In the following comparison table, we will provide you with an extensive guide designed to enable a detailed assessment of Cassandra and OpenSearch. This comparison aims to supply an in-depth exploration of multiple aspects of these two database systems, providing you with the insights required to make informed decisions tailored to your specific use case.
By delving into the intricacies of both Cassandra and OpenSearch, you will gain a deeper understanding of their data models, querying capabilities, distributed architectures, consistency models, indexing and search features, and their respective strengths and use cases.
Cassandra is an open-source, distributed, highly scalable, decentralized NoSQL database system designed to manage extensive amounts of data across many commodity servers, supplying high availability with no single point of failure. Data is replicated across nodes, and if one node fails, the system can still operate without interruption. Unlike traditional relational databases with a centralized master-server architecture, Cassandra's design is decentralized, which contributes to its fault tolerance and scalability.
OpenSearch is an open-source search and analytics engine. The tool is a distributed, RESTful search and analytics engine that can be utilised to index and search large volumes of data quickly and in near real-time. OpenSearch was renamed from OpenDistro in 2021, as it emerged as a fork of Elasticsearch following Elastic's decision to change the license of certain components (read more about OpenSearch's history here). The solution is an open-source search engine, but it has evolved as a community-driven project with a focus on transparency and open governance.
|- Cassandra: Cassandra follows a wide-column store data model. It stores data in a way that is optimized for fast read and write operations, making it suitable for scenarios with high write and read loads. Additionally, Cassandra supports schema-less data, allowing you to add or remove columns from a row without affecting the overall schema. This flexibility makes it well-suited for managing semi-structured or unstructured data, such as time series or sensor data.
|- OpenSearch: OpenSearch (formerly Elasticsearch) deals with structured and semi-structured data, typically in JSON format. While it is capable of handling structured data, it truly shines in scenarios where detailed full-text search and analytics on semi-structured data are needed. OpenSearch enforces a schema on data, meaning you need to define the data structure upfront. It is primarily designed for indexing and searching structured and semi-structured data.
|- Cassandra: Cassandra employs the Cassandra Query Language (CQL) for data access. CQL is similar to SQL but is adapted to the NoSQL paradigm. It allows you to interact with the database using familiar SQL-like commands, but its query capabilities are more limited compared to full-text search engines like OpenSearch. While CQL can handle key-based lookups efficiently, complex queries may require denormalization and additional application logic.
|- OpenSearch: OpenSearch uses a RESTful API and a Query DSL (Domain Specific Language) for searching and filtering data. This Query DSL offers powerful full-text search capabilities, aggregations, filtering, and complex querying. It excels at handling complex queries, making it an excellent choice for search and analytics use cases. The Query DSL allows for in-depth control over how data is retrieved and analyzed.
|- Cassandra: Cassandra is designed with high availability and partition tolerance in mind, following an AP (Available and Partition Tolerant) model in the CAP theorem. It uses a peer-to-peer, masterless architecture, where data is distributed across multiple nodes. Cassandra employs a gossip protocol for inter-node communication and data replication, ensuring fault tolerance. This architecture makes Cassandra suitable for scenarios that require high availability and scalability, even in the presence of network partitions.
|- OpenSearch: OpenSearch also focuses on high availability but leans more towards eventual consistency, making it more of a CP (Consistent and Partition Tolerant) system in the CAP theorem. It uses a distributed, clustered architecture, often leveraging sharding to horizontally partition data across nodes. While it provides strong consistency when needed, it's frequently chosen for use cases where real-time consistency is not as critical, and quick access to data is essential.
|- Cassandra: Cassandra offers tunable consistency levels, allowing you to choose between strong or eventual consistency, depending on the requirements of your application. This flexibility is advantageous when balancing between consistency and availability. Cassandra excels in scenarios where high availability is critical, and strong consistency can be relaxed when needed.
|- OpenSearch: OpenSearch prioritizes availability and quick data access. While it supports strong consistency, its focus on real-time search and analytics makes it more suitable for scenarios where near real-time access to data and quick results are paramount. Consistency can be adjusted to accommodate use cases, but strong consistency may not be the primary goal in many OpenSearch deployments.
|Indexing & Searching
|- Cassandra: Cassandra provides basic indexing capabilities, mainly through secondary indexes. These indexes are helpful for improving the performance of specific queries, but they may not be as robust or flexible as the indexing and searching features of dedicated search and analytics engines like OpenSearch.
|- OpenSearch: OpenSearch excels in indexing and searching structured and semi-structured data. It supports powerful full-text search capabilities, including stemming, tokenization, relevance scoring, and faceted searching. Additionally, it offers aggregations and filtering, making it a valuable tool for complex queries, analytics, and detailed search functionality. OpenSearch's indexing and search capabilities are well-suited for applications like e-commerce search, log analysis, and content discovery.
|- Cassandra: Cassandra is commonly used in applications that require high availability, scalability, and fault tolerance. It is well-suited for scenarios with massive write and read loads, such as Internet of Things (IoT), time series data, and distributed data storage. Its flexibility in handling semi-structured data makes it versatile for various use cases.
|- OpenSearch: OpenSearch is primarily used for search and analytics applications. It is a popular choice for log and event data analysis, monitoring, and full-text search applications. OpenSearch shines in scenarios where quick access to large volumes of structured or semi-structured data is essential. It is widely employed in applications related to e-commerce, security, and content search and discovery.
|Ecosystem & Integration
|- Cassandra: Cassandra has its ecosystem of libraries, tools, and drivers for various programming languages, making it suitable for application development and management. It offers integration with tools like Apache Spark and Apache Hadoop for analytics. Cassandra is widely used in industries such as finance, retail, social media, and other data-intensive domains.
|- OpenSearch: OpenSearch has a rich ecosystem that extends beyond its core capabilities. It is often used in conjunction with other tools like Logstash (for data ingestion) and Kibana (for data visualization and exploration) to form the ELK stack (Elasticsearch, Logstash, and Kibana). This ecosystem is extensively used in application monitoring, security information and event management (SIEM), log analysis, and various other data analytics applications. OpenSearch's broad range of plugins allows it to be customized and integrated into diverse systems.
|- Cassandra: Cassandra is schema-flexible, allowing you to add or remove columns to and from rows without affecting the overall schema. This flexibility makes it an excellent choice for storing semi-structured or unstructured data. It is suitable for scenarios where the data structure evolves over time.
|- OpenSearch: OpenSearch enforces a schema on data, meaning that you need to define the data structure upfront. While it provides some flexibility with dynamic mapping, this schema enforcement is more appropriate for structured or semi-structured data where data consistency and structure are well-defined. OpenSearch excels in use cases where consistent data structure is crucial.
An important factor when choosing between Cassandra or OpenSearch is that if you’re utilizing Jaeger then the Jaeger team recommends OpenSearch/Elasticsearch rather than Cassandra as the storage backend. This is because Cassandra is a key-value database, so it is more effective for retrieving traces by trace ID, yet it doesn’t supply the same powerful search capabilities as OpenSearch. Another reason is that OpenSearch can also be queried directly, e.g. from Kibana dashboards, and provide useful analytics and aggregations.
It’s also important to mention, that whilst we stated that due to Cassandras’ distributed architecture, it's an effective tool when used in applications that require scalability, this is not limited to only Cassandra. OpenSearch is also a highly scalable tool. Both tools are ideal for horizontal scaling, meaning you can tailor your capacity and performance to meet the demands of your data and users. Therefore, if your applications require scalability, selecting Cassandra, solely because of this reason, means that you could be missing some of the benefits that OpenSearch has to offer.
The speed of Cassandra and OpenSearch is an important factor when debating the two solutions as it can help to improve efficiency. Cassandra possess' multi query execution capacity, enabling the tool to be much faster for queries of smaller scripts. In contrast, OpenSearch, formerly Elasticsearch, provides speed queries and transaction capability as well as efficient index searching and storage making it overall the faster of the two tools.
Cassandra has a variety of features and capabilities but a key feature is its linear scalability. Cassandra can scale horizontally by adding more nodes to the cluster, offering linear scalability. Another key feature is high availability and fault tolerance. The solution is renowned for its ability to maintain high availability and fault tolerance. Data is replicated across nodes, guaranteeing that if one node fails, the system will continue to work without data loss. A final key feature of Cassandra is Tunable consistency, users can balance between data consistency and performance by altering the consistency levels to their application’s requirements.
In regards to OpenSearch, a key feature of the solution is that it's distributed ans scalable. OpenSearch is designed to be distributed and can scale horizontally by inputting additional nodes to the cluster, this enables it to manage large amounts of data and traffic. Another key feature of OpenSearch is full-text search, the solution supports full-text search capabilities, enabling users to conduct searches on large volumes of textual data efficiently. A final key feature of OpenSearch is RESTful API. The tool offers a RESTful API, making it simple to integrate into applications and systems. This API enables developers to interact with the search engine for indexing, searching, and managing data.
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