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Apache Solr vs Cassandra: What are the differences?
Introduction
Apache Solr and Cassandra are two popular open-source software used for data management and analysis. While both are designed to handle large volumes of data and provide efficient search and retrieval capabilities, there are several key differences between the two.
Data Structure: Apache Solr is primarily a search platform that uses an inverted index to enable fast full-text search and indexing. It stores data in a schema-based format, allowing for structured search queries. On the other hand, Cassandra is a NoSQL database that follows a distributed database model. It stores data in a columnar format, providing high scalability and availability, especially for write-intensive workloads.
Data Model: Apache Solr is document-oriented, which means it treats each document as a separate entity that can be indexed and searched individually. It allows users to index semi-structured and unstructured data, making it suitable for text search and analysis. In contrast, Cassandra follows a key-value data model, where each record is identified by a unique key. It provides fast read and write operations and is ideal for big data applications that require high scalability.
Consistency Model: Apache Solr guarantees eventual consistency, meaning that updates may take some time to propagate throughout the system, but eventually, all replicas will be consistent. This is suitable for search applications where near real-time updates are not critical. On the other hand, Cassandra offers tunable consistency, allowing users to configure the level of consistency based on their application requirements. It provides strong consistency for immediate read-after-write consistency or eventual consistency for better performance.
Data Replication: Solr uses replication to achieve fault tolerance and high availability. It replicates index data across multiple nodes, allowing for load distribution and failover support. Cassandra also provides replication but follows a masterless architecture known as peer-to-peer replication. Each node in Cassandra can function as a master node, providing horizontal scalability and fault tolerance.
Data Partitioning: In Solr, data is partitioned based on the shard key, and each shard is stored on a separate node. This allows for parallel processing of search queries and helps in distributing the load. Cassandra, on the other hand, uses a distributed hash table to partition data across multiple nodes. It uses consistent hashing to determine the location of data on nodes, allowing for automatic load balancing and scalability.
Query Language: Solr provides a powerful and flexible query language called Solr Query Language (SOLRQL) or Lucene Query Parser Syntax. It allows users to construct complex search queries using operators, wildcards, phrase searching, and more. Cassandra, on the other hand, uses CQL (Cassandra Query Language), which is SQL-like and supports basic query operations like SELECT, INSERT, UPDATE, and DELETE.
In summary, Apache Solr is a search platform with a focus on full-text search and indexing of structured and unstructured data, while Cassandra is a distributed NoSQL database designed for scalability and high availability. Solr uses a document-oriented model and provides eventual consistency, while Cassandra follows a key-value data model and offers tunable consistency. Solr uses replication and shard-based partitioning, whereas Cassandra uses peer-to-peer replication and consistent hashing for data distribution. Solr provides a flexible query language, while Cassandra offers a SQL-like query language.
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.
Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring
Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.
i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra
By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.
Pros of Apache Solr
Pros of Cassandra
- Distributed119
- High performance98
- High availability81
- Easy scalability74
- Replication53
- Reliable26
- Multi datacenter deployments26
- Schema optional10
- OLTP9
- Open source8
- Workload separation (via MDC)2
- Fast1
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Cons of Apache Solr
Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1