Patrick Guillebert – IT-Tage 2015 – Cassandra NoSQL - Architektur und Anwendungsfälle
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The document discusses the design and operational principles of Apache Cassandra, focusing on its capabilities of availability, scalability, and tunable consistency in line with the CAP theorem. It highlights how Cassandra manages data distribution, node dynamics, and offers different consistency levels tailored for applications, which can include immediate or eventual consistency. Additionally, it mentions successful production deployments at major companies like Apple and Netflix, underscoring Cassandra's effectiveness in handling massive datasets and operational simplicity.
Patrick Guillebert – IT-Tage 2015 – Cassandra NoSQL - Architektur und Anwendungsfälle
- 2. Patrick Guillebert Solutions Architect [email protected] Availability, scalability and consistency with Cassandra
- 3. Cassandra and the CAP Theorem In a distributed system, only 2 of these attributes can be fulfilled at a time: Consistency, Availability, Partition tolerance Availability, Partition tolerance is deeply built in by the design. Consistency results programmatically and is tunable. ©2014 DataStax Confidential. Do not distribute without consent. 2 Cassandra is an AP system
- 4. Cassandra’s design goals Massive and predictable scaling Always-On Tunable consistency Geographically distributed Low latency Operationally simple 2016: + User friendly ©2015 DataStax. Do not distribute without consent. 3
- 5. © 2014 DataStax Confidential. Do not distribute without consent. 4 Numbers and facts from production deployments
- 6. Scale at Apple Cassandra can scale from 3 to 1000+ nodes • Footprint @ Apple • 75,000+ nodes • 10’s of petabytes of data • Millions ops/second • Largest cluster 1000+ nodes Apple Inc.: Cassandra at Apple for Massive Scale Video https://www.youtube.com/watch?v=Bc4ql9TDzyg Form Cassandra Summit, USA, September 2014
- 7. Availability at Netflix ©2015 DataStax. Do not distribute without consent. 6
- 8. Consistency at ING Bank ©2015 DataStax. Do not distribute without consent. 7 https://www.youtube.com/watch?v=-sD3x8-tuDU
- 9. Scalability
- 10. Linear and predictable scaling Need to store mode data? Add more nodes. Need more throughput ? Add more nodes. ©2015 DataStax. Do not distribute without consent. 9 Cassandra • Is “future proof” • By scaling out linearly on commodity hardware.
- 11. Partitionning • The dataset is distributed all nodes of the cluster • Data subsets are called “token ranges” • With vnodes, a node could have several small token ranges, 256 by default ©2015 DataStax. Do not distribute without consent. 10 Token Range (Murmur3) Node 1 Node 3 Node 2Node 4 - 263+ 263
- 12. The partitioner ©2015 DataStax. Do not distribute without consent. 11 "@DataSta x" Hash Function 97203 -2245462676723223822 7723358927203680754 • Partitioner is responsible for calculating the token, which is the hash of the key of the data to store • The token is just a number between -2^63 and 2^63 • Partitioners: Murmur3Partitioner (Murmur3), RandomPartitioner (MD5) ByteOrderedPartitioner
- 13. Data distribution within the cluster ©2015 DataStax. Do not distribute without consent. 12 Node 1 Node 3 Node 2Node 4Node 4 Partitioner ’@Datastax' Token 91 Node 1 0100
- 14. 13 Availability
- 15. Node level availability A node failure has no impact • No failover • 0 downtime • 0 data loss • Consistent responses ©2014 DataStax Confidential. Do not distribute without consent. 14 Node 1 1st copy Node 4 Node 5 Node 2 2nd copy Node 3 3rd copy Parallel Writes RF=3 CL=QUORUM If 2 nodes out of the 3 replicas respond, the request is a success Immediate onsistency is ensured Node 4
- 16. Rack level availability A rack failure has no impact on the service ©2014 DataStax Confidential. Do not distribute without consent. 15 Node 1 1st copy Node 4 Node 5 Node 2 2nd copy Node 3 3rd copy Write CL=QUORUM Immediate consistency (RF 3 / CL QUORUM) : Nodes are distributed across at least 3 racks If a rack fails, a quorum of replicas remain available in the remaining racks and the request suceeds. Node 4 RAC 1 RAC 2 RAC 3 RF=3 CL=QUORUM
- 17. Data Centre level availability ©2014 DataStax Confidential. Do not distribute without consent. 16 Node 1 1st copy Node 4 Node 5 Node 2 2nd copy Node 3 3rd copy DC: Frankfurt Node 1 1st copy Node 4 Node 5 Node 2 2nd copy Node 3 3rd copy DC: Dublin A DC failure has no impact on the service
- 18. Internals and practices that enable availability ©2014 DataStax Confidential. Do not distribute without consent. • Cluster topology awareness (“NetworkTopologyStrategy”) in replication A cluster is described as a group of data centres, containining racks, containining nodes. This topology is used to set replicas in a “smart” fashion. • Peer to peer architecture All nodes are strictly equal on the read/write path, any node can be queried for any data Seeds have a special function only in the context of node bootstrap Failover never happens • Trade off on consistency Response consistency is ensured by asking a quorum of nodes to be consistent. There is no single “master” knowing the latest write. • Live operations Whichever the setup is (single or multi-dc) operations can be made live, with no downtime
- 19. Single DC replication ©2014 DataStax Confidential. Do not distribute without consent. Node 1 Node 3 Node 2Node 4 Client Driver Node 4 Node 1 Node 2 Node 3 1-25 26-5051-75 76-0 91 91 Single Data Center CREATE KEYSPACE demo WITH REPLICATION = { 'class':'SimpleStrategy', 'replication_factor':3 }; Primary Range for Token 91
- 20. Multi-DC replication ©2014 DataStax Confidential. Do not distribute without consent. Node 1 Node 3 Node 2Node 4 Client Driver Node 4 Node 1 Node 2 Node 3 1-13 26-38 51-63 76-88 Node 1 Node 3 Node 2Node 4 Data Center - West Rack 1 Rack 2 Node 2 Rack 1 Rack 2 91 91 91 CREATE KEYSPACE demo WITH REPLICATION { 'class':'NetworkTopologyStrategy', 'dc-east':2, 'dc-west’:3 } Remote Coordinator Primary Range for Token 91 14-25 39-50 64-75 89-100 91
- 21. What is different from a master-slave system ? ©2014 DataStax Confidential. Do not distribute without consent. Failovers never happens, having no master irremediable data corruption in split brain situation can’t happen
- 22. Consistency
- 23. Consistency types ©2015 DataStax. Do not distribute without consent. 22 • Immediate consistency The uniquely given consistency type which a relational database provide. A read always return the last written data, whichever node is requested. • Eventual consistency A read will eventually return the last written data. Depending on the node queried, responses could be different. Cassandra offers both types of consistency, it can be tuned.
- 24. Consistency Levels ©2015 DataStax. Do not distribute without consent. 23 Consistency levels are set on each single request. It determines the number of node which need to acknowledge a write or agree on the value of a queried data. Consistency types could be different depending on the table or in time. • Common CLs: ONE, QUORUM, ALL • Multi-DC CLs: LOCAL_ONE, LOCAL_QUORUM, LOCAL_ALL, EACH_QUORUM • Special CLs: ANY, SERIAL, LOCAL_SERIAL
- 25. General consistency levels ©2015 DataStax. Do not distribute without consent. 24 ONE QUORUM ALL ANY SERIAL
- 26. Multi-DC consistency levels ©2015 DataStax. Do not distribute without consent. 25 LOCAL_ONE LOCAL_QUORUM LOCAL_ALL EACH_QUORUM LOCAL_SERIAL
- 27. The rule to tune consistency ©2015 DataStax. Do not distribute without consent. 26 Immediate consistency : CL READ + CL WRITE > REPLICATION READ QUORUM + WRITE QUORUM READ ONE + WRITE ALL READ ALL + WRITE ONE Eventual consistency CL READ + CL WRITE <= REPLICATION READ ONE + WRITE ONE READ ONE + WRITE ANY
- 28. Time base conflict resolution ©2015 DataStax. Do not distribute without consent. 27 Node 1 Node 3 Node 2Node 4 Client Driver timestamp … 523 timestamp … 511 timestamp … 542 The last write wins !
- 29. Empirical verification of the rule ©2015 DataStax. Do not distribute without consent. 28 Node 1 Node 3 Node 2Node 4RF = 3 CL QUORUM WRITE ALL COMBINATIONS OF CL QUORUM
- 30. Use cases
- 31. Domains of use cases ©2015 DataStax. Do not distribute without consent. 30 • IoT • Web / mobile • Gaming • Bank, finance • Telecoms
- 32. Using C* for to back a global reference data service ©2015 DataStax. Do not distribute without consent. 31 Requirements • Always on • High throughput • Low latency • Multi-region replication Little or big data
- 33. Extending use cases with DSE ©2015 DataStax. Do not distribute without consent. 32
- 34. Thank you !