NoSQL Databases. Concept, Types & Use-cases.

Transcription

1 NoSQL Databases Concept, Types & Use-cases 1of93 Hello World Alon Spiegel Mamram grad. Programmer since 1995 DBA since 1997 Co founder and CEO since 2007 Brillix VP products since 2014 Brillix/DBAces 2of93 1

2 Let s talk about What is NoSQL NoSQL Architecture - Distributed database architecture principals NoSQL Flavors exploring the leaders On-premise vs managed service Community vs Enterprise Adoption plan and choosing the right one 3of93 THE OBJECT/RELATIONAL MAPPING PROBLEM 4of93 2

3 Object-relational Mapping (ORM) Mapping is good for C.R.U.D Complex querying becomes very inefficient RS to Obj mapping is tedious 5of93 Thinking out of the box 1998 Carlo Strozzi RDBMS without SQL API NoSQL movement emerges Storzzi refers to it as NoREL Eric Evans (Rackspace employee ) reintroduces the term in an event that discusses open-source distributed dbs 6of93 3

4 FORMATION 7of93 The Jurassic era 60 s Edgar Frank Codd s (IBM): relational model theory 1974 First RDBMS ever System R (IBM) 1979 Oracle v.2 for PDP DB2 for MVS mainframe (IBM) 1984 Sybase is founded but first released in MS branches MSSQL 6 & 6.5 8of93 4

5 RDBMS Built to last Best relationship description Full normalization Powerful queries (joins + indexes) Rich language (SQL, PLSQL,TSQL) 9of93 RDBMS Built to last Easy to use and integrate Rich toolset Many vendors Many drivers Fully transactional 10 of 93 5

6 Expected limited Concurrency 80 s - Terminal based computers Mainframes AS 400 PDP-11, VAX 90 s Client server based application Dos Windows 11 of s - A need emerges Web based application and web 2.0 Social media (facebook, twitter) Huge data-sets Unstructured 12 of 93 6

7 So, what is NoSQL Not Only SQL Modern web-scale database Key principals Often more characteristics Non relational Distributed Open-source Horizontally scalable schema-free easy replication support simple API (CRUD+) eventually consistent (not ACID) huge amount of data 13 of 93 The NoSQL movement s roots Google Big Table Amazon DynamoDB CAP Theorem 14 of 93 7

8 ARCHITECTURE 15 of 93 General layout Records are distributed among chunks in shards Shards are usually installed one per host Shards usually host a mix of primary chunks and replica chunks Some implementations separate primary from replica chunks by the shard 16 of 93 8

9 General layout Application Sahrds Primary/Replica Chunks Primary/Replica Records KV/Documents/Records/Links&Endpoints 17 of 93 General layout scaling (out) The process of adding or removing a shard from the cluster The process is complete after a rebalance redistribute (long) operation 18 of 93 9

10 ARCHITECTURE BY EXAMPLE 19 of 93 Config Config Each is a host Config Arbiter Arbiter Arbiter Arbiter 20 of 93 10

11 MongoDB replication 21 of 93 MongoDB failover 22 of 93 11

12 MongoDB DR 23 of 93 ARCHITECTURE BY EXAMPLE 24 of 93 12

13 25 of 93 Couchbase sharding 26 of 93 13

14 Couchbase replication 27 of 93 Couchbase failover Shard Shard As part of Rebalance Failover 28 of 93 14

15 Couchbase DR 29 of 93 Consistency Availability Partition Tolerance CAP THEOREM 30 of 93 15

16 CAP Theory Proof Theorem 2000 Eric Brewer a Berkeley prof. and Google co. founder conjecture 2002 Gilbert and Lynch proof! asynchronous and partially synchronous network models 31 of 93 Brewer's (CAP) Theorem Consistency - no logical data corruption Availability - service always available Partition tolerance - protect against data discrepancies when network islands form 32 of 93 16

17 CAP Theorem in practice In real life, the network will eventually fail: Inconsistent (AP): Return what every data node holds (Outdated or not yet propagated) Unavailable (CP): Don t return failing node s data MongoDB is AP allows to play with two settings: Write concern and Read reference Couchbase is CP; automatic failover (AP) 33 of of 93 17

18 So why NoSQL Workload diversity flexible design and data modeling Scalability resource elasticity Performance Avoid monolithically limitations Durability and continuous availability no single point of failure no matter what 35 of 93 So why NoSQL Manageability Minimum operational complexity and specialized personnel Cost Cluster of commodity servers with cheap/free licensing Strong Community An important factor to choosing a NoSQL platform 36 of 93 18

19 RDBMS vs NoSQL comparison Types Development history Schemas One type (SQL Database) with minor variations Developed in 1970s to deal with first wave of data storage applications Structure and data types are fixed in advance Scaling Vertically Horizontally Data manipulation Examples SQL mostly ANSI and very similar among all the vendors Oracle, MSSQL, MySQL, PostgreSQL Many different types including key-value stores, document database, wide column stores and graph databases Developed in the 2000s to deal with limitations of SQL databases, particularly concerning scale, replication and unstructured data storage Typically dynamic. Records can add new information on the fly unlike SQL tables Through object-oriented API MongoDB, Cassandra, Aerospike, Couchbase, DynamoDB 37 of 93 EXPLORING NOSQL FLAVORS 38 of 93 19

20 4 flavors of NoSQL databases Key-value store (KVS) Document database Wide Column Store - Column Families Graph database 39 of 93 Key-value store Designed for scaling to huge amounts of data handling massive loads A hash table of unique keys that point to a data store. A very easy model to implement. Only keys are indexed No complex data model 40 of 93 20

21 Key-value store - Aerospike Hybrid memory architecture Key always in memory Value always on disk UDF User defined functions with Optimized for SSD storage Strong consistency Fast as a cache, reliable as a database 41 of 93 Aerospike Popular uses AdTech Travel caching Online gaming 42 of 93 21

22 Key-value cache - Redis Key-value cache and store Referred to as data structure server: Strings, hashes, lists, sets, sorted sets, bitmaps and hyperloglogs Interval checkpoint to disk or log appends Very good and fast as long as dataset is contained in machine s RAM 43 of 93 Redis - Popular uses Main feeds Queues Caching Follow 44 of 93 22

23 Document databases Support a complex, multi-value, nested data model. Non PK indexes updated online or batch Document level operations Recognize special data-formats such as spatial Update partial / entire document 45 of 93 Document databases Organized similarly to key/value store Value is a semi-structured document JSON BSON XML YMAL 46 of 93 23

24 Document based - mongodb Collection of BSON objects Secondary indexes (online) Fully consistent reads Provides embedded docs and linking (joins) Uses a locking mechanism Master-slave replication 10 checkpoints a second 47 of 93 MongoDB - Popular uses Weather alerts Travel arrangement Personalization Custom CMS for readers 48 of 93 24

25 Document based Couchbase A hybrid of CouchDB and memcached Buckets of JSON documents Views of Map/Reduce (batch) for indexing and aggregations. Multi-Master replication with XDCR Integration with Hadoop, Elasticsearch, etc 49 of 93 Couchbase - Popular users Product listing cache Session/Token store cache Follow Answers on Demand big data analytics on consumer purchase behavior and product sales trends 50 of 93 25

26 Document database - DynamoDB Managed service by Amazon Store 3 geographical distributed replicas Read consistency customizable Eventually or Strong PK single hashed attribute (e.g. User ID) or composite hash-range (e.g. User ID, timestamp) 51 of 93 DynamoDB - Popular uses Recommendation platform Kids, you tried your best and you have failed miserably. The lesson is, never try. (Homer J. Simpson) 52 of 93 26

27 Wide column store Extensible record store OR Column family store Can hold a lots of columns (millions) Each key points to a row, each column is a tuple of column name and a value or a triplet adding a timestamp 53 of 93 Wide column store A column family (table) is a list of values that reside together on disk (like a table in RDBMS). Granularity update individual column 54 of 93 27

28 Wide column store A Super column is a list of columns It is actually one nesting level Super columns do not have timestamps 55 of 93 Wide column store - Cassandra Born at Facebook, now under Apache A hybrid of Amazon s Dynamo and Google s Bigtable SQL like query language: CQL Write to one / majority nodes (Tunable consistency) Use gossip protocol for intra-cluster communication Row-level write isolation Use secondary indexes for low cardinality searches 56 of 93 28

29 Wide column store Popular uses Logging, tracking and notifications Fraud detection Social signals Ad serving Audit (fight spam and abusive) Churn tracking, follow requests 57 of 93 Wide column store HBase Part of Hadoop under Apache Strictly consistent reads and writes Intra-cluster communication is handled by zookeeper ACID level semantics on per-row basis Secondary indexes alternates Filter query Periodic-update secondary indexes using MapReduce Dual write index Summary tables using MapReduce Coprocessor secondary index using internal triggers 58 of 93 29

30 Graph database Data is stored in a flexible graph model instead of rigid structure of tables (rows and columns) In the graph each node can be connected to any number of nodes such as network topologies and public transportation system 59 of 93 Graph database - Property graph Entities (nodes) Attributes (KV) Labels, roles, constraints, indexes, metadata Relationships Start node & End node Attributes (KV), Named, typed, Directional (BiDi navigation) 60 of 93 30

31 Graph database neo4j First graph database Fully ACID compliant Visualization 61 of 93 Graph database - Popular uses Online recommendations Social networks Graph based search of content Spatial (Geo) 62 of 93 31

32 PROGRAMMING PARADIGM SHIFT 63 of 93 Write preference Option in the source code (per op) Can write to memory of primary Add write to secondary Can write to storage of primary Add write to secondary If forcing write to secondary and secondary is missing write may fail 64 of 93 32

33 Read preference Option in the source code (per op) Read from primary Read from secondary Force Preferred Read from majority (to minimize probability if inconsistent reads) 65 of 93 Eventually consistent Read from primary replica is consistent But what happens if that replica fails? Persist writes to primary and secondary replica Only when necessary 66 of 93 33

34 Scalable writes Avoid multiple writes to the same shard Sequence or timestamp as range shard key Updates to the same items hotspot 67 of 93 Minimize use of cluster wide ops Secondary indexes are usually co-located with data Querying them performs a cluster wide search Instead model your data to use cluster key whenever possible 68 of 93 34

35 PK and secondary indexes Primary keys are used for sharding Most databases allow secondary indexes Secondary indexes are collocated with data 69 of 93 DEPLOYMENT 70 of 93 35

36 Comparison: On-premise vs Cloud On-premise Managed service (cloud) Cloud VMs Ease/speed of deployment Ease/speed of administration Ease/speed of scaling Upfront cost Dynamically change cost Fixed resources cost Customization Log analysis Ops/DevOps resources Amount of vendors 71 of 93 COMMUNITY VS ENTERPRISE 72 of 93 36

37 Comparison: CE vs EE Community edition Enterprise edition License cost Free Per: Node / RAM / Core / Data volume Features Basic Enhanced Security Basic Enhanced Support None SLA Scalability Low (limited cores / nodes / RAM support) High High 73 of 93 DATA MODELING 74 of 93 37

38 Self contained record Use only one record that contains the complete entity 75 of 93 Sibling records (relational) Indicates the relation using a common key in different sets 76 of 93 38

39 Referencing record (relational) Parent record holds limited information and pointers to other records 77 of 93 Referencing record (relational) Joining records 78 of 93 39

40 Atomicity and isolation Most NoSQL databases commit to atomic and isolated operations on the record level Some allow executing several operations on a single records atomically There are no multi-record transactions 79 of 93 Mixed entity & operations A record that holds both data and logical operations to perform on it. Doing so, allows mutating data and todo list in one atomic operation 80 of 93 40

41 Activity log Use sibling records with an incrementor; <prefix>::<key>::<incr> 81 of 93 Data versioning multi-record mutation atomicity Use referenced records with a version number in the key Committing all mutations is done atomically by the parent record 82 of 93 41

42 Data versioning 83 of 93 Data versioning 84 of 93 42

43 Multi document atomicity Multi document atomicity without a natural parent X <UNDO> Account: Allen Balance: 500 Trans: [115] X <UNDO> Account: Beth Balance: 100 Trans: [115] Account: Allen Balance: 500 Trans: [115] Account: Beth Balance: 100 Trans: [115] Account: Allen Balance: Trans: [115] [] Account: Beth Balance: Trans: [115] [] TranID: 115 Source: Allen Target: Beth Value: 30 State: [ init pending applied done canceling canceled ] LastModified: timestamp 85 of 93 Choosing the right one out of the bunch ADOPTION PLAN 86 of 93 43

44 Adoption plan Organizations usually opt for one of the followings: Convert a lesser important module to NoSQL Convert a main module to NoSQL Convert a suitable module to NoSQL Convert the entire system to NoSQL Organizations usually begin with CE (free) and later upgrade to EE (Availability, scalability, security, SLA and professional services) 87 of 93 Adoption plan RDBMS is not dead Migrate only modules that would really benefit Choose the best NoSQL engine to your application module Note that programming against a NoSQL dictates different paradigms than relational Using several NoSQL engines for different functionalities is advised Beware of falling into a DevOps pit 88 of 93 44

45 When to go NoSQL Most operations are a simple CRUD by Speed and volume is just too much for an RDBMS an average pace of thousands of CRUD operations per seconds Data model and most queries can be achieved without join (SQL JOIN) BASE is applicable (Basic Availability Soft-state Eventual consistency), though some databases support strong consistency 89 of 93 When to NOT go NoSQL Data model is very relational Constraints and referential integrity are mandatory Querying is complexed Even in those cases, usually some modules can be converted to NoSQL and benefit from its advantages 90 of 93 45

46 And there is much more More design patterns fitting for NoSQL More data models Search engines as Elasticsearch and SolR Data grids such as XAP Streams and Queues such as Kafka and RabbitMQ 91 of of 93 46

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48 Diving into RavenDB Michael Yarichuk Prime Directive Be a better database Not less of a database Transactional (ACID) document database For OLTP workloads Distributed, reliable, etc Shouldn t require black magic to run 48

49 CP Cluster wide consistency Consensus protocol (RAFT) For cluster wide operations: Create / Drop DB Add node to cluster Dual Distributed Nature AP Database will always accept writes Gossip protocol (merge) Conflict detection & handling As long as a single node is up, can serve read / writes Distribution of data Data is replicated Self monitoring and active repair Task assignment in cluster Automatic failover and topology updates 49

50 Documents (JSON) Transactions Modeling Features Single document / multiple documents / multiple collections Consistent transactions across machine boundary Attachments Employee photo Counters Document scans Distributed Also part of TX Associated with, but not part of, the document ETL SQL ETL for relational RavenDB ETL for data flow Move & shape data as it moves Integration Subscriptions Long term persistent subscription Batch operations Reliable 50

51 Indexes Query with RQL Query optimizer and dynamic indexes Spatial, full text search, facets, etc Indexes are BASE Batches, optimized Can chose to wait on write Map-Reduce Aggregation that is easy, up to date and available Computed in background Query on the results Cheap to update 51

52 Static Indexes Linq / JS (upcoming) Computation during indexing Queries do not allow computation (therefor, fast) Reducing costs Load Include Lazy Because who wants to go the server twice Reduce network roundtrips 52

53 Licensing Community free 3 Cores 6 GB Up to 3 nodes in cluster Developer free All features & functions Not for production Commercial pre core / year Professional Enterprise Encryption Dynamic cluster behavior Questions? 53

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