nosql-is-a-lie-vdb16



nosql-is-a-lie-vdb16

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nosql-is-a-lie-vdb16


On Github ukmadlz / nosql-is-a-lie-vdb16

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NoSQL is a lie

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Who am I?

Mike Elsmore

Developer Advocate

mike.elsmore@uk.ibm.com

IBM Cloud Data Services

< rant >

NoSQL

- Originally coined by Carlo Strozzi in 1998 for Strozzi_NoSQL, which was relational but without SQL interface

Catch All

try {
    SQL
} catch (Exception $e) {
    Must be NoSQL
}

Not SQL

It's a nasty backronym

- Reintroduced by Johan Oskarsson of Last.fm in 2009 to describe all upcoming DB tech

SQL on NoSQL

SPARQL

CQL (Cassandra Query Language)

Couchbase SQL

Schemaless

Yes, they'll accept anything…but

Schema

Because how else do you know what you’re getting out?

No NoSQL Experts

Many Primary Types

So many distinct types of databases

"X" Expert

~80 Apache Projects

</ rant >

Enter CAP Theorem

Consistency, Availability and Partition Tolerance

Consistent

In = Out

Available

Partition Tolerant

Know's where to look

Pick two?

Uncertainty principle

Why is this important?

History

Consistent & Available

Ignoring the Partition Tolerance by being in the same place

Distributed Systems and Databases

Needs to know what machine X data is on

Partition Tolerance & ___________

Design Decision

The reason why most NoSQL Databases are either AP or CP

NewSQL

Google Spanner

CockroachDB

Types

Key-Value

Document

Column

Graph

Other Database Types

Object, Tabular, Tuple, Triple/Quad store (RDF), Multimodel, Etc

Skipping Multi-model

Different indexing & lookups on the same stored data.

https://bit.ly/nosqllie

Key Value Datastores

Name them?

http://bit.ly/nosql-is-a-lie-kv

Popular Key-Value Datastores

Redis, Memcached, Riak KV, Hazelcast, Ehcache, Aerospike, Oracle Coherence, Berkeley DB, Amazon SimpleDB, Oracle NoSQL, Infinispan, LevelDB, GridGain, ZODB, GT.M, NCache, RocksDB, WiredTiger, WebSphere eXtreme Scale, Tokyo Cabinet, Project Voldemort, XAP, Hibari, MapDB, Tokyo Tyrant, STSdb, Scalaris, GlobalsDB, HyperDex, Kyoto Cabinet, Tarantool, LightCloud, ScaleOut StateServer, Upscaledb, Quasardb, Bangdb, BergDB, Cachelot.io, CodernityDB, CortexDB, Elliptics, Helium, HyperLevelDB, Kyoto Tycoon, LedisDB, Nanolat, Resin, TomP2P

What is a Key-Value Datastore?

- data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary or hash - Dictionaries contain a collection of objects, or records, which in turn have many different fields within them, each containing data - Usually an AP system

Why use a Key-Value store?

- simple model makes them simple to use and powerful Use Cases - Session Stores - Fast search lookups - Queues

Document Datastores

- Considered to be a subclass of key-value - That means: - Records do not need to have a uniform structure, i.e. different records may have different columns. - The types of the values ​​of individual columns can be different for each record. - Columns can have more than one value (arrays). - Records can have a nested structure. - Document stores often use internal notations, which can be processed directly in applications, mostly JSON. JSON documents of course can also be stored as pure text in key-value stores or relational database systems. That would, however, require client-side processing of the structures, which has the disadvantage that the features offered by document stores (such as secondary indexes) are not available.

Name them?

http://bit.ly/nosql-is-a-lie-document

Popular Document Datastores

Cloudant, CouchDB, MongoDB, Couchbase, RethinkDB, RavenDB, GemFire, PouchDB, Microsoft Azure DocumentDB, Datameer, CloudKit, Mnesia, Google Cloud Datastore, TokuMX, Clusterpoint, Terrastore, RaptorDB, EJDB, SisoDb, WhiteDB, Sequoiadb, JasDB, LokiJS, DensoDB, Djondb, FaunaDB, FleetDB, SenseiDB

What is a Document Datastore?

- Considered a subclass of key-value datastores - Relies on the document to provide the meta data to optimise and build further queries - Uses techniques like MapReduce to query - Uses search systems like Apache Lucene for advanced querying

Why use a Document Datastore?

- Operational Datastore - Flexibility in changing the data model whilst presenting the same responses - The majority are designed with AP in mind - Once you model around eventual consistency your about have a lot of reads and writes

Column Datastores

- Wide Column Datastores - Wide column stores, also called extensible record stores, store data in records with an ability to hold very large numbers of dynamic columns. Since the column names as well as the record keys are not fixed, and since a record can have billions of columns, wide column stores can be seen as two-dimensional key-value stores. - Wide column stores share the chracteristic of being schema-free with document stores, however the implementation is very different.

Name them?

http://bit.ly/nosql-is-a-lie-column

Popular Column Datastores

Cassandra, HBase, Accumulo, Hypertable, Google Cloud Bigtable, ScyllaDB

What is a Column Datastore?

- Does use tables, rows and columns for the storage model - Kinda relational - However the names and format of columns can change between rows

Why use a Column Datastore?

- Can use it for operational storage - But due to how you model it, Relational DBs - Is amazing for timeseries - Massive distribution - Network failure

Graph Datastores

- Graph DBMS, also called graph-oriented DBMS or graph database, represent data in graph structures as nodes and edges, which are relationships between nodes. They allow easy processing of data in that form, and simple calculation of specific properties of the graph, such as the number of steps needed to get from one node to another node. - Graph DBMSs usually don't provide indexes on all nodes, direct access to nodes based on attribute values is not possible in these cases.

Name them?

http://bit.ly/nosql-is-a-lie-graph

Popular Graph Datastores

Neo4j, Titan, Giraph, InfiniteGraph, Sparksee, HyperGraphDB, FlockDB, VelocityGraph, InfoGrid, GraphBase

What are Graph Datastores?

- Well it's graph structures - nodes -> edges - Take advantage of distributed computing to cope with X Million+ relationships - Relies more on the relationships than the meta data

Why use Graph Datastores?

- You can do the same in SQL, if the traversal is fixed - Allows for complex iterative and cyclical queries Best use cases - Social Graphs (erdos) - Recommendation engines - Fraud detection

My proof

Ambrosia

Nutrition-as-a-service

The Team

we all wore the same thing. how awkward. pic.twitter.com/viDQRwKEWB

— Sarah Maston (@maston) July 23, 2016

What we've learnt…

NoSQL is a lie

Why NoSQL grew up the way it did

And it's just different databases for different jobs

Some reasons to use different databases

Mike Elsmore

NoSQL is a lie

http://elsmore.me/nosql-is-a-lie-vdb16

@ukmadlz

mike.elsmore@uk.ibm.com

Questions?

© IBM Corporation 2016. All Rights Reserved.
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