Case study: CASSANDRA

Transcription

1 Case study: CASSANDRA Course Notes in Transparency Format Cloud Computing MIRI (CLC-MIRI) UPC Master in Innovation & Research in Informatics Spring Jordi Torres, UPC - BSC

2 Cassandra: main features Cassandra does not support relationships between column families ( tables ), disregarding foreign keys and join operations. Knowing this, the best practice when designing a data model is to keep related data in the same column family. In this section we will review only the main features of Cassandra as an example 2

3 Architecture The architecture of Cassandra is completely decentralized and peer-to-peer, meaning all nodes in a Cassandra cluster are equivalent and provide the same functionality: receive read and write requests, or forward them to other nodes. Peer-to-peer, distributed system All nodes the same Data Partitioned Custom data replication 3

4 Partitioning Cassandra implements automatic partitioning and replication mechanisms to decide which nodes are in charge of each replica. How? PARTITIONER Divide the data across the nodes in the cluster Each node is responsible for a range of the overall data Source: Juan Luis Pérez researcher at BSC (EEDC 2012 master course) 4

5 Partitioning Node A Node B Node C Node D Source: Juan Luis Pérez researcher at BSC (EEDC 2012 master course) 5

6 Partitioning Row Key determines node placement raiser name: john pass: **** url: icann.org trucker name: james pass: **** url: w3.org dumpe r name: maria pass: **** biker name: linda pass: **** 6

7 Partitioning Range of MD5 hash [ ] [ ] [ c00..0] [c ] 7

8 Partitioning Row Key MD5 Hash raiser trucker dumpe r biker 65236c... a113f4... d4ab [ ] [ c00..0] [ ] [c ] 8

9 Partitioning Row Key MD5 Hash raiser trucker dumpe r biker 65236c... a113f4... d4ab [ ] [ c00..0] [ ] [c ] 9

10 Partitioning Row Key MD5 Hash raiser trucker dumpe r biker 65236c... a113f4... d4ab [ ] [ c00..0] [ ] [c ] 10

11 Partitioning Row Key MD5 Hash raiser trucker dumpe r biker 65236c... a113f4... d4ab [ ] [ c00..0] [ ] [c ] 11

12 Partitioning Row Key MD5 Hash raiser trucker dumpe r biker 65236c... a113f4... d4ab [ ] [ c00..0] [ ] [c ] 12

13 Replication Remember: Cassandra implements automatic partitioning and replication mechanisms to decide which nodes are in charge of each replica à The user only needs to configure the number of replicas and the system assigns each replica to a node in the cluster. 13

14 Replication Cassandra stores multiple copies of rows on multiple nodes Replication factor = number of replicas Replica Placement Strategy DEFAULT: SimpleStrategy NetworkTopologyStrategy Configurable both: Replication factor Placement Strategy 14

15 Replication SimpleStrategy First replica determined by the partitioner Additional replicas rows are placed on the next nodes clockwise in the ring Original Row raiser Copy Row raiser 15

16 Replication NetworkTopologyStrategy Allows replication between different racks Racks in a data center or in multiple data centers Reliability & Performance Others 16

17 Consistency The goal of current distributed key-value stores such as Cassandra is to read and write data operations, exactly the same as any database system However, while traditional databases provide strong consistency guarantees of replicated data by controlling the concurrent execution of transactions, Cassandra provides tunable consistency in order to favour scalability and availability. 17

18 Consistency Data consistency is tunable by the user when queries are performed, so depending on the desired level of consistency, operations can either return as soon as possible or wait until a majority or all nodes respond Tunable data consistency Choose between strong and eventual consistency Consistency per-operation (reads & writes) 18

19 Strategy for Read 19 19

20 Strategy for Writes 20 20

21 Strong/Weak consistency? As it can be derived from their description, strong consistency can only be achieved when using (Quorum and) All consistency levels. Operations that use weaker consistency levels, such as Zero, Any and One, aren t guaranteed to read the most recent data. However, this weaker consistency provides certain flexibility for applications that can benefit from better performance and don t have strong consistency needs. imagine your facebook wall!!! 21

22 Caching Data is first written to a commit log for durability Local to the node (for disaster recovery purpouse) Then written to a in-memory structure (memtable) Node that store the row And then to disk (SSTable) once memtable is full Data durability is assured memtable Commit log SSTable Source: Juan Luis Pérez researcher at BSC (EEDC 2012 master course) 22