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Cassandra an overview
- 2. Road Map • The Big Data Challenge • The Cassandra Solution • The CAPTheorem • The Architecture of Cassandra • The Data Partition and Replication
- 3. The Big Data Challenge
- 4. Data Proliferation • Data Produce Growing Exponentially • Sources of Data Growing • Mobile Devices • Cloud Services • Historical Data
- 5. ThreeVs –Trends In Big Data • Volume: • Terabytess -> Zettabyts • Velocity: • Batch -> Streaming • Variety : • Structured -> Unstructured
- 6. RDBMS Scaling • Traditional RDBMS : No Scale out • Cassandra : Linear Scale out
- 8. Objective • Schema Free • Easy Replication • SimpleAPI • Consistence • Can Handle huge data NoSQLDatabase • simplicity of design, • horizontal scaling • finer control over availability
- 9. Relational Database vs. NoSQL Relational Database • Supports powerful query language • It has a fixed schema • Follows ACID (Atomicity,Consistency, Isolation, and Durability) • Supports transactions NoSQL Database • Supports very simple query language • No Fixed Schema • It is only “eventually consistent” • Does not support transactions
- 10. Other NoSQL Database • Apache HBase - HBase is an open source, non-relational, distributed database modeled after Google’s BigTable and is written in Java. It is developed as a part of Apache Hadoop project and runs on top of HDFS, providing BigTable-like capabilities for Hadoop. • MongoDB - MongoDB is a cross-platform document- oriented database system that avoids using the traditional table-based relational database structure in favor of JSON- like documents with dynamic schemas making the integration of data in certain types of applications easier and faster.
- 11. What is Apache Cassandra? • Apache Cassandra™ is a free • Distributed • High performance • Extremely scalable • Fault tolerant (i.e. no single point of failure) • post-relational database solution. Cassandra can serve as both real-time data store (the “system of record”) for online/transactional applications, and as a read intensive database for business intelligence systems.
- 12. Features of Cassandra • Elastic scalability • Always on architecture • Fast linear-scale performance • Flexible data storage • Easy data distribution • Transaction support • Fast writes
- 13. History of Cassandra • Cassandra was developed at Facebook for inbox search. • It was open-sourced by Facebook in July 2008. • Cassandra was accepted into Apache Incubator in March 2009. • It was made an Apache top-level project since February 2010.
- 14. The CapTheorem
- 15. CAPTheorem • Distributed System Can only provide two of • Availability • Consistency • PartitionTolerance • AKA BrewersTheorem
- 16. Cassandra AP • Cassandra Prioritizes Availability and PartitionTolerance • Consistency is not guaranteed • Tradeoffs between latency and Consistency
- 17. Other Approaches -CP • Eg. Hbase • Implements Row locking for consistency • HBase has master/slave & Single point of Failure • No A
- 18. The Architecture of Cassandra
- 19. Architecture Overview • Cassandra was designed with the understanding that system/hardware failures can and do occur • Peer-to-peer, distributed system • All nodes the same • Data partitioned among all nodes in the cluster • Custom data replication to ensure fault tolerance • Read/Write-anywhere design
- 20. Architecture Overview • Each node communicates with each other through the • Gossip protocol, which exchanges information across the • cluster every second • A commit log is used on each node to capture write • activity. Data durability is assured • Data also written to an in-memory structure (memtable) • and then to disk once the memory structure is full (an • SStable)
- 21. Architecture Overview • The schema used in Cassandra is mirrored after Google • Bigtable. It is a row-oriented, column structure • A keyspace is akin to a database in the RDBMS world • A column family is similar to an RDBMS table but is more • flexible/dynamic • A row in a column family is indexed by its key. Other • columns may be indexed as well
- 22. Components of Cassandra • Node − It is the place where data is stored. • Data center − It is a collection of related nodes. • Cluster − A cluster is a component that contains one or more data centers. • Commit log −The commit log is a crash-recovery mechanism in Cassandra. Every write operation is written to the commit log. • Mem-table − A mem-table is a memory-resident data structure. After commit log, the data will be written to the mem-table. Sometimes, for a single-column family, there will be multiple mem-tables. • SSTable − It is a disk file to which the data is flushed from the mem-table when its contents reach a threshold value. • Bloom filter −These are nothing but quick, nondeterministic, algorithms for testing whether an element is a member of a set. It is a special kind of cache. Bloom filters are accessed after every query.
- 24. The Data Partition and Replication
- 25. Partition Process • Data is transparently portioned across the nodes • Data sent to a node is hashed and sent to partition based on hash • The data partitioning strategy is controlled via the partitioner option inside cassandra.yaml file • Once a cluster in initialized with a partitioner option, it can not be changed without reloading all of the data in the cluster
- 26. Partitioning Strategies • Random Partitioning • This is the default and recommended strategy. • Partition data as evenly as possible across all nodes • using an MD5 hash of every column family row key • Ordered Partitioning • Store column family row keys in sorted order across all nodes in the cluster. • Sequential writes can cause hot spots • More administrative overhead to load balance the cluster • Uneven load balancing for multiple column families
- 27. Replication • To ensure fault tolerance and no single point of failure, you can replicate one or more copies of every row across nodes in the cluster • Replication is controlled by the parameters replication factor and replication strategy of a keyspace • Replication factor controls how many copies of a row should be store in the cluster • Replication strategy controls how the data being replicated.
- 28. Replication Strategies • Simple Strategy • Place the original row on a node determined by the partitioner.Additional replica rows are placed on the new nodes clockwise in the ring. • NetworkTopology Strategy • Allow replication between different racks in a data center and or between multiple data centers • The original row is placed according the partitioner.Additional replica rows in the same data center are then placed by walking the ring clockwise until a node in a different rack from previous replica is found. If there is no such node, additional replicas will be placed in the same rack.
Editor's Notes
- #9: Apache Cassandra is a highly scalable, high-performance distributed database designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. It is a type of NoSQL database. Let us first understand what a NoSQL database does. NoSQLDatabase A NoSQL database (sometimes called as Not Only SQL) is a database that provides a mechanism to store and retrieve data other than the tabular relations used in relational databases. These databases are schema-free, support easy replication, have simple API, eventually consistent, and can handle huge amounts of data. The primary objective of a NoSQL database is to have simplicity of design, horizontal scaling, and finer control over availability. NoSql databases use different data structures compared to relational databases. It makes some operations faster in NoSQL. The suitability of a given NoSQL database depends on the problem it must solve.