Non-Relational Databases. Pelle Jakovits

Transcription

1 Non-Relational Databases Pelle Jakovits 25 October 2017

2 Outline Background Relational model Database scaling The NoSQL Movement CAP Theorem Non-relational data models Key-value Document-oriented Column family Graph Example databases 2/36

3 The Relational Model Data is stored in tables Strict relationships between tables Foreign key references between columns The expected format of the data is specified with a restrictive schema Data is typically accessed using Structured Query Language (SQL) 3/36

4 Database Scaling Vertical scaling on one machine Horizontal scaling across a cluster of machines Relational model does not scale well horizontally Because there are too many dependencies in relational model Database sharding is one approach 4/36

5 Sharding 5/36

6 Relational database 6/38

7 The NoSQL Movement Emergence of persistence solutions using non-relational data models Driven by the rise of Big Data and Cloud Computing Non-relational data models are based on Key - Value structure Simpler schema-less key-value based data models scale better than the relational model NoSQL is a broad term with no clear boundaries The term NoSQL itself is very misleading No SQL? Not only SQL? 7/36

8 CAP Theorem It is impossible for a distributed computer system to simultaneously provide all three of the following: Consistency - every read receives the most recent write or an error Availability - every request receives a response Partition/Fault tolerance - the system continues to operate despite arbitrary partitioning (network failures, dropped Have to choose between consistency or availability NoSQL solutions which are more focused on availability try to achieve eventual consistency When trying to aim for both consistency or availability Will have high latency 8/36

9 CAP Theorem 9/36

10 Benefits of the Key-value Model Horizontal scalability Data with the same Key stored close to each other Suitable for cloud computing Flexible schema-less models suitable for unstructured data Fetching data by key can be very fast 10/36

11 Indexing and Partitioning In Key-value model, Key acts as an index Secondary indexes can be created in some solutions Implementations vary from partitioning to local distributed indexes Data is partitioned between different machines in the cluster Usually data is partitioned by rows or/and column families Users can often specify partitioning parameters Gives control over how data is distributed Very important for optimizing query speed 11/36

12 Aggregate-oriented DB Non-relational data models aim to store aggregated data together Aggregate is a collection of data that is treated as a unit E.g. a customer and all of his orders In normalized relational databases aggregates are computed using GroupBy operations Keyed aggregates make for a natural unit of data sharding 12/36

13 Non-relational Data Models

14 The Key-value Model Data stored as key-value pairs The value is an opaque blob to the database Examples: Dynamo, Riak 14

15 The Document-oriented Model Data is also stored as key-value pairs Value is a document and has further structure No strict schema Examples: CouchDB, MongoDB 15/36

16 Example Aggregates described in JSON using map and array data structures 16

17 The Column Family Model Data stored in large sparse tabular structures Columns are grouped into column families Column family is a meaningful group of columns Similar concept as a table in relational database A record can be thought of as a two-level map New columns can be added at any time Examples: BigTable, Cassandra, HBase 17/36

18 Column Family Example a001 username jsmith Names firstname John lastname Smith phone Contacts jsmith@example.com item1 Message1 item2 Message2 Messages itemn MessageN username pauljones Names b014 Contacts item1 new Message Messages 18/36

19 Graph Databases Data stored as nodes and edges of a graph The nodes and edges can have fields and values Aimed at graph traversal queries in connected data Examples: Neo4J, FlockDB 19/36

20 Non-relational Data Models In non-relational data stores data is denormalized It is common to also store JSON documents in keyvalue stores The key-value, document-oriented and column family models are aggregate oriented models The other models are based on the key-value model In reality the classification of databases into different models is not as straight forward Multiparadigm databases also exist (e.g ArangoDB) 20/36

21 Non Relational Database Examples

22 Riak Based on Amazon's Dynamo specification Key-value model Distributed decentralized persistent hash table Keyspace is partitioned between nodes Consistent hashing Avoid hash-to-node remapping when a node is added or removed Eventual Consistency using Multiversion Concurrency Control (MVCC) 22/36

23 Riak RESTful query interface Basic PUT, GET, POST, and DELETE functions Links and link walking One way relationships between data objects Turns Key-Value store into a simple Graph database Higher level querying on-top of Key-Value structure: Riak search is based on Apache Solr search engine MapReduce in Erlang and JavaScript 23/36

24 MongoDB Document oriented (BSON) Query language based on JavaScript GridFS for BLOB file storage Master-slave architecture Linking between documents Supports MapReduce in JavaScript 24/36

25 Query example db.inventory.find({ status: "A", $or: [ {qty: { $lt: 30 }}, { item: /^p/ }] }) Matches the following SQL query: SELECT * FROM inventory WHERE status = "A" AND ( qty < 30 OR item LIKE "p%") 25/38

26 CouchDB Document oriented (JSON) RESTful query interface Built in web server Web based query front-end Futon 26/36

27 CouchDB MapReduce is the primary query method (JavaScript and Erlang) Materialized views as results of incremental MapReduce jobs CouchApps javascript-heavy web applications built entirely on top of CouchDB without a separate web server for the logic layer 27/36

28 Query example Documents: { id : 2, "position" : "programmer", "first_name": "James", "salary" : 100 } { id : 7, "position" : "support", "first_name": "John", "salary" : 23 } Lets extract average salary for each unique position Map: function(doc, meta) { if (doc.position && doc.salary) { emit(doc.position,doc.salary); } } Reduce: function(key, values, rereduce) { return sum(values)/values.length; } Some Reduce functions have been pre-defined: _sum(), _count(), _stats() 28/38

29 Cassandra Column family model Data model from BigTable, distribution model from Dynamo (decentralized) Uses Cassandra Query Language (CQL) SQL-like querying language 29/36

30 Cassandra Provides Availability & Partition-Tolerance from the CAP theorem Static and dynamic column families Dynamic column families as materialized views on data The concept of super-columns Family of column families 30/36

31 Neo4J Open source NoSQL Graph Database Uses the Cypher Query Language for querring graph data Graph consists of Nodes, Edges and Attributes 31/36

32 Neo4J Querry example MATCH (you {name:"you"}) MATCH (expert)-[:worked_with]-> (db:database {name:"neo4j"}) MATCH path = shortestpath( (you)-[:friend*..5]-(expert) ) RETURN db,expert,path 32/38

33 Why use Non-Relational DB? Volume of Data Elasticity Flexible Schemas Cost 33/38

34 Polyglot Persistence 34/36

35 Conclusions In recent years there has been a rise in nonrelational (NoSQL) data stores This is related to the rise of cloud computing key-value models offer better scalability The NoSQL landscape is extremely varied The four basic non-relational data models are: The key-value model The document-oriented model The column family model Graph databases 35/36

36 That is All Next week's practice session Using NoSQL databases Exam times Wednesday - 01 November 2017 Thursday - 02 November 2017 NB! A set of example exam questions are available on the course website 36/36