Cassandra - A Basic Introduction Guide
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Cassandra is a distributed database designed to handle large amounts of structured data across commodity servers. It provides linear scalability, fault tolerance, and high availability. Cassandra's architecture is masterless with all nodes equal, allowing it to scale out easily. Data is replicated across multiple nodes according to the replication strategy and factor for redundancy. Cassandra supports flexible and dynamic data modeling and tunable consistency levels. It is commonly used for applications requiring high throughput and availability, such as social media, IoT, and retail.
Cassandra - A Basic Introduction Guide
- 1. CASSANDRA A Basic Introduction Guide Mohammed Fazuluddin
- 2. TOPICS • Overview • Cassandra Features • Cassandra Architecture • NoSQL Cassandra Database Vs Relational databases • Data Model • Cassandra Use cases/Applications • Components Of Cassandra • Cassandra Data Replication • Supported Programming Languages • Pro’s and Con’s
- 3. OVERVIEW • The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. • Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. • Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages. • Cassandra is a distributed database management system designed for handling a high volume of structured data across commodity servers • Cassandra handles the huge amount of data with its distributed architecture. • Data is placed on different machines with more than one replication factor that provides high availability and no single point of failure.
- 5. CASSANDRA FEATURES • Massively Scalable Architecture: Cassandra has a masterless design where all nodes are at the same level which provides operational simplicity and easy scale out. • Masterless Architecture: Data can be written and read on any node. • Linear Scale Performance: As more nodes are added, the performance of Cassandra increases. • No Single point of failure: Cassandra replicates data on different nodes that ensures no single point of failure. • Fault Detection and Recovery: Failed nodes can easily be restored and recovered.
- 6. CASSANDRA FEATURES • Flexible and Dynamic Data Model: Supports datatypes with Fast writes and reads. • Data Protection: Data is protected with commit log design and build in security like backup and restore mechanisms. • Tunable Data Consistency: Support for strong data consistency across distributed architecture. • Multi Data Center Replication: Cassandra provides feature to replicate data across multiple data center. • Data Compression: Cassandra can compress up to 80% data without any overhead. • Cassandra Query language: Cassandra provides query language that is similar like SQL language. It makes very easy for relational database developers moving from relational database to Cassandra.
- 9. NOSQL CASSANDRA DATABASE VS RELATIONAL DATABASES
- 10. DATA MODEL
- 11. DATA MODEL
- 12. CASSANDRA USE CASES/APPLICATION • Messaging • Cassandra is a great database for the companies that provides Mobile phones and messaging services. These companies have a huge amount of data, so Cassandra is best for them. • Internet of things Application • Cassandra is a great database for the applications where data is coming at very high speed from different devices or sensors. • Product Catalogs and retail apps • Cassandra is used by many retailers for durable shopping cart protection and fast product catalog input and output. • Social Media Analytics and recommendation engine • Cassandra is a great database for many online companies and social media providers for analysis and recommendation to their customers.
- 14. COMPONENTS OF CASSANDRA • NodeNode • is the place where data is stored. It is the basic component of Cassandra. • Data Center • A collection of nodes are called data center. Many nodes are categorized as a data center. • Cluster • The cluster is the collection of many data centers. • Commit Log • Every write operation is written to Commit Log. Commit log is used for crash recovery. • Mem-table • After data written in Commit log, data is written in Mem-table. Data is written in Mem-table temporarily. • SSTable • When Mem-table reaches a certain threshold, data is flushed to an SSTable disk file.
- 15. CASSANDRA DATA REPLICATION • Cassandra places replicas of data on different nodes based on these two factors. • Where to place next replica is determined by the Replication Strategy. • While the total number of replicas placed on different nodes is determined by the Replication Factor. • One Replication factor means that there is only a single copy of data while three replication factor means that there are three copies of the data on three different nodes. • For ensuring there is no single point of failure, replication factor must be there.
- 16. CASSANDRA DATA REPLICATION • SimpleStrategy • SimpleStrategy is used when you have just one data center. • SimpleStrategy places the first replica on the node selected by the partitioner. • After that, remaining replicas are placed in clockwise direction in the Node ring. • NetworkTopologyStrategy • NetworkTopologyStrategy is used when you have more than two data centers. • In NetworkTopologyStrategy, replicas are set for each data center separately. NetworkTopologyStrategy places replicas in the clockwise direction in the ring until reaches the first node in another rack. • This strategy tries to place replicas on different racks in the same data center. This is due to the reason that sometimes failure or problem can occur in the rack. Then replicas on other nodes can provide data.
- 17. SUPPORTED PROGRAMMING LANGUAGES • Java: • You can find the DataStax Java driver at https://github.com/datastax/java-driver and the Hector Java client at http://hector-client.github.io/hector/build/html/index.html • Python: • You can download the drivers for Pythons such as Pycassa at http://github.com/pycassa/pycassa and the DataStax Python CQL driver at https://github.com/datastax/python-driver • Node.js: • You can find the Node.js driver Helenus at https://github.com/simplereach/helenus • PHP: • You can find the Cassandra PDO driver at http://code.google.com/a/apache-extras.org/p/cassandra- pdo/
- 18. PRO’S AND CON’S • Pro’s • Cassandra database automatically replicate the data of failed node to another node without any halt in work. • Cassandra database provide horizontal scaling, and enhance performance with increase load. • Cassandra is durable and provide data consistency, which makes it best fit to hold critical data of companies • Cassandra provides simple query language (CQL) ,very much similar to relational database and so easily adaptable by developers.
- 19. PRO’S AND CON’S • Con’s • Performance is unpredictable, as all the background task are executed in random way and are not scheduled by users. • Here data is modelled around queries instead of its structure due to which same data is store multiple times. • To store huge amount of data, JVM is required to manage the memory which itself is a language, and so garbage collection is not done by application but by a language in Cassandra.
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