apache Ignite supports a wide variety of data models, including key-value, SQL, and file-based models. This allows developers to choose the data model that best fits their application requirements. Cassandra, on the other hand, is primarily a wide-column store that is optimized for write-heavy workloads with a flexible schema.
Secondary Indexes: Lastly, Apache Ignite provides support for secondary indexes, allowing developers to index and query non-primary key columns efficiently. Cassandra, on the other hand, does not natively support secondary indexes. Instead, developers need to use workarounds or third-party tools to achieve similar functionality.

Apache Ignite vs Cassandra

Overview

Apache Ignite vs Cassandra: What are the differences?