Detailed Comparison: Elasticsearch vs. Cassandra

Updated on 09 Sep 2024

OneBrowsing

 4.7

(Reviews: 1.5K)

Est. users: 4.5M

Price Ranges: $50 - $150 - $400

 4.5

(Reviews: 1.2K)

Est. users: 25K

Elasticsearch vs Cassandra

Elasticsearch is a distributed search and analytics engine designed for full-text search capabilities and allows for real-time data exploration and aggregation. Cassandra, on the other hand, is a highly scalable NoSQL distributed database known for its robust write and read performance, making it ideal for managing large amounts of structured data across many servers. While Elasticsearch excels with search operations and analytics, Cassandra is more suited for high throughput transactional data storage and retrieval.

Elasticsearch

  1. Distributed search engine
  2. Real-time data indexing
  3. Full-text search capabilities
  4. Built on Lucene
  5. Supports unstructured data
  6. Analyzes large volumes of data
  7. RESTful API interactions

Cassandra

  1. Distributed NoSQL database
  2. High availability and scalability
  3. Supports wide column store
  4. Handles large amounts of data
  5. No single point of failure
  6. Real-time data analytics
  7. Eventual consistency model

Key Differences

  1. Data Model

    Elasticsearch is a search engine based on the Lucene library and utilizes a document-oriented model with JSON as its format. Cassandra, on the other hand, uses a wide-column store model, which is a type of NoSQL database that stores data in columns instead of rows, making it suitable for high-write applications.

    Winner: Its a tie

  2. Use Case

    Elasticsearch excels in full-text search, logging, and analyzing large volumes of unstructured data quickly. Cassandra is used for handling large volumes of structured data across many commodity servers without any single point of failure, making it suitable for applications requiring high availability and write throughput.

    Winner: Its a tie

  3. Scalability

    Both Elasticsearch and Cassandra are designed to be horizontally scalable across multiple nodes. Elasticsearch allows data to be replicated and distributed across multiple nodes, making the system highly available and resilient. Cassandra also offers excellent horizontal scalability with the ability to scale out by adding more nodes, and its masterless architecture ensures no single point of failure.

    Winner: Its a tie

  4. Querying

    Elasticsearch provides complex querying capabilities, making use of its powerful full-text search and analytics features. Cassandra, however, uses its own CQL (Cassandra Query Language) which is similar to SQL but somewhat limited in its querying capabilities compared to Elasticsearch.

    Winner: Elasticsearch

  5. Consistency

    Cassandra follows the eventual consistency model where data will eventually become consistent across nodes, allowing for very high availability and partition tolerance. Elasticsearch also supports eventual consistency but can be configured for more immediate consistency requirements.

    Winner: Its a tie

  6. Performance

    For read-heavy workloads and complex queries, Elasticsearch generally outperforms Cassandra because of its advanced search capabilities and optimizations for read operations. Cassandra is optimized for write-heavy workloads and can handle high throughput for write operations, making it more performance-efficient for writes.

    Winner: Its a tie

Distinct Features

ElasticsearchCassandra
Full-text search capabilitiesLinear scalability
Real-time analyticsHigh availability with no single point of failure
Distributed document storeWide-column store with flexible schema
Built-in Kibana for visualizationHigh write and read throughput for big data applications
Support for advanced search features like fuzzy matching, phrase matchingSupport for multi-datacenter replication
Native JSON support with indexingEfficient storage of time series data
Near real-time indexing and searchingLightweight transactions and batch operations
Built-in integration with LogstashTunable consistency levels
Ranking and relevance scoringAdvanced compaction strategies
Nested and parent/child documentsSupport for CQL (Cassandra Query Language)
Field collapsingBuilt-in backup and restoration features
Geospatial data and queriesRow-level access control

Pricing Overview

Elasticsearch

View Pricing Plans  

  1. Basic ($50) - Best for individuals and small teams
  2. Standard ($150) - Ideal for medium-sized enterprises
  3. Enterprise ($400) - Tailored for organizations with complex needs

Basic

$50 / monthly

Suitable for small projects and startups

  • Real-time Search and Analytics

  • Scalable and High Availability

  • Alerting and Monitoring

  • Security with HTTPS Encryption

Standard

$150 / monthly

Recommended for growing businesses

  • All Basic features

  • Advanced Machine Learning

  • Scheduled Snapshots

  • Role-based Access Control

Enterprise

$400 / monthly

Designed for large enterprises

  • All Standard features

  • Multi-tenancy Support

  • Dedicated Account Management

  • 24/7 Premium Support

Indepth Overview

ElasticsearchCassandra
Querying and Analytics
Full-text search ★4.8 - It supports powerful full-text search capabilities. ★3.8 - It lacks native support for full-text search but can be achieved using plugins.
Real-time analytics ★4.6 - It provides real-time analytics with near-instant search results.
Faceted navigation ★4.7 - It has built-in support for faceted navigation.
Scalability and Performance
Distributed architecture ★4.7 - It is designed to scale horizontally across multiple nodes. ★4.7 - It is designed to scale horizontally across multiple nodes.
Auto-sharding ★4.6 - It automatically partitions data across shards for distributing workload. ★4.7 - It automatically partitions data across nodes for distributing workload.
Query caching ★4.5 - It provides query caching mechanisms for faster response times.
Data Model
Schema flexibility ★4.5 - It allows for dynamic schema and supports schema-less documents. ★4.7 - It allows for flexible schema changes at runtime.
Nested objects and arrays ★4.6 - It handles nested objects and arrays efficiently.
Upserts ★4.4 - It supports updating documents by merging new data. ★4.7 - It allows for efficient updates of existing rows.
Data Replication and Reliability
Replication ★4.7 - It supports automatic data replication for high availability. ★4.8 - It offers configurable replication options for fault tolerance.
Consistency models ★4.5 - It offers tunable consistency models for different use cases. ★4.7 - It provides tunable consistency models for different use cases.
Backup and restore ★4.6 - It provides backup and restore mechanisms for data preservation. ★4.7 - It has built-in backup and restore capabilities for data protection.
Query Language and API
RESTful API ★4.6 - It provides a RESTful API for easy integration.
Query DSL ★4.8 - It offers a powerful Query DSL for complex queries.
Geo search ★4.7 - It supports geospatial queries out of the box. ★4.3 - It requires additional libraries for geospatial queries.
Concurrency and Locking
Optimistic concurrency control ★4.6 - It supports optimistic concurrency control for concurrent updates.
Distributed locking ★4.5 - It provides distributed locking mechanisms.
Multi-document transactions ★4.7 - It supports atomic multi-document transactions.
Monitoring and Management
Cluster monitoring ★4.7 - It provides built-in cluster monitoring tools. ★4.6 - It provides cluster monitoring features through third-party tools.
Alerting and notification ★4.5 - It supports alerting and notification mechanisms.
Index management ★4.6 - It allows for efficient management of indexes.

Comparision Reviews

Alex Thompson

Seattle, WA

Elasticsearch excels at full-text search capabilities and offers great real-time analytics. Cassandra, on the other hand, is supreme in handling high-speed transactional data. Elasticsearch stands out for its user-friendly interface and extensive documentation. Cassandra’s scalability is unmatched, but it comes with a steeper learning curve.

Elasticsearch 4.5

Cassandra 4

Jane Anderson

Austin, TX

For use cases centered around search functionalities, Elasticsearch is the clear winner with its powerful indexing and query performance. Cassandra, while not as strong in search, shines in its ability to handle heavy loads with virtually no downtime. Elasticsearch's ecosystem and community support are a plus, while Cassandra is praised for its reliability.

Elasticsearch 4.8

Cassandra 4.2

Michael Davis

Chicago, IL

Cassandra's decentralized nature provides excellent data redundancy and fault tolerance, making it ideal for mission-critical applications. Elasticsearch, with its superior search and data visualization tools, is better suited for applications needing advanced analytics. Each has its strengths depending on the specific application requirements.

Elasticsearch 4.4

Cassandra 4.6

Linda Brown

New York, NY

Elasticsearch offers unparalleled ease of setup and fantastic performance for read-heavy workloads. Cassandra’s forte is write-heavy operations where horizontal scaling is crucial. While Elasticsearch is intuitive and straightforward for developers, Cassandra provides robustness and durability for large-scale data operations.

Elasticsearch 4.6

Cassandra 4.3

Chris Wilson

San Francisco, CA

When it comes to integrating with various data sources and providing flexible ingest pipelines, Elasticsearch has an edge. Cassandra excels in maintaining high availability and scaling seamlessly with increased data loads. Both technologies offer excellent performance, but the choice largely depends on the specific needs of the application.

Elasticsearch 4.7

Cassandra 4.5

Comments

John Doe
Hey guys, what do you think about Elasticsearch vs Cassandra for handling our large-scale data?
TechSavvy99
Elasticsearch is great for search and analytics but isn't Cassandra better for write-heavy operations?
Michael Robins
Yeah, ES scales horizontally too though. What kind of data queries do we need most?
Lily Harper
True, but ES can be resource-intensive. Cassandra’s performance remains snappy even at scale.
John Doe
Good point, Lily. I'm worried about the complexity of managing Cassandra clusters.
DataGeek82
John, managing clusters can be tricky, but the write and read performance with Cassandra is top-notch!
TechSavvy99
Agreed, plus Cassandra has no single point of failure. It's pretty robust, don’t you think?
Michael Robins
I’m leaning towards Elasticsearch for its full-text search capabilities. Do we need that?
Lily Harper
Yeah, TechSavvy99 we should consider our actual use case. Do we prioritize search or write-read ops?
John Doe
Both seem crucial. ES handles search better, but Cassandra might be more efficient for our data ingestion needs.
DataGeek82
Maybe a hybrid approach? Using both could leverage the strengths of each.
TechSavvy99
Using both will be complex though. Double management overhead. Worth it?
Michael Robins
Double-edged sword, lmao. Could be amazing or a maintenance nightmare!
Lily Harper
Haha, totally. Let’s do a small-scale test and see what works for us?

External Links For More Information

Related Posts

Leave a Comment