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

Aerospike vs Hazelcast

OverviewComparisonAlternatives

Overview

Hazelcast
Hazelcast
Stacks427
Followers474
Votes59
GitHub Stars6.4K
Forks1.9K
Aerospike
Aerospike
Stacks200
Followers288
Votes48
GitHub Stars1.3K
Forks196

Aerospike vs Hazelcast: What are the differences?

Introduction:

Aerospike and Hazelcast are both distributed database systems that offer unique features and functionalities for various use cases. Here are the key differences between Aerospike and Hazelcast:

  1. Data Model and Querying Language:

    • Aerospike: Aerospike uses a key-value data model, where each record is uniquely identified by a primary key. It supports querying using the Aerospike Query Language (ASQL) or a subset of SQL-like syntax for filtering and manipulating data.
    • Hazelcast: Hazelcast uses a distributed computing platform that provides an in-memory key-value store, but it also supports other data structures like distributed maps, queues, topics, and more. It does not provide a dedicated querying language, but supports querying using MapReduce operations or Predicate-based filtering.
  2. Consistency and Replication:

    • Aerospike: Aerospike ensures strong consistency guarantees through its reliable, distributed replication mechanism. It supports various replication configurations, including synchronous replication with automatic failover and eventual consistency configurations.
    • Hazelcast: Hazelcast provides eventual consistency and best-effort replication with customizable trade-offs between consistency, availability, and partition tolerance. It allows users to define the desired consistency guarantee based on their application requirements.
  3. Scalability and Performance:

    • Aerospike: Aerospike is built for high-performance and scalability. It provides automatic data partitioning and distribution across nodes in a cluster, enabling linear scalability and efficient utilization of resources. It also includes various mechanisms for data compression and optimization to enhance overall performance.
    • Hazelcast: Hazelcast is designed to be highly scalable and can handle large datasets and high throughput workloads. It supports horizontal scaling by adding more nodes to the cluster, and provides partition-based data distribution to ensure optimal performance. Additionally, it offers various optimizations like data storage management and indexing for efficient data access.
  4. Supported Programming Languages:

    • Aerospike: Aerospike provides client libraries and SDKs for multiple programming languages, including Java, C#, C, Node.js, Python, and more, making it accessible to developers from different technology stacks.
    • Hazelcast: Hazelcast supports a wide range of programming languages, such as Java, .NET/C#, C++, Python, Node.js, and more, allowing developers to use their preferred language to interact with the database system.
  5. Data Durability and Persistence:

    • Aerospike: Aerospike provides configurable data durability with the ability to persist data to disk and maintain replicas for fault tolerance. It offers various options for data backup and recovery, ensuring data integrity and availability even in the face of failures.
    • Hazelcast: Hazelcast supports data persistence to disk, facilitating durability in case of server restarts or failures. However, it does not provide built-in replication and fault-tolerance mechanisms, so users need to configure external systems for replication and backup.
  6. Clustering and High Availability:

    • Aerospike: Aerospike utilizes a distributed cluster architecture with built-in fault tolerance and automatic failover. It supports various clustering options like multi-site replication and rack-awareness, ensuring high availability and data accessibility.
    • Hazelcast: Hazelcast relies on a distributed cluster setup to provide high availability and fault tolerance. It supports automatic data replication and recovery in case of node failures, ensuring continuous availability of data and services.

In Summary, Aerospike and Hazelcast differ in terms of data model, querying language, consistency guarantees, scalability, supported programming languages, data durability, and clustering/high availability.

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Detailed Comparison

Hazelcast
Hazelcast
Aerospike
Aerospike

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.

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.

Distributed implementations of java.util.{Queue, Set, List, Map};Distributed implementation of java.util.concurrent.locks.Lock;Distributed implementation of java.util.concurrent.ExecutorService;Distributed MultiMap for one-to-many relationships;Distributed Topic for publish/subscribe messaging;Synchronous (write-through) and asynchronous (write-behind) persistence;Transaction support;Socket level encryption support for secure clusters;Second level cache provider for Hibernate;Monitoring and management of the cluster via JMX;Dynamic HTTP session clustering;Support for cluster info and membership events;Dynamic discovery, scaling, partitioning with backups and fail-over
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
6.4K
GitHub Stars
1.3K
GitHub Forks
1.9K
GitHub Forks
196
Stacks
427
Stacks
200
Followers
474
Followers
288
Votes
59
Votes
48
Pros & Cons
Pros
  • 11
    High Availibility
  • 6
    Distributed compute
  • 6
    Distributed Locking
  • 5
    Sharding
  • 4
    Load balancing
Cons
  • 4
    License needed for SSL
Pros
  • 16
    Ram and/or ssd persistence
  • 12
    Easy clustering support
  • 5
    Easy setup
  • 4
    Acid
  • 3
    Scale
Integrations
Java
Java
Spring
Spring
No integrations available

What are some alternatives to Hazelcast, Aerospike?

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.

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.

Apache Ignite

Apache Ignite

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

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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