Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Solr

Stacks780

Followers644

Votes126

Cassandra vs Solr: What are the differences?

Introduction

Cassandra and Solr are both distributed, highly scalable, and open-source software systems used for managing and searching data. However, there are significant differences between the two.

Data Model: Cassandra is a NoSQL database that follows a wide-column data model, allowing for flexible schemas and fast writes. On the other hand, Solr is a search platform built on Apache Lucene, using a document-based data model with schema definition and indexing for efficient search and retrieval.
Data Replication: Cassandra focuses on providing high availability and fault tolerance through distributed replication. It uses a peer-to-peer architecture with no single point of failure and supports synchronous and asynchronous replication methods. Solr, on the other hand, supports replication for both fault tolerance and load balancing but primarily focuses on providing efficient search capabilities.
Querying and Indexing: Cassandra supports a limited set of query capabilities, mainly based on primary keys and secondary indexes. It is designed for high-volume data ingestion and writes performance, while read performance is optimized for single row retrieval. Solr, on the other hand, provides advanced querying capabilities with full-text search, faceted search, and complex queries using a powerful query syntax. It also supports flexible indexing options to optimize search performance.
Scalability: Cassandra is specifically designed for linear scalability with its distributed architecture. It can handle large volumes of data across multiple nodes while maintaining high performance and low latency. Solr, on the other hand, can also be scaled horizontally to handle large datasets, but its primary focus is on search and retrieval performance rather than distributed data storage.
Data Consistency: Cassandra provides tunable consistency levels to support different trade-offs between data consistency and availability. It offers eventual consistency by default but allows users to configure stronger consistency guarantees if necessary. Solr, on the other hand, focuses on providing near real-time search capabilities and does not prioritize strong consistency guarantees.
Use Cases: Cassandra is commonly used for applications that require high write throughput and high availability, such as time series data, log data, and user activity tracking. Its ability to handle large amounts of data across multiple nodes makes it suitable for distributed architectures. Solr, on the other hand, is primarily used for search and analytics applications, providing advanced search capabilities and faceted navigation for e-commerce, social media, and content-driven websites.

In summary, Cassandra is a distributed NoSQL database with a wide-column data model, emphasizing high availability and fault tolerance, while Solr is a search platform built on Apache Lucene, focusing on advanced querying and efficient search capabilities.

Advice on Cassandra, Solr

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

174k views174k

Comments

Detailed Comparison

Cassandra	Solr
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.	Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
-	Advanced full-text search capabilities; Optimized for high volume web traffic; Standards-based open interfaces - XML, JSON and HTTP; Comprehensive HTML administration interfaces; Server statistics exposed over JMX for monitoring; Linearly scalable, auto index replication, auto-failover and recovery; Near real-time indexing; Flexible and adaptable with XML configuration; Extensible plugin architecture
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 780
Followers 3.5K	Followers 644
Votes 507	Votes 126
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Updates 1 Size	Pros 35 Powerful 22 Indexing and searching 20 Scalable 19 Customizable 13 Enterprise Ready
Integrations
No integrations available	Lucene

What are some alternatives to Cassandra, Solr?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

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.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.