Aerospike vs Cassandra

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Aerospike

Stacks200

Followers288

Votes48

GitHub Stars1.3K

Forks196

Aerospike vs Cassandra: What are the differences?

Introduction

Aerospike and Cassandra are both popular distributed database management systems, but they have key differences that set them apart. In this article, we will outline six key differences between Aerospike and Cassandra.

Data Model: Aerospike follows a key-value data model, where each record is identified by a unique key and can store a value with multiple bins containing various data types. On the other hand, Cassandra uses a wide column data model, where data is organized into rows, each of which contains multiple columns grouped together in a column family.
Consistency Model: Aerospike offers strong consistency by default, ensuring that all replicas of a record are up-to-date before acknowledging a write operation. Cassandra, on the other hand, provides eventual consistency, where updates are propagated asynchronously to replicas, allowing for higher availability and partition tolerance at the cost of potential data inconsistency.
Partitioning Strategy: Aerospike employs a shared-nothing architecture with automatic data partitioning across nodes, allowing for linear scalability and performance. Cassandra also utilizes a shared-nothing architecture but provides configurable partitioning strategies, including random, byte order, and key-based partitioners, enabling more flexibility in data distribution.
Secondary Indexing: Aerospike supports both primary and secondary indexing, allowing queries to efficiently retrieve records based on secondary attributes. Cassandra, on the other hand, does not natively support secondary indexes, requiring the use of external tools like Apache Lucene or Elasticsearch for efficient querying on non-primary keys.
Data Distribution: Aerospike ensures the replication factor and distribution of data across nodes based on a configurable policy, providing fault tolerance and high availability. In contrast, Cassandra utilizes consistent hashing to determine the placement of data replicas in a ring-like architecture, allowing for balanced data distribution among nodes.
Write Performance: Aerospike is optimized for high write throughput, providing sub-millisecond latency and predictable performance for write-intensive workloads. Cassandra, on the other hand, excels in high read and write throughput scenarios, especially in multi-data center environments, where its asynchronous replication and tunable consistency levels provide excellent scalability.

In Summary, Aerospike and Cassandra differ in their data models, consistency models, partitioning strategies, secondary indexing capabilities, data distribution mechanisms, and write performance characteristics. Each database has its strengths and use cases, depending on the specific requirements of the application.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Cassandra, Aerospike

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	Aerospike
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.	Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.
-	99% of reads/writes complete in under 1 millisecond.;Predictable low latency at high throughput – second to none. Read the YCSB Benchmark.;The secret sauce? A thousand things done right. Server code in ‘C’ (not Java or Erlang) precisely tuned to avoid context switching and memory copies. Highly parallelized multi-threaded, multi-core, multi-cpu, multi-SSD execution.;Indexes are always stored in RAM. Pure RAM mode is backed by spinning disks. In hybrid mode, individual tables are stored in either RAM or flash.
Statistics
GitHub Stars 9.5K	GitHub Stars 1.3K
GitHub Forks 3.8K	GitHub Forks 196
Stacks 3.6K	Stacks 200
Followers 3.5K	Followers 288
Votes 507	Votes 48
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 16 Ram and/or ssd persistence 12 Easy clustering support 5 Easy setup 4 Acid 3 Scale

What are some alternatives to Cassandra, Aerospike?

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

Aerospike vs Cassandra: What are the differences?

Introduction

Data Model: Aerospike follows a key-value data model, where each record is identified by a unique key and can store a value with multiple bins containing various data types. On the other hand, Cassandra uses a wide column data model, where data is organized into rows, each of which contains multiple columns grouped together in a column family.
Consistency Model: Aerospike offers strong consistency by default, ensuring that all replicas of a record are up-to-date before acknowledging a write operation. Cassandra, on the other hand, provides eventual consistency, where updates are propagated asynchronously to replicas, allowing for higher availability and partition tolerance at the cost of potential data inconsistency.
Partitioning Strategy: Aerospike employs a shared-nothing architecture with automatic data partitioning across nodes, allowing for linear scalability and performance. Cassandra also utilizes a shared-nothing architecture but provides configurable partitioning strategies, including random, byte order, and key-based partitioners, enabling more flexibility in data distribution.
Secondary Indexing: Aerospike supports both primary and secondary indexing, allowing queries to efficiently retrieve records based on secondary attributes. Cassandra, on the other hand, does not natively support secondary indexes, requiring the use of external tools like Apache Lucene or Elasticsearch for efficient querying on non-primary keys.
Data Distribution: Aerospike ensures the replication factor and distribution of data across nodes based on a configurable policy, providing fault tolerance and high availability. In contrast, Cassandra utilizes consistent hashing to determine the placement of data replicas in a ring-like architecture, allowing for balanced data distribution among nodes.
Write Performance: Aerospike is optimized for high write throughput, providing sub-millisecond latency and predictable performance for write-intensive workloads. Cassandra, on the other hand, excels in high read and write throughput scenarios, especially in multi-data center environments, where its asynchronous replication and tunable consistency levels provide excellent scalability.

Aerospike vs Cassandra

Overview

Aerospike vs Cassandra: What are the differences?