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  5. Aerospike vs Elasticsearch

Aerospike vs Elasticsearch

OverviewDecisionsComparisonAlternatives

Overview

Elasticsearch
Elasticsearch
Stacks35.5K
Followers27.1K
Votes1.6K
Aerospike
Aerospike
Stacks200
Followers288
Votes48
GitHub Stars1.3K
Forks196

Aerospike vs Elasticsearch: What are the differences?

Introduction

Aerospike and Elasticsearch are two popular database systems used for different purposes. While Aerospike is a high-performance NoSQL database, Elasticsearch is a distributed search and analytics engine. Here are the key differences between these two technologies:

1. Data Model:

Aerospike follows a key-value data model, where data is stored and retrieved based on a primary key. On the other hand, Elasticsearch utilizes a document-oriented data model, where data is stored in JSON-like documents and can be queried using full-text search.

2. Search Capabilities:

While both Aerospike and Elasticsearch offer search capabilities, Elasticsearch is designed specifically for searching and achieving high speeds in searching large volumes of data. It supports advanced search features like fuzzy search, autocomplete, and search across multiple fields. Aerospike, on the other hand, focuses more on high-performance data processing rather than search functionalities.

3. Scalability:

Both Aerospike and Elasticsearch are designed to be scalable, but they handle scalability in different ways. Aerospike uses a shared-nothing architecture, where data is distributed and replicated across multiple nodes. It offers automatic data partitioning and replication, making it horizontally scalable. Elasticsearch, on the other hand, uses a distributed architecture with a cluster of nodes. It allows for horizontal scaling by adding more nodes to the cluster and automatically redistributes data across the nodes.

4. Consistency and Durability:

Aerospike guarantees strong consistency and durability by default. It ensures that all nodes in the cluster agree on the state of data and provides various options for durability, including synchronous replication. Elasticsearch, on the other hand, focuses more on availability and eventual consistency. It allows for distributed system failures by providing configurable consistency levels and relies on eventual consistency for performance optimization.

5. Data Replication and Backup:

Aerospike offers multi-node data replication and automatic data backup, ensuring data availability and recovery in case of node failures. Elasticsearch also supports data replication and backup, but it provides additional features like snapshot and restore, which allow taking incremental backups and restoring data even across different clusters.

6. Use Cases:

Aerospike is commonly used in applications requiring low-latency or real-time data processing, such as real-time bidding, fraud detection, and recommendation systems. Elasticsearch, on the other hand, is widely used for full-text search, log analysis, and data analytics, especially in applications dealing with large volumes of textual data.

In summary, Aerospike is a high-performance NoSQL database with a key-value data model, while Elasticsearch is a distributed search and analytics engine with a document-oriented data model. Aerospike focuses more on data processing performance, strong consistency, and durability, while Elasticsearch excels in search capabilities, scalability, and handling large volumes of textual data.

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Advice on Elasticsearch, Aerospike

Rana Usman
Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceonFirebaseFirebaseElasticsearchElasticsearchAlgoliaAlgolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k
Comments

Detailed Comparison

Elasticsearch
Elasticsearch
Aerospike
Aerospike

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

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 and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License
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
-
GitHub Stars
1.3K
GitHub Forks
-
GitHub Forks
196
Stacks
35.5K
Stacks
200
Followers
27.1K
Followers
288
Votes
1.6K
Votes
48
Pros & Cons
Pros
  • 329
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
Cons
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
Pros
  • 16
    Ram and/or ssd persistence
  • 12
    Easy clustering support
  • 5
    Easy setup
  • 4
    Acid
  • 3
    Scale
Integrations
Kibana
Kibana
Beats
Beats
Logstash
Logstash
No integrations available

What are some alternatives to Elasticsearch, 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.

Algolia

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

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.

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

Typesense

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

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.

Amazon CloudSearch

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

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