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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Aerospike vs Apache Ignite

Aerospike vs Apache Ignite

OverviewComparisonAlternatives

Overview

Aerospike
Aerospike
Stacks200
Followers288
Votes48
GitHub Stars1.3K
Forks196
Apache Ignite
Apache Ignite
Stacks110
Followers168
Votes41
GitHub Stars5.0K
Forks1.9K

Aerospike vs Apache Ignite: What are the differences?

Introduction

Aerospike and Apache Ignite are both high-performance, distributed, in-memory database systems that provide fast and scalable data processing capabilities. Despite their similarities, there are several key differences between the two.

  1. Data Model: Aerospike is a key-value store, where data is organized as key-value pairs. It supports complex data types and secondary indexes for efficient querying. On the other hand, Apache Ignite is a versatile data grid platform that supports key-value, SQL, and computing operations. It provides a unified API for data storage and processing, including support for distributed SQL queries and in-memory computing.

  2. ACID Compliance: Aerospike is fully ACID compliant, ensuring the atomicity, consistency, isolation, and durability of transactions. It guarantees consistency even in the face of system failures or concurrent updates. In contrast, Apache Ignite supports ACID transactions but also provides an option for weaker consistency models, such as eventual consistency, to prioritize performance and scalability.

  3. Geographic Distribution: Aerospike has built-in support for geographic distribution, making it suitable for global deployments with multiple data centers. It allows data to be replicated and synchronized across multiple geographic regions, providing fault tolerance and low-latency access to data for users around the world. Apache Ignite also supports data replication and distributed deployments but lacks the native geographically-distributed features of Aerospike.

  4. Indexing Capabilities: Aerospike offers a range of indexing options, including primary indexes, secondary indexes, and geospatial indexes. These indexes enhance query performance by enabling efficient data lookup. Apache Ignite, on the other hand, provides comprehensive indexing capabilities with support for primary and secondary indexes, as well as full-text search indexes and Lucene-based indexing.

  5. Integration with External Systems: Aerospike provides connectors and integrations with various external systems, including Hadoop, Spark, Kafka, and Elasticsearch. These integrations enable seamless data flow between different components of the data ecosystem. Apache Ignite also offers integrations with external systems and technologies, but its focus is more on combining data storage and computation capabilities within a single system.

  6. Data Partitioning: Aerospike uses a hash-based partitioning scheme to distribute data across a cluster. This allows for efficient distribution of data and load balancing. On the other hand, Apache Ignite supports multiple partitioning strategies, including hash-based and affinity-based partitioning. Affinity-based partitioning enables co-location of related data, which can improve query performance in certain scenarios.

In summary, Aerospike and Apache Ignite differ in their data models, ACID compliance, geographic distribution capabilities, indexing options, integration with external systems, and data partitioning strategies. While Aerospike offers a focused key-value store with strong consistency, geographic replication, and extensive indexing, Apache Ignite provides a versatile data grid platform with support for key-value, SQL, and computing operations, along with flexible consistency models and comprehensive indexing capabilities.

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Detailed Comparison

Aerospike
Aerospike
Apache Ignite
Apache Ignite

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.

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

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.
Memory-Centric Storage; Distributed SQL; Distributed Key-Value
Statistics
GitHub Stars
1.3K
GitHub Stars
5.0K
GitHub Forks
196
GitHub Forks
1.9K
Stacks
200
Stacks
110
Followers
288
Followers
168
Votes
48
Votes
41
Pros & Cons
Pros
  • 16
    Ram and/or ssd persistence
  • 12
    Easy clustering support
  • 5
    Easy setup
  • 4
    Acid
  • 3
    Scale
Pros
  • 5
    Multiple client language support
  • 5
    Free
  • 5
    High Avaliability
  • 5
    Written in java. runs on jvm
  • 4
    Rest interface
Integrations
No integrations available
MongoDB
MongoDB
MySQL
MySQL
Apache Spark
Apache Spark

What are some alternatives to Aerospike, Apache Ignite?

Redis

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.

Hazelcast

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

MemSQL

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

SAP HANA

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

VoltDB

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Tarantool

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

Azure Redis Cache

Azure Redis Cache

It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.

KeyDB

KeyDB

KeyDB is a fully open source database that aims to make use of all hardware resources. KeyDB makes it possible to breach boundaries often dictated by price and complexity.

LokiJS

LokiJS

LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser.

BuntDB

BuntDB

BuntDB is a low-level, in-memory, key/value store in pure Go. It persists to disk, is ACID compliant, and uses locking for multiple readers and a single writer. It supports custom indexes and geospatial data. It's ideal for projects that need a dependable database and favor speed over data size.

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