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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on Apache Solr and Cassandra
Vinay Mehta
Needs advice
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CassandraCassandra
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ScyllaDBScyllaDB

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.

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Replies (4)
Recommends
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ScyllaDBScyllaDB

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

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Alex Peake
Recommends
on
CassandraCassandra

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.

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Recommends
on
ScyllaDBScyllaDB

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.

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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 146.1K views
Recommends
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CassandraCassandra

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

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Pros of Apache Solr
Pros of Cassandra
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    • 119
      Distributed
    • 98
      High performance
    • 81
      High availability
    • 74
      Easy scalability
    • 53
      Replication
    • 26
      Reliable
    • 26
      Multi datacenter deployments
    • 10
      Schema optional
    • 9
      OLTP
    • 8
      Open source
    • 2
      Workload separation (via MDC)
    • 1
      Fast

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    Cons of Apache Solr
    Cons of Cassandra
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      • 3
        Reliability of replication
      • 1
        Size
      • 1
        Updates

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      - No public GitHub repository available -

      What is Apache Solr?

      It uses the tools you use to make application building a snap. It is built on the battle-tested Apache Zookeeper, it makes it easy to scale up and down.

      What is Cassandra?

      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.

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      What are some alternatives to Apache Solr and Cassandra?
      Splunk
      It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
      Lucene
      Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
      Elasticsearch
      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).
      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.
      Apache Spark
      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
      See all alternatives